AI & Machine Learning News. 27, January 2020

The Artificial Intelligence and Machine Learning Newsletter by CloudQuant

The Artificial Intelligence and Machine Learning News clippings for Quants are provided algorithmically with CloudQuant’s NLP engine which seeks out articles relevant to our community and ranks them by our proprietary interest score. After all, shouldn’t you expect to see the news generated using AI?

Google CEO Thinks AI Will Be More a Profound Change Than Fire

Google’s chief executive officer has left no doubt in how important he thinks artificial intelligence will be to humanity. “AI is one of the most profound things we’re working on as humanity. It’s more profound than fire or electricity,” Alphabet Inc. CEO Sundar Pichai said in an interview at the World Economic Forum in Davos, Switzerland on Wednesday.

Alphabet, which owns Google, has had to grapple with its role in the development of AI, including managing employee revolts against its work on the technology for the U.S. government. In 2018, a group of influential software engineers successfully delayed the development of a security feature that would’ve helped the company win military contracts. Google has issued a set of AI principles that prohibit weapons work, but doesn’t rule out selling to the military. It has also pledged not to renew its Project Maven contract, which involves using artificial intelligence to analyze drone footage.

Pichai, who’s led Google since 2015, took control of Alphabet after founders Larry Page and Sergey Brin stepped down from day-to-day involvement last month. “AI is no different from the climate,” Pichai said. “You can’t get safety by having one country or a set of countries working on it. You need a global framework.”

2020-01-22 00:00:00+00:00 Read the full story…

CloudQuant Thoughts : Well, this and the Financial Times op-ed suggest Sundar Pichai has something important to tell the public, but honestly it just looks like a spin act. Google spend more on lobbying than any other company. Why does he feel he needs to put his opinions out there in this way. One cannot help but feel manipulated.

High-quality Deepfake Videos Made with AI Seen as a National Security Threat

The FBI is concerned that AI is being used to create deepfake videos that are so convincing they cannot be distinguished from reality.

The alarm was sounded by an FBI executive at a WSJ Pro Cybersecurity Symposium held recently in San Diego. “What we’re concerned with is that, in the digital world we live in now, people will find ways to weaponize deep-learning systems,” stated Chris Piehota, executive assistant director of the FBI’s science and technology division, in an account in WSJPro.

The technology behind deepfakes and other disinformation tactics are enhanced by AI. The FBI is concerned natural security could be compromised by fraudulent videos created to mimic public figures. “As the AI continues to improve and evolve, we’re going to get to a point where there’s no discernible difference between an AI-generated video and an actual video,” Piehota stated.

2020-01-23 22:30:59+00:00 Read the full story…
Weighted Interest Score: 2.3180, Raw Interest Score: 0.9215,
Positive Sentiment: 0.2229, Negative Sentiment 0.2378

CloudQuant Thoughts : If you follow this blog you know how much we love deepfakes. They are such a primal example of the power of ML. With the strides made in the last year it is no wonder the FBI are worried. We should all be worried, we can no longer trust our eyes. Worst of all, it gives plausible deniability to those in positions of power, “I didn’t say that.. its a deepfake”.

The Current State of AI Bias

Fed by massive amounts of data – data that increases in volume every day – AI solutions have the potential to yield unprecedented insights that can be used to steer business, government, and societal progress.

DataRobot surveyed more than 350 U.S. and U.K.-based CIOs, CTOs, VPs, and IT managers involved in AI and machine learning purchasing decisions. Respondents offer a look into how AI is being used by businesses today, current perceptions of AI bias, and what is being done – or should be done – to enhance AI bias prevention efforts in the future.

Key Report Findings

42% of organizations are “very” to “extremely” concerned about AI bias. Most respondents cite “compromised brand reputation” and “loss of customer trust” as the great cause for concern.
83% of respondents have established AI guidelines and are taking steps to avoid bias
85% of respondents also believe AI regulation would be helpful for defining what constitutes AI bias and how it should be prevented.

2020-01-26 00:00:00 Read the full story…
Weighted Interest Score: 5.6922, Raw Interest Score: 1.5759,
Positive Sentiment: 0.3041, Negative Sentiment 0.5529

Bias in AI: Scary Monster or Tamable Beast?

In this first blog in the Views on 2020 series from #Refinitiv, Debra Walton explores the factors contributing to bias in AI and analyzes its impact on the financial services industry. What solutions can be employed to ensure the quality of the data and subsequent outputs are free from such bias?

As the world focuses on the drawbacks of AI in more mainstream society, its impact on the financial services industry could be just as serious.
The impact of bias in AI can create long-term problems and has been seen in such events as the 2016 flash crash in sterling. However, Debra Walton believes that, on balance, the advantages of AI outweigh the disadvantages.
Solutions to bias in AI can be achieved through creating transparent and understandable models; managing data quality; and monitoring outputs and course correcting.

Not since Frankenstein’s monster hopped off the scientist’s bench in Mary Wollstonecraft Shelley’s gothic novel have more people been more afraid and vocal on the confluence of man and machine. In the financial community, leaving decisions to machines highlights the issue of bias in artificial intelligence in a sector that’s worked hard to regain trust since 2008.

2020-01-23 02:52:59+00:00 Read the full story…
Weighted Interest Score: 4.6875, Raw Interest Score: 2.0010,
Positive Sentiment: 0.1636, Negative Sentiment 0.4531

CloudQuant Thoughts : Bias exists, bias exists in AI, identifying bias in AI is extremely difficult. In business today you often do not get a second chance. If your AI chat bot suddenly starts spouting Nazi propaganda then no amount of course correcting will help you (unless you are Microsoft!).

DoD Looks to Scale Predictive Maintenance

In its efforts to make greater use of commercial technologies, a Pentagon innovation office formed to streamline government contracting has expanded an predictive maintenance effort designed to keep front-line aircraft ready for duty.

The Defense Innovation Unit (DIU) created in 2015 to link the military with technology vendors recently awarded a five-year, $95 million contract to C3.ai to boost aircraft readiness. The company said it will provide an AI-based software application that uses machine learning algorithms to monitor aircraft systems. The goal is to spot critical subsystem failures before they occur and help predict the parts and maintenance required to keep aircraft flying.

The AI platform also would serve as a logistics tool, identifying the type of part required to fix an airborne system and where that part can be acquired from DoD’s far-flung logistics network. The ability to anticipate parts failures is seen as a way of saving time and money associated with unscheduled maintenance.

2020-01-21 00:00:00 Read the full story…
Weighted Interest Score: 4.1601, Raw Interest Score: 1.6545,
Positive Sentiment: 0.2545, Negative Sentiment 0.2545

CloudQuant Thoughts : The first introduction I had to Machine Learning was an explanation of how the Military were using ML to predict when parts would fail ahead of time. The idea was that the supply chain would have the parts on hand in advance of the failure, in extreme cases, preventing catastrophic failure. They had the technology, they had massive amounts of data, they had the money and the technology, they had put it into practice and had provable results. So this article makes me feel like I have jumped into a time machine!

Analysing 3,000 tweets about RISE, the opening event of London Borough of Culture 2020

The purpose of this post is to use Twitter data to understand the ebbs and flows of the event and apply machine learning and natural language processing techniques to illuminate social value and cultural value. By analysing the tweets of users who attended RISE, we’re able to gain insight into whether the event achieved the aims of the London Borough of Culture. Let’s give it a go! Please scroll through to see the analysis. Hope you enjoy.

Unlike the opening event of last year, there wasn’t a specific hashtag dedicated to the event. Instead, I collected all tweets containing the official hashtag of the programme #Brent2020 and those which mentioned @LBOC2020, the Twitter handle of Brent 2020. At the time of the event, I used the Twitter API to collect 3,000 tweets in total between 14th January to 21st January 2020. It’s important to note that I only collected tweets that contained #Brent2020 or @LBOC2020; there were, of course, many tweets about RISE that didn’t contain those search terms.

Having collected the tweets, I performed a range of advanced statistical and machine learning natural language processing techniques to help me understand the social and cultural value of the event. Specifically, I used the Google Cloud Natural Language API to calculate the sentiment for each tweet, then I used the gensim library’s Word2Vec model to perform semantic analysis from the entire corpus of the tweets.

2020-01-27 13:17:30.075000+00:00 Read the full story…
Weighted Interest Score: 2.4149, Raw Interest Score: 1.6000,
Positive Sentiment: 0.2000, Negative Sentiment 0.0333

CloudQuant Thoughts : I always love a good Twitter analysis. This looks like a great piece of analysis and extremely interesting but it is such a pity that “Towards Data Science” chose to put their articles on Medium and thus behind a Registration Wall. They must have seen readership drop. Fortunately the author Vishal Kumar, self titled “Cultural Data Scientist” has his own web page, which is very interesting and contains lots of neat articles. Hopefully it will soon contain details of this analysis!

ProBeat: Why Google is really calling for AI regulation

On Sunday, the Financial Times published an op-ed penned by Sundar Pichai titled “Why Google thinks we need to regulate AI.” Whether he wrote it himself or merely signed off on it, Pichai clearly wants the world to know that as the CEO of Alphabet and Google, he believes AI is too important not to be regulated. He has concerns about the potential negative consequences of AI, and like any technology, he believes there needs to be some ground rules.

I simply don’t believe that’s the full story.

2020-01-24 00:00:00 Read the full story…
Weighted Interest Score: 2.6275, Raw Interest Score: 1.1452,
Positive Sentiment: 0.2082, Negative Sentiment 0.2343

MarketAxess deploys H20.ai machine learning tech

H2O.ai, the open source leader in artificial intelligence (AI) and machine learning (ML), today announced that its open source platform, H2O, provides critical machine learning capabilities to MarketAxess, the operator of a leading electronic trading platform for fixed-income securities and the provider of market data and post-trade services for the global fixed-income markets.

MarketAxess’ Composite+, powered by H2O open source, delivers greater insight and price discovery in real-time, globally, for over 24,000 corporate bonds. Composite+ has won several awards for its use of AI including the Risk Markets Technology Award for Electronic Trading Support Product of the Year and the Waters Technology American Financial Technology Award for Best Artificial Intelligence Technology Initiative.

“H2O is an integral part of Composite+ and provides some of the fundamental machine learning tools and support that make our algorithms run as well as they do,” said David Krein, Global Head of Research at MarketAxess. “The Composite+ pricing engine is helping fulfill our clients’ critical liquidity needs with more accurate and timely pricing data, which we make available within the MarketAxess electronic trading workflow. H2O.ai has been a great partner which has contributed to our recent success.”

2020-01-27 11:36:00 Read the full story…
Weighted Interest Score: 6.5515, Raw Interest Score: 3.0534,
Positive Sentiment: 0.5089, Negative Sentiment 0.1018

Human Capital: ACA’s Di Florio on How Tech Is Crucial Tool in Regulators’ Arsenal

Di Florio, a former director of the Securities and Exchange Commission’s Office of Compliance Inspections and Examinations details how regulators’ increasing use of regtech is transforming their supervisory relationships and exam experience with broker-dealers and advisors.
Listen in as di Florio also talks about how regulators are deploying technology and data analytics to identify needles in the regulatory haystack and also how the transition from the London Interbank Offered Rate, or Libor, poses a significant compliance risk.

2020-01-24 00:00:00 Read the full story…
Weighted Interest Score: 4.3478, Raw Interest Score: 2.4648,
Positive Sentiment: 0.0000, Negative Sentiment 0.1761

Community of AI Artists Exploring Creativity with Technology

Artists are using AI to explore original work in new mediums. Refik Anadol, for example, creates art installations using pools of data to create what he calls a new kind of “sculpture.” His “Machine Hallucination” installation ran in Chelsea Market, New York City, last fall.

The Turkish artist used machine learning algorithms on a dataset of more than three million images, to create a synthetic reality experiment. The model generates “a data universe of architectural hallucinations in 512 dimensions,” according to an account of the exhibit in designboom.

2020-01-23 22:30:55+00:00 Read the full story…
Weighted Interest Score: 4.0736, Raw Interest Score: 1.2077,
Positive Sentiment: 0.1812, Negative Sentiment 0.1510

OneStream Software Launches Predictive Analytics for Financial and Operational Planning

According to a recent press release, “OneStream Software, a leader in Corporate Performance Management (CPM) solutions for mid-sized to large enterprises, has introduced a new predictive analytics solution that provides advanced planning and forecasting capabilities for the OneStream XF platform. Predictive Analytics 123 enables finance leaders to create predictive forecasts for financial and operational planning, share these insights and collaborate with their business partners on critical business decisions.”
2020-01-27 08:05:22+00:00 Read the full story…
Weighted Interest Score: 3.9621, Raw Interest Score: 2.1763,
Positive Sentiment: 0.3348, Negative Sentiment 0.0558

Hitachi Vantara Buys Cataloger Waterline Data

Hitachi Vantara is acquiring data catalog startup Waterline Data as the U.S. subsidiary of the Japanese industrial giant seeks to meet growing demand for automation frameworks for data lake management via its AI-driven DataOps platform.

The Hitachi Ltd. (TSE: 6501) unit said Wednesday (Jan. 22) it would offer Waterline Data’s catalog technology based on proprietary “fingerprinting” technology as a separate product as well integrated with its fla…
2020-01-22 00:00:00 Read the full story…
Weighted Interest Score: 3.8912, Raw Interest Score: 2.3440,
Positive Sentiment: 0.2217, Negative Sentiment 0.1584

Five Factors To Consider in Choosing a Modern Data Ingestion Platform

The amount of data available to mid- and enterprise-sized companies continues to grow year over year. Most companies are pulling data from several data sources, making big data intake much more complex. This whitepaper examines the five factors you need to consider when moving from legacy data ingestion platforms to a more modern solution.
2020-01-21 00:00:00 Read the full story…
Weighted Interest Score: 3.8123, Raw Interest Score: 1.7595,
Positive Sentiment: 0.0000, Negative Sentiment 0.0000

Calls for AI Regulation Gain Steam

Should restrictions be placed on the use of artificial intelligence? Google CEO Sundhar Pichai certainly does, and so do a host of other business leaders, including the CEOs of IBM and H2O.ai, as the chorus of calls for putting limits on the spread of the rapidly evolving technology gets louder.

Pichai aired his opinion on the matter in an opinion piece published Monday in the Financial Times, titled “Why Google thinks we need to regulate AI” (story is protected by a paywall).

In the story, Pichai, who is also CEO of Google’s parent company, Alphabet, shared his lifelong love of technology, as well as the breakthroughs that his company is making in using AI to fight breast cancer, improve weather forecasts, and reduce flight delays.

As virtuous as these AI-powered accomplishments are, they don’t account for the negative impacts that AI also can have, Pichai wrote. “There are real concerns about the potential negative consequences of AI, from deepfakes to nefarious uses of facial recognition,” he wrote.

2020-01-24 00:00:00 Read the full story…
Weighted Interest Score: 3.6519, Raw Interest Score: 1.2331,
Positive Sentiment: 0.3045, Negative Sentiment 0.4262

Room for Improvement in Data Quality, Report Says

A new study commissioned by Trifacta is shining the light on the costs of poor data quality, particularly for organizations implementing AI initiatives. The study found that dirty and disorganized data are linked to AI projects that take longer, are more expensive, and do not deliver the anticipated results. As more firms ramp up AI initiatives, the consequences of poor data quality are expected to grow.

The relatively sorry state of data quality is not a new phenomenon. Ever since humans started recording events, we’ve had to deal with errors. But when you couple today’s super-charged data generation and collection mechanisms with the desire of companies to become “data-driven” through advanced analytics and AI, the ramifications of automating decisions based on dirty or disorganized data cannot be ignored.

2020-01-23 00:00:00 Read the full story…
Weighted Interest Score: 3.6208, Raw Interest Score: 1.6564,
Positive Sentiment: 0.1456, Negative Sentiment 0.3458

Qlik Doubles Down on Data Literacy

In parallel with its acquisition this week of an AI startup, data integrator Qlik launched a data literacy service as a means of expanding analytics capabilities across organizations drowning in data.

Separately this week, Qlik announced a deal to acquire RoxAI, developers of software that pushes alerts to analysts. The alerting software also integrates with Qlik’s AI platform, Sense.

Meanwhile, Qlik is pitching its new service as a way to boost data literacy across business operations as a way to wring more value of out of business intelligence and other data.

2020-01-24 00:00:00 Read the full story…
Weighted Interest Score: 3.3487, Raw Interest Score: 1.8836,
Positive Sentiment: 0.1256, Negative Sentiment 0.3349

Qlik Rebrands Attunity Solutions Following Acquisition

Qlik has announced the formal rebranding of the Attunity brand and data integration products into the Qlik brand. The change applies to every product Qlik acquired from Attunity in 2019, which are now integrated into Qlik’s overall data integration platform strategy.

With the acquisition of Attunity, Qlik evolved beyond analytics to include a robust data integration platform. The branding announcement reflects Qlik’s position in both the data in…
2020-01-23 00:00:00 Read the full story…
Weighted Interest Score: 2.7027, Raw Interest Score: 1.5898,
Positive Sentiment: 0.0795, Negative Sentiment 0.0000

What do AML-BSA-CTF Regulators think of Machine Learning?

Prior to 2018, regulators resisted recommending the use of Machine Learning (ML) based Artificial Intelligence (AI) for AML compliance. There was a mindset shift in mid 2018 indicating that proceeding with caution in implementing AI approaches for AML is appropriate. Regulators realize the adoption of recent innovation, such as the use of AI-ML and robotic process automation (RPA) techniques, enables AML compliance improvements not otherwise attainable. A risk-based approach to compliance, underpinned by AI/Machine Learning, creates opportunities for governance and process refinement as well as identifying potential untapped revenues. Reliance on box-ticking approaches familiar to users of legacy rules-based compliance systems is no longer sufficient.

Despite more than $80 Billion global spent annually on AML compliance, current AML approaches are not working as evidenced by the meager 1% of laundered funds intercepted . While banking regulators urge caution in the adoption of advanced analytics and AI based machine learning, reality dictates that adoption of new analytics and automation is imperative and urgent.

2020-01-25 17:39:31 Read the full story…
Weighted Interest Score: 3.1958, Raw Interest Score: 1.6292,
Positive Sentiment: 0.4173, Negative Sentiment 0.3612

How CSIRO boss thinks tech innovation will shape Australia in the 2020s

“Later this year, we’ll be releasing a roadmap outlining Australia’s economic opportunity from quantum technologies, which we think could be a multi-billion-dollar opportunity for Australia and create thousands of jobs – probably more likely over the next 20 years rather than 10 years.

“It will take investment and alignment across industry and research, but we have some of the world’s best people working on it right here, so the opportunity is i…
2020-01-23 00:00:00 Read the full story…
Weighted Interest Score: 3.1508, Raw Interest Score: 1.4807,
Positive Sentiment: 0.5682, Negative Sentiment 0.1894

Iguazio raises $24 million for AI development and management tools

AI adoption levels are higher than they’ve ever been in the enterprise. According to a January survey conducted by Gartner, corporate use of AI grew 270% over the past four years. But developing, deploying, and managing AI applications at scale requires a platform that supports doing so, which is what startup company Iguazio provides. Its investors believe it has legs: Iguazio announced that it has secured $24 million in a funding round led by IN…
2020-01-27 00:00:00 Read the full story…
Weighted Interest Score: 2.9713, Raw Interest Score: 1.7205,
Positive Sentiment: 0.2126, Negative Sentiment 0.1546

So You Want to be a Data Architect?

Being a data architect requires a good understanding of the cloud, databases in general, and the applications and programs used to maximize their potential. A fully functional data architect understands all the phases of Data Modeling, including conceptualization and database optimization. They also understand a continuing education is part of the job.

Typically, a data architect has a degree in information technology, computer science, computer…
2020-01-23 08:35:40+00:00 Read the full story…
Weighted Interest Score: 2.9225, Raw Interest Score: 1.7335,
Positive Sentiment: 0.2222, Negative Sentiment 0.1445

The State of AI in 2020

Learn where our future is headed by understanding AI’s past. This article provides an in depth overview of the past, present, and future of AI.

There is no doubt that artificial intelligence, machine learning, and data science have become the most powerful and forward looking force in technology over the past decade. These technologies have allowed for breakthrough insights and applications that may truly change the world for the better. This is, of course, thanks to the symbiosis of data collection, hardware innovation, and driven researchers that have taken hold over the 2010s. This has led us to be…
2020-01-27 13:21:57.735000+00:00 Read the full story…
Weighted Interest Score: 2.7900, Raw Interest Score: 1.6250,
Positive Sentiment: 0.2734, Negative Sentiment 0.1135

This Google Scientist teaches AI to build better AI (Video)

I got my Masters and Ph.D in electrical and computer engineering at Rice University. I was working on algorithmic, hardware/software code design for large-scale data analytics model and Machine Learning. Towards the end of my Ph.D when Deep Learning took off, I switch my focus to Deep Learning.

Afterwards, I very accidentally saw a flyer for Google Brain Residency Program. The goal was for people with backgrounds other than Deep/Machine Learning to become researchers in the field. I’ve been there 3.5 years now.

2020-01-23 04:34:02.372000+00:00 Read the full story…
Weighted Interest Score: 2.7806, Raw Interest Score: 1.6172,
Positive Sentiment: 0.4227, Negative Sentiment 0.0919

Reading Color Blindness Charts: Deep Learning and Computer Vision

Transforming data to make it compatible with a similar dataset

There are plenty of online tutorials where you can learn to train a neural network to classify handwritten digits using the MNIST dataset, or to tell the difference between cats and dogs. Us, humans, are always very good at these tasks and can easily match or beat the performance of a computer.

However, there are som…
2020-01-26 18:11:47.585000+00:00 Read the full story…
Weighted Interest Score: 2.4579, Raw Interest Score: 1.0101,
Positive Sentiment: 0.1684, Negative Sentiment 0.1684

StoneChecker approval fires up medical imaging diagnostics specialist IQ-AI

This predictive, diagnostic software product helps urologists determine whether a kidney stone will disintegrate under a vibration process called lithotripsy. This capability becomes important in determining whether to subject patients to multiple rounds of lithotripsy or send them straight to surgery. Its products are now sold in the USA, UK, The Netherlands, Switzerland, Germany, Greece and South Korea. The business has been put on sale to free up resources for the company’s medical imaging products.

All the company’s products are based upon digitising information from medical imaging equipment on a voxel-wise basis – a voxel being the three-dimensional (3D) equivalent of a pixel (i.e. a point on a 3D grid).

In the technology IQ-AI is developing, each individual voxel is examined and compared to surrounding voxels, which is often the first step in identifying diseased or compromised tissue.

2020-01-27 00:00:00 Read the full story…
Weighted Interest Score: 2.4048, Raw Interest Score: 1.0541,
Positive Sentiment: 0.1622, Negative Sentiment 0.1622

AI Being Used to Help Diagnose Mental Health Issues; Privacy Concerns Real

Mental health issues are believed to be experienced by one in five US adults and some 16 percent of the global population; the rates seem to be increasing. Meanwhile, many parts of the US have a shortage of healthcare professionals. Some $200 billion is spent annually on mental health services, experts estimate.

Given the constraints, it’s natural for researchers to explore whether AI technology can help ext…
2020-01-23 22:30:18+00:00 Read the full story…
Weighted Interest Score: 2.2564, Raw Interest Score: 1.2133,
Positive Sentiment: 0.0783, Negative Sentiment 0.2218

Be more efficient to produce machine learning pipeline with Metaflow

The DAG is structured around :

Flow: the instance that is managing all the codes for the pipeline. It is a Python object in this case class MyFlow(Flowspec)

Steps: parts of the flow, delimited by decorator @step, they are python functions in the MyFlow object, in this case, def start, fitA, fitB, eval, end.

Transitions: links between the steps they could be of different types (linear, branch and for each); there are more details on the documen…
2020-01-27 13:29:17.967000+00:00 Read the full story…
Weighted Interest Score: 2.2119, Raw Interest Score: 1.2645,
Positive Sentiment: 0.0421, Negative Sentiment 0.0000

Big Data Creates 4 Massive Benefits Of Automated Log Management

Automated log management has led to the proliferation of big data. Some of the changes that it has created help illustrate both the positive and negative implications of big data in general.

Big data is a double-edged sword for countless businesses. It is creating a number of great opportunities for them, as they are using big data technology to boost efficiency, minimize employee turnover, get a better understanding of their customers and solve other challeng…
2020-01-23 20:52:25+00:00 Read the full story…
Weighted Interest Score: 2.1836, Raw Interest Score: 1.6201,
Positive Sentiment: 0.4461, Negative Sentiment 0.7279

Can analysts and statisticians get along?

Inside the subtle war between the data science professions

In a previous article, I explained that typical training programs in statistics and analytics endow graduates with different skillsets.

When you’re dealing with uncertainty, analysts help you ask better questions, while statisticians provide more rigorous answers. Seems like the makings of a collaboration dream, yet somehow these professions end up at one another’s throats. Let’s se…
2020-01-25 00:07:33.422000+00:00 Read the full story…
Weighted Interest Score: 2.0698, Raw Interest Score: 0.7398,
Positive Sentiment: 0.2328, Negative Sentiment 0.2993

AI Weekly: Calls for facial recognition moratorium highlight need for protection from surveillance tech

The debate over whether to ban or place moratoriums on the use of facial recognition started last year in cities like San Francisco, but the debate reignited this week when Alphabet and Google CEO Sundar Pichai said he’s open to a moratorium on facial recognition.

“It [facial recognition regulation] can be immediate, but maybe there’s a waiting period before we really think about how it’s being used,” Pichai said. “It’s up to governments to char…
2020-01-24 00:00:00 Read the full story…
Weighted Interest Score: 2.0487, Raw Interest Score: 1.0555,
Positive Sentiment: 0.1725, Negative Sentiment 0.6090

Workday CEO Aneel Bhusri in Davos on his vision for company growth and more

One of the big themes at the World Economic Forum in Davos is artificial intelligence and breakthrough technologies. Aneel Bhusri, CEO of Workday, joins “Squawk Box” to discuss the latest on A.I. and cloud technology.
2020-01-23 00:00:00 Read the full story…
Weighted Interest Score: 2.0339, Raw Interest Score: 1.3605,
Positive Sentiment: 0.3401, Negative Sentiment 0.0000

Here’s how an ex-CIA officer and a tech entrepreneur are using AI to hunt sex traffickers on Super Bowl Sunday

Emil Mikhailov, founder of XIX, a computer vision startup, and Nic McKinley, founder of Deliver Fund, a nonprofit started by an ex-CIA agent, have teamed up to use AI to fight human trafficking.

Deliver Fund uses XIX’s technology to scan and analyze massive numbers of online images to identify and expose sex trafficking rings. The XIX platform looks for “signals” in the photos — visual elements that suggest a link to the illicit sex trade.

“Hum…
2020-01-26 00:00:00 Read the full story…
Weighted Interest Score: 1.9861, Raw Interest Score: 0.9333,
Positive Sentiment: 0.1343, Negative Sentiment 0.3252

Hotel Disruptor, Life House, Raises a $30M Series B to Make Beautiful Hotels More Affordable & More Authentic

The industry’s leading investors recognize the advantage that Life House’s software-driven platform and scalable authentic brand have in an increasingly competitive hotel landscape. The investment will support the Company’s rapid growth into new markets with a clear path to profitability by 2021.

NEW YORK–(BUSINESS WIRE)–January 24, 2020–

Life House, a tech-enabled lifestyle hotel company, announced the closing of a $30 million Series B fundrai…
2020-01-24 00:00:00 Read the full story…
Weighted Interest Score: 1.9653, Raw Interest Score: 1.2977,
Positive Sentiment: 0.4112, Negative Sentiment 0.0771

Intel’s stock jumps 7% on better-than-expected earnings and revenue

Intel’s revenue rose 8% from a year earlier in the quarter, which ended on Dec. 28, the company said in a statement .

Intel shares jumped as much as 7% in extended trading on Thursday after the chipmaker reported better-than-expected fourth-quarter earnings, extending a rally that’s pushed the stock to its highest since the dot-com bubble in 2000.

Intel’s largest operating segment, the Client Computing Group that makes chips for PCs, laptops an…
2020-01-23 00:00:00 Read the full story…
Weighted Interest Score: 1.9640, Raw Interest Score: 1.3276,
Positive Sentiment: 0.2371, Negative Sentiment 0.0474

Ixia Enhances Active Network Monitoring Platform with Machine Learning

t Technologies, Inc., a leading technology company that helps enterprises, service providers and governments accelerate innovation to connect and secure the world, today announced the addition of new machine learning (ML) features in the Hawkeye active network monitoring platform from Ixia, a Keysight business. The addition of machine learning enables Hawkeye to help enterprises shorten outages and improve network uptime by quickly detecting, identifying and resolving network anomalies.”

The release goes on, “As the volume and velocity of raw network and application data continues to increase, network operat…
2020-01-24 08:05:00+00:00 Read the full story…
Weighted Interest Score: 1.9551, Raw Interest Score: 1.9119,
Positive Sentiment: 0.4056, Negative Sentiment 0.2897

Samasource CEO Leila Janah passes away at 37 – TechCrunch

The startup community has lost another moral leader today.

Leila Janah, a serial entrepreneur who was the CEO and founder of machine learning training data company Samasource, passed away at the age of 37 due to complications from Epithelioid Sarcoma, a form of cancer, according to a statement from the company.

She focused her career on social and ethical entrepreneurship with the goal of ending global poverty, founding three distinct organizations over her career spanning the for-profit and non-profit worlds. She was most well-known …
2020-01-24 00:00:00 Read the full story…
Weighted Interest Score: 1.8754, Raw Interest Score: 1.0620,
Positive Sentiment: 0.2465, Negative Sentiment 0.1328

Dell’s Jeff Clarke Looks to 2020 and the ‘Next Data Decade’

Opinions aren’t exactly hard to come by in technology, especially in December when a veritable cottage industry arises around predictions for the coming year (including here on eWEEK!). That amps up a little further at a decade’s end, with pieces that look both fondly back and adventurously ahead. But the fact is that while opinions are (as usual) easy enough to find, insightful forecasts of longer-term trends tend to be pretty thin on the ground…
2020-01-27 00:00:00 Read the full story…
Weighted Interest Score: 1.7879, Raw Interest Score: 1.1134,
Positive Sentiment: 0.4276, Negative Sentiment 0.0968

Hamish Douglass’ map for investing this decade

n’t even thought of will be $100 billion businesses in ten years.”

If the smartphone was the innovation of the past decade, and if we’re now in the decade of the cloud, the 2030s will be the time of artificial intelligence, Douglass says. “And maybe the decade after that would be the decade of longevity where we’re starting to make massive breakthroughs in terms of life expectancy.”

And if the fund manager is right, our future will also be totally familiar: “I still think people will be eating KFC in a decade,” referring to the franchise owned by Yum! Brands which Magellan has made seven times its money on. “Starb…
2020-01-23 00:00:00 Read the full story…
Weighted Interest Score: 1.7549, Raw Interest Score: 0.9150,
Positive Sentiment: 0.2250, Negative Sentiment 0.1350

CCPA, GDPR and Beyond: The Future of Data Privacy Legislation

The basic concept of regulating how data is stored, protected, and processed is not unique to the past decade. From early inceptions such as Sweden’s Data Act of 1973 to regulations such as Europe’s Data Protection Directive of 1995 – a predecessor to the 2018 General Data Protection Regulation (GDPR) – data privacy has proven to long be a consideration for consumers and governing bodies. Moving forward to modern times, the demand for and impleme…
2020-01-24 11:41:09-05:00 Read the full story…
Weighted Interest Score: 1.7375, Raw Interest Score: 0.9485,
Positive Sentiment: 0.1594, Negative Sentiment 0.2391

How Google Keeps Changing Financial Marketing

In January 2020 in a short blog the head of engineering for Google’s Chrome browser set off a bomb. The company plans to effectively kill third-party cookies on Chrome within two years.

In recognition of privacy concerns, wrote Justin Schuh, Google was taking steps to implement its Privacy Sandbox, announced the previous summer. As part of that implementation, he said, after Google “addressed the needs of users, publishers, and advertisers, and …
2020-01-22 00:05:46+00:00 Read the full story…
Weighted Interest Score: 1.6657, Raw Interest Score: 1.0778,
Positive Sentiment: 0.2530, Negative Sentiment 0.1595

Big tech opts for emollience over exuberance at Davos

Most years, the panel discussions and the talks hosted in the shopfronts are pretty techno-utopian. The fourth industrial revolution is coming to save the world, that sort of thing.

This year, though, although the general “mood of Davos” was relatively upbeat, the tech side was a little more downcast and defensive. Nadella excepted, most of the CEOs were less visible in the public forums. The shopfronts seemed a little quieter.

One tech executi…
2020-01-25 00:00:00 Read the full story…
Weighted Interest Score: 1.6490, Raw Interest Score: 0.8944,
Positive Sentiment: 0.1677, Negative Sentiment 0.3074

4 strategies to redesign entry-level work to be more productive

Automation is changing the world. Namely, it’s changing the way we work by eliminating many of the more menial, repetitive tasks that employees currently focus on multiple hours a day. And while this is happening to employees across all areas of business, perhaps no one is being impacted more quickly—or dramatically—than practitioners and designers of entry-level work.

Soon, entry-level analysts will no longer spend…
2020-01-22 11:00:47 Read the full story…
Weighted Interest Score: 1.6125, Raw Interest Score: 1.2558,
Positive Sentiment: 0.4891, Negative Sentiment 0.1586

Rob Bearden Returns to Lead Cloudera’s Second Act

When Cloudera ran into trouble last June following poor financial results, the board jettisoned senior leadership, including CEO Tom Reilly and Mike Olson, its chief strategy officer. Those moves would open up a path for Hortonworks co-founder Rob Bearden to return to the company that he had a hand in shaping. But with the Hadoop bubble popped, the question remains: Can Bearden succeed in resurrecting Cloudera in the new data age?

Cloudera last …
2020-01-21 00:00:00 Read the full story…
Weighted Interest Score: 1.5381, Raw Interest Score: 1.0704,
Positive Sentiment: 0.1529, Negative Sentiment 0.2243

Geopolitical tensions are high, putting global economy at risk, UN Secretary-General says

The state of global geopolitical tensions is high, and this is having an impact on the global economy, Antonio Guterres, Secretary-General of the United Nations, told CNBC’s Sara Eisen on “Squawk on the Street” from the World Economic Forum at Davos on Thursday.

“It’s the highest I have seen since taking office at the UN,” the Secretary-General said, noting there are a confluence of factors at play. These include the U.S.-Iran conflict, terroris…
2020-01-23 00:00:00 Read the full story…
Weighted Interest Score: 1.4574, Raw Interest Score: 0.9615,
Positive Sentiment: 0.1861, Negative Sentiment 0.6514

Palantir CEO Alex Karp defends his company’s relationship with government agencies

Palantir CEO Alex Karp said he stands by his company’s controversial work for the U.S. government, including Immigration and Customs Enforcement.

The secretive Peter Thiel-backed data analytics start-up ramped up its work for governments in 2019, Karp said Thursday.

“The core mission of our company always was to make the West, especially America, the strongest in the world, the strongest it’s ever been, for the sake of global peace and prosperity, and we feel like this year we really showed what that would mean,” Karp said in an interview with “Squawk Box” co-host Andrew Ross Sorkin f…
2020-01-23 00:00:00 Read the full story…
Weighted Interest Score: 1.4208, Raw Interest Score: 0.9976,
Positive Sentiment: 0.1209, Negative Sentiment 0.4837

4 trends that will shape the cloud-native world in 2020

2019 was a pivotal year for the cloud native community, with lots of announcements that made it hard to get a clear view of what’s happening. But there are 4 key trends in the cloud space that will shape 2020. And if you take a step back, you’ll spot them. A big step back.

In June 2014, Google announces it is embracing Docker, and open-sourcing a new tool to manage compute workloads over large scale computing infrastructure. It is hailed as a re…
2020-01-26 00:00:00 Read the full story…
Weighted Interest Score: 1.1986, Raw Interest Score: 0.8754,
Positive Sentiment: 0.1808, Negative Sentiment 0.0666

Octarine Adds 2 Open Source Projects to Secure Kubernetes

Octarine announced today it has launched two open source projects intended to enhance Kubernetes security. The first project is kube-scan, a workload and assessment tool that scans Kubernetes configurations and settings to identify and rank potential vulnerabilities in applications in minutes. The second project is a Kubernetes Common Configuration Scoring System (KCCSS), a framework for rating security risks involving misconfigurations.

Julian …
2020-01-24 03:04:47-04:00 Read the full story…
Weighted Interest Score: 1.1866, Raw Interest Score: 0.6486,
Positive Sentiment: 0.1081, Negative Sentiment 0.1081

Live from Bett: What’s new in EDU– Change within the Microsoft Educator Center and fostering future-ready skills in students

It’s Day 2 of Bett and we’re back, ready to share the latest innovations in education technology and helping you get you started using the new tools and resources we announced last week. You can tune in live to watch special episodes of “What’s new in EDU” each day of Bett at 5:00PM Local London time or noon EST and 9 am PT. Here’s where you go on Thursday and Friday. In our episode yesterday, we talked about choosing and managing devices and var…
2020-01-23 16:45:45+00:00 Read the full story…
Weighted Interest Score: 1.1726, Raw Interest Score: 0.7912,
Positive Sentiment: 0.3320, Negative Sentiment 0.0636

This news clip post is produced algorithmically based upon CloudQuant’s list of sites and focus items we find interesting. We used natural language processing (NLP) to determine an interest score, and to calculate the sentiment of the linked article using the Loughran and McDonald Sentiment Word Lists.

If you would like to add your blog or website to our search crawler, please email customer_success@cloudquant.com. We welcome all contributors.

This news clip and any CloudQuant comment is for information and illustrative purposes only. It is not, and should not be regarded as investment advice or as a recommendation regarding a course of action. This information is provided with the understanding that CloudQuant is not acting in a fiduciary or advisory capacity under any contract with you, or any applicable law or regulation. You are responsible to make your own independent decision with respect to any course of action based on the content of this post.

The post AI & Machine Learning News. 27, January 2020 appeared first on CloudQuant.

Alternative Data News. 29, January 2020

The AltDataNewsletter by CloudQuant

Finding sources and uses for alternative data can be difficult. At CloudQuant we regularly read and search the internet for new sources of data that can be used in our mission to find alpha signals and build quantitative trading strategies. We recognize that we are technology and data junkies so we wrote our own crawler that specifically seeks out web pages, posts, and news articles that give us a snapshot of what is going on in the world of Alt Data. The following is a collection of articles that we think you will find interesting from the past week.

Alternative data sets … quality is the challenge, not quantity

There are an estimated 445 alternative data providers in the funds industry, serving the needs of both traditional and alternative fund managers. This is, according to alternativedata.org, an industry that is projected to be worth USD350 billion in 2020. But while there is no doubting the quantity and diversity of data sets one can acquire, how valuable really are they?

It is a valid question when one considers that the average dollar spend on alternative data, for 2020, will be USD158K for those running less than USD1 billion in AUM, rising to an estimated USD764K for those running north of USD1 billion.

2020-01-24 14:03:34+00:00 Read the full story(Registration Wall)…
Weighted Interest Score: 6.1564, Raw Interest Score: 2.2838,
Positive Sentiment: 0.1631, Negative Sentiment 0.1631

CloudQuant Thoughts : There are lots of firms offering hundreds of data-sets. Imagine the car industry if the new car lots were not organized by manufacturer and if the car dealers instead bought random cars that they thought they could sell. You want to buy a specific make model color spec, it would be a nightmare! This is what AltData looks like at the moment. Fortunately, at CloudQuant we are seeking out and TESTING the most in-demand Alternative Data Sources. Head over to our Catalog to find out more, we will be adding a number more very shortly.

Unlocking Data Silos to Reach the Promised Land of Smart Analytics

A December 2019 paper by capital markets consultancy GreySpark Partners examined the potential for buy-side and sell-side firms to transform large quantities of big data into actionable intelligence – producing what is known as ‘smart data – through specialized analytics.

2020-01-28 02:06:54+00:00 Read the full story…
Weighted Interest Score: 4.1961, Raw Interest Score: 2.2137,
Positive Sentiment: 0.1050, Negative Sentiment 0.1240

CloudQuant Thoughts : We are constantly coming across extremely interesting exhaust data from firms that have nothing to do with Trading but which, if organized correctly, could be extremely valuable. Simple example, Apple AAPL stopped reporting product sales date on phones and earbuds but Walmart WMT and Best Buy BBY have gobs (technical term!) of historical trend data on those sales. Most of these firms have no idea how to organize the data for Analyst consumption or how to promote it to the Street. Fortunately we have the skillset to take that data and transform it into easy to use and clean data for the Market. If you have some data that you think may have latent value please get in touch!

Discovering millions of datasets on the web

Across the web, there are millions of datasets about nearly any subject that interests you. If you’re looking to buy a puppy, you could find datasets compiling complaints of puppy buyers or studies on puppy cognition. Or if you like skiing, you could find data on revenue of ski resorts or injury rates and participation numbers. Dataset Search has indexed almost 25 million of these datasets, giving you a single place to search for datasets and find links to where the data is. Over the past year, people have tried it out and provided feedback, and now Dataset Search is officially out of beta.

Based on what we’ve learned from the early adopters of Dataset Search, we’ve added new features. You can now filter the results based on the types of dataset that you want (e.g., tables, images, text), or whether the dataset is available for free from the provider. If a dataset is about a geographic area, you can see the map. Plus, the product is now available on mobile and we’ve significantly improved the quality of dataset descriptions. One thing hasn’t changed however: anybody who publishes data can make their datasets discoverable in Dataset Search by using an open standard (schema.org) to describe the properties of their dataset on their own web page.

We have also learned how many different types of people look for data. There are academic researchers, finding data to develop their hypotheses (e.g., try oxytocin), students looking for free data in a tabular format, covering the topic of their senior thesis (e.g., try incarceration rates with the corresponding filters), business analysts and data scientists looking for information on mobile apps or fast food establishments, and so on. There is data on all of that! And what do our users ask? The most common queries include “education,” “weather,” “cancer,” “crime,” “soccer,” and, yes, “dogs”.

2020-01-23 00:00:00 Read the full story…

CloudQuant Thoughts : Google’s entry into Searchable Data Sets is huge, we reported on it when it first came available and they have made many improvements in the Beta period. Take a look, it is probably not ideal for Trading but it is interesting to see what is out there!

New Analytics Platform Screens Investments for ESG Factors

A key frustration of advisors is determining how “green” stocks, ETFs and funds really are. To solve this dilemma, a new portfolio analytics platform that will enable RIAs to compare and construct customized environmental, social and governance portfolios has been released today by Act Analytics, an investment analytics technology firm based in Toronto.

The new product’s analytical tools will be driven by 200 environmental, social and governance factors and industry data, allowing RIAs to “X-ray” securities, funds and portfolios to determine their ESG levels. This will help RIAs build custom portfolios for their entire client base, according to the company.

“Our new ESG analytics platform for RIAs provides a comprehensive and transparent tool to help advisors deliver values-based investments for their clients, enabling them to differentiate their investment offerings in a rapidly commoditizing industry,” according to Mike Unwin, co-founder and CEO of Act Analytics, and a former financial advisor and portfolio manager whose clients were requesting ESG investments, which spurred his team to build the platform.

2020-01-23 00:00:00 Read the full story…
Weighted Interest Score: 3.6496, Raw Interest Score: 1.8273,
Positive Sentiment: 0.1370, Negative Sentiment 0.1370

CloudQuant Thoughts : We have been sounding the trumpet for ESG data for over a year now, apologies to regular readers, but if you need some ESG data that has proven Alpha that has not yet been consumed, head over to the ESG data in our data Catalog.

Defining ESG Investing and Understanding Its Uses

Advisors embracing or considering environmental, social and governance focused investing should understand the different definitions used by asset managers, index providers, stock and bond issuers as well as their clients.

“ESG is not just about values but includes the underlying financial material risks within an industry,” said Mona Naqvi, senior director, ESG, at S&P Dow Jones Indices, who was on a panel about the topic at this week’s Inside ETFs conference in Hollywood, Florida.

Jordan Farris, head of ESG product development at Nuveen, described four different capabilities for ESG:

exclusionary screening of companies based on ethical values, regulations or norm-based violations
best-in-class sectors and companies with superior ESG performance relative to their industry
climate investment solutions, whereby asset allocation is aligned with the transition to a low-carbon economy
ESG integration, which incorporates ESG data into the investment process to improve risk-adjusted returns

2020-01-28 00:00:00 Read the full story…
Weighted Interest Score: 3.2670, Raw Interest Score: 1.6632,
Positive Sentiment: 0.2079, Negative Sentiment 0.0891

GDPR, CCPA, and the AI Explainability Question

In most large organizations, artificial intelligence (AI) and machine learning (ML) are powering key business functions, from big data analytics and customer service to fraud detection and personalized marketing. Insights that AI and ML can produce are powerful, but it’s often difficult, if not impossible, to explain how these algorithms arrived at them. This limitation could pose significant problems for compliance with the EU’s General Data Protection Regulation (GDPR), the California Consumer Privacy Act (CCPA), and other laws governing data and privacy.

Let’s look at GDPR first. When an automated process such as AI or ML makes a decision about an individual based on personal data, GDPR requires the organization to supply an explanation if requested. But what GDPR means by “explanation” isn’t very clear. There are two competing interpretations. The direst for AI would require organizations to detail exactly how an algorithm arrived at its conclusions. Thankfully, the most common thinking is that GDPR simply requires letting people know that decisions pertaining to them will be made by an automated process.

2020-01-28 08:30:33+00:00 Read the full story…
Weighted Interest Score: 4.6779, Raw Interest Score: 1.7268,
Positive Sentiment: 0.0959, Negative Sentiment 0.2686

Human Capital Podcast: ACA’s Di Florio on How Tech Is Crucial Tool in Regulators’ Arsenal (Video)

Weighted Interest Score: 4.3478, Raw Interest Score: 2.4648,
Positive Sentiment: 0.0000, Negative Sentiment 0.1761

Ben Pring’s 50 Thoughts from Davos 50

From the Promenade to behind the velvet rope, here’s what it’s really like at the World Economic Forum’s annual meeting in Davos.

It’s the 50th anniversary of the founding of the World Economic Forum’s (WEF) annual meeting in Davos, Switzerland. In its honor, here are 50 observations from this year’s event. (50?! Don’t worry – I’ll keep it quick and painless.)
2020-01-23 00:00:00 Read the full story…

50 Diverse Takeaways from Davos WEF2020

I am transparently stealing Ben Pring`s format to honor the 50th anniversary of the World Economic Forum’s (WEF) annual meeting in Davos, with my own 50 diverse takeaways. Some are my own big picture opinions from spending two intense full days in Davos and participating in two side events. Others are takeaways from the diverse speakers that I had the privilege to listen to. And some are my Tech innovation picks again from the events I participated.

Data….

The old adage `Location, location, location` is now transformed into `Availability, availability, availability` which means the power is in the accessibility and data.
$4 trillion USD is spent by corporates every year to prepare data to be used by AI algos.
Global Data Excellence is global leader in Data excellence management that maximizes business value with a clear focus on the social value mission.
Trigyan offers an innovative data platform that is domain neutral to link data at scale and real-time – Glide (Graphically Linked Integrated)

2020-01-28 00:00:00 Read the full story…
Weighted Interest Score: 3.4448, Raw Interest Score: 1.5182,
Positive Sentiment: 0.2552, Negative Sentiment 0.0766

Crédit Agricole acquires majority stake in fintech expert Linxo

Upon completion of the transaction, the Crédit Agricole Group will own more than 85% of Linxo Group.

Crédit Agricole Group has earlier today announced the acquisition of a majority stake in fintech expert Linxo Group.

Upon completion of the transaction, the Crédit Agricole Group will own more than 85% of Linxo Group. The acquisition will be led by Crédit Agricole Payment Services and by FIRECA (Fonds d’Investissement et de REcherche du Crédit A…
2020-01-28 11:59:47+02:00 Read the full story…
Weighted Interest Score: 3.2178, Raw Interest Score: 1.4851,
Positive Sentiment: 0.2475, Negative Sentiment 0.0000

So You Want to be a Data Architect?

Typically, a data architect has a degree in information technology, computer science, computer engineering, or a similar field. Like an architect who create homes or buildings, a data architect develops a blueprint representing a data system that supports an organization’s short-term and long-term goals.

A data architect should know how to:

Design models of data processing that implement the intended business model.
Develop diagrams representing key data entities and their relationships.
Generate a list of components needed to build the designed system.

2020-01-23 08:35:40+00:00 Read the full story…
Weighted Interest Score: 2.9225, Raw Interest Score: 1.7335,
Positive Sentiment: 0.2222, Negative Sentiment 0.1445

An Open Source Alternative to AWS SageMaker

There’s no shortage of resources and tools for developing machine learning algorithms. But when it comes to putting those algorithms into production for inference, outside of AWS’s popular SageMaker, there’s not a lot to choose from. Now a startup called Cortex Labs is looking to seize the opportunity with an open source tool designed to take the mystery and hassle out of productionalizing machine learning models.

Infrastructure is almost an aft…
2020-01-28 20:25:44-04:00 Read the full story…
Weighted Interest Score: 3.0151, Raw Interest Score: 1.5228,
Positive Sentiment: 0.2538, Negative Sentiment 0.3807

Apple active devices topped 1.5 billion

Apple’s base of active devices is viewed as the linchpin of its growing services business. The company is targeting 600 million paid subscribers across its various services by the end of 2020. Shares of Apple were rising in after-hours trading after the firm beat earnings and revenue estimates by a solid margin. The number of active Apple devices across the world is growing, as well.

For the December quarter, Apple (AAPL) – Get Report posted overall earnings of $4.99 per share on sales of $91.82 billion, topping Wall Street’s estimates of $4.54 EPS and $88.4 billion. Shares of Apple were trading 2.3% higher shortly after the release. The company said its installed base of iPhones, iPads, wearable devices and Mac computers hit 1.5 billion in the latest quarter, driven in part by many customers buying iPads, Macs and Apple Watches for the first time. Apple CEO Tim Cook added in a statement that Apple’s quarterly revenue, which was its highest ever, was “fueled by strong demand for our iPhone 11 and iPhone 11 Pro models, and all-time records for Services and Wearables.”

2020-01-28 17:37:09-05:00 Read the full story…
Weighted Interest Score: 2.7218, Raw Interest Score: 1.4914,
Positive Sentiment: 0.1864, Negative Sentiment 0.0000

Community Data Platforms Announces Grants to Support Local, Evidence-Based Decision-Making

According to a new press release, “Community Data Platforms, Inc. (CDP) is making an offer that the leaders of America’s smartest communities will find hard to refuse. With support from Schmidt Futures, CDP will help three communities answer pressing questions while also creating a data analytics resource to enhance local decision-making. ‘While C-suite executives regularly rely on data analytics to inform critical decisions, community leaders who manage governments, economic development organizations, and philanthropies tell us that the lack of access to data at the community level is a massive hurdle to addressing their policy challenges. Consequently, critical community-based decisions are too often made on anecdotes or with limited, incomplete data. Community Data Platforms solves this problem,’ says Alan Worden, Founder, and CEO.”
2020-01-28 08:10:08+00:00 Read the full story…
Weighted Interest Score: 2.5656, Raw Interest Score: 1.3126,
Positive Sentiment: 0.1193, Negative Sentiment 0.6563

Reading Color Blindness Charts: Deep Learning and Computer Vision

Transforming data to make it compatible with a similar dataset

However, there are som…
2020-01-26 18:11:47.585000+00:00 Read the full story…
Weighted Interest Score: 2.4579, Raw Interest Score: 1.0101,
Positive Sentiment: 0.1684, Negative Sentiment 0.1684

From Banking and Data Security to Compliance: Blockchain Grows Well Beyond its Cryptocurrency Roots

Cryptocurrency and blockchain came to the forefront of our imaginations as a technological marriage, seen as a revolution, for digital transactions. So vivid was the future for this pairing, at the outset at least, that the terms became linked. In reality, cryptocurrencies and distributed ledger technologies are distinct, the latter supporting the former’s ability to exist.

Blockchain is the technology, and cryptocurrencies are an application of…
2020-01-28 12:43:24 Read the full story…
Weighted Interest Score: 2.3849, Raw Interest Score: 1.2883,
Positive Sentiment: 0.1666, Negative Sentiment 0.1666

Introducing Anaconda Team Edition: Secure Open-Source Data Science for the Enterprise

I’m very excited to announce a new addition to Anaconda’s product line — Anaconda Team Edition!

For the last few years, Anaconda has offered two products: our free Anaconda Distribution, meant for individual practitioners, and Anaconda Enterprise, our full-featured machine learning platform for the enterprise. This left a gap for many data scientists and developers who use Anaconda professionally, but whose companies either do not yet need a ful…
2020-01-28 21:39:50+00:00 Read the full story…
Weighted Interest Score: 1.7467, Raw Interest Score: 1.2048,
Positive Sentiment: 0.2738, Negative Sentiment 0.0274

2020 release wave 1 plans for Dynamics 365 and Power Platform now available

Today, we published the 2020 release wave 1 plans for Dynamics 365 and Microsoft Power Platform, a compilation of new capabilities that will be released between April and September 2020. The new features and enhancements demonstrate our continued investment to power digital transformation for our customers and partners.

Dynamics 365

The first release wave of the year contains hundreds of new features across Dynamics 365 applications including S…
2020-01-27 00:00:00 Read the full story…
Weighted Interest Score: 1.7211, Raw Interest Score: 1.3012,
Positive Sentiment: 0.4879, Negative Sentiment 0.1762

Having A Hard Time Reconnecting With A VC? 5 Practical Tips

Many previous articles have talked about how to connect with investors, including how VCs can suck but you can minimize the pain, doing your own diligence on them, and working with smaller versus larger investors. What this post is focused on is on reconnecting with a VC that you already know. If you have a very strong relationship, such as they funded your previous company, then you should know well how to get their attention. But otherwise, if …
2020-01-27 00:00:00 Read the full story…
Weighted Interest Score: 1.4402, Raw Interest Score: 0.8130,
Positive Sentiment: 0.2555, Negative Sentiment 0.0465

Sales of new homes fell in December, but the future looks bright for the home-building industry

The numbers: Sales of newly-constructed homes in the U.S. dropped 0.4% on a monthly basis in December to a seasonally-adjusted annual rate of 694,000, the government reported Monday.

That’s compared with a seasonally-adjusted pace of 697,000 set in November, which was revised downward from the original report released last month. Because of the sample size of the new-home sales data collected by the U.S. Census Bureau, the report can have swings and significant revisions, as was also seen in the September and October data releases.

While new-home sales dropped on a monthly basis, they were up 23% from the previous year in December. The average rate of new home sales in 2019 was 681,000, which was 10.3% higher than 2018’s pace.

2020-01-27 00:00:00 Read the full story…
Weighted Interest Score: 1.2764, Raw Interest Score: 0.8753,
Positive Sentiment: 0.0729, Negative Sentiment 0.3282

Top Women in WealthTech 2020: Angela Pecoraro CEO of Advicent

Accomplishment(s): Our business started more than 25 years ago, and it is both our challenge and our opportunity to remain relevant and innovative for the next 25 years. I’m thrilled to serve as Advicent’s CEO, leading the company through the largest platform expansion we’ve embarked on in recent years.

As we set out to disrupt an entire industry with a new approach to financial planning, we quickly realized that,…
2020-01-22 00:00:00 Read the full story…
Weighted Interest Score: 1.0382, Raw Interest Score: 0.6530,
Positive Sentiment: 0.6028, Negative Sentiment 0.2344

If you would like to add your blog or website to our search crawler, please email customer_success@cloudquant.com. We welcome all contributors.

The post Alternative Data News. 29, January 2020 appeared first on CloudQuant.

AI & Machine Learning News. 03, February 2020

The Artificial Intelligence and Machine Learning Newsletter by CloudQuant

Play Which Face is Real?!

Defenses Emerge to Combat Adversarial AI

As threats like deep fakes and data poisoning lurk, momentum is building for deploying trusted, pre-trained AI models with embedded security and defenses against the emerging threat known as adversarial AI.

Adversarial AI attacks that can corrupt data used to train AI models are on the rise as companies seek to scale AI applications. In response, the consulting firm Booz Allen Hamilton launched an enterprise AI software product late last year designed to accelerate deployment and management of trusted AI models at scale.
2020-01-30 00:00:00 Read the full story…
Weighted Interest Score: 5.6088, Raw Interest Score: 2.4624,
Positive Sentiment: 0.0000, Negative Sentiment 0.5472

CloudQuant Thoughts : The idea that one can create an image that looks either like random noise or a well know object, add it to an image either algorithmically or by placing it within the captured image and completely fool an ML system is an idea that should give us all pause. These changes can be imperceptible to the human eye. Check out this great Stanford lecture online here (1h 22m) or this very simple example if you want to get caught up on Adversarial AI.

Web scraping is now legal – Here’s what that means for Data Scientists

In late 2019, the US Court of Appeals denied LinkedIn’s request to prevent HiQ, an analytics company, from scraping its data. The decision was a historic moment in the data privacy and data regulation era. It showed that any data that is publicly available and not copyrighted is fair game for web crawlers. But commercial use of scraped data is still limited
The decision does not, however, grant HiQ or other web crawlers the freedom to use data obtained by scraping for unlimited commercial purposes. For example, a web crawler would be allowed to search Youtube for video titles, but it could not re-post the Youtube videos on its own site, since the videos are copyrighted. In general, the copyright for data, including data for media files like video or music, is still enforceable regardless of how the data was obtained.

Some forms of web scraping are also still illegal. The decision also does not grant web crawlers the freedom to obtain data from sites that require authentication.

CloudQuant Thoughts : Tread Carefully! But this is great news for anyone who does their own web scraping for data.

IIT Delhi Startup Creates Buddhi Kit To Make AI Learning A Child’s Play

An IIT-Delhi startup has created a first-of-its-kind interactive DIY education kit based on artificial intelligence (AI). Buddhi AI DIY kit can be used to quickly and easily learn the basics of AI and build AI-based solutions for real-world problems. The kit will also help people without having any prior domain knowledge or training. The idea is to assist young students, tinkerers, makers, innovators, hobbyists, teachers, educationists, artists, parents and professionals from any background.

Buddhi (Build, understand, design, deploy human-like intelligence) kit was launched at IIT Delhi. An IIT statement read — “Buddhi kit helps users develop core skills such as problem-solving, creative thinking and ability to work in teams. With the kit, creative possibilities are endless as it can be used to easily introduce AI in any existing STEAM (science technology, engineering, arts & maths) project.”

2020-01-30 12:54:41+00:00 Read the full story…
Weighted Interest Score: 3.9588, Raw Interest Score: 1.3069,
Positive Sentiment: 0.4356, Negative Sentiment 0.1188

CloudQuant Thoughts : Kits that teach kids how AI works are my favorite things at the moment. This one looks excellent, I would also recommend the “build your own Smart Speaker” kit from ChatterBox.

Top 10 AI Trends for 2020

The rise of artificial intelligence in the workplace to enable and sustain the digital workforce is an apparent trend for 2020.

Artificial intelligence, machine learning, neural networks or whatever other fancy terms industry is coming out with for what is defined as the sophisticated computer technology that is becoming widely utilized to understand and improve business and customer experiences. I assume, you have heard of it before, but they way it is defined today is an area of computer science that emphasizes the creation of intelligent machines that work and react like humans…

2020-02-03 14:11:32.361000+00:00 Read the full story…
Weighted Interest Score: 3.1000, Raw Interest Score: 1.5680,
Positive Sentiment: 0.3877, Negative Sentiment 0.1292

CloudQuant Thoughts : A lightweight blog post but interesting in the observation that “there will be an AI for every worker”! This reminds me of Bill Gates vision of “A computer on every desk and in every home.” which was regarded as extremely unlikely at the time as most people had no concept of what a computer could be used for. But that goal has arguably been achieved, at least in the western world. An AI per person is the 2020 version of that vision. The idea that each of us will be accompanied by an AI assistant for our personal and our work life is actually less difficult for modern humans to imagine than Bill Gates’ vision from the mid 70s. We all interact with AI daily, from Siri to our car navigation to automated banking. This is going to be a wild future!

Top Machine Learning Projects Launched By Google In 2020

It may be that time of the year when new year resolutions start to fizzle, but Google seems to be just getting started.The tech giant has been building tools and services to bring in the benefits of artificial intelligence (AI) to its users. The company has begun upping its arsenal of AI-powered products with a string of new releases this month alone.

Here is a list of the top products launched by Google in January 2020…
2020-02-03 11:30:00+00:00 Read the full story…
Weighted Interest Score: 2.8066, Raw Interest Score: 1.6357,
Positive Sentiment: 0.2431, Negative Sentiment 0.0663

CloudQuant Thoughts : Keeping up with the Googles… Meena is a 2.6 billion parameter end-to-end trained neural conversational model that can conduct conversations that are more sensible and specific than existing state-of-the-art chatbots.

Xignite Now Available in AWS Data Exchange

SAN MATEO, Calif., /PRNewswire/ — Xignite, Inc. announced their financial data APIs are now available in AWS Data Exchange, a new service that makes it easy for millions of Amazon Web Services (AWS) customers to securely find, subscribe to, and use third-party data in the cloud. Xignite is providing AWS Data Exchange customers with end-of-day and historical equities and ETFs trading on North American exchanges: In addition to closing prices, the data includes open, high, low, volume, and simple corporate actions data such as dividend amounts and split ratio for the securities.

Xignite is an Advanced Technology Partner in the AWS Partner Network (APN) and has achieved AWS Financial Competency status . Xignite was also one of the first market data distribution offerings available on the cloud, launching on AWS in 2009. Xignite financial data APIs have been listed in AWS Marketplace since 2014. These designations recognize Xignite for providing deep expertise and relevant technical proficiency delivering solutions seamlessly on AWS.

2020-01-31 02:43:36+00:00 Read the full story…
Weighted Interest Score: 5.1564, Raw Interest Score: 2.6282,
Positive Sentiment: 0.2120, Negative Sentiment 0.0424

Crayon Data to demo maya.ai at FinovateEurope 2020

Singapore/Chennai, January 31, Crayon Data announced today that it will participate in Finovate Europe – one of the largest fintech events in Europe. Crayon will also exhibit and demo their flagship product, maya.ai at the event.

Finovate is a global conference series, focused on financial services technology. This is the first time the annual FinovateEurope conference will take place in Germany. More than 1,200 senior attendees,half of whom are from financial institutions,and more than 150 speakers and 60 demoing companies will be in attendance. FinovateEurope 2020 will be held between Feb 11 -13th, 2020 at the Intercontinental Berlin Hotel in Berlin.

Crayon Data is a big data and AI startup, based out of Singapore. It’s product AI-led personalization product, maya.ai equips top tier enterprises, across industries like banking, e-commerce and travel and hospitality, to have personalized conversations with each of their customers. Across all channels. maya.ai, comes equipped with richly curated vast external datasets, backed by cutting-edge artificial intelligence, delivered through a series of easy-to-use APIs that cater to the needs of the portfolio, campaigns, analytics and alliances teams within an enterprise.

2020-01-31 08:21:11+00:00 Read the full story…
Weighted Interest Score: 4.7734, Raw Interest Score: 1.8322,
Positive Sentiment: 0.2411, Negative Sentiment 0.0000

Investing 2020: The Future of Finance

It’s hard to believe that the first iPhone came out just a little over 12 years ago. It’s even harder to imagine life without our smartphones. The past decade has seen a tornado of innovation that’s mixed our personal lives and our digital lives in an inseparable cocktail that will define this century and beyond. Nowhere has this combination been so powerful than in our wallets.

The finance industry has always been at the forefront of innovation through technology, and for good reason. From open outcry trading at physical exchanges to high-frequency and algorithmic trading via ultra-fast networks; from paper savings account passbooks to robo-advisors that track our spending and investing, technological innovation has been a priority for financial institutions and consumers alike, both eager to make their transactions as fluid as possible.
2020-01-29 17:13:49.654000+00:00 Read the full story…
Weighted Interest Score: 4.6772, Raw Interest Score: 1.9440,
Positive Sentiment: 0.1647, Negative Sentiment 0.0988

Two Sigma’s private-equity arm is building out a data team — it’s a big move that could serve as a case study for PE firms that are behind the ball on AI

Two Sigma’s private-equity arm has plans to build out its data capabilities, recruiting engineers, and data scientists to help provide insights to investment professionals and portfolio companies, Business Insider has learned.

The private-equity arm of hedge fund Two Sigma, known as Sightway Capital, is building out a team of data scientists and engineers to provide deeper insights to investment professionals and portfolio companies, two sources with direct knowledge of the matter have told Business Insider. The goal is to bring its tech-oriented professionals closer in number to its investment professionals, one of these sources said. Sightway Capital’s website lists 17 investment professionals, not counting operating, legal and compliance staff. Meanwhile, there are two data scientists displayed.

Another source said that Sightway Capital will focus on “measured” growth, with not all positions filled immediately. New roles will range from people who will mine data about companies and industries, to project managers who interface with portfolio companies, to software developers, who create dashboards and tools to equip companies with data visualizations created by Sightway Capital.

2020-02-03 00:00:00 Read the full story…
Weighted Interest Score: 4.3572, Raw Interest Score: 2.0898,
Positive Sentiment: 0.0536, Negative Sentiment 0.0536

Unlocking Data Silos to Reach the Promised Land of Smart Data Analytics

With mountains of market data, historical prices, and transactions data stored in disparate systems, securities and investment firms are shifting from a focus on collecting data to extracting value from it. A December 2019 paper by capital markets consultancy GreySpark Partners examined the potential for buy-side and sell-side firms to transform large quantities of big data into actionable intelligence – producing what is known as ‘smart data – through specialized analytics.

The move comes as electronic trading has generated massive data sets across equities, fixed income and currencies. Firms are hiring data scientists and coding analytics to mine this data for trading opportunities or to identify patterns that help lower transaction costs. In the report, titled “Smart Data Analytics Set to Play Key Role in Reducing Buy Side and Sell Side Trading Costs,” GreySpark predicts that smart data inputs and data analytics will become more significant in the next three-to-five years in terms of client performance analytics, competitive differentiation, and value creation.

2020-02-03 14:22:56+11:00 Read the full story…
Weighted Interest Score: 4.2167, Raw Interest Score: 2.2246,
Positive Sentiment: 0.1055, Negative Sentiment 0.1247

Reflections on AI from Davos 2020 – World Economic Forum

What do you say to a Nobel Prize winner when discussing how to make AI explainable in a deep neural network with over one billion parameters? This was my first trip to Davos and it coincided with the World Economic Forum’s (WEF) celebration of its 50th annual meeting. The setting was picture perfect: an idyllic mountain town framed by snow-capped mountains under crystal clear blue skies. The world’s elite were out in force in their designer sunglasses. I spotted senior government ministers, billionaires, tech titans and rock stars all within an hour. And here I was talking to the Nobel Prize winner, Joseph Stiglitz, and making sure that our boutique AI management consultancy, Best Practice AI, was represented at the highest level. We discussed AI explainability, the words that are on everyone’s lips. While Professor Stiglitz has concerns from an academic point of view, I deal with the issue from a different perspective: bringing practical tools to boards who are grappling with AI ethics and how to evidence the management of AI risks.
2020-02-02 13:08:02.392000+00:00 Read the full story…
Weighted Interest Score: 4.1787, Raw Interest Score: 1.4770,
Positive Sentiment: 0.3630, Negative Sentiment 0.2629

3 Top Artificial Intelligence Stocks to Watch in February

Though artificial intelligence (AI) has been a hot topic among technologists for quite some time, it’s only in recent years that semiconductor, software, and cloud capabilities have gotten to the point where companies are now deploying AI on a wider basis.

AI is so powerful because it can help companies on every part of the income statement. It can identify the best leads and better satisfy customers through recommendation engines, helping to boost revenue. AI can also help automate many back office tasks, saving companies on their selling, general, and administrative costs. And perhaps most exciting, AI can also help research and development departments find new solutions or correct flaws in highly technological manufacturing processes, benefiting both research and development as well as costs of goods sold.

February is shaping up to be a crucial month for these three AI leaders across cloud, memory hardware, and software-based analytics. Here’s what investors should watch…

2020-02-03 00:00:00 Read the full story…
Weighted Interest Score: 3.8242, Raw Interest Score: 1.6567,
Positive Sentiment: 0.1999, Negative Sentiment 0.1999

Market contemplates AI standards amidst regulatory pressure

An industry-led artificial intelligence (AI) standard may be forthcoming according to Bill Wardwell, vice president of strategy and product at Bottomline Technologies.

“I think looking at AI and machine learning at this point from a technology provider standpoint, what you’re going to continue to see is probably more of an industry framework related to guidance around AI,” says Wardwell.

“We’ve seen other firms release plans or infor…
2020-01-29 00:00:00 Read the full story…
Weighted Interest Score: 3.7606, Raw Interest Score: 1.4771,
Positive Sentiment: 0.1257, Negative Sentiment 0.0629

Data Freedom- The Path To Unlocking True Enterprise Intelligence

Today, companies across the globe are redefining their business models and seeking innovative ways to extract data, connect it and employ it for meaningful insights and learning. But for any enterprise to become intelligent, they should have the freedom to efficiently utilise all data they are creating and consuming to make smarter business decisions. The smart insights have to be entirely driven by data, not human judgement, or assumptions. This is precisely where the philosophy of Data Freedom comes in.

The concept of Data Freedom is about good-quality data being available within the enterprise in good quality, on an agile platform to all operational users. This includes IT and DevOps, analytical users like data scientists, decision-makers like business executives, and external users who are part of the business value chain.

.
2020-01-27 13:13:00+00:00 Read the full story…
Weighted Interest Score: 3.7082, Raw Interest Score: 1.9605,
Positive Sentiment: 0.4711, Negative Sentiment 0.1064

Britain’s AI industry could boom post-Brexit by escaping damaging Brussels moves

Britain’s artificial intelligence sector could get a boost from Brexit if the country can escape from “poorly-constructed” regulations being drafted in the EU, experts have said.

Already the UK is seen as the third leading geography for AI, behind only China and the US, thanks to its world-class universities where some of the most advanced research is taking place.

European countries have been discussing how to regulate the emerging industry over recent years, with the EC saying it would want to implement rules to make AI “trustworthy and human”.
2020-01-31 00:00:00 Read the full story…
Weighted Interest Score: 3.6904, Raw Interest Score: 1.2613,
Positive Sentiment: 0.1802, Negative Sentiment 0.1802

40 Interview Questions On Statistics For Data Scientists

We frequently come out with resources for aspirants and job seekers in data science to help them make a career in this vibrant field. Cracking interviews especially where understating of statistics is needed can be tricky. Here are 40 most commonly asked interview questions for data scientists, broken into basic and advanced.

Here are some other interview questions resources for data scientists.

2020-02-02 07:20:14+00:00 Read the full story…
Weighted Interest Score: 3.6623, Raw Interest Score: 2.1120,
Positive Sentiment: 0.1014, Negative Sentiment 0.5349

2020 is the Year for Enterprise Data Connectivity

Expect this in 2020: Data will come to the forefront of enterprise priorities. The consumerization of IT and democratization of data have triggered a dramatic shift in data control across small and large organizations. Data control – once the sole domain of the CIO or CTO via the IT department – is increasingly in the hands of users.

The proliferation of applications created a shift within IT departments. Fewer and fewer companies look to IT to control data and instead, these organizations are governing data. Furthermore, with few exceptions, data is now firmly in the hands of business units given the propagation of cloud-based enterprise and functional and departmental applications. Today, organizations are continuously installing new applications to gain a competitive advantage and are establishing yet another source of data with each one. Our enterprise data truth is that digital information is now created by and accessible to the average non-technical user of applications and systems, without having to require the involvement of IT.

But what are those users – and the enterprises they represent – missing? They’re most often missing access to the right data, when and where they need it … in part due to the data fragmentation resulting from the multitudes of departmental apps, data in the cloud, and the diversity of enterprise systems. A Gartner forecast indicated that this year more than 40% of data-based tasks will be automated to bring higher productivity and more democracy to the data user community. Taken together, these trends further escalate the need to solve the massive data fragmentation problem, and the subsequent demand for comprehensive data access.

Consider three key tips for Enterprise Data Management success in 2020:

2020-02-03 08:35:22+00:00 Read the full story…
Weighted Interest Score: 3.6155, Raw Interest Score: 1.9649,
Positive Sentiment: 0.2090, Negative Sentiment 0.1254

Five Ways Artificial Intelligence Improves Contract Management

Cast aside apocalyptic visions of malevolent androids usurping our throne. Artificial intelligence is designed to act as an effective aid for specific tasks that were previously performed by decision-making humans.
If a university professor were scouring students’ essays for plagiarism, for instance, it would be difficult to detect every traceable amount of stolen information in each essay in a reasonable amount of time.

However, an artificially intelligent plagiarism detection service can check submitted documents against its database and the content of other websites to provide a detailed originality score in a matter of minutes or hours, allowing our collegiate example to focus exclusively on the content of those essays. The point is this: AI does not replace the duties of employed human beings, rendering our workforce obsolete. Instead, it assists professionals in performing the myriad of demands of their roles so that their positions can be streamlined into more strategy-based ones.

Recent AI’s machine learning is fostered by the data fed into it by live, human coordinators. This applies to organizations leveraging contract management software as well.
2020-02-03 12:27:57.337000+00:00 Read the full story…
Weighted Interest Score: 3.5681, Raw Interest Score: 2.2382,
Positive Sentiment: 0.3247, Negative Sentiment 0.1740

The human impact of data literacy

As the amount of data continues to grow year on year, a business’s ability to compete will increasingly be driven by how well it can extract insight, apply analytics and implement new technologies. That’s key to stay competitive in today’s ever-changing economy, leveraging rich datasets to get a deeper understanding of their customers, operations and the markets they operate in.

Dealing with an increased amount of data requires an adaptive, agile approach. The organisations that succeed are those that can make sense of the data, spotting the opportunities and assessing ideas quickly. But, before data can be used, it needs to be interpreted and understood properly.

2020-01-31 18:18:45 Read the full story…
Weighted Interest Score: 3.5227, Raw Interest Score: 1.7330,
Positive Sentiment: 0.4830, Negative Sentiment 0.1989

New Library Adds Causality to ML Models

A new open source library is designed to help data scientists and domain experts jointly develop machine learning models based on causal relationships rather than just data correlations. The developers of the new CausalNex library argue that running machine learning projects without considering causality can lead to faulty conclusions.

QuantumBlack, a data analytics unit of McKinsey & Co., said CausalNex is its second open source release after Kedro, a library aimed at production ML code. Its new machine le…
2020-01-28 00:00:00 Read the full story…
Weighted Interest Score: 3.5166, Raw Interest Score: 2.1510,
Positive Sentiment: 0.1218, Negative Sentiment 0.1218

AI And BI Are Vibrantly Sparking New Trends In Affiliate Marketing

The market for affiliate marketing is expected to reach $8.2 billion by 2022. AI is making it easier than ever to succeed in this growing field.

The application of Artificial intelligence and Business Intelligence in affiliate marketing has been actively discussed for quite a time. No wonder, more or less but the majority of marketers have already applied them both at their campaigns. Companies like Propel Media are using machine learning to del…
2020-01-27 16:46:56+00:00 Read the full story…
Weighted Interest Score: 3.4986, Raw Interest Score: 1.4467,
Positive Sentiment: 0.2704, Negative Sentiment 0.2569

Why Is Active Learning Important For Machine Learning

The lack of labelled data is one of the peskiest challenges in machine learning. A classifier that is put to identify spam from proper mails, cats from dogs or any other classifying tasks need to be fed with appropriate annotated data for accurate decision making.

However, this is not the case always; the real-world problems that ML models are tasked with solving come with uncertainties and deficiencies. So, keeping the model updated, in other words, making the model smar…
2020-01-27 05:22:56+00:00 Read the full story…
Weighted Interest Score: 3.3608, Raw Interest Score: 2.1888,
Positive Sentiment: 0.3060, Negative Sentiment 0.3295

Explainable Deep Learning in Breast Cancer Prediction

Understanding Convolutional Neural Network Prediction Results in Healthcare

Advanced machine learning models (e.g., Random Forest, deep learning models, etc.) are generally considered not explainable [1][2]. As described in [1][2][3][4], those models largely remain black boxes, and understanding the reasons behind their prediction results for healthcare is very important in assessing trust if a doctor plans to take actions to treat a disease (e.g., cancer) based on a prediction result. In [2], I …
2020-02-02 22:59:20.051000+00:00 Read the full story…
Weighted Interest Score: 3.3558, Raw Interest Score: 1.5307,
Positive Sentiment: 0.0882, Negative Sentiment 0.0401

Can We Use Medicines Designed By AI On Humans Just Yet

Artificial intelligence has been assisting humans in finding patterns in biological data in order to predict potential diseases. In a few cases, it is even outperforming prominent doctors in determining the ailments. However, with the latest advancements, pharma companies have turned towards AI to expedite the drug discovery. Sooner rather than later, we will be witnessing medicines developed by AI that are used on huma…
2020-02-01 08:30:00+00:00 Read the full story…
Weighted Interest Score: 3.2567, Raw Interest Score: 1.0302,
Positive Sentiment: 0.3115, Negative Sentiment 0.4552

China’s AI Champion IFlyTek Says 2019 Revenue Will Exceed US$1.4 Billion As It Downplays Impact Of Tech War

he US escalated

China’s voice recognition champion iFlyTek said on Monday that its 2019 revenue is expected to surpass 10 billion yuan (US$1.4 billion) on the back of healthy development in its core artificial intelligence business.

Despite being added to a US trade blacklist last October, iFlyTek said it will report a net profit of 732 million to 894 million yuan for 2019, representing year-on-year growth of between 35 to 65 per cent amid a “complicated international and domestic economic environment”, according to a company statement.

Last year was a challenging one for Chinese tech companies as the tech cold w…
2020-02-02 23:15:31-05:00 Read the full story…
Weighted Interest Score: 3.2456, Raw Interest Score: 1.7341,
Positive Sentiment: 0.1806, Negative Sentiment 0.2529

Apple cancels preexisting military drone Pentagon contract after acquiring AI company

Less than three weeks after quietly acquiring artificial intelligence company Xnor.ai, Apple has swiftly canceled the company’s preexisting contract with the Pentagon that would have seen its tech used in the controversial “Project Maven” military drone operation.

That’s according to a report from The Information, citing a person familiar with the matter. Project Maven is the Pentagon’s initiative to use artificial intelligence to identify objec…
2020-01-30 09:26:07 Read the full story…
Weighted Interest Score: 3.2432, Raw Interest Score: 1.4771,
Positive Sentiment: 0.0985, Negative Sentiment 0.4924

Deploying ‘industry 4.0’ technologies in treasury

As a growing number of financial operations move to ‘real-time’, treasurers need to rethink how they organise and manage treasury functions ensuring that investment in technology can support seamless processes, efficiently.

According to BCG’s research “70 percent of treasurers have yet to embrace digitisation in a meaningful way”. Many rely on fragmented data, outdated modeling and analytical tools to optimise balance sheet and risk management. …
2020-01-28 00:00:00 Read the full story…
Weighted Interest Score: 3.1516, Raw Interest Score: 2.0431,
Positive Sentiment: 0.3715, Negative Sentiment 0.0743

Microsoft launches $40M initiative to solve global health challenges with AI

Microsoft launched a major health research initiative Wednesday to address some of the medical world’s most confounding challenges using artificial intelligence.

The $40 million AI for Health initiative will focus on three core areas:

Studying, preventing, and treating diseases

Studying mortality and longevity around the world to protect against the next global health crisis

Reducing inequity in global healthcare

Microsoft will provide grants, data science experts, technology, and other resources to help partner organizations tackle health projects …
2020-01-29 17:30:18+00:00 Read the full story…
Weighted Interest Score: 3.1240, Raw Interest Score: 1.4300,
Positive Sentiment: 0.2328, Negative Sentiment 0.3658

Microsoft Health Innovation Awards 2020 are now open for submission

Accelerating innovation for better experiences, better insights, and better care

There’s never been a more demanding time in healthcare, with many factors driving the need for innovation to solve the industry’s most prevalent and persistent challenges. There has been considerable progress made in this space as we all strive to achieve healthier lives. With that, the submissions for the Microsoft Health Innovation Awards 2020 are now being accept…
2020-01-30 00:00:00 Read the full story…
Weighted Interest Score: 3.0380, Raw Interest Score: 1.3519,
Positive Sentiment: 1.3519, Negative Sentiment 0.1690

Importance of data in the digital age

It isn’t an understatement to say that a day won’t go by when digital transformation isn’t talked about – whether on Twitter, LinkedIN, or in the business press. Whilst too much focus is often put on the technology aspect of a digital transformation or being digital, a key foundation and enabler for the digital age has to be data. A recent Forbes article stated “companies that aren’t continuously reinventing their business – with data at the core – will end up watching from the sidelines while their market is disrupted”. A key industry where this statement is true has to be financial services.
2020-02-02 19:30:46 Read the full story…
Weighted Interest Score: 2.9249, Raw Interest Score: 1.8176,
Positive Sentiment: 0.2773, Negative Sentiment 0.1848

Jumio provides digital onboarding for CIMB mobile customers

Jumio, the leading provider of AI-powered end-to-end identity verification and authentication solutions, has partnered with CIMB Bank Philippines to provide a simple, hassle-free and convenient digital onboarding solution to Filipinos.

In its first full year of formal operations, CIMB Bank Philippines signed in 1.7 million Filipinos via the CIMB Bank PH mobile app, 30% of which are first-time bankers, making CIMB Bank PH the faste…
2020-02-03 11:39:00 Read the full story…
Weighted Interest Score: 2.9032, Raw Interest Score: 1.6144,
Positive Sentiment: 0.2306, Negative Sentiment 0.0000

The Advent And Scope Of AI Marketing In 2020 And Beyond

When it comes to bridging the existing gap between data science and its usage, targeting better marketing results, nothing beats the utilitarian nature of AI. While artificial intelligence alone is capable of sifting through humongous data sets for analyzing the relevant ones, AI marketing is slowly but steadily shaping up into a venture that comes with a host of benefits over the conventional ways of promoting a product or service.

That said, b…
2020-02-03 00:00:00 Read the full story…
Weighted Interest Score: 2.8694, Raw Interest Score: 1.2849,
Positive Sentiment: 0.4007, Negative Sentiment 0.0691

StreamSets Now Integrates with Microsoft SQL Server 2019 Big Data Clusters

StreamSets, provider of a DataOps platform, is supporting and integrating its platform for Microsoft’s recently announced SQL Server 2019 Big Data Clusters.

With this integration, SQL users are empowered to design and operationalize data pipelines for big data workloads without the complexities of coding for big data systems.

With StreamSets DataOps Platform’s new capabilities for Big Data Clusters, SQL developers can accelerate their analytics use cases by:

Design and operate continuous data flows with intuitive, visual tools, eliminati…
2020-01-30 00:00:00 Read the full story…
Weighted Interest Score: 2.8249, Raw Interest Score: 1.6949,
Positive Sentiment: 0.2511, Negative Sentiment 0.0628

Why we’re failing to regulate the most powerful tech we’ve ever faced

Alphabet CEO Sundar Pichai said artificial intelligence is “more profound than fire or electricity.” Author and historian Yuval Noah Harari said, “If you have enough data about me, enough computing power and biological knowledge, you can hack my body, my brain, my life, and you can understand me better than I understand myself.” And a recent Brookings Institution report prophesied that the country or region leading in AI in 2030 will rule the planet u…
2020-02-01 00:00:00 Read the full story…
Weighted Interest Score: 2.8165, Raw Interest Score: 1.1238,
Positive Sentiment: 0.2847, Negative Sentiment 0.3296

How To Write Movie Reviews with AI

Fine-Tuning GPT-2 for Short-Form Nonfiction

Photo by Felix Mooneeram on Unsplash

Imagine collaborating on a Medium article with an artificial intelligence that has read every single word you’ve ever written — a language model that learned from your posts, essays, dictated notes, early drafts, scanned diary entries, research files, favorite quotes, and every other scrap of thought that makes your writing unique.

Your AI writing partner could deliver a smart critique of your essay and make suggestions about how to improve your argument. It could sur…
2020-02-03 06:09:31.163000+00:00 Read the full story…
Weighted Interest Score: 2.8144, Raw Interest Score: 1.3034,
Positive Sentiment: 0.1955, Negative Sentiment 0.3258

StreamSets Announces Support for New Microsoft SQL Server 2019 Big Data Clusters

According to a new press release, “StreamSets®, provider of the industry’s first DataOps platform, announced today support and platform integration for Microsoft’s recently announced SQL Server 2019 Big Data Clusters. With this integration, SQL users are empowered to design and operationalize data pipelines for big data workloads without the complexities of coding for big data systems. Announced at Ignite conference last November, Microsoft SQL Server 2019 Big Data Clusters allows users to deploy scalable clusters of SQL Server, Apache Spark and HDFS containers running on Kubernetes.”

The release co…
2020-01-31 08:05:21+00:00 Read the full story…
Weighted Interest Score: 2.8087, Raw Interest Score: 1.6969,
Positive Sentiment: 0.2341, Negative Sentiment 0.0585

Five Data Ethics Considerations for 2020

During the past two years, data theft and privacy concerns have emerged as a heavy counterweight to the benefits of big data and data analytics. Data ethics, the right or wrong conduct related to handling data, is in daily public discourse. Professionals who work in data-related fields are rethinking long-held beliefs about its management and use. The debate centers on the responsibility of companies to ethically protect the rights of data source…
2020-01-30 00:00:00 Read the full story…
Weighted Interest Score: 2.8033, Raw Interest Score: 1.4835,
Positive Sentiment: 0.2522, Negative Sentiment 0.4747

AI Driving Personalization Efforts at Restaurants; Dynamic Drive-Thru Menus Coming

ample, is incorporating AI into its mobile app used by five million customers, through a partnership with Certona, a personalization engine, according to a recent account in Forbes. The app relies on machine learning to present content to users based on their individual behavior. It can differentiate menu items and pricing based on geographic region.

“Instead of displaying generic, or static, product recommendations, we use Certona’s AI engine to determine what the best products are to display to a customer,” stated Derrick Chan, Taco Bell’s director of e-commerce. “So, for example, if it is a first-time use…
2020-01-30 22:30:23+00:00 Read the full story…
Weighted Interest Score: 2.7267, Raw Interest Score: 1.1585,
Positive Sentiment: 0.3724, Negative Sentiment 0.0000

Dice 2020 Salary Report: Which Cities, Skills, and Occupations Paid the Most?

Welcome to the 2020 edition of the Dice Salary Report. In order to obtain the latest data on the top technology salaries, we surveyed more than 12,800 technologists over two months. Whether you’re brand-new to the industry or a longtime veteran, there’s data in here that’s relevant to your current career and future goals. Let’s jump in!

Certain skills saw a significant year-over-year bump, suggesting heightened demand by employers, and certain c…
2020-01-29 00:00:00 Read the full story…
Weighted Interest Score: 2.5744, Raw Interest Score: 1.8039,
Positive Sentiment: 0.2627, Negative Sentiment 0.0350

Expanding Your Data Science and Machine Learning Capabilities

Expanding Your Data Science

and Machine Learning Capabilities

SPECIAL DBTA ROUNDTABLE WEBINAR THURSDAY, JUNE 25, 2020 – 11:00 am PT / 2:00 pm ET

Surviving and thriving with data science and machine learning means not only having the right platforms, tools and skills, but identifying use cases and implementing processes that can deliver repeatable, scalable business value. The challenges are numerous, from selecting dat…
2020-06-25 00:00:00 Read the full story…
Weighted Interest Score: 2.5744, Raw Interest Score: 1.7004,
Positive Sentiment: 0.2429, Negative Sentiment 0.0810

Modern Data Warehousing: Enterprise Must-Haves

Modern Data Warehousing:

Enterprise Must-Haves

SPECIAL DBTA ROUNDTABLE WEBINAR THURSDAY, NOVEMBER 19, 2020 – 11:00 am PT / 2:00 pm ET

To fit into modern analytics ecosystems, legacy data warehouses must evolve – both architecturally and technologically – to deliver the agility, scalability and flexibility that business need to thrive in today’s data-driven economy. Alongside new architectural approaches, a variety of technologies have emerge…
2020-11-19 00:00:00 Read the full story…
Weighted Interest Score: 2.5448, Raw Interest Score: 1.6053,
Positive Sentiment: 0.0944, Negative Sentiment 0.0000

Data Lake Modernization for Speed, Scale and Agility

DBTA ROUNDTABLE WEBINAR THURSDAY, MARCH 19, 2020 – 11:00 am PT / 2:00 pm ET

Data lake adoption has more than doubled over the past three years. Currently in use by 45% of DBTA subscribers to support data science, data discovery and real-time analytics initiatives, data lakes are still underpinned by Hadoop in many cases, although cloud-native approaches are on the rise. The technologies and best practices surrounding data lakes continue to evolve, as well as the challenges, from data governance and security, to integration and architecture. Join us for a special roundtable webinar on March 19th to learn …
2020-03-19 00:00:00 Read the full story…
Weighted Interest Score: 2.5355, Raw Interest Score: 1.6227,
Positive Sentiment: 0.2028, Negative Sentiment 0.1014

Unlocking the Power of DataOps

DBTA ROUNDTABLE WEBINAR THURSDAY, MAY 7, 2020 – 11:00 am PT / 2:00 pm ET

A new methodology is on the rise at insights-hungry enterprises looking to bring improved quality and reduced cycle times to data analytics. Borrowing from Agile Development, DevOps and statistical process control, DataOps is poised to revolutionize data analytics with its eye on the entire data lifecycle, from data preparation, to reporting. However, improving the flow of data between managers and consumers within an organization through greater communication, integration and automation is no simple task, and it requires cultural ch…
2020-05-07 00:00:00 Read the full story…
Weighted Interest Score: 2.5234, Raw Interest Score: 1.4953,
Positive Sentiment: 0.6542, Negative Sentiment 0.0000

The Secret to Data and Analytics Success Is…People

One of the most astonishing facts culled from the data and analytics field is the persistently high rate of failure. Despite the billions of dollars and millions of hours invested, the majority of data analytics projects simply do not succeed. There are many reasons for this, of course. Sometimes the technology is not up to par, and the data is almost always dirty. But arguably, the biggest factor is a lack of investment in people.

There’s a growing realization that people are the key to data and analytics success. Of course, every organization that fancies itself to be “data-driven” would like to have …
2020-01-30 00:00:00 Read the full story…
Weighted Interest Score: 2.5152, Raw Interest Score: 1.2424,
Positive Sentiment: 0.2222, Negative Sentiment 0.2727

Saving Penguins with AI

Penguin populations (like those of most adorable animals) are threatened by looming environmental catastrophes, including climate change – but, as in the case of whales, protecting those populations first requires that we understand them and the challenges they are encountering. Now, Intel and Gramener are highlighting new research that uses AI to analyze Antarctic penguin populations, paving the way for better protection of their fragile ecosyst…
2020-01-29 00:00:00 Read the full story…
Weighted Interest Score: 2.5103, Raw Interest Score: 1.0116,
Positive Sentiment: 0.1499, Negative Sentiment 0.3747

On the Radar: Promethium, StreamNative, Inzata

Welcome to On the Radar, a new Datanami feature about interesting new startups in the big data and analytics space. In this week’s edition, we explore three vendors making news, including Promethium, NativeStreams, and Inzata.

Promethium, a provider of AI-powered data management software that’s based in Menlo Park, California, has landed on our radar with a $6 million round of funding led by .406 Ventures. The round, which the company tells us is a pre-Series A round, comes on the heels o…
2020-01-29 00:00:00 Read the full story…
Weighted Interest Score: 2.4160, Raw Interest Score: 1.4103,
Positive Sentiment: 0.0261, Negative Sentiment 0.0783

If you would like to add your blog or website to our search crawler, please email customer_success@cloudquant.com. We welcome all contributors.

The post AI & Machine Learning News. 03, February 2020 appeared first on CloudQuant.

Performance review of ESG Dataset available from CloudQuant DataSet Catalog… As Managed Funds decrease in popularity and Passive Funds take over there has been one bright spark for Fund Managers as Investors have realized that they can dramatically influence the behavior of companies through their investments. With each passing month more Millennials come into the trading environment with their well established trends of making purchasing and investing decisions influenced heavily by environmental and social impact. Analysis of ESG friendly ETFs and investment funds have shown them out performing all other asset classes. We have also seen, over the last year, a dramatic increase in the number of News postings regarding ESG data. This January 31st post from Barron’s is one of the most recent… “As sustainable investing becomes more popular, independent firms with these principles are more likely to be acquired.” … “…competition has increased among asset managers” …”the number of annual mergers among publicly traded asset managers doubled.” “Sustainable investing has been a bright spot. Last year, flows into these funds in the U.S. more than tripled, marking the fourth year of record flows.”

CloudQuant Alternative Data Catalog

CloudQuant strives to dramatically increase your odds of finding a suitable, appropriate and valuable Alternative Data Set. If you have been in the Alternative Data environment you know how difficult it is to find Alternative Data Sets with proven performance. Do you trust the results supplied by the data vendor (rarely reproducible) or do you carry out your own analysis (too often a monumental waste of time and resources). CloudQuant researches the most in demand data types, finds sets that have not already had their Alpha consumed. We test the performance of the data and provide a white paper of the results and Python Code to reproduce and confirm the results to your satisfaction (using our leading Python Cloud Backtester CloudQuant Mariner). Head over to our Data Catalog to find out more about our tried and tested Data Sets which include the ESG Data Set from G&S Quotient.

The post Environmental Social and Governance (ESG) Alternative Data Sets appeared first on CloudQuant.

Alternative Data News. 05, February 2020

The AltDataNewsletter by CloudQuant

Top Tech Trends To Watch in 2020 – Alternative Data is #5

Alternative data drives financial services.

Investors and hedge funds are looking beyond financial statements for a competitive edge. They’re harnessing “alternative data” from job listings, social media posts and satellite images to foot traffic at stores and the flight paths of corporate jets. Thanks to artificial intelligence, machine learning and natural language processing, these vast pools of data are easier to search and more accurate in predicting trends. Alternative data startups include AlphaSense, BattleFin and Ireland-based Eagle Alpha.

2020-02-03 15:34:55 Read the full story…

CloudQuant Thoughts : Alternative Data makes it to # 5 on Forbes top 5 Trends to watch in 2020! You are ahead of the curve baby! Stay ahead by heading over to the CloudQuant Alternative Data Catalog.

Unlocking Data Silos to Reach the Promised Land of Smart Data Analytics

2020-02-03 15:34:55 Read the full story…
Weighted Interest Score: 4.2167, Raw Interest Score: 2.2246,
Positive Sentiment: 0.1055, Negative Sentiment 0.1247

CloudQuant Thoughts : Smart Data is just automated Feature Engineering. In my experience the Smartest data is produced by a Human Expert working with the source data to create unique and quality data. For example, in human traffic analysis, the years of expertise may point a Human Expert to immediately check the duration of Amber on a high accident rate intersection. Whilst we would expect an ML system, given the right data, to come to the same conclusion, a Human Expert can shortcut the data leaving the ML to focus on what is the ideal solution.

Ex-Google Cloud engineers raise $8M for Seattle machine learning startup Kaskada

Machine learning is all the rage in big tech, but still largely unavailable to most companies that don’t have the resources or the knowhow to build it. Seattle startup Kaskada wants to change that.

The company just raised a $8 million Series A round to grow its software platform for big companies to deploy machine learning — systems that can learn from experience without requiring additional programming. The round brings the startup to $9.8 million in lifetime funding, and investors include Voyager Capital, NextGen Venture Partners, Founders’ Co-op, and Walnut Street Capital Fund.

Kaskada’s software is geared primarily toward enterprise companies outside the tech world that still rely on some form of data science for their products. The startup wants to make it easier for the two main roles involved with machine learning products — data scientists and data engineers — to work together.

2020-02-04 17:00:13+00:00 Read the full story…
Weighted Interest Score: 2.7668, Raw Interest Score: 1.8651,
Positive Sentiment: 0.2778, Negative Sentiment 0.1587

Kaskada raises $8 million to facilitate AI feature engineering

Feature engineering — the process of using domain knowledge to extract features from raw data — is essential to tuning machine learning performance. It’s also typically arduous and involves rewriting features before they’re deployed, which is why in 2018 Ben Chambers and Davor Bonaci cofounded Kaskada, which uses mining techniques to compute and serve AI features in real time.

Today the Seattle, Washington-based startup announced it has raised $8 million in a series A round of funding, with participation from Voyager Capital, NextGen Venture Partners, Founders’ Co-op, and Walnut Street Capital Fund. This brings Kaskada’s total raised to $9.8 million, following a $1.8 million seed round in September 2018. CEO Bonaci says the capital will be used to accelerate the company’s growth, expand its team of software engineers, and fulfill customer demand ahead of its flagship product’s launch in the first half of 2020.

2020-02-04 00:00:00 Read the full story…
Weighted Interest Score: 4.1863, Raw Interest Score: 2.4801,
Positive Sentiment: 0.0886, Negative Sentiment 0.0000

CloudQuant Thoughts : This is obviously a hot topic this week!

Labelbox raises $25 million to grow its data-labeling platform for AI model training

Labelbox today announced the close of a $25 million series B round to grow its platform that helps customers label the data needed to train AI systems. The round was led by Andreessen Horowitz, with participation from Google’s AI-focused Gradient Ventures fund, Kleiner Perkins, and First Round Capital.

The funds will be used to develop and accelerate Labelbox’s roadmap for machine learning and computer vision models by doubling the size of its engineering and sales teams. Labelbox also enables users to automate some labeling so a company can manually label all data except any that falls below a particular prediction confidence threshold, COO Brian Rieger told VentureBeat in a phone interview.

2020-02-04 00:00:00 Read the full story…
Weighted Interest Score: 3.2148, Raw Interest Score: 1.7570,
Positive Sentiment: 0.0976, Negative Sentiment 0.0000

CloudQuant Thoughts : Break your data so that you manually label the high prediction confidence data and leave a machine to label the low prediction confidence data. Hmmmm… Interesting…

NGD raises $20 million for storage hardware designed to handle AI workloads

IDC predicts that worldwide data will grow 61% to 175 zettabytes by 2025, which will invariably put a strain on infrastructure as organizations look to sort, search, scan, and otherwise derive value from this data. That’s where NGD Systems comes in — it’s an Irvine, California-based company offering a computational storage drive tailor-made for on-device AI and machine workloads. To lay a runway for growth, NGD this week closed a $20 million series C financing round led by Western Digital Capital, with participation from existing investors including Orange Digital Ventures, Partech Ventures, BGV, and Plug-N-Play.

The round brings NGD’s total raised to date to $45 million following a $12.4 million series B in February 2018, and CEO Nader Salessi says the funding will enable NGD to execute its go-to-market strategy by supporting production as well as sales and marketing efforts.

2020-02-05 00:00:00 Read the full story…
Weighted Interest Score: 3.4074, Raw Interest Score: 1.8519,
Positive Sentiment: 0.2593, Negative Sentiment 0.1481

New Books and Resources for Data Science Central Members

We are in the process of writing and adding new material (compact eBooks) exclusively available to our members, and written in simple English, by world leading experts in AI, data science, and machine learning. We invite you to sign up here to not miss these free books.

Statistics: New Foundations, Toolbox, and Machine Learning Recipes
Deep Learning and Computer Vision with CNNs
Getting Started with TensorFlow 2.0
Book: Classification and Regression In a Weekend
Online Encyclopedia of Statistical Science
Book: Azure Machine Learning in a Weekend
Book: Enterprise AI – An Application Perspective
Book: Applied Stochastic Processes

2020-02-04 16:04:32+00:00 Read the full story…
Weighted Interest Score: 3.8875, Raw Interest Score: 1.5997,
Positive Sentiment: 0.0849, Negative Sentiment 0.0425

2020 is the Year for Enterprise Data Connectivity

2020-02-03 08:35:22+00:00 Read the full story…
Weighted Interest Score: 3.6155, Raw Interest Score: 1.9649,
Positive Sentiment: 0.2090, Negative Sentiment 0.1254

These China Companies Can Survive The Wuhan Coronavirus

Workers use their laptops near a display showing sales data at the command center at the … [+] headquarters of e-commerce retailer JD.com in Beijing. Investors perception is that e-commerce players will weather the coronavirus outbreak better than most. The KraneShares China Internet ETF was up over 3% on Monday, a down day for China. (AP Photo/Mark Schiefelbein) ASSOCIATED PRESS

China’s stock market fell on Monday. Anyone who was surprised by that is …
2020-02-03 00:00:00 Read the full story…
Weighted Interest Score: 2.7916, Raw Interest Score: 1.3462,
Positive Sentiment: 0.1122, Negative Sentiment 0.2083

If You Need Somebody — Not Just Anybody — Data Literacy Help Is Here

The report “Data Literacy Matters: The Writing’s On The Wall” presents Forrester’s data literacy framework, which outlines the components of a comprehensive data literacy program. The second report in our series on data literacy, “Build A Data Literacy Curriculum Of ACES,” which is coming soon, will address the question of external training. Organizations needing guidance on building a data literacy program can turn to:

Peer exchanges.
Data literacy specialists.
Data and analytics tools vendors.
Insights service providers.

2020-02-05 09:37:14-05:00 Read the full story…
Weighted Interest Score: 2.4778, Raw Interest Score: 1.3246,
Positive Sentiment: 0.2805, Negative Sentiment 0.1247

How to ensure your data science projects are successful every time

In a 2017 survey, Gartner analysts found that more than half of data science projects never deploy. This might lead some to believe there are flaws within the data, analytics tools or underlying ML models, but that’s not the case. At Plotly, we know from experience that failures to launch typically stem from an inability to bridge model outputs with real business or organizational next steps.

As the statistic suggests, this is a major sticking point across our industry. To help navigate this roadblock, we’ve pulled together our learnings from work we’ve done with our clients over the years and what we’ve heard from the Plotly community. It consistently comes back to three main points — if you get these right, you’re on your way to ensuring your projects will have real business impact each time they are developed.

Ask the right questions
Build the right UI
Define the structure for operationalizing your application

2020-02-03 17:10:44.286000+00:00 Read the full story…
Weighted Interest Score: 2.4703, Raw Interest Score: 1.2586,
Positive Sentiment: 0.2517, Negative Sentiment 0.1831

Does Google Cloud Stand A Chance Against AWS?

Google’s cloud computing business may have a hit $10 billion annual revenue run rate with the fourth-quarter 2019 earnings, but is it on course to topple Amazon in the enterprise cloud war? With Google’s parent company, Alphabet Inc’s showing worst fourth-quarter revenue growth since 2015, the company will need to aggressively pursue more growth opportunities and strengthen its capabilities within this vertical if it wants to have a fighting chance.

A marked shift from typical financial disclosures, the company published its Q4 results on a minute basis, including fresh data on Google Search, YouTube and Cloud. The growth in cloud, which has been up 53% year-over-year, was driven by Google Cloud Platform (GCP) and brought in $2.6 billion, up from $1.7 billion in the year before. By comparison, Amazon Web Services (AWS) brought in nearly $10 billion in sales in its quarterly results, hitting a $40 billion annual run rate, which is four times the projection for Google.
2020-02-05 05:42:17+00:00 Read the full story…
Weighted Interest Score: 2.2682, Raw Interest Score: 1.4676,
Positive Sentiment: 0.2224, Negative Sentiment 0.0445

Google Faces Fresh EU Probe Over User Location Data

Alphabet-owned Google (GOOGL) – Get Report is facing a fresh probe in the European Union over concerns the company’s collection and processing of user location data through its various programs and apps could be violating the region’s stringent privacy rules. In a statement on Tuesday, the Irish Data Protection Commission said the Mountain View, Calif.-based search-engine giant was being questioned about “the legality of Google’s processing of location data and the transparency surrounding that processing.”

Of concern to regulators is whether Google and other companies that offer location-tracking apps such as mapping and other functions are collecting and analyzing more than just individuals’ locations, such as information about their shopping and commuting habits, sexual orientation and even political affiliations.

2020-02-04 06:47:33-05:00 Read the full story…
Weighted Interest Score: 2.1696, Raw Interest Score: 1.5779,
Positive Sentiment: 0.1578, Negative Sentiment 0.4734

If you would like to add your blog or website to our search crawler, please email customer_success@cloudquant.com. We welcome all contributors.

The post Alternative Data News. 05, February 2020 appeared first on CloudQuant.

AI & Machine Learning News. 10, February 2020

The Artificial Intelligence and Machine Learning Newsletter by CloudQuant

YouTuber uses neural networks to upscale 1896 short film to 4K 60 fps

Upscaled and resounded version of a classic B&W movie: Arrival of a Train at La Ciotat, The Lumière Brothers, 1896

Source used to upscale: https://youtu.be/MT-70ni4Ddo
To upscale to 4k – Gigapixel AI – Topaz Labs https://vc.ru/76580
To add FPS – Dain, https://sites.google.com/view/wenbobao/dain

There are many times when we see ‘future technology’ that just feels so out of place. We call it a marketing gimmick and move on. However, a YouTuber has shown us that not everything is unreal. There are some technological advancements that require using the adage “so good to be true.”

A YouTuber, Denis Shiryaev, has used the technological advancements available and turned a black and white movie from the year 1896 into a movie with 4k crystal clarity, running at 60 frames per second.

We have all been so numbed to the technological buzzword of Artificial Intelligence, claimed to be used by every tech firm out there. Artificial intelligence has had a long history with humans and software development.

The idea kicked off with the objective of understanding and imitating how a human brain learns. Coupled with this are the concepts of machine learning, deep learning, neural networking, etc. While they all probably sound like a gimmick, just words without meaning, it is not so. There is actual scientific technology behind this.

2020-02-06 00:00:00+00:00 Read the full story…

CloudQuant Thoughts : The future looks rosy, All our old media, restored and enhanced by AI. Video Games up-rendered by Nvidia’s DLSS (Deep Learning Super Scaler), OK that one did not work out well, but in the future, who knows!?

Alternative Data, It’s Not Just For Hedge Funds Any More • Integrity Research

lternative data has become ubiquitous on Wall Street, especially among quantitative hedge funds. However, in the past few years many new client segments have started to include the use of alternative data as a key part of their business decision making processes.

The Changing Face of the Alternative Data Industry. In the past, most investment professionals built models and did their research, using conventional, widely available data sets, like financial statement data and historical stock prices. The advent of big data tools and enhanced computing power, have allowed larger and less structured, unconventional alternative data sets to be studied, in search of leading edge insights and advantages in the investment process. Enterprising alternative data providers are searching for new markets to sell their data and are always on the lookout for new data sets that may be capable of improving alpha. As with any product, opening new markets to alternative data increases the potential revenue that can be generated.
2020-02-10 02:30:04+00:00 Read the full story…
Weighted Interest Score: 7.3444, Raw Interest Score: 2.3708,
Positive Sentiment: 0.3003, Negative Sentiment 0.0474

CloudQuant Thoughts : Check out our Wednesday blogpost which is more focussed on AltData. Knowing that AltData is trending is one thing, knowing how to use it and how to find great quality AltData without all the leg work can be extremely difficult. Head over to our Data Catalog to see what we are doing to make it easier for you!

The “Rise of Alternative Data:” So, What the Heck Is It?

Big data adoption is fairly widespread, with 53% of companies using it in 2017. But it’s just the tip of the iceberg. Now, an increasing number of businesses are recognizing the value of alternative data that may not have been on their radar a few years ago. In fact, total buy-side spend jumped from $232 million in 2016 to $1.08 billion in 2019, and it’s predicted to reach over $1.7 billion by 2020. Companies that know how to properly harness alternative data can greatly improve their decision-making, and gain a significant competitive advantage.

What is alternative data? “Alternative data draws from non-traditional data sources so that when you apply analytics to the data, they yield additional insights that complement the information you receive from traditional sources,” explains Krishna Nathan, CIO of S&P Global.
2020-02-07 11:00:00+00:00 Read the full story…
Weighted Interest Score: 5.7850, Raw Interest Score: 1.9083,
Positive Sentiment: 0.2726, Negative Sentiment 0.0341

CloudQuant Thoughts : First two stories relate to Alternative Data! Alternative Data and your own Exhaust Data are going to be huge drivers for change in the markets ahead. If you have Exhaust Data that you think may be useful for Investors and Traders please get in touch. CloudQuant can help you make those connections, provide proof of the value of the data and help you to present the data in a format that the market expects.

How To Fool AI With Adversarial Attacks

Research in adversarial attacks has been the latest trend in technology, where developers, experts, and scientists are trying to trick AI bots by making subtle changes. Undoubtedly, ML models perform miserably if they are evaluated in a completely different environment as we are yet to develop an AI that can generalise and deliver superior results in new situations. But what has drawn interest from experts is that the outputs of these AI-based solutions can be swayed even with the smallest of changes. Such flaws depict that we are still a long way away from achieving an AI that we all dream of. In this article, we will show you how some researchers have deceived AI bots.

Researchers from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) developed a tool called TextFooler to trick AI bots. The tool forces Alexa and Siri to predict wrong with adversarial attacks, where inputs were deliberately created to fool the ML algorithms.

TextFooler attacks natural language processing (NLP) systems such as Alexa and Siri. The framework takes the input as text and then determines the word that will be vital for NLP-based systems to make predictions. Post that, the TextFooler replaces the word with a contextual synonym while ensuring that the grammar and original meaning has not been altered. For one, instead of using the input ‘The characters, cast in impossibly contrived situations, are estranged from reality,’ TextFooler replaced it with “The characters, cast in impossibly engineered circumstances, are fully estranged from reality’ to get different outputs. TextFooler was even used with some of the most popular open-source NLP model, BERT. Researchers were successfully able to bring down the 90 plus accuracy of BERT to under 20% by only changing 10% of the input words.

2020-02-10 12:30:00+00:00 Read the full story…
Weighted Interest Score: 3.8062, Raw Interest Score: 1.6984,
Positive Sentiment: 0.2071, Negative Sentiment 0.4557

MIT CSAIL’s TextFooler generates adversarial text to strengthen natural language models

AI and machine learning algorithms are vulnerable to adversarial samples that have alterations from the originals. That’s especially problematic as natural language models become capable of generating humanlike text, because of their attractiveness to malicious actors who would use them to produce misleading media. In pursuit of a technique that illustrates the extent to which adversarial text can affect model predictio…
2020-02-07 00:00:00 Read the full story…
Weighted Interest Score: 2.5924, Raw Interest Score: 1.5155,
Positive Sentiment: 0.2312, Negative Sentiment 0.4367

CloudQuant Thoughts : As mentioned last week, Adversarial Attacks run the risk of affecting your AI model. Defending against such attacks must be part of your planning process.

How To Watermark Your Dataset With Radioactive Data Technique

Large scale machine learning projects require vast amounts of data, i.e. large datasets. Training models on these datasets is tedious and therefore poses a danger of running into redundancy. It makes no sense to train a model on some data if it has already been trained? A responsible developer would always like to know this information in order to track biases in models.

But how would one know if the data has already been used for training? To answer these questions, Facebook’s AI team, in collaboration with INRIA, proposed a new technique called Radioactive Data, in their paper titled the same. “Our objective in this paper is to enable the traceability for datasets.” Their technique, believed the authors, is robust to data augmentation and offers a higher signal to noise ratio than data poisoning methods.

2020-02-10 07:00:00+00:00 Read the full story…
Weighted Interest Score: 4.6498, Raw Interest Score: 1.8491,
Positive Sentiment: 0.1491, Negative Sentiment 0.3281

CloudQuant Thoughts : Curve fitting and use of Out of Sample data for training are difficult things to track. As demand for data scientists ramps up even more, more rookie mistakes will be made. Anything that can help prevent this will be a huge benefit. But then again.. it’s FaceBook.

Fintech workforce to expand 19% by 2030 thanks to AI, Cambridge University predicts

n a recent report, the Cambridge Centre for Alternative Finance (CCAF) and the World Economic Forum (WEF) found that rather than observing AI as a single instrument for blanket application across the industry, AI can be viewed as a toolkit that is being used to tinker and build services in an abundance of ways to achieve a variety of objectives. Using data collected in a global survey during 2019, the report analysed a sample of 151 fintechs and incumbents across 33 countries to paint a rich picture of how artificial technology is being developed and deployed within the financial services sector.

While 77% of respondents noted that they expect AI to become an essential business driver across the financial services industry in the near term, the report found that the way incumbents and fintechs are leveraging AI technologies differ in a number of ways. A higher share of fintechs tend to be creating AI-based products and services, employing autonomous decision-making systems, and relying on cloud-based systems. Whereas incumbents appear to focus on “harnessing AI to improve existing products. This might explain why AI appears to have a higher positive impact on fintechs’ profitability.” 30% of the fintechs surveyed indicated a substantial increase in profit as a result of AI, while only 7% of incumbents indicated such profitability.

2020-02-07 12:00:00 Read the full story…
Weighted Interest Score: 5.3806, Raw Interest Score: 2.1761,
Positive Sentiment: 0.4002, Negative Sentiment 0.1001

What’s Ahead in Data for 2020—And the Coming Decade

We stand at the start of a new year and on the precipice of a new decade—the 2020s. For data managers, these will likely be the “Roaring ’20s” with data at the heart of every key business initiative, accented by a growing sophistication in technologies and methodologies focused on increasing the intelligence of the enterprise.

In the year ahead, organizations will intensify their efforts to manage and process the data that underpins many of today’s cutting-edge initiatives such as AI and machine learning. “The competitive edge goes to the organizations that understand and treat data analytics as one discipline,” said Gavin Day, SVP of technology at SAS. “Organizations are forgetting the critical nature that timely and fit-for-purpose data plays within training, model development, and model deployment. The use of AI within data management technologies will change the role and job function of our data workers—including data scientists. The days of data workers spending time configuring data quality and data integration jobs is behind us. Data analytics platforms use AI to do the routine, heavy lifting so we can focus on what we’re good at—creativity and solving analytical challenges that move our business forward.”

In the process, AI and analytics teams will merge into one as the new foundation of the data organization. “As the importance of data grows, a multitude of ways to get insights has emerged,” said Haoyuan Li, founder and CTO of Alluxio. “Yesterday’s Hadoop platform teams are today’s AI teams. It’s no longer just about managing your data lake.” AI takes a new approach to deriving value from structured and unstructured data, said Li. “What used to be statistical models has converged with computer science, becoming AI and ML.”
2020-02-10 00:00:00 Read the full story…
Weighted Interest Score: 5.1348, Raw Interest Score: 2.3518,
Positive Sentiment: 0.1611, Negative Sentiment 0.0644

Man and machine need each other – Systematica CEO

Self-driving cars may have already begun to replace human drivers but the end goal of AI in asset management should not be fully autonomous investing, according to Leda Braga, chief executive of $8.2 billion quant hedge fund Systematica Investments. “Autonomous investing is not the target. The target is the powerful association of machine learning and human investment management skills,” she said, speaking at the Cayman Alternative Investment Summit on February 6. Braga described a case study

2020-02-10 09:51:36+00:00 Read the full story (Registration Wall)…
Weighted Interest Score: 4.7904, Raw Interest Score: 2.8056,
Positive Sentiment: 0.0000, Negative Sentiment 0.0000

60 Interview Questions On Machine Learning

We frequently come out with resources for aspirants and job seekers in data science to help them make a career in this vibrant field. Cracking interviews especially where understating of machine learning is needed can be tricky. Here are 60 most commonly asked interview questions for data scientists, broken into linear regression, logistic regression and clustering.

2020-02-09 08:39:52+00:00 Read the full story…
Weighted Interest Score: 3.7911, Raw Interest Score: 1.6377,
Positive Sentiment: 0.1136, Negative Sentiment 0.2741

15 Python Libraries for Data Science You Should Know – Dataquest

Python is one of the most popular languages used by data scientists and software developers alike for data science tasks. It can be used to predict outcomes, automate tasks, streamline processes, and offer business intelligence insights. It’s possible to work with data in vanilla Python, but there are quite a few open-source libraries that make Python data tasks much, much easier.

You’ve certainly heard of some of these, but is there a helpful library you might be missing? Here’s a line-up of the most important Python libraries for data science tasks, covering areas such as data processing, modeling, and visualization.

Scrapy
BeautifulSoup
NumPy
SciPy
Pandas
Keras
SciKit-Learn
PyTorch
TensorFlow
XGBoost
Matplotlib
Seaborn
Bokeh
Plotly
pydot

2020-02-05 16:14:09+00:00 Read the full story…
Weighted Interest Score: 4.0674, Raw Interest Score: 2.1138,
Positive Sentiment: 0.3325, Negative Sentiment 0.0356

New Books and Resources for Data Science Central Members

We are in the process of writing and adding new material (compact eBooks) exclusively available to our members, and written in simple English, by world leading experts in AI, data science, and machine learning. We invite you to sign up here to not miss these free books.

Statistics: New Foundations, Toolbox, and Machine Learning Recipes
Deep Learning and Computer Vision with CNNs
Getting Started with TensorFlow 2.0
Book: Classification and Regression In a Weekend
Online Encyclopedia of Statistical Science
Book: Azure Machine Learning in a Weekend
Book: Enterprise AI – An Application Perspective
Book: Applied Stochastic Processes

2020-02-04 16:04:32+00:00 Read the full story…
Weighted Interest Score: 3.8875, Raw Interest Score: 1.5997,
Positive Sentiment: 0.0849, Negative Sentiment 0.0425

Finmechanics and MathLabs to provide advanced AI to corporate banks to improve processes and conduct risk in capital markets.

Finmechanics Pte. Ltd. (Finmechanics) and Math Labs Research Ltd. (MathLabs) have announced a technology partnership to provide corporate banks with enhanced client experience, as well as automated detection of trade errors in Capital Markets, using advanced AI.

“Finmechanics is executing projects providing corporate banks with digital tools to trade and negotiate a broad variety of financial instruments. We bring cutting edge AI algorithms to banks, to help direct their clients to the products they need, and prioritise those clients accordingly”, says Anindya Sarkar, CEO of Finmechanics.

“Partnering with a fast growing fintech such as Finmechanics is a key accelerator in our journey to provide the financial industry with AI-Optimal strategies.” says Prodipto Ghosh, Chief AI Scientist at MathLabs. “Finmechanics products are ideal carriers of our algorithms into banks’ architecture; they also allow for rapid development of a portfolio of intelligent tools”, he adds.

2020-02-04 00:00:00 Read the full story…
Weighted Interest Score: 4.4301, Raw Interest Score: 1.8952,
Positive Sentiment: 0.4032, Negative Sentiment 0.1613

GNY Launches ML As A Service Tool In AWS Marketplace

Studies have shown becoming artificial intelligence (AI) ready by 2022 is a major priority for the US companies, many of them fearing that they will lose market share to their competition if they don’t adapt. However, many of them lack the expertise and resources to get there. AI feeds on data, and many companies need to determine exactly what data they should be collecting.

GNY, a decentralised machine learning (ML) platform, has announced the launch of a new software-as-a-service (SaaS) tool designed to allow businesses to check the AI-readiness of their data.

The GNY Data Diagnostic analyses a company’s datasets and detects if the historical data is strong enough for an effective ML, or if weak or inconsistent datasets are skewing the predictions and weakening the business. GNY’s Data Diagnostic team offers everything a business would need to become AI-ready, from education about AI basics and how predictive analytics works, to learning about the company’s data collection practices and offering a tailored solution. The result includes a detailed analysis of the historical data — what needs to be done to support ML, as well as an analysis of the digital architecture’s ability to support the company’s business goals.

2020-02-06 10:27:19+00:00 Read the full story…
Weighted Interest Score: 4.3812, Raw Interest Score: 1.9623,
Positive Sentiment: 0.3078, Negative Sentiment 0.1154

Unlocking Data Silos to Reach the Promised Land of Smart Data Analytics

2020-02-03 03:22:56+00:00 Read the full story…
Weighted Interest Score: 4.2167, Raw Interest Score: 2.2246,
Positive Sentiment: 0.1055, Negative Sentiment 0.1247

Benchmark Analysis of Popular Image Classification Models

6 Popular Image classification models on Keras were benchmarked for inference under adversarial attacks

Image classification models have been the torchbearers of the machine learning revolution over the past couple of decades. From medical diagnosis to self-driving cars to smartphone photography, the field of computer vision has its hold on a wide variety of applications. The advent of customized hardware for machine learning applications has propelled more research into image recognition techniques. Conventional deep learning models were tweaked and better architectures were developed. Today there are tens of good image classification models that have demonstrated state of the art results and we wanted to know how these models perform under adversarial attacks.

In this work, we use pre-trained Keras models trained on the ImageNet dataset to benchmark them for adversarial attacks. We test the accuracy of these models with and without noise using random images that are not part of the ImageNet dataset. An adversarial attack on an image can be something as simple as a blur. Keras has become popular with developers ever since the introduction because of its lightweight, written in Python and offers high-level APIs to run models with great ease. For this very reason; i.e. the ease of execution, we have used pre-trained models offered by Keras.

2020-02-04 11:01:24+00:00 Read the full story…
Weighted Interest Score: 3.9389, Raw Interest Score: 2.2694,
Positive Sentiment: 0.2960, Negative Sentiment 0.1973

ECB runs AI coding marathon to get new insights into supervisory data

The European Central Bank (ECB) has teamed up with digital innovation outfit Reply to run a 48-hour coding marathon focused on the application of AI and machine learning. Taking place in the last days of February at the ECB in Frankfurt, the supervisory data hackathon will gather more than 80 participants from the ECB, Reply and other firms.

Participants will use AI and machine learning to try to gain deeper and faster insights into the swathes of supervisory data gathered by the ECB from financial institutions through regular reporting for risk analysis. Participants will submit projects in the areas of data quality, interlinkages in supervisory reporting and risk indicators ahead of the event. The most promising submissions will be worked on for 48 hours by the multidisciplinary teams.

2020-02-06 00:01:00 Read the full story…
Weighted Interest Score: 3.9360, Raw Interest Score: 1.8541,
Positive Sentiment: 0.2472, Negative Sentiment 0.0000

Make Your Own AI

Artificial intelligence is slated to have a profound impact on the future of business. We’re seeing evidence of that every day. But the manner in which AI will change business is not always straightforward. H2O.ai CEO Sri Ambati, who has given the matter some thought, recently filled us in on the radical ramifications of the looming AI transformation.

The way Ambati sees it, we are on the cusp of a new era, one that’s born largely out of the power of data and software. With limitations in storage and compute quickly melting away thanks in large part to the cloud, the key differentiator becomes how companies use AI to transform data into competitive advantage.

“Algorithms plus data has a lot of value,” Ambati tells Datnami. “If you can inspire a small band of technologists in a company, you can transform the overall company into a powerhouse of innovation.”

There are a multitude of ways that companies can adopt AI to improve aspects of their business. They can tackle smaller pieces, such as increasing the clickthrough rate of an email campaign or reducing customer churn, which can provide an iterative boost to the bottom line. But those are table stakes compared to the big, macro-level changes and entirely new business models that can be unlocked with data and AI. The former might help pay the bills, but Ambati has his sights set on the latter.

2020-02-06 00:00:00 Read the full story…
Weighted Interest Score: 3.9354, Raw Interest Score: 1.4250,
Positive Sentiment: 0.2791, Negative Sentiment 0.0588

GSA Unit Launches AI Community of Practice to Boost Agency Adoption

By AI Trends Staff

The General Services Administration’s Technology Transformation Services (TTS) unit has launched an AI community of practice (AI CoP) to capture advances in AI and accelerate adoption across the federal government. The founding was announced in November via a blog post written by Steve Babitch, head of the AI portfolio for TTS.

The action is a follow-up to an Executive Order signed by President Trump in February on Maintainin…
2020-02-06 22:30:25+00:00 Read the full story…
Weighted Interest Score: 3.7838, Raw Interest Score: 1.6487,
Positive Sentiment: 0.2274, Negative Sentiment 0.0000

Flashback 2019: Top 6 Tech Talks From The Rising

‘The Rising’ has been the biggest meeting of women data science leaders from across the domain and women professionals from the industry as well as academia. This year, the conference is going to be held in Hotel Radisson Blu, on March 20, 2020, which will serve as a forum for exchanging ideas to build a better environment for women participating in STEM. The conference will also highlight the achievements and career interests of women in data science.

In this article, we list down top tech talks from the last year, The Rising 2019.

The list is in no particular order.

‘Driving towards a cleaner future through data’ by Deepika Sandeep
‘Fighting prejudice in artificial intelligence’ by Smitha Ganesh
‘Unlock the power of intelligent enterprise with augmented analytics’ by Dharani Karthikeyan
‘Enabling early-stage breast cancer detection using AI’ by Geetha Manjunath
‘Applying ML at scale for new user experiences’ by Vikram Vij
‘My experiences with data’ by Mathangi Sri

2020-02-10 06:09:24+00:00 Read the full story…
Weighted Interest Score: 3.4515, Raw Interest Score: 1.6805,
Positive Sentiment: 0.1657, Negative Sentiment 0.2367

Explainable AI: But Explainable to Whom?

As the power of AI and machine learning have become widely recognized, and as people see the value that these approaches can bring to an increasingly data-heavy world, a new need has arisen: the need for explainable AI. How will people know the nature of the automated decisions that are made by machine learning models? How will they make use of the insights provided by AI-driven systems if they do not understand and trust the automated decisions that underlie them?

The biggest challenge to the next level of adoption of AI and machine learning is not the development of new algorithms, although of course that continues to be done. The biggest challenge is building confidence and trust in intelligent machine learning systems. Some call this need for confidence and trust a barrier for AI – and in a way it is – but I prefer to think of it as a very reasonable requirement of AI and machine learning. Explainable or interpretable AI involves the ability to present explanations for model-based decisions to humans. So explainable AI is of critical importance for the success of AI and ML systems. But explainable to whom?

2020-02-06 00:00:00 Read the full story…
Weighted Interest Score: 3.4474, Raw Interest Score: 1.6013,
Positive Sentiment: 0.1601, Negative Sentiment 0.1068

Why You Should Enhance Your Email Campaigns With AI

You can enhance your email campaigns with the help of AI, or artificial intelligence. Here’s how artificial intelligence can make your emails better.

Artificial intelligence is slowly working its way into everything. One of the newest iterations is AI-driven email marketing. Many companies are already using this and you may not have noticed. That’s because these programs are undetectable unless you know what to look for and they are highly relevant to your interests. Using AI allows you to better segment your market and it can help signal when someone is prepared to sign.

2020-02-05 14:15:49+00:00 Read the full story…
Weighted Interest Score: 3.2002, Raw Interest Score: 1.0836,
Positive Sentiment: 0.4575, Negative Sentiment 0.0482

Kaskada Accelerates ML Workflow with Its Feature Store

There’s a lot of surface area in the typical data science workflow for the purveyors of automation to attack. What moves the needle for the folks at the startup Kaskada is the feature engineering and deployment stage, which it’s seeking to streamline with a new automated feature store.

The typical data science workflow is fraught with inefficiency, according to Kaskada CEO and co-founder Davor Bonaci, who previously was a senior engineer at Google who …
2020-02-05 00:00:00 Read the full story…
Weighted Interest Score: 3.1397, Raw Interest Score: 1.9706,
Positive Sentiment: 0.1471, Negative Sentiment 0.0735

Facebook Releases Open-Source Library For 3D Deep Learning: PyTorch3D

Rendering a simple shape into a proper object with geometry, texture, and other material properties is a painstakingly long process; however, with AI, researchers can now do this rendering ten times faster than the real-time.

A machine learning model is trained on images that are closer to the target. When it is presented with a shape and matching properties, it would recommend a photorealistic image. This opened a whole new field altogether — differentiable programming. Traditional rendering engines are not differentiable, so they can’t be incorporated into deep learning pipelines. Projects, such as OpenDR, Neural Mesh Renderer, Soft Rasterizer, and redner, have showcased how to build differentiable renderers that can be cleanly integrated with deep learning.

In a significant boost to 3D deep learning research, Facebook AI has released PyTorch3D, a highly modular and optimised library with unique capabilities to make 3D deep learning easier with PyTorch. PyTorch3d provides efficient, reusable components for 3D Computer Vision research with PyTorch.

2020-02-10 10:52:20+00:00 Read the full story…
Weighted Interest Score: 3.0925, Raw Interest Score: 1.8260,
Positive Sentiment: 0.3297, Negative Sentiment 0.1522

Data Scientists Key to Winning Deals at Blackstone

Investment banks and hedge funds aren’t alone in incorporating data science into their business models. Private equity funds are also turning to data science, both to win deals in the first place and to help them manage portfolio companies after a purchase.

Speaking at the recent Alternative Investments Conference in London, Lionel Assant, head of European private equity at Blackstone, said the fund now has 14 analytics professionals, “up from zero five years ago.”

Ana…
2020-02-05 00:00:00 Read the full story…
Weighted Interest Score: 3.0402, Raw Interest Score: 1.5797,
Positive Sentiment: 0.2385, Negative Sentiment 0.0000

dotData Achieves AWS Machine Learning Competency Status

dotData, focused on delivering full-cycle data science automation and operationalization for the enterprise, has achieved Amazon Web Services (AWS) Machine Learning (ML) Competency status, reaching the milestone after 8 months of joining the AWS Partner Network.

“From its foundation, dotData’s vision has been to make AI and ML accessible to as many people in the enterprise as possible,” said Ryohei Fujimaki, founder and CEO of dotData. “Achieving AWS ML Competency status in just eight months recognizes our ability to deliver an outstanding product that dram…
2020-02-04 00:00:00 Read the full story…
Weighted Interest Score: 2.9525, Raw Interest Score: 1.6710,
Positive Sentiment: 0.4284, Negative Sentiment 0.0428

The Advent And Scope Of AI Marketing In 2020 And Beyond

That said, b…
2020-02-10 00:00:00 Read the full story…
Weighted Interest Score: 2.8694, Raw Interest Score: 1.2849,
Positive Sentiment: 0.4007, Negative Sentiment 0.0691

Yseop Launches Augmented Analyst, the Next-Generation AI NLG Platform

A recent press release reports, “Yseop, the world-leading AI software company and pioneer in Natural Language Generation (NLG), today announced the launch of Augmented Analyst, a new enterprise-wide NLG automated report generation platform. Augmented Analyst is designed to help financial companies accelerate their digital transformation. Stemming from the imperative to move away from high-cost individual point solutions, Yseop leveraged over 10 y…
2020-02-10 08:05:15+00:00 Read the full story…
Weighted Interest Score: 2.8571, Raw Interest Score: 2.0752,
Positive Sentiment: 0.1683, Negative Sentiment 0.0561

Four factors businesses need to consider when it comes to automation and decisioning

As we enter a new decade, many financial providers plan to harness the power of automation and invest in advanced analytics such as Machine Learning and Artificial Intelligence to help transform their processes.

With this transformation, many are currently investing in major projects to bring together disparate data sources together into one coherent framework. And although a central, accessible data source is essential, organisations need to consider several factors if they want to take full advantage of the myriad of ways data can improve …
2020-02-10 10:34:19 Read the full story…
Weighted Interest Score: 2.8053, Raw Interest Score: 1.7808,
Positive Sentiment: 0.4452, Negative Sentiment 0.1370

The big questions businesses must ask before AI gets bigger

Meanwhile the court of public opinion has never been so vocal. Politicians and regulators are keen to be seen as standing up for the average person in the street, so companies may have to answer the question ‘how does the algorithm used in your business meet community expectations?’ This will be particularly challenging as those expectations continue to evolve.

2020-02-06 00:00:00 Read the full story…
Weighted Interest Score: 2.7650, Raw Interest Score: 0.8801,
Positive Sentiment: 0.1048, Negative Sentiment 0.3772

Top Tech Skills of 2020 Include Swift, Kafka

“Which skills are most valuable to me?” That’s a question that some technologists constantly ask, and it’s a good one: Knowing the right skills can keep you employed, and give you the leverage to negotiate for better salary and benefits from your employer. But as with so many things in tech, the “right” skills constantly shift, and 2020 is no different, according to the latest Dice Salary Report.

For example, there are certain, often-popular skills—such as Python, or how to best utilize various tools for back-end development—that never go out of style. However, those skills also evolve at a pretty rapid clip, and you risk falling behind if you don’t constantly keep your knowledge sharply honed. It’s an exhausting treadmill for any technologist to step onto, but it’s worth it if you can truly stand out from the pack because you’ve mastered the latest updates.

With new technologies, especially complicated ones such as machine learning or artificial intelligence (A.I.), a relatively small pool of experts can command high salaries and lots of benefits from hungry employers. For example, top-tier A.I. researchers, such as the autonomous-driving experts at Google, have managed to rack up millions of dollars in compensation over the past few years.

Very old technologies, such as mainframes that were first brought online in the 1960s and 1970s, can also draw high salaries, if only because there are relatively few people left who’ve mastered them.

2020-02-06 00:00:00 Read the full story…
Weighted Interest Score: 2.6894, Raw Interest Score: 1.8823,
Positive Sentiment: 0.1390, Negative Sentiment 0.0505

Automatic Speech Transcription And Speaker Recognition Simultaneously Using Apple AI

Last year, Apple witnessed several controversies regarding its speech recognition technology. To provide quality control in the company’s voice assistant Siri, Apple asked its contractors to regularly hear the confidential voice recordings in the name of the “Siri Grading Program”. However, to this matter, the company later apologised and published a statement where it announced the changes in the Siri grading program.

This year, the tech giant has been gearing up a number of researchers regarding speech recognition technology to upgrade its voice assistant. Recently, the researchers at Apple developed an AI model which can perform automatic speech transcription and speaker.

2020-02-08 08:30:00+00:00 Read the full story…
Weighted Interest Score: 2.6497, Raw Interest Score: 1.4020,
Positive Sentiment: 0.1020, Negative Sentiment 0.1275

How enterprises combine Natural language generation (NLG) and BI

It has never been easier to measure and monitor business operations — the amount of data available to organizations is staggering. Access to insight provides businesses with a clear competitive advantage, but many enterprises struggle to make sense of the seemingly endless reams of data at their disposal.

To overcome hurdles with data literacy, smart businesses have embraced various business intelligence (BI) solutions to collect, aggregate, tra…
2020-02-07 16:34:42+00:00 Read the full story…
Weighted Interest Score: 2.6275, Raw Interest Score: 1.8753,
Positive Sentiment: 0.3464, Negative Sentiment 0.1553

What is the Data Architecture We Need?

In the new era of Big Data and Data Sciences, it is vitally important for an enterprise to have a centralized data architecture aligned with business processes, which scales with business growth and evolves with technological advancements. A successful data architecture provides clarity about every aspect of the data, which enables data scientists to work with trustable data efficiently and to solve complex business proble…
2020-02-09 05:23:37.326000+00:00 Read the full story…
Weighted Interest Score: 2.5904, Raw Interest Score: 1.4458,
Positive Sentiment: 0.2238, Negative Sentiment 0.1549

How to make the most hated task at work suck less

A recent study showing that data entry is one of the most redundant and hated workplace tasks raises questions about why, in the age of artificial intelligence, data mining, and smart technologies, this task is still being done manually.

Is there any way it could be less despised? My ongoing fieldwork in a data-driven startup, referred to as Sage (a real company, but not its real name due to confidentiality requirements), suggests that technological solutions are not nearly as sophisticated as many assume—and are not going…
2020-02-09 08:00:14 Read the full story…
Weighted Interest Score: 2.5656, Raw Interest Score: 1.5175,
Positive Sentiment: 0.0973, Negative Sentiment 0.1167

Top Databases Used In Machine Learning Projects

One of the most critical components in machine learning projects is the database management system. With the help of this system, a large number of data can be sorted and one can gain meaningful insights from them. According to the Stack Overflow Survey report 2019, Redis is the most loved database, whereas MongoDB is the most wanted database.

In this article, we list down 10 top databases used in machine learning projects.

2020-02-07 12:09:07+00:00 Read the full story…
Weighted Interest Score: 2.5216, Raw Interest Score: 1.7218,
Positive Sentiment: 0.2609, Negative Sentiment 0.1565

Startup Stellus Claims New Data Platform Drives Massive Throughput

It’s rare that a new file system of any type becomes available to the enterprise IT market. This is because so many enterprises have long been standardized on older, well-trampled file systems—even if they long predate the servers, storage and networking they handle.

However, the time has come for a new-gen file system engineered specifically for unstructured file and object-based data, wherever it resides in a system. A new player in the busine…
2020-02-07 00:00:00 Read the full story…
Weighted Interest Score: 2.5154, Raw Interest Score: 1.5511,
Positive Sentiment: 0.2341, Negative Sentiment 0.1171

Is the DBA dead… or alive and preparing for the future?

Sure, there’s some doom and gloom out there about the future of the DBA. But in this session, you’ll learn how to adapt, evolve, survive and even thrive in a changing database world. You’ll get to see real-world statistics on the evolving role of the DBA, common DBA concerns and valuable insights for career success. You’ll learn how the trends of DevOps, cloud, NoSQL, big data and more will shape the future
2020-02-07 00:00:00 Read the full story…
Weighted Interest Score: 2.4331, Raw Interest Score: 1.2165,
Positive Sentiment: 0.4866, Negative Sentiment 0.2433

If You Need Somebody — Not Just Anybody — Data Literacy Help Is Here

Some organizations need a little help with data literacy just to get their feet on the ground. Maybe they can’t seem to move the bar in terms of using data to make decisions. Or they sense that their employees struggle to understand and aren’t so self-assured when it comes to data. Others lack the resources and talent needed to deliver timely insights or scale existing internal efforts. The report “Data Literacy Matters: The Writing’s On The Wall…
2020-02-05 09:37:14-05:00 Read the full story…
Weighted Interest Score: 2.4272, Raw Interest Score: 1.2684,
Positive Sentiment: 0.2819, Negative Sentiment 0.1253

Incorta 4.6 Platform Release Makes Cloud Data Lakes Analytics-ready

According to a recent press release, “Incorta, the industry’s only Unified Data Analytics Platform powered by Direct Data Mapping, today announced the latest version of its platform, Incorta 4.6. With this release, Incorta introduces new features, capabilities, and performance enhancements that double down on its commitment to bringing data engineers, data scientists, and data analysts together on a single platform. Specifically, Incorta 4.6—the “cloud data lake release”—enables seaml…
2020-02-07 08:15:31+00:00 Read the full story…
Weighted Interest Score: 2.4207, Raw Interest Score: 1.4524,
Positive Sentiment: 0.2152, Negative Sentiment 0.0538

Crumbling Infrastructure and AI Autonomous Cars

I should sue! That’s what my friends told me to do.

They were looking at the damage done to the right fender and front right tire of my car. I had been driving innocently down a street in downtown Los Angeles and encountered a whopper of a pothole. I was doing the legal speed limit and was not driving recklessly.

When I turned a corner, an unexpected pothole loomed just after making the turn, and the right side of my car was doomed to enter into the gaping asphalt gash.

2020-02-06 22:30:27+00:00 Read the full story…
Weighted Interest Score: 2.3958, Raw Interest Score: 0.7268,
Positive Sentiment: 0.1284, Negative Sentiment 0.2787

The Benefits of Building Predictive Analytics on Unified Customer Data

Predictive customer lifetime value (CLV) is a key element in modern marketing analytics, allowing marketers to prioritize customers that have the highest predicted business value. The most popular data science approach to predicting CLV is the extended Pareto/NBD model (EP/NBD) generative model which leverages a few summary statistics about customer transactions: the frequency of repeat purchases, the total customer age, most recent purchase, and the historical average order value. Despite using only a few signals and being over fifteen years old, the EP/NBD models has maintained strong relative performance according to a recent comparison of several CLV prediction approaches.

There have been many attempts to substantially improve CLV prediction via more sophisticated modeling techniques (SVMs, boosted decision trees, and neural networks), but these models also assume the time-series of past customer transactions as the primary data signal. Further improvements to CLV prediction, and predictive analytics generally, are more likely to come from exploiting new sources of customer data rather than modeling techniques or feature engineering. To quote Rule #41 from Google’s Rule of Machine Learning: “When performance plateaus, look for qualitatively new sources of information to add rather than refining existing signals.”

2020-02-05 00:00:00 Read the full story…
Weighted Interest Score: 2.3690, Raw Interest Score: 1.4326,
Positive Sentiment: 0.2956, Negative Sentiment 0.1251

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Alternative Data News. 12, February 2020

The AltDataNewsletter by CloudQuant

From Reddit : Data Is Beautiful

Change in United States Presidential Election margins from 2012 to 2016

Quandl 2020 Alternative Data Conference

Alternative data conferences connect actors in the buy-side ecosystem to explore novel use cases as the demand for alternative data is increasingly intense. Only the largest and most sophisticated players with distinctly unique roles can leverage their critical edge. Increasing demands on data remains a challenge that only few can solve.

With annual purchases of alternative data by U.S.-based buy-side firms projected to reach $900 million by 2021, the competition to find, extract, refine, package, and ultimately sell alternative data is immense. Quandl, a subsidiary of NASDAQ, is the largest alternative data provider for financial professionals transforming the investment management processes not only for the buy-side but also for private equity and venture capital.
Conferences such as Quandl’s 2020 alternative data conference offer a unique opportunity to explore the future of alternative data: potentially new data sources, new approaches to extract additional signals, and novel use cases. My few observations from the conference.

2020-02-10 00:00:00 Read the full story…
Weighted Interest Score: 9.0559, Raw Interest Score: 2.3658,
Positive Sentiment: 0.3286, Negative Sentiment 0.1314

CloudQuant Thoughts : An interesting overview of the state of Alt Data at the moment. Alpha Decay, Black Boxes, refined data sets produced by AI Black Boxes, Data Scientists reluctant to use unexplained data, Glass Boxes, Graphical rendering of data, NLP etc.

Alternative Data, It’s Not Just For Hedge Funds Any More • Integrity Research

Alternative data has become ubiquitous on Wall Street, especially among quantitative hedge funds. However, in the past few years many new client segments have started to include the use of alternative data as a key part of their business decision making processes.

In the past, most investment professionals built models and did their research, using conventional, widely available data sets, like financial statement data and historical stock prices. The advent of big data tools and enhanced computing power, have allowed larger and less structured, unconventional alternative data sets to be studied, in search of leading edge insights and advantages in the investment process.

Enterprising alternative data providers are searching for new markets to sell their data and are always on the lookout for new data sets that may be capable of improving alpha. As with any product, opening new markets to alternative data increases the potential revenue that can be generated.

2020-02-10 02:30:04+00:00 Read the full story…
Weighted Interest Score: 7.3444, Raw Interest Score: 2.3708,
Positive Sentiment: 0.3003, Negative Sentiment 0.0474

CloudQuant Thoughts : This road runs both ways, if you have data that you think may be of interest to Investment Funds or Traders in general but do not know how to reach out to them or organize the data in a way that will be of interest, we can provide that service. Get in touch with us at CloudQuant.com

Neuberger Berman bolsters ESG offering with Global High Yield Sustainable Action fund

Neuberger Berman, a private, independent, employee-owned investment manager, is launching an actively managed, differentiated global high yield fund focusing on corporate credit securities that meet sustainable investment criteria.

The strategy will target best-in-class issuers through systematic evaluation of environmental, social and governance (ESG) factors and negative exclusion criteria, emphasising active engagement with issuers on ESG factors. Engagement objectives for each issuer are established and aligned with the United Nations Sustainable Development Goals, with progress for the portfolio reported to investors annually.

The fund will invest in securities across the global high yield fixed income universe, with an emphasis on income generation. The portfolio will be diversified by industry and issuer, comprising 90-150 issuers, with a quality focus on BB and B credit.

2020-02-10 00:00:00 Read the full story…
Weighted Interest Score: 5.1985, Raw Interest Score: 2.2684,
Positive Sentiment: 0.2363, Negative Sentiment 0.0473

CloudQuant Thoughts : The most interesting quote in this article for me was “This will allow us to source best-in-class issuers and quality opportunities that are positively contributing to the future sustainability of the planet.”. I recently saw WSJ (paywall) and WealthProfessional articles quoting research done by RBC Capital Markets into the constituent components of the top ESG Funds. It turns out that “The five most commonly held S&P 500 stocks in actively managed sustainable equity funds last fall were Microsoft Corp., Alphabet Inc., Visa Inc., Apple Inc. and Cisco Systems Inc.”. At least we have the UNs Principles for Responsible Investing (PRI) to guide us.

The State of Sustainable Investing in 2020

During the past decade, governments across the world have promulgated more than 500 new measures that seek to promote the use of environmental, social, and governance (ESG) criteria in making investment decisions. A comprehensive report from global public accounting and consulting firm KPMG International explores these three broad issues related to sustainable investing and its impact on the alternative investment industry: the rate of progress in implementing sustainable investing, the barriers to more rapid progress, and responses to these barriers. A total of 135 investment managers and pension consultants from 13 countries in all key regions of the globe participated in this research project.

According to hedge fund managers surveyed for the study, institutional investors are by far the leaders in promoting ESG-driven investing. However, only 15% of these hedge fund managers “have embedded ESG factors across their strategies.”. More widespread adoption is hindered by the fact that 63% of them them find a “lack of robust templates, consistent definitions and reliable data.”

2020-02-07 16:02:43.463000+00:00 Read the full story…
Weighted Interest Score: 3.7088, Raw Interest Score: 2.0665,
Positive Sentiment: 0.2411, Negative Sentiment 0.3444

From nice-to-have to must-have: ESG becoming central to hedge fund processes, study finds

A growing number of investors require hedge funds to build environmental, social and governance (ESG) elements into their investment processes – with traditional risk-return metrics being overhauled to include ESG factors, a wide-ranging industry study has found.

The report, ‘Sustainable Investing: Fast Forwarding Its Evolution’, published jointly today by KPMG, the Alternative Investment Management Association (AIMA), the Chartered Alternative Investment Analyst Association (CAIA), and CREATE-Research, underlines the far-reaching impact that ESG is having on the investor-manager dynamic.

2020-02-06 14:14:18+00:00 Read the full story…
Weighted Interest Score: 4.8658, Raw Interest Score: 3.0201,
Positive Sentiment: 0.0000, Negative Sentiment 0.0000

CloudQuant Thoughts : If you are a regular reader of this blog you probably know what is coming. It can be hard to find quality data, there are so many sellers, so many white papers with no data to back them up. Acquiring and researching potential data feeds for Alpha is a never ending and increasingly difficult task. CloudQuant can help. We have developed a process to test Alternative Data sets, we publish white papers WITH CODE. The data can be made available in a format that is already processed and cleaned. We have examples including an ESG dataset from G&S Quotient over at our Data Catalog.

America’s Data Addiction Enabled China’s Equifax Hack – Barron’s

U.S. prosecutors announced on Monday indictments of members of the Chinese military for the great Equifax hack of 2017. The news instantly made a familiar geopolitical story of what was originally a massive failure of cybersecurity and privacy protections. But something bigger is at stake: The data-economy bubble is now bursting. And individuals everywhere are becoming the collateral damage of an industry that’s collapsing under the weight of exponential growth fueled by cheap capital, both data and venture.

I’m not the only one who sees the signs. In 2017, the highly sensitive personal and financial data of 150 million Americans spilled out from Equifax’s secure servers like oil from the Exxon Valdez tanker. On Monday, U.S. Attorney General William Barr condemned the hack as an “attack on American industry,” in effect shifting blame away from a multibillion-dollar corporation.

“For years we have witnessed China’s voracious appetite for the personal data of Americans,” Barr said. “This data has economic value, and these thefts can feed China’s development of artificial intelligence tools as well as the creation of intelligence targeting packages.”

2020-02-11 00:00:00 Read the full story…

CloudQuant Thoughts : Take care of your user’s data. Learn from every attack.

The new age of Fintech – What you need to know about data aggregators

Earlier last month, news feeds were abuzz about a major acquisition within the financial industry, when Visa purchased fintech startup – Plaid, for a substantial sum of $5.3 billion. Although major news, for the majority of the readers this was the first time they have heard about Plaid, raising the questions of how could this company that many have never heard of before suddenly get such massive valuation.

Despite the obliviousness of many, chances are that if you have ever used any sort of financial app, you already are Plaid’s customer. Plaid is one of the key players among the Financial data aggregation companies – which, through partnerships with banks and other financial institutions, have direct access to consumers’ financial data. Since its foundation, Plaid has been rising to the top of the fintech industry, using data aggregation techniques, such as screen scraping and APIs, to gain direct access to consumers’ financial data, cleaning, categorizing and supplying the acquired data accordingly. What Plaid has done is it has taken a key position within the world of Fintech, acting as the connecting link between fintech startups and banks, with its services used by a number of companies, such as Venmo, TransferWise, and Level Money, utilizing Plaid’s software to power their services.

2020-02-11 11:07:21 Read the full story…
Weighted Interest Score: 4.3328, Raw Interest Score: 1.8341,
Positive Sentiment: 0.1630, Negative Sentiment 0.1630

15 Python Libraries for Data Science You Should Know – Dataquest

Scrapy
BeautifulSoup
NumPy
SciPy
Pandas
Keras
SciKit-Learn
PyTorch
TensorFlow
XGBoost
Matplotlib
Seaborn
Bokeh
Plotly
pydot

2020-02-05 16:14:09+00:00 Read the full story…
Weighted Interest Score: 4.0674, Raw Interest Score: 2.1138,
Positive Sentiment: 0.3325, Negative Sentiment 0.0356

Sentieo Q&A: research workflows still using “outdated technology stacks”

Productivity provides an edge in equity research, and as equity-focused hedge funds navigate the markets this year, any opportunity to use next generation technology tools to trade more adroitly will likely be welcomed. In the following Q&A with Hedgeweek’s managing editor, James Williams, Nick Mazing, Head of Research at Sentieo Inc, discusses how its platform is using the latest AI technology to improve the research workflow and reduce the time taken to ingest structured and textual data sets, and empower portfolio managers as they look to build alpha-generating investment theses.
2020-02-10 00:00:00 Read the full story…
Weighted Interest Score: 3.3462, Raw Interest Score: 1.6096,
Positive Sentiment: 0.3604, Negative Sentiment 0.1441

Top 10 Tools For No-Code AI & ML

Enterprises primarily rely on the two domains — artificial intelligence (AI) and machine learning (ML) in order to build and deploy various kinds of models for the smooth operation of their business. However, it requires programmers or data scientists with adequate knowledge of coding, which enterprises often lack. In a bid to ease such woes of the enterprises, tech giants are now open-sourcing their platforms and providing developer tools to ensure businesses can match the ongoing pace without the need for a coding expert.

In this article, we list down ten such tools which can be used to develop models without being an expert in coding.

The list is in no particular order.

Create ML By Apple
Teachable Machine
Accelerite ShareInsights by Amazon Web Services
What-If Tool
Google AI Platform
Data Robot
RapidMiner Studio
Microsoft Azure Automated Machine Learning
BigML
Google ML Kit

2020-02-11 10:38:02+00:00 Read the full story…
Weighted Interest Score: 3.3456, Raw Interest Score: 1.9481,
Positive Sentiment: 0.2238, Negative Sentiment 0.1053

Top Skills To Show On Your Data Science Resume Beyond Just The Tools

With the evolving data science landscape, organisations are expecting more from data scientists. Consequently, various companies are seeking skills in data scientists that were ignored a few years ago. And, therefore, aspirants should devise a data science resume that aligns with the latest needs of the enterprise to increase their chance of getting job offers. Failing to do so can drastically decrease the likelihood of data scientists to stay relevant or differentiate themselves from others, resulting in losing opportunities.

Today, data scientists should not confine their data science resumes only with fancy tools, ML and DL techniques, and certifications. Instead, they must include skills that can demonstrate their capabilities that are now a prime focus for organisations.

Work On Specific Projects
Converting Business Problems Into Data Science Problems
Explainability
Business Acumen
Communication Skills

2020-02-11 06:30:00+00:00 Read the full story…
Weighted Interest Score: 3.1868, Raw Interest Score: 1.6038,
Positive Sentiment: 0.2708, Negative Sentiment 0.4999

Top 6 Must-Attend AI & ML Conferences In India For 2020

The emergence of artificial intelligence (AI) and machine learning (ML) has led to a proliferation of tech-focused conferences and summits in India. Not only do they offer a platform for researchers, tech enthusiasts, policymakers and business people to deepen their understanding of emerging technologies, but also present a fitting setting to discover potential collaborations and make critical connections.

Without implying any ranking in the order, here is a list of AI & ML conferences for 2020 that you should mark on your calendars:

2020-02-10 12:03:52+00:00 Read the full story…
Weighted Interest Score: 3.1354, Raw Interest Score: 1.4668,
Positive Sentiment: 0.3603, Negative Sentiment 0.1801

Four factors businesses need to consider when it comes to automation and decisioning

analytics such as Machine Learning and Artificial Intelligence to help transform their processes.

With this transformation, many are currently investing in major projects to bring together disparate data sources together into one coherent framework. And although a central, accessible data source is essential, organisations need to consider several factors if they want to take full advantage of the myriad of ways data can improve decisioning, efficiency, and customer journeys.

Here are four aspects businesses need to consider:

Relevancy of data
New data sources
System issues
Exploring new models

2020-02-10 10:34:19 Read the full story…
Weighted Interest Score: 2.8053, Raw Interest Score: 1.7808,
Positive Sentiment: 0.4452, Negative Sentiment 0.1370

Facts & Figures of Amazon lending and the Goldman Sachs X-factor

As I was listening to Cathy Wood`s interview, CEO of ArkInvest, from the Exponential Africa Show; she triggered an insight around investing and disruptive innovation.

One can`t be a banking analyst or an automobile industry analyst anymore, with the same silo-ed focus required over the past decades. Industry-specific analysts bring a lot of experience from their respective sectors but lack the insights of innovative business models enabled by the `future technologies` (that are already here by the way).

I will elaborate on this topic over the next couple of weeks with several examples and insights on where I see the market heading to.

2020-02-11 00:00:00 Read the full story…
Weighted Interest Score: 2.7652, Raw Interest Score: 1.3111,
Positive Sentiment: 0.1669, Negative Sentiment 0.0715

Top 9 ETL Tools For Data Integration In 2020

One of the essential aspects of data warehousing is the ETL (Extract Transform Load) tool. An ETL tool is a combination of three different functions in a single tool. One most crucial property of ETL is to transform the heterogeneous data into homogeneous one, which later helps data scientists to gain meaningful insights from the data.

In this article, we list down the top nine ETL tools one must use for data integration in 2020.

The list is in alphabetical order.

2020-02-11 12:36:18+00:00 Read the full story…
Weighted Interest Score: 2.5847, Raw Interest Score: 1.6221,
Positive Sentiment: 0.2317, Negative Sentiment 0.0535

Sebi Shortlists IBM India, Infy & Wipro For Data Analytics Project

Sebi has shortlisted around eight companies, including Infosys, Wipro and IBM India, in order to implement a data analytics project through which the regulator wants to track possible market manipulations such as insider trading and front running.

This move is part of Sebi’s efforts to address the challenges arising out of technological advancements in the markets. In November, the regulator had invited expression of interest (EoI) from “reputed and reliable solution providers for implementation of data analytics project and building of data models at Sebi”.

The analytics/model development would include developing new models, implementing analytics project, establishing linkages between various entities in the market, automated extraction of details from documents filed with Sebi, and prediction of market manipulations such as insider trading and front running.

2020-02-11 11:30:00+00:00 Read the full story…
Weighted Interest Score: 2.5424, Raw Interest Score: 1.7295,
Positive Sentiment: 0.1774, Negative Sentiment 0.2217

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AI & Machine Learning News. 18, February 2020

The Artificial Intelligence and Machine Learning Newsletter by CloudQuant

“We trained our AI on a few minutes of audio from youtube interviews of those artists and celebrities, and created the voices you heard in this video by typing up a script and choosing from a palette of Celebrity ‘Replica’ voices. The video demonstrates how powerful this technology is, and also how easy it is for us to produce any number of Replica voices like these, by analysing a few minutes of speech. The more audio data we train on, the better the voices will sound in terms of quality and smoothness.”

Replica Blog

EU’s new AI rules will focus on ethics and transparency

The European Union is set to release new regulations for artificial intelligence that are expected to focus on transparency and oversight as the region seeks to differentiate its approach from those of the United States and China. On Wednesday, EU technology chief Margrethe Vestager will unveil a wide-ranging plan designed to bolster the region’s competitiveness. While transformative technologies such as AI have been labeled critical to economic survival, Europe is perceived as slipping behind the U.S., where development is being led by tech giants with deep pockets, and China, where the central government is leading the push.

Europe has in recent years sought to emphasize fairness and ethics when it comes to tech policy. Now it’s taking that approach a step further by introducing rules about transparency around data-gathering for technologies like AI and facial recognition. These systems would require human oversight and audits, according to a widely leaked draft of the new rules. In a press briefing in advance of Wednesday’s announcement, Vestager noted that companies outside the EU that want to deploy their tech in Europe might need to take steps like retraining facial recognition features using European data sets. The rules will cover such use cases as autonomous vehicles and biometric IDs.

2020-02-17 00:00:00 Read the full story…
Weighted Interest Score: 2.4914, Raw Interest Score: 1.2773,
Positive Sentiment: 0.1558, Negative Sentiment 0.1246

CloudQuant Thoughts : “Slipping behind” or taking sensible precautions.

Facebook, Apple And Google Heads Visit Europe, Seek Help Navigating New AI Rules

Tech company heads are visiting Brussels one after another. After Alphabet CEO Sundar Pichai and Apple’s senior vice-president for artificial intelligence and machine learning John Giannandrea visited earlier this month, Facebook CEO Mark Zuckerberg visited the city Monday.

The reason that tech giants are lining up to meet EU officials, such as Vice-President Margrethe Vestager, is because they are worried about a new artificial intelligence law that is being debated in the European parliament. The policy is the first-of-its-kind and is made to regulate artificial intelligence.

All tech companies have bet big on AI and the laws are expected to affect how these companies do business in the E.U.

2020-02-17 08:02:26-05:00 Read the full story…
Weighted Interest Score: 2.6846, Raw Interest Score: 1.2102,
Positive Sentiment: 0.0448, Negative Sentiment 0.2689

CloudQuant Thoughts : Let’s be honest here, the reason these companies having been saying for over a year that they want the government to take the lead in deciding the rules for AI is because they have extremely effective methods of controlling government narrative in the US via their lobbyists. When it comes to Europe they have no such control. So they want the US to take the lead, and if Europe takes the lead they have to get on their planes and get out there. To quote the San Francisco Chronicle on why Pichai welcomes government regulation “One big reason is to head off the kind of regulation he doesn’t want. Both the U.S. and the European Union are moving closer to instituting rules for artificial intelligence, and their approaches are already diverging.”

New Facial Recognition Technology Uses AI to Recognize Faces in the Dark, Far Away

For many, normal facial recognition — used in the daylight — has become a facet of everyday life. Whether it’s for identity verification to unlock a smart phone, or trivial social media camera filters — it seems the technology is everywhere.

However, at the U.S. Combat Capabilities Development Command Army Research Laboratory just outside of Washington, D.C., scientists are on the forefront of bringing facial recognition technology into the future, capable of identifying figures in the dark, as experimental tests kick off.

The cutting-edge technology uses artificial intelligence, machine learning techniques, and state-of-the-art infrared cameras to identify facial patterns by using the heat signatures from living skin tissue any time of day, said Dr. Sean Hu, U.S. Army Research Laboratory Intelligent Perception Branch team lead.

2020-02-17 00:00:00 Read the full story…
Weighted Interest Score: 2.4914, Raw Interest Score: 1.2773,
Positive Sentiment: 0.1558, Negative Sentiment 0.1246

CloudQuant Thoughts : Night Vision plus AI. Not just identifying people in the dark but also from “a few hundred meters” away!!

Roboflow: Popular autonomous vehicle data set contains critical flaws

A machine learning model’s performance is only as good as the quality of the data set on which it’s trained, and in the domain of self-driving vehicles, it’s critical this performance isn’t adversely impacted by errors. A troubling report from computer vision startup Roboflow alleges that exactly this scenario occurred — according to founder Brad Dwyer, crucial bits of data were omitted from a corpus used to train self-driving car models.

Dwyer writes that Udacity Dataset 2, which contains 15,000 images captured while driving in Mountain View and neighboring cities during daylight, has omissions. Thousands of unlabeled vehicles, hundreds of unlabeled pedestrians, and dozens of unlabeled cyclists are present in roughly 5,000 of the samples, or 33% (217 lack any annotations at all but actually contain cars, trucks, street lights, or pedestrians). Worse are the instances of phantom annotations and duplicated bounding boxes (where “bounding box” refers to objects of interest), in addition to “drastically” oversized bounding boxes.

It’s problematic considering that labels are what allow an AI system to understand the implications of patterns (like when a person steps in front of a car) and evaluate future events based on that knowledge. Mislabeled or unlabeled items could lead to low accuracy and poor decision-making in turn, which in a self-driving car could be a recipe for disaster.

2020-02-14 00:00:00 Read the full story…
Weighted Interest Score: 2.4793, Raw Interest Score: 0.9360,
Positive Sentiment: 0.1088, Negative Sentiment 0.6530

CloudQuant Thoughts : GIGO – Garbage In Garbage Out. However, I would not be so fast to discount this data as critically flawed, particularly after seeing the annotated video from a Tesla and its AI unit driving through Paris.

TOP 10 TOOLS FOR NO-CODE AI & ML

In this article, we list down ten such tools which can be used to develop models without being an expert in coding.

The list is in no particular order.

Create ML By Apple
Teachable Machine
Accelerite ShareInsights by Amazon Web Services
What-If Tool
Google AI Platform
Data Robot
RapidMiner Studio
Microsoft Azure Automated Machine Learning
BigML
Google ML Kit

2020-02-11 00:00:00 Read the full story…

Weighted Interest Score: 7.7708, Raw Interest Score: 3.0992,
Positive Sentiment: 0.0989, Negative Sentiment 0.0659

CloudQuant Thoughts : Time to try a different tack? Lots of options here!

Need to Build Trustworthy AI Systems Gains Importance as AI Progresses

The push is on to build trusted AI systems with an eye toward instilling confidence that results will be fair, accuracy will be sufficient, and safety will be preserved.

Gary Marcus, the successful entrepreneur who sold his startup Geometric Intelligence to Uber in 2016, issued a wakeup call to the AI industry as co-author with Ernest Davis of “Rebooting AI,” (Pantheon, 2019) an analysis of the strengths and weaknesses of current AI, where the field is going, and what we should be doing. Marcus spoke about building trusted AI in a recent interview with The Economist. Here are some highlights:

“Trustworthy AI has to start with good engineering practices, mandated by laws and industry standards, both of which are currently largely absent. Too much of AI thus far has consisted of short-term solutions, code that gets a system to work immediately, without a critical layer of engineering guarantees that are often taken for granted in other fields. The kinds of stress tests that are standard in the development of an automobile (such as crash tests and climate challenges), for example, are rarely seen in AI. AI could learn a lot from how other engineers do business.” AI developers, “can’t even devise procedures for making guarantees that given systems work within a certain tolerance, the way an auto part or airplane manufacturer would be required to do.” “The assumption in AI has generally been that if it works often enough to be useful, then that’s good enough, but that casual attitude is not appropriate when the stakes are high.”

IBM Team Identifies Four Pillars of Trusted AI…

2020-02-13 22:30:28+00:00 Read the full story…
Weighted Interest Score: 5.4069, Raw Interest Score: 1.8151,
Positive Sentiment: 0.2433, Negative Sentiment 0.1871

Israel risks falling behind in AI despite growth

There are more than 1,150 AI-focused startups in Israel, and that number is growing. Even so, some people within the government are concerned that the nation risks falling behind because it lacks a unified AI policy.

AI policy refers to national strategies like the U.S.’s American AI Initiative and Canada’s Pan-Canadian Artificial Intelligence Strategy, which implement whole-government efforts to promote technological innovation. Implicitly, it incorporates a funding component that bolsters those efforts with capital.

“AI is affecting every element of our society, and it’ll continue to affect change further and further. Those [who don’t adopt it] might find themselves [behind],” Aharon Aharon, CEO of the Israel Innovation Authority, the arm charged with fostering industrial R&D within the state, told members of the press during a roundtable discussion at the Jerusalem Press Club last week. “Just [funding] and capital is not enough. You need companies that grow … so they can invest back, and so they can continue to develop the Israeli economy.”

2020-02-17 00:00:00 Read the full story…
Weighted Interest Score: 5.3222, Raw Interest Score: 1.9763,
Positive Sentiment: 0.2288, Negative Sentiment 0.0832

Join the innovators in enterprise AI at Transform 2020

The AI event of the year for business leaders, Transform 2020 doubles down on results-driven content that helps executives at the senior director level and above maintain their competitive edge. Expect two days of the most transformative trends in conversational AI, computer vision, IoT and AI at the edge, and automation, plus a special emphasis on women in AI, diversity, and expanded networking opportunities.

Each year, we gather corporate decision-makers from around the world to discuss “big picture” trends within artificial intelligence, as well as practical ways to move the needle on implementing AI. In 2020, we’re looking at the effects of AI through the lens of four key industries: retail, health, finance, and industrial manufacturing.

2020-02-18 00:00:00 Read the full story…
Weighted Interest Score: 4.5833, Raw Interest Score: 1.6753,
Positive Sentiment: 0.3697, Negative Sentiment 0.1271

A former Amazon and Google engineer wants to make AI more accessible to smaller companies so that Big Tech doesn’t have a stranglehold on the future

Bindu Reddy wants to give more people access to artificial intelligence. The former Amazon Web Services and Google engineer, wants small and medium sized companies to be able to use AI in the same way that large companies do, and make sure big tech companies aren’t dominating the sector. Reddy previously started the AI verticals division at AWS, creating AI for particular domains or use-cases. Earlier in her career she was the Head of Product for Google social apps, where she helped build Google+, Blogger Google Video, Google Docs and Google Sites.

She said during her time at Google and Amazon Web Services she noticed that big companies had a gap between the research being done in AI and the products being developed. She realized that a startup could be more nimble and forge a deeper connection between research and product development. She decided to start a company to address that need. She founded the startup, called RealityEngines.ai, with two of her former colleagues from Google, and it makes cloud based software to help companies make their own AI models for the workplace tools they are using.

2020-02-15 00:00:00 Read the full story…
Weighted Interest Score: 4.5351, Raw Interest Score: 1.9559,
Positive Sentiment: 0.1457, Negative Sentiment 0.0208

Oracle Announces Oracle Cloud Data Science Platform

y announced the availability of the Oracle Cloud Data Science Platform. At the core is Oracle Cloud Infrastructure Data Science, helping enterprises to collaboratively build, train, manage and deploy machine learning models to increase the success of data science projects. Unlike other data science products that focus on individual data scientists, Oracle Cloud Infrastructure Data Science helps improve the effectiveness of data science teams with capabilities like shared projects, model catalogs, team security policies, reproducibility and auditability. Oracle Cloud Infrastructure Data Science automatically s…
2020-02-14 08:15:15+00:00 Read the full story…
Weighted Interest Score: 4.2044, Raw Interest Score: 2.1356,
Positive Sentiment: 0.4805, Negative Sentiment 0.0000

These Are the 100 Most Sustainable Companies in America

As companies get more sustainable, investors are benefiting. Shares of the companies on Barron’s third annual ranking of America’s Most Sustainable Companies outperformed the S&P 500 index in 2019.

America’s corporations are getting more sustainable, and investors are benefiting, along with the planet and the rest of its inhabitants. The third annual Barron’s ranking of America’s Most Sustainable Companies also makes for a pretty good portfolio: Shares of the 100 companies on our list returned 34.3%, on average, in 2019, beating the S&P 500 index’s 31.5%. More than half of our honorees, 55, outperformed the mighty index, which has been nearly unbeatable for a decade.

With companies in general adopting ambitious…

2020-02-07 00:00:00 Read the full story (Registration Wall)…
Weighted Interest Score: 4.0233, Raw Interest Score: 1.8409,
Positive Sentiment: 0.2180, Negative Sentiment 0.0363

5 Free Data Science Courses For Beginners

Companies across all the industries in the world are always looking for data science personnel to help them garner insights from big data. The hiring experts are constantly on the lookout for personnel with high skills regarding programming, data mining, statistical modelling etc. With the huge gap existing between required skills and talent available, these industries have become more resilient towards finding skilled data scientists and scraping out the less talented ones. One way for the people going into data science to enhance their knowledge is taking up the data science courses online, these data science courses help one to learn about the sector and acquire the in-demand skills.

Below we have listed some of the best free online data science courses available: (List is in random order)

Data Science Essentials From: Microsoft through edX.
Data-Driven Decision Making Offered by: PwC through Coursera.
CS109 Data Science Offered by: Harvard
Data Science Foundations Offered by: IBM on their portal.
Machine Learning Offered by: Stanford on Coursera

2020-02-17 11:30:00+00:00 Read the full story…
Weighted Interest Score: 4.0118, Raw Interest Score: 2.2396,
Positive Sentiment: 0.1621, Negative Sentiment 0.0147

The New Business of AI (and How It’s Different From Traditional Software)

At a technical level, artificial intelligence seems to be the future of software. AI is showing remarkable progress on a range of difficult computer science problems, and the job of software developers – who now work with data as much as source code – is changing fundamentally in the process.

Many AI companies (and investors) are betting that this relationship will extend beyond just technology – that AI businesses will resemble traditional software companies as well. Based on our experience working with AI companies, we’re not so sure.

We are huge believers in the power of AI to transform business: We’ve put our money behind that thesis, and we will continue to invest heavily in both applied AI companies and AI infrastructure. However, we have noticed in many cases that AI companies simply don’t have the same economic construction as software businesses. At times, they can even look more like traditional services companies.

2020-02-16 00:00:00 Read the full story…
Weighted Interest Score: 4.0065, Raw Interest Score: 1.7184,
Positive Sentiment: 0.2571, Negative Sentiment 0.2014

How the 80/20 Rule can help decide which skills you need to start a career in Data Science

Using the Pareto Principle to increase your confidence as a Data Scientist

1. The daunting task to learn Data Science

Data Science is an exciting field with an increasing demand for skilled and experienced professionals. A traditional Data Scientist has a background in a field related to Computer Science, Mathematics, Engineer or Physics. But we also find ot…
2020-02-18 04:13:31.638000+00:00 Read the full story…
Weighted Interest Score: 3.8864, Raw Interest Score: 2.0927,
Positive Sentiment: 0.1495, Negative Sentiment 0.1495

AI Weekly: Machine learning could lead cybersecurity into uncharted territory

Once a quarter, VentureBeat publishes a special issue to take an in-depth look at trends of great importance. This week, we launched issue two, examining AI and security. Across a spectrum of stories, the VentureBeat editorial team took a close look at some of the most important ways AI and security are colliding today. It’s a shift with high costs for individuals, businesses, cities, and critical infrastructure targets — data breaches alone are expected to cost more than $5 trillion by 2024 — and high stakes.

Throughout the stories, you may find a theme that AI does not appear to be used much in cyberattacks today. However, cybersecurity companies increasingly rely on AI to identify threats and sift through data to defend targets.

2020-02-14 00:00:00 Read the full story…
Weighted Interest Score: 3.8440, Raw Interest Score: 1.1978,
Positive Sentiment: 0.1114, Negative Sentiment 0.5292

Understanding the Uses of Artificial Intelligence

Artificial intelligence (AI) has provided a critical competitive advantage for those organizations able and willing to use it. AI has gained significant momentum in the last few years, acting as personal assistants for some, while processing business transactions and providing technical services to others. AI systems have the ability to manage large amounts of data in a number of ways. Different types of artificial intelligence have been evolved to handle a variety of tasks, ranging from facial recognition to drug design to driving cars.

In terms of logistics, an AI can optimize the routing of delivery traffic, thereby improving fuel efficiency and providing faster delivery times. It has become a valuable response tool, providing customer service centers with a phone answering service. In the world of sales, combining customer demographics with past transaction data and social media can result in recommendations tailored to the customer. An artificial intelligence can improve predictive maintenance, analyzing large amounts of data from images and audio to detect anomalies in auto engines or assembly lines. Specific deep learning techniques can be used to tailor an AI for accomplishing specific goals and tasks.

2020-02-13 08:35:24+00:00 Read the full story…
Weighted Interest Score: 3.7897, Raw Interest Score: 1.9047,
Positive Sentiment: 0.2450, Negative Sentiment 0.1559

Discussing Knowledge Graphs as the Next Big Thing

The extraordinary growth in complex data has left many enterprises struggling to create an integrated, comprehensive view of that data. In recent years, knowledge graphs have emerged as a powerful tool for integrating large volumes of distributed, data, both structured and unstructured. A simplified graph data model available in graph databases helps enterprises achieve this goal in fewer steps and without locking into a single notion of the analytics needed.

DBTA recently held a webinar with Steve Sarsfield, VP product, AnzoGraph DB, who discussed the powerful side benefits of graph databases, like graph algorithms, and how they go beyond standard analytics to uncover relationships in the data.

A knowledge graph can mean different things to different people, Sarsfield explained. For executives, they see a common understanding of all disparate data while data architects see knowledge graphs as one method to integrate data from multiple data sets, structured or unstructured, and to leverage standard industry ontologies to enhance analytics.

2020-02-14 00:00:00 Read the full story…
Weighted Interest Score: 3.6344, Raw Interest Score: 1.9273,
Positive Sentiment: 0.2753, Negative Sentiment 0.0551

PyKrylov: Accelerating Machine Learning Research at eBay

A recent eBay Tech Blog article1 presented the Unified AI platform called Krylov. In this article, we show how Krylov users interact with the platform to build and manage powerful workflows in a pythonic and efficient way.
The experience while accessing the AI platform and running machine learning (ML) training code on the platform must be smooth and easy for the researchers. Migrating any ML code from a local environment to the platform should not require any refactoring of the code at all. Infrastructure configuration overhead should be minimal. Our mission while developing PyKrylov was to abstract the ML logic from the infrastructure and Krylov core components (Figure 1) as much as possible in order to achieve the best experience for the platform users.
2020-02-11 00:00:00 Read the full story...
Weighted Interest Score: 3.5581, Raw Interest Score: 2.4436,
Positive Sentiment: 0.7519, Negative Sentiment 0.0000

SGX RegCo uses AI to enhance surveillance activities

Singapore Exchange Regulation (SGX RegCo) is making its surveillance and regulation of the securities market more targeted and effective with the application of artificial intelligence (AI) enhancements to its real-time monitoring system.

The introduction of AI can help to better isolate unusual activity, by learning from historical trading patterns and filtering out noise caused by developments across intricate relationships between multiple markets. This allows regulatory attention to be more sharply focused on a smaller set of potentially unusual trading signals identified through the surveillance system, which are then further analysed and reviewed by the surveillance team.

2020-02-12 00:00:00 Read the full story…
Weighted Interest Score: 3.5039, Raw Interest Score: 1.4025,
Positive Sentiment: 0.4909, Negative Sentiment 0.2104

How NVIDIA Set A World Record For Training BERT And What Does This Mean

The deep learning community, especially those who work on Natural Language problems, had a great run in 2019. Top players like Google, NVIDIA and Microsoft have set new benchmarks with their every release. With time the models keep getting larger and the training times too, surprisingly, have somehow come down.

What really turned heads was NVIDIA’s world record for training state of the art BERT-Large models in just 47 minutes, which usually takes a week’s time.

This record was created by utilising 1,472 V100 SXM3-32GB 450W GPUs, 8 Mellanox Infiniband compute adapters per node, and running PyTorch with Automatic Mixed Precision to accelerate throughput.

2020-02-18 09:30:00+00:00 Read the full story…
Weighted Interest Score: 3.4804, Raw Interest Score: 1.8455,
Positive Sentiment: 0.3044, Negative Sentiment 0.1712

An Enterprise Formula for AI Success

One of the great things about the current wave of AI innovation is the large number of open source tools, technologies, and frameworks. From TensorFlow to Python, Kafka to PyTorch, the we’re in the midst of an explosion in diversity of data science and big data toolchains. However, when it comes to putting these toolchains together and building real-world AI applications, regular companies suffer from a serious technology gap compared to technology firms.

The technology giants have a curious habit of releasing powerful technology onto the unsuspecting masses. For example, in 2015 Google unveiled TensorFlow, which enables users to build and deploy very large and very accurate neural network models. A year later, Facebook, released PyTorch, which some say is an easier-to-use framework for machine learning development. Both are among the most heavily used technologies for machine learning today.

Nobody is complaining too much about Google’s and Facebook’s decisions to release such ground-breaking technology. After all, they’ve been at this for many years. While the tech giants do benefit by getting the open source community to continue to develop and maintain technology that it puts into the public realm, it’s safe to say that the open source community receives bigger benefit than the tech giants. But these AI gains have not flowed equally. Many of the latest open source AI technologies are not known for being easy to work with, and typically require highly skilled data scientists to use. This puts a cap the applicability of the AI tech, and limits its use to companies that have the budget to hire experienced data scientists. That leaves a lot of companies out of luck when it comes to leveraging the latest in AI innovation, according to Phil Gurbacki, the senior vice president of product and customer experience for DataRobot, a provider of automated machine learning and enterprise AI offerings based in Boston, Massachusetts.

2020-02-11 00:00:00 Read the full story…
Weighted Interest Score: 3.3022, Raw Interest Score: 1.3697,
Positive Sentiment: 0.2848, Negative Sentiment 0.1221

L3Harris Technologies Selected by US Air Force for Artificial Intelligence Contract

“The Air Force Life Cycle Management Center has awarded L3Harris Technologies a multimillion-dollar contract to develop a software platform that will make it easier for analysts to use artificial intelligence (AI) to identify objects in large data sets. The U.S. military and intelligence community are inundated with massive amounts of data generated by remote sensing systems. Automated searches using algorithms that can identify pre-loaded images of objects makes pinpointing them easier. However, in order to train these algorithms, real images are often unavailable because they are either rare or do not exist. The L3Harris tool creates sample images used to train search algorithms to identify hard-to-find objects in the data, which will help make it easier for the military and intelligence community to adopt artificial intelligence.”

Ed Zoiss, President of Space and Airborne Systems at L3Harris, commented, “L3Harris is a premier provider of modeling and simulation capabilities that provide risk reduction for our customers who rely on advanced geospatial systems and data… Accelerating the use of AI will help automate analysis of large geospatial data sets so warfighters receive trusted data faster and more efficiently.”
2020-02-18 08:05:16+00:00 Read the full story…
Weighted Interest Score: 3.2861, Raw Interest Score: 1.5873,
Positive Sentiment: 0.3401, Negative Sentiment 0.1701

Fastest-Growing Tech Occupations Include Data Scientists, Engineers

The 2020 edition of Dice’s Salary Report showed significant salary growth for certain kinds of skills, including Swift and Kafka. Now let’s take a deeper dive into the tech occupations that enjoyed the biggest increases in salary between 2018 and 2019. Based on our analysis, it’s clear that employers are hungry for technologists who can carry out a variety of tasks, from analyzing data to building applications (as well as making sure those applications go into the world relatively bug-free).

The data from the Salary Report was drawn from 12,837 technologists. In addition, the occupations below the chart also incorporated data from Burning Glass, which collects and analyzes millions of job postings from across the country, for data-points such as time-to-fill and most-requested skills. (Burning Glass defines “Defining skills” as the day-to-day tasks and responsibilities of a particular job, while “Distinguishing skills” are advanced skills that are called for occasionally, and really come into play with highly specialized employees.)

As you can see from the following list, a broad spectrum of occupations enjoyed gains, which is a reflection of all kinds of companies and industries needing a range of technology services:
2020-02-13 00:00:00 Read the full story…
Weighted Interest Score: 3.1851, Raw Interest Score: 2.1032,
Positive Sentiment: 0.0935, Negative Sentiment 0.1496

Revamp Your Life With Artificial Intelligence Training

When we hear the word “ Artificial Intelligence “, digital assistants, chat bots, robots, and self-driving cars are what strikes our mind but that’s not enough. These are few more examples of artificial intelligence which are terrifying yet interesting. Unlike other technologies, we will continue to see the advancements of Artificial Intelligence and Machine Learning in 2020 and beyond.

Some other technologies will grow as well but technologies like deep learning and machine learning will creep on us. Many experts in Artificial Intelligence believe that AI is going to be bigger than the internet revolution. Artificial intelligence is a field of computer science and is sometimes called machine intelligence. In simple words, It is a field in computer science that teaches the machine how to understand the human mind and react exactly like humans. The main aim of AI is to build machines that can think, behave, and understand the way humans do work.

Learning ability and dynamically updating your skills will prepare you to take advantage of unseen opportunities and pave the path for a successful career. Today’s Simple AI has now started offering advanced Artificial Intelligence Training with help of industry experts to help the AI career enthusiasts to transform into industry leaders. When you are deciding for a career strategy, start with self-awareness, have a good mindset, and seek out for long-term skills that need to be adopted or enhanced.

2020-02-17 00:00:00 Read the full story…
Weighted Interest Score: 3.1044, Raw Interest Score: 2.2621,
Positive Sentiment: 0.4290, Negative Sentiment 0.0390

How is data growth affecting real-world enterprises? 4 key findings

Data volumes are expanding at an unprecedented rate. It’s very easy to get caught up in how many zettabytes of data we’ll all be producing by 2025, 2030, and beyond, and how data and cloud computing affect us all on a global scale. But what do data growth and other issues mean for how you do business right now? The results of a recent Matillion and IDG Research survey are very illuminating.

Matillion and IDG Research recently conducted an IDG MarketPulse survey, “Optimizing Business Analytics by Transforming Data in the Cloud”. The survey polled more than 200 IT, data science, and data engineering professionals. The research exposes the challenges companies face and the strategies they use to prepare data for BI and analytics, with faster time-to-value for implementing analytics projects becoming the main driver for migrating to a cloud approach. Here are some of the key findings:

Data growth is hitting home for enterprises
That data is coming from hundreds (even thousands) of sources
Everyone will have data in the cloud within two years
Transforming data to make it analytics-ready is challenging for nearly all respondents

2020-02-17 00:00:00 Read the full story…
Weighted Interest Score: 3.0769, Raw Interest Score: 1.8084,
Positive Sentiment: 0.1350, Negative Sentiment 0.2429

Making Better Data-Driven Decisions

Is your organization investing heavily in data, yet not necessarily making better decisions or seeing meaningful results? Research shows that companies are spending massive amounts on data and analytics. Yet, as many as 85% of big data projects fail.

Part of the problem is that in this era of data, the numbers on a computer screen or in a report take on a special air of authority. Users rarely ask where the data came from, how it’s been modified, or whether it is fit for its intended purpose.

On February 26, 2020, in a live Harvard Business Review webinar, Eric Haller of Experian DataLabs will discuss the four questions leaders and organizations need to ask and answer about data:

2020-02-26 18:00:00+00:00 Read the full story…
Weighted Interest Score: 3.0111, Raw Interest Score: 1.6640,
Positive Sentiment: 0.3170, Negative Sentiment 0.3962

Automatic Data Labeling Gains Momentum with New IBM and Labelbox Announcements

Data is powerful, but labeling data makes it useful. Labeled data (data that has been appended with informative tags about its contents – say, whether a photo is of a person or an animal) can be used to quickly train machine learning models for identification. Furthermore, automated, AI-driven labeling tools can help to speed the initial process of labeling the data. Now, a pair of back-to-back announcements from IBM and Labelbox are signaling new momentum in the data labeling tool space.

IBM made the first of the two announcements: a new automated labeling tool called “Cloud Annotations.” The tool, which is open-source and accessible on GitHub, allows users to feed 200-500 hand-labeled images into it, after which AI takes the wheel and automatically labels the remaining image set. Cloud Annotations also allows for real-time collaboration as well as cloud data storage and access through IBM’s public cloud.

2020-02-11 00:00:00 Read the full story…
Weighted Interest Score: 2.9683, Raw Interest Score: 1.5705,
Positive Sentiment: 0.2048, Negative Sentiment 0.0683

Data science is becoming software engineering

Editor’s note: The Towards Data Science podcast’s “Climbing the Data Science Ladder” series is hosted by Jeremie Harris, Edouard Harris and Russell Pollari. Together, they run a data science mentorship startup called SharpestMinds. You can listen to the podcast below:

When I think of the trends I’ve seen in data science over the last few years, perhaps the most significant and hardest to ignore has been the increased focus on deployment and prod…
2020-02-17 15:22:56.879000+00:00 Read the full story…
Weighted Interest Score: 2.8914, Raw Interest Score: 1.9095,
Positive Sentiment: 0.3080, Negative Sentiment 0.0616

The Advent And Scope Of AI Marketing In 2020 And Beyond

When it comes to bridging the existing gap between data science and its usage, targeting better marketing results, nothing beats the utilitarian nature of AI. While artificial intelligence alone is capable of sifting through humongous data sets for analyzing the relevant ones, AI marketing is slowly but steadily shaping up into a venture that comes with a host of benefits over the conventional ways of promoting a product or service.

That said, before we move any further into a detailed discussion regarding AI marketing and its role in diverse industries, it is necessary to underst…
2020-02-18 00:00:00 Read the full story…
Weighted Interest Score: 2.8694, Raw Interest Score: 1.2849,
Positive Sentiment: 0.4007, Negative Sentiment 0.0691

The integration and use of AI in mobile apps

In recent years, Artificial Intelligence has emerged as a bubbling buzzword in various industries. And, companies are making hefty investments in projects that utilize AI technology. As per the forecast by International Data Corporation (IDC), global spending on AI systems will hit a $79.2 billion mark by 2022, rising at a CAGR (compound annual growth rate) of 38.0% during the 2018-2022 period. The mobile app development industry is no exception to this trend and developers are experimenting with varied ideas and concepts that entail AI technology. The power of artificial intelligence can be harnessed in mobile applications for obtaining a competitive edge as well as provide the awe-inspiring experience to users.
2020-02-14 18:11:28+00:00 Read the full story…
Weighted Interest Score: 2.8330, Raw Interest Score: 1.5420,
Positive Sentiment: 0.2856, Negative Sentiment 0.0571

Importance of AI in personal finance

With the enhancement of technology the is more to spend and then actually maintain the budget. There are many institutes trying to figure out how to maintain the budget using artificial intelligence. With AI in budgeting and managing personal finance will help in making life better as consumers and also allow them to track their spending and stick to the budget set.

Why is AI required in personal finance? With new technologies getting evolved AI for personal finance will be a major help in getting people out from the financial trap . In current situation, financial health is very important and a lot difficult to maintain. With growing technologies it is a lot easier to spend the money as there are various templation. E.g Food app. Earlier consumer had to order food, one needs to go to the restaurant or call the same, but with the food delivery app and laziness of users the food is just a click away. I personally spent around Rs 50,000 last year on a food delivery app.

2020-02-17 00:00:00 Read the full story…
Weighted Interest Score: 2.7176, Raw Interest Score: 1.1816,
Positive Sentiment: 0.4332, Negative Sentiment 0.4332

Banking for Humanity: Technology to Increase the Human Touch

Technology versus Humanity?

Banks have been struggling with the concept of being more personal with their customers. ‘Banking for Humanity’, a concept explored by fintech guru Chris Skinner, is considered as a remedy for this; with new banking technology innovation spearheading the way for banks to adopt a human touch and be more empathetic.

Chris Skinner stated that the concept is about “how banks can make their services more human”. Accor…
2020-02-17 00:00:00 Read the full story…
Weighted Interest Score: 2.6632, Raw Interest Score: 1.3918,
Positive Sentiment: 0.2595, Negative Sentiment 0.1887

Do not underestimate the need for DevOps in AI. Enter Deep Learning DevOps — DL Infrastructure Engineering.

Why should you care?

As machine learning is getting more mature, the need to build infrastructure that supports running these workflows is even greater. In a large enterprise setting on an average, there are at least 200+ data scientists/DL/ ML engineers that run their model training and inferencing jobs. Ensuring that these users get easy hardware/software access to train their models is imperative. This sounds like an easy task, I’m h…
2020-02-18 04:35:08.274000+00:00 Read the full story…
Weighted Interest Score: 2.6193, Raw Interest Score: 1.5309,
Positive Sentiment: 0.1816, Negative Sentiment 0.1557

How To Make Sure Your Robot Doesn’t Drop Your Wine Glass

From microelectronics to mechanics and machine learning, the modern-day robots are a marvel of multiple engineering disciplines. They use sensors, image processing and reinforcement learning algorithms to move the objects around and move around the obstacles as well.

However, this is not the case when it comes to handling objects such as glass. The surface properties of glass are transparent, and non-uniform light reflection makes it difficult for the sensors mounted on the robot to understand how to engage in a simple pick and place operation.

To address this problem, researchers at Google AI along with Synthesis AI and Columbia University devised a novel machine-learning algorithm called ClearGrasp, that is capable of estimating accurate 3D data of transparent objects from RGB-D images.

2020-02-18 05:41:08+00:00 Read the full story…
Weighted Interest Score: 2.5093, Raw Interest Score: 1.2019,
Positive Sentiment: 0.1335, Negative Sentiment 0.2671

African crowdsolving startup Zindi scales 10,000 data scientists – TechCrunch

Cape Town based startup Zindi has registered 10,000 data-scientists on its platform that uses AI and machine learning to crowdsolve complex problems in Africa.

Founded in 2018, the early-stage venture allows companies, NGOs or government institutions to host online competitions around data-oriented challenges. Zindi opens the contests to the African data scientists on its site who can join a competition, submit solution sets, move up a leader board and win — for a cash prize payout. The highest purse so far has been $12,000, according to Zindi co-founder Celina Lee. Competition hosts receive the results, which they can use to create new products or integrate into their existing systems and platforms.

It’s free for data scientists to create a profile on the site, but those who fund the competitions pay Zindi a fee, which is how the startup generates revenue. Zindi’s model has gained the attention of some notable corporate names in and outside of Africa. Those who have hosted competitions include Microsoft, IBM and Liquid Telecom.

2020-02-17 00:00:00 Read the full story…
Weighted Interest Score: 2.4375, Raw Interest Score: 1.4948,
Positive Sentiment: 0.1830, Negative Sentiment 0.2135

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The post AI & Machine Learning News. 18, February 2020 appeared first on CloudQuant.

Alternative Data News. 19, February 2020

The AltDataNewsletter by CloudQuant

CloudQuant Thoughts : China and China data

Even though none of our news scraper articles for last week seem to cover it, the corona virus has undoubtedly been the key driver of recent trading results and even if the virus is brought under control shortly this will drag on for at least a year. Last week we reported on how some traders were using AltData to identify if/when and by how much China was restoring production. Specifically we pointed out their use of Tom Tom’s data on Beijing traffic patterns. Amongst others CNN has reported the enormous stress that the shutdown is causing China’s small business’ “About 30 million small and medium-sized businesses contribute more than 60% of the country’s GDP, according to government statistics published last September. The taxes they pay account for more than half of government revenue, and they employ more than 80% of China’s workers.”, “According to the Tsinghua University and Peking University survey of firms, 85% of respondents said they would go out of business if the outbreak lasts three months. By the six month mark, 90% of the companies would collapse.” Rumors abound that more than 50% will not be able to manage the crisis as it stands and despite the central governments announcements of assistance through the People’s Bank of China, local banks’ “fear of failure” (the same fear of failure that was undoubtedly responsible for the slow response to the initial outbreak) may prevent small business’ getting access to these loans. Flights are at 20% of normal levels, long distance buses are at 50% capacity. Even if an employee is able to get back to the working cities they will be on two week lock down. Electricity consumption is well below normal usage suggesting industry has not yet powered up. Nitrous Dioxide emissions are at a third of their normal level post the lunar new year. Shops are shut, logistics to move product around the country are still fairly locked down. The ripple effects on the finances of ordinary citizens, their ability to locate food and purchase it are all under extreme pressure. Add to this that the goverment is using its tracking facilities to track minute by minute movement of those infected and assisting in the crack down on those who break the mandatory two-week isolation/quarantine plus the videos of people being forcibly removed from their homes and the long term damage to the Chinese psyche cannot be underestimated.

ESG ETFs Hit New Highs

ESG investments are getting hot. So far in 2020, investors in ETFs appear to be showing a growing preference for investments that follow ESG (Environmental, Social & Governance) criteria, some of which represent disruptive technologies. As evidence of this, new 52-week highs were reached in daily trading by the Vanguard ESG U.S. Stock ETF (ESGV) and the iShares ESG MSCI USA Leaders ETF (SUSL). KEY TAKEAWAYS

ESG-focused ETFs from iShares and Vanguard have hit 52-week highs.
Big tech firms tend to dominate the portfolios of ESG funds.
“Green” technologies and practices are only part of the selection criteria.

Significance For Investors – ESG investing exploded in 2019 as a record $20.6 billion flowed into the sector via mutual funds and ETFs. That’s nearly four times the previous record in 2018. While still a fraction of investable assets, more and more money managers, global banking giants, and philanthropic investors are setting hard and fast rules about not funneling more money to companies or funds that include fossil fuel stocks, boards that lack diversity, and companies that are not contributing to sustainability goals.

2020-02-18 16:12:06.819000+00:00 Read the full story…
Weighted Interest Score: 4.8718, Raw Interest Score: 1.7664,
Positive Sentiment: 0.1140, Negative Sentiment 0.2564

CloudQuant Thoughts : No surprises here, we have been banging on about ESG forever, part of our service is to provide access to curated data sets (we check the data, clean it, write the code, test it, and produce a white paper that wraps all our testing in one complete package) and one of our most interesting is the ESG data set from G&S Quotient. BUT…. most of these ESG ETFs and investment funds do not go much further than investing in Apple, Google, Amazon and Microsoft as four firms that score highly on most ESG based scorecards, are these really what you think of when you think ESG? You might as well just invest in FAANG! Is the future move going to be towards firms that more realistically epitomize the ESG spirit?

Five Data Science and Machine Learning Trends That Will Define Job Prospects in 2020

Data Science and ML have been one of the most talked-about trends in 2019 and without any surprise, they will continue to be so in 2020 as well. From shopping online, hailing rides, ordering food to show binging and digital courses, today everything you indulge in is regulated or influenced by AI and Data Science in known and unknown ways. This dramatic adoption of AI & Data Science in recent years has transitioned this trend from a niche into a mainstream. And with 2020 fueling this transition further, these two trends are going to be the norm of every industry soon. This transitional phase is perfectly described in a Harvard Business Review article as, “Sooner or later, every technology transition from an elite niche to a mainstream tool. AI is now undergoing a similar transformation.… We’re entering an age in which just about anyone can leverage the power of intelligent algorithms to solve the problems that matter to them.” To keep up with the trend and demand, businesses are also increasing their appetite for AI resources, resulting in an upsurge of AI jobs. According to Indeed, a leading job portal, AI job postings rose 57.9% from May 2017 to May 2018 and a whopping 136.3% between May 2016 and May 2017.

Data Science in the Cloud
Natural Language Processing
Machine Learning as a Service
Embedded Analytics
Data Privacy and Cyber Security

2020-02-19 00:00:00 Read the full story…
Weighted Interest Score: 3.7265, Raw Interest Score: 2.0507,
Positive Sentiment: 0.1611, Negative Sentiment 0.0586

SEC Proposes to Modernize Market Data Structure

The Securities and Exchange Commission Friday proposed to modernize the infrastructure for the collection, consolidation, and dissemination of market data for exchange-listed national market system (NMS) stocks. The proposal would update and expand the content of NMS market data to better meet the diverse needs of investors in today’s equity markets. The Commission has not significantly updated the rules that govern the content and dissemination of NMS market data since their initial implementation in the late 1970s. The proposal would also seek to introduce competitive forces into this core component of the national market system for the first time. The introduction of and competition among these new data consolidators could, in turn, allow all market participants, including investors, to access and benefit from the expanded content of NMS market data. “Today’s proposal is part of our larger initiative to modernize our equity market regulatory structure to address significant changes in our trading markets. In particular, today’s proposals are designed to improve data quality and data access for all market participants,” said Chairman Jay Clayton. “Both the content of NMS market data and the technologies used to collect, consolidate, and disseminate that data have lagged meaningfully behind proprietary data products and systems offered by the exchanges. By expanding the content of this data and introducing competitive forces into the market, the proposals would enhance transparency and ensure that improved NMS market data is available on terms that are accessible to a wide variety of participants in today’s markets.” 2020-02-18 15:38:12+00:00 Read the full story…
Weighted Interest Score: 3.3628, Raw Interest Score: 1.9561,
Positive Sentiment: 0.3180, Negative Sentiment 0.0867

Challenges of Integrating Heterogeneous Data Sources

With enterprise data pouring in from different sources – CRM systems, web applications, databases, files, etc. – streamlining data processes is a significant challenge as it requires integrating heterogeneous data streams. In such a scenario, standardizing data becomes a pre-requisite for effective and accurate analysis. The absence of the right integration strategy will give rise to application-specific and intradepartmental data silos, which can hinder productivity and delay results. Consolidating data from disparate structured, unstructured, and semi-structured sources can be complex. A survey conducted by Gartner revealed that one-third of respondents consider “integrating multiple data sources” as one of the top four integration challenges. Understanding the common issues faced during this process can help enterprises successfully counteract them. Here are three challenges generally faced by organizations when integrating heterogeneous data sources as well as ways to resolve them:

2020-02-17 08:35:43+00:00 Read the full story…
Weighted Interest Score: 3.3461, Raw Interest Score: 1.7553,
Positive Sentiment: 0.2468, Negative Sentiment 0.6308

Leading with Data Analytics – TCU

The volume and importance of business data and analytics are growing at an exponential rate. Today, more organizations are taking advantage with data science methodologies to understand the current nature of the firm’s business operations and strategy as well as predict “what may happen” and be prepared for “what might be a good course of action to take,” based on the application of data analytics. This course will help you better understand how to leverage data analytics to make decisions and enable business success. Additionally, you will gain a basic understanding of three popularly applied approaches to data analysis. Who should attend? – Business leaders, managers, and functional-area knowledge workers at all levels who want to take advantage of the real opportunity to better utilize data analytics to develop knowledge and insights that drive improved decision-making.

2020-04-07 00:00:00 Read the full story…
Weighted Interest Score: 3.3281, Raw Interest Score: 1.9017,
Positive Sentiment: 0.9509, Negative Sentiment 0.0000

Snowflake Announces General Availability on Google Cloud

A new press release reports, “Snowflake, the cloud data platform, today announced general availability on Google Cloud, bringing together Snowflake’s cloud-native data platform with Google Cloud’s capabilities in AI, ML and analytics. Snowflake is now available in the us-central1 (Iowa) and europe-west4 (Netherlands) regions with additional regions coming later this year. Combined with the new database replication feature, Snowflake makes it easy for customers to migrate their data to Google Cloud or keep their database data synchronized between multiple cloud providers for business continuity. General availability of Snowflake on Google Cloud is a continuation of the company’s commitment to providing customers with the flexibility of choosing a preferred- or multi-cloud environment that best supports their business and users.”
2020-02-19 08:05:57+00:00 Read the full story…
Weighted Interest Score: 3.1366, Raw Interest Score: 2.1809,
Positive Sentiment: 0.1596, Negative Sentiment 0.0000

10 Apps For Data Scientists To Enhance Their Skills

Finding time to learn a new skill can be difficult, especially when it comes to the competitive field of data science. With the rapid increase in the usage of mobiles, mobile apps have revolutionised the system for learning. Apart from adding excitement to the whole process, mobile apps also make it convenient for data science enthusiasts to learn and upskill themselves on the go. In this article, we have shared a few interesting apps that can help data scientists to learn, practice and enhance their skills.

Data Science 101
Elevate
Lumosity
NeuroNation
Math Workout
QPython
Basic Statistics
Probability Distributions
Programming Hub
Learn Python

2020-02-19 05:33:53+00:00 Read the full story…
Weighted Interest Score: 2.9449, Raw Interest Score: 1.5059,
Positive Sentiment: 0.3364, Negative Sentiment 0.1602

Data Architects, It’s Time to Improve Your Data Classification

Team Data — data architects in particular, whatever their official title may be — have a job to do. That is to take more aggressive steps to protect data assets on behalf of customers and constituents. The responsibility falls to data architects because few in most organizations are thinking about protecting data from the data point of view, so to speak. The lawyers and the security group think about it from the standpoint of data breaches, applications, SQL injections, and so on. Everything starts with a data catalog for classification and data integration. It revolves around “the identification of data elements — privacy, security, confidentiality, usability, context, allowed uses, or any other data protection-like requirements,” said Karen Lopez, Senior Project Manager and Architect at InfoAdvisors. She was speaking on the topic at the DATAVERSITY® Enterprise Data World Conference during her presentation titled Data Categorization for Data Architects.

2020-02-19 08:35:24+00:00 Read the full story…
Weighted Interest Score: 2.8509, Raw Interest Score: 1.4255,
Positive Sentiment: 0.0407, Negative Sentiment 0.1222

Ten Expert Tips And Advice For Aspiring Data Scientists

Thinking about pursuing a career in data science but not sure where to start? With endless archives of information available online and few that may be coming from verified sources, sifting through this information could be challenging. As a result, we have compiled a valuable collection of tips and thoughts from a variety of experts from the data science community. Here are individual insights and pieces of advice from data scientists themselves that could help you carve out a profitable career in this field…

2020-02-18 13:00:30+00:00 Read the full story…
Weighted Interest Score: 2.7120, Raw Interest Score: 1.3058,
Positive Sentiment: 0.2583, Negative Sentiment 0.2439

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The post Alternative Data News. 19, February 2020 appeared first on CloudQuant.

AI & Machine Learning News. 24, February 2020

The Artificial Intelligence and Machine Learning Newsletter by CloudQuant

Elon Musk and Talosian Jeff Bezos in Star Trek’s pilot episode ‘The Cage’ deepfake.

AI is Changing the Pattern for How Software is Developed

Software developers are using AI to help write and review code, detect bugs, test software and optimize development projects. This assistance is helping companies to deploy new software more efficiently, and to allow a new generation of developers to learn to code more easily.

These are conclusions of a recent report on AI in software development published by Deloitte and summarized in a recent article in Forbes. Authors David Schatsky and Sourabh Bumb describe how a range of companies have launched dozens of AI-driven software development tools over the past 18 months. The market is growing with startups raising $704 million in the year ending September 2019.

The new tools can be used to help reduce keystrokes, detect bugs as software is being written and automate many of the tests needed to confirm the quality of software. This is important in an era of increasing reliance on open source code, which can come with bugs. While some fear automation may take jobs away from coders, the Deloitte authors see it as unlikely. “For the most part, these AI tools are helping and augmenting humans, not replacing them,” Schatsky stated. “These tools are helping to democratize coding and software development, allowing individuals not necessarily trained in coding to fill talent gaps and learn new skills. There is also AI-driven code review, providing quality assurance before you even run the code.”

2020-02-21 12:37:45+00:00 Read the full story…
Weighted Interest Score: 3.8938, Raw Interest Score: 1.9255,
Positive Sentiment: 0.1590, Negative Sentiment 0.1943

CloudQuant Thoughts : I use KITE in PyCharm which I find very useful. Just having an AI buddy next to you saying “I think you meant to put a double equals there!” is an astonishing saving in time. If you do a lot of programming, particularly in Python, you should check out AI assistance.

Python Dominates, Usage Survey Confirms

Data scientists, machine learning developers and data engineers are turning decisively to the Python programming language, according to a new study.

An annual usage analysis released this week by O’Reilly Media also found a decided shift towards cloud native design for software, IT infrastructure and DevOps. The study tracked the most popular search terms on O’Reilly’s platform in 2019. The fastest growing search terms were “coding practices,” which jumped nearly 40 percent year-on-year.

Another hot topic as data and applications shift to the cloud was security. A pair of security certifications developed by the industry group CompTIA spiked over the past year, reflecting the need for more security skills as companies move to the cloud. Overall, security registered the strongest growth as a topic search in 2019, jumping nearly 30 percent.

Meanwhile, Python’s growing popularity was fueled by machine learning development. The survey found that Python usage for AI, deep learning and natural language processing projects grew by 9 percent over 2018. Java ranked second in 2019, but usage actually declined slightly year-on-year.

2020-02-19 00:00:00 Read the full story…
Weighted Interest Score: 3.8080, Raw Interest Score: 2.2084,
Positive Sentiment: 0.3118, Negative Sentiment 0.0520

CloudQuant Thoughts : No surprise here at CloudQuant, we obviously use Python extensively both in our scripting language for ANYONE to develop auto-trading models and for all of our back end processes. Everyone in the firm understands Python, not just our programmers. It makes for a very fertile environment.

Autonomous cars ‘won’t kill insurance’

“It might not be how the car and another vehicle interact, but it might be about how the car interacts with the environment or the road system that it’s driving on.”

About 60 per cent of IAG’s overall revenue comes from motor vehicle insurance premiums, and Ms Batch said IAG spent considerable time preparing for an increasingly AI-dense world across its business lines.

This goes far beyond driverless cars to incorporate preparations for big str…
2020-02-17 00:00:00 Read the full story…
Weighted Interest Score: 3.5977, Raw Interest Score: 1.4196,
Positive Sentiment: 0.1052, Negative Sentiment 0.1840

CloudQuant Thoughts : Wanna bet?

Green Bond Indices And ESG Futures Outperform

Green bond indices have, in general, performed better than traditional indices over the past four years according to NN Investment Partners, the Dutch fund manager.

Bram Bos, lead portfolio manager green bonds at NN IP, said in a statement that investing in green bonds is an easy way to invest in fixed income more sustainably without having to compromise on performance.

“Green bonds are typically issued by innovative, forward-looking issuers, whose activities are adapting to the urgency of climate change. As a result, these companies are less exposed to climate and environmental, social and governance risks and are more transparent in their activities,” added Bos. “The consistent outperformance of green bond indices versus regular bond indices underscores this and also makes a compelling argument for green bonds in a broader context.”

2020-02-21 14:10:12+00:00 Read the full story…
Weighted Interest Score: 3.2173, Raw Interest Score: 1.7629,
Positive Sentiment: 0.2063, Negative Sentiment 0.2626

CloudQuant Thoughts : We are strong proponents of ESG, one of the leading datasets on our Alternative Data Catalog is an ESG dataset from G&S Quotient. Though there has been some concern recently that some ESG Indices and Funds are promoting themselves as ESG and loading up on the FAANG stocks. Check out this mornings “DAILY” Podcast from The New York Times where they discuss last weeks letter from Larry Fink where he states “I believe we are on the edge of a fundamental reshaping of finance.“. Jeff Bezos’ Amazon Monday promised to donate $10 billion dollars “to explore new ways of fighting the devastating impact of climate change on this planet we all share” including changing all their delivery trucks to Electric. Delta have promised to go Carbon Neutral within 10 years. Microsoft followed up with a $10b 10 year commitment to climate change, promising to go Carbon Negative. Of course a lot of these extreme promises are based on future technology such as Carbon sequester/capture. However without the buy in from the current major polluters of China and India its impact will be negligible.

The Ultimate Beginner Guide to TensorFlow

Why TensorFlow? We already have Keras! When we build a machine learning model, for example, a convolutional neural network for classifying images, we usually design our network on top of high-level libraries such as Keras. At the end of the day, a Keras model is converted into a TensorFlow program.

TensorFlow, open sourced to the public by Google in 2015, is the result of years of lessons learned from a dilemma: should we attempt to do research with inflexible libraries so that we don’t have to reimplement code, or should we use one library for research and a completely different library for production? TensorFlow was made to be flexible, efficient, extensible, and portable (source). Computers of any shape and size can run it, from smartphones all the way up to huge computing clusters. TensorFlow embraces both open source communities and stability of a large corporation.

2020-02-24 05:07:40.742000+00:00 Read the full story…
Weighted Interest Score: 5.3957, Raw Interest Score: 2.2490,
Positive Sentiment: 0.1719, Negative Sentiment 0.1003

10 Datasets For Data Cleaning Practice For Beginners

In order to create quality data analytics solutions, it is very crucial to wrangle the data. The process includes identifying and removing inaccurate and irrelevant data, dealing with the missing data, removing the duplicate data, etc. Thus, eliminating the major inconsistencies and making the data more efficient to work with.

In this article, we list down 10 datasets for beginners, which can be used for data cleaning practice or data preprocessing.

2020-02-21 09:30:00+00:00 Read the full story…
Weighted Interest Score: 5.2324, Raw Interest Score: 1.8160,
Positive Sentiment: 0.0726, Negative Sentiment 0.0726

AI Resistance is Futile!

We had our first 2020 meetings of Nordic Finance Innovation last week. The theme was digital transformation and its implementation, and was co-hosted by our partner Swedbank. One of the slides struck me as particularly noteworthy. It came from a presentation by Stephan Erne, Chief Digital Officer at Handelsbanken, in reference to artificial intelligence (AI).

Tieto surveyed 3,659 people in Sweden, Norway and Finland in 2019, to understand the general public’s views on the development and use of AI in different areas. The survey consists of two parts, the first part is focusing on industries and occupations, and the second part covers ethical considerations. Notably, most people aren’t too worried about AI’s impact on society. Only a third are really concerned.

2020-02-20 07:13:18+00:00 Read the full story…
Weighted Interest Score: 4.5959, Raw Interest Score: 1.6647,
Positive Sentiment: 0.1189, Negative Sentiment 0.1585

Data Sourcebook (Winter 2019) Issue

From modern data architecture and hybrid clouds, to data science and machine learning, Data Sourcebook is your guide to the latest technologies and strategies in managing, governing, securing, integrating, governing and analyzing data today. Download your copy today to learn about the latest trends, innovative solutions and real-world insights from industry experts on pressing challenges and opportunities for IT leaders and practitioners.

2020-02-19 00:00:00 Read the full story (PDF behind registration wall)…
Weighted Interest Score: 4.2683, Raw Interest Score: 2.0408,
Positive Sentiment: 0.4082, Negative Sentiment 0.4082

Why AI companies don’t always scale like traditional software startups

2020-02-22 00:00:00 Read the full story…
Weighted Interest Score: 4.1357, Raw Interest Score: 1.7738,
Positive Sentiment: 0.2690, Negative Sentiment 0.2052

Equipping the Enterprise for Deep Learning: What IT Leaders Need to Know

Deep learning is a form of artificial intelligence that utilizes neural networks, which are computing systems inspired by the human brain and nervous system — essentially a multi-layered “mesh” architecture. Neural networks are not new, but their use in tackling machine learning problems has become so specialized and valuable, it has emerged as the discipline of deep learning. The magic of DL models is in how well they handle data with a huge number of input variables and/or very complex relationships between input variables.

Performance: Deep Learning vs. Machine Learning : When the number of input variables and the complexity of relationships between them are very great, deep learning techniques outperform traditional machine learning. This is often the case with image classification, natural language processing, and complex anomaly detection. For example, a relatively common DL model for image classification takes as input 150,000 values (per image!) and predicts one of 20,000 image categories. This would be extremely hard to handle with other ML techniques. DL models are also commonly used for natural language processing (NLP) and complex anomaly detection, such as the detection of fraud and manufacturing defects.

2020-02-19 00:00:00 Read the full story…
Weighted Interest Score: 4.0110, Raw Interest Score: 2.4096,
Positive Sentiment: 0.3109, Negative Sentiment 0.0972

9 Free E-Books One Must Read To Learn Big Data In 2020

Currently, organisations have been dealing with a huge amount of data, which are both structured and unstructured. According to research, the Hadoop big data analytics market is forecasted to grow at a CAGR of 40% over the next four years. It is one of the biggest reasons behind the rapid industry growth.

In this article, we list down 9 free e-books to learn big data.

Big Data Now
Cloudera Impala
Data Mining and Analysis
Data-Intensive Text Processing with MapReduce
Disruptive Possibilities: How Big Data Changes Everything
Hadoop Explained
Machine Learning and Big Data
Migrating Big Data Analytics into the Cloud
Real-Time Big Data Analytics: Emerging Architecture

2020-02-24 06:27:30+00:00 Read the full story…
Weighted Interest Score: 3.9405, Raw Interest Score: 1.9836,
Positive Sentiment: 0.1488, Negative Sentiment 0.0248

Data and AI in Banking: More Hype Than Reality

Hardly a week goes by without some major consultancy or industry publication talking about how data and AI will transform banking. Effectively leveraged, data and advanced analytics can cut costs, enhance customer experiences, reduce risks and improve returns. Despite the hype, the reality is still discouragingly modest.

Over the past year, the Digital Banking Report has conducted several research studies on the deployment and potential impact of data and artificial intelligence on the banking industry. We have found that the improved use of data and advanced analytics can improve customer experiences, generate better marketing results, streamline deposit and lending operations, increase consumer engagement, support innovation, and be a foundation for digital transformation.

Being a data-driven financial institution is no longer optional (if it ever was). In every industry, winners will be determined by how well data and AI can be used for the benefit of the consumer. Big tech firms such as Google, Apple, Facebook and Amazon (GAFA) are setting the pace, delivering experiences that are improving valuations and providing the foundation for entry into financial services. Fintech firms and non-traditional banking challengers are using data and insights to steal business from legacy banks and credit unions.

2020-02-24 00:05:59+00:00 Read the full story…
Weighted Interest Score: 3.7181, Raw Interest Score: 1.7921,
Positive Sentiment: 0.4630, Negative Sentiment 0.2240

How Big Data Has Changed the Financial Industry

The accessibility and value of consumer data has grown substantially in the past several years. These days, nearly every company bigger than a “mom and pop” shop works to gather and analyze terabytes of data from their customers, hoping to better understand and serve them while one-upping the competition.

In the financial industry, these efforts are particularly intense. Data has the power to shape not only financial decisions (like how and when…
2020-02-21 08:10:39+00:00 Read the full story…
Weighted Interest Score: 3.5833, Raw Interest Score: 1.7226,
Positive Sentiment: 0.5098, Negative Sentiment 0.3340

Is The Recent Criticism For OpenAI by MIT Technology Review Unfair?

OpenAI had earned plenty of plaudits for its transparent and collaborative culture, but the research organization received a drubbing in MIT Technology Review for allegedly breaching the principles it was founded upon. The caustic article exposed a misalignment between the startup’s magnanimous mission and how it operates behind closed doors.

Although some doubts were raised about its mission at the time of Microsoft’s billion-dollar investment …
2020-02-21 05:49:54+00:00 Read the full story…
Weighted Interest Score: 3.3568, Raw Interest Score: 1.5335,
Positive Sentiment: 0.2914, Negative Sentiment 0.2300

AI is not just another technology project

AI, unlike any other initiative is a business transformation enabler and not another technology system implementation that business users need to be trained on. Traditionally, businesses choose either the classic waterfall approach of linear tasks, or the agile approach, where teams review and evaluate solutions as they are tested out.

In contrast, implementing AI technology requires a different approach altogether. AI requires that you look at a problem and see if there’s a way to solve it by reframing the business process itself. Instead of solving a problem with a 10-step strategy, is there a way to cut it down to six steps using data already available or by using new types of untapped internal or publicly available data and applying AI to it? A study by IDC last year found that 60% of organizations reported changes in their business model that were associated with AI adoption.

2020-02-23 00:00:00 Read the full story…
Weighted Interest Score: 3.3073, Raw Interest Score: 1.1809,
Positive Sentiment: 0.2191, Negative Sentiment 0.3531

10 Best Machine Learning Engineering Practices For A Better Product

Machine learning is the hottest topic in the industry. Therefore, they are one of the highest-paid professionals in the industry. ML and its services are only going to extend their influence and push the boundaries to new realms of the technology revolution. However, deploying ML comes with great responsibility. The black box modeling, though is shedding off its black box reputation, it is crucial to establish trust in both in-house teams and stakeholders.

This can be done by practising a few routines that have been tested at the heart of Google AI research departments. Here are a few best practices, which can help ML engineers in a hassle-free model building…

2020-02-24 09:09:37+00:00 Read the full story…
Weighted Interest Score: 3.2681, Raw Interest Score: 1.7572,
Positive Sentiment: 0.3232, Negative Sentiment 0.2020

Phyto Launches Phyto III Cannabis Focused Venture Capital Fund

Want exposure to cannabis but need some professional advice?

Don’t want to go one toke over the line?

Then consider Phyto Partners, a cannabis investment fund, is launching a third cannabis-focused private equity fund modeled after its first two funds, Phyto Partners I, LP and Phyto II, LP. Phyto Partners plans to leverage its early mover advantage and industry expertise to source early and later stage privately held companies that solve critic…
2020-02-20 15:26:01+00:00 Read the full story…
Weighted Interest Score: 3.1646, Raw Interest Score: 1.6275,
Positive Sentiment: 0.3165, Negative Sentiment 0.2260

Companies Bringing AI Training Inside; Udacity Well-Positioned

Companies are taking AI training into their own hands, hiring outside firms to help their employees to learn about AI and often picking up the expense.

Training is a big market. The annual North American workplace training market is estimated to be $169 billion, according to an estimate on Statista. Spending on annual workplace training averaged $83 billion from 2012 to 2019. The share of US companies that partially or fully outsource training is 53%.

Royal Dutch Shell could be a model. The company is expanding an online program that teaches AI skills as part of an effort to cut costs, improve business processes, and generate revenue, according to a recent account in WSJ Pro. Of its 82,000 employees, about 2,000 have expressed interest or been approached by management about taking AI courses through Udacity, the online education company. These include petroleum engineers, chemists, and geophysicists.

2020-02-21 12:21:40+00:00 Read the full story…
Weighted Interest Score: 3.1614, Raw Interest Score: 1.3581,
Positive Sentiment: 0.1509, Negative Sentiment 0.1358

Data Science in 30 Minutes: Medical Metrics that Matter – The Partnership Between Data Science and the Medical Field

The medical field has been one of the fastest adopters of new data science technology. Finding new ways to treat and manage patient health has become a growing industry for data science.

On Wednesday, February 19th, at 5PM ET, we chatted with Bill Lynch, lead data scientist at NeuroFlow, as he discussed the way his team and company are revolutionizing the medical field with their tools. NeuroFlow has built natural language processing tools and other predictive analytic…
2020-02-20 15:18:59-05:00 Read the full story…
Weighted Interest Score: 3.0669, Raw Interest Score: 1.9143,
Positive Sentiment: 0.6381, Negative Sentiment 0.1823

Okera Enhances Automatic Discovery of Sensitive Data Using Machine Learning

According to a recent press release, “Okera announced today version 2.0 of its secure data access platform. The new version uses machine learning to enhance the automatic discovery of sensitive data such as social security numbers and credit card numbers so that organizations are able to protect their consumers’ data and comply with data privacy regulations like GDPR and CCPA. With a visual policy builder, Okera’s secure data access platform allows data owners and stewards to easily create policies that can be enforced dynamically now on Microsoft Azure Data Lake Storage Gen2 in addition to the previously available support of ADLS Gen1 and Amazon S3. Okera is further enhancing its ecosystems by adding support for AWS Glue Data Catalog.”
2020-02-21 08:05:52+00:00 Read the full story…
Weighted Interest Score: 3.0547, Raw Interest Score: 1.7723,
Positive Sentiment: 0.5908, Negative Sentiment 0.1611

Making Better Data-Driven Decisions

2020-02-26 18:00:00+00:00 Read the full story…
Weighted Interest Score: 3.0111, Raw Interest Score: 1.6640,
Positive Sentiment: 0.3170, Negative Sentiment 0.3962

BEAT THE STOCK MARKET WITH MACHINE LEARNING: the Lazy Strategy

Is it possible to have a machine learning model learn the differences between stocks that perform well and those that don’t, and then leverage this knowledge in order to predict which stock will be worth buying? Moreover, is it possible to achieve this simply by looking at financial indicators found in the 10-K filings?

2020-02-23 15:05:42.582000+00:00 Read the full story…
Weighted Interest Score: 3.0042, Raw Interest Score: 1.5617,
Positive Sentiment: 0.2477, Negative Sentiment 0.2800

Data Science Infrastructure and MLOps

Editor’s note: The Towards Data Science podcast’s “Climbing the Data Science Ladder” series is hosted by Jeremie Harris, Edouard Harris and Russell Pollari. Together, they run a data science mentorship startup called SharpestMinds. You can listen to the podcast below:

You train your model. You check its performance with a validation set. You tweak its hyperparameters, engineer some features and repeat. Finally, you try it out on a tes…
2020-02-23 16:42:15.205000+00:00 Read the full story…
Weighted Interest Score: 2.9930, Raw Interest Score: 1.6650,
Positive Sentiment: 0.0999, Negative Sentiment 0.2331

Explainable AI Needs More Humans

In the midst of the technical jargon of LIME, Shap and the rest, you can forget that the goal is explaining something to a person.

For a lot of people, explainable or interpretable AI or ML means layering a new set of algorithms on top of an older set of algorithms to better understand the older set of algorithms output. Hence, the discussion around interpretable ML can sometimes revolve…
2020-02-24 05:32:36.074000+00:00 Read the full story…
Weighted Interest Score: 2.9632, Raw Interest Score: 1.0177,
Positive Sentiment: 0.0299, Negative Sentiment 0.2394

Google launches TensorFlow library for optimizing fairness constraints

Google AI today released TensorFlow Constrained Optimization (TFCO), a supervised machine learning library built for training machine learning models on multiple metrics and “optimizing inequality-constrained problems.”

The library is designed to help address issues like fairness constraints and predictive parity and help machine learning practitioners better understand things like true positive rates on residents of certain countries, or recall illness diagnoses depending on age and gender.

2020-02-21 00:00:00 Read the full story…
Weighted Interest Score: 2.9263, Raw Interest Score: 1.5263,
Positive Sentiment: 0.5088, Negative Sentiment 0.1696

Top States for Technology Salaries and Growth

The 2020 edition of the Dice Salary Report revealed some key things about the tech industry and the technologists who work in it. Nationally, average annual pay within the tech industry hit $94,000 last year—just a 1.3 percent increase from 2018.

But that’s not the whole story. Salary can vary wildly from state to state, driven by a huge number of factors—everything from the presence of a major tech hub to the average cost of living (which can have a substantial impact on pay). In that spirit, we also broke down average pay on a state-by-state basis; these are the states for which we received a significant number of responses:

2020-02-20 00:00:00 Read the full story…
Weighted Interest Score: 2.8776, Raw Interest Score: 1.8750,
Positive Sentiment: 0.1677, Negative Sentiment 0.0762

Google’s AI drops ‘man’ and ‘woman’ gender labels to avoid possible bias

Google has announced that its image recognition AI will no longer identify people in images as a man or a woman, reports Business Insider. The change was revealed in an email to developers who use the company’s Cloud Vision API that makes it easy for apps and services to identify objects in images.

In the email, Google said it wasn’t possible to detect a person’s true gender based simply on the clothes they were wearing. But Google also said that they were dropping gender labels for another reason: they could create or reinforce biases. From the email:

Given that a person’s gender cannot be inferred by appearance, we have decided to remove these labels in order to align with the Artificial Intelligence Principles at Google, specifically Principle #2: Avoid creating or reinforcing unfair bias.

2020-02-20 07:48:28 Read the full story…
Weighted Interest Score: 2.8524, Raw Interest Score: 1.4819,
Positive Sentiment: 0.0549, Negative Sentiment 0.2195

Insurance Companies Using AI to Build Safety Systems, Optimize Rates

Leading insurance companies in the $500 billion/year insurance industry are studying what types of ML applications to try to gain a business advantage, and startups are using AI to disrupt the industry.

Safety is a big focus, timely considering that motor-vehicle fatalities in 2016 peaked at 40,200; the highest amount recorded in nearly a decade. The estimated healthcare costs to people injured in car crashes totaled over $80…
2020-02-21 12:25:56+00:00 Read the full story…
Weighted Interest Score: 2.7646, Raw Interest Score: 1.2701,
Positive Sentiment: 0.3629, Negative Sentiment 0.3402

Gurucul Introduces Platform for Hunting Security Threats

Gurucul, a provider of unified security and risk analytics technology, is introducing automated intelligent threat hunting that uses artificial intelligence (AI) and machine learning (ML) to detect behaviors associated with cyber attacks and data breaches.

“One of the biggest challenges associated with threat hunting is the manual labor involved in piecing together data from various sources to trace the origin, tactics and techniques across different stages of an attack,” said Nilesh Dherange, CTO of Gurucul. “By combining link analysis and chaining, Gurucul automatically co…
2020-02-21 00:00:00 Read the full story…
Weighted Interest Score: 2.7569, Raw Interest Score: 1.5993,
Positive Sentiment: 0.1263, Negative Sentiment 0.7155

Automation Anywhere Announces World’s First Integrated Process Discovery Solution

grated artificial intelligence (AI)-driven process discovery solution that discovers business processes and with one-click creates bots to automate them. Automation Anywhere Discovery Bot uses AI and machine learning to automatically capture and analyze user actions to uncover common, repetitive process steps as employees navigate between business applications. It then prioritizes automation opportunities by potential return on investment (ROI) and develops RPA bots – accelerating the process automation journey for organizations. Research by Automation Anywhere shows that nearly 80 percent of manual, repetiti…
2020-02-21 08:15:57+00:00 Read the full story…
Weighted Interest Score: 2.7144, Raw Interest Score: 1.9053,
Positive Sentiment: 0.1633, Negative Sentiment 0.1089

Artificial Intelligence Ushers in a New Era of Cost-Effective Clinical Trials

Contributed Commentary by James Streeter, Global Vice President Life Sciences Product Strategy, Oracle Health Sciences

Clinical trials have changed significantly over the past several years. As drugs and devices—and the conditions they are trying to impact—have become increasingly more complex, so has the design and structure of clinical trials. But protocols are costly to change and identifying and enrolling the right patient cohorts is also no…
2020-02-21 12:30:28+00:00 Read the full story…
Weighted Interest Score: 2.6958, Raw Interest Score: 1.2726,
Positive Sentiment: 0.1985, Negative Sentiment 0.2802

Want to get more from your data? Stop focusing on efficiency

Ever since Henry Ford came up with the idea of using moving assembly lines to build automobiles faster at lower cost and at higher quality, efficiency has been the driving force in industry.

Unfortunately, it’s the wrong approach for modern businesses. We’re no longer in an industrial economy. Today, information powers our world. Efficiency drives profit in the short term, but making it your primary focus doesn’t drive innovation; it opens the d…
2020-02-23 00:00:00 Read the full story…
Weighted Interest Score: 2.6771, Raw Interest Score: 1.5008,
Positive Sentiment: 0.8543, Negative Sentiment 0.1847

Britain’s top cop calls for law on police use of AI

Britain’s most senior police officer on Monday called on the government to create a legal framework for police use of new technologies such as artificial intelligence.

Speaking about live facial recognition, which police in London started using in January, London police chief Cressida Dick said that she welcomed the government’s 2019 manifesto pledge to create a legal framework for the police use of new technology like AI, biometrics and DNA.

“The best way to ensure that the police use new and emerging tech in a way that has the country’s support is for the …
2020-02-24 13:18:18+00:00 Read the full story…
Weighted Interest Score: 2.6520, Raw Interest Score: 1.3583,
Positive Sentiment: 0.1294, Negative Sentiment 0.1294

Government must act fast so police can use AI without undermining public trust

Public attention is increasingly focussed on the regulation of police technology. Recent debate has centred around live facial recognition (LFR), following the Met Police’s decision to deploy LFR technology on the streets of London. Proponents argue that LFR will enhance the police’s ability to detect and prevent crime, by enabling officers to more efficiently locate wanted individuals. Privacy campaigners argue that these technologies present a …
2020-02-22 00:00:00 Read the full story…
Weighted Interest Score: 2.6495, Raw Interest Score: 1.0667,
Positive Sentiment: 0.3009, Negative Sentiment 0.5197

Leaked Document Shows How Big Companies Buy Credit Card Data on Millions of Americans

Yodlee, the largest financial data broker in the U.S., sells data pulled from the bank and credit card transactions of tens of millions of Americans to investment and research firms, detailing where and when people shopped and how much they spent. The company claims that the data is anonymous, but a confidential Yodlee document obtained by Motherboard indicates individual users could be unmasked.

The findings come as multiple Senators have urged…
2020-02-19 15:47:10+00:00 Read the full story…
Weighted Interest Score: 2.6489, Raw Interest Score: 1.2696,
Positive Sentiment: 0.0944, Negative Sentiment 0.1373

Four Artificial Intelligence Use Cases in 2020

As the new year and decade have dawned, the buzz surrounding artificial intelligence and its impact in 2020 and beyond shows no signs of slowing down.

AI has now been incorporated into the everyday life of consumers in the developed world, largely driven by the emergence of virtual assistants such as the Alexa, Siri, and Google Assistant ecosystems of devices. This would not have been possible without the maturation of technologies such as voice and image recognition, which cont…
2020-02-24 08:30:01+00:00 Read the full story…
Weighted Interest Score: 2.5952, Raw Interest Score: 1.1274,
Positive Sentiment: 0.2392, Negative Sentiment 0.1025

Data Lake Modernization for Speed, Scale and Agility

DBTA ROUNDTABLE WEBINAR THURSDAY, MARCH 19, 2020 – 11:00 am PT / 2:00 pm ET

AI Weekly: Why a slow movement for machine learning could be a good thing

In 2019, the number of published papers related to AI and machine learning was nearly 25,000 in the U.S. alone, up from roughly 10,000 in 2015. And NeurIPS 2019, one of the world’s largest machine learning and computational neuroscience conferences, featured close to 2,000 accepted papers from thousands of attendees.

There’s no question that the momentum reflects an uptick in publicity and funding — and correspondingly, competition — within the AI research community. But some academics suggest the relentless push for progress might be causing more harm than good.

2020-02-21 00:00:00 Read the full story…
Weighted Interest Score: 2.4176, Raw Interest Score: 1.4201,
Positive Sentiment: 0.2517, Negative Sentiment 0.4134

ProBeat: AI is helping Microsoft rethink Office for mobile

e app is not just for consuming content and maybe a little light editing on the side, but actually creating content on the go. Most interestingly, a lot of these features fundamentally require AI and machine learning to achieve this new mobile productivity paradigm.

Microsoft has been adding AI-driven features to its once most profitable product line for years now — we did a recap of just a handful last year. This week’s Office launch, however, showed Microsoft’s embrace of AI as not merely augmenting what you can already do with the productivity suite, but added new use cases altogether. Most of the new fea…
2020-02-21 00:00:00 Read the full story…
Weighted Interest Score: 2.4014, Raw Interest Score: 1.1773,
Positive Sentiment: 0.1472, Negative Sentiment 0.0981

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Alternative Data News. 26, February 2020

The AltDataNewsletter by CloudQuant

A Scalable Prediction Engine for Automating Structured Data Prep | Ihab Ilyas

Ihab Ilyas is a professor of Computer Science at the University of Waterloo and co-founder of Tamr.

“Data scientists spend big chunk of their time preparing, cleaning, and transforming raw data before getting the chance to feed this data to their well-crafted models. Despite the efforts to build robust predication and classification models, data errors still the main reason for having low quality results. This massive labor-intensive exercises to clean data remain the main impediment to automatic end-to-end AI pipeline for data science. In this talk, I focus on data prep and cleaning as an inference problem, which can be automated by leveraging modern abstractions in ML. I will describe the HoloClean framework, a scalable prediction engine for structured data.”

Markets finally reacting to Corona Virus and Google search auto-complete can’t help but reveal how its users are reacting!!

CloudQuant Thoughts : Markets have their largest two day drop in years and the reaction of ordinary investors is totally expected but still very, very interesting. If you are not a broker with access to retail flow then how do you know what the small trader is thinking and when? Is it Stocktwits, is it by crawling CNBC’s subtitles? If you come up with your own unique way of finding the signal then you have unique Alpha.

Leaked Document Shows How Big Companies Buy Credit Card Data on Millions of Americans

CloudQuant Thoughts : “Let me be blunt. This is bullshit anonymization” Nicholas Weaver, a senior researcher at the International Computer Science Institute at UC Berkeley. This comes as no surprise to most Data Scientists!

ESG – Environmental, Social and Governance

CloudQuant Thoughts : Today the WSJ have an article titled “Investors Cast Off Coal Stakes, Miners Rely on a Few Big Funds”. ESG is the hot Alternative Data right now. CloudQuant provide services to review and test Alternative Data sets. We produce white papers where we investigate whether or not the data has demonstrable Alpha, we give access to the code and the data used to produce the white paper (I know! – Crazy!). Head over to our Data Catalog for more information.

Recent Moves in Sell-Side ESG Research: Credit Suisse & JPMorgan • Integrity Research

Two major investment banks, Credit Suisse and JPMorgan, have recently experienced senior personnel changes in their research teams overseeing environmental, social and governance (ESG) research.

Recent ESG Research Personnel Moves

Last month, US investment bank JPMorgan recently named Jean-Xavier Hecker and Hugo Dubourg as its new co-heads of ESG equity research for its Europe, Middle East and Africa (EMEA) region. Hecker joins JPMorgan following more than 2 ½ years as an SRI analyst at Exane BNP Paribas and close to 4 years as a corporate governance and SRI analyst at …
2020-02-24 02:30:01+00:00 Read the full story…
Weighted Interest Score: 3.1308, Raw Interest Score: 1.9521,
Positive Sentiment: 0.0368, Negative Sentiment 0.0368

ESG Research Set For Significant Growth

Environmental, social and governance research remains one of the few growth areas in the research market according to consultancy Integrity Research.

Michael Mayhew, founder of Integrity Research, said in a blog that ESG has been one of the fastest growing segments of sell-side equity research in the past few years and he expects this to continue to grow for the foreseeable future.

“We would not be surprised to see significant ESG…
2020-02-24 14:37:28+00:00 Read the full story…
Weighted Interest Score: 3.6182, Raw Interest Score: 2.2139,
Positive Sentiment: 0.1862, Negative Sentiment 0.1655

Australian investors warm to sustainable themes

Australia’s mutual fund assets in sustainable investments surged 23.0 per cent year-on-year (y-o-y) to AUD66.8 billion (USD46.7 billion) in 2019, and gathered AUD1.2 billion in inflows over the same period, Cerulli Associates’ estimates show.

This could be attributed to Australian investors’ increased awareness of climate change. According to the Lowy Institute Poll 2019, for the first time in its 15-year history, climate change topped the list …
2020-02-26 00:00:00 Read the full story…
Weighted Interest Score: 2.8713, Raw Interest Score: 1.5692,
Positive Sentiment: 0.1538, Negative Sentiment 0.2462

Defining ESG Investing and Understanding Its Uses

Photo: ESB Professional/Shutterstock

Advisors embracing or considering environmental, social and governance focused investing should understand the different definitions used by asset managers, index providers, stock and bond issuers as well as their clients.

“ESG is not just about values but includes the underlying financial material risks within an industry,” said Mona Naqvi, senior director, ESG, at S&P Dow Jones Indices, a panelists at the …
2020-02-24 00:00:00 Read the full story…
Weighted Interest Score: 2.5574, Raw Interest Score: 1.5155,
Positive Sentiment: 0.1184, Negative Sentiment 0.0710

ESG proposals at Amazon signal ongoing concerns

New slate of ESG proposals at Amazon signal ongoing shareholder concerns

Investors say proposals citing health and safety risks to workers, liabilities related to third-party sellers, and due diligence around the sale of surveillance tech, among other issues, indicate the company is failing to adequately manage risks.

Get Our Activist Investing Case Study! Get the entire 10-part series on our in-depth study on activist investing in PDF. Save it…
2020-02-25 17:41:48+00:00 Read the full story…
Weighted Interest Score: 2.1791, Raw Interest Score: 1.2938,
Positive Sentiment: 0.2043, Negative Sentiment 0.3064

Data Architecture and Data Science: What is the Intersection?

Data Science, in practice, should ultimately combine the best practices of information technology, analytics, and business. On the other hand, Data Architecture enables data scientists to analyze and share data throughout the enterprise for strategic decision-making. Thus, without a sound Data Architecture in place, data scientists will remain severely handicapped in their abilities to develop and productionize data models. This is the primary point of intersection between Data Architecture and Data Science.

According to Science Direct, Data Architecture accomplishes the two following goals for the enterprise Data Science teams:

It allows “strategic development” of data models by “insulating the data from the business as well as the technology process.”
It provisions an “environmental foundation” for ensuing model-development activities with approval from the data owner.

Thus, it is logical to assume that the data architect and the data scientist play complementary roles in an enterprise Data Science team.

2020-02-26 08:35:54+00:00 Read the full story…
Weighted Interest Score: 4.9917, Raw Interest Score: 2.4966,
Positive Sentiment: 0.1379, Negative Sentiment 0.0552

“Obviously AI” Rolls Out First Natural Language-Powered Machine Learning Platform for Predicting Outcomes from Any Data

Data at organizations can be incredibly siloed, difficult to access, and overwhelming for thousands of business users across the globe. From finding a list of items in a haystack of data, to running complex predictive analytics, business users often have to wait for weeks for data engineers to get a single question answered. Today, with the public launch of Obviously AI, a no-code platform designed to put the power of machine learning and analytics in the hands of non-technical business users, this solution can enable anyone to access crucial information and data predictions, simply by asking questions in natural language.

Obviously AI’s no-code tool is extremely easy to use with results on any query returned in under a minute. Users simply upload their dataset from CSV, databases or CRMs and then get a Google-like search bar to ask a question in natural language. For predictive questions, such as “Which customers are likely to cancel their subscriptions?” the platform will understand what the user is asking, find the right data, and build a machine learning algorithm on the fly. It also shows you exactly what factors drove your results, so you don’t have to guess how it got them. Similarly, the platform can answer analytical questions that look for existing patterns in data, such as “What is the average daily foot traffic for my retail stores?” Users do not need any familiarity with writing complex SQL queries or working with programming languages to code regressions, neural networks and other ML algorithms.

2020-02-25 08:05:32+00:00 Read the full story…
Weighted Interest Score: 4.5292, Raw Interest Score: 2.1505,
Positive Sentiment: 0.1792, Negative Sentiment 0.4779

SambaNova Systems raises $250 million for software-defined AI hardware

The infrastructure required to handle AI workloads is often as complex as it is sprawling, but a cottage industry of startups has emerged whose focus is developing solutions for end customers. SambaNova Systems is one such startup — the Palo Alto, California-based firm, which was founded in 2017 by Rodrigo Liang and Stanford professors Kunle Olukotun and Chris Ré, provides systems that run AI and data-intensive apps from the datacenter to the edge. In a reflection of investors’ voracious appetite for the market, it today announced that it’s raised $250 million in series C funding.

“Raising $250 million in this funding round with support from new and existing investors puts us in a unique category of capitalization,” said CEO Liang, a veteran of Sun Microsystems and Oracle. “This enables us to further extend our market leadership in enterprise computing.”
2020-02-25 00:00:00 Read the full story…
Weighted Interest Score: 3.9004, Raw Interest Score: 2.0033,
Positive Sentiment: 0.2956, Negative Sentiment 0.0657

StreamSets Expands Databricks Partnership With New Connector for Delta Lake

“StreamSets®, provider of the industry’s first DataOps platform, today announced an expansion of its partnership with Databricks by participating in Databricks’ newly launched Data Ingestion Network. As part of the expanded partnership, StreamSets is offering additional functionality with a new connector for Delta Lake, an open source project that provides reliable data lakes at scale. With it, users can configure their pipelines to write data from any source moving in batch or streaming mode directly into Delta Lake. Now, data teams can deliver all of their data in a shorter time frame, driving BI, analytics and ML. Today, companies require systems for diverse data applications like real-time monitoring, machine learning and data science — and that can process unstructured data like text, images, video and audio. A decade ago, data lakes replaced data warehouses as the best repositories for this raw data; however, they neither support transactions nor enforce data quality. In addition, they lack consistency, making it almost impossible to mix batch and streaming jobs and appends and reads.”
2020-02-25 08:10:00+00:00 Read the full story…
Weighted Interest Score: 3.6474, Raw Interest Score: 1.8763,
Positive Sentiment: 0.2535, Negative Sentiment 0.1521

The Rising Value of Data in Financial Markets

‘New oil’ or ‘new gold’ are just some of the phrases used to describe the value of data in financial markets. And rightly so. Data fuels every aspect of the trading process. From the very beginning, trading has always been about information. Whoever has the best and the fastest information gains the edge.

Today, traders are also challenged with managing the sheer amount of data in financial markets. The edge that traders gain now is all about who can consume and make sense of the data the fastest by leveraging technologies such as artificial intelligence. In the second of three reports on the trading desk of the future, Refinitiv partners with Greenwich Associates to explore data’s impact on financial markets over the next three to five years, including which types of data will be most valuable, who will provide that data, and how traders expect to use it.

2020-02-13 00:00:00+00:00 Read the full story…

SimCorp launches new machine learning initiative with start-up, Alkymi, targeting institutional investment challenges

SimCorp, a leading provider of investment management solutions and services to the global financial services industry, today announces a partnership with New York based start-up, Alkymi, to launch a new Machine Learning (ML) initiative. It’s arrival comes as institutional investors raise a number of data concerns, including the ability to quickly extract insights from unstructured data, for faster, more informed decision-making.
2020-02-26 00:00:00 Read the full story…
Weighted Interest Score: 3.4483, Raw Interest Score: 2.2040,
Positive Sentiment: 0.4271, Negative Sentiment 0.1367

Business – Integrating data is getting harder, but also more important

GEEKS ARE not known for being poets. But sometimes even they have a way with words, for example when trying to describe the main challenge of dealing with data. It is the search, they say, for “a single version of the truth”.

This also nicely describes what has been the goal of corporate information technology since it emerged 60 years ago. And the adage encapsulates the main tension for businesses in the data economy: finding digital truth—tha…
2020-02-20 00:00:00 Read the full story…
Weighted Interest Score: 3.0650, Raw Interest Score: 1.4636,
Positive Sentiment: 0.1591, Negative Sentiment 0.1803

Is artificial intelligence Sexist? The answer is Yes And No

With advanced research happening in the realm of artificial intelligence (AI), the technology is poised to become smarter than its human creators. But until that day, it is like to harbour sexist, racist and even homophobic tendencies – all inherited from its makers’ social and cultural biases.

This was discussed at some length last year at Rising, one of the country’s biggest gatherings of women trailblazers in the fields of data science and AI. Held on March 8 to commemorate Women’s Day, the one-day event hosted more than 250 participants and featured more than 15 sessions led by industry leaders, mostly women.

One of the speakers on the occasion, Director of Citi Saraswathi Ramachandra, provoked a discussion around a hotly debated topic – Is AI sexist. According to her, this cannot be firmly answered in the affirmative since AI models can only respond to what it has learned. This means that the real culprit is essentially the training dataset we feed it, and not the technology by itself.

2020-02-25 14:30:00+00:00 Read the full story…
Weighted Interest Score: 3.0166, Raw Interest Score: 0.9555,
Positive Sentiment: 0.1257, Negative Sentiment 0.2012

Buy Apple and Amazon? Now May Be Just the Time — ICYMI (In Case you Missed It)

The U.S. stock market is in a tizzy as the coronavirus could become a pandemic. That means the economic shock may last longer than most of us expected. And that in turn hurts in the shorter term but could create favorable revenue and earnings comparisons for 2021. This would be especially true for big tech. Institutional funds have been buying up big tech stocks, which is one piece of evidence that the sector has significant upside.

U.S. stocks broadly are closer to a correction – a 10% drop from record highs. The S&P 500 is 8% lower than its all-time high, reached last week.

The coronavirus is threatening global supply chains, many of which originate in China and have been mostly shut down. The spread of the virus to Italy and other Asian countries is worsening the problem. This may bleed into the U.S. if and when American importers can’t access goods from abroad to meet demand at home.

2020-02-25 21:18:46+00:00 Read the full story…
Weighted Interest Score: 2.9284, Raw Interest Score: 1.3498,
Positive Sentiment: 0.1373, Negative Sentiment 0.2745

Interested in Data Science? Here’s a Breakdown of Data, ML Platforms

Data science is a rapidly growing area of focus for many companies, and for good reason: The right kind of data, analyzed correctly, can yield insights that translate into better and more profitable strategies. That’s why “data scientist” has become a much sought-after role for companies to fill, with high salaries and generous benefits to match.

Once you begin your data-scientist journey, you very quickly d…
2020-02-24 00:00:00 Read the full story…
Weighted Interest Score: 2.9257, Raw Interest Score: 1.8256,
Positive Sentiment: 0.3363, Negative Sentiment 0.1681

Lesser-Known AI-Based Research Labs In India

In order to accomplish breakthroughs in the space of artificial intelligence, it is crucial for researchers to mitigate a plethora of existing challenges. Due to this reason, researchers must have access to a state-of-the-art laboratory to have a free hand in terms of researching. Consequently, companies are rigorously developing AI research labs to facilitate exceptional infrastructure across the country.

In India, the artificial intelligence space has buzz primarily by big giants like Google, Bosch, Mercedes, Microsoft, Accenture and PayPal, to name a few. However, there are a number of lesser-known players who are providing a great contribution to artificial intelligence (AI) with the help of their AI labs. In this article, we will have a look at a few of the lesser-known companies with AI labs.
2020-02-25 10:30:00+00:00 Read the full story…
Weighted Interest Score: 2.7714, Raw Interest Score: 1.5430,
Positive Sentiment: 0.2919, Negative Sentiment 0.0626

INSOFE Launches PGP (Honours) in Data Science, in Collaboration with IIT Ropar and CICE, Canada

INSOFE teamed up with IIT Ropar and CICE, Carleton University to offer – “PGP (Honours) in Data Science”, a specialization program that will prepare students with data science, application architecture, and engineering skills.

There is a great demand for engineers who can not only build models but actually scale and deploy complex AI applications. The salaries of such engineers are roughly twice that of data scientists who build offline models which itself is 50-70% higher than software engineers….
2020-02-26 13:00:00+00:00 Read the full story…
Weighted Interest Score: 2.6932, Raw Interest Score: 1.4052,
Positive Sentiment: 0.3123, Negative Sentiment 0.0781

How To Start A Career In Data Science

A data scientist is someone who helps an organisation to make critical decisions through data analysis, modelling, visualisation, among others. According to the survey reports, Data Science and analytics ecosystem has been witnessing an overall growth in the number of jobs with India contributing to 6% of open job openings worldwide.

Currently, the total number of analytics and data science job positions available are more than 90,000 and compar…
2020-02-26 05:30:00+00:00 Read the full story…
Weighted Interest Score: 2.5253, Raw Interest Score: 1.6456,
Positive Sentiment: 0.2161, Negative Sentiment 0.1828

How To Learn A Programming Language As Fast As Possible

Whether it is for a newly emerging language like Dart, Swift or some of the most established ones like Python, R, etc., the process of learning a new programming language is daunting. People learn programming languages for various reasons like getting a certification for a job hunt, building a project, among others. People want to learn a programming language as fast as possible. However, learning a programming language quickly doesn’t mean that …
2020-02-25 13:30:00+00:00 Read the full story…
Weighted Interest Score: 2.2623, Raw Interest Score: 1.4632,
Positive Sentiment: 0.1829, Negative Sentiment 0.0914

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AI & Machine Learning News. 02, March 2020

The Artificial Intelligence and Machine Learning Newsletter by CloudQuant

How Data and Technology are Changing Active Portfolio Management

We have witnessed a permanent shift in the role that data and technology are playing in investment decision-making. Idea generation techniques that had mainly been seen as emerging or experimental are now increasingly being adopted as mainstream.

However, one of the biggest challenges for asset managers is how to incorporate, assimilate and integrate many of these techniques into the daily investment processes of the various investment teams. Regardless of the approach taken, data and how it is integrated and analyzed is going to play an increasingly pivotal role across all investment strategies.

I will touch upon some key themes in this blog, but will go into more detail in a series to follow.

Quantamental investing
Data science and AI
Sustainable investing
Talent

2020-02-28 15:53:44+00:00 Read the full story…
Weighted Interest Score: 6.0569, Raw Interest Score: 2.3309,
Positive Sentiment: 0.1650, Negative Sentiment 0.1856

CloudQuant Thoughts : The combination of fundamental and Quant techniques, the application of Data Science and AI, the importance of finding and retaining talent have long been important factors in Data Science but the Sustainable Investing is the new Data Point in town. CloudQuant have an ESG dataset available via our Data Catalog. We are constantly looking for new data sets, we test them and produce our own White Papers with reproducible results using our own Backtesting system.

Women in Data Science (WiDS) Conference Livestream – March 2nd

The Women in Data Science (WiDS) initiative aims to inspire and educate data scientists worldwide, regardless of gender, and to support women in the field.

WiDS started as a conference at Stanford in November 2015. Now, WiDS includes a global conference, with approximately 150+ regional events worldwide; a datathon, encouraging participants to hone their skills using a social impact challenge; and a podcast, featuring leaders in the field talking about their work, their journeys, and lessons learned.

If you’re looking for the livestream for the fifth annual Women in Data Science (WiDS) Conference taking place today (March 2) from 9 a.m. to 5 p.m. PT, then you’ve come to the right place.

2020-03-02 00:00:00 Read the full story…
Weighted Interest Score: 2.9308, Raw Interest Score: 1.8171,
Positive Sentiment: 0.4103, Negative Sentiment 0.2345

CloudQuant Thoughts : Here at CloudQuant we are happy to promote WiDS and to encourage increased diversity in Data Science.

AI Algorithm Improves Crop Yield Prediction

As climate change puts greater and greater stressors on crops, precision agriculture – which pursues lower inputs and higher yields – is a booming market, poised to reach nearly $13 billion by the late 2020s. Technology improvements are at the core of many of the solutions that guide the search for climate-resilient and precision agriculture, and now, a team from the University of Illinois has created a new AI algorithm that can use data to help guide crop management decisions in real-time.

A few years ago, the university undertook an effort called the Data-Intensive Farm Management Project. Through this project, researchers seeded and fertilized over 200 fields in the U.S., South America, and even South Africa at varying rates. These researchers used that data – specifically, data from a subset of Midwestern corn fields – as their starting point. They broke the fields down into 5-meter squares and fed data from the project (including elevation, nitrogen application, seed rate, and more) into a convolutional neural network (CNN). Then, they tasked the neural network with predicting yield.

2020-02-27 00:00:00 Read the full story…
Weighted Interest Score: 4.3618, Raw Interest Score: 1.7564,
Positive Sentiment: 0.1756, Negative Sentiment 0.0585

CloudQuant Thoughts : As AI and ML functionality gets easier to use, more and more industries will be revolutionized by their discoveries. The idea of monitoring entire farms in 5m x 5m squares (for multiple data points) was pure science fiction just a few years ago, let alone feeding the information into a convolutional neural network to make sense of the data.

Financial AI Is the Missing Key to Ending Human Trafficking

Technology has opened up a world of possibility, for good and for bad. Some enable criminals to operate on a level previously unseen, but the solution to stopping them often also lies in tech. At today’s rapid pace of development, catching the bad guys is usually a matter of having the most advanced tools.

Some of the most pressing criminal concerns have moved to the digital sphere. As a result, cyber justice can take the form of a technological arms race. Clever implementation of technology may have allowed criminals to avoid capture in the first place, but as security tech improves, it may prove to be their downfall.

Human trafficking is one crime that has proved historically challenging to address. Those guilty of this heinous activity have repeatedly slipped the grasp of law enforcement, but thanks to new tools like artificial intelligence (AI), that may be changing.

2020-02-27 22:30:16+00:00 Read the full story…
Weighted Interest Score: 2.7935, Raw Interest Score: 1.2319,
Positive Sentiment: 0.3715, Negative Sentiment 0.6648

CloudQuant Thoughts : These are the AI and ML stories that fill me with hope for the future. Human Traffickers have been utilizing new technology to expand their scope so it is great to see that AI is being used to close the net on these offenders.

AI Put to Work to Help US Steel Industry Stay Competitive

As the US steel industry looks for ways to lower costs in a global market facing slowing demand, a modern steel plant in Arkansas is using AI to help it become more competitive.

The Big River Steel Mill, which began operating in January 2017, melts scrap metal and produces steel for more than 200 customers, including four automakers, according to a recent account in WSJPro.

The plant’s AI system has been designed by Noodle Analytics of San Francisco, which uses deep learning and neural networks to continually train algorithms on data captured by thousands of sensors.

“We’re using the best available technology and pressing that technology farther, we think, than anyone in the steel industry,” stated Big River Chief Executive David Stickler, a veteran of the steel, mining and recycling industries. “Any future steel facilities that are built will try to capitalize on what we’ve done and replicate it.”

2020-02-27 22:30:23+00:00 Read the full story…
Weighted Interest Score: 2.7727, Raw Interest Score: 1.3185,
Positive Sentiment: 0.2728, Negative Sentiment 0.1591

CloudQuant Thoughts : If we move fast enough we can reduce our costs of production to lower than the current cost of Chinese labor. Adidas tried to move production of Sneakers back to Germany and the US but will be closing its SpeedFactory sites later this year and moving some of the technology to Asia. The shortages of medical supplies for treatment of the CoronaVirus as a result of the over reliance on China for production demonstrates how important it is for us to do better!

IEX’s plan to ‘Thwart predatory trading with AI’ gets pension backing

A group of North American retirement plans with more than $3.3 trillion in assets has backed a proposal by exchange operator IEX Group to use artificial intelligence to counter technology some high-speed traders use to get a trading edge. New technologies and regulations have made the U.S. equity market more efficient. But they have also created speed advantages when executing stock orders, the group, led by pension plans in Ontario and Quebec, as well as the New York City Comptroller, said in a letter to the U.S. Securities and Exchange Commission.

“These speed advantages have tilted the playing field in favor of firms specializing in ‘latency arbitrage,’ reducing the willingness of both long-term investors and market makers to display quotes,” the group, which also includes retirement plans in California, Wyoming, and Arizona, said in the Feb. 24 letter. In latency arbitrage, when a stock price changes on one of the 13 U.S. exchanges, a firm uses advanced technology to race ahead electronically to the other exchanges microseconds before the price updates to buy or sell at an advantageous level.

2020-02-25 20:11:46+00:00 Read the full story…
Weighted Interest Score: 3.2593, Raw Interest Score: 1.3463,
Positive Sentiment: 0.2244, Negative Sentiment 0.1496

CloudQuant Thoughts : For more information see this Medium post and the letters from IEX to the SEC and Virtu to the SEC.

Top Google AI Tools for Everyone

“We want to use AI to augment the abilities of people, to enable us to accomplish more and to allow us to spend more time on our creative endeavors.” — Jeff Dean, Google Senior Fellow
Calling Google just a search giant would be an understatement with how quickly it grew from a mere search engine to a driving force behind innovations in several key IT sectors. Over the past couple of years, Google has planted its roots into almost everything digital, be it consumer electronics such as smartphones, tablets, laptops, its underlying software such as Android and Chrome OS or the smart software backed by Google’s AI.

Google has been actively innovating in the smart software industry. Backed by its expertise in search and analytical data acquired over the years have helped Google create various tools like TensorFlow, ML Kit, Cloud AI and many more for enthusiasts and beginners alike, trying to understand the capabilities of AI. Google AI is focused on bringing the benefits of AI to everyone. The following sections will shed more light on how Google has targeted its suite of tools to specific groups of users, such as Developers, Researchers and Organizations and how they can benefit from the AI tools by Google.

2020-02-29 14:43:52.706000+00:00 Read the full story…
Weighted Interest Score: 3.5372, Raw Interest Score: 1.9825,
Positive Sentiment: 0.3130, Negative Sentiment 0.1252

Rethinking how we value data – Looking at the world’s most precious resource through new eyes

Everyone knows that data are worth something. The biggest companies in the world base their businesses on them. Artificial-intelligence algorithms guzzle them in droves. But data are not like normal traded goods and services, such as apples and haircuts. They can be used time and again, like public goods. They also have spillover effects, both positive, such as helping to improve health care, and negative, such as breaches of personal information. That makes them far from easy to value.

A new report, led by Diane Coyle, an economist at the University of Cambridge, attempts to address this by understanding the value of data and who stands to benefit from it. She says market prices often do not ascribe full value to data because, in many cases, trading is too thin. Moreover, while much of society’s emphasis is on the dangers of misuse of personal data, the report chooses to highlight data’s contribution to “the broad economic well-being of all of society.” That gives it a much deeper value than a simple monetary one.

2020-02-27 00:00:00 Read the full story (Paywall)…
Weighted Interest Score: 4.9312, Raw Interest Score: 1.8729,
Positive Sentiment: 0.1422, Negative Sentiment 0.1660

Pope weighs in on AI ethics debate

The Pontiff is the latest public figure to offer an opinion on the ethics of using artificial intelligence (AI), issuing a set of principles on the use of new technology. The Vatican has produced the Rome Call for AI Ethics, which calls for AI technology to respect privacy, work reliably and without bias, operate transparently and “consider the needs of all human beings”.

Tech giants Microsoft and IBM have been recruited to act as technology sponsors for a project that apparently grew out of concerns raised by Pope Francis more than a year ago about the societal impact of AI. “His major concerns were, will it be available to everyone, or is it going to further bifurcate the haves and the have-not’s?” said John Kelly II, executive vice president of IBM and one of the signatories for the document, in comments reported by Reuters.

2020-02-28 11:18:00 Read the full story…
Weighted Interest Score: 3.2947, Raw Interest Score: 1.2821,
Positive Sentiment: 0.1832, Negative Sentiment 0.3663

Rome Call For AI Ethics: A Humanising Pledge Signed By The Tech Giants

Artificial intelligence (AI) has been seen as a transformative tech, where AI is now used to do significant functionalities of our lives. AI has enabled companies and governments to keep a constant tab on what humans are doing, therefore several questions have been raised, by critics, about its privacy and ethics. To promote ethical use of artificial intelligence (AI), for protecting the planet and the rights of the peo…
2020-03-02 09:30:00+00:00 Read the full story…
Weighted Interest Score: 2.6587, Raw Interest Score: 1.2859,
Positive Sentiment: 0.1944, Negative Sentiment 0.2093

Healthcare Providers Beginning to Apply AI More in Patient Care

Hospitals and doctors’ offices collect vast amounts of data on their patients, everything from blood pressure to genetic sequencing. While the data may be digitized, using it to help in patient treatment can be challenging. But the healthcare industry is getting better at using AI to find patterns in data that can help in patient care.

“I think the average patient or future patient is already being touched by AI in health care. They’re just not necessarily aware of it,” stated Chris Coburn, chief innovation officer for Partners HealthCare System, a hospital and physicians network based in Boston, in an account in WebMD. The application of AI to patient care is in an early stage and is spreading.

“I could not easily name a [health] field that doesn’t have some active work as it relates to AI,” stated Coburn, who mentioned pathology, radiology, spinal surgery, cardiac surgery, and dermatology as examples.

2020-02-27 22:30:03+00:00 Read the full story…
Weighted Interest Score: 4.6781, Raw Interest Score: 1.5464,
Positive Sentiment: 0.3651, Negative Sentiment 0.1503

DOD Adopts Ethical Principles for Artificial Intelligence

The U.S. Department of Defense officially adopted a series of ethical principles for the use of Artificial Intelligence today following recommendations provided to Secretary of Defense Dr. Mark T. Esper by the Defense Innovation Board last October.

The recommendations came after 15 months of consultation with leading AI experts in commercial industry, government, academia and the American public that resulted in a rigorous process of feedback and analysis among the nation’s leading AI experts with multiple venues for public input and comment. The adoption of AI ethical principles aligns with the DOD AI strategy objective directing the U.S. military lead in AI ethics and the lawful use of AI systems.
2020-03-02 08:53:20+00:00 Read the full story…
Weighted Interest Score: 4.6243, Raw Interest Score: 1.5424,
Positive Sentiment: 0.4499, Negative Sentiment 0.0000

Syncsort Partners with Databricks to Support Cloud Initiatives

Syncsort is partnering Databricks to support cloud initiatives for critical mainframe and IBM i data, enabling enterprises to leverage Syncsort Connect products to access, transform, and deliver mainframe data to Delta Lake.

Organizations rely on Databricks to process massive amounts of data in the cloud and power AI, machine learning and business insights. Syncsort Connect features a design once, deploy anywhere architecture that provides a graphical interface to deploy mainframe to cloud data transformation pipelines.

Integration with Syncsort Connect products enables the combination of the Databricks platform with Syncsort’s unrivaled ability to integrate previously inaccessible mainframe and IBM i data for analytics and data science.

2020-02-24 00:00:00 Read the full story…
Weighted Interest Score: 4.3342, Raw Interest Score: 2.1038,
Positive Sentiment: 0.1791, Negative Sentiment 0.2686

Qlik Expands Partnership With Databricks by Joining its Data Ingestion Network

According to a recent press release, “Qlik today announced it has expanded its partnership with Databricks, joining Databricks’ Data Ingestion Network. Qlik Data Integration simplifies loading data into Delta Lake, an open source project that provides reliable data lakes at scale, accelerating the creation of lakehouses for analytics and machine learning (ML). Lakehouse, a new data management paradigm, combines elements of data lakes and data warehouses, enabling business intelligence (BI) and ML on all of a business’s data.”

Itamar Ankorion, SVP of Technology Alliances at Qlik, commented, “We’re excited to be one of the inaugural partners for the launch of Databricks’ Data Ingestion Network. This is the latest development in our growing relationship focused on helping enterprises accelerate time to value with data in the cloud.. Our deeper integration provides Databricks’ customers with a more seamless on-ramp of data from any enterprise data source to their Delta Lake, with the best-fit modern data integration strategy to fuel future targets as their data platforms evolve.”

2020-02-28 08:05:33+00:00 Read the full story…
Weighted Interest Score: 4.1667, Raw Interest Score: 2.2596,
Positive Sentiment: 0.4056, Negative Sentiment 0.0000

Databricks, Partners, Open a Unified ‘Lakehouse’

Coalescing around an open source storage layer, Databricks is pitching a new data management framework billed as combining the best attributes of data lakes and warehouses into what the company dubs a “lakehouse.”

The new data domocile is promoted as a way of applying business intelligence and machine learning tools across all enterprise data. The company and its lakehouse partners also have assembled a “data ingestion network” that allows users to load siloed data into Delta Lake, a storage layer released by Databricks to the open source community last year.

Among the applications that can be integrated into the lakehouse are Google analytics, Salesforce and SAP along with Cassandra, Kafka, Oracle, MySQL and MongoDB databases. Those along with mainframe and file data would be available in one location for BI and machine learning use cases.

2020-02-24 00:00:00 Read the full story…
Weighted Interest Score: 3.5194, Raw Interest Score: 1.8564,
Positive Sentiment: 0.0913, Negative Sentiment 0.1826

Experts Debunk The Biggest Myths About AI In Business

Themarket for AI is growing at an unprecedented rate. Market Watch released a report last year showing that the market size is growing about 55% a year over the course of the decade between 2016 and 2025. The sudden growth of AI is not at all surprising. Businesses in all industries are starting to realize the potential AI (artificial intelligence) can bring to their sectors, strengthening decision-making while automating complex and time-consuming tasks.

Through the power of artificial intelligence, businesses can help to streamline their operations, increasing efficiency and overall productivity. Companies are now increasing the adoption of this technology in a range of different industries, which covers diverse sectors such as healthcare, finance, marketing and more. Through the incorporation of AI, industries have seen major shifts in how they run. While the true potential of AI is now being recognized by businesses from all different sectors, many myths have floated around causing scepticism and unnecessary fear over this transformative technology. If AI is to reach its true potential in businesses across all industries, it’s important to explore, and further debunk, these common misconceptions.

AI Will Steal Our Jobs
AI Is Hard to Integrate with Business
Businesses Don’t Need AI
AI is a Net Positive – Not a Destructive Force to the Economy

2020-02-28 17:05:53+00:00 Read the full story…
Weighted Interest Score: 4.2568, Raw Interest Score: 1.4572,
Positive Sentiment: 0.2732, Negative Sentiment 0.2049

CNAS Announces New Members of AI Task Force

The Center for a New American Security (CNAS) is pleased to announce the addition of three new members to the Task Force on Artificial Intelligence (AI) and National Security. The AI Task Force examines how the United States should respond to the national security challenges AI poses and is co-chaired by Robert O. Work, former Deputy Secretary of Defense, and Dr. Andrew W. Moore, Head of Google Cloud Artificial Intelligence.

The new AI Task Force members are…

2020-02-27 01:19:11+00:00 Read the full story…
Weighted Interest Score: 4.1733, Raw Interest Score: 1.9889,
Positive Sentiment: 0.0398, Negative Sentiment 0.3182

Top 3 Artificial Intelligence Research Papers – February 2020

At the beginning of every month, we decipher three research papers from the fields of machine learning, deep learning and artificial intelligence, that left the biggest impact on us in the previous month. Apart from that, at the end of the article, we add links to other papers that we have found interesting but were not in our focus that month. So, you can check those as well. In February, we explored papers that, as we see it, are going to leave a big impact on the future of machine learning and deep learning. In essence, we think that these proposals are going to change the way we do our jobs. Have fun!

The Tree Ensemble Layer: Differentiability meets Conditional Computation
The Microsoft Toolkit of Multi-Task Deep Neural Networks for Natural Language Understanding
SUOD: Toward Scalable Unsupervised Outlier Detection

2020-03-02 00:00:00 Read the full story…
Weighted Interest Score: 4.1407, Raw Interest Score: 1.8718,
Positive Sentiment: 0.1134, Negative Sentiment 0.2836

Common Pitfalls That The Deep Learning Startups Fail To Recognise

Today, AI’s significant applications are being recognised in the world when it comes to solving complex problems. AI and its branch, deep learning have considerably contributed across the sector with machine translation, natural language processing and computer vision. And over the last few years, we have witnessed a spur in deep learning startups, but like any other software-based ones, these startups encounter some pitfalls, but these mistakes are a little unique to them.

Not Investing Enough in Data and Powerful Processors
Not Accounting for the Cloud Charges
Expensive Data Cleansing
The Edge Cases
Hiring the Right People

2020-03-02 10:30:00+00:00 Read the full story…
Weighted Interest Score: 4.0104, Raw Interest Score: 2.2657,
Positive Sentiment: 0.2201, Negative Sentiment 0.3755

Micron Bridges Memory Bandwidth Gap for ML

Deep learning accelerators based on chip architectures coupled with high-bandwidth memory are emerging to enable near real-time processing of machine learning algorithms. Memory chip specialist Micron Technology argues that hardware accelerators linked to higher memory densities are the best way to accelerate “memory bound” AI applications.

Micron (NASDAQ: MU) announced a partnership this week with German automotive supplier Continental (OTCMKTS: CTTAY) to adapt the memory maker’s deep learning accelerator to machine learning auto applications. The partners said they would focus on vehicle automation systems.

Micron’s deep learning accelerator also has machine learning applications for other edge deployments where memory bandwidth used for local data processing has so far failed to keep pace with microprocessor core growth.

2020-02-26 00:00:00 Read the full story…
Weighted Interest Score: 3.8473, Raw Interest Score: 2.0243,
Positive Sentiment: 0.2180, Negative Sentiment 0.0623

Talend Data Fabric’s Latest Version Features Variety of Self-Service Enhancements

Talend, a provider of cloud data integration and data integrity, is introducing the Winter ‘20 release of Talend Data Fabric, unveiling the new Talend Cloud Data Inventory.

Talend Cloud Data Inventory automatically calculates the Data Intelligence Score of all data across an organization and presents it in a service-self cloud app for every user.

Winter ’20 also provides several other capabilities including AI features and cutting-edge cloud co…
2020-02-27 00:00:00 Read the full story…
Weighted Interest Score: 3.7983, Raw Interest Score: 2.0854,
Positive Sentiment: 0.4373, Negative Sentiment 0.0336

SimCorp Launches New ML Initiative With Alkymi

Corp, a leading provider of investment management solutions and services to the global financial services industry, today announces a partnership with New York based start-up, Alkymi, to launch a new Machine Learning (ML) initiative. It’s arrival comes as institutional investors raise a number of data concerns, including the ability to quickly extract insights from unstructured data, for faster, more informed decision-making.

Many investment firms are currently buckling under a number of operational constraints, including the burden of processing unstructured data or outsourcing it, inadequate cross-asset co…
2020-02-26 14:30:54+00:00 Read the full story…
Weighted Interest Score: 3.7037, Raw Interest Score: 2.3941,
Positive Sentiment: 0.5064, Negative Sentiment 0.1842

How Going All-In on Machine Learning Changed Data Collection at Morningstar

Shariq Ahmad set an ambitious goal for Morningstar’s data collection team in 2019: to have at least 50 percent of its engineers working on machine learning initiatives by year’s end.

Ahmad joined Morningstar, which provides research and proprietary tools to investors, in 2010 and stepped into the role of head of technology for the data collection group in the summer of 2018. His first order of business was to automate the data collection process which, up until that point, had relied on analysts to gather information from numerous sources — ranging from SEC filings to managed investment documents — and verify its quality.

“Collecting financial data from various sources is an exhaustive process,” Ahmad said. “With an ever-increasing demand for new datasets, I realized we needed some form of automation to help us scale.”

2020-02-28 00:00:00 Read the full story…
Weighted Interest Score: 3.5430, Raw Interest Score: 1.9403,
Positive Sentiment: 0.1455, Negative Sentiment 0.2183

5 Key Differences Between a Data Lake vs Data Warehouse

A data lake is not a direct replacement for a data warehouse; they are supplemental technologies that serve different use cases with some overlap. Most organizations that have a data lake will also have a data warehouse. Let’s compare the properties of a data lake in comparison to a (data warehouse & separate ETL server).

Data in data lakes is stored in its native format
Data in data lakes can be accessed flexibly
Data lakes provide schema-on-read access
Data lakes provide decoupled storage and compute
Data lakes go with cloud data warehouses

2020-02-25 00:00:00 Read the full story…
Weighted Interest Score: 3.4128, Raw Interest Score: 1.7946,
Positive Sentiment: 0.1765, Negative Sentiment 0.0588

Actionable big data: How to bridge the gap between data scientists and engineers

The buzz around big data has created a widespread misconception: that its mere existence can provide a company with actionable insights and positive business outcomes.

The reality is a bit more complicated. To get value from big data, you need a capable team of data scientists to sift through it. For the most part, corporations understand this, as evidenced by the 15x – 20x growth in data scientist jobs from 2016 to 201…
2020-02-29 00:00:00 Read the full story…
Weighted Interest Score: 3.3882, Raw Interest Score: 1.9234,
Positive Sentiment: 0.4075, Negative Sentiment 0.2445

How Chicago Tech Companies Use AI to Drive Decision-Making

Humans are inferior to technology when it comes to making objective decisions. According to Harvard Business Review, cognitive biases heavily influence judgment, often steering us away from objective decision-making. Companies are now turning to artificial intelligence to optimize their data-based business decisions. AI-driven workflows crunch data, consolidate insights and provide best possible outcomes, saving humans time, money and of course, error.

At dealmaking software company Ansarada, Vice President of Sales, Americas Sean Elder said their AI Bidder Engagement Score assesses 57 separate data metrics to determine a bidder’s behavior. “This allows dealmakers — whose time is stretched thin during these events — to prioritize the serious bidders and focus their time and energy where it counts,” said Elder.

2020-02-25 00:00:00 Read the full story…
Weighted Interest Score: 3.3720, Raw Interest Score: 1.8073,
Positive Sentiment: 0.3772, Negative Sentiment 0.1257

Intel uses AI to find new customers in specific industries

How does Intel, which expects the market opportunity for AI hardware to grow from $2.5 billion in 2017 to $10 billion in 2022, find new customer opportunities? With AI, of course. In a blog post today, Intel detailed a tool its IT Advanced Analytics team developed internally to mine millions of public business pages and extract actionable segmentation for both current and potential customers. The chipmaker says that its sales and marketing staff have used the new system to discover new leads faster and more accurately than before.

“Intel sales and marketing staff have traditionally used manual search and vendor tools in order to identify potential leads; however, these methods lack the ability to align with the internal language used by Intel staff to properly segment and tailor their outreach plans,” wrote Intel. “Additionally, in the era of globalized business, existing customers are often expanding into new domains, requiring sales and marketing staff to constantly keep current with changes in a wide variety of industries.”

As Intel explains it, the system focuses on two key classification aspects: (1) an industry segment ranging from verticals such as “healthcare” to more specific fields such as “video analytics” and (2) functional roles like “manufacturer” or “retailer” that further distinguish potential sales and marketing opportunities. The AI model acquires a constant stream of textual data from millions of sites, updating the multi-million node knowledge graph with gigabytes of data every hour, which then gets passed along to a set of machine learning models for segmenting potential customers.

2020-02-27 00:00:00 Read the full story…
Weighted Interest Score: 3.2248, Raw Interest Score: 1.5195,
Positive Sentiment: 0.3482, Negative Sentiment 0.0317

Machine Learning on the Edge, Hold the Code

Many companies are scrambling to find machine learning engineers who can build smart applications that run on edge devices, like mobile phones. One company that’s attacking the problem in a broad way is Qeexo, which sells an AutoML platform for building and deploying ML applications to microcontrollers without writing a line of code.

Qeexo emerged from Carnegie Mellon University in 2012, just at the dawn of the big data age. According to Sang Won Lee, the company’s co-founder and CEO, the original plan called for Qeexo to be a machine learning application company.

The company landed a big fish, the Chinese mobile phone manufacturer Huawei, right out the gate. Huawei liked the ML-based finger-gesture application that Qeexo (pronounced “Key-tzo”) developed, and the company wanted Qeexo to ensure that it could run across all of its phone lines. That was a good news-bad news situation, Lee says.

“Our first commercial implementation with Huawei kept the whole company in China for two months, to finish one model with one hardware variant,” Lee tells Datanami. “We came back and it was difficult to keep the morale high for our ML engineers because nobody wanted to constantly go abroad to do this type of repetitive implementation.

2020-02-25 00:00:00 Read the full story…
Weighted Interest Score: 3.0297, Raw Interest Score: 1.7888,
Positive Sentiment: 0.1750, Negative Sentiment 0.0972

AI Weekly: U.S. and EU strike contrasting tones on AI regulatory policy

This week, the White House Office of Science and Technology Policy (OSTP) released a year-one report card on its American Artificial Intelligence Initiative. Earlier this month, the European Commission (EC) published a major set of proposals for its strategy on AI. Both of these follow AI principles and regulations proposed in May 2019 by the multi-nation Organization for Economic Co-operation and Development (OECD), which includes the U.S. and European countries.

Despite that shared international work, the U.S. and Europe have also gone their own respective ways. It’s clear that the rhetoric of both is strongly bound to geography — U.S.-first here, Europe-first there — but the aforementioned announcements also show a slight but important difference in tone between the two. Whereas Europe sounds largely optimistic, the U.S. comes off as more fearful.

Just over a week ago, EC president Ursula von der Leyen took to the podium and gave a speech announcing and explaining Europe’s new AI strategy. She discussed Eurocentric concerns first, adding, “We want the digital transformation to power our economy, and we want to find European solutions in the digital age.”

2020-02-28 00:00:00 Read the full story…
Weighted Interest Score: 2.9688, Raw Interest Score: 1.1739,
Positive Sentiment: 0.2594, Negative Sentiment 0.3140

Freshworks To Improve Customer Experience Through Acquisition Of AI Startup AnsweriQ

Freshworks — a SaaS-based company — has acquired Seattle-based AI startup AnsweriQ for an undisclosed amount. Founded in 2015, Freshworks is now valued at $3.5 billion and has closed about 10 acquisitions since then. The company offers a wide range of products such as freshdesk, freshsales, freshchat, among others. And its platforms include Freddy AI — an analytics solution — that provides predictive insights across the customer journey.
2020-02-28 02:46:55+00:00 Read the full story…
Weighted Interest Score: 2.9187, Raw Interest Score: 1.2440,
Positive Sentiment: 0.4306, Negative Sentiment 0.0957

Real-time Data Streaming, Kafka,and Analytics Part 2: Going Beyond Pure Streaming

Data transaction streaming is managed through many platforms, with one of the most common being Apache Kafka. In our first article in this data streaming series, we delved into the definition of data transaction and streaming and why it is critical to manage information in real-time for the most accurate analytics. As more individuals increase their data literacy and use data to make business decisions, real-time data is becoming a critical factor. To handle this effectively, companies are implementing modern data architectures that can support this real-time requirement, including change data capture (CDC) and Apache Kafka as their streaming platform components of choice.

Apache Kafka is a strong choice to handle real-time data streaming as it ingests, persists and presents streams of data for consumption and use by individuals for analytics. Basically, Kafka operates through three basic components to move data in real-time: producers, brokers and consumers. The producer is a process that writes or sends the data to Kafka. It is then sent along to the Kafka broker, which runs the process and responds to requests from products and consumers. Finally, the consumer is the end process – an application program that reads the records at the end of the stream.
2020-02-25 00:00:00 Read the full story…
Weighted Interest Score: 2.8375, Raw Interest Score: 1.8397,
Positive Sentiment: 0.2339, Negative Sentiment 0.0780

Unveiling The IT Stack To Support The Artificial Intelligence Of Things At CES 2020

Flying taxis, concept cars, curved screens, and folding PCs are lighting up Las Vegas nights. This is a place where the world’s best tech gets unveiled. As intelligence will transform most verticals, including transportation, smart home, healthcare, and public services, emerging technologies such as 5G, the internet of things (IoT), and edge computing are forming the foundation of and heating up the data economy to support pervasive AI-enabled applications.

Intelligence Goes Vertical And Pervasive. Vendors at CES are actively embedding AI into their products. Open source frameworks and AI suites available on public clouds have lowered the barrier to develop AI applications and empower products with intelligence. To win against fierce competition, however, firms must differentiate their intelligence with exceptional performance, identify the problems created by increasing customer expectations, and solve them with smart algorithms. At this stage, even small steps garner huge investment.

2020-02-28 02:09:36-05:00 Read the full story…
Weighted Interest Score: 2.8306, Raw Interest Score: 1.3927,
Positive Sentiment: 0.3296, Negative Sentiment 0.2143

Airlines take no chances with our safety. And neither should artificial intelligence

You would thinking flying in a plane would be more dangerous than driving a car. In reality it’s much safer, partly because the aviation industry is heavily regulated. Airlines must stick to strict standards for safety, testing, training, policies and procedures, auditing and oversight. And when things do go wrong, we investigate and attempt to rectify the issue to improve safety in the future.

It’s not just airlines, either. Other industries where things can go very badly wrong — such as pharmaceuticals and medical devices — are also heavily regulated. Artificial intelligence is a relatively new industry, but it’s growing fast and has great capacity to do harm. Like aviation and pharmaceuticals, it needs to be regulated.

2020-03-02 11:30:00+11:00 Read the full story…
Weighted Interest Score: 2.7745, Raw Interest Score: 1.0875,
Positive Sentiment: 0.1338, Negative Sentiment 0.4517

Data Versioning Matters to Data Science

Amazon Web Services (AWS) recently published a case study about how the Allen Institute for Cell Science — which was founded by Microsoft’s Paul Allen to research how the human brain works in health and disease — is taking new steps to make its data and metadata easy to access, search, and redistribute for internal and external users on the web.

The Institute has a lot of data: more than 7 terabytes and over 288,000 objects on the Amazon S3 web …
2020-02-27 08:35:45+00:00 Read the full story…
Weighted Interest Score: 2.7371, Raw Interest Score: 1.5520,
Positive Sentiment: 0.1207, Negative Sentiment 0.1035

How robots explain themselves matters more than you might think

Artificial intelligence is entering our lives in many ways—on our smartphones, in our homes, in our cars. These systems can help people make appointments, drive, and even diagnose illnesses. But as AI systems continue to serve important and collaborative roles in people’s lives, a natural question is: Can I trust them? How do I know they will do what I expect?

Explainable AI (XAI) is a branch of AI research that examines how artificial agents can be made more transparent and trustworthy to their human users. Trustworthiness is essential if robots and people are to work together. XAI seeks to develop AI systems that human beings find trustworthy—while also performing well to fulfill designed tasks.

At the Center for Vision, Cognition, Learning, and Autonomy at UCLA, we and our colleagues are interested in what factors make machines more trustworthy, and how well different learning algorithms enable trust. Our lab uses a type of knowledge representation—a model of the world that an AI uses to interpret its surroundings and make decisions—that can be more easily understood by humans. This naturally aids in explanation and transparency, thereby improving trust of human users.

2020-02-29 07:00:44 Read the full story…
Weighted Interest Score: 2.7027, Raw Interest Score: 1.3343,
Positive Sentiment: 0.3160, Negative Sentiment 0.1404

All Machine Learning Products Launched By Google In February 2020

When it comes to artificial intelligence, it is hard to keep Google away from bringing in a new array of services and products on a regular basis. In the month of January, the tech giant launched a number of products such as LaserTagger, Meena and Reformer, to name a few. Just like the previous month, Google has rolled down a number of new tools/techniques to look at, which will benefit a host of people in regard to artificial intelligence and other related streams.

Here is a list of the products launched by Google in February 2020:

T5: The Text-To-Text Transfer Transformer
AutoFlip: An Open-Source Framework For Intelligent Video Reframing
Learning To See Transparent Objects Via ClearGrasp
Setting Fairness Goals With The TensorFlow Constrained Optimisation Library
Generating Diverse Synthetic Medical Image Data For Training Machine Learning Models

2020-03-01 04:30:00+00:00 Read the full story…
Weighted Interest Score: 2.6724, Raw Interest Score: 1.5215,
Positive Sentiment: 0.2355, Negative Sentiment 0.2174

Busted! New Ways Your Boss Knows You’re About to Quit

Are you conveying a more subdued emotional tone in your emails to your boss? Have you increased the distance between yourself and your colleagues when chatting around the coffee machine? Today, changing your routines and habits (even in small ways) may peg you as a flight risk.

Don’t worry: If your boss suspects you’re about to quit, they haven’t become a soothsayer. Chances are good they’re being tipped off by HR and a burgeoning practice that uses data analytics and machine-learning algorithms to predict in real time which employees are likely to leave. What’s more, the highly accurate “quit algorithms” can reveal your intentions even before you start accepting calls from recruiters or post your résumé.

Here are the new ways that your boss can identify you as someone who is likely to quit…

2020-02-26 00:00:00 Read the full story…
Weighted Interest Score: 2.6404, Raw Interest Score: 1.3101,
Positive Sentiment: 0.1872, Negative Sentiment 0.2433

The Challenges Of Building Inferencing Chips

Putting a trained algorithm to work in the field is creating a frenzy of activity across the chip world, spurring designs that range from purpose-built specialty processors and accelerators to more generalized extensions of existing and silicon-proven technologies.

What’s clear so far is that no single chip architecture has been deemed the go-to solution for inferencing. Machine learning is still in its infancy, and so is the entire edge concept where most of these inferencing chips ultimately will be deployed. Moreover, how to utilize this technology across multiple end markets and use cases, let alone choose the best chip architectures, has shifted significantly over the past 12 to 18 months as training algorithms continue to evolve. That makes it difficult, if not impossible, for any single architecture to dominate this field for very long.

“Machine learning can run on a range of processors, depending on what you are most concerned about,” said Dennis Laudick, vice president of marketing for the machine learning group at Arm. “For example, all machine learning will run on an existing CPU today. Where you only want to do light ML, such as keyword spotting, or where response time is not critical, such as analyzing offline photos, then the CPU is capable of doing this. It can still carry out other tasks, which cuts the need for additional silicon investment. Where workloads become heavier, and where performance is critical or power efficiency is a concern, then there are a range of options.”

2020-02-27 08:02:48+00:00 Read the full story…
Weighted Interest Score: 2.6375, Raw Interest Score: 1.3795,
Positive Sentiment: 0.2024, Negative Sentiment 0.1125

In AI, the objective is subjective!

What is “Ground Truth” in AI? (A warning.) : A demo that shows why you shouldn’t treat AI like a magical box of magic

With all the gratuitous anthropomorphization infecting the machine learning (ML) and artificial intelligence (AI) space, many businessfolk are tricked into thinking of AI as an objective, impartial colleague that knows all the right answers. Here’s a quick demo that shows you why that’s a terrible misconception.

A task that practically every AI student has to suffer through is building a system that classifies images as “cat” (photo contains a cat) or “not-cat” (no cat to be seen). The reason this is a classic AI task is that recognizing objects is a task that’s relatively easy for humans to perform, but it’s really hard for us to say how we do it (so it’s difficult to code explicit rules that describe “catness”). These kinds of tasks are perfect for AI.
2020-02-28 21:32:23.975000+00:00 Read the full story…
Weighted Interest Score: 2.6358, Raw Interest Score: 1.0379,
Positive Sentiment: 0.1800, Negative Sentiment 0.2118

Scrabble Chinese Room and AI Understanding

By Lance Eliot, the AI Trends Insider

If you are a Scrabble fan, you might remember the headlines in 2015 that blared that the winner of the French Scrabble World Championship was someone that did not understand a word of French.

Sacrebleu!

Note that I spelled this stereotypical French phrase as it is spelled in the French language, as one word, rather than the Americanized version of two words with the accent (sacre bleu), which would be impo…
2020-02-25 12:40:07+00:00 Read the full story…
Weighted Interest Score: 2.5648, Raw Interest Score: 0.9780,
Positive Sentiment: 0.2404, Negative Sentiment 0.1064

Deep Transfer Learning for Image Classification

tep-by-step tutorial from data import to accuracy evaluation

The following tutorial covers how to set up a state of the art deep learning model for image classification. The approach is based on the machine learning frameworks “Tensorflow” and “Keras”, and includes all the code needed to replicate the results in this tutorial (unfortunately, the syntax when including code blocks in medium articles does not look very nice, but it should hopefully be readable).

The prerequisites for setting up the model is access to labelled data, and as an example case I have used images of various traffic signs (which can b…
2020-02-28 15:12:12.983000+00:00 Read the full story…
Weighted Interest Score: 2.4999, Raw Interest Score: 1.3773,
Positive Sentiment: 0.0915, Negative Sentiment 0.1706

Addressing Catastrophic Forgetting In ML With ANWL & Meta-Learning

eural networks sometimes suffer from forgetting the last tasks it has done upon learning new information, something which is called catastrophic forgetting.

This catastrophic forgetting prevents the machine learning systems from ‘continual learning which is the ability to remember previous tasks while still learning new things. But all hope is not lost, some systems can still be trained to remember, enter, ANML (a neuromodulated meta-learning algorithm).

What is ANML?

While a lot of work has been done on keeping the machine learning models from catastrophically forgetting the previous knowledge, all of it …
2020-02-27 13:30:00+00:00 Read the full story…
Weighted Interest Score: 2.4840, Raw Interest Score: 1.4458,
Positive Sentiment: 0.2370, Negative Sentiment 0.3792

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Alternative Data News. 04, March 2020

The AltDataNewsletter by CloudQuant

Airborne Nitrogen Dioxide Plummets Over China

NASA and European Space Agency (ESA) pollution monitoring satellites have detected significant decreases in nitrogen dioxide (NO2) over China. There is evidence that the change is at least partly related to the economic slowdown following the outbreak of coronavirus.

At the end of 2019, medical professionals in Wuhan, China, were treating dozens of pneumonia cases that had an unknown source. Days later, researchers confirmed the illnesses were caused by a new coronavirus (COVID-19). By January 23, 2020, Chinese authorities had shut down transportation going into and out of Wuhan, as well as local businesses, in order to reduce the spread of the disease. It was the first of several quarantines set up in the country and around the world.

2020-02-28 00:00:00 Read the full story…

CloudQuant Thoughts : These images just cause me concern at the damage the manufacturing engine in China is doing to the planet, at the same time those clear images weirdly give me hope for the future.

How investors are using ‘alternative’ data to track China’s recovery from coronavirus

Only a week ago market pundits were still telling investors to fade the panic from the COVID-19 outbreak. But Tracy Chen didn’t share in the optimism.

The fund manager for Brandywine Global said an array of “alternative” datasets which offered frequent updates of how swiftly Chinese workers were returning to the factory floor painted a more dire picture of the world’s second largest economy. Based on such sources, hopes for China to see a V-shap…
2020-02-27 00:00:00 Read the full story…
Weighted Interest Score: 3.9141, Raw Interest Score: 1.5876,
Positive Sentiment: 0.0847, Negative Sentiment 0.2752

CloudQuant Thoughts : There is so much alternative data coming out of China at the moment to help traders make their decisions. This is a really opening a lot of people’s eyes to the added value of unusual data sources. We have traffic monitoring, pollution monitoring, daily coal consumption, box office revenues, container volumes, container pricing, Intra City Travel Data, NASA pollution change data 2019 vs 2020 , increasing number of airlines seats, night street lights and much much more. If you were not already a believer in the value of Alternative Data then you should be now!

Study: Gender Diversity In Analytics

Women are breaking the glass ceiling across industries and enterprises, rising to the top echelons of company departments and management. Nevertheless, the participation of women in the technology sector, and more specifically across the data science domain, is still significantly constrained by low participation fueled by possible hiring stereotypes and mindsets around women in science functions v/s women in art functions.

The greater adoption and scaling of data science services, such as predictive analytics, artificial intelligence (AI), and machine learning (ML), across all industries from Industrials to Media, enterprises ranging from large to small, and organization functions covering HR and Marketing, renders the representation of women across the data science domains all the more significant as the representation impacts not a single, isolated enterprise function but the entire organizational ecosystem across industries.

2020-03-02 08:30:00+00:00 Read the full story…
Weighted Interest Score: 3.7926, Raw Interest Score: 1.5687,
Positive Sentiment: 0.1514, Negative Sentiment 0.0138

CloudQuant Thoughts : We must do all we can to encourage diversity in the newest industry on the planet. A number of the significant difficulties we have had with bias would not have existed at all if our teams more closely reflected our society. If your management have a hard time understanding how bias can affect outcomes I would suggest you have them watch this recent video by Cassie Kozyrkov of Google and the accompanying article.

ESG SECTION

CloudQuant Thoughts : Every week the number of ESG articles keeps rising, our process has identified a very strong data supplier in the ESG area, head over to our data catalog for more information.

Large Pension Schemes Emphasize Sustainable Growth

CalSTRS, alongside Japan’s Government Pension Investment Fund and the UK’s Universities Superannuation Scheme, released a statement emphasizing the importance of long-term, sustainable growth.

“Our commitments to our members span decades, and we must prioritize long-term value creation over short-term gain,” said Chief Investment Officer Christopher J. Ailman. “We urge our partners and the companies in which we invest to improve their disclosures and enhance their integration of environmental, social and governance factors into their decision-making processes.”
2020-03-04 11:01:28+00:00 Read the full story…
Weighted Interest Score: 4.1715, Raw Interest Score: 2.7810,
Positive Sentiment: 0.3476, Negative Sentiment 0.0000

JP Morgan targets sustainability alpha with new ESG-focused long/short hedge fund launch

JP Morgan Alternative Asset Management is rolling out a new long/short ESG-focused hedge fund strategy, which aims to generate alpha by trading a range of global sustainability themes – but warns that more work is needed on ESG education within the hedge fund industry.

The USD100 million JP Morgan Multi-Manager Sustainable Long/Short Fund will utilise long/short equity managers and high-conviction trades, focusing on companies that lead their pe…
2020-03-03 00:00:00 Read the full story…
Weighted Interest Score: 6.3152, Raw Interest Score: 3.0752,
Positive Sentiment: 0.3295, Negative Sentiment 0.1647

Public pension funds lag in climate issues for ESG

Environmental, social and governance issues have become more and more important for institutional investors, and public pension funds are no different. However, it seems most U.S. public pension funds are lagging when it comes to the climate initiatives in the United Nations’ Sustainable Development Goals.

Get The Full Warren Buffett Series in PDF Get the entire 10-part series on Warren Buffett in PDF. Save it to your desktop, read…
2020-03-03 20:53:40+00:00 Read the full story…
Weighted Interest Score: 3.4144, Raw Interest Score: 1.5763,
Positive Sentiment: 0.0409, Negative Sentiment 0.2047

New research highlights strong correlation between ESG factors and developed markets sovereign spreads

A new Federated Hermes study conducted with Beyond Ratings, ‘Pricing ESG risk in sovereign credit, part II’, reveals a meaningful relationship between sovereign environmental, social and governance (ESG) scores and CDS spreads in developed markets.

The study analyses five-year CDS spreads and ESG scores from 28 developed market (DM) countries and 31 emerging market (EM) countries during 2009-2018.

In 2019, Federated Hermes and Beyond Ratings published the first part of this study which assessed the correlation between ESG factors and sovereign credit spreads. The results established an …
2020-03-03 00:00:00 Read the full story…
Weighted Interest Score: 3.1708, Raw Interest Score: 1.6091,
Positive Sentiment: 0.1420, Negative Sentiment 0.1183

Investors look to navigate market uncertainty in 2020, according to BlackRock’s annual institutional client survey

Entering 2020, institutional investors in BlackRock’s global rebalancing survey stated that their biggest concerns were the possibility of the economic cycle turning, declining global interest rates, and geopolitical instability.

When weighing the effects of lower rates with the risks of decreased growth and increased geopolitical tensions, investors see private markets as the asset class most suited to the current environment. And wherever they are investing, institutions increasingly view environmental, social and governance (ESG) factors as critical to their investment processes. Recent events underscore the importance of these long-term trends as investors confront rapidly evolving macroeconomic and investment risks.

These were the dominant themes in BlackRock’s annual Institutional Client Survey, which covered 271 institutional clients, representing over US $9.8 trillion in investible assets globally.

2020-02-27 00:00:00 Read the full story…
Weighted Interest Score: 3.0145, Raw Interest Score: 1.4756,
Positive Sentiment: 0.0922, Negative Sentiment 0.3689

ESG Allocations Rise From Fixed Income And Passive

Significant amounts of money from fixed income and passive investments are being allocated to environmental, social and governance strategies, which have traditionally been dominated by active equity funds.

Aled Jones, head of sustainable investment, Europe, at FTSE Russell told Markets Media: “Before joining FTSE Russell in 2017 I was an investment consultant and we used to think that between £100m ($127.7m) and £200m of active being allocated to ESG was a good start. Now significant chunks of passive money are being alloc…
2020-03-02 20:11:10+00:00 Read the full story…
Weighted Interest Score: 2.9439, Raw Interest Score: 1.9153,
Positive Sentiment: 0.2186, Negative Sentiment 0.0625

Australian investors warm to sustainable themes

Interactive Brokers’ TWS platform offers expanded TipRanks ratings

TipRanks ratings have been expanded to include non-US stocks, with added coverage for over 6,700 Canadian and European companies.

Electronic trading expert Interactive Brokers continues to beef up the functionalities of its TWS platform.

In the latest (beta) version of the platform, TipRanks ratings have been expanded to include non-US stocks, with added coverage for over 6,700 Canadian and European companies. TipRanks evaluates recommendations by financial analysts and bloggers, and then ranks their recommendations based on historical accuracy and performance.
2020-03-04 09:39:05+02:00 Read the full story…
Weighted Interest Score: 5.2655, Raw Interest Score: 2.2262,
Positive Sentiment: 0.0890, Negative Sentiment 0.0890

Data Architecture and Data Science: What is the Intersection?

However, both Data Science and Data Architecture specialists need to have a sound understanding of business issues before they can design a model-development and testing environment for business use. An IBM developer explores the architectural thinking embedded in Data Science.
2020-02-26 08:35:54+00:00 Read the full story…
Weighted Interest Score: 4.9917, Raw Interest Score: 2.4966,
Positive Sentiment: 0.1379, Negative Sentiment 0.0552

Why 2020 Will Be The Year Of AutoML

AutoML, with its ability to perform data pre-processing, ETL tasks, and transformation, will likely become the most popular trend for the year 2020. With the advent of big data, advanced analytics, and predictive models, data scientists today are expected to possess more talent and updated skills when it comes to handling artificial intelligence and machine learning. But these highly skilled data scientists are rare to find. However, bridging the skills gap, the other side of the herd has not only been able to survive but are also capable of building models using the best diagnostic and predictive analytics tools, and part of the reason is AutoML.

AutoML packages like auto-sklearn can automatically do the model selection, scoring, and hyperparameter optimisation. Services like Amazon Forecast and Google’s Cloud AutoML also help in determining the algorithm to fit best with the data.
2020-03-04 10:30:00+00:00 Read the full story…
Weighted Interest Score: 4.3969, Raw Interest Score: 2.3601,
Positive Sentiment: 0.2536, Negative Sentiment 0.0585

How Blue Sky Analytics Is Utilising ML To Minimise Air Pollution

Founded by Abhilasha Purwar and Kshitij Purwar in 2018, Blue Sky Analytics is a Big Data and AI startup with a mission to provide actionable intelligence on environmental indicators.

In a recent report, PWC India stats show that over 60% Indians firmly believe in the power of AI to accentuate economic growth, stimulate delivery of health services, improve access to education, especially in remote areas of the country, enhance customer interaction, and pave the way for a more inclusive India. AI-enabled solutions can help close the demand-supply gaps. In 2019, the Indian AI startup market received funding of USD 760+ million, a 44% increase from the 2018 figures. Also, with the National AI Programme, the AI startup ecosystem is set to get a boost.
2020-03-04 04:30:00+00:00 Read the full story…
Weighted Interest Score: 4.0895, Raw Interest Score: 2.2170,
Positive Sentiment: 0.2877, Negative Sentiment 0.0338

CIBC Innovation Banking Provides Precision Crop Management Company SemiosBio With a $25 Million Growth Capital Financing to Support Expansion

CIBC Innovation Banking is pleased to announce a $25 million growth capital financing with Vancouver-based SemiosBio Technologies Inc., a leader in developing technology solutions for the precision crop management industry. The capital will be used by the company to support growth into new crops and new geographies, and for strategic acquisitions.

An industry-leading precision crop management platform, Semios provides a proprietary in-field Internet of Things (IoT) network of over 1 million sensors that monitor and predict insect, disease, water, and frost risk in near real-time. Collecting 350 million data points daily, this information is fed through established and proprietary models and then provided to the grower in a simple, powerful mobile or web interface.

2020-03-03 00:00:00 Read the full story…
Weighted Interest Score: 3.6044, Raw Interest Score: 2.0485,
Positive Sentiment: 0.4328, Negative Sentiment 0.0000

SimCorp launches new machine learning initiative with start-up, Alkymi, targeting institutional investment challenges

Many investment firms are currently buckling under a number of operational constraints, including the burden of processing unstructured data or outsourcing it, inadequate cross-asset coverage within Enterprise Data Management (EDM) systems, and exceptionally low Straight Through Processing (STP) rates suffered in asset classes like alternatives, which are predicted to reach $14 trillion AUM by 2023.

2020-02-26 00:00:00 Read the full story…
Weighted Interest Score: 3.4483, Raw Interest Score: 2.2040,
Positive Sentiment: 0.4271, Negative Sentiment 0.1367

Exchange Data International (EDI) Partners with BondCliQ to offer real-time and historical corporate bond data product

BondCliQ, a consolidated quote system for the US corporate bond market, has partnered with Exchange Data International (EDI) to distribute real-time and historical corporate bond data. Through this collaboration, EDI can now distribute institutional corporate bond quote (pre-trade) information and enriched transaction data (TRACE) that is competitive to the established providers.

Chris White, Chief Executive Officer of BondCliQ, says: “There is huge demand for higher quality institutional pricing information in the US corporate bond market. By coordinating directly with 35 dealers, BondCliQ has produced the first centralised data feed. The partnership with EDI makes this valuable data set more accessible to their comprehensive network of clients.”

2020-03-04 00:00:00 Read the full story…
Weighted Interest Score: 3.2946, Raw Interest Score: 2.8681,
Positive Sentiment: 0.3824, Negative Sentiment 0.0000

Emerging Artificial Intelligence and Machine Learning Trends

“2020 Is An Important Year For AI Adoption.”

I was not surprised when I read this news headline. AI and machine learning markets are already growing at a fast pace. According to a recent report by IDC, the global spending for AI systems will be around $97.9 billion in the year 2023. This year alone $37.5 billion will be spent on the same.

So, 2020 is the year when you can expect to see a lot of innovations in the AI and machine learning space. Many opportunities will also open. Hence, it is important for companies to keep track of new trends so that they know in which areas they should invest.

With that said, here we present Six Emerging AI and Machine Learning Trends in 2020 :

Augmented Analytics
Natural Language Processing / Conversational Analytics
Conversational AI or Chatbots
Self-learning Systems
AI Security
RPA (Robotic Process Automation) Solutions

2020-03-04 08:30:39+00:00 Read the full story…
Weighted Interest Score: 3.0780, Raw Interest Score: 1.5715,
Positive Sentiment: 0.2095, Negative Sentiment 0.2270

Pros & Cons Of Choosing A Career In Data Science

The internet may be saturated with articles on why data science is the ‘sexiest job of the 21st century’, but little has been spoken about its possible cons. The draw of a career in this field is undeniable — it is in demand, pays well, and has a right mix of technology, statistics, and business. However, this field is massive and paradoxical, which comes with its share of limitations.

This article seeks to provide you with the necessary insights about data science that will help you run a self-assessment and take the right course.

2020-03-04 12:30:00+00:00 Read the full story…
Weighted Interest Score: 3.0738, Raw Interest Score: 1.7015,
Positive Sentiment: 0.2255, Negative Sentiment 0.2665

Best Practices For A Superior Machine Learning Portfolio

In any profession, it is important to create an image of oneself, which is reflected by materials included in a portfolio. It does not matter if an individual is a veteran or a fresher because a good portfolio always works in the direction of betterment. Through a well-organised machine learning portfolio, one can increase his or her value in the industry as a portfolio is similar to a brochure that a buyer goes through before making a purchase.

In this article, we will discuss how to create a machine learning portfolio, which will help an individual grab a better opportunity.

2020-03-03 08:30:13+00:00 Read the full story…
Weighted Interest Score: 3.0228, Raw Interest Score: 1.3508,
Positive Sentiment: 0.4988, Negative Sentiment 0.0623

Health Data Analytics Institute nabs $16 million for AI platform that predicts outcomes

AI excels at making sense of data. Health Data Analytics Institute (HDAI) is a case in point — its AI-powered platform analyzes over a billion patient encounters to improve health care outcomes. After several years of operating quietly under the radar, the company today revealed that it has raised $16 million, which it will use to launch an API that widens access to its clinical prediction service.

HDAI founder and CEO Nassib Chamoun says the company is already collaborating with organizations to measure the impact of its soon-to-be-expanded platform. HDAI recently received funding from the Robert Wood Johnson Foundation to support a project that will analyze demand for long-term care. Separately, it’s working with New England Baptist Hospital to study the effectiveness of surgical and nonsurgical options for spinal procedures. It’s also partnering with the Cleveland Clinic to match patients with the appropriate level of presurgical support, using AI. And HDAI plans to team up with Houston Methodist to test clinical applications that would use its risk predictors.

2020-03-03 00:00:00 Read the full story…
Weighted Interest Score: 3.0215, Raw Interest Score: 1.4717,
Positive Sentiment: 0.1577, Negative Sentiment 0.1314

Alphalion and Euromoney TRADEDATA join forces

Hong Kong based technology provider Alphalion, strikes derivatives data deal with Euromoney TRADEDATA.

Today, Hong Kong based Alphalion, a supplier of innovative technology to the capital markets financial sectors, confirmed its strategic partnership with Euromoney TRADEDATA, who will provide reference data sets for Alphalion’s derivatives platform. Euromoney TRADEDATA’s core reference data sets have been integrated within the Alphalion middle and back office platform and can be permissioned for each new Alphalion client, with a range of licensing options available to reflect intended use.
2020-02-25 00:00:00 Read the full story…
Weighted Interest Score: 2.9039, Raw Interest Score: 1.6367,
Positive Sentiment: 0.6072, Negative Sentiment 0.1056

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AI & Machine Learning News. 09, March 2020

The Artificial Intelligence and Machine Learning Newsletter by CloudQuant

Women in Data Science (WiDS) Stanford Conference Replay (8 hours)

On March 2, 2020, 400+ people gathered at Stanford University and thousands more gathered across the globe for the 5th annual Women in Data Science (WiDS) Conference.

Watch the recorded livestream, or you can watch individual WiDS Stanford speaker presentations on YouTube.

WiDS Welcome | Margot Gerritsen, Karen Matthys and Judy Logan
Opening Address | Persis Drell
Machine Learning: A New Approach to Drug Discovery | Daphne Koller
Why a World with AI Needs More EQ | Tsu-Jae King Liu
Interpretability For Everyone | Been Kim
How Data Science Can Unlock Teaching & Learning at Scale | Emily Glassberg Sands
Building Water Security From the Bottom Up by Leveraging Big Data | Newsha Ajami
Creating Global Economic Opportunity with Responsible Data Science | Ya Xu
Ethics Panel
Don’t Look. See! Are We Blinded by Data (Visualization)? | Fanny Chevalier
Know Thyself: Introspective Personal Data Mining | Talithia Williams
Data Science in a Cloud World: What Every Data Scientist Needs to Know | Nhung Ho
Polyglot AI: The Role of Natural Language Processing (NLP) | Rama Akkiraju
Career Panel

2020-03-02 00:00:00 Read the full story…
Weighted Interest Score: 3.0048, Raw Interest Score: 1.8630,
Positive Sentiment: 0.4207, Negative Sentiment 0.2404

CloudQuant Thoughts : We gave it a shout out last week and here is the recap, you can watch at your leisure.

Data science and AI are a mess… and your startup might be making it worse – Cassie Kozyrkov

Data science has been called “the sexiest job of the 21st century” but sometimes I wonder whether we’re off by a century here. Is the world ready for us? I’ve looked into this question before, but the tools for data science issue warrants more discussion. The tools available to data scientists put a cap on their effectiveness, so it would be great to see toolmakers paying more attention to their needs. Instead, it feels like the tools are made for buzzwords instead of people.

This article was inspired by my friend Clemens Mewald—one of the best product managers I’ve had the honor of working with—who wrote a piece titled “Your Deep-Learning-Tools-for-Enterprises Startup Will Fail” …which I read with the same emotion I feel when my toys are about to be yanked away. It feels as though the tools are made for buzzwords instead of people.

On the one hand, he’s right: if you go about making ML/AI developer tools like all the rest of ’em, your startup will probably go under. On the other hand, I don’t want startup folk to run away screaming.
I say this purely selfishly, as a data scientist pleading on behalf of her people. I recognize my privilege of working in an environment that suffers relatively little from the problems I’m about to bring up, so I want you to know that these words are inspired by my experience of how things used to be (even here at Google!) and by the stories you share with me daily. Let me lend you my voice.
2020-03-07 14:02:38.722000+00:00 Read the full story…
Weighted Interest Score: 2.1915, Raw Interest Score: 1.2262,
Positive Sentiment: 0.3783, Negative Sentiment 0.2087

CloudQuant Thoughts : Cassie’s posts are always entertaining and this one is no different.

New Open Source App: Data Science Education

The principles underpinning open source software development that are transforming the digital economy are now being extended to new sectors such as education, where proponents hope to leverage the collaborative approach to advance the teaching of data science.

An open source project shepherded by the Linux Foundation aims to accelerate data science curricula while benefitting from the contributions of students and teachers. OpenDS4All is funded by IBM (NYSE: IBM) and is being developed by the University of Pennsylvania. The effort would give educators free access to information needed to develop data science coursework. In return, successful approaches would be folded back into what project promoters call “constantly evolving and improving” curricula.

2020-03-04 00:00:00 Read the full story…
Weighted Interest Score: 3.2821, Raw Interest Score: 1.9335,
Positive Sentiment: 0.1813, Negative Sentiment 0.0302

CloudQuant Thoughts : Don’t you hate when you discover a white paper that speaks exactly to your current interest but you cannot source the data or reproduce the results. The Data Science Foundation should help resolve this. But in the environment of Stock Markets and Trading, data is like gold and access is restricted. So it would be useful to find white papers on important trading and alternative data trends that actually worked out of the box, contained all the data. CloudQuant create their own white papers to provide this exact service over at our data catalog.

Babylon to train its health chatbot to recognise coronavirus symptoms

Digital doctor app Babylon Health is searching for ways to train its artificial intelligence-driven chatbot to detect coronavirus symptoms as the number of cases of the deadly Covid-19 strain rises in the UK.

The British start-up, which has been lauded by Health Secretary Matt Hancock, operates an AI chatbot in its GP at Hand app. it claims it can “identify likely causes” of an illness once patients input symptoms.

Dr Keith Grimes, clinical AI director at Babylon, cautioned that changes to the AI would be difficult due to the lack of “robust data” needed to train the underlying algorithms.

Babylon is currently urging patients not to use its AI symptom checker if they think they have been infected by the new coronavirus strain….

2020-03-08 00:00:00 Read the full story…
Weighted Interest Score: 3.8017, Raw Interest Score: 1.3223,
Positive Sentiment: 0.1653, Negative Sentiment 0.4959

CloudQuant Thoughts : I love that final line in the article preview!!!

Oracle Rolls Out Data Science and Machine Learning Services

Oracle recently announced the availability of the Oracle Cloud Data Science Platform with Oracle Cloud Infrastructure Data Science at the core, helping enterprises to collaboratively build, train, manage, and deploy machine learning models.

The goal with Oracle’s Cloud Infrastructure Data Science is to improve the collaboration and effectiveness of data science teams with capabilities such as shared projects, model catalogs, team security policies, reproducibility, and auditability. Oracle Cloud Infrastructure Data Science automatically selects the most optimal training datasets through AutoML algorithm selection and tuning, model evaluation and model explanation.

2020-03-04 00:00:00 Read the full story…
Weighted Interest Score: 5.4091, Raw Interest Score: 2.7588,
Positive Sentiment: 0.3876, Negative Sentiment 0.0912

AI Spawning New Products in Investment Business

Liquidnet recently announced plans to launch a data service for money management firms that uses AI to search for hidden information that can affect investment decisions, according to an account in WSJPro. Liquidnet operates a dark pool, a private financial forum for buying and selling securities, that lets investors stay hidden until a trade is executed.

The new service, called Investment Analytics, will use AI to analyze financial reports, earnings calls, news articles, and other sources. The company appointed Vicky Sanders, a co-founder of RSRCHXchange, which Liquidnet acquired last year, as the global head of Investment Analytics. RSRCHXchange was a financial tech company that provided asset management firms with a repository of reports based in the cloud.

The plan is to combine the services with two other recent Liquidnet acquisitions, OTAS Technologies and Prattle, to use AI for a new investment product. OTAS uses AI to analyze data on liquidity, volumes, and spreads. Prattle uses natural language processing and machine learning to analyze publicly-available content.

2020-03-05 22:30:34+00:00 Read the full story…
Weighted Interest Score: 4.5622, Raw Interest Score: 1.8949,
Positive Sentiment: 0.2369, Negative Sentiment 0.0646

TradeTech 2020

TradeTech 2020 unites Europe’s top equity trading leaders including regulators, sell side, trading platforms, technology partners and over 500 senior buy side. This is your unique opportunity to join the leading buy side in using data and technology to thrive in the post-MiFID II liquidity landscape.

Brand new for our 20th and best-ever TradeTech: buy side are invited to join our exclusive data science day. Sign-up for free today – very limited availability.

TradeTech 2020 has evolved:

Connect with 1200+ equity trading and tech leaders representing the world’s leading financial institutions such as Blackrock, UBS Asset Management, Generali, JP Morgan Asset Management and Vanguard Asset Management amongst other influential players in this exciting field.
Hear 500+ buy-side heads of trading and learn how to adopt smarter approaches to access liquidity, build the best execution strategies, and implement Machine Learning techniques and smart data analysis to automate trading.
Whether you work in Data Science, Trading or Technology/IT, Tradetech 2020 has tailored streams to provide you with the latest updates on regulation, market structure developments and cutting-edge execution strategies.

2020-03-09 00:00:00 Read the full story…
Weighted Interest Score: 4.5417, Raw Interest Score: 2.1066,
Positive Sentiment: 0.3304, Negative Sentiment 0.0000

Syncsort Partners with Databricks to Support Cloud Initiatives

2020-03-09 00:00:00 Read the full story…
Weighted Interest Score: 4.3342, Raw Interest Score: 2.1038,
Positive Sentiment: 0.1791, Negative Sentiment 0.2686

AI in Human Resources: 5 Trends in 2020 and Beyond

Many new and emerging technologies are adding value to human resources. This explains their high adoption rate. One of these technological marvels is artificial intelligence or AI. Is there a place for AI in HR? Apparently, there is. More than 66% of CEOs in IBM’s survey report they believe cognitive computing has an important role in HR. It’s definitely worth exploring the AI trends in HR in 2020 and beyond.

HR Task Automation
AI-Powered Chatbots As New Recruiters
Smart LMS System As New HR Training Software Solution
AI Facilitates Data-Driven Decisions
AI-Powered HR Helpdesk

2020-03-09 00:00:00 Read the full story…
Weighted Interest Score: 4.1653, Raw Interest Score: 1.7968,
Positive Sentiment: 0.3023, Negative Sentiment 0.1175

Dataiku Announces Global 2020 EGG Conference Series, Expanding to 8 Cities

Today, Dataiku, one of the world’s leading Enterprise AI and machine learning platforms, announced the lineup for its 2020 EGG Conferences, with events planned in eight cities worldwide. EGG, The Human-Centered AI Conference, is a series of one-day gatherings focused on issues at the forefront of data science, machine learning, and AI, exploring real-life Enterprise AI use cases and how to create organizational change with scalable AI systems that enhance – not replace – humans.

Since 2017, EGG has attracted more than 5,000 data leaders at the world’s first conference focused not just on what AI can do, but, practically, how companies can get there. As human-centered AI as a concept continues to gain traction globally, the conference is expanding to new and exciting hubs for AI: Montreal, Frankfurt and Sydney.

The 2020 EGG Series includes:

New York City – June 11
London – June 23
Montreal – Sept. 24
Sydney – Oct. 12
San Francisco – Oct. 21
Paris – Nov. 3
Amsterdam – Nov. 10
Frankfurt – Nov. 24

2020-03-06 00:00:00 Read the full story…
Weighted Interest Score: 4.1015, Raw Interest Score: 1.5914,
Positive Sentiment: 0.2850, Negative Sentiment 0.1188

Billionaire investor Steve Cohen is reportedly raising money for a new fund focused on the AI space

Billionaire investor Steve Cohen is stepping outside the hedge fund industry and creating a private-markets fund, The Wall Street Journal reported Friday.

The fund, named Point72 Hyperscale, will act as a hybrid between a venture capital firm and a private-equity fund and focus on companies in the artificial intelligence space, sources told The Journal. Hyperscale aims to raise $500 million to $900 million in 2020, with Cohen as its anchor investor. The private-markets fund is Cohen’s first investor offering outside the hedge fund space. He previously founded SAC Capital Advisors and Point72 Asset Management.

2020-03-10 00:00:00 Read the full story…
Weighted Interest Score: 3.9870, Raw Interest Score: 1.9131,
Positive Sentiment: 0.0000, Negative Sentiment 0.1297

AI is Being Used to Discover New Antibiotics and Genes Linked to Disease

New types of antibiotics are being developed using an AI machine-learning approach that scans a pool of more than 100 million molecules, including one that works against strains of bacteria previously considered untreatable, according to a recent account in Nature.

The antibiotic, called halicin (named after the HAL 9000 computer from 2001: A Space Odyssey), is believed to be the first discovered with AI. While AI had been applied to parts of the antibiotic-discovery process, the researchers said this was the first time AI had helped to identify a completely new kind of antibiotic from scratch. Led by synthetic biologist Jim Collins at MIT, the paper is published in Cell.

New drugs are needed to fight growing bacterial resistance to antibiotics worldwide, resulting in infections that could kill 10 million people per year by 2050. New drug development has slowed over the past several decades. “People keep finding the same molecules over and over,” stated Collins. “We need novel chemistries with novel mechanisms of action.”

2020-03-05 22:30:19+00:00 Read the full story…
Weighted Interest Score: 3.7242, Raw Interest Score: 1.7188,
Positive Sentiment: 0.1023, Negative Sentiment 0.1023

Kubernetes Gets an Automated ML Workflow

A stable version of an automation tool released this week aims to make life easier machine learning developers training and scaling models, then deploying ML workloads atop Kubernetes clusters.

Roughly two years after its open source release, Kubeflow 1.0 leverages the de facto standard cluster orchestrator to aid data scientists and ML developers in tapping cloud resources to run those workloads in production. Among the stable workflow applications released on Monday (March 2) are a central dashboard, Jupyter notebook controller and web application along with TensorFlow and PyTorch operators for distributed training.

2020-03-03 00:00:00 Read the full story…
Weighted Interest Score: 3.6762, Raw Interest Score: 2.0732,
Positive Sentiment: 0.1944, Negative Sentiment 0.0324

3 important trends in AI/ML you might be missing

According to a Gartner survey, 48% of global CIOs will deploy AI by the end of 2020. However, despite all the optimism around AI and ML, I continue to be a little skeptical. In the near future, I don’t foresee any real inventions that will lead to seismic shifts in productivity and the standard of living. Businesses waiting for major disruption in the AI/ML landscape will miss the smaller developments.

Here are some trends that may be going unnoticed at the moment but will have big long-term impacts:

Specialty hardware and cloud providers are changing the landscape
Innovative solutions are emerging for, and around, privacy
Robust model deployment is becoming mission critical

2020-03-08 00:00:00 Read the full story…
Weighted Interest Score: 3.5708, Raw Interest Score: 1.6596,
Positive Sentiment: 0.2202, Negative Sentiment 0.3895

7 Ways To Kill Your Data Scientist Career (Without Knowing It)

In the era where data is the most valuable asset for a company, nurturing data skills has to become the topmost priority for any aspiring to mid-level data scientist.

The feeling of self-satisfaction with your current skillsets can land you at a miserable spot where your company may choose a candidate who not only has better experience in using new analytics tools but also has a deeper understanding of the latest trends. This, in turn, can result as an end of your career.

To solve this problem, we asked multiple data scientists from the AIM Expert Network community, to share their key insight on how to avoid unusual pitfalls and get out of the cocoon to build a bulletproof career.

Not defining the problem accurately
Not nurturing the data
Focusing only on coding
Ignoring Visualization at the cost of Modeling
Extreme focus on tools and technologies instead of fundamental concepts.
Disregarding the latest trends
Avoiding participation in external and community events

2020-03-06 04:30:00+00:00 Read the full story…
Weighted Interest Score: 3.5473, Raw Interest Score: 1.6942,
Positive Sentiment: 0.2515, Negative Sentiment 0.3177

Power Analytics Global & Molecula Announce Strategic Partnership

A recent press release reports, “Power Analytics Global, a next generation technology platform company that specializes in critical network infrastructure software monitoring, prediction, simulation and data-analytics, and Molecula, a Cloud Data Access company that simplifies, accelerates, and enables instantaneous, secure access to large, fragmented, and geographically distributed data sets to support the most demanding Machine Learning (ML), Artificial Intelligence (AI), and IoT workloads, announced today a strategic partnership.”

The release goes on, “The goal of the partnership is to deliver a combined offering for customers to cost effectively protect and enhance their critical revenue resulting in higher margins, customer retention and more informed capital allocation decisions. The combined offering will allow customers to drastically reduce the cost, time and complexity of accessing critical, operational data from globally distributed network assets and infrastructure. The hardened tools and real-time capabilities of this powerful combination enable our clients to incorporate, predict trends, and take action in real-time across relevant components of distributed data sets while lowering the hardware footprint needed to store, process and make decisions from the operational data streaming from these critical networks.”

2020-03-09 07:15:54+00:00 Read the full story…
Weighted Interest Score: 3.4889, Raw Interest Score: 2.6316,
Positive Sentiment: 0.1815, Negative Sentiment 0.3630

Top 10 Technical AI and Machine Learning Conferences in 2020

The AI & ML field is growing at a very fast rate, and as a research scientist or ML engineer, you definitely want to keep track of the latest research advances, especially in your area of interest. To stay aware of the most important research breakthroughs, you can follow our regular research summaries across the main ML research fields.

For connecting with other researchers and practitioners in your subject area, you will want to attend at least a few technical AI conferences during the year. For your convenience, we’ve compiled a list of the key AI & ML research conferences held in 2020…

2020-03-05 18:23:01+00:00 Read the full story…
Weighted Interest Score: 3.3991, Raw Interest Score: 1.8951,
Positive Sentiment: 0.2815, Negative Sentiment 0.0657

How does Data Governance Differ from Data Platform Governance?

Data Governance guides personnel in better managing data. The guidance is ensured through policy and ownership of data in an organization. The emphasis is on formalizing the Data Management function along with the associated data ownership roles and responsibilities.

Data Platform Governance defines and governs how data platforms, databases and data warehouses can be better planned, and managed. Information Technology takes the responsibility of deploying platforms. Procedures for platform governance are developed by IT and are aligned to the Data Management Strategy. These procedures can be as simple as a selection of Tool stack.

2020-03-09 07:35:36+00:00 Read the full story…
Weighted Interest Score: 3.3408, Raw Interest Score: 2.2272,
Positive Sentiment: 0.0891, Negative Sentiment 0.1336

Why the AI we rely on can’t get privacy right (yet)

Whiile artificial intelligence (AI) powered technologies are now commonly appearing in many digital services we interact with on a daily basis, an often neglected truth is that few companies are actually building the underlying AI technology. A good example of this is facial recognition technology, which is exceptionally complex to build and requires millions upon millions of facial images to train the machine learning models.

Consider all of the facial recognition based authentication and verification components of all the different services you use. Each service did not reinvent the wheel when making facial recognition available in their service; instead, they integrated with an AI technology provider. An obvious case of this is iOS services that have integrated FaceID, for example, to quickly log into your bank account. Less obvious cases are perhaps where you are asked to verify your identity by uploading images of your face and your identity document to a cloud service for verification, for example if you are looking to rent a car or open up a new online bank account.

2020-03-07 00:00:00 Read the full story…
Weighted Interest Score: 3.2170, Raw Interest Score: 1.1343,
Positive Sentiment: 0.1829, Negative Sentiment 0.2195

Data Science and ML Platform Market Heats Up

If you’re in the market for data science and machine learning tools, we have great news: The market is absolutely booming in 2020. With a ton of healthy competition, vendors are investing heavily to differentiate their products and drive innovation. The vibrant market is also diversifying, with separate tracks evolving for users with different skill levels and goals.

Gartner isn’t typically one to get overly exuberant about things. That’s just not the way in Stamford, Connecticut. But analysts with the storied firm opened up a bit in a recent report and stated that the market for data science and machine learning platforms is “beyond healthy” and “thrillingly innovative.”

“The broad mix of vendors offer a granular range of capabilities, with solutions appropriate for most levels of maturity,” the company wrote in its Magic Quadrant for Data Science and Machine Learning Platforms. “The definitions and parameters of data science and data scientists continue to evolve, and the space is dramatically different from this Magic Quadrant’s inception in 2014.”

2020-03-03 00:00:00 Read the full story…
Weighted Interest Score: 3.1809, Raw Interest Score: 1.9262,
Positive Sentiment: 0.4322, Negative Sentiment 0.0247

Real-Time Data Streaming, Kafka and Analytics Part 3: Effective Planning for Data Streaming Improves Data Analytics

Data stream processing is defined as a system performing transformations for creating analytics on data inside a stream. In Part 1 of this series, we defined data streaming to provide an understanding of its importance. In Part 2, we got a bit more technical in explaining data integration and ingestion into one of the more popular stream platforms, Apache Kafka. This final piece will explore the benefits of data stream processing with Kafka, as well as how to best plan for implementing data streams.

Companies who wish to take advantage of real-time data streams for analytics require a modern data architecture. This infrastructure can be managed through the emerging DataOps, which applies principles of lean manufacturing, DevOps and agile software development to data pipeline management. By leveraging DataOps, companies can implement a fully governed data management strategy that promotes team collaboration and use of data for business-driven data analytics. A direct benefit is that as individuals work together on data analysis, they gain greater understandings of the information and raise their own data literacy.

2020-03-04 00:00:00 Read the full story…
Weighted Interest Score: 3.1777, Raw Interest Score: 2.0331,
Positive Sentiment: 0.3916, Negative Sentiment 0.0602

Outsourced Trading: Buy-Side Questions Answered

The popularity of asset managers outsourcing their trading desks to third parties and execution providers is on the rise. Well documented now, consultancy Opimas estimates that a fifth of investment managers overseeing more than $50 billion will outsource at least parts of their trading by 2020. In addition, almost half of outsourced firms expect their outsource revenues to grow between 50-100% in the next few years, according to a recent study by Tabb Group. Yet some investment management firms are still hesitant about whether they should move some of their trading functions over to third parties. In order to help asset managers weigh up the pros and cons of outsourcing, we have taken a look at some of the key benefits in the context of maintaining a healthy marketplace and answered some of the most commonly asked questions posed by asset managers before making a decision.

2020-03-05 18:30:16+00:00 Read the full story…
Weighted Interest Score: 3.1694, Raw Interest Score: 1.4333,
Positive Sentiment: 0.4730, Negative Sentiment 0.2150

How to Deliver a Data Science Project Successfully

It is demanding to know where to begin once zoućve decided that, yes, you wish to dive into the fascinating world of data and AI. Just having a look at all the technologies you need to understand all the tools you’re supposed to master is enough to make you confused.

Well, luckily for you, creating your first data project is actually not difficult as it seems. Becoming data-powered is first and most foremost about having to learn the basic steps and following them to go from raw data to create a machine learning model, and in the end to operationalization.

Let’s jump into the following steps that will help you in successfully delivering a data science project.

2020-03-08 18:21:32+00:00 Read the full story…
Weighted Interest Score: 3.0904, Raw Interest Score: 1.5218,
Positive Sentiment: 0.1806, Negative Sentiment 0.1806

Vatican, DoD Weigh in on Ethical AI Principles in Same Week

The Vatican and the Department of Defense both took stances on AI ethics last week.

The Department of Defense on Monday held a press conference to announce its principles of AI ethics to guide development of new systems. The Vatican on Friday received support from IBM and Microsoft for its guidance for developers of AI rooted in Catholic social teaching.

The Rome Call for AI Ethics was drafted by the Pontifical Academy for Life, an advisory body to Pope Francis. It outlines six principles to define the ethical use of AI, to ensure that AI is developed and used to serve and protect people and the environment. Microsoft and IBM announced support for the charter, reported WSJPro on Feb. 28.

2020-03-05 22:30:48+00:00 Read the full story…
Weighted Interest Score: 3.0680, Raw Interest Score: 0.9656,
Positive Sentiment: 0.1408, Negative Sentiment 0.1408

The Overview of Artificial Intelligence in Medicine

Artificial intelligence in healthcare, just like AI in general, mimics neurons’ structure and human brain organization in a very simplistic but very powerful way. It approximates its conclusions without direct human input while analyzing complex medical data. The essence of AI usage in healthcare is to analyze relationships between prevention, treatment, and outcome of human illnesses. The AI in healthcare is specific since it cannot be disruptive in a way it can be in other industries. Doctors don’t want to be disrupted. They would rather adopt a tool that would ease their administrative burden so they could focus on their patients. Another set of tools, doctors would approve, are some assistance tools, that would help them with problematic differential diagnoses. Finally, tools that provide therapeutic and surgical assistance would also be in demand. As a conclusion, the process of implementing AI methodologies in the healthcare industry cannot be disruptive. It must be gradual, thoroughly tested, proven and understood. There are simply too many moral, ethical and legal implications for doctors to just embrace novelty in a way that is advertised and embraced in other industries.
2020-03-09 00:00:00 Read the full story…
Weighted Interest Score: 3.0385, Raw Interest Score: 1.1338,
Positive Sentiment: 0.3175, Negative Sentiment 0.1814

How AWS Nudged Out IBM, Google & Microsoft From The Cloud AI Space

The cloud space is ever-evolving, which in turn offers incredible opportunities for companies wishing to establish themselves as leaders in cloud computing. According to a report, the cloud market is expected to grow more than double in three years, to $195 billion by the end of 2020. A decade ago, the space was simply known as “cloud” comprising infrastructure-as-a-service for virtualised workloads, however, with the fractalisation of offerings, companies now need to be more specific in terms of what aspect of cloud they are dealing with.

In the recent era, artificial intelligence has been considered an important segment of the cloud space, which involves machine learning and deep learning. And, to analyse companies engaged in this space, Gartner, has come up with their report on the ‘AI Developer Services‘, which focuses on the platforms that deliver AI services via APIs. The companies involved in the study were Aible, AWS, Google, H2O.ai, IBM, Microsoft, Prevision.io, Salesforce, SAP and Tencent; however, Alibaba and Baidu were excluded for this analysis.

2020-03-09 11:30:00+00:00 Read the full story…
Weighted Interest Score: 2.8921, Raw Interest Score: 1.4754,
Positive Sentiment: 0.2350, Negative Sentiment 0.2350

“The 3 ingredients to our success.” | Winners dish on their solution to Google’s QUEST Q&A Labeling | Kaggle Winner’s Interview

Congratulations to the (four!) first-place winners of the Quest Q&A Labeling competition, Dmitriy Danevskiy, Yury Kashnitsky, Oleg Yaroshevskiy, and Dmitry Abulkhanov who make up the team “Bibimorph”!

In the QUEST Q&A Labeling competition by Google, participants were challenged to build predictive algorithms for different subjective aspects of question-answering. The provided dataset contained several thousand question-answer pairs, mostly from StackExchange. These pairs were human-labeled to reflect whether the question was well-written, whether the answer was relevant, helpful, satisfactory, contained clear instructions, etc. Results from the competition will hopefully foster the development of Q&A systems, contributing to them becoming more human-like. In this winner interview, we catch up with team Bibimorph to learn more about their approach to solving this unique challenge:

2020-03-05 14:47:40.061000+00:00 Read the full story…
Weighted Interest Score: 2.8394, Raw Interest Score: 1.4569,
Positive Sentiment: 0.2343, Negative Sentiment 0.2050

How AI will change the mobile app development industry

The tech world has been permeated by a plethora of disruptive technologies such as Artificial Intelligence, Machine Learning, AR/VR and so forth. The following post emphasizes on how the concept of AI seems to be revolutionizing the mobile app industry in one go!

We have reached 2020, a world that’s even more fast-paced and user-centric, a space that surely holds a wide range of promising trends for the industry ranging from chatbots to augmented reality. But above all, artificial intelligence steals the show due to its prompt, real-time access to the content aspect. Every industry using AI for some time now like eCommerce, retail, energy, mobile & software app development etc. Earlier smartphone applications incorporated cloud-based and internet-dependent solutions but now things have certainly changed and of course, for good.

Let me show you how!

2020-03-09 04:29:44+00:00 Read the full story…
Weighted Interest Score: 2.8025, Raw Interest Score: 1.3155,
Positive Sentiment: 0.4063, Negative Sentiment 0.0967

‘It Has Never Been Easier to Get into Machine Learning’ – Interview with Machine Learning Tokyo

As an Applied Linguist, Suzana Ilic was introduced to machine learning through her specialization in data and text analysis. Through working on projects in sentiment analysis and emotion recognition, she began writing code and now works on deep learning projects for natural language processing. Currently based in Tokyo, Japan, her work has seen her collaborate with companies like Google and research organizations such as RIKEN.

Ilic is also the founder of Machine Learning Tokyo, a non-profit organization that has brought together a passionate community of engineers and researchers to work on projects, study new technology, and gather for regular events with industry experts.

In this interview, we ask Ilic about ML trends in Japan, challenges facing engineers, how MLT grew into a community of +4800 members, and what’s in store for the future.

2020-03-07 22:00:58+00:00 Read the full story…
Weighted Interest Score: 2.7128, Raw Interest Score: 1.5421,
Positive Sentiment: 0.2341, Negative Sentiment 0.0964

Nobel laureate Robert Shiller identifies a rising ‘existential threat’ to the economy’s expansion — and tells us why it’s similar to what made the Great Depression so severe

The fear of artificial intelligence and its ability to displace workers pose an “existential threat” to our sense of economic strength, Robert Shiller, the Nobel Memorial Prize-winning Yale University economist, said.

He exclusively showed Business Insider the similarity between concerns about AI and the so-called technological-unemployment narrative that sprang up just before the Great Depression.

2020-03-06 00:00:00 Read the full story (Registration Wall)…
Weighted Interest Score: 2.6177, Raw Interest Score: 1.4554,
Positive Sentiment: 0.2117, Negative Sentiment 0.6880

A hybrid AI model lets it reason about the world’s physics like a child

A new data set reveals just how bad AI is at reasoning—and suggests that a new hybrid approach might be the best way forward. Questions, questions: Known as CLEVRER, the data set consists of 20,000 short synthetic video clips and more than 300,000 question and answer pairings that reason about the events in the videos. Each video shows a simple world of toy objects that collide with one another following simulated physics. In one, a red rubber ball hits a blue rubber cylinder, which continues on to hit a metal cylinder.

The questions fall into four categories: descriptive (e.g., “What shape is the object that collides with the cyan cylinder?”), explanatory (“What is responsible for the gray cylinder’s collision with the cube?”), predictive (“Which event will happen next?”), and counterfactual (“Without the gray object, which event will not happen?”). The questions mirror many of the concepts that children learn early on as they explore their surroundings. But the latter three categories, which specifically require causal reasoning to answer, often stump deep-learning systems. Fail: The data set, created by researchers at Harvard, DeepMind, and MIT-IBM Watson AI Lab is meant to help evaluate how well AI systems can reason. When the researchers tested several state-of-the-art computer vision and natural language models with the data set, they found that all of them did well on the descriptive questions but poorly on the others.

2020-03-09 00:00:00 Read the full story…
Weighted Interest Score: 2.5862, Raw Interest Score: 1.6863,
Positive Sentiment: 0.2353, Negative Sentiment 0.4706

DocuSign acquires Seal Software for $188 million

DocuSign announced its intent to acquire the contract analytics and AI technology provider Seal Software for $188 million in cash. The deal reflects the increasingly important role that artificial intelligence (AI) will play in digital document management.

The news builds on the existing relationship between the two companies. DocuSign already resells Seal’s flagship analytics and machine learning application as part of the DocuSign Agreement Cloud—its suite of applications and integrations for automating and connecting the entire agreement process. DocuSign also made a strategic investment in Seal in March last year.

With the acquisition, DocuSign can integrate Seal’s technology and value proposition more comprehensively across the Agreement Cloud—and therefore deliver greater value to companies looking to prepare, sign, act-on and manage the agreements that are critical to their business, the company said.

2020-03-04 09:38:50+11:00 Read the full story…
Weighted Interest Score: 2.5656, Raw Interest Score: 1.3046,
Positive Sentiment: 0.2552, Negative Sentiment 0.1134

Will Artificial Intelligence Render Human Transcriptionists Obsolete?

How will artificial intelligence, or AI, impact the transcriptionist industry? Here’s what to know about how AI compares to human transcriptionists. Artificial intelligence is changing the nature of human language. We are seeing computers that can understand language in very nuanced ways. Towards Data Science has a very interesting analysis of this trend in their article Understanding Natural Language Process, How AI Understands Our Languages. AI is still unable to grasp the complexities of language to the level of trained transcriptionists. However, that may change in the future. Will it eventually make them obsolete?

The world of professional transcription is something that is not to be taken lightly. Many companies in multiple industries put plenty of stock in a transcriptionist’s ability to do their jobs both quickly and accurately. It can be a demanding job that can be overwhelming for those who are unaware of just how challenging it can be.

2020-03-06 19:05:49+00:00 Read the full story…
Weighted Interest Score: 2.5353, Raw Interest Score: 0.9998,
Positive Sentiment: 0.1707, Negative Sentiment 0.3414

Rise of the Customer Data Platform

Retail businesses have moved from a product-centric model to a customer-centric model. This transformation has a significant impact on how companies are engaging with their customers who are spoilt for choice, well informed and tech-savvy.

Traditional approaches to managing customers were through Customer Relationship Management (CRM) processes and systems, however, in today’s hyperconnected world we see the emergence of Customer Data Platforms (CDP) as a robust model for handling customer data from a multitude of online and offline sources. The rising interest in Customer Data Platforms (CDPs) is reflected in the higher number of vendors providing these services as well as more venture capital funding (currently estimated at 2.4 billion USD).

What is a Customer Data Platform? Customer Data Platforms give a unified view of the customer from a multitude of touchpoints that are beyond the realm of traditional CRM systems. CDP’s can integrate data from both structured and unstructured sources, as well as online and offline sources to build a unified customer profile. The key here is traceability of the customer profile through the lifecycle of a customer and their interactions across the lifecycle. The key difference with CRM is its ability to handle wider touchpoints, ability to trace customers from site visitors to actual customers.

2020-03-09 06:31:00+00:00 Read the full story…
Weighted Interest Score: 2.5288, Raw Interest Score: 1.4288,
Positive Sentiment: 0.2023, Negative Sentiment 0.0253

Predicting 2020 Trends in Modern Data Architecture

From AI and machine learning, to data discovery and real-time analytics, a strong data architecture strategy is critical to supporting an organization’s data-driven goals.

Greater speed, flexibility, and scalability are common wish-list items, alongside smarter data governance and security capabilities.

DBTA recently held a roundtable webinar featuring Jeff Bayntun, manager, customer success, Simba Technologies, a Magnitude company; Suphatra Rufo, principal product marketing manager, Couchbase; and Ali LeClerc, director of product marketing, Alluxio, who discussed the top trends in modern data architecture for 2020.

Right now there is a bottleneck of market fragmentation, Bayntun explained, there are many different types of databases and database offerings to choose from.

2020-03-03 00:00:00 Read the full story…
Weighted Interest Score: 2.5034, Raw Interest Score: 1.5244,
Positive Sentiment: 0.1694, Negative Sentiment 0.1355

Women in finance: How AI is shining a light on diversity

While AI is shining a light on diversity, issues around bias and ensuing discrimination are in the spotlight too as the technology is being used in recruitment and is having a detrimental impact, questioning whether AI is ready to make hiring decisions on its own.

AI has helped in circumstances where there is a high-volume recruitment challenge and machine learning has supported the process of sifting through vast quantities of applications, with chatbots being implemented to answer candidate questions and screen applications at the first stages – claiming it is free of human prejudice and subconscious hiring bias.

However, technology can be as biased as humans if it replicates past hiring decisions and in the past, AI recruitment tools have realised that it discriminated against women because it attempted to find employees like its current workforce, namely, men.

While AI stands the chance of democratising access to capital for women, the irony is that only 22% of the AI workforce is made up of women. The financial services industry can address these imbalances by driving a greater focus on inclusion, empowerment and equality. Potentially, with more women working in the technology industry, writing algorithms and feeding into product development change, they can imagine and develop technology too.

2020-03-09 00:00:00 Read the full story…
Weighted Interest Score: 2.4596, Raw Interest Score: 0.9962,
Positive Sentiment: 0.4575, Negative Sentiment 0.3253

AI Weekly: Coronavirus, facial recognition, and the future of privacy

Global cases of COVID-19 surpassed 100,000 today. As President Trump signs into law an $8.3 billion emergency aid package to address the crisis, the chief of the World Health Organization (WHO) said yesterday that this is “a time for pulling out all the stops.” New cases are emerging in countries around the world, but COVID-19 appears to be flat or declining in China, where the novel virus first emerged. Earlier this week, VentureBeat took a look at some ways AI is being applied to fight COVID-19.

AI and big data played a significant role in China’s response to COVID-19, according to a WHO report compiled by about a dozen outside health professionals and released last month. The assessment finds that swift action by Chinese authorities to limit travel and quarantine entire cities potentially kept hundreds of thousands of people from being infected. But many also criticized China’s measures as draconian.

It’s unclear to what extent facial recognition played a role in enforcement of public safety in China, but a coauthor of the WHO study told Science that China is making strides on COVID-19 through “good old social distancing and quarantining, very effectively done because of that on-the-ground machinery at the neighborhood level facilitated by AI and big data.”

2020-03-06 00:00:00 Read the full story…
Weighted Interest Score: 2.4444, Raw Interest Score: 1.0398,
Positive Sentiment: 0.0912, Negative Sentiment 0.4196

Could Brexit open the gates to AI in the UK?

The UK is outwardly pursuing an adequacy decision from Europe regarding its current data protection and privacy regulations. However, the idea of eschewing outright assimilation with relevant EU laws is gaining traction as it provides an opportunity to sculpt a more appealing privacy framework. As it stands, come 31 December 2020, the UK will have departed the EU, and the transition period which ensured the stability of continuing UK/EU regulation to bridge the exit will cease.

In conversation with Finextra Research, Miriam Everett, partner and global head of data and privacy at Herbert Smith Freehills, refers to a statement made by the UK Prime Minister Boris Johnson in February 2020 in which he alludes to the possibility of altering the application of current privacy laws under GDPR: “The UK will in future develop separate and independent policies in areas such as[…]data protection, maintaining high standards as we do so…the UK would see the EU’s assessment processes on financial services equivalence and data adequacy as technical and confirmatory of the reality that the UK will be operating exactly the same regulatory frameworks as the EU at the point of exit.”

2020-03-09 11:01:00 Read the full story…
Weighted Interest Score: 2.4373, Raw Interest Score: 1.1207,
Positive Sentiment: 0.2537, Negative Sentiment 0.2396

Ex-AWS, Azure employees raise $3.3M for Seattle startup that helps companies save on cloud costs

Aran Khanna, Nikhil Khanna, and Daniel Christianto know a lot about the complexity of cloud computing, having worked for industry leaders such as Amazon Web Services and Microsoft Azure. Now the entrepreneurs are using their knowledge and expertise to help other companies save money on cloud-related costs. The three co-founders head up Reserved.AI, a Seattle startup that just raised $3.3 million to fuel growth. Amplify Partners and Pioneer Square Labs invested in the round.

Founded less than a year ago, Reserved.AI already has more than 20 customers who use its software to automate cost management of their AWS cloud spend. The startup says its clients average 35 percent savings. “The Reserved.ai product uses proprietary machine learning algorithms to analyze a customer’s AWS usage patterns and match them to an optimal set of AWS purchasing options, designed to maximize savings and minimize risk,” said CEO Aran Khanna, a Harvard grad who spent 18 months at Amazon as an AWS engineer.

2020-03-04 18:20:48+00:00 Read the full story…
Weighted Interest Score: 2.4025, Raw Interest Score: 1.3612,
Positive Sentiment: 0.0000, Negative Sentiment 0.1361

Top 10 Powerful Data Modeling Tools For 2020

Data science in 2020 allows business owners to process massive amounts of information and obtain the valuable nuggets that once took days to compute. With data modeling, you can take a complex software process and create a diagram that is much easier to understand.

If your business deals with big data at all, then data modeling is a concept you may already know. You can use tools for data modeling to create an overall IT strategy for your business or in the task of developing new databases.

2020-03-04 20:23:53+00:00 Read the full story…
Weighted Interest Score: 2.3719, Raw Interest Score: 1.5983,
Positive Sentiment: 0.2801, Negative Sentiment 0.0000

Big Data Is Changing The Way People Learn New Languages

Big data ischanging the way people learn, and the fact of the matter is that language is one of the most complicated yet sought after packets of information you seek. Because language and communication are so important, people will go and try different means to learn a new language. Using the ability of big data to access and process large sets of information, language can become much easier to learn and communicate.
2020-03-09 12:11:23+00:00 Read the full story…
Weighted Interest Score: 2.2599, Raw Interest Score: 1.4438,
Positive Sentiment: 0.2929, Negative Sentiment 0.3557

New Tech Platforms Hold the Key to Retail Banking’s Future

Today’s more powerful enabling technologies didn’t exist when most banks and credit unions implemented their current core platforms, EY notes in its report. the result has been mostly ad hoc deployment of AI and other cognitive technologies. Instead, they need to be managed as part of everyday IT operations, and modern core technologies enable this.

Battles tells The Financial Brand that the next big push in AI and machine learning will be extracting data from within banks’ own firewalls.

“If you have somebody’s primary checking account, you essentially understand their spending habits and can build a pro forma cash flow just with the data you have. That helps you direct [more personalized] offers to them.” He notes, however, that traditional institutions need to be sure their data models are supervised and certified — allowing them to “learn and adapt in the background at a pace that demonstrates the right amount of control.”

2020-03-02 00:13:10+00:00 Read the full story…
Weighted Interest Score: 2.2220, Raw Interest Score: 1.3680,
Positive Sentiment: 0.2098, Negative Sentiment 0.1277

K2 and Celonis Join Forces to Accelerate Digital Transformation

SourceCode Technology Holdings, Inc., the maker of K2 Software and a leader in intelligent process automation, and Celonis, a leader in AI-enhanced Process Mining and Process Excellence software, today announced a partnership leveraging process mining to accelerate digital transformation results for enterprises worldwide that are focused on mission-critical processes.

Integration of K2’s deep domain expertise and digital process automation capabilities with Celonis’ process mining capabilities will enable enterprises to better define and eliminate bottlenecks and inefficiencies. Customers can extract data from their operational systems and leverage powerful artificial intelligence (AI) and machine learning (ML) techniques to continuously optimize and adapt their business processes to accelerate digital transformation and deliver improved outcomes.

“As digital transformation initiatives drive the need for end-to-end automation, cataloging and prioritizing manual and inefficient automated processes becomes a logical starting point. Using process mining to underpin digital transformation automates daunting, error-prone manual discovery,” wrote Rob Koplowitz, vice president and principal analyst at Forrester Research in the February 2019 report, Process Mining: Your Compass For Digital Transformation.

2020-03-09 07:10:52+00:00 Read the full story…
Weighted Interest Score: 2.1529, Raw Interest Score: 1.9407,
Positive Sentiment: 0.3774, Negative Sentiment 0.2156

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Alternative Data News. 11, March 2020

The AltDataNewsletter by CloudQuant

50 Must-Read Free Books For Every Data Scientist in 2020

In this article, we are listing down some excellent data science books which cover the wide variety of topics under Data Science.

Data science is an inter-disciplinary field which contains methods and techniques from fields like statistics, machine learning, Bayesian etc. They all aim to generate specific insights from the data. In this article, we are listing down some excellent data science books which cover the wide variety of topics under Data Science.
2020-03-05 00:00:00 Read the full story…
Weighted Interest Score: 5.3947, Raw Interest Score: 2.0809,
Positive Sentiment: 0.2780, Negative Sentiment 0.1348

CloudQuant Thoughts : A very nice and well put together list!!

The U.S 10 Year Bond Falls Below 1%

While everyone has been concerned about the sell off in the stock market in the past two weeks, this decline should be contrasted with the rapid rise in the price of government bonds. For the first time in history, the yield on the 10-year government bond fell below 1%.

As Figure 1 illustrates, the 75-year interest rate pyramid is continuing its path toward new lows. The pyramid began on November 30, 1945 when the 10-year bond yielded 1.55%. The…
2020-03-09 14:31:33+00:00 Read the full story…
Weighted Interest Score: 5.3394, Raw Interest Score: 2.0362,
Positive Sentiment: 0.1131, Negative Sentiment 0.4525

CloudQuant Thoughts : 10 year bond below 1%, Interest rate above 1%, you do the math!

FINRA Starts Reporting Data on Treasury Securities Trading Volume

For the first time, the Financial Industry Regulatory Authority has started reporting aggregate data on the trading volume of Treasury securities reported to FINRA’s Trade Reporting and Compliance Engine, the regulator said Tuesday.

The move was designed to “provide more transparency” to the marketplace, FINRA said. Securities firms started reporting Treasury transactions to FINRA in July 2017 in a move that was designed to provide regulators and policymakers with additional information to boost understanding and “enhance surveillance of this bellwether market,” according to FINRA.
2020-03-10 00:00:00 Read the full story…
Weighted Interest Score: 4.2970, Raw Interest Score: 1.8750,
Positive Sentiment: 0.2717, Negative Sentiment 0.0272

CloudQuant Thoughts : I trawled around for REPO data for days at the end of last year during the REPO crisis. Making more of this data available in a timely and frequent fashion can only help traders get a better view of the health of our financial system…. HOWEVER…. During government shutdowns, the time when you probably need this data most of all… IT WILL GO OFFLINE.

How To Build An AI Marketplace For The Next Decade

Any company that releases a brand new car has to wait at least for the first-quarter sales to come in before deciding on the future of production. If a new software product is launched, the number of downloads might play a deciding factor in the future versions of the product. Now, what if a products’ sales in real time are combined with customers’ likelihood of purchase to fuel a profitable marketplace?
2020-03-11 10:30:58+00:00 Read the full story…
Weighted Interest Score: 4.2604, Raw Interest Score: 1.5892,
Positive Sentiment: 0.2249, Negative Sentiment 0.3898

CloudQuant Thoughts : “According to a Teradata survey, the lack of IT infrastructure (40%) and the shortage of talent (34%) are the two most significant barriers to AI realisation”. You are in a strong position my friend!

Alt data’s Wild West days may be ending as Congress and privacy advocates zero in on the industry. Nearly a dozen insiders tell us how data streams going dark is an ‘unhedgeable’ risk.

Recent congressional inquiries into Envestnet’s Yodlee and Avast’s Jumpshot highlight the risks aggregators of alternative data and their hedge-fund clients have as they rely on datasets that could disappear overnight.

Business Insider spoke with about a dozen alternative-data providers and consumers to get a sense of how to sell and use a product, respectively, that is not guaranteed to always be there.
2020-03-05 00:00:00 Read the full story…
Weighted Interest Score: 4.6156, Raw Interest Score: 1.8880,
Positive Sentiment: 0.0878, Negative Sentiment 0.3293

CloudQuant Thoughts : Going Dark and Disappearing are not the same thing. If regulators decide they do not like certain data sets they will simply retreat into the shadows and sell directly to large private customers who are happy to keep the transaction secret.

Superwise.ai raises $4.5 million for AI lifecycle management tools

Tel Aviv-based Superwise.ai today announced that it’s raised $4.5 million in seed funding, which it plans to use to scale its AI lifecycle management platform. This expanded platform, it says, will allow business and operational teams to take ownership of the health of AI models, giving them the ability to better trust the operation of their AI-driven processes.
2020-03-10 00:00:00 Read the full story…
Weighted Interest Score: 4.7175, Raw Interest Score: 1.6608,
Positive Sentiment: 0.1748, Negative Sentiment 0.3205

TradeTech 2020

21 – 23 April, 2020 Buy Side Only Evaluation Day 21 April, 2020 Main Conference 22 – 23 April, 2020 Palais des Congrès de Paris.

2020-03-09 00:00:00 Read the full story…
Weighted Interest Score: 4.5417, Raw Interest Score: 2.1066,
Positive Sentiment: 0.3304, Negative Sentiment 0.0000

ML Engineer, Data Scientist, Research Scientist: What’s the Difference?

If you have to write an artificial intelligence (AI) or machine learning (ML) job description, it can be difficult to convey precisely what kind of new employee you want to hire. Doing so requires using the right language, plus understanding what type of role is most appropriate for what you want to achieve.

To guide you through the challenging process of recruiting top AI talent, we’ll start by looking at the differences between different AI & …
2020-03-10 16:35:47+00:00 Read the full story…
Weighted Interest Score: 4.0748, Raw Interest Score: 2.0385,
Positive Sentiment: 0.2016, Negative Sentiment 0.1008

Heads Up! Slew of SEC Risk Alerts on Their Way, Exam Chief Warns

The Securities and Exchange Commission’s exam division plans to release soon — likely in March — risk alerts indicating how it will conduct exams on Regulation Best Interest and Form CRS and is zeroing in on the “race to zero commissions” by brokerage firms, Pete Driscoll, head of the Office of Compliance Inspections and Examinations, said Friday. Speaking at the Investment Adviser Association’s annual compliance conference in Washington, Driscoll told attendees to brace for eight upcoming risk alerts this year on: fixed income cross-trading (very close to release); Reg BI and Form CRS (coming very soon); cryptocurrency/digital assets; Libor; alternative data gathered from outside sources; top findings in the compliance space; and top findings on private funds.

Driscoll said OCIE will continue to focus on “fees and expenses,” a mainstay for the SEC, and is continuing exams of private funds and robo-advisors. Driscoll says he sees two groups of robos: those that provide “another style of offering investment advice by firms that have robust compliance, legal and risk programs” and those run by tech-based startups that are typically “not terribly familiar” with securities laws. “We see risks there,” he said.
2020-03-06 00:00:00 Read the full story…
Weighted Interest Score: 3.8348, Raw Interest Score: 1.6964,
Positive Sentiment: 0.0679, Negative Sentiment 0.1810

Having a digital strategy will help fund managers embrace transformational change

Investment managers stand at a crossroads today. Faced with a rapidly changing digital world, they must determine which path to take to help them transform their business models and respond to the needs of a younger generation of investors.

One key aspect to this transformation story is how investment managers successfully integrate people, processes and technology, as part of a sophisticated digital strategy, to adapt to the digital age. Data, …
2020-03-09 00:00:00 Read the full story…
Weighted Interest Score: 3.7082, Raw Interest Score: 1.7094,
Positive Sentiment: 0.2442, Negative Sentiment 0.0733

Data Cleansing: Everything You Wanted to Know About It

Today there is harsh competition in the market for companies to grow and even to survive. Data is the most important factor now for organizations and is being seen as the cause of all successful or bad decisions. Data is rightly said to be the factor which enables businesses to make confident business decisions and gain actionable insights. Clean, accurate, validated, and standardized data is wh…
2020-03-11 07:30:17+00:00 Read the full story…
Weighted Interest Score: 3.4962, Raw Interest Score: 1.9810,
Positive Sentiment: 0.6533, Negative Sentiment 0.4215

Data Scientist: Education, Training, Interviewing

Your typical data scientist works with various forms of data to discover insights and knowledge. Then they develop products and services that support optimal decision-making.

The data can be structured (coming from a pre-defined data model and residing in relational databases) or unstructured (having no pre-defined format, such as text files or user-generated content).

A data scientist is responsible for understanding and aggregating these diff…
2020-03-11 00:00:00 Read the full story…
Weighted Interest Score: 3.4727, Raw Interest Score: 1.9001,
Positive Sentiment: 0.2643, Negative Sentiment 0.1007

Compliance, Risk and Financial Experts to Headline COMPLY2020 Conference

PerformLine, the leading provider of multi-channel compliance technology and the organizer of COMPLY2020, today announced leaders from some of the world’s most recognized brands in banking, fintech and compliance are joining the COMPLY2020 speaker lineup.

Taking place May 5-6 in New York City, COMPLY2020 will unite a comprehensive gathering of regulators, compliance and risk professionals, sales and marketing leaders, innovators, investors and…
2020-03-10 00:00:00 Read the full story…
Weighted Interest Score: 3.4146, Raw Interest Score: 1.7497,
Positive Sentiment: 0.2238, Negative Sentiment 0.0610

Fidelity Introduces Funds Targeting HSAs: Portfolio Products

Fidelity Investments launched two new mutual funds specifically designed for investors looking to grow their health Health Savings Account savings to meet future medical expenses.

The Fidelity Health Savings Fund is available in a retail share class (FHLSX with annual expenses of 47 basis points) and a K share/institutional class (FHLKX, 37 basis points). The Fidelity Health Savings Index Fund, meanwhile, is available onl…
2020-03-09 00:00:00 Read the full story…
Weighted Interest Score: 3.2116, Raw Interest Score: 1.8581,
Positive Sentiment: 0.1883, Negative Sentiment 0.0126

How to Deliver a Data Science Project Successfully

It is demanding to know where to begin once you’ve decided that, yes, you wish to dive into the fascinating world of data and AI. Just having a look at all the technologies you need to understand all the tools you’re supposed to master is enough to make you confused.

Well, luckily for you, creating your first data project is actually not difficult as it seems. Becoming data-powered is first and most foremost about having to learn the basic steps…
2020-03-08 18:21:32+00:00 Read the full story…
Weighted Interest Score: 3.0904, Raw Interest Score: 1.5218,
Positive Sentiment: 0.1806, Negative Sentiment 0.1806

The Insights Beat: Stay On Course With Smart Data And Analytics Choices

As companies navigate through murky waters amid global health and economic headwinds, as a data and analytics leader, it may feel like there are many things that are out of your hands today. As you weather these conditions, it is still important to chart a steady course and be laser-focused on the parts you can control — which is to actively shape your enterprise’s ability to continue getting smarter about markets, competitors, products, and cust…
2020-03-10 19:22:13-04:00 Read the full story…
Weighted Interest Score: 3.0691, Raw Interest Score: 1.7070,
Positive Sentiment: 0.2276, Negative Sentiment 0.0284

IBM Project Debater: AI System That Can Debate People

IBM is announcing several new IBM Watson technologies designed to help organizations begin identifying, understanding and analyzing some of the most challenging aspects of the English language with greater clarity, for greater insights.

The new technologies represent the first commercialization of key Natural Language Processing (NLP) capabilities to come from IBM Research’s Project Debater, the only AI system capable of debating humans on compl…
2020-03-11 11:51:57+00:00 Read the full story…
Weighted Interest Score: 2.9711, Raw Interest Score: 1.5968,
Positive Sentiment: 0.3327, Negative Sentiment 0.1663

All The Latest AI Innovations By IIT Madras

ia if not the world. The institute always collaborates with other institutions, companies, government or other bodies to make new developments in AI. With the establishment of Robert Bosch Center for Data Science and Artificial Intelligence (RBC-DSAI) on February 6, 2019, it has accelerated and opened up the scope of research even more and widened the range of projects that are undertaken.

Below highlight some of the latest projects undertaken by the well-known institution in India:

Drones To Counter The Rogue Ones

A team mentored by Dr Ranjith Mohan, Ass. Prof, Dept. of Aerospace Engineering designe…
2020-03-11 06:30:00+00:00 Read the full story…
Weighted Interest Score: 2.9329, Raw Interest Score: 1.2787,
Positive Sentiment: 0.1880, Negative Sentiment 0.2445

Are You Data-driven? It Will Take More Than a Piece of Software to Achieve Success

Organizations want and need to be data-driven. We have been talking about achieving this level of insight for years, but the majority of companies have made surprisingly little progress. Most organizations still rely on gut intuitions for decision-making and have a long way to go to leveraging trusted data for data insights across the business.

For the past decade, I have had the privilege of wor…
2020-03-10 07:30:13+00:00 Read the full story…
Weighted Interest Score: 2.9091, Raw Interest Score: 1.7281,
Positive Sentiment: 0.2729, Negative Sentiment 0.2729

How Working Data Scientists Can Continue To Increase Their Knowledge

Working alone as a data scientist is not enough to boost one’s career because organisations are always looking to find something extra from their employees. In short, an organisation might expect data scientists to deliver different functions. So the question is how can a data scientist learn to increase their knowledge while still working full-time?

In this article, we will discuss a few pointers through which data scientists can increase their…
2020-03-10 11:30:25+00:00 Read the full story…
Weighted Interest Score: 2.9040, Raw Interest Score: 1.7677,
Positive Sentiment: 0.3788, Negative Sentiment 0.1263

The Advent And Scope Of AI Marketing In 2020 And Beyond

When it comes to bridging the existing gap between data science and its usage, targeting better marketing results, nothing beats the utilitarian nature of AI. While artificial intelligence alone is capable of sifting through humongous data sets for analyzing the relevant ones, AI marketing is slowly but steadily shaping up into a venture that comes with a host of benefits over the conventional ways of promoting a product or service.

That said, before we move…
2020-03-11 00:00:00 Read the full story…
Weighted Interest Score: 2.8694, Raw Interest Score: 1.2849,
Positive Sentiment: 0.4007, Negative Sentiment 0.0691

“The 3 ingredients to our success.” | Winners dish on their solution to Google’s QUEST Q&A Labeling | Kaggle Winner’s Interview

First place foursome, ‘Bibimorph’ share their winning approach to the QUEST Q&A Labeling competition by Google, and more!

Weekly Self-Study Plan To Ace Data Science and Machine Learning

Data Science is a vast field where statistics and programming go hand-in-hand. In order to ace this field, enthusiasts must follow a learning routine that involves practising, reading, competing as well as engaging with the community. This is a 4-weeks plan which can be repeated every month to enhance your depth of understanding in Data Science. It includes both theoretical and real-world practical resources …
2020-03-11 09:30:00+00:00 Read the full story…
Weighted Interest Score: 2.8460, Raw Interest Score: 1.7344,
Positive Sentiment: 0.2797, Negative Sentiment 0.0746

5 lesser-known pandas tricks

Pandas provides high-performance, easy-to-use data structures and data analysis tools for the Python

pandas needs no introduction as it became the de facto tool for data analysis in Python. As a Data Scientist, I use pandas daily and I am always amazed by how many functionalities it has. In this post, I am going to show you 5 pandas tricks that I learned recently and using them helps me to be more productive.

For pandas newbies — Pandas provide…
2020-03-02 20:54:02.073000+00:00 Read the full story…
Weighted Interest Score: 2.6658, Raw Interest Score: 1.2083,
Positive Sentiment: 0.0919, Negative Sentiment 0.0788

Go Beyond Upskilling In Data Science & Machine Learning

The Big Data boom has birthed many flourishing opportunities for India Inc in 2020. Most businesses are now energetically adopting data science, machine learning and data analytics in their operations for improved business models and optimisation of their operations. Now, with data science and machine learning gaining more and more prominence across sectors, enterprises are now looking at filling the talent gap to keep up with the swift pace of the technological progress. The year 2019 saw over 97,000 job openings in data science alone. …
2020-03-11 10:47:42+00:00 Read the full story…
Weighted Interest Score: 2.6290, Raw Interest Score: 1.8637,
Positive Sentiment: 0.4659, Negative Sentiment 0.1359

Will Artificial Intelligence Render Human Transcriptionists Obsolete?

Artificial intelligence is changing the nature of human language. We are seeing computers that can understand language in very nuanced ways. Towards Data Science has a very interesting analysis of this trend in their article Understanding Natural Language Process, How AI Understands Our Languages.

AI is still unable to grasp the complexities of language to the level of trained transcriptionists. However, that may change in the future. Will it eventually make them obsolete?

AI is Changing the Nature of Human Language – How Will Transcriptionists Respond?…
2020-03-06 19:05:49+00:00 Read the full story…
Weighted Interest Score: 2.5353, Raw Interest Score: 0.9998,
Positive Sentiment: 0.1707, Negative Sentiment 0.3414

BackboneAI raises $4.7 million to unify disparate enterprise data sets with AI

BackboneAI, a startup providing a data automation platform for enterprises, today emerged from stealth with $4.7 million in seed funding. CEO Rob Bailey says the round will be used to scale the company’s product for data collection within and among organizations, which he believes could improve data science team productivity by leveraging AI to integrate data from a range of sources.

BackboneAI’s offering reconciles records in a number of industries, like bills of materials across construction vendors and companies. It ingests data from various sources, preprocesses and standardizes it, and then delivers it to systems. A combination of data synchronization, API connectivity, and third-party app and database support can identify and resolve anomalies while tracking the provenance of products from source to customer and managing global rights and shipments for companies with affiliates and distributors.
2020-03-10 00:00:00 Read the full story…
Weighted Interest Score: 2.5145, Raw Interest Score: 1.6119,
Positive Sentiment: 0.1289, Negative Sentiment 0.0967

If you would like to add your blog or website to our search crawler, please email customer_success@cloudquant.com. We welcome all contributors.

The post Alternative Data News. 11, March 2020 appeared first on CloudQuant.

The Value in Machine Learning Alternative Data for Investment Managers

Chicago, Illinois, USA, March 12, 2020 – CloudQuant LLC has proven the value in the Precision Alpha Machine Learning Signals (PA Signals) alternative data set. Its detailed data science study shows a long-short portfolio outperforms the equal-weight S&P 500 ETF by an average of 37.9% per year after transaction costs. CloudQuant found that over 91.5% of the total return is pure alpha. The results of the study are significant to the 99th percent level.

Alternative Data Portfolio Returns on Intraday Hold Trading Strategy

A Long-Short portfolio outperforms $RSP by 37.9%/year using Precision Alpha AltData.

“Backtesting on CloudQuant’s Mariner showed that a long top 5%-short bottom 5% quantile intraday strategy achieved overall Sharpe Ratio of 5.36 and a very low CAPM beta,” said Morgan Slade, Chief Executive Officer of CloudQuant.

The growing quality and quantity of Alternative Data Sets have created a dilemma for many investment managers. Profitable information is contained in new data but most investors lack the resources to onboard and then research the data. CloudQuant’s quantamental researchers have studied the PA Signals and provide a detailed white paper, and backtesting algorithm with source code (free upon qualified request) that allows any portfolio manager to replicate the research and immediately begin to reproduce the results.

“With CloudQuant investment professionals can jumpstart their research without incurring the cost of dataset ingress and curation. They are able to see the value in the data,” says Mark Temple-Raston, Ph.D. – Chief Data Scientist of Precision Alpha.

About CloudQuant

CloudQuant provides quantamental data showcasing services to alternative data providers including bespoke AI, Machine Learning, and data science services. Fundamental and quantitative investors utilize the cloud-based institutional-grade analytics technology and detailed backtests to quickly research alternative datasets in a unique “try-before-you-buy” data shopping experience.

www.cloudquant.com

Twitter: @CloudQuant

About Precision Alpha

Precision Alpha uses probabilistic mathematics, information theory and machine learning to expose alpha for investors. They calculate a set of exact, unbiased, equity measurements that reveal market price moves before they occur for every security on 85+ global financial exchanges. Precision Alpha’ proprietary technology leverages machine learning to generate accurate, predictive Alpha for Investment Funds, Family Offices, Traders and professional investors.

www.precisionalpha.com

Twitter: @PrecisionAlpha

For Media Inquiries Please Contact:

Tayloe Draughon, Senior Product Manager

tdraughon@CloudQuant.com

+ 1 512.439.8152

The post The Value in Machine Learning Alternative Data for Investment Managers appeared first on CloudQuant.

ESG for Selecting Short Or Under-weight Positions with AltData

March 13, 2020

Environmental, Social and Governance (ESG) investing is based on the premise that firms with higher quality management and resources will operate well and their value will rise in the market. The reverse is also true. A negative ESG event for a company can be a precursor to the shares underperforming or decreasing in value.

CloudQuant’s research shows that short or under-weight opportunities exist using ESG Alternative Data.

The following charts show some of the short signals of $GM and $VIAC where the GSQ Short Term Price Predictor Score fell to ≈ -25. The trading strategy (source code and white paper available upon request) suggested that a 5-day hold works with Alternative Data.

$GM #ESG

$VIAC #ESG

The Disclaimer

The post ESG for the Short Seller with Alt Data appeared first on CloudQuant.

AI & Machine Learning News. 16, March 2020

The Artificial Intelligence and Machine Learning Newsletter by CloudQuant

AlphaGo – The Movie | Full Documentary

With more board configurations than there are atoms in the universe, the ancient Chinese game of Go has long been considered a grand challenge for artificial intelligence. On March 9, 2016, the worlds of Go and artificial intelligence collided in South Korea for an extraordinary best-of-five-game competition, coined The DeepMind Challenge Match. Hundreds of millions of people around the world watched as a legendary Go master took on an unproven AI challenger for the first time in history.

Directed by Greg Kohs with an original score by Academy Award nominee, Hauschka, AlphaGo chronicles a journey from the halls of Oxford, through the backstreets of Bordeaux, past the coding terminals of Google DeepMind in London, and ultimately, to the seven-day tournament in Seoul. As the drama unfolds, more questions emerge: What can artificial intelligence reveal about a 3000-year-old game? What can it teach us about humanity?

CloudQuant Thoughts : Self Isolating? Here is a way to spend an entertaining hour and a half learning all about the AlphaGo project.

CloudQuant proves value in PA Signals alt data set

CloudQuant says it has proven the value in the Precision Alpha Machine Learning Signals (PA Signals) alternative data set. Its detailed data science study shows a long-short portfolio outperforms the equal-weight S&P 500 ETF by an average of 37.9 per cent per year after transaction costs.

CloudQuant found that over 91.5 per cent of the total return is pure alpha. The results of the study are significant to the 99th per cent level.

Cutting-edge machine learning is transforming quantitative analysis for portfolio managers and traders. PA Identifies structural breaks and exposes investment signals that market participants are currently unable to see. The PA Signal offers a favourable risk-adjusted return that can be used to create large-scale investment algorithms.
2020-03-13 00:00:00 Read the full story…
Weighted Interest Score: 12.5769, Raw Interest Score: 3.3426,
Positive Sentiment: 0.2228, Negative Sentiment 0.2228

CloudQuant Thoughts : Our news scraper did a good job this week picking up this excellent article and rating it very highly. This is what we do at CloudQuant.

AI Can Detect Coronavirus Infections Far Faster Than Humans

Using 5,000 confirmed cases as their training data, scientists at the Alibaba DAMO Academy built an algorithm they claim can detect coronavirus infections in CT scans in just 20 seconds and with 96% accuracy, according to Chinese outlet Sina Tech News.

In February, Qiboshan Hospital in Zhengzhou became the first place to use Alibaba’s AI to detect coronavirus, and an additional 100 hospitals reportedly plan to adopt the system.

Alibaba’s isn’t the only AI helping doctors to detect coronavirus in CT scans, either…

2020-03-10 00:00:00 Read the full story…

CloudQuant Thoughts : Lots of moves in the industry to assist in this global emergency including free access to GPUs for Corona AI research, demands for open access to all data and White House requests for Silicon Valley to assist in developing solutions and Google web page for citizens to figure out if they need a test and where the nearest test center was.

An implant uses machine learning to give amputees control over prosthetic hands

Researchers have been working to make mind-controlled prosthetics a reality for at least a decade. In theory, an artificial hand that amputees could control with their mind could restore their ability to carry out all sorts of daily tasks, and dramatically improve their standard of living.

However, until now scientists have faced a major barrier: they haven’t been able to access nerve signals that are strong or stable enough to send to the bionic limb. Although it’s possible to get this sort of signal using a brain-machine interface, the procedure to implant one is invasive and costly. And the nerve signals carried by the peripheral nerves that fan out from the brain and spinal cord are too small.

A new implant gets around this problem by using machine learning to amplify these signals. A study, published in Science Translational Medicine today, found that it worked for four amputees for almost a year. It gave them fine control of their prosthetic hands and let them pick up miniature play bricks, grasp items like soda cans, and play Rock, Paper, Scissors.

2020-03-04 00:00:00 Read the full story…

CloudQuant Thoughts : Non invasive nerve signal detection will not only assist many people with a disabilities but will also provide assistance for the elderly and open up the huge future (sci-fi!) market for physical augmentation.

20+ Machine Learning Datasets & Project Ideas

To Build a perfect model, you need a large amount of data. But finding the right dataset for your machine learning and data science project is sometimes quite a challenging task. There are many organizations, researchers, and individuals who’ve shared their work, and we will use their datasets to build our project.

So in this article, we are going to discuss 20+ Machine learning and Data Science dataset and project ideas that you can use for practicing and upgrading your skills.

2020-03-20 00:00:00 Read the full story…
Weighted Interest Score: 7.5461, Raw Interest Score: 2.1555,
Positive Sentiment: 0.1130, Negative Sentiment 0.0969

ING Invests In Natural Language Processing Technology

ING’s reputation for adopting cutting edge technologies was enhanced today with the announcement that it would make an investment in London-based provider of natural language processing (NLP) technology Eigen Technologies (‘Eigen’).

ING has invested in London-based #fintech @Eigen_Tech, a provider of natural language processing (#NLP) technology. Together they will work on establishing best-in-class NLP models for the #financialindustry. #innova…
2020-03-13 11:25:26+00:00 Read the full story…
Weighted Interest Score: 5.4463, Raw Interest Score: 2.5210,
Positive Sentiment: 0.4449, Negative Sentiment 0.0000

Reinforcement Learning In Finance – A Newbie In Portfolio Selection And Allocation

Ever heard about financial use cases of reinforcement learning, yes but very few. One such use case of reinforcement learning is in portfolio management. Earlier Markowitz models were used, then came the Black Litterman models but now with the advent of technology and new algorithms, reinforcement learning finds its place in the financial arena.

Portfolio selection and allocation have been a manual task majorly. Using reinforcement learning, the…
2020-03-16 11:30:00+00:00 Read the full story…
Weighted Interest Score: 5.1796, Raw Interest Score: 2.6539,
Positive Sentiment: 0.3611, Negative Sentiment 0.0181

Data Science Fails: Building AI You Can Trust

Industries from insurance and healthcare to banking and retail are aggressively working to integrate AI and machine learning models into their operations to maximize profits, reduce customer churn, operate more efficiently, and gain a significant advantage over competitors. However, before any business can make AI technology a central part of their organization’s success, they must first be able to trust the technology.

Regulate Your AI Bias : Businesses need to make sure that any AI solutions they implement are free from human biases and are built using best data science practices. Toward this end, it’s important to take care to avoid common data science mistakes, including:

Don’t buy into hype: Make sure your data science team is not caught up in the hype of a new algorithm that is generating lots of buzz.
Choose the right AI model: Avoid algorithm bias by allowing a competition between a champion and a challenger model to decide which is the better option.
Leave presumption at the door: Don’t assume you know which algorithm is best for your data in advance.

2020-03-09 00:00:00 Read the full story…
Weighted Interest Score: 5.1462, Raw Interest Score: 1.7707,
Positive Sentiment: 0.2724, Negative Sentiment 0.3859

Is Machine Learning or Deep Learning Best for Your AI Project?

When a business is engaged in digitization, adopting digital technologies to change a business model and provide new opportunities, the discussion inevitably rolls around to how to incorporate AI.

Software developers face decisions on which advanced analytic techniques are within reason to incorporate. Viewing members of a team assembled to work on projects incorporating AI, the data scientist is likely to have the best grasp of the risks versus the rewards of different tools and approaches, suggests a recent article in Data Science Central.

Powerful and reasonably-mature machine learning techniques are the most widely adopted. Deep learning describes deep neural networks and reinforcement learning. Deep learning encompasses convolutional neural networks (CNNS), recurrent neural networks (RNNs), long short-term memory networks (LSTMs) and generative adversarial networks (GANs). In applications, these would cover image and video processing and search, text and audio processing, game play as optimization and several versions of time series forecasting.

However, deep learning solutions typically require a larger volume of data, are difficult to train and require specialized skills to build, implement and maintain. These all heighten the risk. So whether deep learning techniques should be recommended for a company’s digital journey needs to be carefully considered.

2020-03-12 21:30:17+00:00 Read the full story…
Weighted Interest Score: 4.5245, Raw Interest Score: 2.3549,
Positive Sentiment: 0.2411, Negative Sentiment 0.1298

Top Challenges Startups Face While Implementing Artificial Intelligence

Artificial intelligence (AI) is the crown of every tech-powered business enterprise — whether small or big. And embracing new opportunities with AI is something every business must do to stay relevant in their industry. Implementing artificial intelligence in business will provide a direct impact on the success of the companies ranging from improved decision-making to better use of the extensive data generated.

However, business-friendly it may sound; the path to implementing artificial intelligence in business is not a smooth ride. While larger businesses find themselves in a better position, the same cannot be said about startups. There are some typical challenges that startups face when it comes to implementing AI in their organisation. In this article, we will discuss six such challenges to implement AI in startups vs in larger organisations.

2020-03-14 10:30:00+00:00 Read the full story…
Weighted Interest Score: 4.3458, Raw Interest Score: 2.0090,
Positive Sentiment: 0.2411, Negative Sentiment 0.4018

Syncsort Partners with Databricks to Support Cloud Initiatives

2020-03-09 00:00:00 Read the full story…
Weighted Interest Score: 4.3342, Raw Interest Score: 2.1038,
Positive Sentiment: 0.1791, Negative Sentiment 0.2686

Google’s Neural Tangents library gives ‘unprecedented’ insights into AI models’ behavior

Google today made available Neural Tangents, an open source software library written in JAX, a system for high-performance machine learning research. It’s intended to help build AI models of variable width simultaneously, which Google says could allow “unprecedented” insight into the models’ behavior and “help … open the black box” of machine learning.

As Google senior research scientist Samuel S. Schoenholz and research engineer Roman Novak explain in a blog post, one of the key insights enabling progress in AI research is that increasing the width of models results in more regular behavior and makes them easier to understand. By way of refresher, all neural network models contain neurons (mathematical functions) arranged in interconnected layers that transmit signals from input data and slowly adjust the synaptic strength (weights) of each connection. That’s how they extract features and learn to make predictions.

2020-03-13 00:00:00 Read the full story…
Weighted Interest Score: 4.2634, Raw Interest Score: 2.2261,
Positive Sentiment: 0.2087, Negative Sentiment 0.1391

Google Launches Beta Version of Cloud AI Platform Pipelines

A scalable machine learning workflow involves several steps and complex computations. These steps include data preparation and preprocessing, training and evaluating models, deploying these models and much more. While prototyping a machine learning model can be seen as a simple and easygoing task, it eventually becomes hard to track each and every process in an ad-hoc manner.

To simplify the development of machine learning models, Google launches the beta version of Cloud AI Platform Pipelines, which will help to deploy robust, repeatable machine learning pipelines along with monitoring, auditing, version tracking, and reproducibility. It ensures to deliver an enterprise-ready, easy to install, a secure execution environment for the machine learning workflows.

2020-03-14 12:30:00+00:00 Read the full story…
Weighted Interest Score: 4.1419, Raw Interest Score: 2.0460,
Positive Sentiment: 0.3197, Negative Sentiment 0.0000

ML Engineer, Data Scientist, Research Scientist: What’s the Difference?

To guide you through the challenging process of recruiting top AI talent, we’ll start by looking at the differences between different AI & ML roles. Then, we’ll discuss who should be your first hires depending on the approach you choose for your ML projects. We also recommend you make sure that you don’t do these seven things to scare off the AI talent you’re trying to hire.

THE DIFFERENCES BETWEEN POPULAR AI & ML JOBS
Someone who’s unfamiliar with the various job titles associated with AI & ML may quickly get overwhelmed by the perceived lack of distinction between them. This breakdown should help.

2020-03-10 16:35:47+00:00 Read the full story…
Weighted Interest Score: 4.0761, Raw Interest Score: 2.0392,
Positive Sentiment: 0.2017, Negative Sentiment 0.1008

Tips For Data Scientists & Data Engineers To Work Together

Noticeably, the demand for big data professionals has been higher than ever, where data scientists, data engineers and machine learning engineers are being ranked among the top emerging jobs of the industry. We agree that data is king; however, many companies are struggling to integrate a proper data science team into their engineering workflows. This is because of lack of knowledge and an improper understanding of the field.

Ever since big data and analytics became one of the lucrative career paths for the youth, there has been an ongoing discussion about the differences between various data-related roles — especially data scientists and data engineering. And for people to get into this field or for organisations to build a strong team to handle their data, it is imperative to understand the field properly.

One needs to understand that a data science degree isn’t suitable for a data engineering role. While data science deals heavily with mathematics, data engineers, in contrast, primarily deal with the tech side of data — building data pipelines. However, both roles have ‘big data’ common in them.

2020-03-16 12:30:00+00:00 Read the full story…
Weighted Interest Score: 3.8611, Raw Interest Score: 2.1773,
Positive Sentiment: 0.4147, Negative Sentiment 0.1944

Eigen and ING target financial industry with NLP-driven data extraction

Eigen Technologies, a U.K. startup that offers natural language processing (NLP) technology to help companies extract meaningful data from documents, has closed a $42 million series B round of funding. This includes a fresh $5 million tranche from Dutch finance giant ING, following an initial $37 million raise back in November.

Additionally, Eigen and ING announced a deeper working partnership to establish “best-in-class NLP models” that are ful…
2020-03-13 00:00:00 Read the full story…
Weighted Interest Score: 3.7037, Raw Interest Score: 1.9166,
Positive Sentiment: 0.1127, Negative Sentiment 0.1879

Data Scientist: Education, Training, Interviewing

Your typical data scientist works with various forms of data to discover insights and knowledge. Then they develop products and services that support optimal decision-making.

The data can be structured (coming from a pre-defined data model and residing in relational databases) or unstructured (having no pre-defined format, such as text files or user-generated content).

How Automation, AI, and Data Integration are Transforming the Pharmaceutical Industry

Click to learn more about author Joe Rymsza.

Pharma companies are more challenged than ever to bring drugs to market safely and cost-effectively. Roadblocks to success include the ever-evolving regulatory environment, growing patient safety concerns, and the burden of outdated technology solutions. Today many businesses find themselves burdened with rigid and costly compliance processes. Furthermore, there is a schism between organizations and t…
2020-03-16 07:35:26+00:00 Read the full story…
Weighted Interest Score: 3.4321, Raw Interest Score: 1.9044,
Positive Sentiment: 0.3304, Negative Sentiment 0.2915

Machine Learning Leading to Revolution in Clinical Data Management

“What I’ve been looking at for the past few years is how things are evolving within the clinical trial space, and what impact that’s going to have on clinical data management,” said Francis Kendall, Senior Director of Biostatistics and Programming at Cytel, explained to attendees in Orlando during the Summit for Clinical Ops Executives (SCOPE).

We’re going to see a shift in how clinical evidence is produced and where it’s produced from, said Kendall. “It’s a new paradigm about data usage,” he said “We have traditional clinical trials, and they will always remain, but we’re starting to see things like pragmatic trials or synthetically controlled models. How do we deal with that data?”

2020-03-12 21:30:19+00:00 Read the full story…
Weighted Interest Score: 3.2823, Raw Interest Score: 1.8018,
Positive Sentiment: 0.1461, Negative Sentiment 0.0974

Google Launches TensorFlow Quantum

The worlds of quantum computing and machine learning are coming together with TensorFlow Quantum (TFQ), a new library unveiled today by Google.

Google has been one of the leaders in the emerging field of quantum computing, where computers are able to manipulate multiple qubits, compared to the binary bits that regular computers can use. The Mountain View, California tech giant declared “quantum supremacy” last year as a result of its progress in the field.

But for all advancement that’s been made, it found that “there’s been a lack of research tools to discover useful quantum ML models that can process quantum data and execute on quantum computers available today,” the company says.

2020-03-09 00:00:00 Read the full story…
Weighted Interest Score: 3.2662, Raw Interest Score: 1.7462,
Positive Sentiment: 0.1722, Negative Sentiment 0.0738

How to Deliver a Data Science Project Successfully

Let’s jump into the following steps that will help you in successfully delivering a data science project.

2020-03-08 18:21:32+00:00 Read the full story…
Weighted Interest Score: 3.0904, Raw Interest Score: 1.5218,
Positive Sentiment: 0.1806, Negative Sentiment 0.1806

Three Tricks to Amplify Small Data for Deep Learning

It’s no secret that deep learning lets data science practitioners reach new levels of accuracy with predictive models. However, one of the drawbacks of deep learning is it typically requires huge data sets (not to mention big clusters). But with a little skill, practitioners with smaller data sets can still partake of deep learning riches.

Deep learning has exploded in popularity, with good reason: Deep learning approaches, such as convolutional neural networks (used primarily for image data) and recurrent neural networks (used primarily for language and textual data) can deliver higher accuracy and precision compared to “classical” machine learning approaches, like regression algorithms, gradient-boosted trees, and support vector machines.

But that higher accuracy comes at a cost. Deep learning models are much more complex and typically require much more data to deliver better predictions. And of course, running all that data requires more computer horsepower, typically in the form of GPU-equipped clusters. It’s no wonder that the world’s leaning practitioners of deep learning are companies with names like Google, Facebook, and Microsoft, which have a ton of data and compute capacity on which to develop and train advanced predictive models.

2020-03-10 00:00:00 Read the full story…
Weighted Interest Score: 3.0804, Raw Interest Score: 2.2181,
Positive Sentiment: 0.1969, Negative Sentiment 0.0656

The Insights Beat: Stay On Course With Smart Data And Analytics Choices

AI Weekly: Coronavirus spurs adoption of AI-powered candidate recruitment and screening tools

As COVID-19 continues to spread — as of the time of writing (March 12), there were over 139,600 confirmed cases and over 5,100 deaths — companies are increasingly adopting alternatives to in-person job interviews and talent recruitment. Recruiters PageGroup and Robert Walters have announced plans to move some job interviews and interactions online, following on the heels of tech giants Amazon, Facebook, Google, and Intel.

At least a few have beg…
2020-03-13 00:00:00 Read the full story…
Weighted Interest Score: 2.9792, Raw Interest Score: 1.0860,
Positive Sentiment: 0.1614, Negative Sentiment 0.2201

Can We Judge An Machine Learning Model Just From Weights

Any machine learning solution comes with two primary challenges — accuracy and training time. There is always a trade-off between these two. There are places where you can’t trade accuracy, such as in the case of self-driving cars. That’s why we don’t see these cars on the road yet as the engineers are training the models with a vast number of features. However, there are applications where long hours of training time can’t be spared.

So, what if we just look at the weights and decide whether to train a model or not? This would dramatically reduce the computational costs of any ML pipeline. To study the prediction of the accuracy of a neural network given only its weights, the researchers from Google Brain propose a formal setting that frames this task.
2020-03-11 04:30:00+00:00 Read the full story…
Weighted Interest Score: 2.9790, Raw Interest Score: 1.5737,
Positive Sentiment: 0.0954, Negative Sentiment 0.0715

Should Early-Stage Startups Hire A Data Scientist?

With data scientists emerging as one of the most sought after positions, organisations across the world are looking to increase their data science capabilities. This has resulted in a deluge of jobs, with demands coming in from new startups as well. But do they need to hire a full-time data scientist early in its life cycle?

Indeed, there may be great uses for hiring data scientists, especially given that they can gather insights that can significantly contribute to overall business success. But without adequate customers and a proper data…
2020-03-16 05:30:34+00:00 Read the full story…
Weighted Interest Score: 2.9561, Raw Interest Score: 1.6603,
Positive Sentiment: 0.2025, Negative Sentiment 0.1822

A Brief History of Data Quality

The term “Data Quality” focuses primarily on the level of accuracy possessed by the data, but also includes other qualities such as accessibility and usefulness. Some data isn’t accurate at all, which, in turn, promotes bad decision-making. Some organizations promote fact checking and Data Governance, and, as a consequence, make decisions that give them an advantage. The purpose of ensuring accurate data is to support good decision-making in both…
2020-03-11 07:35:59+00:00 Read the full story…
Weighted Interest Score: 2.9191, Raw Interest Score: 1.7716,
Positive Sentiment: 0.2573, Negative Sentiment 0.1781

3i Infotech Launches AI Based Anti-Money Laundering Tool Called AMLOCK Analytics

3i Infotech Limited, a global Information Technology company, launched AMLOCK Analytics, its advanced anti-money laundering (AML) solution powered by Artificial Intelligence (AI) and Machine Language (ML), which enables banks and financial institutions to identify complex and hidden AML patterns. It helps organizations to meet their most critical challenge of managing high false positives and provides a holistic view of investigating an alert. AMLOCK Analytics uses various statistical methods and machine learning algorithms to derive analyses and predictions based on…
2020-03-12 06:17:01+00:00 Read the full story…
Weighted Interest Score: 2.8913, Raw Interest Score: 1.7270,
Positive Sentiment: 0.3621, Negative Sentiment 0.6128

The Advent And Scope Of AI Marketing In 2020 And Beyond

2020-03-16 00:00:00 Read the full story…
Weighted Interest Score: 2.8694, Raw Interest Score: 1.2849,
Positive Sentiment: 0.4007, Negative Sentiment 0.0691

Data Security: How to move to a data-centric approach

Take a moment and think about some of the changes in computing over the last decade. The iPad debuted in 2010. Smartphones weren’t in everybody’s pockets yet. 4G was starting to branch out from major cities around the world. And we had only a taste of the Big Data.

The way everyone uses computers has fundamentally changed. It has redefined the role networks play in our lives. Employees are no longer limited to corporate systems and workstations to do their job. Students can connect to academic servers from any place on earth. The use of mobile devices, laptops, and tablets have now fostered the growth of the telecommute era.

It’s time for companies to embrace the data-centric approach. It’s the only way to adapt to technological advances and prime themselves for the changes to come.

2020-03-13 07:31:12+00:00 Read the full story…
Weighted Interest Score: 2.8563, Raw Interest Score: 1.6424,
Positive Sentiment: 0.1714, Negative Sentiment 0.4427

How AI will change the mobile app development industry

We have reached 2020, a world that’s even more fast-paced and user-centric, a space that surely holds a wide range of promising trends for the industry ranging from chatbots to augmented reality. But above all, artificial intelligence steals the show due to i…
2020-03-09 04:29:44+00:00 Read the full story…
Weighted Interest Score: 2.8025, Raw Interest Score: 1.3155,
Positive Sentiment: 0.4063, Negative Sentiment 0.0967

Will Quantum Computing Define The Future Of AI?

Google, this week, has launched a new version of their TensorFlow framework — TensorFlow Quantum (TFQ), which is an open-source library for prototyping quantum machine learning models.

Quantum computers aren’t mainstream yet; however, when they do arrive, they will need algorithms. So, TFQ will bridge that gap and will make it possible for developers/users to create hybrid AI algorithms combining both traditional and quantum computing techniques…
2020-03-14 07:30:00+00:00 Read the full story…
Weighted Interest Score: 2.7679, Raw Interest Score: 1.3022,
Positive Sentiment: 0.2485, Negative Sentiment 0.4076

AI sector reacts to increased policy oversight and market uncertainty

Despite extreme economic uncertainty, policy makers continue attempts to regulate artificial intelligence (AI) as tech vendors adapt to the latest guidelines set by the European Commission last month, calling for a European AI strategy. Legal counsels are advising small fintechs and companies leveraging AI to utilise regulation for a competitive edge.

“The advice that we’re giving is to feed into any consultation process as soon as you can, because it can be used almost to get a competitive advantage because if you’re the person that’s feeding in and saying ‘this is how we think you should do this’ then you put yourself in a very good position,” says Mardi MacGregor, senior associate at Fox Williams. “Then you can make sure that the rules when they do come out are something that can work for you, but also sometimes you can make sure that your business is one of the businesses that succeeds under the new regulation.”

2020-03-12 00:00:00 Read the full story…
Weighted Interest Score: 2.7361, Raw Interest Score: 1.2358,
Positive Sentiment: 0.1696, Negative Sentiment 0.1938

Vida Diagnostics raises $11 million to diagnose lung diseases with AI

Vida Diagnostics, a provider of AI-powered lung imaging analysis tools, today announced that it has raised $11 million in a series C round. CEO Susan A. Woods said the funds will be used to accelerate the commercialization and expansion of the company’s product portfolio, which she says could address market deficits in the early assessment, monitoring, and treatment of lung disease.

“We are driven to continuously raise the standard of care for p…
2020-03-12 00:00:00 Read the full story…
Weighted Interest Score: 2.6757, Raw Interest Score: 1.0276,
Positive Sentiment: 0.1622, Negative Sentiment 0.1622

Importance Of Hypothesis Testing In Data Science

Data Science has two parts to it “Data” and “Science”. Alone both are having their individual meanings but when it is combined together “Data” gets power. Yes, you heard it right, but the question here is how “Data” gets power? Data alone is not interesting, it Is the interpretation and insights from the data that make it worthy. How to achieve that is another question pondering in our minds. So I would say statistics is the answer to this questi…
2020-03-12 12:30:00+00:00 Read the full story…
Weighted Interest Score: 2.6706, Raw Interest Score: 1.3891,
Positive Sentiment: 0.1736, Negative Sentiment 0.5209

Australian AI Startup Reejig Raises $2.2m

Reejig, a Sydney based startup that uses big data and AI to predict problems and opportunities in large complex workforces, today announced a $2.2 million first capital raise.

The company says the money will be used to scale its software as a service platform which connects various HR systems, collating the data on employees and potential employees to use in predictive analytics.

2020-03-10 11:47:58+11:00 Read the full story…
Weighted Interest Score: 2.6004, Raw Interest Score: 1.3686,
Positive Sentiment: 0.3193, Negative Sentiment 0.2281

Amazon’s AI predicts context from search queries

Amazon is using AI and machine learning to predict context from customers’ queries. In a preprint paper accepted to the ACM SIGIR Conference on Human Information Interaction and Retrieval scheduled to take place this month, Amazon researchers describe a system that predicts activities like “running” from queries like “Adidas men’s pants.” It could help to improve the quality of search results on Amazon.com, which could enhance the overall Amazon shopping experience.

As Adrian Boteanu, contributing author and Amazon Search customer experience applied scientist, explains in a blog post, most product discovery algorithms look for correlations between queries and products. By contrast, the researchers’ AI identifies the best matches depending on the context of use.
2020-03-12 00:00:00 Read the full story…
Weighted Interest Score: 2.5853, Raw Interest Score: 1.5102,
Positive Sentiment: 0.1798, Negative Sentiment 0.0000

Could Brexit open the gates to AI in the UK?

As it stands, come 31 December 2020, the UK will have departed the EU, and the transition period which ensured the stability of continuing UK/EU regulation to bridge the exit will cease.

2020-03-09 11:01:00 Read the full story…
Weighted Interest Score: 2.4373, Raw Interest Score: 1.1207,
Positive Sentiment: 0.2537, Negative Sentiment 0.2396

Raising Deposits Amid Coronavirus Rate-Slashing and Stock Volatility

The U.S. has returned to the low-interest environment of pre-2018, following the Federal Reserve’s 50 basis point cut to the Fed funds rate, and its subsequent cut, both in an effort to contain the economic damage from the COVID-19 outbreak.

Bankers had been dusting off their 2017 playbooks to revisit deposit growth strategies for when the Fed funds rate was last in the 1%-1.25% range, and now must assess things in light of the Fed’s reduction in that key rate to a range of 0%-0.25%. When yields are low, consumers have little incentive to lock up funds for any length of time and are equally unlikely to move deposits for marginally higher returns.

Management has been focusing on protecting employees and serving people in a highly unusual period. But the economics of banking still march on. Banks and credit unions must be wary of falling into the old trap of resorting to undifferentiated, headline-grabbing rate promotions to accelerate deposit growth and stem deposit outflows. These competitive knee-jerk reactions can increase deposit costs and “hot money.” This is a pivotal time for financial institutions to move beyond myopic attempts at quick fixes.

Weighted Interest Score: 2.4308, Raw Interest Score: 1.3474,
Positive Sentiment: 0.2567, Negative Sentiment 0.2727

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Alternative Data News. 18, March 2020

The AltDataNewsletter by CloudQuant

Data Science @ The New York Times

About the speaker : Chris Wiggins is an associate professor of applied mathematics at Columbia University and the Chief Data Scientist at The New York Times. At Columbia he is a founding member of the executive committee of the Data Science Institute, and of the Department of Applied Physics and Applied Mathematics as well as the Department of Systems Biology, and is affiliated faculty in Statistics. https://www.linkedin.com/in/wiggins/

About the talk : The Data Science group at The New York Times develops and deploys machine learning solutions to newsroom and business problems. Re-framing real-world questions as machine learning tasks require not only adapting and extending models and algorithms to new or special cases but also sufficient breadth to know the right method for the right challenge. The speaker will first outline how unsupervised, supervised, and reinforcement learning methods are increasingly used in human applications for description, prediction, and prescription, respectively. The speaker will then focus on the ‘prescriptive’ cases, showing how methods from the reinforcement learning and causal inference literatures can be of direct impact in engineering, business, and decision-making more generally.

2020-03-17 14:57:27.479000+00:00 Read the full story…
Weighted Interest Score: 3.3898, Raw Interest Score: 1.7004,
Positive Sentiment: 0.0000, Negative Sentiment 0.2429

CloudQuant Thoughts : A very interesting presentation from a very fast moving and innovative business, given at the Toronto Machine Learning Summit (TMLS) .

CloudQuant proves value in PA Signals alt date set

CloudQuant says it has proven the value in the Precision Alpha Machine Learning Signals (PA Signals) alternative data set. Its detailed data science study shows a long-short portfolio outperforms the equal-weight S&P 500 ETF by an average of 37.9 per cent per year after transaction costs.

CloudQuant found that over 91.5 per cent of the total return is pure alpha. The results of the study are significant to the 99th per cent level.

CloudQuant Thoughts : The news system has picked up our release and we have picked up their postings with our news scraper… All is right with the world! Head over to our data catalog to find out about this excellent data set and other quality datasets.

Killi Introduces Passive Data Dividend for Qualifying Users

Killi, a consumer-led privacy application wholly owned by Freckle Ltd. (TSXV: FRKL) (the “Company”), today announced the launch of its Data Dividend program to users who consent and share specific pieces of data over a predetermined time frame. Subscribers who share data will automatically receive:

notification of their dividends via SMS and/or email
the amount, in cash, of each dividend that will automatically be credited to their account,
a clear, transparent description of who purchased their data and,
clear opt-out functionality for future dividends and data sales

All of the above are market firsts.

The first Data Dividend payment was made to U.S. and Canadian users who shared their location data during the month of January 2020, and again to those who shared their data in February. Consumers in February saw a doubling of dividends vs those distributed in January. Starting in March Killi will pay users a weekly dividend for users who have shared location for the previous seven days, multiplied by the number of companies that purchased this data.

2020-03-17 07:10:28+00:00 Read the full story…
Weighted Interest Score: 3.8627, Raw Interest Score: 1.4715,
Positive Sentiment: 0.0613, Negative Sentiment 0.0000

CloudQuant Thoughts : Not a first, this idea has been around for a long time, even Tim Berners Lee (inventor of the internet) was touting something similar last year. But it does not seem to want to go away so it is likely to be in our future. I know I buy things online and get adverts for weeks after, waste of money. Or my daughter uses my computer and I get ads for crazy things everywhere I go. I removed the TV from my life when my daughter was born and it was interesting to ask her each birthday/Christmas what she wanted. She could not come up with anything “I don’t know, what do I want?”. No advertisers had got to her! How do we find an adult balance, I will tell you what I am interested in and what I am willing to have advertised to me (no sugar/candy or meds!). You get access to my desires. You can even see how long it takes me to make a buying decision. In return, the company holding this data for the advertisers pays my internet bill.

Is Python storming ahead of Javascript in fintech?

The use of Python is catching up to Java in banking and fintech applications, but what are the reasons behind the emergence of Python? While three million developers have joined the Java community in the past year, in the banking sector, Python is fast closing in on Java’s position in top spot.

2020-03-18 00:00:00 Read the full story…
Weighted Interest Score: 3.7618, Raw Interest Score: 2.0342,
Positive Sentiment: 0.3137, Negative Sentiment 0.1720

CloudQuant Thoughts : Erm, Yes!

ESG fund investment grows amidst coronavirus and oil turmoil

Environmental, social and governance (ESG) funds may see a continued spike in investment as oil prices crash, despite the Securities and Exchange Commission (SEC) cracking down on funds to clarify their intents, according to Bryan McGannon, director of policy and programs at the Forum for Sustainable and Responsible Investment (USSIF).

McGannon says the oil market crash may lead to long-term ESG investment strategies.

“I think maybe it does put a fine point on how volatile and how much risk is involved in the fossil fuel markets. That might point to a stronger direction towards ESG funds and still being broadly invested in the market, but without that component which is bringing in a lot more risk than you may not want,” says McGannon.

2020-03-17 00:00:00 Read the full story…
Weighted Interest Score: 2.9742, Raw Interest Score: 1.4590,
Positive Sentiment: 0.2245, Negative Sentiment 0.2245

CloudQuant Thoughts : Again, head over to our Data Catalog for an ESG data set that we have already tested for you and demonstrated its Alpha opportunity.

How Automation, AI, and Data Integration are Transforming the Pharmaceutical Industry

Pharma companies are more challenged than ever to bring drugs to market safely and cost-effectively. Roadblocks to success include the ever-evolving regulatory environment, growing patient safety concerns, and the burden of outdated technology solutions. Today many businesses find themselves burdened with rigid and costly compliance processes. Furthermore, there is a schism between organizations and the critical insights they need to manage their regulatory, safety, and reporting data.

Organizations must adopt more integrated, automated solutions to align safety and regulatory compliance with solving critical business challenges. There are four critical trends pharma and med-tech organizations are embracing related to digital transformation.

2020-03-16 07:35:26+00:00 Read the full story…
Weighted Interest Score: 3.4321, Raw Interest Score: 1.9044,
Positive Sentiment: 0.3304, Negative Sentiment 0.2915

Intercontinental Exchange Update on Global Operations of Exchanges, Clearing Houses, and Data Services

Platforms operating and functioning normally
Contingency plans of exchanges and clearing houses working as designed
Measures enacted globally to protect health and safety

ICE Data Services

ICE Data Services continues to deliver and support its customer offerings. These include evaluated bond prices for nearly three million securities, real-time exchange data, which is essential for powering global markets, and fixed income indices, which track more than $68 trillion in debt across 40 currencies. Additionally, the ICE Global Network has provided an uninterrupted backbone for financial and commodity markets, offering its ultra-secure, highly resilient network where global financial firms can access one of the broadest ranges of data sources and trading venues.

Equity Exchanges

At the New York Stock Exchange, which is both functionally and symbolically important to public confidence in the market during volatile times, the NYSE Group’s five equity and two options exchanges remain fully functional and operating as designed. The NYSE trading floor, as well as our options floors in New York and San Francisco, remain open and operating. The members of the trading floor community, exercising their human judgement over trades, play a vital role in reducing volatility of individual stocks during historic fluctuations in the market.

2020-03-17 00:00:00 Read the full story…
Weighted Interest Score: 3.2892, Raw Interest Score: 1.5845,
Positive Sentiment: 0.1366, Negative Sentiment 0.1229

Verta.ai Announces the Release of ModelDB 2.0

According to a new press release, “Verta.ai, provider of Verta Enterprise, an open-core end-to-end MLOps platform, today announced the launch of ModelDB 2.0, an industry-leading, open-source model versioning system to make machine learning (ML) development and deployment reliable, safe, and reproducible. In a field that is rapidly evolving but lacks infrastructure to operationalize and govern models, ModelDB 2.0 provides the ability to track and …
2020-03-18 07:15:57+00:00 Read the full story…
Weighted Interest Score: 3.2647, Raw Interest Score: 1.9105,
Positive Sentiment: 0.2011, Negative Sentiment 0.1508

Planixs Releases Realiti® Version 10 – The Most Complete Real-Time Cash, Collateral and Intraday Liquidity Management Suite in the Market

Planixs, the leading provider of real-time, intraday cash, collateral and liquidity management solutions, today announced that it has released GA10 (Generally Available Version 10) to the market. GA10 represents the most complete, function rich and technically capable real-time treasury software in the market.

Realiti GA10 took input from customers, industry experts, regulators and the latest technology advances as part of the release development. GA10 includes a number of enhancements across the software suite including …
2020-03-11 00:00:00 Read the full story…
Weighted Interest Score: 3.2084, Raw Interest Score: 1.6986,
Positive Sentiment: 0.4583, Negative Sentiment 0.1078

Machine Learning methods to aid in Coronavirus Response

With Coronavirus on everyone’s mind and forcing almost all of us indoors, many in the ML community are wondering how they might help. While there have been other articles on fighting coronavirus with AI, few have offered a truly comprehensive view. Therefore, I decided to bring together a list of datasets and use cases of machine learning applied to coronavirus. I understand the criticism that when you have a hammer every problem seems like a nail; in other words, to a machine learning practitioner/data scientist every problem seems to have a ML solution. Nevertheless, I believe that machine learning and data analytics can help accelerate solutions and minimize the impacts of the virus in conjunction with all the other great research and planning going.

As we will see below, machine learning can help expedite the drug development process, provide insight into which current antivirals might provide benefits, forecast infection rates, and help screen patients faster. Additionally, although not currently researched, I think there are several other appropriate application areas. That said there are many barriers related to lack of limited training data, the ability to integrate complex structures into DL models, and, perhaps most importantly, access to the available data. I’m not going to detail the techniques below (not that I could as my chemistry/drug-development knowledge is severely lacking), but instead aim to summarize the different resources. Also, I will be creating a central GitHub repository to list resources for using AI to combat coronavirus. Feel free to make a pull request if you find another resource/dataset that you find helpful.
2020-03-18 02:40:25.754000+00:00 Read the full story…
Weighted Interest Score: 2.8335, Raw Interest Score: 1.4156,
Positive Sentiment: 0.2014, Negative Sentiment 0.2073

AI sector reacts to increased policy oversight and market uncertainty

Legal counsels are advising small fintechs and companies leveraging AI to utilise regulation for a competitive edge.

2020-03-12 00:00:00 Read the full story…
Weighted Interest Score: 2.7361, Raw Interest Score: 1.2358,
Positive Sentiment: 0.1696, Negative Sentiment 0.1938

AI vs. Coronavirus: How artificial intelligence is now helping in the fight against COVID-19

GeekWire’s Health Tech Podcast goes in-depth with tech innovators bringing new ideas and ingenuity to health and wellness.

Artificial intelligence often raises concerns about privacy, bias and trickery in areas such as facial recognition and deep fake videos. But amidst the outbreak of the novel coronavirus, some technology companies and scientists are looking to AI for a positive impact instead.

“AI and high tech in general have gotten something of a bad rap recently, but this crisis shows how AI can potentially do a world of good,” said Oren Etzioni, CEO of Seattle’s Allen Institute for Artificial Intelligence (AI2) and a University of Washington computer science professor.

Etzioni was speaking on a call Monday organized by the White House Office of Science and Technology Policy, as part of an announcement of a project called the COVID-19 Open Research Dataset, aka CORD-19.

2020-03-17 15:31:57+00:00 Read the full story…
Weighted Interest Score: 2.7296, Raw Interest Score: 1.5484,
Positive Sentiment: 0.3011, Negative Sentiment 0.2151

Some of the Top Free Online Data Science Courses For 2020

Organisations across the world are turning to data science professionals to help businesses extract insights from the vast reserves of data. This means that there is a resilient push by recruitment agencies for people skilled in data mining, programming, and statistical modelling, among others.

Although the demand for talent is high, it is a travesty that this has not been met by appropriate skill sets among people. How can candidates plug this gap without taking on the burden of a massive education loan?

With the proliferation of online courses and tutorials, students can enhance their knowledge for a lucrative career in data science. What is more, a lot of these courses are available for free.

2020-03-18 10:30:00+00:00 Read the full story…
Weighted Interest Score: 2.5911, Raw Interest Score: 1.4520,
Positive Sentiment: 0.1613, Negative Sentiment 0.0993

6 Spectacular Reasons You Must Master the Data Sciences in 2020

Everyone has heard about Data Science in 2020. But not many people understand what it really is and how it’s going to change the world. It’s a skill that you would want to learn this year considering how its demand is growing. The field might already be too saturated before you can enter the profession. However, this doesn’t mean you should jump right in without any research. First, you should learn how Data Science is relevant to you, whether you will like, and if there are opportunities for you. Let’s start by first understanding what this field is, and then we will discuss why you need to learn it.

Data Science is a field that extracts useful information from loads of structured and unstructured data using algorithms, statistics, and programming. Its primary focus is to use user-generated data to good use. The insights extracted from data are presented to a human in a friendly form, or a computer program uses it to make decisions without any hardcoded instructions.

2020-03-17 20:26:08+00:00 Read the full story…
Weighted Interest Score: 2.5872, Raw Interest Score: 1.5685,
Positive Sentiment: 0.2811, Negative Sentiment 0.0888

The Shift Towards Sustainable Pensions: How Plan Beneficiaries are Shaping the Future of Pension Systems

Many institutional investors are addressing global issues by making allocations into ‘sustainable investments’. Sustainable investing is an umbrella term referring to a spectrum of investment approaches such as ethical screening, ESG (Environmental, Social, and Governance) investing, impact investing in alignment with the Sustainable Development Goals. Pension funds are one of the asset owner groups taking a plunge into the domains of sustainable development. USSIF Foundation reported that, as of 2018, public pension funds accounted for more than half of the $8.6 trillion worth sustainable, responsible and impact investing assets that were managed on behalf of US-based institutional investors¹.

Pension fund asset managers are bound by their fiduciary responsibility to act in the best interests of their plan participants. Over time, these beneficiaries have grown outspoken about aligning their finances with personal values. Most recently in Australia, Mark McVeigh, a 24-year old council worker, took his pension provider REST (Retail Employees Superannuation Trust, $57 billion) to court over limited disclosure on climate-change-related risks.
2020-03-13 00:00:00 Read the full story…
Weighted Interest Score: 2.4901, Raw Interest Score: 1.5308,
Positive Sentiment: 0.1148, Negative Sentiment 0.0510

The Corona Correction Isn’t Blind Panic. It’s Markets Drilling the Data

Panic. That’s how the world’s media characterized last week’s sharp drops on financial markets as the threat from coronavirus (COVID-19) became clear. Over $5 trillion was wiped off stock indices ‪in five days and the S&P500 had its worst week since the Global Financial Crisis. No wonder the Fed moved to cut rates on Tuesday (3 March 2020). A turbulent week, no doubt about it. But is this characterization of panic (and the implication that markets acted on emotion) a fair one? If not, what does it say about where markets go next as we enter uncharted territory on COVID-19?

Historically, heart has ruled head when it comes to the way in which markets have reacted to black swan events. But is this still the case in our data-driven age? Just because our 17th Century forebears manically piled into tulip contracts, or in 1929 went with their gut in dumping everything, we shouldn’t assume today’s ‘corona correction’ is the triumph of emotion (i.e. fear) over hard-headed analysis. In fact, I would argue that what happened last week was not panic at all, but a clear, news and data-driven response to a fast-shifting set of circumstances.
2020-03-11 01:48:25+00:00 Read the full story…
Weighted Interest Score: 2.2939, Raw Interest Score: 1.2119,
Positive Sentiment: 0.0433, Negative Sentiment 0.3895

Chinese recovery offers “springboard”, with a revalued yuan boosting post-virus global economy

firepower helping to swerve a deep recession in the next year, according to Toscafund Asset Management’s Savvas Savouri.

Savouri, chief economist and partner at Toscafund, the renowned GBP4 billion multi-strategy London-based hedge fund founded by Martin Hughes, said China is “an engine which will fire up again and far sooner and more powerfully” than the current consensus indicates.

As global stock markets went into freefall this week on the back of the Covid-19 pandemic, Savouri believes Beijing’s response is “the only response that matters”.

Savouri told Hedgeweek: “I have every confidence it will be…
2020-03-13 00:00:00 Read the full story…
Weighted Interest Score: 2.1727, Raw Interest Score: 1.1871,
Positive Sentiment: 0.1696, Negative Sentiment 0.3674

How To Use Effective Data Storytelling For Business Impact

An extraordinary amount of data passes through businesses on an ordinary day.

Data-driven insights are driving a new wave of business intelligence, helping move the needle with quick business impact.

However, with the increasing dependency and usage of analytics that is embedded into day to day decision making at enterprises, the demand for easily consumable and interactive data dashboards is on the rise.

Analytics dashboards have become critical in helping managers and executives make fast decisions. For these dashboards to be truly effective and impactful, the data insights need to be communicated using the best practices of design. But it should not stop there. Analysts need to communicate the data insights to the stakeholders with infectious passion and enthusiasm.

2020-03-17 15:30:00+00:00 Read the full story…
Weighted Interest Score: 2.1660, Raw Interest Score: 1.0506,
Positive Sentiment: 0.3113, Negative Sentiment 0.1686

Path Solutions achieves Microsoft Gold Partner Competency for Data Analytics

Path Solutions demonstrates best-in-class capability and market leadership through demonstrated technology success and customer commitment

March 11, 2020 – Path Solutions, a global Islamic software provider, today announced it has achieved the Microsoft Gold Partner Competency for Data Analytics, demonstrating a best-in-class ability and commitment to meet Microsoft customers’ evolving needs in today’s mobile-first, cloud-first world, and distinguishing itself within the Microsoft partner ecosystem.

To earn a Microsoft Gold Data Analytics Competency, partners must submit client references that demonstrate successful projects in data analytics and must also complete training and assessments to prove their level of technology expertise, thereby ensuring the required level of competency.

2020-03-11 00:00:00 Read the full story…
Weighted Interest Score: 2.0495, Raw Interest Score: 1.2753,
Positive Sentiment: 0.5751, Negative Sentiment 0.0500

Machine learning has uncertainty. Design for it.

We can productize and ship more data science insights — even imperfect, probabilistic ones — with the right designs.

We live in the age of machine learning. That means fewer and fewer of the products we build deal in facts as we know them: instead, they rely more and more on probabilistic things like inferences, predictions, and recommendations. By definition, these things have uncertainty. Inevitably, they will be wrong.

But that doesn’t mean they have no product value. After all, you’d probably rather know there is a 50% chance of rain than have no forecast at all. How can we unlock user value from algorithms that are bound to be wrong? We can do what forecasts do: design our products to be upfront about uncertainty.

In the age of machine learning, designing products that communicate their degree of certainty can be a huge competitive advantage…

2020-03-16 22:51:32.393000+00:00 Read the full story…
Weighted Interest Score: 2.0202, Raw Interest Score: 1.2380,
Positive Sentiment: 0.2251, Negative Sentiment 0.2532

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AI & Machine Learning News. 23, March 2020

The Artificial Intelligence and Machine Learning Newsletter by CloudQuant

Harvard’s (free) Introduction to Artificial Intelligence with Python, with CS50’s own Brian Yu.

Hello, World : This course explores the concepts and algorithms at the foundation of modern artificial intelligence, diving into the ideas that give rise to technologies like game-playing engines, handwriting recognition, and machine translation. Through hands-on projects, students gain exposure to the theory behind graph search algorithms, classification, optimization, reinforcement learning, and other topics in artificial intelligence and machine learning as they incorporate them into their own Python programs. By course’s end, students emerge with experience in libraries for machine learning as well as knowledge of artificial intelligence principles that enable them to design intelligent systems of their own.

CS50’s OpenCourseWare Introduction to Artificial Intelligence with Python

CloudQuant Thoughts : With little news this week that was not Corona Virus related I had to look long and hard to find something interesting for you. And at the last moment I discovered that the CS50 team at Harvard had just launched an excellent FREE introduction to AI in Python. Enjoy!

Growth in Machine Learning Leading to Demand for Automated ML

Machine learning has been used successfully in many disciplines that increasingly depend on it. However, the success relies on human machine learning experts to perform many tasks, according to an account on AutoML.org, a website of the community. These tasks include: Preprocessing and cleaning the data; selecting and constructing appropriate features; selecting an appropriate model family; optimizing model hyper parameters; post-processing machine learning models; and critically analyzing the results.

The growth of machine learning applications has created a demand for off-the-shelf machine learning methods that can be used more easily and without necessarily expert knowledge. The goal is to progressively automate these manual tasks in what is being called AutoML. Most companies offering AutoML solutions are positioning them as tools to increase the production of data scientists, and to simplify the process to make it more accessible to new AI developers, according to an account in Towards Data Science written by Justin Tennenbaum, a data scientist.

2020-03-19 21:30:38+00:00 Read the full story…
Weighted Interest Score: 3.4601, Raw Interest Score: 1.8878,
Positive Sentiment: 0.3464, Negative Sentiment 0.1039

CloudQuant Thoughts : Perhaps the most important note in this article is the concern from Microsoft that AutoML may increase the risk of Over Fitting. “In the most egregious cases, an over-fitted model will assume that the feature value combinations seen during training will always result in the exact same output for the target.”

Turing Award For Pixar, EfficientNet Lite Release And More:Top AI News

Regardless of what is happening around the world, the AI community are one productive bunch, and they have something interesting to share almost every day. So, here’s a compilation of all the important releases for the ML developers from top companies like Google and Uber.

Here’s what is new this week:

Google Open-Sources Neural Tangent Library
3D Object Detection With MediaPipe
EfficientNet-Lite For Mobiles By TensorFlow
Introducing Piranha: An Open-Source Tool to Automatically Delete Stale Code
Pixar’s Pioneers Get 2019 Turing Award
AlphaGo The Movie

2020-03-20 03:30:00+00:00 Read the full story…
Weighted Interest Score: 2.3338, Raw Interest Score: 1.5695,
Positive Sentiment: 0.3176, Negative Sentiment 0.1495

CloudQuant Thoughts : This is a nice summary of the week in AI with some interesting articles.

Multi-Agent Seasonal Dataset For Autonomous Car Development by Ford

The automotive industry has been working hard for a few years now on one of the most challenging problems of transportation, which is a fully autonomous self-driving vehicle. Currently, the autonomous systems use a combination of 3D scanners, high-resolution cameras and GPS/INS, to enable autonomy. However, in order to deal robustly on roads, handle a number of scenarios and maintain operating conditions, these systems will have to evolute into multi-agent autonomous systems.

Tech giants such as Apple, Facebook, Microsoft, among others, have been developing intelligent machine learning models to reduce collisions of self-driving cars. However, in all these years, Ford has always been on the quieter side when it comes to being open regarding their plans on autonomous driving projects until now.

Recently, researchers from the multinational automaker, Ford launched a challenging multi-agent seasonal dataset for autonomous cars. This multi-agent autonomous vehicle data presents the seasonal variation in weather, lighting, construction and traffic conditions experienced in dynamic urban environment.
2020-03-23 12:06:15+00:00 Read the full story…
Weighted Interest Score: 4.1347, Raw Interest Score: 1.3129,
Positive Sentiment: 0.1239, Negative Sentiment 0.1486

Top Hyperparameter Optimisation Tools For Your Machine Learning Models

A key balancing act in machine learning is choosing an appropriate level of model complexity: if the model is too complex, it will fit the data used to construct the model very well but generalise poorly to unseen data (overfitting). And if the complexity is too low, the model won’t capture all the information in the data (underfitting).

In a deep learning context, a model’s performance depends heavily on the hyperparameter optimisation, given that the vast search space of features, evaluation of each configuration can be expensive.

Generally, there are two types of toolkits for HPO: open-source tools and services that rely on cloud computing resources.

In the next section, we list down a few tools that have helped in making hyperparameter optimisation easier:

2020-03-23 09:30:20+00:00 Read the full story…
Weighted Interest Score: 4.0618, Raw Interest Score: 1.5456,
Positive Sentiment: 0.1482, Negative Sentiment 0.0847

How AI Can Revolutionize Banking

AI brings the potential for disruption and transformation due to its ability to make decisions and take action much quicker than its human counterparts. It has been seen as a means of increasing productivity within a company and improving revenues through better customer engagements.

But the use of AI is not without pitfalls, risks and detractors. Will AI discriminate between classes of people? Will AI used for good or just corporate greed? How should the use of AI be regulated?

To discuss the opportunities and challenges of AI in banking, we interviewed Dan Faggella, founder and CEO of the artificial intelligence research agency, Emerj. Dan is a globally recognized speaker on the use-cases of artificial intelligence in business, and has presented to the World Bank, the United Nations, INTERPOL, and global banking companies.

2020-03-17 06:00:44+00:00 Read the full story…
Weighted Interest Score: 3.8479, Raw Interest Score: 1.9483,
Positive Sentiment: 0.3172, Negative Sentiment 0.1812

AI at the Edge Enabling a New Generation of Apps, Smart Devices

Enabling an edge-computing architecture with AI is seen as a way forward for advances in strategic applications. And at the advent of 5G network speeds, AI is seen as essential to the endpoints.

A new network paradigm based on virtualization enabled by Software Defined Networking (SDN) and Network Function Virtualization (NFV), presents an opportunity to push AI processing out to the edge in a distributed architecture, suggests a recent report from Strategy Analytics.

Three types of edge computing are foreseen: device as the edge, in which an IoT device generates and consumes data and has embedded AI that can send and receive data to and from additional AI systems; enterprise premise network edge, that can support AI processing on a piece of hardware in a vehicle, drone or machinery, and can collect and process data from smart devices; and operator network edge, with an AI stack/platform to host applications and services, which may be located at a micro data center in a radio tower, edge router, base station or internet gateway.

2020-03-19 21:30:02+00:00 Read the full story…
Weighted Interest Score: 3.8267, Raw Interest Score: 2.0214,
Positive Sentiment: 0.2297, Negative Sentiment 0.2144

Is Python storming ahead of Java in fintech?

Python’s backstory in banking. Across all sectors, Python has reached seven million active developers fuelled in part by a staggering 62% of machine learning developers and data scientists who now use the programming language. This popularity gathered momentum back in 2015, with numerous financial institutions hiring Python developers. At around the same time, the sheer volume of fintechs – both funded growth businesses and bootstrapping startups – also started to make their presence felt in the developer skills marketplace. Despite its recent popularity, particularly across the investment banking and hedge fund industries, Python is not a new language. The first versions of Python emerged in 1991, five years before HTTP 1.0 and four years before Java.
2020-03-18 00:00:00 Read the full story…
Weighted Interest Score: 3.7964, Raw Interest Score: 2.0435,
Positive Sentiment: 0.3081, Negative Sentiment 0.1746

Dataiku 7 Brings Deeper Collaboration and More Granular Explainability to Enterprise AI

Today Dataiku, the leading Enterprise AI and machine learning platform, announced the release of Dataiku 7, bringing deeper integration for technical data professionals to work on machine learning project development and row-level explainability for white-box AI. Additional feature highlights with this latest release include Kubernetes-powered web apps to expand on the capabilities introduced in Dataiku 6 and a machine learning-assisted data labeling plugin.

“Collaboration has been at the core of Dataiku since our founding in 2013, and with Dataiku 7, we’re continuing to add features that deepen our philosophy to effectively democratize AI in the enterprise”

2020-03-20 07:15:09+00:00 Read the full story…
Weighted Interest Score: 3.6422, Raw Interest Score: 1.6196,
Positive Sentiment: 0.2776, Negative Sentiment 0.1388

Hitchhiker’s guide to learning Data Science

With companies raising huge funds using the term “Data Science”, the grounds for the value of the skill has been established for quite some time now. Billions are being invested to hire talented Data Scientists who can build a state of the art deduction machines putting zettabytes of data to use. The revolution can now be witnessed as our lives are…
2020-03-23 12:28:09.894000+00:00 Read the full story…
Weighted Interest Score: 3.3169, Raw Interest Score: 1.7397,
Positive Sentiment: 0.2060, Negative Sentiment 0.2060

What is the Future of Machine Learning?

Machine Learning has been one of the hottest topics of discussion among the C-suite. The blog speaks about the future of Machine Learning. Read this to know more.

With its incredible potential to compute and analyze huge amounts of data, advanced ML techniques are being used in businesses to perform complex tasks quicker and more efficiently.

The machine learning market is expected to grow from USD 1.03 Billion in 2016 to USD 8.81 Billion by 20…
2020-03-21 16:05:43.755000+00:00 Read the full story…
Weighted Interest Score: 3.2152, Raw Interest Score: 1.8626,
Positive Sentiment: 0.2714, Negative Sentiment 0.0123

Building a Winning Multi-Asset Execution Strategy

The road to digitization is paved with promises. Great stuff–like cost efficiencies, speed, smart automation, decision-ready intelligence, and real-time optimization. With payoffs so attractive, our industry has been pursuing greater digitization for quite some time. Despite our obsession with speed however, we’ve largely overlooked the more straightforward route-learning from the native digital companies who paved the way in the first place. If the buy-side wants to make the most of digital’s next level possibilities, we should take a page from these companies’ innovation roadmap. Instead of reacting to the acceleration of technology and the proliferation of venues, we can proactively transform to not only keep up with but capitalize on, the pace of change.

2020-03-23 01:42:04+00:00 Read the full story…
Weighted Interest Score: 3.0863, Raw Interest Score: 1.7499,
Positive Sentiment: 0.4541, Negative Sentiment 0.0222

Are AI and Machine Learning the Key to Understanding the U.S. Economy?

GPUs fuel AI and machine learning. Initially created for video games, they are used in sports and business analysis by fantasy baseball enthusiasts, oddsmakers, and front office executives who want to enhance their understanding of the hidden value of often obscure players. Other uses of this technology’s extreme processing power include the recognition of animals, such as dog breeds or endangered species, to allow biologists to gain a more accurate understanding of species populations in a geographical area.

GPUs and advanced statistics constitute an incredible advancement in 21st-century technology and can result in a more precise and deeper understanding of the data that is captured by a plethora of sources. So, why is so much of the economic data used by the news media and the U.S. Bureau of Labor Statistics for unemployment or gross domestic product (GDP) analysis still the same as what was used decades ago?

The initial concept of the GDP (originally referred to as gross national product [GNP] and calculated slightly differently than GDP) was first conceptualized in the 17th century. The GDP measures the value of all goods and services produced within a country’s border within a specific point in time. The modern version of the GDP was developed in 1934 by Simon Kuznets for a U.S. Congress report to measure the U.S. recovery from the Great Depression. Kuznets’ work ultimately resulted in a Nobel Prize in Economics.

2020-03-17 00:00:00 Read the full story…
Weighted Interest Score: 3.0332, Raw Interest Score: 1.4432,
Positive Sentiment: 0.2698, Negative Sentiment 0.3777

OANDA Launches MT5 Support for Clients in Japan

OANDA, a leading player in online multi-asset class trading and data analytics sector revealed the launch of new platform support for its products. As per statement released by the firm, the New York headquartered firm will now offer MT5 Support trading platform to its clients who are based in Japan. The service was initially made available for use of its clients for demo accounts back in December of 2019, but it has now made the service accessible for its clients who hold …
2020-03-22 16:29:56+00:00 Read the full story…
Weighted Interest Score: 2.9299, Raw Interest Score: 1.4316,
Positive Sentiment: 0.2444, Negative Sentiment 0.0000

The Chief Data Officer and the Chief Digital Officer: Work Together, Not Apart

Data vs. digital: That’s a big tension within many organizations.

Chief Data Officer s and Chief Digital Officers don’t always agree about some important things, said Joe Caserta, president of consulting firm Caserta, during his DATAVERSITY® Enterprise Data WorldConference presentation titled Building a Foundation for Disruption and Advanced Analytics. What’s the disconnect between the two roles that share the CDO acronym?

The Chief Digital Officer is really about the customer experience, about being the customer advocate. The Chief Digital Officer wants the customers to buy something as quickly and as easily as possible, no matter what device, and to capture and share the data about the transaction with any other part of the application and with any other part of the business that’s going to make that experience better, Caserta said. And to do it all as quickly as possible.

2020-03-19 07:35:13+00:00 Read the full story…
Weighted Interest Score: 2.7915, Raw Interest Score: 1.5427,
Positive Sentiment: 0.2160, Negative Sentiment 0.1388

How Machine Learning Fights Financial Fraud

We have e-shops, online banking, online insurances, and tons of other online services. But there’s one more thing we have – online fraud, as powerful as ever.

Fraudsters take advantage of any weak spot they find to steal millions before security teams can see and patch up the breach. So companies are forced to look for new solutions to prevent, detect, and eliminate fraud. And machine learning seems to be the best answer to financial fraud. How does it work, what are the benefits, and who uses it?

2020-03-19 11:00:00+00:00 Read the full story…
Weighted Interest Score: 2.7804, Raw Interest Score: 1.4731,
Positive Sentiment: 0.2487, Negative Sentiment 0.6505

24 Best (and Free) Books To Understand Machine Learning

“What we want is a machine that can learn from experience” – Alan Turing

We have compiled a list of some of the best (and free) machine learning books that will prove helpful for everyone aspiring to build a career in the field. There is no doubt that Machine Learning has become one of the most popular topics nowadays. According to a study, Machine Learning Engineer was voted one of the best jobs in the U.S. in 2019. Looking at this trend, we have compiled a list of some of the best (and free) machine learning books that will prove helpful for everyone aspiring to build a career in the field. Enjoy!

2020-03-24 00:00:00 Read the full story…
Weighted Interest Score: 2.7794, Raw Interest Score: 2.2373,
Positive Sentiment: 0.4195, Negative Sentiment 0.1199

Yes, You Can Do AI Without Sacrificing Privacy

In general, the more data you have, the better your machine learning model is going to be. But stockpiling vast amounts of data also carries a certain privacy, security, and regulatory risks. With new privacy-preserving techniques, however, data scientists can move forward with their AI projects without putting privacy at risk.

To get the low down on privacy-preserving machine learning (PPML), we talked to Intel’s Casimir Wierzynski, a senior di…
2020-03-17 00:00:00 Read the full story…
Weighted Interest Score: 2.7272, Raw Interest Score: 1.5198,
Positive Sentiment: 0.2266, Negative Sentiment 0.1600

Domo Shines a Light on Dark Data with New Augmented Capabilities in the Business Cloud

According to a recent press release, “Today Domo announced it is making it even easier to get BI leverage at cloud scale in record time through new augmented capabilities in the Domo Business Cloud. In a new Dimensional Research study sponsored by Domo, 92% of individuals surveyed said they’ve made decisions in the past three months without having all the information they wanted, with most reporting that data is just too hard to access. And while…
2020-03-20 07:10:25+00:00 Read the full story…
Weighted Interest Score: 2.6652, Raw Interest Score: 1.6676,
Positive Sentiment: 0.3891, Negative Sentiment 0.0556

Balancing Hard Data, Panic to Combat Pandemic

The early success of South Korea and Taiwan in slowing the spread of the novel coronavirus has underscored the necessity for a top-down, data-driven approach in which tech-savvy officials, prepared after earlier Asian epidemics, applied their know-how to mitigate a public health crisis.

While SARS-CoV-2 data trackers reported a sudden, sharp and unexplained spike in Taiwan infections on Thursday (March 19), South Korea’s infection rate continues…
2020-03-19 00:00:00 Read the full story…
Weighted Interest Score: 2.5754, Raw Interest Score: 1.4761,
Positive Sentiment: 0.1256, Negative Sentiment 0.4083

SoftBank to buy back $41 billion in assets to trim debt

BANKGOK (AP) — The Japanese technology and telecoms company SoftBank said Monday it plans to buy back up to 4.5 trillion yen ($41 billion) of its assets as it seeks to trim its gigantic debt burden.

The company’s founder, Masayoshi Son, said the move reflected “the firm and unwavering confidence we have in our business.”

Tokyo-based SoftBank will buy up to 2 trillion yen ($18.1 billion) of its shares, Son said in a statement. Earlier, SoftBank …
2020-03-23 00:00:00 Read the full story…
Weighted Interest Score: 2.5632, Raw Interest Score: 1.6968,
Positive Sentiment: 0.0722, Negative Sentiment 0.3249

IQ-AI’s Imaging Biometrics at the forefront of brain tumour analysis

What IQ-AI does:

( ) is the company formerly known as Flying Brands. The name change reflected its aspiration of becoming a leader in the field of medical imaging diagnostics. It currently comprises two businesses – Stone Checker Software and Imaging Biometrics (IB).

StoneChecker

This predictive, diagnostic software product helps urologists determine whether a kidney stone will disintegrate under a vibration process called lithotripsy. This ca…
2020-03-18 00:00:00 Read the full story…
Weighted Interest Score: 2.5269, Raw Interest Score: 1.1293,
Positive Sentiment: 0.1882, Negative Sentiment 0.1076

Digital Outcomes Now Signs Strategic Partnership with Yellowbrick Data

A new press release reports, “The next wave of Mobile 5G and IOT capabilities will generate volumes of data unlike anything previously imaginable. The organizations who are able to process this data as fast as possible to create valuable outcomes will rule the digital future. ‘Yellowbrick Data has created a breakthrough in processing analytic datasets that is extremely well positioned to deliver on this coming 5G opportunity. In 1/20th the rack s…
2020-03-20 07:05:32+00:00 Read the full story…
Weighted Interest Score: 2.4987, Raw Interest Score: 1.4278,
Positive Sentiment: 0.6629, Negative Sentiment 0.0510

Startup Cuberg Uses AI To Build Energy Dense, Lightweight Batteries

Startup Cuberg is working on developing lighter, safer, more energy-dense batteries, and they’re using a machine learning platform developed by Aionics Technologies to do it faster. “The exciting thing we do is make batteries that are very energy dense. They are much lighter than lithium ion batteries but they have much more energy in them,” said Olivia Risset, PhD, senior scientist at Cuberg. The batteries that Cuberg makes are safer than lithium ion batteries because the liquid component, the electrolyte, is nonflammable as opposed to what has been used traditionally in lithium ion batteries. “Because of that,” says Risset, “electric aviation is a great place for us because they are very sensitive to weight, but also to safety.” Most of Cuberg’s current sales are within the drone industry, but their biggest investor is Boeing. “People talk about electric vehicles a lot, but electric aircraft and flying taxis are emerging tech that is coming soon. By 2022 there are going to be fleets of electric aircraft,” predicts Risset.

2020-03-19 21:30:16+00:00 Read the full story…
Weighted Interest Score: 2.4978, Raw Interest Score: 1.1231,
Positive Sentiment: 0.2695, Negative Sentiment 0.0898

Databricks Delivers Security and Scalability Enhancements

s, today announced new features within its platform that provide deeper security controls, proactive administration and automation across the data and ML lifecycle. As data teams enable analytics and machine learning (ML) applications across their organizations, they require the ability to securely leverage data at massive scale. Doing this can be complex and risky, especially when operating in a multi-cloud environment. Security is fragmented, which makes corporate access policies difficult to extend, administration is reactive and inefficient, and devops processes like user management or cluster provisionin…
2020-03-23 07:15:45+00:00 Read the full story…
Weighted Interest Score: 2.4490, Raw Interest Score: 1.7534,
Positive Sentiment: 0.3507, Negative Sentiment 0.3507

Unlock Machine Learning for the New Speed and Scale of Business

Vertica is transforming the way organizations build, train and operationalize machine learning models. Are you ready to embrace the power of Big Data and accelerate business outcomes with no limits and no compromises?

Read our white paper to find out how you can bring predictive analytics projects to market faster than ever before with:
2020-03-17 00:00:00 Read the full story…
Weighted Interest Score: 2.3460, Raw Interest Score: 1.7699,
Positive Sentiment: 0.0000, Negative Sentiment 0.0000

Behavox CEO explains backing from SoftBank’s Vision Fund 2

Behavox, a New York startup that uses artificial intelligence to scan employee conversations, said in a statement in February that it had raised $100 million from SoftBank’s Vision Fund 2.

Erkin Adylov, the company’s CEO and founder, told Business Insider he was originally hoping to raise between $25 million and $50 million in fall 2019.

to raise between $25 million and $50 million in fall 2019. Adylov said the Vision Fund 2 investors asked him: “What if …
2020-03-21 00:00:00 Read the full story…
Weighted Interest Score: 2.2601, Raw Interest Score: 1.3090,
Positive Sentiment: 0.2380, Negative Sentiment 0.1983

AI Weekly: How data scientists are helping to flatten the pandemic curve

In any time of trouble, the archetypal hero usually takes a specific form — soldiers in World War II, firefighters on 9/11, and now health care professionals in the time of COVID-19. But data scientists are playing an indispensable role in fighting the global pandemic. While medical professionals are on the front lines caring for the sick, data scientists have shouldered the responsibility of helping keep everyone else healthy by disseminating crucial information to the world.

A case in point is the “flatten the curve” mantra. Originating from the Centers for Disease Control (CDC), the idea is that we have to slow down rates of infection in order to keep health care systems from collapsing. Those three words are potentially going to save millions of lives. But they mean nothing without the simple graphs illustrating how slowed infections can improve outcomes, and the graphs don’t exist without data.

These visualizations tell a story of exponential growth that can be difficult for the average person to immediately comprehend. “COVID-19 is … a tricky thing to reason about — very tricky thing to reason about. Intuition breaks down,” said Jeremy Howard in an interview with VentureBeat. Howard is the cofounder of Fast.ai, which offers free courses on deep learning, and he is on the faculty at the University of San Francisco. He pointed out that there’s a long gap between an outbreak occurring and the visible results. The nature of an illness is that it takes a while for the disease to show itself in a person, and it takes longer to see it at scale.

“This is a perfect storm of what the human brain is bad at,” said Howard. “We respond to what we can see. And we respond to stories. A pandemic doesn’t give you those things.”

He continued, “But what we do have is data. Data scientists are people who know how to look at data and find out what story it’s telling us.” He joked that data scientists aren’t very good at telling the story — except through visualizations like those illustrating the need to “flatten the curve.”

2020-03-20 00:00:00 Read the full story…
Weighted Interest Score: 2.1182, Raw Interest Score: 1.0901,
Positive Sentiment: 0.1689, Negative Sentiment 0.3071

How A Data Scientist Can Effectively Network While Working From Home

With COVID-19 impacting daily lives and disrupting industries around the world, a major setback is being witnessed by data scientists in terms of the networking they indulge in, to help the data science community move forward. With every major conference and meetups being postponed or cancelled at the moment, the chances of networking with new people coming into the community and discussing different topics have come to a stop. However, networking is a crucial aspect for any data scientist as it is imperative to remain highly active in the community. Due to the above-mentioned reason, data scien…
2020-03-19 10:30:59+00:00 Read the full story…
Weighted Interest Score: 2.0206, Raw Interest Score: 1.0945,
Positive Sentiment: 0.2526, Negative Sentiment 0.2315

Coronavirus: the data behind the disease

In mid-January, China launched an official investigation into a string of unusual pneumonia cases in Hubei province. Within two months, that cluster of cases would snowball into a full-blown pandemic, with hundreds of thousands — perhaps even millions — of infections worldwide, with the potential to unleash a wave of economic damage not seen since the 1918 Spanish influenza or the Great Depression.
The exponential growth that led us from a few isolated infections to where we are today is profoundly counterintuitive. And it poses many challenges for the epidemiologists who need to pin down the transmission characteristics of the coronavirus, and for the policy makers who must act on their recommendations, and convince a generally complacent public to implement life-saving social distancing measures.
With the coronas in full bloom, I thought now would be a great time to reach out to Jeremy Howard, co-founder of the incredibly popular Fast.ai machine learning education site. Along with his co-founder Rachel Thomas, Jeremy authored a now-viral report outlining a data-driven case for concern regarding the coronavirus.

2020-03-20 17:00:16.174000+00:00 Read the full story…
Weighted Interest Score: 1.9709, Raw Interest Score: 0.9405,
Positive Sentiment: 0.2427, Negative Sentiment 0.4551

Top 20 Data Science YouTube Channels you should subscribe to in 2020

Here are the best YouTubers you should follow to learn about programming, Machine learning and AI, mathematics and Data Science.

YouTube is a great platform for both entertainment and education. The best thing about it is, there isn’t a 10 dollar per month subscription to watching videos on Youtube, it’s all free for you to watch. Except for the only currency, you pay for watching them is your time, and what you decide to watch is entirely up to you….
2020-03-23 05:49:19.304000+00:00 Read the full story…
Weighted Interest Score: 1.8913, Raw Interest Score: 1.0887,
Positive Sentiment: 0.5645, Negative Sentiment 0.1613

Can AI bots lend a virtual hand in the virus crisis?

“It’s not us looking for more business. It’s us trying to speed up processes to help,” Adrian Jones, Automation Anywhere’s regional executive vice president, told the The Australian Financial Review.

Before it was a cost saving initiative. Now it’s arguably a life-saving initiative. — Hanno Blankenstein

“We’ve had many inbound approaches saying ‘Hey, can we somehow use video to manage the crisis,” said Hanno Blankenstein, Unleash Live chief exe…
2020-03-19 00:00:00 Read the full story…
Weighted Interest Score: 1.8400, Raw Interest Score: 0.8805,
Positive Sentiment: 0.0267, Negative Sentiment 0.1868

My Python Pandas Cheat Sheet

A mentor once told me that software engineers are like indexes not textbooks; we don’t memorize everything but we know how to look it up quickly. Being able to look up and use functions fast allows us to achieve a certain flow when writing code. So I’ve created this cheatsheet of functions I use everyday building web apps and machine learning models. This is not a comprehensive list but contains the functions I use most, an example, and my incites as to when it’s most useful.

Setup
Importing
Exporting
Viewing and Inspecting
Selecting
Adding / Dropping
Combining
Filtering
Sorting
Aggregating
Cleaning
Other
Conclusion

2020-03-22 23:53:14.462000+00:00 Read the full story…
Weighted Interest Score: 1.7990, Raw Interest Score: 1.0318,
Positive Sentiment: 0.0688, Negative Sentiment 0.0000

Convolutional Neural Networks With Heterogeneous Metadata

In autonomous driving, convolutional neural networks are the go-to tool for various perception tasks. Although CNNs are great at distilling information from camera images (or a sequence of them in form of a video clip), I constantly bump into all kinds of metadata that do not lend themselves to convolutional neural networks.

Metadata, by traditional definition, means a set of data used to describe other data. Here in this post, by metadata, we mean:

heterogeneous, unstructured or unordered data that accompanies camera image data as auxiliary information. In the sense of the traditional definition, these data “describe” the camera data.
The size of metadata is usually much less than camera image data, ranging from a few to at most a few hundred numbers per image.
And unlike image data, metadata cannot be represented by a regular grid, and the length of metadata per image may not be constant.

All these properties make it hard for CNN to consume the metadata directly as CNN assumes a data representation on a regular-spaced grid, and neighboring data on the grid has a closer spatial or semantic relationship as well.
2020-03-18 16:40:02+00:00 Read the full story…
Weighted Interest Score: 1.7859, Raw Interest Score: 0.9985,
Positive Sentiment: 0.0742, Negative Sentiment 0.1027

Banks, Finance IT Hiring Technologists Despite COVID-19 Crunch

In London, on Wall Street, in Paris and in Frankfurt, COVID-19 has unleashed some crazy times. Things that were taken for granted last month (or even last week) no longer hold as societies go into lockdown and markets crash. This isn’t likely to quickly pass: this week’s COVID-19 modelling document from London’s Imperial College suggests that if social distancing measures and quarantines are maintained until August and then lifted, there will simply be an even bigger surge in the virus in the coming autumn and winter.

What does all of this mean if you were hoping to change jobs this year? With health considerations to the fore, some of you will shelve plans to find new roles.

But as we noted last week, banks are still hiring despite the COVID-19 threat. New jobs continue to be released and candidates continue to be interviewed… albeit by video and telephone rather than face-to-face. However, hiring right now is far more likely in the middle and back office than in the front, even though this is traditionally front office hiring season for finance.

2020-03-20 00:00:00 Read the full story…
Weighted Interest Score: 1.6468, Raw Interest Score: 1.1467,
Positive Sentiment: 0.2150, Negative Sentiment 0.2389

What Could a Future of AI-augmented Infectious Disease Surveillance Look Like?

What Could a Future of AI-augmented Infectious Disease Surveillance Look Like? And how close are we to this today?

Before the Covid-19 pandemic, an estimated 40 million flights, carrying almost 5 billion people were estimated to operate globally in 2020. This number is growing year upon year, meaning that if a new infectious strain of a disease emerges, it can spread locally and internationally at a speed and scale not seen in the past.

Despite enormous technological advances over the past few centuries, infectious diseases still threaten global health. The possibility of the rapid, unexpected spread of an infectious agent across the world has become much higher, due to increasing globalisation. Rapid urbanization, an increase in international travel and trade, and the modification of agriculture and environmental changes have increased the spread of vector populations, putting more people at risk.
2020-03-23 09:20:11.742000+00:00 Read the full story…
Weighted Interest Score: 1.5925, Raw Interest Score: 0.8964,
Positive Sentiment: 0.1358, Negative Sentiment 0.3350

Most In-Demand Skills, February and March 2020: Python, SQL, and More

A new analysis of data from Burning Glass, which collects millions of job postings from across the country, reinforces what many technologists may already know: That older and much-used languages and platforms such as SQL, Java, JavaScript, Python, and Linux continue to dominate employers’ needs.

That should come as no surprise, of course, since companies not only rely on these languages to build new apps and services—they also need technologists who can maintain mountains of legacy code. (In this respect, the Burning Glass list also echoes the most-popular-languages lists that update periodically, such as TIOBE.) The following chart represents tech skills that popped up in open job postings between February 18 and March 18, 2020:

2020-03-20 00:00:00 Read the full story…
Weighted Interest Score: 1.4406, Raw Interest Score: 0.9610,
Positive Sentiment: 0.1502, Negative Sentiment 0.1502

The Growing Importance of Customer Data Mining for SMEs

Every SME needs to get the most value of their customer data. They can find that this will significantly increase the ROI of their marketing campaigns.

Big data is changing the direction of small and medium sized businesses. They can use big data for many purposes. However, the value of their big data strategies will vary considerably. Using big data to get a better understanding of your customers is important. Wired author Mike Dickey has written a great article on 10 ways that big data can be used to get a more thorough understanding of your customers. Unfortunately, not all SMEs use this data effectively. There are two factors that affect the value of a company’s SME customer big data strategy:

The quality and volume of the customer data the organization has acquired
The strategy the company employs for leveraging this data

Customer data can be very valuable, but it needs to be utilized effectively. Fortunately, there are a number of ways companies can make the most of their customer data.
2020-03-18 20:16:37+00:00 Read the full story…
Weighted Interest Score: 1.4026, Raw Interest Score: 0.7683,
Positive Sentiment: 0.3415, Negative Sentiment 0.1341

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The post AI & Machine Learning News. 23, March 2020 appeared first on CloudQuant.