AI & Machine Learning News. 02, November 2020

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?

Soccer AI cannot tell the linesman’s head from the ball!

Second-tier Scottish football club Inverness Caledonian Thistle doesn’t have a camera operator for matches at their stadium so the club uses an AI-controlled camera that’s programmed to follow the ball for their broadcasts. But in a recent match against Ayr United, the AI controller kept moving the camera off the ball to focus on the bald head of the linesman, making the match all but unwatchable. No fans allowed in the stadium either, so the broadcast was the only way to watch.

2020-11-02 Read the Full Story…

CloudQuant Thoughts : I tried to write an AI to pan and zoom my wide camera shots for school basketball games, it is much harder than you would imagine!

CloudQuant nominated for Benzinga Fintech Awards 2020

In just 8 days CloudQuant will be attending the Benzinga Fintech Awards 2020 where we have been nominated for an award in the Best Data Analysis Tool catagory.

Head over to Benzinga to register and do not forget to click here to pop a vote in for CloudQuant.

Read the Full Story…

MIT develops new app which detects coronavirus by listening to your cough

Researchers have developed a new artificial intelligence (AI) system which they claim can detect coronavirus by analysing the sound of people coughing.

Scientists at Massachusetts Institute of Technology (MIT) said the AI works because the virus causes temporary muscular impairment which can cause small differences to people’s speech or the sound of their cough – even if they have no other symptoms.
2020-11-02 00:00:00 Read the full story…
Weighted Interest Score: 2.3931, Raw Interest Score: 1.2736,
Positive Sentiment: 0.3057, Negative Sentiment 0.1528

CloudQuant Thoughts : This is excellent but the news article is behind a paywall, see the details of the experiment at MIT news.

AI Weekly: In a chaotic year, AI is quietly accelerating the pace of space exploration

The year 2020 continues to be difficult here on Earth, where the pandemic is exploding again in regions of the world that were once successful in containing it. Germany reported a record number of cases this week alongside Poland and the Czech Republic, as the U.S. counted 500,000 new cases. It’s the backdrop to a tumultuous U.S. election, which experts fear will turn violent on election day. Meanwhile, Western and Southern states like Oregon, Washington, California, and Louisiana are reeling from historically destructive wildfires, severe droughts, and hurricanes.

Things are calmer in outer space, where scientists are applying AI to make exciting new finds. Processes that would have taken hours each day if performed by humans have been reduced to minutes, a testament to the good AI can achieve when used in a thoughtful way. While not necessarily groundbreaking, unprecedented, or state-of-the-art with regard to technique, the innovations are inspiring stories of discovery at a time when there isn’t a surfeit of hope.

2020-10-30 00:00:00 Read the full story…
Weighted Interest Score: 3.3789, Raw Interest Score: 0.9754,
Positive Sentiment: 0.1797, Negative Sentiment 0.3850

CloudQuant Thoughts : AI in space helping us to discover new planets from old NASA data, new craters in Mars for us to explore and new ways of seeing through the clouds. Nice to see AI streching its legs out there in space!

Intel Acquires Model Optimizer SigOpt

Intel Corp. is acquiring AI optimization software vendor SigOpt, a move the chip maker said would complement its existing AI software portfolio while integrating SigOpt’s tools with its AI hardware to accelerate and scale AI software used by model developers.

The acquisition also addresses the growing complexity of machine learning and neural network models and the resulting inability of hardware to keep pace.

Terms of the transaction announced…
2020-10-29 00:00:00 Read the full story…
Weighted Interest Score: 5.4913, Raw Interest Score: 2.6125,
Positive Sentiment: 0.1814, Negative Sentiment 0.1451

Federal Government Investments in AI Beginning to Pay Off

The federal government’s investments in AI since President Donald Trump signed an Executive Order calling for the US to maintain its leadership in AI in early 2019 have been substantial and are playing out in a range of agencies, as several speakers outlined on day two of the Second Annual AI World Government conference and expo held virtually this week.

Erwin Gianchandani, Deputy Assistant Director, Computer and Information …
2020-10-29 21:28:18+00:00 Read the full story…
Weighted Interest Score: 5.3111, Raw Interest Score: 1.9802,
Positive Sentiment: 0.2666, Negative Sentiment 0.0571

Broadridge’s LTX and Charles River Development to Take Corporate Bond Trading to the Next Level Using Artificial Intelligence

Broadridge Financial Solutions, a global fintech leader, today announced that its new artificial intelligence (AI)-driven digital trading platform, LTX , has been integrated with the Charles River Investment Management Solution (Charles River IMS) as part of a strategy to improve efficiency in the corporate bond market.Integrating with LTX enables Charles River’s order and execution management system (OEMS) users to digitize workflows in order to help improve liquidity, efficiency and best execution for …
2020-10-27 00:00:00 Read the full story…
Weighted Interest Score: 5.1665, Raw Interest Score: 2.3919,
Positive Sentiment: 0.7973, Negative Sentiment 0.0420

An Important Guide To Unsupervised Machine Learning

We’re living in an era of digital switch-over with only one constant – evolve. And that digital transformation is being introduced by high-tech solutions. Hence, it comes as no surprise that mundane business tasks are being completely taken over by tech advancements. Machines, artificial intelligence (AI), and unsupervised learning are reshaping the way businesses vie for a place under the sun. With that being said, let’s have a closer look at ho…
2020-11-01 18:08:28+00:00 Read the full story…
Weighted Interest Score: 5.1657, Raw Interest Score: 1.9476,
Positive Sentiment: 0.1885, Negative Sentiment 0.0628

Is Your Data Ready for AI?

We’ve already figured out that AI has an immense potential to enhance business processes of many kinds in almost any industry imaginable. AI is poised to redefine conventional business models, enhance productivity, and drive value overall. When deploying it, companies tend to stick to a traditional scenario: first, outline an elaborate strategy, then attract excellent talent, secure the budget, develop a PoC, and so on. However, artificial intell…
2020-10-29 06:48:28 Read the full story…
Weighted Interest Score: 4.4589, Raw Interest Score: 1.7616,
Positive Sentiment: 0.2381, Negative Sentiment 0.3333

Understanding XGBoost Algorithm In Detail

XGBoost or extreme gradient boosting is one of the well-known gradient boosting techniques(ensemble) having enhanced performance and speed in tree-based (sequential decision trees) machine learning algorithms. XGBoost was created by Tianqi Chen and initially maintained by the Distributed (Deep) Machine Learning Community (DMLC) group. It is the most common algorithm used for applied machine learning in competitions and has gained popularity through winning solutions in structured and tabular data. It is open-source software. Earlier only python and R packages were built for XGBoost but now …
2020-11-02 05:30:27+00:00 Read the full story…
Weighted Interest Score: 4.4030, Raw Interest Score: 2.0504,
Positive Sentiment: 0.3629, Negative Sentiment 0.2540

Why Building a Machine Learning Model is Like Cooking

The first step in building a machine learning model is to prepare the data. Depending on the data infrastructure this may involve pulling raw data from a variety of sources to load into a database. In a mature company, data is in a database and the data scientist just needs to find the data they need for the model.

Likewise, the first step in cooking is to get the ingredients (the data). You may need to go to the grocery store to buy ingred…
2020-11-02 02:47:46.265000+00:00 Read the full story…
Weighted Interest Score: 4.2620, Raw Interest Score: 2.1752,
Positive Sentiment: 0.2003, Negative Sentiment 0.0572

How Can You Build a Career in Data Science & Machine Learning?

Machine Learning is the crux of Artificial Intelligence. With increasing developments in AI, IoT and other smart technologies, machine learning jobs are gaining higher exposure and demand in the technology market. If you are currently an IT professional, you might be interested in a career switch because of the exciting opportunities the industry offers to its aspirants. Or, you might have an interest that you have wanted to pursue long.

2020-11-01 09:49:59+00:00 Read the full story…
Weighted Interest Score: 3.9340, Raw Interest Score: 2.2061,
Positive Sentiment: 0.2134, Negative Sentiment 0.1024

AI-Enabled DevOps: Reimagining Enterprise Application Development

Today, advances in artificial intelligence (AI) and machine learning (ML) have opened up significant application possibilities, from sensor-driven weather prediction to driverless cars to intelligent chatbots. Development teams have enabled these breakthroughs by leveraging automation to rapidly prototype, iterate, and improve applications. As the scale, scope, and complexity of AI use cases increase, DevOps is fast becoming the preferred mode of build and deliver…
2020-10-28 00:00:00 Read the full story…
Weighted Interest Score: 3.6137, Raw Interest Score: 1.9127,
Positive Sentiment: 0.6302, Negative Sentiment 0.3096

Exploratory Data Analysis: Functions, Types & Tools

Discovered in the 1970s by American mathematician John Tukey, exploratory data analysis (EDA) is a method of analysing and investigating the data sets to summarise their main characteristics. Scientists often use data visualisation methods to discover patterns, spot anomalies, check assumptions or test a hypothesis through summary statistics and graphical representations.

EDA goes beyond the formal modelling or hypothesis to give maximum insight…
2020-11-01 12:30:40+00:00 Read the full story…
Weighted Interest Score: 3.5150, Raw Interest Score: 1.6729,
Positive Sentiment: 0.0376, Negative Sentiment 0.0376

Westpac-backed akin prepares to pitch human AI for float

Some of the hot money has moved on from ASX-listed artificial intelligence tearaway “billion dollar” Brainchip Holdings, but brokers and the founders of similar companies certainly have not.

Street Talk understands investor appetite for Brainchip – some fleeting, some more permanent – has helped inspire Sydney/San Francisco based AI company akin to accelerate its listing plans.

akin CEO Liesl Yearsley is expected to pitch Australian investors for a sharemarket float e…
2020-11-01 00:00:00 Read the full story…
Weighted Interest Score: 3.4739, Raw Interest Score: 1.4901,
Positive Sentiment: 0.0000, Negative Sentiment 0.0000

How the U.S. patent office is keeping up with AI

Technology keeps creating challenges for intellectual property law. The infamous case of the “monkey selfie” challenged the notion of not just who owns a piece of intellectual property, but what constitutes a “who” in the first place. Last decade’s semi-sentient monkey is giving way to a new “who”: artificial intelligence. The rapid rise of AI has forced the legal field to ask difficult questions about whether an AI can hold a patent at all, how …
2020-10-29 00:00:00 Read the full story…
Weighted Interest Score: 3.4049, Raw Interest Score: 1.3317,
Positive Sentiment: 0.2589, Negative Sentiment 0.2515

A Modern Architecture for Interactive Analytics on AWS Data Lakes

Built upon cost-efficient cloud object stores such as Amazon S3, cloud data lakes benefit from an open and loosely-coupled architecture that minimizes the risk of vendor lock-in as well as the risk of being locked out of future innovation. However, the many benefits of cloud data lakes are negated if data is duplicated into a data warehouse and then again into cubes, BI extracts and aggregation tables. Because of this, many organizations are now …
2020-11-10 00:00:00 Read the full story…
Weighted Interest Score: 3.3884, Raw Interest Score: 1.9008,
Positive Sentiment: 0.4959, Negative Sentiment 0.0000

Data Summit Connect Fall 2020 Presentations Now Available On-Demand

Videos of presentations from Data Summit Connect Fall 2020, a 3-day series of data management and analytics webinars presented last week by DBTA and Big Data Quarterly, are now available for on-demand viewing on the DBTA YouTube channel.

Whether your interests lie in the technical possibilities and challenges of new and emerging technologies or using the wealth of data your company is collecting for business intelligence, analytics, and other business strategies, Data Summit Connect Fall 2020 presentations have something for you—so take another look,…
2020-10-26 00:00:00 Read the full story…
Weighted Interest Score: 3.3012, Raw Interest Score: 1.7882,
Positive Sentiment: 0.0000, Negative Sentiment 0.4127

NASA’s New AI Tool Can Spot Craters On Mars

Amid NASA’s progress in AI research starting from ML model to predict hurricanes to partnering with Google to make quantum computing accessible, it has now developed a new AI tool to classify a cluster of craters on Mars.

The launch of this new AI tool, built on a machine learning algorithm, was aimed at helping scientists to reduce their process time of scanning a single Context Camera image. Thus, researchers from Jet Propulsion Laboratory (JP…
2020-10-26 08:06:58+00:00 Read the full story…
Weighted Interest Score: 3.2827, Raw Interest Score: 1.4302,
Positive Sentiment: 0.1117, Negative Sentiment 0.0670

Wine giants taps The Yield for microclimate data

ASX listed Treasury Wine Estates has signed a multiyear partnership with agtech The Yield to use its harvest predictions and weather notifications technology.

For the next three years Treasury Wine Estates will use The Yield’s Sensing+, a system for large scale growers to monitor and predict growing conditions based on microclimate data collected by farm sensors.

Sensing+ analytics help growers make decisions on when to irrigate, feed, plant, p…
2020-11-02 00:04:30+00:00 Read the full story…
Weighted Interest Score: 3.1954, Raw Interest Score: 1.1001,
Positive Sentiment: 0.3667, Negative Sentiment 0.0524

Trump faces executive order lawsuit as critical race theory fuels AI research

Today, civil rights groups — including the NAACP Legal Defense Fund — filed a lawsuit against the Trump administration on the grounds that a Trump executive order violates free speech rights and will “undermine efforts to foster diversity and inclusion in the workplace.” The lawsuit follows opposition to the executive order from a range of groups, including the U.S. Chamber of Commerce, as well as a federal agency’s recent intervention in a Micro…
2020-10-29 00:00:00 Read the full story…
Weighted Interest Score: 3.1328, Raw Interest Score: 1.5979,
Positive Sentiment: 0.0903, Negative Sentiment 0.6044

Databricks Plotting IPO in 2021, Bloomberg Reports

Databricks, which runs a unified data platform in the cloud and is the driving force behind Apache Spark, is preparing for an initial public offering (IPO), possibly in the first half of 2021, according to a report in Bloomberg last week.

The San Francisco company is looking at going public with a valuation in excess of $6.2 billion, which is what the company was worth a year ago when it raised $400 million in a Series F round, according to Bloo…
2020-10-26 00:00:00 Read the full story…
Weighted Interest Score: 3.0769, Raw Interest Score: 1.9475,
Positive Sentiment: 0.2264, Negative Sentiment 0.1359

Forrester: Top Emerging Technology Trends To Watch In 2021 And Beyond

CAMBRIDGE, Mass. , Oct. 22, 2020 /PRNewswire/ — According to Forrester (FORR: NASDAQ), the next decade will require CIOs to both respond to digital acceleration and proactively manage uncertainty. Rapidly changing consumer trends, complex security concerns, the ethical use of artificial intelligence, and the increasing impacts of climate change will drive businesses to incorporate systemic risk into their long-term planning.

The Forrester repor…
2021-11-02 00:00:00 Read the full story…
Weighted Interest Score: 3.0519, Raw Interest Score: 1.4881,
Positive Sentiment: 0.2790, Negative Sentiment 0.0775

Quantifying How COVID-19 Is Accelerating Digital Transformation

At eWEEK, we report on advances in conventional data centers and in cloud services providers, but we don’t report on interconnection centers often enough. That changes here in this article.

Data center interconnect (DCI) hardware and software connect two or more data centers together over short, medium or long distances using high-speed packet-optical connectivity. There is little data storage in a DCI facility; data in “flight” is directed and …
2020-10-28 00:00:00 Read the full story…
Weighted Interest Score: 2.9812, Raw Interest Score: 1.6078,
Positive Sentiment: 0.1473, Negative Sentiment 0.0368

Resiliency And Security: Future-Proofing Our AI Future

On the first day of the Second Annual AI World Government conference and expo held virtually October 28-30, a panel moderated by Robert Gourley, cofounder & CTO of OODA, raised the issue of AI resiliency. Future-proofing AI solutions requires keeping your eyes open to upcoming likely legal and regulatory roadblocks, said Antigone Peyton, General Counsel & Innovation Strategist at Cloudigy Law. She takes a “use as l…
2020-10-28 21:16:59+00:00 Read the full story…
Weighted Interest Score: 2.9633, Raw Interest Score: 1.1165,
Positive Sentiment: 0.1218, Negative Sentiment 0.3654

Multivariate Time Series Forecasting with LSTM in Tensorflow 2.0

Prerequisites: The reader should already be familiar with neural networks and, in particular, recurrent neural networks (RNNs). Also, knowledge of LSTM or GRU models is preferable. If you are not familiar with LSTM, I would prefer you to read LSTM- Long Short-Term Memory.

In Sequence to Sequence Learning, an RNN model is trained to map an input sequence t…
2020-10-29 06:42:30+00:00 Read the full story…
Weighted Interest Score: 2.9380, Raw Interest Score: 1.6523,
Positive Sentiment: 0.0198, Negative Sentiment 0.0989

A Primer on Getting Started with Data Science for Beginners

This article was published as a part of the Data Science Blogathon. Introduction

. “Companies are investing hugely in data science.”

After completing my engineering and starting my job I was continuously been bombarded with these statements on the internet. I was puzzled and like Lord Buddha wanted to know life’s truth, I also wanted to clarify my doubts. For seeking answers, I searched the internet, approached many people in/out of this domain…
2020-11-02 11:00:42+00:00 Read the full story…
Weighted Interest Score: 2.8779, Raw Interest Score: 1.5904,
Positive Sentiment: 0.1383, Negative Sentiment 0.0807

Employee Attrition Analysis using Logistic Regression with R

“To win in the market place you must win in the workplace” –Steve Jobs, founder of Apple Inc.

Introduction

This article was published as a part of the Data Science Blogathon

Why are we using logistic regression to analyze employee attrition?

Whether an employee is going to stay or leave a company, his or her answer is just binomial i.e. it can be “YES” or “NO”. So, we can see our dependent variable Employee Attrition is just a categorical variable. In the case of a dependent categorical variable, we can not use linear regression, in that case, we have to …
2020-11-01 09:53:41+00:00 Read the full story…
Weighted Interest Score: 2.8280, Raw Interest Score: 1.4519,
Positive Sentiment: 0.2600, Negative Sentiment 0.2709

Modern Data Warehousing: Enterprise Must-Haves

my. Alongside new architectural approaches, a variety of technologies have emerged as key ingredients of modern data warehousing, from data virtualization and cloud services, to Hadoop and Spark, and machine learning and automation. To educate IT decision makers and data warehousing professionals about the must-have capabilities for modern data warehousing today – how they work and how best to use them – DBTA is hosting a special roundtable webinar on November 19th.

Reserve your seat today!

Register Now for this webinar titled Modern Data Warehousing: Enterprise Must-Haves.

Audio is streamed over the…
2020-11-19 00:00:00 Read the full story…
Weighted Interest Score: 2.7871, Raw Interest Score: 1.7877,
Positive Sentiment: 0.1117, Negative Sentiment 0.0000

China’s bull market has room to run

Foreign holdings of A-shares, or shares listed on mainland markets, had increased from less than 1 per cent in 2015 to 7 per cent of total free-float market capitalisation.

Ms Werkun said valuations weren’t as stretched as during the 2015 boom and margin lending was playing a smaller role in the market’s current advance.

She also sees greater retail investor participation over time.

“Financial assets including stocks account for just a very sm…
2020-11-02 00:00:00 Read the full story…
Weighted Interest Score: 2.7768, Raw Interest Score: 1.5040,
Positive Sentiment: 0.3302, Negative Sentiment 0.1101

Is analytics the missing piece in a complex settlement jigsaw?

At a time when the costs of doing business are skyrocketing, is it sustainable for banks to persevere with the current approach to trade settlement failure?While there will always be trades that fail, the tolerance levels of banking boardroom execs must be at breaking point right now if the latest Esma Trends Risks and Vulnerabilities (TVR) report is anything to go by. The study shows a dramatic surge in the level of settlement fails during the s…
2020-10-29 00:00:00 Read the full story…
Weighted Interest Score: 2.7442, Raw Interest Score: 1.4278,
Positive Sentiment: 0.0824, Negative Sentiment 0.7688

Root Out Bias at Every Stage of Your AI-Development Process

Bias mitigation is a fairly technical process, where certain techniques can be deployed depending on the stage in the machine learning pipeline: pre-processing, in-processing and post-processing. Each offers a unique opportunity to reduce underlying bias and create a technology that is honest and fair to all. Leaders must make it a priority to take a closer look at the models and techniques for addressing bias in each of these stages to identify how best to implement the models across their technology. Ultimately, there is no wa…
2020-10-30 12:25:08+00:00 Read the full story…
Weighted Interest Score: 2.7106, Raw Interest Score: 1.2609,
Positive Sentiment: 0.2546, Negative Sentiment 0.3152

Python Attracts Rookies as Well as Pros

It is no secret that the Python programming language is peaking in popularity among data scientists. Now, would-be coders are increasingly trying to “self-master” the big data framework via online tutorials, according to an analysis of Google and YouTube searches.

Indeed, a quick YouTube search turned up a lengthy list of “Python for Beginners” tutorials promising to help novice coders master the programming language in just a few hours.
2020-10-30 00:00:00 Read the full story…
Weighted Interest Score: 2.6776, Raw Interest Score: 1.8565,
Positive Sentiment: 0.1785, Negative Sentiment 0.0000

Kx Systems: A Historical Need for Speed

There are a few things that Kx Systems is not. It’s not a new company, for starters, and it’s not a big supporter of open source technology. But if there’s one thing that defines Kx Systems and its approach to helping its clients develop advanced analytics and machine learning systems to work on big data, it’s a relentless focus on speed.

Speed is the defining mantra, the raison d’etre, for Kx Systems and how it seeks to differentiate itself from other providers of advanced analytics software. Speed is why both Wall Street investment banks and Formula 1 racing teams in Europe use the company’s platform, which combines a database, a programming language, and a collection of add-ons for machine learning, real time analytics, and other interesting things that can be done with data.
2020-10-29 00:00:00 Read the full story…
Weighted Interest Score: 2.6687, Raw Interest Score: 1.4546,
Positive Sentiment: 0.1086, Negative Sentiment 0.0868

XBRL News: SEC enforcement, investment-grade sustainability data, invoicing

Here is our pick of the 3 most important XBRL (eXtensible Business Reporting Language) news stories this week.

The SEC has invested heavily in ramping up its ability to leverage “big data” in order to spot anomalies or inconsistences that are potential indicia of financial reporting violations.

With its robust stance towards enforcement not just in words, but in action and preparedness, the SEC should serve as an example for other authorities around the world.

Following the recent announcement that the Sustainability Accounting Standards Board (SASB) is working with PwC to devel…
2020-10-29 00:00:00 Read the full story…
Weighted Interest Score: 2.6504, Raw Interest Score: 1.1359,
Positive Sentiment: 0.0000, Negative Sentiment 0.1683

Privitar and StreamSets Collaborate to Streamline and Minimize the Privacy Risk in Data Pipelines

Privitar, a data privacy platform provider, and StreamSets, provider of a DataOps platform, are forming a new partnership and native product integration designed to help organizations accelerate access to data-driven insights.

Through the new native integration, users can leverage StreamSets to design and run data pipelines to their data science and data analytics applications, seamlessly applying Privitar’s data privacy policies across all of their execution environments.

“Business users are increasingly demanding continuous access to shared data as it is generated across organizations and ecosystems, enabling fast decisions on fresh data, while simultaneously complying with increasingly complex regulation,” said Jobi George, general m…
2020-10-30 00:00:00 Read the full story…
Weighted Interest Score: 2.6196, Raw Interest Score: 1.6325,
Positive Sentiment: 0.4176, Negative Sentiment 0.0000

Expanding Your Data Science and Machine Learning Capabilities

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 data sets and data platforms, to architecting and optimizing data pipelines, and model training and deployment. As a result, new solutions have emerged to deliver key…
2021-06-24 00:00:00 Read the full story…
Weighted Interest Score: 2.5974, Raw Interest Score: 1.6536,
Positive Sentiment: 0.2611, Negative Sentiment 0.1741

Buyers’ brief: how tech is helping boutique asset managers compete in a scale game

Within our venture capital and private equity business there is a large universe of potential businesses to invest in but these need to be assessed and reduced down to a smaller number that best fit our investment criteria,” according to Andrew Hampshire, chief operating officer and chief technology officer at Gresham House. “Historically, that is a process that requires a lot of manpower, with investment managers proactively reviewing a host of …
2020-11-02 00:00:00 Read the full story…
Weighted Interest Score: 2.5734, Raw Interest Score: 1.6253,
Positive Sentiment: 0.3251, Negative Sentiment 0.1170

What Does Data Archiving Bring To Healthcare Intelligence?

Healthcare organizations house enormous amounts of data – amounts that have been multiplied many times over since the widespread adoption of electronic health records (EHR) systems over the last decade. What few of these groups know how to reckon with, though, is how to best manage data that’s no longer in use – particularly data from systems the organization has since retired. What’s the best way to handle this information?

When data is no long…
2020-10-28 07:09:10+00:00 Read the full story…
Weighted Interest Score: 2.5530, Raw Interest Score: 1.5696,
Positive Sentiment: 0.1513, Negative Sentiment 0.2458

10 Tech Jobs That Could Grow in 5 Years Thanks to A.I. and Automation

Over the past few years, there’s been quite a bit of chatter over the jobs that artificial intelligence (A.I.) will destroy. However, it’s also worth examining the human jobs that A.I. will help grow—many of them in tech.

The World Economic Forum, which regularly analyzes the potential impact of A.I. on the economy and unemployment, has a new report that examines how the COVID-19 pandemic could accelerate the automation of many jobs and tasks. A…
2020-10-30 00:00:00 Read the full story…
Weighted Interest Score: 2.4873, Raw Interest Score: 1.6609,
Positive Sentiment: 0.1615, Negative Sentiment 0.0923

Unlocking the Power of DataOps

DataOps is on the rise at enterprises looking to bring improved quality and reduced cycle times to data analytics. Borrowing from Agile Development, DevOps and statistical process control, this new methodology is poised to revolutionize data analytics with its eye on the entire data lifecycle. 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 process changes as well as enabling…
2021-05-13 00:00:00 Read the full story…
Weighted Interest Score: 2.4819, Raw Interest Score: 1.4478,
Positive Sentiment: 0.7239, Negative Sentiment 0.1034

The Top Trends in Data Management for 2021

From the rise of hybrid and multicloud architectures, to the impact of machine learning and automation, the business of data management is constantly evolving with new technologies, strategies, challenges and opportunities. The demand for fast, wide-range access to information is growing. At the same time, the need to effectively integrate, govern, protect and analyze data is also intensifying. All the while, data environments are increasing in size and complexity — traversing relat…
2020-12-10 00:00:00 Read the full story…
Weighted Interest Score: 2.4678, Raw Interest Score: 1.6129,
Positive Sentiment: 0.1075, Negative Sentiment 0.1075

UK report spotlights the huge investment gap facing diverse founders – TechCrunch

New research looking into how UK VC has been invested over the past decade according to race, gender and educational background makes for grim reading — with all-ethnic teams and female entrepreneurs receiving just a fraction of available funding vs all-white teams and male founders.

The finding of baked in bias holds true across all funding stages, per the findings.
2020-11-02 00:00:00 Read the full story…
Weighted Interest Score: 2.4327, Raw Interest Score: 1.3784,
Positive Sentiment: 0.1775, Negative Sentiment 0.1671

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CloudQuant will be at the Benzinga Global Fintech Awards November 10th 2020

CloudQuant will be attending the Benzinga Global Fintech Awards November 10th 2020. CEO Morgan Slade will be taking part in a fireside chat and our sales team will be available throughout the event to answer your questions and discuss our huge range of alternative datasets.

Those of you who already know us know that we deliver this huge dataset using possibly the best unified alternative data research archive in the world – The Liberator API. A temporal dataset technology which works seamlessly with our industry-leading applications and our customers’ private tools.

CloudQuant has opened the world of data analysis through sharing research tools, advanced analysis, white papers, data, and source code. We have overcome the Cambrian explosion of alternative data through finely tuned data onboarding and temporal APIs. Clients rapidly move from ideas to value. Unique datasets, trading algorithms, stock market backtesting, and support help investment managers launch new funds and new investment algorithms all while maintaining proper privacy and security.

We are proud to announce that our industry leading technology has been nominated for a Benzinga Fintech Award 2020 in the category of Best Data Analysis Tool.

CloudQuant Data Liberator

Data Liberator API: Our single, simple data access platform resolves the ETL, timestamp, symbology, and access issues that bedevil quality research. It also serves data into our industry-leading, research applications including :

• CQ Explorer: Visualising historical time series, alternative, and stock market data
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CloudQuant CEO to speak on Panel at CRUX Virtual Summit – November 18th 2020

CloudQuant CEO Morgan Slade will be taking part in a Panel Discussion at the CRUX Summit at 9am on November 18th 2020.

The Panel Discussion is title “What’s Missing in Data Preparation & Distribution”.

Register here.

For more information about our Alternative Data Sets Email Sales@CloudQuant.com,

Make an appointment or fill in the form on the right to be contacted back by a CloudQuant Representative.

See also our Data Catalog and our Repository of White Papers.

Alternative Data News. 04, November 2020

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.

CloudQuant are in the running for a top prize at the Benzinga Global Fintech Awards November 10th 2020

CloudQuant will be attending the Benzinga Global Fintech Awards November 10th 2020. CEO Morgan Slade will be taking part in a fireside chat and our sales team will be available throughout the event to answer your questions and discuss our huge range of alternative datasets.

We are also proud to announce that our industry leading technology has been nominated for a Benzinga Fintech Award 2020 in the category of Best Data Analysis Tool.

2020-11-02 Read the Full Story…

It’s about to start…

Reddit User : JustGlowing

Data source : Google Trends

Tools : Python with the libraries matplotlib and pytrends

2020-10-29 Read the Full Story…

CloudQuant Thoughts : I bet you thought I was going to pick some Election Data Analysis, well I think we need to move on.. to Christmas.

Information Services Q&A: Lauren Dillard, Nasdaq

Lauren Dillard joined Nasdaq as Head of Global Information Services in May 2019. Markets Media recently caught up with Lauren for an update on the business.

“I think we’ve seen the most growth, however, in our analytics business. Whether it’s about supply chain, consumer spending, indications of travel, or anything else, the need for alternative data sets increased dramatically this year.”

2020-10-27 07:01:39+00:00 Read the full story…
Weighted Interest Score: 3.5517, Raw Interest Score: 1.6086,
Positive Sentiment: 0.3312, Negative Sentiment 0.0473

CloudQuant Thoughts : If you are not already using Alternative Data, or do not know where to start, reach out to us and we can help you get started – Fast. If you have an Alternative Data Set that you want to promote far and wide, get in touch and let us explain what we can do to turbocharge your data sales. Email Sales@CloudQuant.com, Make an appointment to speak to a CloudQuant Representative, or fill in the form on the right to be contacted back by a CloudQuant Representative. Also see our Data Catalog and our Repository of White Papers.

ESG Section

CloudQuant also provides Alternative Data Sets including an excellent ESG data set with proven Alpha. Head over to our data catalog for more information.

Environmentally friendly funds have been drawing cash as Biden polling lead holds

Funds focused on sustainable investing are attracting record inflows as investors increasingly prioritize ESG metrics, or a company’s environmental, social and governance factors.

Democrat Joe Biden’s ascent in the polls and his environmentally friendly proposals have driven more investors into climate-focused funds, some of which have seen their shares more than double this year.

Here’s a list of the most popular sustainable funds over the last month, according to data compiled by FactSet:…
2020-11-03 00:00:00 Read the full story…
Weighted Interest Score: 4.0367, Raw Interest Score: 2.0183,
Positive Sentiment: 0.3670, Negative Sentiment 0.0000

Number Of ESG Indices Globally Rise By 40.2%

hmark survey. This year’s survey shows an industry that is growing and diversifying its products and services to meet expanding investor needs. Main growth drivers this year include indices measuring environmental, social and governance (ESG) criteria, which saw a 40.2% increase, and fixed income indices, which had a 7.1% increase.

Rick Redding, the CEO of IIA, commented: “The survey’s 2020 results demonstrate a highly competitive industry that continues to broaden its offerings to meet investor demand. Indices today are transparent and reliable representations of market segments covering a wide spectrum of asset classes and in…
2020-11-03 05:35:46+00:00 Read the full story…
Weighted Interest Score: 2.7453, Raw Interest Score: 1.5360,
Positive Sentiment: 0.1617, Negative Sentiment 0.0404

What Matters Most In ESG Investing: How To Spot Opportunities Across Market Cycles And The Capital Structure

Pensions, insurers, endowments, and foundations are asking asset managers to incorporate elements of sustainability and inclusion into their investing. Responding to investor interest is complex. There are over 600 environmental, social, and governance (ESG) frameworks and standards, and materiality—focusing on sustainability issues that drive stakeholder decision-making—varies across industries, the capital structure, and economic cycles. Sustainability Accounting Standards Board (SASB) is perhaps the most widely accepted of the sustainability standards. SASB’s Materiality Map SASB is championed by an Investor Advisory Group with an aggregate $40 trillion in assets, including BlackRock BLK +2.6%, which in January asked the 15,000 companies in its portfolio to publish disclosure in line with industry-specific SASB standards by the end of the year. SASB’s Materiality Map outlines how material ESG factors vary across 77 industries. Institutional investors and issuers would benefit from analogous materiality maps by asset class, strategy, and phase in the economic cycle.
2020-11-03 00:00:00 Read the full story…
Weighted Interest Score: 2.6693, Raw Interest Score: 1.5476,
Positive Sentiment: 0.4422, Negative Sentiment 0.1769

How Data Gravity Is Forcing a Shift to a Data-Centric Enterprise Architecture

Data is the output of society and everything we do — and the enterprise is fast becoming the world’s data steward. Digital-enabled interactions are becoming the norm, increasing enterprise data exchange volumes. In fact, it’s estimated that by 2024, Global 2000 Enterprises will create data at a rate of 1.1 million gigabytes per second and will require 15,635 exabytes of additional data storage annually. While applications like artificial intelligence (AI) and machine learning (ML) are fast becoming the center of today’s digital enterprise, helping to create efficiencies and improve customer experience, they also add to the accumulation of data that must be processed, analyzed, and applied to keep businesses running smoothly and spur innovation.

The accumulation of this data describes an effect similar to what occurs with the gravity between objects like the earth and the moon — data gravity. The data becomes harder to move, which can cause complexity and prevent digital transformation from occurring. For instance, if enterprises aren’t monitoring their data gravity challenges, it can cause slow response times, create information silos, and ultimately stall profitability and growth.

2020-11-02 Read the Full Story…

How consumer data provider Yodlee can help bolster the buy-side portfolio-building process

Envestnet | Yodlee, a data aggregation and analytics platform specialising in consumer spending data analytics, says asset managers are increasingly seeking out such alternative data insights in their hunt for alpha. Nikhil Nadkarni, Vice President, Data Products, explains how the consumer spending data analytics can help provide asset managers a view into consumer interactions with brands and incorporating insights into the investment research processes.

“Equity Researchers and Investment Managers can use consumer spending data analytics in their fundamental research to understand and forecast revenues, customer retention, customers’ lifetime values, customer churn and competitive analysis. Learning consumer spending patterns around online versus offline provides visibility into how consumer discretionary spending is shaping consumer behaviour especially during Covid-19.”

2020-11-03 00:00:00 Read the full story…
Weighted Interest Score: 6.1352, Raw Interest Score: 2.3926,
Positive Sentiment: 0.0443, Negative Sentiment 0.0443

An Important Guide To Unsupervised Machine Learning

It’s become very clear that unsupervised machine learning and artificial intelligence can be very helpful for business growth, but how do they work? There are some key methods you’ll want to know so your market research, trend predictions, and other machine learning uses are effective.

We’re living in an era of digital switch-over with only one constant – evolve. And that digital transformation is being introduced by high-tech solutions. Hence, it comes as no surprise that mundane business tasks are being completely taken over by tech advancements. Machines, artificial intelligence (AI), and unsupervised learning are reshaping the way businesses vie for a place under the sun. With that being said, let’s have a closer look at how unsupervised machine learning is omnipresent in all industries.
2020-11-01 18:08:28+00:00 Read the full story…
Weighted Interest Score: 5.1657, Raw Interest Score: 1.9476,
Positive Sentiment: 0.1885, Negative Sentiment 0.0628

Is Your Data Ready for AI?

We’ve already figured out that AI has an immense potential to enhance business processes of many kinds in almost any industry imaginable. AI is poised to redefine conventional business models, enhance productivity, and drive value overall. When deploying it, companies tend to stick to a traditional scenario: first, outline an elaborate strategy, then attract excellent talent, secure the budget, develop a PoC, and so on. However, artificial intelligence experts argue that this traditional roadmap is missing one integral component of successful AI adoption. Namely, data readiness. In fact, even when data gets enough attention, it still remains a solid roadblock on an already thorny path.

The common trap that organizations tend to fall into is to assume that large amounts of data imply it’s usable. In reality, most data that have been collected without solid governance principles can’t be fed into AI algorithms. Data becomes useful only when it’s properly cleansed, labeled, and structured. Contrary to popular opinion, it’s usually a bad idea to purchase datasets from other vendors as in most cases each company requires its unique data to extract maximum value.

These are a few steps that companies can make to prepare their data for AI implementation.

2020-10-29 06:48:28 Read the full story…
Weighted Interest Score: 4.4589, Raw Interest Score: 1.7616,
Positive Sentiment: 0.2381, Negative Sentiment 0.3333

Top Open Source Recommender Systems In Python For Your ML Project

Recommender systems have found enterprise application by assisting all the top players in the online marketplace, including Amazon, Netflix, Google and many others. These systems are the decision support systems that make the personalisation process better as well as smoother. It predicts and estimates the content of user preferences by extracting from various data sources such as previous database, data history, among others.

Here, we have listed the top eight open-source recommender systems in Python, in no particular order, that you must try for your next project.

• LensKit
• Crab
• Surprise
• Rexy
• TensorRec
• LightFM
• Case Recommender
• Spotlight

2020-11-04 11:30:01+00:00 Read the full story…
Weighted Interest Score: 4.2817, Raw Interest Score: 1.4205,
Positive Sentiment: 0.2029, Negative Sentiment 0.0609

AIM Partners With NASSCOM To Invite Organisations For AI Case Studies

Analytics India Magazine (AIM), in association with NASSCOM, has launched an initiative to unearth some of the best India-based AI use cases that have transformed organisations’ value-chain. To drive this initiative, AIM is conducting an online survey for businesses to jump in and share relevant details of the AI implementations.

Various enterprises and organisations in India, in recent years, have implemented artificial intelligence across their value chain to boost operational and business efficiency. And to implement these AI solutions, these enterprises, in most instances, have partnered with various organisations and service providers who specialise in AI and other technology services. In an attempt to identify these best use cases, Analytics India Magazine is inviting organisations to share their AI/ML case studies with the larger ecosystem.

The initiative has been aimed to create an AI Case Study Compendium for the industry by discovering some of the critical use cases, spanning across all sectors, implemented by key solution providers – both public and private. These use cases will be covering the implementation journey of artificial intelligence at any particular level or all levels of the organisation.

2020-11-02 10:19:13+00:00 Read the full story…
Weighted Interest Score: 3.5520, Raw Interest Score: 1.4220,
Positive Sentiment: 0.2091, Negative Sentiment 0.0836

The Ultimate Guide to Data Engineer Interviews

What to expect and how to prepare for data engineering interviews.

Although data engineer (DE) was the fastest-growing tech job role in 2019, there aren’t many online resources on what to expect in a data engineering interview and how to prepare for it.

In the past year, I have interviewed for data engineer roles with several tech companies in the Bay Area and helped many connections succeed in their interviews. In this blog post, I will explain the most important technical topics in data engineering interviews: your resume, programming, SQL, and system design. I will also teach you how to prepare for the non-technical part of the interview, which I believe is key to a successful job interview but is often ignored by candidates.

2020-11-02 22:01:27.975000+00:00 Read the full story…
Weighted Interest Score: 3.0175, Raw Interest Score: 1.7927,
Positive Sentiment: 0.3006, Negative Sentiment 0.2895

AWS releases models and datasets to help predict COVID-19’s spread

Amazon Web Services (AWS) today open-sourced a new simulator and machine learning toolkit for anticipating and mitigating the spread of COVID-19. AWS says that the suite, which comprises a disease progression simulator and models to test the impact of various intervention strategies, can help to accurately capture many of the complexities of the virus in the world.

While there have been a number of breakthroughs in understanding COVID-19, such as how soon an exposed person will develop symptoms, building an all-encompassing epidemiological model remains an uphill battle. Challenges in model building include identifying variables that influence disease spread across cities, countries, and populations. A performant model must also combine intervention strategies such as closures and stay-at-home orders and explore hypotheticals by incorporating trends from COVID-19-like diseases.

2020-10-30 00:00:00 Read the full story…
Weighted Interest Score: 2.5451, Raw Interest Score: 1.2741,
Positive Sentiment: 0.0593, Negative Sentiment 0.1778

Embracing the new reality. A Bank’s perspective

Highlighted in Deloitte’s report on the “outlook” for the industry, a new, forceful, wave of disruption is coming, even before the pandemic, so imagine the need for digitalisation post-Covid. The combined effects of this technological disruption will greatly affect the banking industry. Banks need to re-evaluate their platforms across multiple dimensions in order to exploit the opportunity that comes with every disruption, modernising their systems to support:

• Tailored products – customers are the asset, the offering needs to be close to their needs, while safeguarding Bank’s revenue
  Real-time transactions – this becomes the norm for the banking of younger generations
• Top quality of data – data analytics is paramount now, and no Bank can rely on poor data to make decisions, nor have large cost overheads on data normalisation and cleansing
• Deployment to take place in stages as the bank grows – it is imperative that system modernisation develops in line with the Bank’s current operations with technology efficiently supporting this approach
• Human capital through automation of processes – Bank’s personnel are valuable assets that may shift their focus from system operational tasks to more productive activities, provided that the majority of operations are automated by the systems.

2020-10-29 00:00:00 Read the full story…
Weighted Interest Score: 2.5104, Raw Interest Score: 1.6352,
Positive Sentiment: 0.4146, Negative Sentiment 0.1842

How NVIDIA Powered America’s Fastest Supercomputer In Fight Against COVID-19

“Using Dask, RAPIDS, BlazingSQL, and NVIDIA GPUs, researchers are leveraging Summit supercomputers from their laptops.”

Working on data-intensive projects like protein folding research, drug discovery, or deep space leads to several TBs of data. And, using queries on CPUs to sort information can take days. Time is a key constraint while fighting global pandemics. Research labs and governments around the world have accommodated money and manpower to speed up drug discovery. But this isn’t sufficient. There is a need for a smart, diligent solution that combines the existing technologies without trying to reinvent the wheel.

At Oak Ridge National Laboratory, which has been at the forefront of the fight against COVID-19, the researchers have been leveraging the powerful SUMMIT supercomputer to skim large datasets in search of solutions. SUMMIT, the world’s second-fastest supercomputer is powered by NVIDIA’s Tesla V100s and the team at OLCF (Oakridge Leadership Computing Facility) has been looking for solutions that would fit well into their technology stack.

2020-11-04 05:30:43+00:00 Read the full story…
Weighted Interest Score: 2.4012, Raw Interest Score: 1.2632,
Positive Sentiment: 0.2071, Negative Sentiment 0.0828

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.

For information on this dataset or any of our white papers either…

Email Sales@CloudQuant.com, Make an appointment to speak to a CloudQuant Representative, or fill in the form on the right and we will get in touch.

See also our Data Catalog and our Repository of White Papers.

For information on this dataset or any of our white papers either…

Email Sales@CloudQuant.com, Make an appointment to speak to a CloudQuant Representative, or fill in the form on the right and we will get in touch.

See also our Data Catalog and our Repository of White Papers.

SpiderRock Alternative Data Sets provide robust historical options market data and analytics

CloudQuant unleashes the power of SpiderRock’s historical intraday options vol surface with its Liberator Data Fabric.

For more information, see the SpiderRock cards in our Data Catalog.

Make an appointment to speak to a CloudQuant Representative, Email Sales@CloudQuant.com, or fill in the form on the right and we will get in touch.

See also our Repository of White Papers.

AI & Machine Learning News. 09, November 2020

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?

Best Deep Fakes Yet by Try Parker and Matt Stone of South Park

CloudQuant Thoughts : Irreverent, as you would expect from the creators of South Park, but the most impressive 15 minutes of Deep Fakes I have seen yet. With Donald Trump, Al Gore, Ivanka Trump, Mark Zuckerberg, Michael Caine, a truly amazing Sound of Music aged Julie Andrews, Chris Wallace, Anthony Fauci, Jared Kushner,

CloudQuant will be at the Benzinga Global Fintech Awards TOMORROW – November 10th 2020

CloudQuant will be attending the Benzinga Global Fintech Awards, Tomorrow – November 10th 2020.

Our Industry leading Alternative Data Fabric “Liberator” is up for an industry award, our CEO Morgan Slade will be taking part in a fireside chat and our sales team will be available throughout the event to answer your questions and discuss our huge range of alternative datasets. Look forward to seeing you there.

Read the Full Story…

Consumer Reports: Tesla Autopilot “Distant Second” to GM’s Driver Assistance

“The evidence is clear: If a car makes it easier for people to take their attention off the road, they’re going to do so.”

Nonprofit product testing group Consumer Reports has determined that Tesla’s Autopilot driving assistance feature is a “distant second” to General Motors’ Super Cruise.

Consumer Reports tested a total of 17 vehicles equipped with a variety of active driving assistance systems — which, as Consumer Reports emphasized, is still not the same as a fully autonomous vehicle. “To be clear, active driving assistance doesn’t make a car ‘self-driving,’ but rather it’s intended to support the driver — a well-designed system can help relieve driver fatigue and stress, such as on long highway road trips or in stop-and-go traffic,” writes Consumer Reports. A Cadillac CT6 equipped with Super Cruise, a hands-free driving feature that’s designed to make highway commutes more convenient, beat a Tesla Model Y with Autopilot when it came to safety and keeping the driver engaged.

2020-10-28 Read the Full Story…

CloudQuant Thoughts : For “ACTIVE ASSISTANCE” which is not the same as fully autonomous. Tesla are definitely closer to Full Autonomy but I would not trust any system to take complete control just yet!

Triggerless backdoors: The hidden threat of deep learning

Hackers can implant backdoors on deep neural networks without leaving a trace, researchers at the Germany-based CISPA Helmholtz Center for Information Security have found
This article is part of our reviews of AI research papers, a series of posts that explore the latest findings in artificial intelligence.

In the past few years, researchers have shown growing interest in the security of artificial intelligence systems. There’s a special interest in how malicious actors can attack and compromise machine learning algorithms, the subset of AI that is being increasingly used in different domains.

Among the security issues being studied are backdoor attacks, in which a bad actor hides malicious behavior in a machine learning model during the training phase and activates it when the AI enters production.

2020-11-05 Read the Full Story…

CloudQuant Thoughts : Triggerless backdoor, misidentification of a turtle as a rifle, the many stories of tricking Teslas to change the speed limit or stop at a sign that was not a stop sign. We still have a long way to go before we can have secure but easily updated AI.

How to Build a Twitter Sentiment Analysis System

In the field of social media data analytics, one popular area of research is the sentiment analysis of twitter data. Twitter is one of the most popular social media platforms in the world, with 330 million monthly active users and 500 million tweets sent each day. By carefully analyzing the sentiment of these tweets—whether they are positive, negative, or neutral, for example—we can learn a lot about how people feel about certain topics.

Understanding the sentiment of tweets is important for a variety of reasons: business marketing, politics, public behavior analysis, and information gathering are just a few examples. Sentiment analysis of twitter data can help marketers understand the customer response to product launches and marketing campaigns, and it can also help political parties understand the public response to policy changes or announcements.

However, Twitter data analysis is no simple task. There are something like ~6000 tweets released every second. That’s a lot of Twitter data! And though it’s easy for humans to interpret the sentiment of a tweet, human sentiment analysis is simply not scalable. In this article, we’re going to look at building a scalable system for Twitter sentiment analysis, to help us better understand the role of machine learning in social media data analytics.

2020-11-05 Read the Full Story…

CloudQuant Thoughts : A neat beginners to mid level intro to Sentiment Analysis.

Guided Labeling Episode 6: Comparing Active Learning with Weak Supervision

Welcome to the sixth episode of our Guided Labeling Blog Series. In the last episode, we made an analogy with a number of “friends” labeling “movies” with three different outcomes: “good movie” (?), “not seen movie” ( – ), “bad movie” (?). We have seen how we can train a machine learning model, also predicting movies no friend has watched before and adding to the model additional feature data about such movies.

The other episodes are here:

• 1: An Introduction to Active Learning
• 2: Label Density
• 3: Model Uncertainty
• 4: From Exploration to Exploitation
• 5: Blending Knowledge with Weak Supervision

Let’s pick up where we left off. You can blend friends’ movie opinions into a single model, but how is this useful if you don’t have any labels to train a generic supervised model? How can weak supervision become an alternative to active learning in a generic classification task? How can this analogy with many “friends” labeling “movies” work better than a single human expert like in active learning?

2020-11-06 08:30:58+00:00 Read the full story…
Weighted Interest Score: 2.6458, Raw Interest Score: 1.3082,
Positive Sentiment: 0.1595, Negative Sentiment 0.0638

Deutsche Börse Invests In Clarity AI

Clarity AI announced that it has closed a USD $15 million funding round led by Deutsche Börse AG and co-investor Mundi Ventures. Clarity AI empowers investors to manage the impact of their portfolios through a proprietary technology platform that leverages big data and machine learning to assess sustainability for all societal stakeholders.

“Our purpose is simple: to measure the impact of companies on our society and planet,” said Rebeca Minguela, Founder and CEO of Clarity AI. “Investors attempting to evaluate impact have faced fragmented and unreliable data, inconsistent subjective definitions, and a lack of standards and tools for comprehensive analysis. Historically it has been too hard and resource-intensive to get accurate and transparent insights. Clarity AI provides a solution for that.”

2020-11-05 06:14:35+00:00 Read the full story…
Weighted Interest Score: 4.8108, Raw Interest Score: 2.0093,
Positive Sentiment: 0.4206, Negative Sentiment 0.1636

Financial Institutions Benefit from AI, But Consumers Remain Skeptical

There’s no doubt that retail banking leaders understand the potential of artificial intelligence technology to improve customer experience. Nearly every one (94%) of more than 300 banking and insurance executives surveyed by The Capgemini Research Institute agreed that improving CX is the key objective behind launching new AI-enabled initiatives.

In fact, more than half of the international sample say that at least 40% of customer interactions are already enabled by various AI applications, including conversational agents, prescriptive modeling, process automation, and complex analytics.
2020-11-09 02:35:04+00:00 Read the full story…
Weighted Interest Score: 4.7914, Raw Interest Score: 1.8185,
Positive Sentiment: 0.3550, Negative Sentiment 0.2078

Intel Buys Another AI Startup

Intel Corp. has quietly acquired another AI platform developer, Israeli-based Cnvrg.io.

The acquisition, confirmed by Intel late Tuesday (Nov. 3) to the web site TechCrunch.com, is the latest in a flurry of deals by the chip maker (NASDAQ: INTC) as it fills out its AI and machine learning portfolio.

Terms of the acquisition were not disclosed. Intel did say the Cnvrg.io would continue to operate as an independent company.

Founded in 2016, Jerusalem-based Cnvrg.io’s data science platform is designed to help development teams manage and scale AI models. Early backers include Prashant Malik, co-creator of the Cassandra NoSQL database management system.

The startup’s most recent release is an MLOps dashboard designed to boost machine learning server utilization, a gap the startup refers to as “computational debt.” The framework is designed to boost utilization of CPUs, graphics processors and memory resources by as much as 80 percent.

2020-11-03 Read the Full Story(TheTechee)…
2020-11-04 00:00:00 Read the full story… (Datanami)
2020-11-07 10:30:24+00:00 Read the full story…(AnalyticsIndia)
Weighted Interest Score: 4.5634, Raw Interest Score: 2.3526,
Positive Sentiment: 0.1222, Negative Sentiment 0.1222

Intel To Purchase SigOpt, An AI Software Optimisation Platform

Intel has announced that the company is planning to acquire a San Francisco-based software optimisation startup — SigOpt. The company further stated that the terms of the deal are expected to close this quarter, however, weren’t disclosed to the media.

According to the company’s statement to the media, Intel is planning to leverage SigOpt’s technologies across its products to accelerate, amplify, and scale AI software tools for developers. The company further stated that SigOpt’s software technologies would be combined with Intel hardware products to gain a competitive advantage and provide differentiated value for data scientists and developers.
2020-11-02 14:41:24+00:00 Read the full story…
Weighted Interest Score: 3.7229, Raw Interest Score: 1.9669,
Positive Sentiment: 0.1380, Negative Sentiment 0.1035

Microsoft Partners With Adobe and C3.ai on Advanced CRM

Microsoft Corporation (MSFT) has announced a partnership with Adobe Inc. (ADBE) and privately held startup C3.ai to offer customer relationship management (CRM) software solutions utilizing artificial intelligence (AI).

“This year has made clear that businesses fortified by digital technology are more resilient and more capable of transforming when faced with sweeping changes like those we are experiencing,” said Satya Nadella, CEO of Microsoft. “Together with C3.ai and Adobe, we are bringing to market a new class of industry-specific AI solutions,” he added.1

The joint effort is designed to leverage the combined resources of the three partners to mount a more effective challenge to the dominance of salesforce.com in the CRM field than any of them could achieve individually. According to research firm Gartner Inc. (IT), salesforce is the top vendor of CRM software, with a 20% share of this $56 billion market in 2019, while Microsoft’s Dynamics 365 offering registered a mere 2.6% market share.

2020-11-05 19:42:48.140000+00:00 Read the full story…
Weighted Interest Score: 4.2816, Raw Interest Score: 1.7321,
Positive Sentiment: 0.1732, Negative Sentiment 0.0577

AI Can Make Bank Loans More Fair

As banks increasingly deploy artificial intelligence tools to make credit decisions, they are having to revisit an unwelcome fact about the practice of lending: Historically, it has been riddled with biases against protected characteristics, such as race, gender, and sexual orientation. Such biases are evident in institutions’ choices in terms of who gets credit and on what terms. In this context, relying on algorithms to make credit decisions instead of deferring to human judgment seems like an obvious fix. What machines lack in warmth, they surely make up for in objectivity, right?

Sadly, what’s true in theory has not been borne out in practice. Lenders often find that artificial-intelligence-based engines exhibit many of the same biases as humans. They’ve often been fed on a diet of biased credit decision data, drawn from decades of inequities in housing and lending markets. Left unchecked, they threaten to perpetuate prejudice in financial decisions and extend the world’s wealth gaps.

2020-11-06 13:25:26+00:00 Read the full story…
Weighted Interest Score: 3.6202, Raw Interest Score: 1.4401,
Positive Sentiment: 0.1290, Negative Sentiment 0.2472

Green Data Centers Seen as Helping Manage AI Power Demands

Huawei has built a cloud data center in Ulanqab; it is holding out to be a model green data center. The Chinese multinational technology company published the account in a sponsored post in The Register, describing its efforts to build the new data center in Ulanqab, a city in Mongolia.

Power usage effectiveness (PUE), is seen as a measure of “greenness” or energy efficiency, PUE was introduced in 2006 by the Green Grid, a non-profit organization of IT professionals; it has become the most commonly used metric for reporting the energy efficiency of data centers. The higher the value, the less the efficiency.

Huawei reports its cloud data center in Ulanqab achieves an annual PUE as low as 1.15, compared to an average PUE of 1.58 in 2020, according to the Uptime Institute. “Data Efficiency Gains Have Flattened Out,” stated the headline on a line chart showing gains in energy efficiency from 2007 to 2013, and an essentially flat line since then.

2020-11-05 22:35:01+00:00 Read the full story…
Weighted Interest Score: 3.5211, Raw Interest Score: 1.9531,
Positive Sentiment: 0.2155, Negative Sentiment 0.0404

How to create stunning visualizations using python from scratch

A step-by-step guide using Matplotlib and Seaborn libraries

Visualization is an important skill set for a data scientist. A good visualization can help in clearly communicating insights identified in the analysis also it is a good technique to better understand the dataset. Our brain is wired in a way that makes it easy for us to extract patterns or trends from visual data as compared to extracting details based on reading or other means.

In this article, I will be covering the visualization concept from the basics using python. Below are the steps to learn visualization from basic,

1. Importing data
2. Basic visualization using Matplotlib
3. More advanced visualizations, still using Matplotlib
4. Building quick visualizations for data analysis using Seaborn
5. Building interactive charts

2020-11-08 03:30:36.025000+00:00 Read the full story…
Weighted Interest Score: 3.4597, Raw Interest Score: 1.3460,
Positive Sentiment: 0.1706, Negative Sentiment 0.0379

Software Suppliers Responding to Market Opportunity for AI in Government

The US government has woken up to the importance of AI, the work of AI scientists is paying off, and the investment community is supporting the industry. AI entrepreneurs and suppliers of AI-related products and services are staring at a market poised for dramatic growth.

Perspective is called for. “What we need to be more concerned about is the thoughtfulness around everything that we do,” suggested Joanne Lo, PhD, the CEO of Attica AI, speaking on data governance challenges at the opening morning of the 2nd Annual AI World Government conference and expo held virtually last week. Attica said her company designs systems tools for the DoD and first responders.

She spends time with clients assessing the foundation in place that new AI systems will be built to support. “We tell the client to think about the foundation, to clean up the data before we can build something new on top of it, so it does not crumble,” she said.

Her company also encourages clients to think beyond making applications work faster, on bigger computers and with faster chips. “We are working on those, and they should be done, but we say you need to think about what you as a human have to offer. What is the human 2.0 you can become,” she said.

2020-11-05 22:53:25+00:00 Read the full story…
Weighted Interest Score: 3.4308, Raw Interest Score: 1.6063,
Positive Sentiment: 0.1452, Negative Sentiment 0.1724

What Does a Data Engineer’s Career Path Look Like?

Big data is changing the future of almost every industry. The market for big data is expected to reach $23.5 billion by 2025.

Data science is an increasingly attractive career path for many people. However, the outlook is hazy for people that are not as familiar with the career path.

If you want to become a data scientist, then you should start by looking at the career options available. Northwestern University has a great list of ways that people can pursue a career in data science. You should also learn the career path that you need to follow to get started, which includes learning the right programming languages.
2020-11-08 19:38:27+00:00 Read the full story…
Weighted Interest Score: 3.3677, Raw Interest Score: 2.0106,
Positive Sentiment: 0.2011, Negative Sentiment 0.1005

Algorithmia, Datadog Team on MLOps

Tools continue to be introduced to allow machine learning developers to monitor model and application performance as well as anomalies like model and data drift—a trend one market tracker dubs “ModelOps.”

The latest comes from Algorithmia, which this week launched an enterprise platform for monitoring machine learning model performance. The Seattle-based MLOps and management software vendor also said it has partnered with DataDog on a pipeline designed to stream metrics through Apache Kafka to Datadog via its Metrics API.

Algorithmia’s enterprise platform provides access to algorithm inference and MLOps metrics. Along with improving model performance and compliance with data governance regulations, Algorithmia CEO Diego Oppenheimer said the monitoring platform helps reduce the risk of model failure.
2020-11-05 00:00:00 Read the full story…
Weighted Interest Score: 3.3480, Raw Interest Score: 1.8650,
Positive Sentiment: 0.1356, Negative Sentiment 0.3391

Must-Have Elements of a Modern Data Approach

he ability to embed analytics within every element of the modern data platform provides business leaders with the capabilities to understand information in context and achieve situational awareness to act in the moment. For example, high-performance predictive and machine learning algorithms can reveal meaningful patterns in data and build applications that automate manual business processes. With search capability analysis that helps extracts insights from unstructured data, business leaders can generate conclusions and more importantly, apply them to the business, faster than ever before.

2020-11-04 00:00:00 Read the full story…
Weighted Interest Score: 3.3110, Raw Interest Score: 1.8358,
Positive Sentiment: 0.3750, Negative Sentiment 0.0197

Digital Banking Strategies Hampered By AI Talent Gap

There is a massive increase in demand for data, advanced analytics and AI skills in the banking industry at the same time as there is a lack of data and AI talent within most organizations. This gap between need and availability of talent shows no signs of abating. The solution is to combine outsourced solutions with a focus on upskilling your workforce.

In research done by the Digital Banking Report, financial organizations of all sizes indicate a low level of data maturity despite an increasing array of AI solutions offered by third-party vendors. In the research, only 12% of organizations believed they were “very effective” or “extremely effective” at using data and advanced analytics. This is lower than prior to the onset of COVID-19. While legacy systems are cited as the primary reason for the shortfall, the second most cited challenge is the lack of expertise within the organization to deploy AI technology effectively.

In other words, while banks and credit unions can purchase sophisticated AI solutions, there usually isn’t a defined path to achieving strategic goals or increasing business value. This creates a digital transformation paradox; where decision makers and employees believe in the power of data and AI, yet the appropriate actions are not being taken to leverage these tools for the benefit of digital transformation solutions.
2020-11-02 00:05:37+00:00 Read the full story…
Weighted Interest Score: 3.2499, Raw Interest Score: 1.5065,
Positive Sentiment: 0.2912, Negative Sentiment 0.1772

Japan’s SoftBank back in the black as investments improve

Son said his investments will focus on AI, or artificial intelligence, which he said will prove vital to all the companies he’s banking on, like robots doing deliveries and automated driving.

“We used to say whoever rules the mobile net will rule the net,” he said. “We think whoever rules AI will rule the future.”

2020-11-09 00:00:00 Read the full story…
Weighted Interest Score: 3.0568, Raw Interest Score: 1.6733,
Positive Sentiment: 0.2390, Negative Sentiment 0.1992

Forrester: Top Emerging Technology Trends To Watch In 2021 And Beyond

According to Forrester (FORR: NASDAQ), the next decade will require CIOs to both respond to digital acceleration and proactively manage uncertainty. Rapidly changing consumer trends, complex security concerns, the ethical use of artificial intelligence, and the increasing impacts of climate change will drive businesses to incorporate systemic risk into their long-term planning.

The Forrester report “Top Trends And Emerging Technologies, Q3 2020” highlights important trends and organizes emerging technologies into seven key domains that will play a big role in accelerating this shift: artificial intelligence; business automation and robotics; enterprise risk management; human experience and productivity; new compute architectures; next-generation communications; and Zero Trust security. Key trends include:

• Rising demand for ethical AI.
• Recasting of automation roadmaps.
• Moving toward hyperlocal business operations.
• Driving innovation everywhere using cloud-native technologies.
• Shifting cloud strategies toward the edge.

2021-11-09 00:00:00 Read the full story…
Weighted Interest Score: 3.0519, Raw Interest Score: 1.4881,
Positive Sentiment: 0.2790, Negative Sentiment 0.0775

Provizio closes $6.2M seed round for its car safety platform using sensors and AI – TechCrunch

Provizio, a combination hardware and software startup with technology to improve car safety, has closed a seed investment round of $6.2million. Investors include Bobby Hambrick (the founder of Autonomous Stuff); the founders of Movidius; the European Innovation Council (EIC); ACT Venture Capital.

The startup has a “five-dimensional” sensory platform that — it says — perceives, predicts and prevents car accidents in real time and beyond the line-of-sight. Its “Accident Prevention Technology Platform” combines proprietary vision sensors, machine learning and radar with ultra-long range and foresight capabilities to prevent collisions at high speed and in all weather conditions, says the company. The Provizio team is made up of experts in robotics, AI and vision and radar sensor development.
2020-11-06 00:00:00 Read the full story…
Weighted Interest Score: 3.0395, Raw Interest Score: 1.5221,
Positive Sentiment: 0.2283, Negative Sentiment 0.6088

Yann LeCun’s Deep Learning Course Is Now Free & Fully Online

Yann LeCun’s deep learning course — Deep Learning DS-GA 1008 — at NYU Centre for Data Science has been made free and accessible online for all. The course will be led by Yann LeCun himself, along with Alfredo Canziani, an assistant professor of computer science at NYU, in Spring 2020.

This deep learning course will focus on the latest techniques in deep learning and representation learning. It will also focus on an in-depth understanding of supervised and unsupervised deep learning, embedding methods, metric learning, and convolutional and recurrent nets. The course will further talk about the applications to computer vision, natural language understanding, and speech recognition. The course, however, comes with a prerequisite of completing the introductory course of deep learning — DS-GA 1001 Intro to Data Science or a graduate-level machine learning course.

2020-11-09 09:58:10+00:00 Read the full story…
Weighted Interest Score: 3.0379, Raw Interest Score: 2.1832,
Positive Sentiment: 0.0716, Negative Sentiment 0.0000

AMD Acquires Xilinx To Bolster Its HPC Portfolio

Last week, AMD entered into agreement to acquire chip manufacturer Xilinx through a $35 billion all-stock transaction. This is the latest gargantuan M&A deal in the semiconductor market to focus on new opportunities in processing, after the NVIDIA-Arm announcement. The AMD-Xilinx partnership is meant to broaden AMD’s product portfolio, which focuses on high-performance CPUs and GPUs (graphics processing units). AMD CEO Lisa Su aims to establish the company as “the industry’s high-performance computing leader.” That would mean expanding AMD into a customer audience that Xilinx currently leads in high-performance field programmable gate array (FPGA) and system on a chip (SoC) components for data centers. These products target verticals that include communication, automotive, and defense markets. FPGAs can also serve AI infrastructure for machine learning (ML) inferencing.

Although the two companies differ in product and markets, the product lines will be complementary. AMD anticipates US$300 million in synergistic revenue. Short term, the companies will focus on combining go-to-market strategies. However, it’s likely they’ll start combining IP for their respective SoCs — such as Xilinx’s adaptable SoCs — to use more of AMD’s processor and GPU IP. Both companies face an enormous opportunity in AI. Expect a leadership position in high-performance computing (HPC) to also mean a massive stake in AI infrastructure. HPC is comprised of clusters of computational nodes conjoined with high volumes of storage and bandwidth to handle very complex scientific, engineering, and artificial intelligence workloads.
2020-11-04 01:22:11-05:00 Read the full story…
Weighted Interest Score: 3.0204, Raw Interest Score: 1.4936,
Positive Sentiment: 0.1821, Negative Sentiment 0.0729

HR-Tech Startup Leena AI Raises $8M In Series A Funding To Accelerate Hiring & Product Development

Leena AI, an artificial intelligence-based employee experience platform, has announced an $8 million funding in Series A round led by Greycroft. According to the official release of the company, the funding is going to be used for expanding its go-to-market programs, hiring talent as well as accelerating product development.

In 2018 the company, Leena AI, was working on building an intelligent virtual assistant, aka chatbot, for addressing Human Resources-related issues. However, recently, the company has moved its focus on to more broader HR service delivery. This funding would allow Leena AI to continue its growth and momentum and would enable them to continue developing solutions to modernise legacy employee experience.
2020-11-04 07:59:54+00:00 Read the full story…
Weighted Interest Score: 3.0042, Raw Interest Score: 1.3091,
Positive Sentiment: 0.2083, Negative Sentiment 0.1190

The cutting-edge computer architecture that’s changing the AI game (VB Live)

AI and machine learning demand new approaches to computer architecture — but, of course, there are more factors. Large amounts of data, the arrival of industry-standard frameworks such as TensorFlow and PyTorch, and the death of Moore’s Law, are all signs that it’s time for the next generation of computing systems. And it’s one of the biggest transitions that the computer industry has seen since the changes demanded by the Internet and online connectivity.

The new wave of computer architecture is being driven by three main issues:

• First, data centers are growing larger, the amount of data that needs to be processed is growing exponentially, and compute is getting more expensive, which means companies need new more effective, powerful, and efficient architectures for data processing.
• Second is the difficulty — in time, expense, and resources — of turning that massive amount of data into actual value for a business. The companies that manage this transmutation will have a dramatic competitive edge over the ones that are falling behind.
• Third, applications are evolving in sophistication and ability, and companies want the computing architecture that allows them to take advantage of these new possibilities.

2020-11-05 00:00:00 Read the full story…
Weighted Interest Score: 2.8640, Raw Interest Score: 1.5131,
Positive Sentiment: 0.2124, Negative Sentiment 0.0796

Brainome Right-Sizes Your Data Before ML Training

A startup called Brainome today launched a new product designed to help data scientists determine how much data they need to sufficiently train their machine learning models. In addition to cutting costs, the software can also help data scientists avoid overfitting their models.

Called Daimensions, the Python-based tool essentially works as a compiler that generates the “memory equivalent capacity” of one’s data. Based on this figure, data scientists can whether there’s enough data to extract a meaningful signal. The tool also tells the user about the capability to generalize from the data, and also helps them with feature selection.

2020-11-04 00:00:00 Read the full story…
Weighted Interest Score: 2.8502, Raw Interest Score: 1.7268,
Positive Sentiment: 0.0714, Negative Sentiment 0.1998

Data Lakes Are Legacy Tech, Fivetran CEO Says

By some accounts, data lakes appear poised to supplant data warehouses as the center of gravity of modern analytics systems, particularly with today’s sophisticated data virtualization capabilities. But with the advent of cloud data warehouses that separate compute and storage, companies should take a hard look at their data lakes.

That’s the message that Fivetran CEO and co-founder George Fraser delivered during his keynote address for the “Modern Data Stack Conference 2020,” his company’s virtual conference that took place two weeks ago.

“In my opinion, data lakes are not part of the modern data stack. Data lakes are legacy,” Fraser said. “There are organizational [and] quasi-political reasons why people adopt data lakes. But there are no longer technical reason for adopting data lakes.”
2020-11-05 00:00:00 Read the full story…
Weighted Interest Score: 2.7517, Raw Interest Score: 1.6673,
Positive Sentiment: 0.3176, Negative Sentiment 0.1059

How to Approach Technology From a Non-Computing Background

If you’re a professional in another field who’s interested in a career as a technologist, we have good news for you: It’s very possible to plunge into learning the technology specialization of your choice without any previous tech experience. For example, you might have a background as a marketer or political scientist, and realize you need to build up your programming or data-science skills to further your career—don’t be intimidated about jumping in.

The term for this is a “non-tech” or “non-computing” background. That means a working knowledge of tech but little working experience when it comes to programming, data algorithms and data structures, according to Tiffani L. Williams, teaching professor and director of onramp programs at the University of Illinois at Urbana-Champaign.

2020-11-04 00:00:00 Read the full story…
Weighted Interest Score: 2.7021, Raw Interest Score: 1.4221,
Positive Sentiment: 0.1659, Negative Sentiment 0.0237

Insurance giant leads $13.6m funding round into Oxford University AI spin-out

An Oxford University spin-out which specialises in one of the most promising applications of artificial intelligence (AI) has raised $13.6m (£10.4m) in funding.

Mind Foundry develops AI for sectors including financial services and aerospace, specialising in the development of machine learning, in which computer systems gradually teach themselves to carry out tasks like identifying data.

The company raised the funding from Aioi Nissay Dowa Insurance, part of Japanese insurance giant MS&AD. Other backers includ…
2020-11-09 00:00:00 Read the full story…
Weighted Interest Score: 2.6838, Raw Interest Score: 1.2288,
Positive Sentiment: 0.1755, Negative Sentiment 0.1170

An Important Guide To Unsupervised Machine Learning

It’s become very clear that unsupervised machine learning and artificial intelligence can be very helpful for business growth, but how do they work? There are some key methods you’ll want to know so your market research, trend predictions, and other machine learning uses are effective.

And that digital transformation is being introduced by high-tech solutions. Hence, it comes as no surprise that mundane business tasks are being completely taken over by tech advancements. Machines, artificial intelligence (AI), and unsupervised learning are reshaping the way businesses vie for a place under the sun. With that being said, let’s have a closer look at how unsupervised machine learning is omnipresent in all industries.
2020-11-01 18:08:28+00:00 Read the full story…
Weighted Interest Score: 5.1657, Raw Interest Score: 1.9476,
Positive Sentiment: 0.1885, Negative Sentiment 0.0628

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.

Alternative Data News. 11, November 2020

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.

3D Map of COVID Cases by Population, March through Today

This vizualization uses county-level COVID-19 case data obtained from the US Covid Atlas from UChicago’s Center for Spatial Data Science, where I’m a software engineer. The data comes originally from 1Point3Acres, packaged as a Geojson (a few Python and R daily scripts) and displayed on the front end using Deck.gl. Original color scheme is based off of d3’s Yellow-Orange-Red color interpolation.

Here’s a non population-normalized version for reference — raw COVID-19 count numbers.

2020-11-10 Read The Full Story…

CloudQuant Thoughts : Another Fabulous Reddit DataIsBeautiful post by especiallySpatial. Momentary spikes are apparently down to backlogs in the data. This animation is of reported cases, the reported deaths normally trail the cases by a month or so, also our doctors and nurses have been getting better at treating this disease and so the outcomes are not AS BAD as at the outset, however Americans are still dying in the thousands. Here is especiallySpatial‘s same animation for deaths. This pandemic must not be allowed to spiral out of control just because we are in a political limbo.

CloudQuant CEO to speak on Panel at CRUX 3 day Virtual Summit – November 18th 2020

CloudQuant CEO Morgan Slade will be taking part in a Panel Discussion at the CRUX Summit 9:00am – 9:45am Eastern on November 18th 2020.

The Panel Discussion is title “What’s Missing in Data Preparation & Distribution”.

Read the Full Story…

Why Should Biden Focus On Getting A National Chief Data Scientist

Data science for Joe Biden, the newly elected President of the US, is not a new field. To boost his presidential campaign and to broaden the appeal with younger voters and small donors, he had relied on a data analytics startup — Civis Analytics, which is backed by former Google chairman Eric Schmidt. In his campaigns, he has also been a strong advocate of making investments in technology.

With a strong dependency on data science for his election campaigns, it would not be unlikely for him to rely on the technology to drive his memos and future missions. The question now is, like his contemporary, Barack Obama who had for the first time appointed a Chief Data Scientist to bring about data-driven tasks, will Biden focus on getting a National Chief Data Scientist? Most popular views and experts believe that, in fact, he should get one — and here’s why.

2020-11-11 04:30:32+00:00 Read the full story…
Weighted Interest Score: 2.3947, Raw Interest Score: 1.3519,
Positive Sentiment: 0.4056, Negative Sentiment 0.2704

CloudQuant Thoughts : The last presidency ramped up AI, now Data Science and belief in a Scientific approach to data is the healthiest thing we can do for the American people. When discussing national talking points with my daughter, I spend the majority of my time referring to data as such I hope we can move to a more provable facts driven society.

How consumer data provider Yodlee can help bolster the buy-side portfolio-building process

Envestnet | Yodlee, a data aggregation and analytics platform specialising in consumer spending data analytics, says asset managers are increasingly seeking out such alternative data insights in their hunt for alpha. Nikhil Nadkarni, Vice President, Data Products, explains how the consumer spending data analytics can help provide asset managers a view into consumer interactions with brands and incorporating insights into the investment research processes.

“Equity Researchers and Investment Managers can use consumer spending data analytics in their fundamental research to understand and forecast revenues, customer retention, customers’ lifetime values, customer churn and competitive analysis. Learning consumer spending patterns around online versus offline provides visibility into how consumer discretionary spending is shaping consumer behaviour especially during Covid-19.”

Usage of alternate data, like consumer spending data analytics, is getting a lot of attention from institutional money managers as it delivers additional alpha in investment research. Such data analytics provide a lens for researchers and portfolio managers to validate an investment thesis and generate differentiated insights outside of just forecasting revenue.

2020-11-03 Read the Full Story…

CloudQuant Thoughts : Evestnet | Yodlee were the main sponsors of the excellent Benzinga Fintech Awards which took place yesterday.  We were nominated for Best Data Analysis Tool (Unfortunately we didn’t win!). I believe you can still watch the recordings of the two live-streams. I would personally recommend the Boot Camp Track for variety!

ESG Section

CloudQuant is a major provider of Alternative Data Sets, including a quite excellent ESG data set. For many of the datasets we provide we have taken the claims of the vendors and tested them on our publicly available Mariner Backtesting system, we produce White Papers detailing our results and even make the Python code we used in the analysis so you can re-run it yourself on Mariner.

For more information see our Data Catalog, make an appointment to speak to a CloudQuant Representative, Email Sales@CloudQuant.com, or fill in the form on the right and we will get in touch.

Moody’s Acquires Stake In Alternative Data Provider

Moody’s Corporation announced that it has acquired a minority stake in MioTech, a leading provider of alternative data and insights serving the environmental, social, and governance (ESG) and know your customer (KYC) markets in Greater China. The investment reflects Moody’s commitment to providing China’s evolving financial markets with innovative ESG and KYC solutions.

MioTech uses artificial intelligence (AI) to track and scan alternative data sources related to ESG and KYC factors, supply chains, and financial information for over 800,000 public and private companies in China. Its analytical tools are designed to turn unstructured datasets into insights for portfolio managers, research analysts, and risk managers, and its AI algorithms detect entities’ vulnerabilities by monitoring news, social media, disclosure, and other forms of alternative data in real-time.

2020-11-06 Read the Full Story…

Alternative fund managers demonstrated resilience in adapting to the new Covid-19 reality

Despite extreme levels of market volatility, increased trading volumes and disruptions to society due to Covid-19, alternative fund managers have persevered, and even exceeded, performance expectations from investors. Nonetheless, managers continue to face challenges in addressing important areas of focus, including environmental, social and governance (ESG) products, and diversity and inclusion (D&I), according to the 2020 EY Global Alternative Fund Survey.

In times of change, does accelerated adaptation present obstacles or opportunities? – the 14th annual survey (formerly the EY Global Hedge Fund Survey) – reveals that total allocations to alternative investments remain relatively unchanged; however, the competition between asset classes continues to intensify. Following a multiyear trend, allocations to hedge funds shrunk again to just 23 per cent in 2020, compared to 33 per cent in 2019 and 40 per cent in 2018. Investments in private equity and venture capital remained stable at 26 per cent, while investments in private credit increased from 5 per cent to 11 per cent as many market participants anticipate Covid-19 initiating a credit cycle that will create opportunities for these managers.
2020-11-11 00:00:00 Read the full story…
Weighted Interest Score: 3.1797, Raw Interest Score: 1.5793,
Positive Sentiment: 0.2154, Negative Sentiment 0.1292

Must-Have Elements of a Modern Data Approach

The current global situation has highlighted the importance of digitalization for organizations of every kind—from businesses to hospitals and schools. But data-driven organizations must be able to access all the relevant data, store it cost efficiently, ensure it is of the highest quality, and make its insights available in real time to all users. Now more than ever, a strong data strategy is essential to every enterprise’s success.

According to the “2017 Gartner Chief Data Officer” survey, 86% of data and analytics leaders said defining such a strategy was a top responsibility, up 64% from 2016. As many leaders have realized, a large part of this responsibility requires them to implement new strategies that empower citizen and specialist users with self-service capabilities. To make this possible and accelerate digital transformation, enterprises need to adopt a modern data platform and approach.

2020-11-04 Read the Full Story…

How alternative data is a research necessity in today’s dynamic market – PODCAST

Economist John Kenneth Galbraith once remarked: “One of the greatest pieces of economic wisdom is to know what you do not know.”

As global markets experience greater volatility in 2020, the opportunities for active fund managers to seek out alpha-generating positions have improved. However, with heightened volatility comes heightened risk. This webinar looks at how Covid-19 has accelerated the use of alternative data to aid global equities portfolio managers respond to dynamic markets. And whether accessing alternative data sets has helped them in this endeavour; both with respect to idea generation, and risk management.

In this webinar, the panelists discussed:

• How equity l/s managers are evolving their investment process using non-traditional data sets.
• How is data management improving/solidifying their approach to risk management?
• Why not all alternative data sets are created equal (GPS data on shopping density in supermarkets doesn’t necessarily mean people are spending money)
• Why accessing anonymous credit card data sets, as one example, can provide deeper insights into consumer retail activity (i.e. more tactical long/short positions for those trading consumer stocks)
• How should analysts/PMs best approach incorporating alternative data sets into their existing infrastructure?
• What are the risks and opportunities, in today’s market environment?

2020-11-06 00:00:00 Read the full story…
Weighted Interest Score: 6.9069, Raw Interest Score: 2.3725,
Positive Sentiment: 0.4152, Negative Sentiment 0.0000

Nasdaq Leads Europe for SME Listings

Bjørn Sibbern, president of European markets at Nasdaq, said the exchange has listed more than 50 small and medium-size enterprises in Europe this year despite the Covid-19 pandemic. Sibbern told Markets Media: “It was important to keep markets open and functionality normally during the volatility caused by Covid-19. We did not feel there was a need to shorten hours or ban short selling.”

He took on his European role in June last year after moving from New York where he had been Nasdaq’s head of the global information services business. Last year he said he wanted Nasdaq to become more visible in Europe outside the Nordics. “We are the leading European venue for SME listings with more than 50 so far this year despite Covid-19,” he added. “The success is due to a cluster of institutional and sophisticated retail investors in the Nordics and advisers who support these initial public offerings.”

2020-11-10 04:37:24+00:00 Read the full story (TradersMagazine)…
2020-11-06 13:07:02+00:00 Read the full story (MarketsMedia)…
Weighted Interest Score: 5.2300, Raw Interest Score: 2.0762,
Positive Sentiment: 0.2212, Negative Sentiment 0.0340

How Digital is Your Bond Desk?

Digital transformation is at the top of every bank’s strategic agenda as possibly the most important initiative necessary to remain competitive and drive future value. According to an Accenture Survey (1) of investment banks, 60% believe that in three years technology will have a significant impact on trading and that data analytics is the technology that will have the greatest impact. The opportunity is reflected in the continued growth in IT spending on new technologies and the talent required to develop it. Boston-based research and advisory company Celent, projects global bank IT spending to reach $309b in 2022 (2), and much of that spending is going to new and disruptive technologies. But there are many questions that management must address about what a digital transformation strategy means and how to ensure the results generate real value.
2020-11-11 08:10:34+00:00 Read the full story…
Weighted Interest Score: 3.5156, Raw Interest Score: 1.7196,
Positive Sentiment: 0.2707, Negative Sentiment 0.1194

How to create stunning visualizations using python from scratch

A step-by-step guide using Matplotlib and Seaborn libraries

Visualization is an important skill set for a data scientist. A good visualization can help in clearly communicating insights identified in the analysis also it is a good technique to better understand the dataset. Our brain is wired in a way that makes it easy for us to extract patterns or trends from visual data as compared to extracting details based on reading or other means.

In this article, I will be covering the visualization concept from the basics using python. Below are the steps to learn visualization from basic,

1. Importing data
2. Basic visualization using Matplotlib
3. More advanced visualizations, still using Matplotlib
4. Building quick visualizations for data analysis using Seaborn
5. Building interactive charts

2020-11-09 16:37:44.772000+00:00 Read the full story…
Weighted Interest Score: 3.4597, Raw Interest Score: 1.3460,
Positive Sentiment: 0.1706, Negative Sentiment 0.0379

Data science at scale with PySpark on Amazon EMR cluster

Have you ever run into a situation where your computer simply fails to process the kind of data you trying to work with? Hmmm, so you are probably dealing with a big data that is most likely too large and complex to be processed by a single machine on CPU. Well, what do we mean by big data? How much data is considered as big data? Well, we can argue endlessly about that — so let’s not do that here. Instead, let’s just say that you have a large enough data and your computer is struggling to process it. Hopefully, there is no any flames or smoke coming out of your machine. Joke aside, this is a very common problem and the most common approach to solve it is to process such large datasets on a distributed computing platform. Apache Spark is an open-source parallel computing framework and is designed to enable the processing of such large datasets on a cluster of computers.

There are a few different providers for distributed computing platform you can choose from and some popular choices include Cloudera, Hortonworks, Databricks, Amazon AWS and Microsoft Azure. In this article, I will show you how to set up a distributed computing platform for your needs on AWS cloud, in particular Amazon EMR. We will be using PySpark which is the Python API to Apache Spark.

2020-11-11 13:21:28.119000+00:00 Read the full story…
Weighted Interest Score: 3.3743, Raw Interest Score: 1.2304,
Positive Sentiment: 0.0932, Negative Sentiment 0.1491

US Treasury Could Issue Inaugural Green Bond Under Biden

The US could issue its first sovereign green bond market under the Biden administration which could spark significant growth in the market.

Bram Bos, lead porfolio manager green bond at NN Investment Partners, the Dutch fund manager, said in an email that President-elect Joe Biden’s commitment to rejoining the Paris agreement when he is inaugurated symbolises a broader transformative shift in climate policy. “The Biden administration will invest heavily in sustainable infrastructure and clean energy, which could lead to an inaugural green US Treasury issue and further boost the global green bond market,” he added.

2020-11-10 06:09:49+00:00 Read the full story…
Weighted Interest Score: 3.1717, Raw Interest Score: 1.6781,
Positive Sentiment: 0.1831, Negative Sentiment 0.1831

What Is The Hiring Process For Data Scientists At ZS

With a focus on building scalable capabilities for generating, operationalising, and measuring data-driven insights for their clients, ZS has a strong advanced data science group within their Business Consulting function. The team focuses on integrating transformative AI-enabled solutions and data products across multiple industries such as healthcare, life sciences, telecommunication, high tech, and retail. As the company leverages deep industry expertise and leading-edge analytics to create solutions that work in real life, the data science team plays a vital role in driving these functions.

What Do ZS Look For In A For Data Scientist? From foundational research in deep learning, natural language processing (NLP), optimisation, and operational research — the advanced data science team at ZS works across various solutions. They are involved in developing solutions to full-scale productisation. In other words, the team works on completing the entire life cycle from innovation to industrialisation. To fit into these roles, ZS look for AI technocrats who can effectively lead a hybrid team of scientists and engineers. This requires mastery in either advanced data science or engineering and working proficiency.

2020-11-11 07:30:23+00:00 Read the full story…
Weighted Interest Score: 3.1582, Raw Interest Score: 1.6029,
Positive Sentiment: 0.4654, Negative Sentiment 0.0259

3 Types of Data Science Engineer Interview Questions

Nail the data science engineer interview with confidence

My background is primarily in software engineering and data science. As I began looking for a job in data science, interviewers noticed my experience in software. Many interviewing individuals did not have software backgrounds but were from mathematics, physics, or signal processing. During my interviews, I commonly saw questions in three main areas: data ingestion and cleaning, scalability, and research and development.

1. Data Ingestion and Cleaning
2. Scalability
3. Research and Development

2020-11-11 04:08:54.068000+00:00 Read the full story…
Weighted Interest Score: 3.0885, Raw Interest Score: 1.0225,
Positive Sentiment: 0.0417, Negative Sentiment 0.2504

How one bank’s tech team is teaching ‘on-the-fly’ data

Following her virtual session, ‘Democratising data-driven decisions with self-service tools’ at the Apidays conference, Yojas Samarth, senior data engineer, tech speaker and tech trainer at DBS Bank, speaks to Finextra Research regarding the nuance of training teams to effectively utilise data platforms.

Scenario based workshops and animated tools are some of the favoured methods Samarth employs in her efforts to teach the DBS workforce how to make use of the data assets at their fingertips – a resource that is quickly becoming a necessary competitive differentiator rather than a luxury.

Data democratisation is a force for good in financial services, however training is needed not only to interact with data legally and securely, but in order to distil relevant and valuable insights in a manner that works to promote the business goal.

2020-11-10 15:15:00 Read the full story…
Weighted Interest Score: 2.7633, Raw Interest Score: 1.5595,
Positive Sentiment: 0.1915, Negative Sentiment 0.1915

A Development Environment for Data with CI/CD

Data engineering is the science and art of producing good and timely data. Its goal is to deliver data to users even more than to deliver applications. There are great methods and tools that help deliver applications with consistently high quality. What are the methods and tools that help us deliver high-quality data? In this article, I will take the three following concepts: development environments, continuous integration, and continuous deployment, and demonstrate what they should look like in the world of data delivery. I will also provide examples of tools that can help you build this foundation for your data application.

What Is a Development Environment for Data? When developing data-intensive applications, we need to experiment with new code, new data sets, changes to the code, or data changes to data analysis tools — for example, new ETL, format or schema changes, a new compression algorithm, accuracy, a Spark/Presto version upgrade, and so on. While the type of experiment varies, the need remains the same: We should be able to run isolated experiments of data pipelines in an environment that is similar to our production environments without the fear of compromising it.

2020-11-11 08:35:03+00:00 Read the full story…
Weighted Interest Score: 2.5238, Raw Interest Score: 1.3874,
Positive Sentiment: 0.2643, Negative Sentiment 0.1321

India’s New AWS Data Center, Intel’s AI Acquisitions & More: Top News

As the world watched American election drama unfold, the stock market was rallying in favor of big tech. The internet companies which were under fire from both sides will benefit from a divided Congress as it makes it harder to pass legislation against tech giants. However, Twitter and Facebook continue to stumbled into more goof ups. Twitter even blocked President Trump’s tweets on election night. Looks like the gatekeepers of the internet and their semi-algorithmic content moderation will be looked at more closely in the coming months given the recent events. As we write this article, the results are still not yet declared and the big tech continues to influence one of the biggest events. But, it looks like Californians are not at all happy with so much of big tech and they voted in favor of a privacy law that can give one full control of their own data. Read more about this new law in this week’s top news brought to you by Analytics India Magazine.
2020-11-07 12:30:54+00:00 Read the full story…
Weighted Interest Score: 2.5181, Raw Interest Score: 1.3183,
Positive Sentiment: 0.1809, Negative Sentiment 0.1422

Top Tips and Tools for a Data Science Career in Finance

We caught up with Graham Giller, the former head of data science research at JPMorgan and ex-head of primary research at Deutsche. These days, Giller is CEO of his own firm, Giller Investments, and has written a book, Adventures in Financial Data Science, out later this month.

If you’re looking for develop a career in financial data, these are Giller’s tips.

2020-11-10 00:00:00 Read the full story…
Weighted Interest Score: 2.4685, Raw Interest Score: 1.3773,
Positive Sentiment: 0.2850, Negative Sentiment 0.1662

What Does a Data Engineer’s Career Path Look Like?

Big data is changing the future of almost every industry. The market for big data is expected to reach $23.5 billion by 2025.

Data science is an increasingly attractive career path for many people. However, the outlook is hazy for people that are not as familiar with the career path.

If you want to become a data scientist, then you should start by looking at the career options available. Northwestern University has a great list of ways that people can pursue a career in data science. You should also learn the career path that you need to follow to get started, which includes learning the right programming languages.

2020-11-08 19:38:27+00:00 Read the full story…
Weighted Interest Score: 3.3677, Raw Interest Score: 2.0106,
Positive Sentiment: 0.2011, Negative Sentiment 0.1005

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 CloudQuant data catalog just got smarter – Welcome our new partner BRAIN!

Brain’s excellent Alternative Data Sets are now available via the industry leading Liberator API.

We have added Brain’s Sentiment Indicator, Machine Learning Stock Ranking and Language Metrics Company Filings datasets to the Liberator Firehose!

For more information, see the BRAIN cards in our Data Catalog.

Make an appointment to speak to a CloudQuant Representative, Email Sales@CloudQuant.com, or fill in the form on the right and we will get in touch.

See also our Repository of White Papers.

S3 Partners Short Interest now of Interest to all CloudQuant clients!

S3’s unique Short Interest and Securities Finance Alternative Dataset is the latest to join CloudQuant’s Liberator Firehose!

Accurate and timely Short Interest data is of huge interest in the current market and we are pleased to offer S3’s unique dataset to all our clients via our Liberator API.

For more information, see the S3 card in our Data Catalog, make an appointment to speak to a CloudQuant Representative, Email Sales@CloudQuant.com, or fill in the form on the right and we will get in touch.

See also our Repository of White Papers.

AI & Machine Learning News. 16, November 2020

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?

The Machine Learning Behind Hum to Search

CloudQuant Thoughts : How did I not already know about this? Because it is only on Google App! Musical creation is about to blow up big time. Once we have software that free’s people from having to learn ANY instrument to be creative, millions of new musical creators will be born.

What’s Missing in Data Preparation & Distribution – CloudQuant CEO to speak on CRUX Summit Panel

Crux 3 Day Virtual Summit – November 17th – 19th 2020 – Full Agenda.

CloudQuant CEO Morgan Slade will be taking part in a Panel Discussion at the CRUX Summit on November 18th 2020 at 9:00am – 9:45am Eastern Time. The Panel Discussion title is “What’s Missing in Data Preparation & Distribution”

Register here.

Boosting Stop-Motion to 60 fps using AI

Scientists have found a new, neural network based approach for video frame interpolation. I was curious to see how this would work on Stop Motion movies. The network called DAIN interpolates your Brickfilm or animation in a top quality, as I present on my Apollo 11 Lego Stop Motion movie.

Link to download DAIN (for free): https://www.patreon.com/DAINAPP

Link to the source code: https://github.com/baowenbo/DAIN

Original Apollo 11 movie

Interpolated version

Link to Two Minute Papers, a channel that presents videos in the style of this one each week: https://www.youtube.com/user/keeroyz

CloudQuant Thoughts : This opens up huge possibilities for small animation studios to go up against the big guys. Very Very impressive!

Top 40 COMPLETELY FREE Coursera Artificial Intelligence and Computer Science Courses

Wikipedia: “Coursera is an American massive open online course (MOOC) provider founded in 2012 by Stanford University’s computer science professors Andrew Ng and Daphne Koller that offers massive open online courses (MOOC), specializations, degrees, professional and master track courses.

Coursera works with universities and other organizations to offer online courses, certifications, and degrees in a variety of subjects, such as engineering, data science, machine learning, mathematics, business, financing, computer science, digital marketing, humanities, medicine, biology, social sciences, 3000 plus a variety of courses giving students a very broad range of information & experience in different fields.”

In this article, we are going to talk about the best FREE courses at Courses, from areas like Artificial Intelligence and Computer Science.

2020-11-11 Read The Full Story…

CloudQuant Thoughts : Human knowledge and potential expanding at an exponential rate and for free!

AWS Launches Visual Data Prep Tool

AWS this week unveiled Glue DataBrew, a new visual data preparation tool for AWS Glue that’s designed to help users clean and normalize data without writing code.

Data preparation is the Achille’s Heel of advanced analytics and machine learning, as it regularly consumes upwards of 80% of data scientists and analysts’ time. However, without spending this time to clean, transform, and prepare data for analysis or for training machine learning models, the analysis or ML activity risks being flawed.

Many individuals and software vendors have attempted to reduce the time spent on data prep by automating the process. They have been met with mixed success, however, and ETL remains an entrenched part of the process.

2020-11-12 00:00:00 Read the full story…
Weighted Interest Score: 2.5273, Raw Interest Score: 1.6120,
Positive Sentiment: 0.0864, Negative Sentiment 0.2015

CloudQuant Thoughts : Very nice, and every little helps. But when it comes to Alternative Data, why do all the legwork? CloudQuant provides Alternative Data, easy to query (Python just one line of code!) and the data is returned, tickerized. in an easy to handle standardized format. If you are utilizing the data for investment purposes, CloudQuant has already carried out extensive research into the strengths and weaknesses of the data for many of its top datasets. This research includes results and code used (which you can run on our publicly available Back Tester CloudQuant Mariner).

AI Weekly: Tech, power, and building the Biden administration

After U.S. President Donald Trump was defeated in the recent election, President-elect Joe Biden and running mate Kamala Harris moved quickly from celebratory speeches to conversations about transition team members and key administration appointments.

Among the first names to emerge were people with tech backgrounds, like former Google CEO Eric Schmidt, who may be tapped to lead a tech industry panel in the White House. Since leaving Google, Schmidt has extended his services to the Pentagon, with a focus on machine learning. He has also led the Defense Innovation Board at the Pentagon and the National Security Commission on AI, which advised Congress that the U.S. needs to allocate more federal spending on AI to compete with China. NSCAI commissioners have also recommended steps like the creation of a government-run AI university and an increase in public-private partnerships in the semiconductor industry.

Schmidt and others have also raised questions about how close the administration will get with Big Tech companies that are increasingly viewed as the next Big Tobacco. Sentiment has shifted since 2009, when Biden first entered the White House, with industry experts warning that Big Tech’s concentration of power accelerates inequality.

2020-11-13 00:00:00 Read the full story…
Weighted Interest Score: 2.8599, Raw Interest Score: 1.4308,
Positive Sentiment: 0.1590, Negative Sentiment 0.3498

CloudQuant Thoughts : Tech is treading a fine line between being blamed for censorship to being blamed for the spreading of false truths. It would be nice to see some of the smartest people in the country re-engaging with government.

The changing landscape of asset management in China

Harvest Fund Management believes its local knowledge and approach to research gives it an edge over international asset managers on critical market issues such as the development of ESG investing.
As a leading asset manager, Harvest Fund Management is committed to the welfare and sustainability of domestic financial markets. With ESG considerations increasingly impacting upon Chinese companies and their stock prices the firm believes fiduciary managers must incorporate these considerations into their investment research and decision-making.

ESG is crucial to the sustainable development of Chinese financial markets. As wealth increases, people are also increasingly demanding improvements in quality of life in areas such as air and water quality, product safety, and cybersecurity and privacy.

2020-11-09 09:46:23.903000 Read the full story…
Weighted Interest Score: 3.5390, Raw Interest Score: 1.8758,
Positive Sentiment: 0.3288, Negative Sentiment 0.1160

The AI-Powered Cybersecurity Arms Race and its Perils

The advancement in the field of artificial intelligence (AI) is still one of the most important technological achievements in recent history. The prominence and prevalence of machine learning and deep learning algorithms of all types, being able to unearth and infer valuable conclusions about the world surrounding us without being explicitly programmed to do so, has sparked both the imagination and primordial fears of the general public.

The cybersecurity industry is no exception. It seems that wherever you go, you can’t find a cybersecurity vendor that doesn’t rely, to some extent, on Natural Language Processing (NLP), computer vision, neural networks, or other technology strains of what could be broadly categorised or branded as ‘AI’.

2020-11-12 12:00:00 Read the full story…
Weighted Interest Score: 4.5247, Raw Interest Score: 1.6106,
Positive Sentiment: 0.1683, Negative Sentiment 0.7091

Webinar: Managing data today – real time, real AI, real application

Join FinTech Futures and SmartStream’s team of innovation specialists and learn how real artificial intelligence can overcome the challenges organisations face when it comes to managing data integrity and validation processes.

In real time, you will see how artificial intelligence (AI) can be applied to no less than three complex data reconciliation activities with immediate results.

Join this webinar on 19 November to understand how effortless it is to transform your operations and take data processes to the next level. You will gain insight into:

• How to compare complex data sets, in huge variety of non-standard formats and structures
• How real time AI and observational learning transforms processes that would usually be measured in weeks and months, to just seconds
• How to significantly increase match rates
• SmartStream Air – the most advanced AI solution in the market for reconciliations

2020-11-10 13:43:05+00:00 Read the full story…
Weighted Interest Score: 4.3478, Raw Interest Score: 2.0871,
Positive Sentiment: 0.3630, Negative Sentiment 0.1815

Charles Schwab launches cross-channel algorithm to boost user experience

It was built by Schwab’s internal Digital Services organization, which develops innovative tools for the client journey. Last year, Schwab acquired fellow broker TD Ameritrade for $26 billion, bringing its total client assets to more than $5 trillion at the time. The cross-channel analytics tool will provide a more seamless client experience and drive Schwab’s operational efficiencies. The algorithm processes billions of pieces of client data, and is able to discern what clients want or are trying to do in real time, which allows the broker to offer more personalized services much faster.

For example, the algorithm can directly connect a call-in client to the relevant representative based on what they have recently been researching on Schwab’s platforms, avoiding drawn out waiting times or being frustratingly shuffled around multiple departments to find the right customer rep. In addition, the new tool skims across Schwab’s webpages and searches to identify the areas that are driving highest call volumes, enabling the broker to direct resources more efficiently and focus its efforts on improving the most common client roadblocks.

2020-11-16 00:00:00 Read the full story…
Weighted Interest Score: 4.3199, Raw Interest Score: 1.6355,
Positive Sentiment: 0.4381, Negative Sentiment 0.0584

Google Releases New Dataset For Advanced 3D Object Understanding

Machine learning models for computer vision tasks have been largely trained on photos. However, there lies a large possibility for scaling up to a wider range of applications such as augmented reality, autonomy, robotics, and image retrieval tasks if we train these models on 3D objects. To achieve this has been an uphill task since there is a dearth of large real-world datasets of objects in 3D, as compared to 2D datasets such as ImageNet, COCO, and Open Images.

Now, Google has released the Objectron dataset, which is a collection of short, object-centric video clips that capture a large set of common objects from various angles. Along with the dataset, the research also details a new 3D object detection solution.

2020-11-14 04:30:21+00:00 Read the full story…
Weighted Interest Score: 4.0131, Raw Interest Score: 1.3779,
Positive Sentiment: 0.0934, Negative Sentiment 0.0234

C3.ai Files to Go Public

C3.ai, the predictive analytics firm founded by CRM giant Tom Siebel, today announced plans for an initial public offering (IPO) of stock. It intends to trade shares on the New York Stock Exchange under the ticker symbol “AI.”

While the rest of the big data world was focused on using open source software like Spark and Hadoop to build giant clusters, Siebel was quietly assembling his own cloud-based application for collecting and analyzing huge amounts of data at scale.

Founded in 2009 as C3 IoT, the company successfully attracted several large public utilities to its platform. It eventually added a host of larger customers, including banks, healthcare companies, manufacturers, and oil and gas companies to its customer roll.

2020-11-13 00:00:00 Read the full story…
Weighted Interest Score: 3.8145, Raw Interest Score: 1.7241,
Positive Sentiment: 0.1999, Negative Sentiment 0.0250

How Compute Divide Leads To Discrimination In AI Research

Science doesn’t discriminate, but probably technology does, at least in terms of accessibility. New research has found that the unequal distribution of compute power in academia is promoting inequality in the era of deep learning. The study conducted jointly by AI researchers from Virginia Tech and Western University found that this de-democratisation of AI has pushed people to leave academia and opt for high-paying industry jobs.

The study found that the amount of compute power at elite universities, ranked among top 50 as per QS World University Rankings, is much more than at mid-to-low tier institutions. For the research, authors analysed over 170,000 papers presented across 60 prestigious computer science conferences such as ACL, ICML, and NeurIPS in categories like computer vision, data mining, NLP, and machine learning.
2020-11-16 10:30:37+00:00 Read the full story…
Weighted Interest Score: 3.5442, Raw Interest Score: 1.6701,
Positive Sentiment: 0.1237, Negative Sentiment 0.1649

Univariate and Multivariate Gaussian Distribution: Clear Understanding with Visuals

Guassian Distribution in details and its relationship with mean, standard deviation, and variance

Gaussian distribution is the most important probability distribution in statistics and it is also important in machine learning. Because a lot of natural phenomena such as the height of a population, blood pressure, shoe size, education measures like exam performances, and many more important aspects of nature tend to follow a Gaussian distribution.

I am sure, you heard this term and also know it to some extent. If not, do not worry. This article will explain it clearly. I found some amazing visuals in Professor Andrew N…
2020-11-15 23:52:17.955000+00:00 Read the full story…
Weighted Interest Score: 3.5293, Raw Interest Score: 1.8378,
Positive Sentiment: 0.1575, Negative Sentiment 0.1470

Must-Have Elements of a Modern Data Approach

The current global situation has highlighted the importance of digitalization for organizations of every kind—from businesses to hospitals and schools. But data-driven organizations must be able to access all the relevant data, store it cost efficiently, ensure it is of the highest quality, and make its insights available in real time to all users. Now more than ever, a strong data strategy is essential to every enterprise’s success.

According to the “2017 Gartner Chief Data Officer” survey, 86% of data and analytics leaders said defining such a strategy was a top responsibility, up 64% from 2016. As many leaders have realized, a large part of this responsibility requires them to implement new strategies that empower citizen and specialist users with self-service capabilities. To make this possible and accelerate digital transformation, enterprises need to adopt a modern data platform and approach.
2020-11-18 00:00:00 Read the full story…
Weighted Interest Score: 3.3110, Raw Interest Score: 1.8358,
Positive Sentiment: 0.3750, Negative Sentiment 0.0197

Data science… without any data?!

Why it’s important to hire data engineers early. “What challenges are you tackling at the moment?” I asked. “Well,” the ex-academic said, “It looks like I’ve been hired as Chief Data Scientist… at a company that has no data.” I don’t know whether to laugh or to cry. You’d think it would be obvious, but data science doesn’t make any sense without data. Alas, this is not an isolated incident. So, let me go ahead and say what so many ambitious data scientists (and their would-be employers) really seem to need to hear.

What is data engineering? If data science is the discipline of making data useful, then you can think of data engineering as the discipline of making data usable. Data engineers are the heroes who provide behind-the-scenes infrastructure support that makes machine logs and colossal data stores compatible with data science toolkits. Unlike data scientists, data engineers tend not to spend much time looking at data. Instead, they look at and work with the infrastructure that holds the data. Data scientists are the data-wranglers, while data engineers are the data-pipeline-wranglers.

2020-11-13 14:56:17.278000+00:00 Read the full story…
Weighted Interest Score: 3.2845, Raw Interest Score: 1.8466,
Positive Sentiment: 0.1692, Negative Sentiment 0.1551

How to turn Text into Features

A comprehensive guide into using NLP for Machine Learning

Simple question: How to turn text into features? Imagine you’ve been tasked with the activity of building a Sentiment Analysis tool for your company product reviews. As a seasoned Data Scientist, you built many insights about future sale predictions and was even able to classify customers based on their purchase behavior.
But now, you’re intrigued: you have this bunch of text entries and have to turn them into features for a Machine Learning model. How can that be done? That’s a common question when Data Scientists meet text for the first time.

As simple as it may look for experienced NLP Data Scientists, turning text into features is not that trivial for newcomers in the area. The purpose of this article is to provide a guide into turning Text to Features, as a continuation to the NLP Series that I’ve been building for the last months (and its been a while since the last article, I know).

2020-11-13 10:45:18.180000+00:00 Read the full story…
Weighted Interest Score: 3.1118, Raw Interest Score: 1.7099,
Positive Sentiment: 0.0919, Negative Sentiment 0.0735

Forrester: Top Emerging Technology Trends To Watch In 2021 And Beyond

The Forrester report “Top Trends And Emerging Technologies, Q3 2020” highlights important trends and organizes emerging technologies into seven key domains that will play a big role in accelerating this shift: artificial intelligence; business automation and robotics; enterprise risk management; human experience and productivity; new compute architectures; next-generation communications; and Zero Trust security. Key trends include:

• Rising demand for ethical AI.
• Recasting of automation roadmaps.
• Moving toward hyperlocal business operations.
• Driving innovation everywhere using cloud-native technologies.
• Shifting cloud strategies toward the edge.

2021-11-16 00:00:00 Read the full story…
Weighted Interest Score: 3.0519, Raw Interest Score: 1.4881,
Positive Sentiment: 0.2790, Negative Sentiment 0.0775

Splice Machine Launches Platform for Industrial IoT

Splice Machine, a scale-out SQL database with built-in machine learning, is releasing Livewire, its new open source Operational AI platform for industrial IoT use cases. The Splice Machine Livewire platform enables teams of data engineers, operators, and data scientists to work together with unprecedented speed and agility. By using an integrated platform these teams can deploy machine learning models 100x faster with half the staff.

Livewire is built upon the Splice Machine SQL RDBMS with built-in machine learning. The system is elastic and deployable anywhere. If the plant has already leveraged cloud computing, then the Splice Machine Livewire cloud service may be the perfect solution. Companies can provision a Livewire solution in minutes and easily manage and operate it with few people.

2020-11-09 00:00:00 Read the full story…
Weighted Interest Score: 2.9308, Raw Interest Score: 1.6626,
Positive Sentiment: 0.1635, Negative Sentiment 0.1363

Tech Employers, Jobs Cause for Optimism: Q3 Dice Tech Job Report

Although revenue and profits for many technology companies have remained relatively robust throughout 2020, the technology industry as a whole hasn’t been immune from the economic impacts of the pandemic. While third quarter tech hiring showed the continuing effects of COVID-19, as revealed by Dice’s latest Tech Job Report, the data was tempered by encouraging results in terms of posting volumes for top employers and trending occupations.

Overall, the unemployment rate for technologists remains lower (3.5 percent) than the national average. That’s cause for optimism. Furthermore, some large organizations have increased hiring—of the top 50 employers in the third quarter, 68 percent created more job postings than the second quarter, while only 32 percent created an equal or lesser number of job postings.  In this article, we’ll dig more deeply into the data for top employers, as well as looking at the occupations seeing the highest growth for the quarter.

2020-11-11 00:00:00 Read the full story…
Weighted Interest Score: 2.8202, Raw Interest Score: 2.0928,
Positive Sentiment: 0.1079, Negative Sentiment 0.1726

Snap To Acquire Israel’s Voca.ai — A Maker Of AI-Based Voice Agents

Snap, the parent company of Snapchat — a messaging app for millennials, has announced the news of acquiring an Israel-based AI-based voice assistants company — Voca.ai for an estimated $70 million. According to the news, Voca.ai, so far, has raised a mere $6 million from investors like American Express Ventures, lool Ventures, Group 11, and Flint Capital. And, post this acquisition, the company will be integrated into Snap along with its 35 employed people.

According to Voca — seven out of 10 customers still prefer speaking with a human agent; however, Voca offers an AI-based agent natural, human-like conversations that leave the customers wondering if they have spoken to a virtual or human agent. The platform serves as a kind of triage system, which addresses simple inbound queries and then hands over to human agents seamlessly for more complex issues. Voca.ai was founded in 2017 by Dr Alan Bekker and Einav Itamar.

2020-11-12 07:41:37+00:00 Read the full story…
Weighted Interest Score: 2.7344, Raw Interest Score: 1.0817,
Positive Sentiment: 0.1502, Negative Sentiment 0.0601

Deep Unsupervised Learning in Energy Sector – Autoencoders in Action

In this short article, I will talk about unsupervised learning especially in the energy domain. The blog would mainly focus on the application of Deep Learning in real-time than emphasizing the underlying concepts. But first, let us see what an Unsupervised Machine Learning mean? It is a branch of machine learning which deals with identifying hidden patterns from the datasets and does not depend on the necessity of the target variable in the data to be labeled. So here the algorithms are used to discover the underlying structure of the data like the presence of data clusters, odd data detection, etc.

When the Target value of the desired variable under study is unknown, the unsupervised form of Deep learning techniques is used to find the relations between the target (Desired Variable) and other variables in the data to arrive at the outcome ( That is the probable value of the Target).

2020-11-12 09:19:13+00:00 Read the full story…
Weighted Interest Score: 2.7155, Raw Interest Score: 1.4941,
Positive Sentiment: 0.0776, Negative Sentiment 0.1067

Finding Important Features using Genetic Algorithms (for Heart Failure Survival Prediction)

This data set has 12 features and you can download it from the UCI Machine Learning Repository. It is a binary classification, supervised learning problem, with “DEATH_EVENT” as the target variable, 1 meaning died and 0 meaning survived.

Here’s the question: what is the most efficient way to find the best learner (algorithm) and best feature subset? Sometimes, surprisingly small subsets of the features perform better than the complete feature set. What is the best way of finding that set?

First, let’s put the question of the best learner aside. One thing we know is that some learners are faster to train than others. If you want to test out the genetic learning algorithm for feature selection, you’ll find that using Logistic Regression is the fastest way to go, whereas something tree-based like the Random Forest or LightGBM takes a lot longer, and might not even work properly, depending on the library you are using.

2020-11-09 18:40:45.734000+00:00 Read the full story…
Weighted Interest Score: 2.7132, Raw Interest Score: 1.3130,
Positive Sentiment: 0.2820, Negative Sentiment 0.0881

How the MiFID II review and Covid-19 are reshaping the hedge fund operational landscape

With certain aspects of the EU’s ongoing MiFID II review affected by the coronavirus pandemic, Hedgeweek explores how a fresh overhaul of the framework may further impact hedge fund operations, and why the Covid-19 crisis may provide an easing of the regulatory burden. Introduced in January 2018, the European Union’s Markets in Financial Instruments Directive (MiFID) II brought sweeping changes to transparency rules and transaction reporting requirements across the financial markets spectrum.

Among the major reforms impacting hedge funds was a package of measures covering third party research. These included extra scrutiny over the ways that asset managers pay for sell-side analysis, and the unbundling of research from brokerage fees, a move aimed at curbing inducements to trade. Almost three years on, industry consensus indicates MiFID II has led to a reduction in hedge fund research spend. But anecdotal evidence also suggests portfolio managers have sought to capitalise on the reduced amount of stock analysis with targeted research budgets to help them gain an edge.

2020-11-10 00:00:00 Read the full story…
Weighted Interest Score: 2.6651, Raw Interest Score: 1.3711,
Positive Sentiment: 0.1808, Negative Sentiment 0.1959

Android gains support for hardware-accelerated PyTorch inference

Google’s Android team today unveiled a prototype feature that allows developers to use hardware-accelerated inference with Facebook’s PyTorch machine learning framework. This enables more developers to leverage the Android Neural Network API’s (NNAPI) ability to run computationally intensive AI models on-device. Google says this partnership between the Android team and Facebook will allow millions of Android users to benefit from experiences powered by real-time computer vision and audio enhancement models, like Facebook Messenger’s 360-degree virtual backgrounds.

On-device machine learning can bolster features that run locally without transferring data to a remote server. Processing the data on-device results in lower latency and can improve privacy, allowing apps to work without connectivity.

2020-11-12 00:00:00 Read the full story…
Weighted Interest Score: 2.6297, Raw Interest Score: 1.7191,
Positive Sentiment: 0.3292, Negative Sentiment 0.0000

Hospitals Make Massive Inroads on COVID-19 Battle with EMS Data

We have previously talked about some of the biggest ways that policy makers and the healthcare sector are using big data to fight COVID-19. Their approach has evolved in recent weeks, as they find ways to use EMS data to streamline their response.

COVID-19 has changed how hospitals and EMS services function dramatically. Due to the impact that the virus has had on their systems, the healthcare field will likely be changed forever. This sector is using data to control the pandemic and will likely use these new approaches to curb future healthcare crises in the months to come. All areas of EMS are being stretched thin; ambulances, fire departments, and even the police are playing crucial roles in today’s healthcare system.

2020-11-12 15:44:49+00:00 Read the full story…
Weighted Interest Score: 2.6252, Raw Interest Score: 1.2478,
Positive Sentiment: 0.2755, Negative Sentiment 0.2269

Expanding Your Data Science and Machine Learning Capabilities

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 data sets and data platforms, to architecting and optimizing data pipelines, and model training and deployment. As a result, new solutions have emerged to deliver key capabilities in areas including visualization, self-service and real-time analytics. Along with the rise of DataOps, greater collaboration and automation have been identified as key success factors.

To educate IT decision-makers and practitioners about new technologies and strategies for expanding data science and machine learning capabilities, DBTA is hosting a special roundtable webinar on June 24th. Reserve your seat today!

2021-06-24 00:00:00 Read the full story…
Weighted Interest Score: 2.5974, Raw Interest Score: 1.6536,
Positive Sentiment: 0.2611, Negative Sentiment 0.1741

Impact of Coronavirus on Businesses

Savanta recently conducted some research with 500 business decision makers across the US in regards to working in a pandemic environment and the impact of coronavirus.

• Impact of Coronavirus on Businesses
• Approach to work from home
• Company approach to remote working
• Impact of Covid-19 on business Increase / Positive impact Decrease / Negative impact
• Covid-19 impact over time
• How companies are dealing with coronavirus?
• Strategies to mitigate business risks
• Priorities of the organizations
• Post Covid-19 technological changes in US corporate sector

2020-11-12 18:28:45+00:00 Read the full story…
Weighted Interest Score: 2.5945, Raw Interest Score: 1.4476,
Positive Sentiment: 0.1304, Negative Sentiment 0.1304

Researchers investigate why popular AI algorithms classify objects by texture, not by shape

In a paper accepted to the 2020 NeurIPS conference, Google and Stanford researchers explore the bias exhibited by certain kinds of computer vision algorithms — convolutional neural networks (CNNs) — trained on the open source ImageNet dataset. Unlike humans, ImageNet-trained CNNs tend to classify images by texture rather than by shape. Their work indicates that CNNs’ bias toward textures may arise not from differences in their internal workings but from differences in the data that they see.

CNNs attain state-of the-art results in computer vision tasks including image classification, object detection, and segmentation. Although their performance in several of these tasks approaches that of humans, recent findings show that CNNs differ in key ways from human vision. For example, recent work compared humans to ImageNet-trained CNNs on a dataset of images with conflicting shape and texture information (e.g. an elephant-textured knife), concluding that models tend to classify according to material (e.g. “checkered”) and humans to shape (e.g. “circle”).

2020-11-13 00:00:00 Read the full story…
Weighted Interest Score: 2.5513, Raw Interest Score: 1.4395,
Positive Sentiment: 0.1469, Negative Sentiment 0.1469

Palantir reports 52% sales growth in first earnings statement since public market debut

Palantir, the maker of software and analytics tools for the defense industry and large corporations, reported 52% revenue growth in its first earnings announcement since going public in September. The stock bounced around in extended trading, falling more than 8% before bouncing back and gaining more than 1%. It plunged 8.7% during the regular trading day. The software and analytics company went public in September, 17 years after it was co-founded by Peter Thiel, CEO Alex Karp and others. Palantir said that its “customer concentration is decreasing,” and that it now gets a smaller percentage of revenue from its top clients.

2020-11-12 00:00:00 Read the full story…
Weighted Interest Score: 2.5346, Raw Interest Score: 1.2842,
Positive Sentiment: 0.0676, Negative Sentiment 0.2028

AI Holistic Adoption for Manufacturing and Operations: Data

For the executive leader who is taking their enterprise on a journey of Digital Transformation and AI Holistic Adoption, we started this series with the foundation of Value and then moved to the framework of the Program. Although these are the fundamental building blocks required for success, the results of any enterprise’s analytics, do, in the end, rely on the Data.

The executive leader has the responsibility to ensure that they and their team are dedicated to mastering data fluency and data excellence in the enterprise. The facets of Data Management are vast with the standard areas of focus including data discovery, collection, preparation, categorization and protection. Strategies for achieving maturity in these areas are well-established in most industries, and yet many industries still struggle. These standard areas of focus in Data Management are indeed necessary but are not sufficient for the needed AI Holistic Adoption.

2020-11-12 23:36:45+00:00 Read the full story…
Weighted Interest Score: 4.7808, Raw Interest Score: 2.2741,
Positive Sentiment: 0.3876, Negative Sentiment 0.0689

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.

Alternative Data News. 18, November 2020

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.

Industry Stats

This page contains relevant facts and figures related to the alternative data industry for institutional investors. Unless otherwise noted, these figures are per internal alternativedata.org estimates or based on our database of providers.

Read the Full Story…

CloudQuant Thoughts : Just a nice pie chart of our industry. Where do you fit?

In 2021, off-the-shelf datasets will be on the rise for AI model development

If there’s one thing that companies large and small can agree on, it’s that deploying effective artificial intelligence (AI) is challenging. Not every organization has the funds, specialized teams, and annotators required for a large-scale AI deployment, and even those that do struggle with collecting enough high-quality data to build accurate models quickly, or update them with the right frequency. Deploying and maintaining AI with speed is essential for a competitive advantage in this rapidly-evolving space, which is why many companies are looking to third-party options that enable them to scale quickly.

In particular, organizations are increasingly relying on off-the-shelf, or pre-built, datasets to provide needed data conveniently with limited risk. These datasets are cost-effective alternatives that can accelerate deployments and provide that last percentage or two of accuracy required to meet desired confidence thresholds. In part two of our five part series on 2021 predictions, we focus on the rise of off-the-shelf datasets.

2020-11-18 00:00:00 Read the full story…
Weighted Interest Score: 4.4556, Raw Interest Score: 1.5044,
Positive Sentiment: 0.2925, Negative Sentiment 0.0975

CloudQuant Thoughts : Off the shelf suggests mediocrity, CloudQuant’s industry leading data access fabric –  CloudQuant Data Liberator – lets you access thousands of datasets using the same simple code. Head over to our data catalog to see huge range of Alternative data we have available.

ESG Section

ICE Reports Record Activity Across Its Environmental Complex

Intercontinental Exchange, a leading operator of global exchanges and clearing houses and provider of mortgage technology, data and listings services, today reported record open interest across its environmental complex as participants price climate risk.

The environmental complex – which includes futures and options connected to ICE’s European (EUA) and California Carbon allowances (CCA), Regional Greenhouse Gas Initiative (RGGI) and renewable energy credits (RECs) – hit record open interest of approximately 2.65 million contracts on November 12, 2020.
2020-11-17 05:35:03+00:00 Read the full story…
Weighted Interest Score: 3.1124, Raw Interest Score: 1.6399,
Positive Sentiment: 0.2343, Negative Sentiment 0.0335

Morningstar Formally Integrates ESG Into Analysis

As assets and interest in sustainable investing continue to grow, Morningstar unpacks ESG risks to meet investor demand for in-depth qualitative and quantitative research to curate ESG investment choices

Morningstar, a leading provider of independent investment research, today announced it has begun formally integrating environmental, social, and governance (ESG) factors into its analysis of stocks, funds, and asset managers.

Morningstar equity research analysts will employ a globally consistent framework to capture ESG risk across over 1,500 stocks. Analysts will identify valuation-relevant risks for each company using Sustainalytics’ ESG Risk Ratings, which measure a company’s exposure to material ESG risks, then evaluate the probability those ri…
2020-11-18 05:01:20+00:00 Read the full story…
Weighted Interest Score: 5.8561, Raw Interest Score: 2.3620,
Positive Sentiment: 0.0980, Negative Sentiment 0.0544

Panelists discuss Covid-19, ESG and investor appetite in “extraordinary” year for hedge funds

This year’s Hedgeweek LIVE Europe digital summit opened with a broad overview of the prevailing hedge fund industry landscape in 2020, exploring a range of themes including the shifting perception of hedge funds among investors, the impact of Covid-19 on market opportunities, the increased importance of ESG among both managers and allocators, and the start-up environment for fledgling funds.

Underlining the importance of emerging managers within the hedge fund industry, Jack Inglis, CEO of the Alternative Investment Management Association, said those firms managing USD500 million and below comprise the majority of managers, and described new funds as the “lifeblood” of this business.
2020-11-11 00:00:00 Read the full story…
Weighted Interest Score: 4.2398, Raw Interest Score: 1.8611,
Positive Sentiment: 0.2091, Negative Sentiment 0.1673

US Sustainable Investing Assets Reach $17.1 Trillion

The US SIF Foundation’s 2020 biennial Report on US Sustainable and Impact Investing Trends, found that sustainable investing assets now account for $17.1 trillion—or 1 in 3 dollars—of the total US assets under professional management. This represents a 42 percent increase over 2018.

• The Trends report counts two main strategies as sustainable investing: ESG incorporation—applying various environmental, social and governance (ESG) criteria in investment analysis and portfolio selection—and filing shareholder resolutions on ESG issues.
• The total US-domiciled assets under management using sustainable investing strategies grew from $12.0 trillion at the start of 2018 to $17.1 trillion at the start of 2020, an increase of 42 percent.
• This is 33 percent – or 1 in 3 dollars – of the total US assets under professional management.
• The top three specific issues for money managers and their institutional investor clients are climate change/carbon emissions, sustainable natural resources/agriculture and board issues.
• From 2018 through the first half of 2020, 149 institutional investors and 56 investment managers controlling $1.98 trillion in AUM led or co-led shareholder resolutions on ESG issues.

2020-11-17 05:44:07+00:00 Read the full story…
Weighted Interest Score: 3.9395, Raw Interest Score: 2.5750,
Positive Sentiment: 0.0387, Negative Sentiment 0.1162

US Treasury Could Issue Inaugural Green Bond Under Biden

The US could issue its first sovereign green bond market under the Biden administration which could spark significant growth in the market.

Bram Bos, lead porfolio manager green bond at NN Investment Partners, the Dutch fund manager, said in an email that President-elect Joe Biden’s commitment to rejoining the Paris agreement when he is inaugurated symbolises a broader transformative shift in climate policy.

The US could issue its first sovereign green bond market under the Biden administration which could spark significant growth in the market.

“The Biden administration will invest heavily in sustainable infrastructure and clean energy, which could lead to an inaugural green US Treasury issue and further boost the global green bond market,” he added.

2020-11-10 06:09:49+00:00 Read the full story…
Weighted Interest Score: 3.1717, Raw Interest Score: 1.6781,
Positive Sentiment: 0.1831, Negative Sentiment 0.1831

Nasdaq’s Quandl just hired a new executive for what it believes is the next big opportunity for alternative data — Europe

Alternative data on US companies and trends have become table stakes for sophisticated investors, but the same information is harder to come by in “fragmented” Europe.

The opportunity for legitimate and trustworthy datasets on European consumers is “untapped,” according to Bill Dague, head of alternative data for Nasdaq, which bought Quandl roughly two years ago.

2020-11-18 00:00:00 Read the full story…
Weighted Interest Score: 6.8009, Raw Interest Score: 2.1500,
Positive Sentiment: 0.1344, Negative Sentiment 0.2419

Goldman Sachs in talks to acquire Trading Technologies

Goldman Sachs is reportedly in advance talks for a $500 million takeover of Trading Technologies.

Citing four unidentified sources, Global Investor Group says the US investment bank is in late-stage talks about buying the Chicago-based trading and data analytics software firm. Rumours that TT was in the shop window first began circulating last month. Founded in 1994, TT supplies software to most of the world’s biggest investment banks, brokers and trading firms.

2020-11-12 14:13:00 Read the full story…
Weighted Interest Score: 5.7432, Raw Interest Score: 3.5473,
Positive Sentiment: 0.0000, Negative Sentiment 0.1689

What to Learn to Become a Data Scientist in 2021

…and why the data science generalist will triumph

When I started learning data science a few years ago most job ads requested a PhD, or at the very least a masters, in maths, statistics or a similar subject as an essential requirement.

Over the last couple of years, things have evolved. With the development of machine learning libraries that abstract away much of the complexity behind the algorithms, and a realisation that practically applying machine learning to solve business problems requires a set of skills that are not usually acquired through academic study alone. Companies are now hiring data scientists based on their ability to perform applied data science rather than research.

Applied data science that delivers value to a business in the fastest possible time requires a very practical skillset. Additionally, as more companies migrate their data and machine learning solutions to the cloud, It is becoming paramount for data scientists to have an understanding of the new tools and technology relating to this.

2020-11-15 18:35:33.545000+00:00 Read the full story…
Weighted Interest Score: 3.4218, Raw Interest Score: 1.9608,
Positive Sentiment: 0.1751, Negative Sentiment 0.0350

What Does a Data Engineer’s Career Path Look Like?

Data science is a very attractive career path, but finding your niche can be tricky. Big data is changing the future of almost every industry. The market for big data is expected to reach $23.5 billion by 2025. Data science is an increasingly attractive career path for many people. However, the outlook is hazy for people that are not as familiar with the career path.

If you want to become a data scientist, then you should start by looking at the career options available. Northwestern University has a great list of ways that people can pursue a career in data science. You should also learn the career path that you need to follow to get started, which includes learning the right programming languages.

2020-11-08 Read the Full Story…

After Free Statistics Course, IIT Kanpur Brings Free Online Data Science Courses

After the recent announcement by Indian Institute of Technology, Kanpur to offer free statistics courses during the lockdown period, they have announced two new free online courses on data science. This time they have offered it on SWAYAM NPTEL platform, which is a two-part course, as below:

• Essentials of Data Science With R Software – 1: Probability and Statistical Inference
• Essentials of Data Science With R Software – 2: Sampling Theory and Linear Regression Analysis

SWAYAM is a programme initiated by the Government of India to provide best teaching-learning resources to all, including the most disadvantaged. SWAYAM seeks to bridge the digital divide for students to make them digitally enabled.

2020-11-16 13:49:17+00:00 Read the full story…
Weighted Interest Score: 3.3630, Raw Interest Score: 1.9714,
Positive Sentiment: 0.1546, Negative Sentiment 0.0773

Capital allocation – Stewards’ inquiry

If investors buy stocks in an index, who watches managers?

Robert Fleming has a claim to be a pioneer of active asset management. His First Scottish investment trust pledged to invest mostly in American securities, with choices informed by on-the-ground research. Fleming saw that shareholders needed to act as stewards in the governance of the businesses that they part-owned. So once the fund was launched, in 1873, he sailed directly to America. It was the first of many fact-finding trips across the Atlantic over the next 50 years, according to Nigel Edward Morecroft’s book, “The Origins of Asset Management”.

The art of asset management is capital allocation. It is easy to miss this amid confusing talk of alpha and beta, active and passive, private and public markets. For investors of Fleming’s kind the work of finding the best investment opportunities and engaging with business was inseparable.
2020-11-12 00:00:00 Read the full story…
Weighted Interest Score: 3.3213, Raw Interest Score: 1.8116,
Positive Sentiment: 0.3136, Negative Sentiment 0.2323

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.

Alqami’s Unique Private Alternative Datasets now available via CloudQuant API!

Alqami’s unique Private Alternative Datasets are now available from CloudQuant!

We are delighted to announce that we are currently onboarding Alqami’s very special private data sets so they can be available via our high speed, simple to use Data Liberator data fabric.

Due to their nature, we cannot reveal too much information about the datasets but you can get a flavor of them from our Data Catalog. Keep checking back as we will be adding more Alqami datasets in the coming days.

For more information, see the Alqami cards in our Data Catalog, make an appointment to speak to a CloudQuant Representative, Email Sales@CloudQuant.com, or fill in the form on the right and we will get in touch.

See also our Repository of White Papers.

Alternative Data News. 04, November 2020

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.

CloudQuant are in the running for a top prize at the Benzinga Global Fintech Awards November 10th 2020

CloudQuant will be attending the Benzinga Global Fintech Awards November 10th 2020. CEO Morgan Slade will be taking part in a fireside chat and our sales team will be available throughout the event to answer your questions and discuss our huge range of alternative datasets.

We are also proud to announce that our industry leading technology has been nominated for a Benzinga Fintech Award 2020 in the category of Best Data Analysis Tool.

2020-11-02 Read the Full Story…

It’s about to start…

Reddit User : JustGlowing

Data source : Google Trends

Tools : Python with the libraries matplotlib and pytrends

2020-10-29 Read the Full Story…

CloudQuant Thoughts : I bet you thought I was going to pick some Election Data Analysis, well I think we need to move on.. to Christmas.

Information Services Q&A: Lauren Dillard, Nasdaq

Lauren Dillard joined Nasdaq as Head of Global Information Services in May 2019. Markets Media recently caught up with Lauren for an update on the business.

“I think we’ve seen the most growth, however, in our analytics business. Whether it’s about supply chain, consumer spending, indications of travel, or anything else, the need for alternative data sets increased dramatically this year.”

2020-10-27 07:01:39+00:00 Read the full story…
Weighted Interest Score: 3.5517, Raw Interest Score: 1.6086,
Positive Sentiment: 0.3312, Negative Sentiment 0.0473

CloudQuant Thoughts : If you are not already using Alternative Data, or do not know where to start, reach out to us and we can help you get started – Fast. If you have an Alternative Data Set that you want to promote far and wide, get in touch and let us explain what we can do to turbocharge your data sales. Email Sales@CloudQuant.com, Make an appointment to speak to a CloudQuant Representative, or fill in the form on the right to be contacted back by a CloudQuant Representative. Also see our Data Catalog and our Repository of White Papers.

ESG Section

CloudQuant also provides Alternative Data Sets including an excellent ESG data set with proven Alpha. Head over to our data catalog for more information.

Environmentally friendly funds have been drawing cash as Biden polling lead holds

Funds focused on sustainable investing are attracting record inflows as investors increasingly prioritize ESG metrics, or a company’s environmental, social and governance factors.

Democrat Joe Biden’s ascent in the polls and his environmentally friendly proposals have driven more investors into climate-focused funds, some of which have seen their shares more than double this year.

Here’s a list of the most popular sustainable funds over the last month, according to data compiled by FactSet:…
2020-11-03 00:00:00 Read the full story…
Weighted Interest Score: 4.0367, Raw Interest Score: 2.0183,
Positive Sentiment: 0.3670, Negative Sentiment 0.0000

Number Of ESG Indices Globally Rise By 40.2%

hmark survey. This year’s survey shows an industry that is growing and diversifying its products and services to meet expanding investor needs. Main growth drivers this year include indices measuring environmental, social and governance (ESG) criteria, which saw a 40.2% increase, and fixed income indices, which had a 7.1% increase.

Rick Redding, the CEO of IIA, commented: “The survey’s 2020 results demonstrate a highly competitive industry that continues to broaden its offerings to meet investor demand. Indices today are transparent and reliable representations of market segments covering a wide spectrum of asset classes and in…
2020-11-03 05:35:46+00:00 Read the full story…
Weighted Interest Score: 2.7453, Raw Interest Score: 1.5360,
Positive Sentiment: 0.1617, Negative Sentiment 0.0404

What Matters Most In ESG Investing: How To Spot Opportunities Across Market Cycles And The Capital Structure

Pensions, insurers, endowments, and foundations are asking asset managers to incorporate elements of sustainability and inclusion into their investing. Responding to investor interest is complex. There are over 600 environmental, social, and governance (ESG) frameworks and standards, and materiality—focusing on sustainability issues that drive stakeholder decision-making—varies across industries, the capital structure, and economic cycles. Sustainability Accounting Standards Board (SASB) is perhaps the most widely accepted of the sustainability standards. SASB’s Materiality Map SASB is championed by an Investor Advisory Group with an aggregate $40 trillion in assets, including BlackRock BLK +2.6%, which in January asked the 15,000 companies in its portfolio to publish disclosure in line with industry-specific SASB standards by the end of the year. SASB’s Materiality Map outlines how material ESG factors vary across 77 industries. Institutional investors and issuers would benefit from analogous materiality maps by asset class, strategy, and phase in the economic cycle.
2020-11-03 00:00:00 Read the full story…
Weighted Interest Score: 2.6693, Raw Interest Score: 1.5476,
Positive Sentiment: 0.4422, Negative Sentiment 0.1769

How Data Gravity Is Forcing a Shift to a Data-Centric Enterprise Architecture

Data is the output of society and everything we do — and the enterprise is fast becoming the world’s data steward. Digital-enabled interactions are becoming the norm, increasing enterprise data exchange volumes. In fact, it’s estimated that by 2024, Global 2000 Enterprises will create data at a rate of 1.1 million gigabytes per second and will require 15,635 exabytes of additional data storage annually. While applications like artificial intelligence (AI) and machine learning (ML) are fast becoming the center of today’s digital enterprise, helping to create efficiencies and improve customer experience, they also add to the accumulation of data that must be processed, analyzed, and applied to keep businesses running smoothly and spur innovation.

The accumulation of this data describes an effect similar to what occurs with the gravity between objects like the earth and the moon — data gravity. The data becomes harder to move, which can cause complexity and prevent digital transformation from occurring. For instance, if enterprises aren’t monitoring their data gravity challenges, it can cause slow response times, create information silos, and ultimately stall profitability and growth.

2020-11-02 Read the Full Story…

How consumer data provider Yodlee can help bolster the buy-side portfolio-building process

Envestnet | Yodlee, a data aggregation and analytics platform specialising in consumer spending data analytics, says asset managers are increasingly seeking out such alternative data insights in their hunt for alpha. Nikhil Nadkarni, Vice President, Data Products, explains how the consumer spending data analytics can help provide asset managers a view into consumer interactions with brands and incorporating insights into the investment research processes.

“Equity Researchers and Investment Managers can use consumer spending data analytics in their fundamental research to understand and forecast revenues, customer retention, customers’ lifetime values, customer churn and competitive analysis. Learning consumer spending patterns around online versus offline provides visibility into how consumer discretionary spending is shaping consumer behaviour especially during Covid-19.”

2020-11-03 00:00:00 Read the full story…
Weighted Interest Score: 6.1352, Raw Interest Score: 2.3926,
Positive Sentiment: 0.0443, Negative Sentiment 0.0443

An Important Guide To Unsupervised Machine Learning

It’s become very clear that unsupervised machine learning and artificial intelligence can be very helpful for business growth, but how do they work? There are some key methods you’ll want to know so your market research, trend predictions, and other machine learning uses are effective.

We’re living in an era of digital switch-over with only one constant – evolve. And that digital transformation is being introduced by high-tech solutions. Hence, it comes as no surprise that mundane business tasks are being completely taken over by tech advancements. Machines, artificial intelligence (AI), and unsupervised learning are reshaping the way businesses vie for a place under the sun. With that being said, let’s have a closer look at how unsupervised machine learning is omnipresent in all industries.
2020-11-01 18:08:28+00:00 Read the full story…
Weighted Interest Score: 5.1657, Raw Interest Score: 1.9476,
Positive Sentiment: 0.1885, Negative Sentiment 0.0628

Is Your Data Ready for AI?

We’ve already figured out that AI has an immense potential to enhance business processes of many kinds in almost any industry imaginable. AI is poised to redefine conventional business models, enhance productivity, and drive value overall. When deploying it, companies tend to stick to a traditional scenario: first, outline an elaborate strategy, then attract excellent talent, secure the budget, develop a PoC, and so on. However, artificial intelligence experts argue that this traditional roadmap is missing one integral component of successful AI adoption. Namely, data readiness. In fact, even when data gets enough attention, it still remains a solid roadblock on an already thorny path.

The common trap that organizations tend to fall into is to assume that large amounts of data imply it’s usable. In reality, most data that have been collected without solid governance principles can’t be fed into AI algorithms. Data becomes useful only when it’s properly cleansed, labeled, and structured. Contrary to popular opinion, it’s usually a bad idea to purchase datasets from other vendors as in most cases each company requires its unique data to extract maximum value.

These are a few steps that companies can make to prepare their data for AI implementation.

2020-10-29 06:48:28 Read the full story…
Weighted Interest Score: 4.4589, Raw Interest Score: 1.7616,
Positive Sentiment: 0.2381, Negative Sentiment 0.3333

Top Open Source Recommender Systems In Python For Your ML Project

Recommender systems have found enterprise application by assisting all the top players in the online marketplace, including Amazon, Netflix, Google and many others. These systems are the decision support systems that make the personalisation process better as well as smoother. It predicts and estimates the content of user preferences by extracting from various data sources such as previous database, data history, among others.

Here, we have listed the top eight open-source recommender systems in Python, in no particular order, that you must try for your next project.

• LensKit
• Crab
• Surprise
• Rexy
• TensorRec
• LightFM
• Case Recommender
• Spotlight

2020-11-04 11:30:01+00:00 Read the full story…
Weighted Interest Score: 4.2817, Raw Interest Score: 1.4205,
Positive Sentiment: 0.2029, Negative Sentiment 0.0609

AIM Partners With NASSCOM To Invite Organisations For AI Case Studies

Analytics India Magazine (AIM), in association with NASSCOM, has launched an initiative to unearth some of the best India-based AI use cases that have transformed organisations’ value-chain. To drive this initiative, AIM is conducting an online survey for businesses to jump in and share relevant details of the AI implementations.

Various enterprises and organisations in India, in recent years, have implemented artificial intelligence across their value chain to boost operational and business efficiency. And to implement these AI solutions, these enterprises, in most instances, have partnered with various organisations and service providers who specialise in AI and other technology services. In an attempt to identify these best use cases, Analytics India Magazine is inviting organisations to share their AI/ML case studies with the larger ecosystem.

The initiative has been aimed to create an AI Case Study Compendium for the industry by discovering some of the critical use cases, spanning across all sectors, implemented by key solution providers – both public and private. These use cases will be covering the implementation journey of artificial intelligence at any particular level or all levels of the organisation.

2020-11-02 10:19:13+00:00 Read the full story…
Weighted Interest Score: 3.5520, Raw Interest Score: 1.4220,
Positive Sentiment: 0.2091, Negative Sentiment 0.0836

The Ultimate Guide to Data Engineer Interviews

What to expect and how to prepare for data engineering interviews.

Although data engineer (DE) was the fastest-growing tech job role in 2019, there aren’t many online resources on what to expect in a data engineering interview and how to prepare for it.

In the past year, I have interviewed for data engineer roles with several tech companies in the Bay Area and helped many connections succeed in their interviews. In this blog post, I will explain the most important technical topics in data engineering interviews: your resume, programming, SQL, and system design. I will also teach you how to prepare for the non-technical part of the interview, which I believe is key to a successful job interview but is often ignored by candidates.

2020-11-02 22:01:27.975000+00:00 Read the full story…
Weighted Interest Score: 3.0175, Raw Interest Score: 1.7927,
Positive Sentiment: 0.3006, Negative Sentiment 0.2895

AWS releases models and datasets to help predict COVID-19’s spread

Amazon Web Services (AWS) today open-sourced a new simulator and machine learning toolkit for anticipating and mitigating the spread of COVID-19. AWS says that the suite, which comprises a disease progression simulator and models to test the impact of various intervention strategies, can help to accurately capture many of the complexities of the virus in the world.

While there have been a number of breakthroughs in understanding COVID-19, such as how soon an exposed person will develop symptoms, building an all-encompassing epidemiological model remains an uphill battle. Challenges in model building include identifying variables that influence disease spread across cities, countries, and populations. A performant model must also combine intervention strategies such as closures and stay-at-home orders and explore hypotheticals by incorporating trends from COVID-19-like diseases.

2020-10-30 00:00:00 Read the full story…
Weighted Interest Score: 2.5451, Raw Interest Score: 1.2741,
Positive Sentiment: 0.0593, Negative Sentiment 0.1778

Embracing the new reality. A Bank’s perspective

Highlighted in Deloitte’s report on the “outlook” for the industry, a new, forceful, wave of disruption is coming, even before the pandemic, so imagine the need for digitalisation post-Covid. The combined effects of this technological disruption will greatly affect the banking industry. Banks need to re-evaluate their platforms across multiple dimensions in order to exploit the opportunity that comes with every disruption, modernising their systems to support:

• Tailored products – customers are the asset, the offering needs to be close to their needs, while safeguarding Bank’s revenue
  Real-time transactions – this becomes the norm for the banking of younger generations
• Top quality of data – data analytics is paramount now, and no Bank can rely on poor data to make decisions, nor have large cost overheads on data normalisation and cleansing
• Deployment to take place in stages as the bank grows – it is imperative that system modernisation develops in line with the Bank’s current operations with technology efficiently supporting this approach
• Human capital through automation of processes – Bank’s personnel are valuable assets that may shift their focus from system operational tasks to more productive activities, provided that the majority of operations are automated by the systems.

2020-10-29 00:00:00 Read the full story…
Weighted Interest Score: 2.5104, Raw Interest Score: 1.6352,
Positive Sentiment: 0.4146, Negative Sentiment 0.1842

How NVIDIA Powered America’s Fastest Supercomputer In Fight Against COVID-19

“Using Dask, RAPIDS, BlazingSQL, and NVIDIA GPUs, researchers are leveraging Summit supercomputers from their laptops.”

Working on data-intensive projects like protein folding research, drug discovery, or deep space leads to several TBs of data. And, using queries on CPUs to sort information can take days. Time is a key constraint while fighting global pandemics. Research labs and governments around the world have accommodated money and manpower to speed up drug discovery. But this isn’t sufficient. There is a need for a smart, diligent solution that combines the existing technologies without trying to reinvent the wheel.

At Oak Ridge National Laboratory, which has been at the forefront of the fight against COVID-19, the researchers have been leveraging the powerful SUMMIT supercomputer to skim large datasets in search of solutions. SUMMIT, the world’s second-fastest supercomputer is powered by NVIDIA’s Tesla V100s and the team at OLCF (Oakridge Leadership Computing Facility) has been looking for solutions that would fit well into their technology stack.

2020-11-04 05:30:43+00:00 Read the full story…
Weighted Interest Score: 2.4012, Raw Interest Score: 1.2632,
Positive Sentiment: 0.2071, Negative Sentiment 0.0828

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AI & Machine Learning News. 30, November 2020

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?

‘Superintelligence’ in Seattle: AI researchers do a reality check on a movie that adds romance to tech

Seattle, Microsoft and the field of artificial intelligence come in for their share of the spotlight in “Superintelligence” — an HBO Max movie starring Melissa McCarthy as the rom-com heroine, and comedian James Corden as the world’s new disembodied AI overlord.

But how much substance is there behind the spotlight? Although the action is set in Seattle, much of the principal filming was actually done in Georgia. And the scientific basis of the plot — which involves an AI trying to decide whether or not to destroy the planet — is, shall we say, debatable.

Fortunately, we have the perfect team to put “Superintelligence” to the test, as a set-in-Seattle movie as well as a guide to the capabilities of artificial intelligence.

2020-11-26 18:00:00+00:00 Read the full story…
Weighted Interest Score: 2.0458, Raw Interest Score: 0.7833,
Positive Sentiment: 0.0490, Negative Sentiment 0.1567

CloudQuant Thoughts : A new movie about AI, no matter how silly it is, how could I not put it’s trailer at the top?

Ethical AI isn’t the same as trustworthy AI, and that matters

Artificial intelligence (AI) solutions are facing increased scrutiny due to their aptitude for amplifying both good and bad decisions. More specifically, for their propensity to expose and heighten existing societal biases and inequalities. It is only right, then, that discussions of ethics are taking center stage as AI adoption increases.

In lockstep with ethics comes the topic of trust. Ethics are the guiding rules for the decisions we make and actions we take. These rules of conduct reflect our core beliefs about what is right and fair. Trust, on the other hand, reflects our belief that another person — or company — is reliable, has integrity and will behave in the manner we expect. Ethics and trust are discrete, but often mutually reinforcing, concepts.

So is an ethical AI solution inherently trustworthy?

2020-11-28 00:00:00 Read the full story…
Weighted Interest Score: 3.2303, Raw Interest Score: 0.9299,
Positive Sentiment: 0.1894, Negative Sentiment 0.3272

CloudQuant Thoughts : I would imagine, and the article seems to back this up, that once we get past the initial problems with AI/ML (where human biases unduly influence the machines decisions), most peoples problems will not be with the AI but how it is used. I bristle whenever I see an insurance advert for using monitoring devices to “lower” car insurance. Why would an insurance company behave any more ethically with AI guidance.

Should Companies Pay Users For Their Data?

Every day, humans produce an astonishing amount of data, to the tune of about 2.5 quintillion bytes. Google processes 40,000 searches a second, 1.4 billion people login into Facebook every day. Every minute, 16 million text messages are sent out, 156 million emails are shared, and 600 new pages are created in Wikipedia. Needless to say that the amount of data generated every day, including this very moment is astronomical. However, the companies and enterprises, who ultimately benefit from this data, are not complaining; in fact, they are glad.

Data is the cog in the wheel of modern society. Given how data is an essential driving factor, should not the users who provide this data in the first place, be paid for use by the tech companies? This question has gained prominence over the recent years, with even experts and influential figures are raising.

2020-11-30 09:30:23+00:00 Read the full story…
Weighted Interest Score: 1.9592, Raw Interest Score: 1.1462,
Positive Sentiment: 0.2159, Negative Sentiment 0.2159

CloudQuant Thoughts : Yes, Yes and thrice Yes. My data is my data. Also, most people would be happy to give up more data for better services. Don’t call me to ask me if I want new windows… wait until I add a desire for new windows to my data vault.

Google Enables Machine Learning for Gunshot Recognition in the Rainforest

The World Wildlife Fund (WWF) estimates that poaching is the root of a $20 billion-a-year industry. This thriving, illegal practice is largely possible because enforcement of poaching laws is so difficult: personnel must monitor enormous swaths of land for a handful of rare animals and sneaky humans – and they must identify infractions quickly enough to make a meaningful difference. For the past three years, the Zoological Society of London (ZSL) has been partnered with Google Cloud to use machine learning to streamline these processes and protect endangered species. Now, ZSL and Google Cloud are highlighting a new tool on that front: acoustic data monitoring.

“The analysis of acoustic (sound) data to support wildlife conservation is one of the major lines of work at ZSL’s monitoring and technology programme,” wrote Omer Mahmood, a head of customer engineering for Google Cloud, UK and Ireland. “Compared to camera traps that are limited to detection at close range, acoustic sensors can detect events up to 1 kilometre (about half a mile) away. This has the potential to enable conservationists to track wildlife behaviour and threats over much greater areas.”

2020-11-24 00:00:00 Read the full story…
Weighted Interest Score: 1.9690, Raw Interest Score: 1.1656,
Positive Sentiment: 0.1494, Negative Sentiment 0.2391

CloudQuant Thoughts : Cannot help but think this is a solution they had to hand (ie shotspotter.com) that just happened to get a nice greenwash!

DeepMind solves 50-year-old ‘grand challenge’ with protein folding A.I.

• DeepMind has developed a piece of AI software called “AlphaFold” that can accurately predict the structure that proteins will fold into in a matter of days.
• Predicting the shape that a protein will fold into is important because it determines their function and nearly all diseases, including cancer and dementia, are related to how proteins function.
• “DeepMind has jumped ahead,” said Professor John Moult, who is the chair of a group called CASP (Critical Assessment for Structure Prediction).

Alphabet-owned DeepMind has developed a piece of artificial intelligence software that can accurately predict the structure that proteins will fold into in a matter of days, solving a 50-year-old “grand challenge” that could pave the way for better understanding of diseases and drug discovery.

Every living cell has thousands of different proteins inside that keep it alive and well. Predicting the shape that a protein will fold into is important because it determines their function and nearly all diseases, including cancer and dementia, are related to how proteins function.

2020-11-30 00:00:00 Read the full story…
Weighted Interest Score: 1.9317, Raw Interest Score: 1.0608,
Positive Sentiment: 0.3536, Negative Sentiment 0.2593

CloudQuant Thoughts : 50 year grand challege… Pfft…Tick.. NEXT!!!

A developer’s guide to re:Invent 2020 machine learning sessions – STARTS TODAY

This year will be remembered for many reasons. It has been a year of big changes in our lives and habits and a time when we found new ways to do the things we love. 2020 will be remembered as the first year without Amazonians from everywhere gathering in Vegas for the traditional re Invent.

Luckily, it won’t be a year without re:Invent because AWS decided to shift the conference completely online, with a catalog of almost 2000 unique sessions ranging from IoT to machine learning applications to infrastructure and serverless.

As a developer and a machine learning practitioner, digging into such a vast list to extract the best sessions to watch while doing our everyday job is not easy. Here this article comes in handy, trying to enucleate the talks you can’t miss, grouped into four main themes, with a bit of context to support deep diving into each topic.

2020-11-30 14:14:23.359000+00:00 Read the full story…
Weighted Interest Score: 2.3385, Raw Interest Score: 1.4198,
Positive Sentiment: 0.3395, Negative Sentiment 0.0926

CloudQuant Thoughts : 2000 unique sessions sounds amazing but unmanageable. Unsurprisingly, as an Amazon event it Amazon appear to be the powerhouse, from Amazon Comprehend to Amazon Personalize they have their fingers in so many pies!

S&P Global And IHS Markit To Merge

All-Stock Transaction Valuing IHS Markit at $44 Billion, Powering the Markets of the Future

Joins Two World-Class Organizations with Unique, Highly Complementary Assets to Enhance Customer Value Proposition

Combined Company to Benefit from Increased Scale and Mix Across Core Markets with Attractive Growth Adjacencies

2020-11-30 07:07:33+00:00 Read the full story…
Weighted Interest Score: 2.9668, Raw Interest Score: 1.6110,
Positive Sentiment: 0.4658, Negative Sentiment 0.1359

CloudQuant Thoughts : Two major players in the Alternative Data business coming together. S&P is expanding its data empire.

Researchers Release An AI-Based Research Paper Summariser

In an attempt to simplify the process of summarising complex scientific research papers, researchers at the Allen Institute for Artificial Intelligence have released a new AI-based tool that summarises the text from scientific papers and present it in a few sentences.

Considering scientific research papers are complex to understand because of the language it is presented in, it becomes a challenge for many who are willing to work on the same or trying to be updated with scientific literature. And, that is why the researchers from Allen Institute for Artificial Intelligence came out with this new AI-based model — Semantic Scholar — that automatically generates a single-sentence summary using GPT-3 style techniques. This helps in locating the right paper and deciding whether to dedicate time to read that complex paper or not, stated by the official website.

2020-11-24 12:35:25+00:00 Read the full story…
Weighted Interest Score: 3.0324, Raw Interest Score: 1.9054,
Positive Sentiment: 0.1411, Negative Sentiment 0.0353

The U.S. government needs to get involved in the A.I. race against China, Nasdaq executive says

The U.S. needs to take a “strategic approach” as it competes with China on artificial intelligence, according to a Nasdaq executive.

AI is an area that is going to only develop in partnership with government, and U.S. authorities need to get involved, said Edward Knight, vice chairman of Nasdaq.

The Chinese government has already started “investing heavily” and working with their private sector to develop new technologies based on artificial intelligence, he said.

Beijing in 2017 said it wa…
2020-11-25 00:00:00 Read the full story…
Weighted Interest Score: 5.0746, Raw Interest Score: 1.8905,
Positive Sentiment: 0.0995, Negative Sentiment 0.0000

Research provision hits digital inflection point

Research providers must have been wondering what else could be thrown at them as they entered 2020, given the upheaval that the unbundling of execution and research has brought to their industry during the past few years. The answer was Covid and the switch to remote delivery.

They still face the challenge of making sure they provide quality research at the right valuation, as budgets are capped, but now also have to do this with the shift to digital distribution. Establishing a clear valuation and pricing mechanism for research provision remains a challenge for asset managers, even as they expand their consumption of different types of analysis.The demand for research in areas such as ESG and artificial intelligence (AI) has seen an acceleration in the use of expert networks and greater discussion over what should be deemed a research expense and what should come from the market data budget.

2020-11-23 12:29:36.409000 Read the full story…
Weighted Interest Score: 2.9026, Raw Interest Score: 1.5933,
Positive Sentiment: 0.1874, Negative Sentiment 0.2812

Liquidnet Adds Senior Data Science Staff to IA Unit • Integrity Research

Liquidnet, a New York-based technology driven trading and analytics provider, recently announced that it has added three new senior staff to its Investment Analytics (IA) data science team, and appointed former Prattle executive Steven Nichols as Head of NLP and Unstructured Data for the firm.

Liquidnet recently promoted Steven Nichols to the role of Head of NLP and Unstructured Data. Nichols joined Liquidnet through the firm’s 2019 acquisition of technology company Prattle, where he was a Director of Data Science. Steven has been instrumental in the development of Liquidnet’s NLP capabilities and their integration into the Liquidnet Investment Analytics product suite, combining AI tools like machine learning and NLP with traditional and alternative data to help uncover the actionable insights hidden within data and content. In his new role, Steven will guide the strategic direction of the NLP team while serving as one of the leaders on the Liquidnet data science team.

2020-11-23 07:30:00+00:00 Read the full story…
Weighted Interest Score: 4.9827, Raw Interest Score: 2.0127,
Positive Sentiment: 0.1059, Negative Sentiment 0.0794

TensorFlow 2 on Raspberry Pi. TensorFlow Lite on Raspberry Pi 4 can…

With the new Raspberry Pi 400 shipping worldwide, you might be wondering: can this little powerhouse board be used for Machine Learning?

The answer is, yes! TensorFlow Lite on Raspberry Pi 4 can achieve performance comparable to NVIDIA’s Jetson Nano at a fraction of the dollar and power cost. You achieve real-time performance with state-of-the-art neural network architectures like MobileNetV2 by adding a Coral Edge TPU USB Accelerator.

2020-11-22 19:13:30.090000+00:00 Read the full story…
Weighted Interest Score: 4.8173, Raw Interest Score: 1.5105,
Positive Sentiment: 0.1092, Negative Sentiment 0.0546

AI Weekly: The state of machine learning in 2020

It’s hard to believe, but a year in which the unprecedented seemed to happen every day is just weeks from being over. In AI circles, the end of the calendar year means the rollout of annual reports aimed at defining progress, impact, and areas for improvement.

The AI Index is due out in the coming weeks, as is CB Insights’ assessment of global AI startup activity, but two reports — both called The State of AI — have already been released.

Last week, McKinsey released its global survey on the state of AI, a report now in its third year. Interviews with executives and a survey of business respondents found a potential widening of the gap between businesses that apply AI and those that do not.

2020-11-27 00:00:00 Read the full story…
Weighted Interest Score: 4.3369, Raw Interest Score: 1.6162,
Positive Sentiment: 0.1515, Negative Sentiment 0.1515

How software engineers and data scientists can collaborate together

Data scientists are great mathematicians with a lot of cross-disciplinary knowledge and a super ability for analysis. The task of this specialist is to find the ideal formula for training artificial intelligence. Among all the existing algorithms, they should look for the one that is better suited to solving the project’s problems and understand what exactly is going wrong. However, in order to increase the competitive advantage of the company, data scientists need to cooperate with software engineers, like dedicated Laravel engineer

Working with data is more research-oriented than software development, for instance, Laravel application development. Laravel developer can take over the technical side of the issue. At any stage of the work, both data scientists and engineers must feel responsible for the problem and be able to contribute. There is continuous communication, so that potential inconsistencies are identified early. In this article, we’ll take a closer look at the challenges a software developer and data scientist face in the process and how collaboration between them can be improved.

2020-11-24 12:36:23+00:00 Read the full story…
Weighted Interest Score: 4.2899, Raw Interest Score: 2.3059,
Positive Sentiment: 0.3334, Negative Sentiment 0.3751

Humans can’t escape accountability for decisions made by artificial intelligence

Algorithms are in theory a useful tool, but their “black box” decisions shouldn’t be accepted without question

The way organisations make decisions is changing. An explosion in the volume of data, coupled with the growing sophistication and accessibility of algorithms, means that increasingly organisations have opportunities to use machine learning and artificial intelligence to support decision-making….

This requires good, anticipatory governance. Many of the high profile cases of algorithmic bias could have been anticipated with careful evaluation and mitigation of the potential risks. Organisations, and more specifically their leaders, need to make sure that the right capabilities and structures are in place to ensure that this happens both before algorithms are introduced into decision-making processes, and throughout the process. Doing this …
2020-11-27 00:00:00 Read the full story…
Weighted Interest Score: 4.0816, Raw Interest Score: 1.1946,
Positive Sentiment: 0.2987, Negative Sentiment 0.1991

AI in Finance: how to finally start to believe your backtests [3/3]

Understanding strategy risk and the probability of overfitting: small numbers that change everything

Note from Towards Data Science’s editors: While we allow independent authors to publish articles in accordance with our rules and guidelines, we do not endorse each author’s contribution. You should not rely on an author’s works without seeking professional advice. See our Reader Terms for details.

This is the third and the last article in a short series about “how to believe the backtests”. We have started with an overview o…
2020-11-30 14:06:55.841000+00:00 Read the full story…
Weighted Interest Score: 4.0279, Raw Interest Score: 2.1446,
Positive Sentiment: 0.2437, Negative Sentiment 0.2275

ON THE MOVE: Khan Joins Nasdaq’s Quandl

Nasdaq’s Quandl, an alternative data provider, appointed Hamza Khan as Head of European Data. Khan was formerly the CEO and founder of Suburbia, a technology company that specialized in alternative data solutions. Based in Amsterdam with connections across the continent on the buy and sell side, Khan will lead the organization’s data strategy in Europe and help expand its presence in the European market. Khan began his career as a quantitative analyst and was the head of commodities strategy at ING prior to founding Suburbia.

2020-11-23 04:12:22-05:00 Read the full story…
Weighted Interest Score: 3.9007, Raw Interest Score: 1.8514,
Positive Sentiment: 0.1322, Negative Sentiment 0.0000

GrAI Matter Labs Raises $14M to Bring Fastest AI per Watt to Every Device on the Edge

GrAI Matter Labs, a pioneer of brain inspired ultra-low latency computing, today announced its latest financing round of $14 million. The round was led by iBionext, joined by all existing investors and newly welcomed Bpifrance through the Future Investment Program and Celeste Management. The company will utilize the funds to accelerate design and market launch of its first GrAI® full-stack AI system-on-chip platform, to deliver on customer needs at the edge.

GrAI Matter Labs’ programmable NeuronFlow technology enables industry-leading inference latency efficiently – more than an order of magnitude better than competing solutions. Its current accelerator chip GrAI One and the GrAI One HDK are available for product evaluation and application programming. The upcoming GrAI® full-stack AI system-on-chip platform will drive a significant step in visual inference capabilities in robotics, industrial automation, AR/VR and surveillance products and markets.

2020-11-23 00:00:00 Read the full story…
Weighted Interest Score: 3.7072, Raw Interest Score: 1.6497,
Positive Sentiment: 0.4124, Negative Sentiment 0.0317

How Much Does The Future Depend Upon Artificial Intelligence?

AI has changed the world and it is not going to stop. It’s time for you to know what it offers.

Artificial Intelligence has grown and been adapted to become a game-changer for conducting businesses in the 21st century. From eliminating guesswork from your decision making to making repetitive and mindless tasks redundant, AI has already become a major attraction among the biggest businesses in the world. As good trickle-down effect works, the rest of the world is also going through this inevitable development.

Let’s begin and understand what makes AI the need of the hour and where it drives our future.

2020-11-23 13:50:44+00:00 Read the full story…
Weighted Interest Score: 3.5768, Raw Interest Score: 1.3609,
Positive Sentiment: 0.1864, Negative Sentiment 0.1864

3 Ways To Utilize The Power Of Artificial Intelligence For Your Marketing Today

3 Reasons You Should Consider Artificial Intelligence For Your Marketing purposes with some useful links to help you get started.

Artificial Intelligence is the main talking point of the entire century. With the advancements in Artificial Intelligence (AI) and technology, nobody wants to fall behind in their marketing strategies, especially tech giants and modern start-ups. This scenario leads to some engrossing questions in our minds.

Some of these questions are — What are the ways you could utilize this growing trend to your benefit? How can you make the best possible us…
2020-11-30 14:55:22.374000+00:00 Read the full story…
Weighted Interest Score: 3.4955, Raw Interest Score: 1.9270,
Positive Sentiment: 0.5506, Negative Sentiment 0.3441

AI rewrites 21st century debt collection practices

Artificial Intelligence (AI) technologies are the focus of intense excitement at the moment, and nowhere more so than in the fintech sector. In just the last year, we’ve looked at the rise of AI in retail banking, and also explored whether AI is the future of commercial lending. And while many of the promises of AI have yet to be realised, it’s increasingly apparent that everyone is looking to the technology to revolutionise the banking industry.

The same cannot be said for debt collection firms. To say that these firms have an image problem is to understate the negativity with which they are regarded by both consumers and a new breed of responsible fintech companies. Think of debt collection, and the immediate image that comes to mind is a string of threateningly incessant phone calls demanding money from scared debtors. Forward-thinking creditors have begun to wonder if such tactics are efficient or effective.

It doesn’t have to be like this, some debt collectors have realised, and are looking to change stereotypes via innovative uses of technology. AI is a key component of this. In this article, we’ll look at how they are doing so.

2020-11-25 00:01:01+00:00 Read the full story…
Weighted Interest Score: 3.3481, Raw Interest Score: 1.2923,
Positive Sentiment: 0.2891, Negative Sentiment 0.3741

Optimizing AI and Deep Learning Performance

As AI and deep learning uses skyrocket, organizations are finding they are running these systems on similar resource as they do with high-performance computing (HPC) systems – and wondering if this is the path to peak efficiency.

Ostensibly AI and HPC architectures have a lot in common, as AI has evolved into even more data-intensive machine learning (ML) and deep learning (DL) domains (Figure 1). Workloads often require multiple GPU systems as a cluster, and share those systems in a coordinated way among multiple data scientists. Secondly, both AI and HPC workloads require shared access to data at a high level of performance and communicate over a fast RDMA-enabled network. Especially in scientific research, the classic HPC systems nowadays tend to have GPUs added to the compute nodes to have the same cluster suitable for classic HPC and new AI/DL workloads.

Yet AI and DL are different from HPC, their applications needs are different, and the deep learning process in particular (Figure 2) has requirements that simply buying more GPU servers won’t fix.

2020-11-26 00:00:00 Read the full story…
Weighted Interest Score: 3.3331, Raw Interest Score: 1.7056,
Positive Sentiment: 0.2360, Negative Sentiment 0.1066

Bayesian probability mass estimation using TensorFlow

When all you have are categorical variables

I have spent some time studying data with categorical variables trying to explore many ways to encode them into numeric features. What if all your variables are categorical? One of the mechanism to describe this scenario known as contingency tables.

Contingency tables in their essence are (potentially multidimensional) tables where rows, columns and other dimensions represent categorical variables, and the cells contain counts of the occurrences of the combinations. As an example, consider a simple contingency table that represents salary (rows) vs. years of experience (columns). The data are taken from [1] and reported by a study conducted by the Department of Energy.

The task of probability mass estimation is to learn probabilities of every combination of categories. A naïve approach would be to set the probabilities as fractions of observed cell count and total sample size:

2020-11-23 13:46:21.146000+00:00 Read the full story…
Weighted Interest Score: 3.1882, Raw Interest Score: 1.4814,
Positive Sentiment: 0.1470, Negative Sentiment 0.1244

Why Data Republic is hitting reset

Data Republic, a company that attracted millions in funding from the likes of Qantas, NAB, Westpac and Singtel for its data marketplace platform has reset its strategy and will now offer enterprise software.

The company sees more potential in facilitating accessibility and innovation than in data commercialisation, and wants to change now to move with the evolving data economy.

2020-11-29 19:00:51+00:00 Read the full story… GONE: Closest story
Weighted Interest Score: 3.1780, Raw Interest Score: 1.8064,
Positive Sentiment: 0.2529, Negative Sentiment 0.0361

Implement Expectation-Maximization(EM) in Python from scratch

Unsupervised and Semi-supervised Gaussian Mixture Models (GMM)

When companies launch a new product, they usually want to find out the target customers. If they have data on customers’ purchasing history and shopping preferences, they can utilize it to predict what types of customers are more likely to purchase the new product. 
2020-11-27 16:27:21.505000+00:00 Read the full story…
Weighted Interest Score: 3.1173, Raw Interest Score: 1.4003,
Positive Sentiment: 0.0289, Negative Sentiment 0.1444

Industry Looks to Unlock Liquidity in Chats

Maryanne Richter, executive director, electronic credit trading strategy, global credit at Morgan Stanley, said the bank is looking for a way to automatically convert chats into request for quotes in credit markets.

Richter spoke in a webinar, Unlocking Liquidity through People and Machines, hosted by consultant Greenwich Associates last week.

2020-11-25 10:36:45-05:00 Read the full story…
Weighted Interest Score: 3.0705, Raw Interest Score: 1.5181,
Positive Sentiment: 0.2693, Negative Sentiment 0.1959

The Maturation of Data Science

Data science used to be somewhat of a mystery, more of a dark art than a repeatable, scientific process. Companies basically entrusted powerful priests called data scientists to build magical algorithms that used data to make predictions, usually to boost profits or improve customer happiness. But in recent years, the field has matured to a remarkable degree, and that is enabling progress to be made on multiple fronts, from ModelOps and reproducibility to ethics and accountability.

About five years ago, the worldwide scientific community was suffering a “reproducibility crises” that impacted a wide range of scientific endeavors, including so-called hard sciences like physics and chemistry. One of the hallmarks of the scientific method is that experiments must be reproducible and will give the same results, but that lofty goal too often was not met.

Data science was not immune to this problem, which should not be surprising, given the relative newness and the probabilistic nature of the field. And when you mix in the black box nature of deep learning models and data that reflects a rapidly changing world, sometimes it seems a miracle that an algorithm of any complexity could generate the same result at two points in time.

2020-11-25 00:00:00 Read the full story…
Weighted Interest Score: 2.8104, Raw Interest Score: 1.2893,
Positive Sentiment: 0.2904, Negative Sentiment 0.2207

Future of Business Intelligence in the Data-Driven Economy

As the use of intelligence technologies is staggering, knowing the latest trends in business intelligence is a must. The market for business intelligence services is expected to reach $33.5 billion by 2025. Here we’ve prepared a detailed outline about the future of BI, including main trends, challenges, specifics, BI-as-a-Service, and most promising BI services of today.

In this article, you’ll discover:

• upcoming trends in business intelligence
• what benefits will BI provide for businesses in 2020 and on?
• top 5 key platforms that control the future of business intelligence
• impacts BI may have on your business in the future

2020-11-24 21:05:51+00:00 Read the full story…
Weighted Interest Score: 2.7900, Raw Interest Score: 1.5959,
Positive Sentiment: 0.4622, Negative Sentiment 0.1541

Amazon Targeting Banks, Financial Firms for Fresh AWS Talent

When Amazon Web Services (AWS), Amazon’s cloud division, announced its third quarter results for 2020 a few weeks ago, it disclosed a 37 percent year-on-year increase in revenues. That revenue growth appears to have catalyzed hiring, and Amazon has a very particular target in mind when it comes to poaching top talent.

AWS currently has over 360 vacancies in New York City, mostly for solutions architects, but also for roles such as software developers and account managers (including two roles for account managers in financial services). Recent history suggests that at least some of these roles will be filled by people moving from leading investment banks.

AWS likes to hire from Goldman Sachs. One of its most recent Goldman hires is Roger Li, a former Goldman VP and member of the machine learning team in Jersey City. Li joined AWS this month as an applied scientist in NYC, and he will be in good company: In June, AWS also poached Jeff Savio, a VP in Goldman’s asset management business, to manage accounts with fintech start-ups.

2020-11-23 00:00:00 Read the full story…
Weighted Interest Score: 2.7750, Raw Interest Score: 1.7863,
Positive Sentiment: 0.1323, Negative Sentiment 0.0662

How to Go Beyond an Ordinary Data Scientist

Ways to be Distinguished in the Age of Data Science Boom

Suppose you are the hiring manager for a data scientist position, and interviewing a prospective candidate. The candidate starts to express the skills hoping they are enough for the position and the best card among these skills is MS Excel capability. What would you think about this candidate? I suppose most of you would consider this candidate as mediocre, which is ineligible for most of the companies. Let’s make a little change in our hypothetical interview by replacing MS Excel with predictive modelling. In the new scenario, the candidate obviously has more chances to be recruited and considering the high need for data science skilled people in today’s business world, it is fairly reasonable. But how long will being proficient for just machine learning be enough to stay ahead of the game? I cannot say precisely, however, I do believe the destiny of basic machine learning skills will be similar to Excel.

2020-11-30 13:41:12.081000+00:00 Read the full story…
Weighted Interest Score: 2.7581, Raw Interest Score: 1.3675,
Positive Sentiment: 0.4156, Negative Sentiment 0.2815

The Top Trends in Data Management for 2021

From the rise of hybrid and multicloud architectures, to the impact of machine learning and automation, the business of data management is constantly evolving with new technologies, strategies, challenges and opportunities. The demand for fast, wide-range access to information is growing. At the same time, the need to effectively integrate, govern, protect and analyze data is also intensifying. All the while, data environments are increasing in size and complexity — traversing relat…
2020-12-10 00:00:00 Read the full story…
Weighted Interest Score: 2.7460, Raw Interest Score: 1.7571,
Positive Sentiment: 0.0764, Negative Sentiment 0.0764

How Hasty uses automation and rapid feedback to train AI models and improve annotation

Computer vision is playing an increasingly pivotal role across industry sectors, from tracking progress on construction sites to deploying smart barcode scanning in warehouses. But training the underlying AI model to accurately identify images can be a slow, resource-intensive endeavor that isn’t guaranteed to produce results. Fledgling German startup Hasty wants to help with the promise of “next-gen” tools that expedite the entire model training process for annotating images.

The global computer vision market was pegged at $11.4 billion in 2020, a figure that is projected to rise to more than $19 billion by 2027. Data preparation and processing is one of the most time-consuming tasks in AI, accounting for around 80% of time spent on related projects. In computer vision, annotation, or labeling, is a technique used to mark and categorize images to give machines the meaning and context behind the picture, enabling them to spot similar objects. Much of this annotation work falls to trusty old humans.

2020-11-24 00:00:00 Read the full story…
Weighted Interest Score: 2.6510, Raw Interest Score: 1.4578,
Positive Sentiment: 0.1310, Negative Sentiment 0.2785

Top AI Based Smartphone Apps Of 2020

For many years, AI has eluded smartphones owing to its own resource-heavy nature. With the introduction of tools like TFLite and other frameworks, machine learning models became lighter; light enough to be boarded onto the limited space on a mobile or an edge device. This allowed app developers to incorporate models into their apps for better experience. From Maps to face filters, from e-learning to contact tracing during COVID-19, AI came in quite handy. In this article we list few of the most popular AI based apps (not necessarily created this year) that have made their presence felt this year.

• COVID-19 Sounds App
• Microsoft Math Solver
• AI Dungeon

2020-11-28 04:30:00+00:00 Read the full story…
Weighted Interest Score: 2.5899, Raw Interest Score: 1.0234,
Positive Sentiment: 0.0981, Negative Sentiment 0.1542

This is how we’ll merge with AI

The relationship between humans and AI is something of a dance. We and AI come close together operating collaboratively, then are pushed away by the impossibility, only to stumble but return attracted by the potential. It is perhaps fitting that the dance community is beginning to embrace robots, with AI helping to create new movements and choreography, and with robots sharing the stage with human dancers.

The relationship between society and technology is yin and yang, with every massive enhancement accompanied by the potential for danger. AI, for example, offers the promise to end boring, repetitive jobs, enabling us to engage in higher level and more fulfilling tasks. It helps with any number of efficiency efforts, such as fraud detection, and it can even paint masterpiece artworks and compose symphonies. Sam Altman, CEO of OpenAI, hopes AI will unlock human potential and let us focus on the most interesting, most creative, most generative things.
2020-11-23 00:00:00 Read the full story…
Weighted Interest Score: 2.4650, Raw Interest Score: 1.0822,
Positive Sentiment: 0.4200, Negative Sentiment 0.1615

A vision for data driven lending

“The past is a foreign country – they do things differently there” – so runs the infamous first line of L.P. Hartley’s novel, The Go-Between. And so the unprecedented economic circumstances associated with Covid-19 are set to alter the lending landscape: lenders are dealing with material impairments, degradation of loan portfolios and spikes in forbearance requests. With the potential for cyclical lockdowns and business restarts on the horizon, lenders are having to monitor credit risk with limited visibility. Historic data sets previously used to drive a credit decision are less predictive of future circumstances.

Advanced data and analytics capabilities – the access to and mining of transactional data; real-time monitoring and advanced decisioning are integral to the solution. For traditional, incumbent institutions, the crisis represents perhaps the single most compelling opportunity to digitalise their systems. And for challenger banks and alternative lenders, an opportunity to steal a march: placing the plethora of open data sources available at the heart of their propositions.

2020-11-30 08:00:00 Read the full story…
Weighted Interest Score: 2.3681, Raw Interest Score: 1.3411,
Positive Sentiment: 0.3245, Negative Sentiment 0.2055

7 Things I Learned during My First Big Project as an ML Engineer

Important advice about machine learning from development to production

Apparently Covid-19 is not the only one that keeps on increasing significantly (hopefully it will end up pretty soon!), NLP research has also grown exponentially over the last couple of years.

One of my biggest mistakes was using only 1 method for any type of data. Simply because it works best with certain data. In other words, I only did an experiment at the beginning of the project. After finding the best method, I keep on…
2020-11-30 15:03:20.541000+00:00 Read the full story…
Weighted Interest Score: 2.2950, Raw Interest Score: 1.2547,
Positive Sentiment: 0.3326, Negative Sentiment 0.2721

IT Departments Find Timing is Good to Modernize Legacy Systems; AI Can Help

The pandemic era of increased remote work and powerful available AI is motivating IT departments to examine legacy software systems for renewal. A legacy application, as defined by Gartner, is “an information system that may be based on outdated technologies, but is critical to day-to-day operations.”

This process of renewal can also be called modernization and often involves a move from on-premises hardware to the cloud.

2020-11-24 22:59:42+00:00 Read the full story…
Weighted Interest Score: 2.1773, Raw Interest Score: 1.1020,
Positive Sentiment: 0.1090, Negative Sentiment 0.1453

AWS Infrastructure Solutions BrandVoice: How The Edge Drives Innovation In 3 Major Industries

Edge computing systems are managing an increasing amount of data as businesses across industries realize how the technology can drive innovation.

The edge represents locations beyond centralized clouds where data is either processed or analyzed close to where it’s created. In many cases, this data is generated by a device, such as a camera or sensor, that is connected to the Internet of Things.

2020-11-25 00:00:00 Read the full story…
Weighted Interest Score: 2.1248, Raw Interest Score: 1.1754,
Positive Sentiment: 0.2486, Negative Sentiment 0.1808

Data Management Best Practices for Machine Learning – Webinar

Machine learning is on the rise at businesses hungry for greater automation and intelligence. A recent study fielded amongst the subscribers of DBTA found that 48% currently have machine learning initiatives underway with another 20% considering adoption. At the same time, most projects are still in the early phases. Machine learning is the new kid on the block. From data quality issues, to architecting and optimizing models and data pipelines, there are many success factors to keep in mind.
2021-01-21 00:00:00 Read the full story…
Weighted Interest Score: 2.1106, Raw Interest Score: 1.7224,
Positive Sentiment: 0.4053, Negative Sentiment 0.1013

Improving virtual assistants’ performance using semantic search and Sentence Transformers

How is our well designed virtual assistant able to capture (or detect) the user intention? Here is where natural language understanding plays its role by performing the typical cognitive task that is extremely (sometimes) easy for humans and an authentic nightmare for computers.

The NLP/NLU techniques allow us to tackle this task in different ways. In the scope of the intent detection paradigm, we need to define the top…
2020-11-29 13:19:28.316000+00:00 Read the full story…
Weighted Interest Score: 1.9063, Raw Interest Score: 1.1499,
Positive Sentiment: 0.3493, Negative Sentiment 0.2475

Database Management Today: New Strategies and Technologies

From machine learning and automation, to hybrid and multicloud environments, technology trends continue to reshape the practice of database management. As a result, database professionals face new challenges and opportunities. Today, the average database team is tasked with managing more databases, bigger databases and a greater variety of databases – from the ground to the cloud. At the same time, businesses are hung…
2021-04-22 00:00:00 Read the full story…
Weighted Interest Score: 1.8834, Raw Interest Score: 1.4376,
Positive Sentiment: 0.3594, Negative Sentiment 0.1797

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.

Alternative Data News. 02, December 2020

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.

Why did renewables become so cheap so fast? And what can we do to use this global opportunity for green growth?

For the world to transition to low-carbon electricity, energy from these sources needs to be cheaper than electricity from fossil fuels.

Fossil fuels dominate the global power supply because until very recently electricity from fossil fuels was far cheaper than electricity from renewables. This has dramatically changed within the last decade. In most places in the world power from new renewables is now cheaper than power from new fossil fuels.

The fundamental driver of this change is that renewable energy technologies follow learning curves, which means that with each doubling of the cumulative installed capacity their price declines by the same fraction. The price of electricity from fossil fuel sources however does not follow learning curves so that we should expect that the price difference between expensive fossil fuels and cheap renewables will become even larger in the future.

2020-12-01 Read the Full Story…

CloudQuant Thoughts : The future for Energy looks very rosy indeed!

S&P Global And IHS Markit To Merge

All-Stock Transaction Valuing IHS Markit at $44 Billion, Powering the Markets of the Future

Joins Two World-Class Organizations with Unique, Highly Complementary Assets to Enhance Customer Value Proposition

Combined Company to Benefit from Increased Scale and Mix Across Core Markets with Attractive Growth Adjacencies

Expected to be Accretive to Earnings by the End of the Second Full Year Post-Closing with ~$480 Million of Annual Run-Rate Cost …
2020-11-30 07:07:33+00:00 Read the full story…
Weighted Interest Score: 2.9668, Raw Interest Score: 1.6110,
Positive Sentiment: 0.4658, Negative Sentiment 0.1359

S&P Global Gobbles Up IHS Markit In $44 Bn Deal

Credit ratings giant S&P Global reached an all-stock deal to buy IHS Markit for $44 billion, the companies announced Monday.

The merger will create a $126 billion financial services behemoth that will be headquartered in New York.

The firms said joining forces will bolster offerings to investor clients and provide complementary information streams in growth areas, such as in the shifts towards renewable energy and environmental, social and governance (ESG) investing.

2020-12-01 12:11:27+11:00 Read the full story…
Weighted Interest Score: 2.5915, Raw Interest Score: 1.5245,
Positive Sentiment: 0.2595, Negative Sentiment 0.0649

S&P agrees to pay a staggering $44 billion for IHS Markit, but will it be allowed?

Although the deal is yet to be approved by regulators, and could be thwarted if deemed to create a data monopoly, IHS Markit’s sale represents a potentially massive transaction and shows that data is the absolute essence of the future of markets.

Data, and its vital importance in capital markets, is most certainly the order of the day.

The astonishing price that S&P has agreed to pay for IHS Markit, about which CEO Lance Uggla told employees in…
2020-11-30 16:24:19+02:00 Read the full story…
Weighted Interest Score: 2.5731, Raw Interest Score: 1.4295,
Positive Sentiment: 0.0953, Negative Sentiment 0.2224

CloudQuant Thoughts : This week’s big news in the Alternative Data Universe!

Pandas Sidetable: A Smarter Way of Using Pandas

A great merge of value counts and cross tab functions

Pandas is a very powerful and versatile Python data analysis library that expedites the preprocessing steps of data science projects. It provides numerous functions and methods that are quite useful in data analysis.

Although the built-in functions of Pandas are capable of performing efficient data analysis, custom made functions or libraries add value to Pandas. Sidetable is one of these add-ons which makes it easier to create summaries of dataframes. It can be considered as a combination of value counts and cross tab functions.
2020-12-02 13:11:07.448000+00:00 Read the full story…
Weighted Interest Score: 3.1081, Raw Interest Score: 1.2838,
Positive Sentiment: 0.4054, Negative Sentiment 0.0000

CloudQuant Thoughts : Sidetable looks like a very nice, useful library!

A.I. Specialist, Robotics Engineer Top Emerging Jobs List

Which technology jobs are poised to go from niche to mainstream over the next few years? That’s a vital question to answer, especially for those technologists who spend lots of time and resources acquiring highly specialized skills in arenas such as machine learning and artificial intelligence (A.I.).

For the third year in a row, LinkedIn has produced an Emerging Jobs Report (PDF) that tries to guess which jobs will experience “tremendous growth” over the next few years. As you might expect, most of these roles are technology-related; once businesses realize that embracing A.I. or data science can mean the difference between wild success and complete implosion, they rush to employ as many technologists as they can.

LinkedIn’s report concludes that it’s never a bad time to become an engineer or a data scientist: “engineering roles across the board are still seeing tremendous growth. More than 50% of this year’s list was made up of roles related to engineering or development, with the emerging field of robotics appearing for the first time.” Here’s the breakdown of the top positions, along with anticipated growth and the necessary skills for each:

2020-12-02 00:00:00 Read the full story…
Weighted Interest Score: 2.8398, Raw Interest Score: 1.8247,
Positive Sentiment: 0.1935, Negative Sentiment 0.1106

CloudQuant Thoughts : AI Specialist is streaking out in the lead!!

14 Data Science projects to improve your skills

There’s a lot of data out there and so many data science techniques to master or review. Check out these great project ideas from easy to advanced difficulty levels to develop new skills and strengthen your portfolio.

Take this time in isolation to learn new skills, read books, and improve yourself. For those interested in data, data analytics, or data science, I’m providing a list of fourteen data science projects that you can do during your spare time!

There are three types of projects:

• Visualization projects
• Exploratory data analysis (EDA) projects
• Prediction modeling

2020-12-14 00:00:00 Read the full story…
Weighted Interest Score: 4.6355, Raw Interest Score: 1.4865,
Positive Sentiment: 0.2973, Negative Sentiment 0.2973

CloudQuant Thoughts : Lots of Easy level projects for people to try!

ESG Section

CloudQuant also provides Alternative Data Sets including an excellent ESG data set with proven Alpha. Head over to our data catalog for more information.

Diligend launches new module for ESG & Diversity data disclosures

Diligend, a financial data collection and analysis software provider, has laucnhed an ESG and Diversity disclosure module for Limited Partners (LPs), General Partners (GPs), consultants, fund of funds, fund managers and fund administrators.

The tailored and flexible set of new features allows the clients to request specific ESG & Diversity data quickly, then, swiftly rate, and assess considerations at the firm or fund level.

Diligend provides clients with readily available and automated ESG & Diversity questionnaires that they can instantly distribute to hundreds of fund managers or portfolio companies. Clients can also adjust questions and refine scoring metrics to fit their process.

2020-12-01 00:00:00 Read the full story…
Weighted Interest Score: 6.5068, Raw Interest Score: 3.0822,
Positive Sentiment: 0.0000, Negative Sentiment 0.0000

NASDAQ’s VC arm invests in Danish sustainable analytics firm as part of European Data business

Data continues to be the order of the day, as NASDAQ takes a shine to Matter, which provides retail and institutional investors with in-depth insight of the ESG impact of their portfolios.

Nasdaq Ventures, Nasdaq’s (Nasdaq: NDAQ) investment arm, announced today a strategic investment in Matter, the sustainability analysis and reporting provider, as it continues to innovate capabilities that help enlighten sustainable investing decisions. Nasdaq’s investment in Copenhagen-based Matter further extends and complements its existing partnership with Matter via Nasdaq’s European data business.

The Nasdaq ESG Footprint solution is powered by Matter’s analytics technology and provides retail and institutional investors with in-depth insight of the ESG impact of their portfolios.
2020-12-01 14:16:55+02:00 Read the full story…
Weighted Interest Score: 5.1819, Raw Interest Score: 2.4763,
Positive Sentiment: 0.4370, Negative Sentiment 0.1092

Nasdaq Ventures Invests In ESG Analytics Provider

“We are excited to welcome Nasdaq Ventures as an investor and look forward to strengthening our partnership with Nasdaq around sustainability,” said Niels Fibæk-Jensen, CEO of Matter. “The investment will enable us to continue the expansion of our sustainability analysis and reporting solutions for financial institutions. Together with Nasdaq, we can enable a better understanding of the impact of capital by delivering sustainability insights in a…
2020-12-01 06:10:02+00:00 Read the full story…
Weighted Interest Score: 4.3011, Raw Interest Score: 2.0417,
Positive Sentiment: 0.5326, Negative Sentiment 0.0888

ICE And CTBC Investments Collaborate On ESG

r of global exchanges and clearing houses and provider of mortgage technology, data and listings services, today announced that ICE Data Services and CTBC Investments Co., will collaborate to develop environmental, social and governance (ESG) indices and financial products for market participants.

ICE has launched two new ESG bond indices, the ICE 15+ Year Large Cap USD Emerging Markets External Sovereign Carbon Reduction Index and the ICE 15+ Year Ultra Large Cap Developed Markets US Corporate Best-in-Class ESG Index. The new ESG indices are administered and calculated by ICE Data Indices, LLC (“IDI”) and are broadly dissemina…
2020-12-01 05:59:03+00:00 Read the full story…
Weighted Interest Score: 3.4572, Raw Interest Score: 1.5578,
Positive Sentiment: 0.3462, Negative Sentiment 0.0000

ICE looks to clean up its act with the launch of new fixed income ESG indices

CTBC Investments will launch investable tracking products, such as ETFs to reflect the performance of the ESG indices.

ESG, a three letter acronym for Environmental Social and Governance, has been the buzzword-du-jour for financial markets throughout 2020 and seem likely to remain so into 2021 and beyond.

Asset managers have been busy launching new funds and repurposing old vehicles to meet the demand from investors for more ethically surefooted products, whilst rating agencies are actively promoting their own scoring and ranking systems for ESG and sustainability criteria.

The…
2020-12-01 14:49:42+02:00 Read the full story…
Weighted Interest Score: 3.2168, Raw Interest Score: 1.4040,
Positive Sentiment: 0.1053, Negative Sentiment 0.0351

The problem with data on investing in ESG stocks

Investing in ESG stocks has been heating up in recent years, but the firms that provide the data may be the only ones that are winning in the ESG revolution. In a recent report, Factor Research examined the ESG data from one provider and analyzed stock performance.

Author Nicolas Rabener found that the stocks with the worst ESG rating in the data set he looked at outperformed over the last 12 months. This suggests that proponents who claim ESG boosts stock prices may not be seeing the whole story when it comes to the data.

Rabener explained that the “ESG ecosystem” has three categories of players. They are the data providers, data consumers like asset managers and index providers, and asset allocators. However, he argues that there’s only one winner in ESG investing, and that’s the data providers that sell ESG ratings.

2020-12-01 18:59:09+00:00 Read the full story…
Weighted Interest Score: 3.1592, Raw Interest Score: 1.8075,
Positive Sentiment: 0.2312, Negative Sentiment 0.1681

NVIDIA Launches MONAI Framework To Accelerate AI In Healthcare

In an attempt to accelerate artificial intelligence in the healthcare industry, NVIDIA has launched MONAI — a Medical Open Network for AI, a domain-optimised, open-source framework for healthcare. According to the official release by NVIDIA, MONAI is now ready for production with the upcoming release of the company’s Clara application framework for AI-powered healthcare and life sciences.

The framework, MONAI, is a PyTorch-based framework which was introduced in April 2020 and has already been adopted by some of the leading healthcare research institutions. This framework advances the usage of artificial intelligence for medical imaging. This is done with industry-specific data handling, reproducible reference implementations of state-of-the-art approaches, and high-performance training workflows.

2020-12-01 07:45:10+00:00 Read the full story…
Weighted Interest Score: 2.9425, Raw Interest Score: 1.4828,
Positive Sentiment: 0.2399, Negative Sentiment 0.0436

Next gen of alt data

While the relationship between systematic hedge funds and alternative data sources is not new, quants have recently raised concerns that alt data is not giving the same alpha as it used to. This is because people are increasingly accessing the same data sets (web scraping, search analytics, satellite feeds etc.), making it difficult to find investing signals and high-frequency insight amongst the noise.

New data sets are needed to satisfy the demand for data-driven insights to spot long-term trends, improve trading decisions and ultimately drive performance. And while the industry understands the benefits of real-time (or near real-time) data, market participants are waking up to the predictive power of pricing data that comes from having access to vast amounts of historic data. Having a minimum of 5 years’ worth of historic Level 3 order book data is what is needed to have a meaningfully predictive data set. Or in other words…the next generation of alternative data.

2020-12-01 00:00:00 Read the full story…
Weighted Interest Score: 6.2447, Raw Interest Score: 2.7538,
Positive Sentiment: 0.1689, Negative Sentiment 0.1352

Is synthetic data the key to the next data boom in financial services?

Across industries, data is recognised as an organisation’s most valuable asset. From data comes knowledge and new insights that can be used to improve every function of a business, from new and better products and services for customers, to operational efficiencies. As data strategies mature, firms are turning an increasingly expectant eye toward the possibilities enabled by advanced technologies such as AI, machine learning and data science.

AI and ML models have delivered unprecedented value in many industries. In financial services, they have unlocked incredible efficiencies by automating decision-making processes, risk calculation and enabled the creation of ever more intelligent solutions. In each case, the quality of the models is determined by two major factors: quantity and quality.
2020-11-27 Read the Full Story…

TORA’s OEMS Integrates with Liquidnet’s IA Trader to offer real time actionable decision making tools

has integrated its order & execution management system (OEMS) with global institutional investment network, Liquidnet’s IA Trader.

The integration offers access to advanced artificial intelligence, data analytics tools and a broad range of Liquidnet trading algorithms.

Integrating IA Trader directly in the TORA OEMS helps equity traders enhance their decision making process at the point of trade. The system delivers actionable signals, alerts, compact stamp summaries and data visualisation capabilities that are designed to provide clients greater day-to-day efficiency and enhanced operational workflow …
2020-12-01 00:00:00 Read the full story…
Weighted Interest Score: 4.9705, Raw Interest Score: 2.5304,
Positive Sentiment: 0.6326, Negative Sentiment 0.0973

TORA’s OEMS integrates with Liquidnet’s IA Trader to offer real time actionable decision making tools

t has integrated its order & execution management system (OEMS) with global institutional investment network, Liquidnet’s IA Trader. The integration offers access to advanced artificial intelligence, data analytics tools and a broad range of Liquidnet trading algorithms.

Integrating IA Trader directly in the TORA OEMS helps equity traders enhance their decision making process at the point of trade. The system delivers actionable signals, alerts, compact stamp summaries and data visualisation capabilities that are designed to provide clients greater day-to-day efficiency and enhanced operational workflow in…
2020-12-01 14:12:04+02:00 Read the full story…
Weighted Interest Score: 4.8099, Raw Interest Score: 2.4479,
Positive Sentiment: 0.6346, Negative Sentiment 0.0907

How software engineers and data scientists can collaborate together

Data scientists are great mathematicians with a lot of cross-disciplinary knowledge and a super ability for analysis. The task of this specialist is to find the ideal formula for training artificial intelligence. Among all the existing algorithms, they should look for the one that is better suited to solving the project’s problems and understand what exactly is going wrong. However, in order to increase the competitive advantage of the company, data scientists need to cooperate with software engineers, like dedicated Laravel engineer

2020-11-24 12:36:23+00:00 Read the full story…
Weighted Interest Score: 4.2899, Raw Interest Score: 2.3059,
Positive Sentiment: 0.3334, Negative Sentiment 0.3751

Consolidated Tape: A Vaccine for Fragmentation?

Since MiFID introduced competition to our equity markets in 2007, the resulting fragmentation of liquidity had a significant impact on the efficiency of sourcing liquidity. Whilst competition resulted in lower explicit costs (spreads), the implicit costs of accessing liquidity and data from a broad spectrum of competing markets have offset the gains. Costs aside, the visibility of liquidity in the market has been impaired to the point that investors are flying blind – ask 5 different people how much turnover traded in Air France KLM today and you will get 5 different answers.

Soon after MiFID was implemented, when these issues were first observed in equity markets, the immediate conclusion was the need for a consolidated tape which would logically provide a single source of information about market liquidity. A closer look, however, reveals a more complex challenge resulting from the quality of this information. Aside from aggregating the fragmented sources of trading venue data, the data provided by APAs for OTC trading has proven to be wildly inaccurate thereby undermining any attempts to assemble a clear picture of market liquidity. Unfortunately, these problems have little to do with how data is consolidated and reveal the actual challenge of how data is collected by APAs who are not empowered nor motivated to address these issues.
2020-11-25 10:30:08-05:00 Read the full story…
Weighted Interest Score: 2.6992, Raw Interest Score: 1.0497,
Positive Sentiment: 0.3187, Negative Sentiment 0.6560

How This Startup Is Using Computer Vision To Provide Video KYC

The potential of IoT and AI can be said as limitless. The key to finding the potential is basically to find practical use-cases where businesses can rapidly realise the value of these AI and IOT heavy investments.

At the present scenario, the whole world is moving towards a digitally connected and automated world revolving around smart gadgets, homes and cars to animated customer engagement outposts, chatbots, and personalisation of products and services.

Regarding the practical use-case of AI and IoT, one such use-case is known as Video KYC. Video KYC with its embedded capabilities can offer many enhancements, such as geo-tagging, liveness detection, image recognition, fuzzy matching, computer vision and optical character recognition, neural networks, and scalable infrastructure- to deliver better accuracy at a fraction of the cost.

2020-11-27 12:30:16+00:00 Read the full story…
Weighted Interest Score: 2.6842, Raw Interest Score: 1.5558,
Positive Sentiment: 0.0980, Negative Sentiment 0.0613

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.

AI & Machine Learning News. 07, December 2020

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?

Introducing ArtLine, Create Amazing Line art portraits.

The main aim of the project is to create amazing line art portraits.

The amazing results that the model has produced has a secret sauce to it. The initial model couldn’t create the sort of output I was expecting, it mostly struggled with recognizing facial features.

Even though APDrawingGAN produced great results it had limitations like (frontal face photo similar to ID photo, preferably with clear face features, no glasses and no long fringe.)

I wanted to break-in and produce results that could recognize any pose. Achieving proper lines around the face, eyes, lips and nose depends on the data you give the model. APDrawing dataset alone was not enough so I had to combine selected photos from Anime sketch colorization pair dataset. The combined dataset helped the model to learn the lines better.

2020-12-05 Read the Full Story…

3 Stocks Profiting From Unstoppable Trends That Could Make You Rich

Making money by investing in stocks isn’t all that complicated. There are basically three steps involved. First, identify major trends that will drive new markets. Second, find the leading companies linked to those trends. Third, buy the stocks and hold them over a long period. The last part of that third step is usually the Achilles’ heel for many investors. Holding stocks for a long time can be more difficult than it seems. It can be tempting to sell when a stock sinks or to lock in profits when a stock soars. However, the first two steps aren’t really difficult. Here are three stocks profiting from unstoppable trends that could make you rich.

1. NVIDIA (artificial intelligence) : Artificial intelligence (AI) is here to stay. Just ask Alexa or Siri. Billionaire Mark Cuban even predicts that world’s first trillionaire will make his or her fortune in AI. While there are plenty of companies that are likely to be big winners in AI, NVIDIA (NASDAQ:NVDA) looks like a sure-fire top pick. NVIDIA originally developed its graphics chips to power video games. Along the way, though, the company found that those same chips were also ideally suited for use in datacenter servers that performed AI processing. The data center market over time became a huge business for NVIDIA.

2020-12-07 00:00:00 Read the full story…
Weighted Interest Score: 2.5170, Raw Interest Score: 1.2302,
Positive Sentiment: 0.2417, Negative Sentiment 0.0879

CloudQuant Thoughts : A slow AI week if you are not Google but Nvidia comes up tops again. Motley Fool are a very well trusted trading advice site!

TensorFlow 2 on Raspberry Pi.

TensorFlow Lite on Raspberry Pi 4 can achieve performance comparable to NVIDIA’s Jetson Nano at a fraction of the cost.

With the new Raspberry Pi 400 shipping worldwide, you might be wondering: can this little powerhouse board be used for Machine Learning?

The answer is, yes! TensorFlow Lite on Raspberry Pi 4 can achieve performance comparable to NVIDIA’s Jetson Nano at a fraction of the dollar and power cost. You can achieve real-time performance with state-of-the-art neural network architectures like MobileNetV2 by adding a Coral Edge TPU USB Accelerator.

This performance boost unlocks interesting offline TensorFlow applications, like detecting and tracking a moving object.

2020-12-03 00:19:24.450000+00:00 Read the full story…
Weighted Interest Score: 4.8138, Raw Interest Score: 1.5094,
Positive Sentiment: 0.1091, Negative Sentiment 0.0546

CloudQuant Thoughts : Running Tensorflow on a full programmable computer that costs as little as $35 is extremely impressive!

Google at NeurIPS 2020

Google AI Researcher Fired?

Google AI Researcher Says She Was Fired for Critical Views

A prominent Google artificial-intelligence researcher said she was fired over an email she authored expressing dismay with management and the way it handled a review of her research.

In a tweet, Timnit Gebru, 37, who is Black, claimed she was fired from Alphabet-owned Google for refusing to retract a research paper that said AI discriminates against darker-skinned people, and complained about the company in an email to colleagues. She also criticized Google over its approach to hiring minorities and not doing enough to stamp out biases in AI systems. Gebru, a renowned scientist and one of the few Black women in the field of artificial intelligence, had been co-head of the team at Google examining the ethical ramifications of AI.

The email itself, which was reviewed by The Wall Street Journal, began with “Hi friends,” and then proceeded to criticize her superiors, alleging among other things that Google executives quashed her research and ignored her feedback on issues like the proportion of female employees in the company.

2020-12-04 12:00:16+00:00 Read the full story…
Weighted Interest Score: 3.6727, Raw Interest Score: 1.8363,
Positive Sentiment: 0.0399, Negative Sentiment 0.8782

Google AI Ethics Co-Head Reportedly Sacked for Critical Views

A prominent Google (GOOGL) – Get Report artificial-intelligence researcher says she was fired over an email she authored expressing disappointment with management and the way a review of her research was handled internally. Timnit Gebru, 37, who is Black, claimed in a tweet she was fired from Alphabet-owned Google for refusing to retract a research paper that said AI discriminates against darker-skinned people – and for complaining about the company in an email to colleagues.

The email itself, which was reviewed by The Wall Street Journal, began with “Hi friends,” and then proceeded to criticize her superiors, alleging among other things that Google executives quashed her research and ignored her feedback on issues like the proportion of female employees in the company. Gebru also criticized Google over its approach to hiring minorities and not doing enough to stamp out biases in AI systems. Gebru, a renowned scientist and one of the few Black women in the field of artificial intelligence, had been co-head of the team at Google examining the ethical ramifications of AI.

2020-12-04 16:16:29+00:00 Read the full story…
Weighted Interest Score: 3.3480, Raw Interest Score: 1.8502,
Positive Sentiment: 0.0000, Negative Sentiment 0.8811

Google AI ethics co-lead Timnit Gebru says she was fired over an email

Timnit Gebru, one of the best-known AI researchers today and co-lead of an AI ethics team at Google, no longer works at the company. She was featured in Google promotional material as recently as May. According to Gebru, she was fired Wednesday for sending an email to “non-management employees that is inconsistent with the expectations of a Google manager.” She said Google AI employees who report to her were emailed and told that she accepted her resignation when she did not offer her resignation. VentureBeat reached out to Gebru and Google AI chief Jeff Dean for comment. This story will be updated if we hear back.
2020-12-03 00:00:00 Read the full story…
Weighted Interest Score: 3.3661, Raw Interest Score: 1.3897,
Positive Sentiment: 0.1469, Negative Sentiment 0.4519

AI Weekly: In firing Timnit Gebru, Google puts commercial interests ahead of ethics

This week, leading AI researcher Timnit Gebru was fired from her position on an AI ethics team at Google in what she claims was retaliation for sending colleagues an email critical of the company’s managerial practices. The flashpoint was reportedly a paper Gebru coauthored that questioned the wisdom of building large language models and examined who benefits from them and who is disadvantaged.

Google AI lead Jeff Dean wrote in an email to employees following Gebru’s departure that the paper didn’t meet Google’s criteria for publication because it lacked reference to recent research. But from all appearances, Gebru’s work simply spotlighted well-understood problems with models like those deployed by Google, OpenAI, Facebook, Microsoft, and others. A draft obtained by VentureBeat discusses risks associated with deploying large language models, including the impact of their carbon footprint on marginalized communities and their tendency to perpetuate abusive language, hate speech, microaggressions, stereotypes, and other dehumanizing language aimed at specific groups of people.

2020-12-04 00:00:00 Read the full story…
Weighted Interest Score: 3.2784, Raw Interest Score: 1.9025,
Positive Sentiment: 0.1268, Negative Sentiment 0.5255

Thousands petition Google for more answers on departed researcher

• More than 2,000 Google employees and industry supporters are petitioning Google for answers on its firing of renown researcher Timnit Gebru.
• Gebru, who claims she was terminated for conditional disagreements regarding one of her research papers, has been vocally critical of the company’s treatment of research, diversity and inclusion efforts, and treatment of black employees.
• Employees promptly took to Twitter, saying her managers’ explanations didn’t line up with their experiences.

Employees from Google and other organizations are asking Google for answers about how it handled the departure of renown researcher Timnit Gebru in an online petition that had more than 2,000 signatures as of Friday afternoon.

Gebru, a well-known artificial intelligence researcher, technical co-lead of Google’s “Ethical AI” team and vocal critic of tech companies’ treatment of Black workers, tweeted Wednesday night that her corporate account had been abruptly shut off after she discussed potentially resigning over a disagreement about a research paper that scrutinized bias in artificial intelligence, which the company asked her to retract.

2020-12-04 00:00:00 Read the full story…
Weighted Interest Score: 2.7477, Raw Interest Score: 1.6220,
Positive Sentiment: 0.2478, Negative Sentiment 0.5632

Google staff hit out at effort to ‘silence’ AI ethics leader

Hundreds of Google staff have accused the US firm of “unprecedented research censorship” in an open letter supporting Timnit Gebru, a high-profile artificial intelligence researcher who said she was fired by the organisation this week.

More than 400 members of Google’s workforce and 521 academic, industry, and civil society supporters signed a letter demanding transparency over why Dr Gebru was terminated from her post.

Earlier this week, the renowned researcher posted on Twitter that she had been abruptly fired from her role at Google, as a co-leader of the eth…
2020-12-04 00:00:00 Read the full story…
Weighted Interest Score: 2.4827, Raw Interest Score: 1.6661,
Positive Sentiment: 0.0629, Negative Sentiment 1.1317

Renowned AI researcher says Google abruptly fired her, spurring industrywide criticism of the company

• Timnit Gebru, an artificial intelligence researcher at Google, said on Thursday that she has been abruptly fired by Jeff Dean, Google’s head of AI.
• Gebru, who was the technical co-lead of the Ethical AI Team at Google, shared on Twitter what she claims is a dismissal email.
• Gebru said she had made a number of requests and threatened to leave if they weren’t met but didn’t expect immediate termination.

Renowned AI researcher says Google abruptly fired her, spurring industrywide criticism
Timnit Gebru, a well-known artificial intelligence researcher at Google, tweeted on Wednesday that the company abruptly fired her, drawing widespread statements of support from other Google employees and tech workers throughout the industry.

2020-12-03 00:00:00 Read the full story…
Weighted Interest Score: 2.5641, Raw Interest Score: 1.5202,
Positive Sentiment: 0.0950, Negative Sentiment 0.4751

ServiceNow Acquires AI Pioneer Element AI For Smarter AI Capabilities

Popular cloud-based platform, ServiceNow recently acquired leading artificial intelligence (AI) company with deep AI capabilities, Element AI. According to sources, Element AI will significantly enhance ServiceNow’s commitment to building the world’s most intelligent workflow platform, enabling employees to work smarter and faster, streamline business decisions, and unlock new levels of productivity.

Element AI will allow ServiceNow to offer purpose-built AI for our customers’ enterprise-specific use cases. With the acquisition of Element AI, ServiceNow will create an AI Innovation Hub in Canada to accelerate customer-focused AI innovation in the Now Platform.

2020-12-01 08:42:49+00:00 Read the full story…
Weighted Interest Score: 5.8421, Raw Interest Score: 1.9178,
Positive Sentiment: 0.6849, Negative Sentiment 0.0457

Making it Real: Effective Data Governance in the Age of AI

Customer trust is not only gained with delightful service offerings but also by ensuring that their data is safe. This is one of the key factors why organizations across the globe are now considering data security, compliance, and governance as a key business objective.

Data governance means laying down set of consistent rules and processes to ensure the quality and integrity of data throughout the business lifecycle. A data governance framework is a pre-requisite for any organization to convert data into assets and meet their strategic goals.

2020-12-01 00:00:00 Read the full story…
Weighted Interest Score: 4.5833, Raw Interest Score: 2.3716,
Positive Sentiment: 0.5026, Negative Sentiment 0.0942

President Trump signs an executive order guiding how federal agencies use AI tech

BOT or NOT? This special series explores the evolving relationship between humans and machines, examining the ways that robots, artificial intelligence and automation are impacting our work and lives.

President Donald Trump today signed an executive order that puts the White House Office of Management and Budget in charge of drawing up a roadmap for how federal agencies use artificial intelligence software.

The roadmap, due for publication in 180 days, will cover AI applications used by the federal government for purposes other than defense or national security. The Department of Defense and the U.S. intelligence community already have drawn up a different set of rules for using AI.

Today’s order could well be the Trump administration’s final word on a technology marked by rapid innovation — and more than a little controversy.

2020-12-04 02:00:00+00:00 Read the full story…
Weighted Interest Score: 4.0862, Raw Interest Score: 1.9349,
Positive Sentiment: 0.3085, Negative Sentiment 0.0280

Deep Q-Learning Tutorial: minDQN

A Practical Guide to Deep Q-Networks : Reinforcement Learning is an exciting field of Machine Learning that’s attracting a lot of attention and popularity. An important reason for this popularity is due to breakthroughs in Reinforcement Learning where computer algorithms such as Alpha Go and OpenAI Five have been able to achieve human level performance on games such as Go and Dota 2. One of the core concepts in Reinforcement Learning is the Deep Q-Learning algorithm. Naturally, a lot of us want to learn more about the algorithms behind these impressive accomplishments. In this tutorial, we’ll be sharing a minimal Deep Q-Network implementation (minDQN) meant as a practical guide to help new learners code their own Deep Q-Networks.

2020-12-05 19:23:09.257000+00:00 Read the full story…
Weighted Interest Score: 4.0216, Raw Interest Score: 2.2496,
Positive Sentiment: 0.3100, Negative Sentiment 0.1590

Shortcut Learning, The Reason ML Models Often Fail in Practice

TLDR, models always take the route of least effort.

Training machine learning models is far from easy. In fact, the unaware data scientist might trip and fall in as many pitfalls as there are living AWS instances. The list is endless but divides itself nicely into two broad categories: underfitting, your model is bad, and overfitting, your model is still bad, but you think it isn’t. While overfitting can manifest itself in various ways, shortcut learning is a recurring flavor when dealing with custom datasets and novel problems. It affected me; it might be affecting you.

Informally, shortcut learning occurs whenever a model fits a problem on data not expected to be relevant or present, in general.
A practical example is a dog/cat classifier that, instead of properly recognizing dog- and cat-features, specializes in detecting leashes. Assuming leashes means dogs will likely do well most of the time, but leashes are not a general descriptor of dogness. That’s lazy work!
2020-12-01 13:21:54.182000+00:00 Read the full story…
Weighted Interest Score: 3.9326, Raw Interest Score: 1.6197,
Positive Sentiment: 0.0704, Negative Sentiment 0.3169

UOB boosts AML efforts with AI

UOB is hailing the accuracy of its new AI anti-money laundering technology in helping the Singaporean bank cut through large volumes of transactions to pinpoint suspicious activities.

The bank is using AI concurrently in two AML risk dimensions – transaction monitoring and name screening, helping it to pinpoint higher-priority cases from the more-than-5700 average monthly suspicious transaction alerts.

Once the AI system – which was built with regtech Tookitaki – flags suspicious activity, the bank’s compliance officers step in to conduct in-depth investigations and report to authorities.

2020-12-07 00:01:00 Read the full story…
Weighted Interest Score: 3.8520, Raw Interest Score: 1.8141,
Positive Sentiment: 0.0756, Negative Sentiment 0.7559

Deutsche Bank Partners With Google Cloud

• Deutsche Bank and Google Cloud to co-innovate the next generation of cloud-based financial services
• The bank’s move to the cloud will improve resilience, deliver new capabilities to market quicker and reduce cost over time
• Co-innovation use cases already being explored include new lending products, one retail bank interface and enhancements to the Autobahn platform
• Deutsche Bank and Google Cloud intend to selectively co-innovate with promising start-ups and fintechs and plan to make Deutsche Bank products available on Google Cloud Marketplace for the first time

Deutsche Bank and Google Cloud have finalised a strategic, multi-year partnership to accelerate the bank’s transition to the cloud and co-innovate new products and services. It is the first partnership of this kind for the financial services industry.

2020-12-07 05:31:04+00:00 Read the full story…
Weighted Interest Score: 3.6523, Raw Interest Score: 2.1353,
Positive Sentiment: 0.5403, Negative Sentiment 0.0515

Sber unveils cloud-based AI model training platform

Sber has developed a cloud platform for AI model training that the Russia bank says will help data scientists push ahead with their experiments.

Developed by the bank’s SberCloud unit, Machine Learning Space (ML Space) takes advantage of Sberbank’s supercomputer, Christofari, to ensure that resource-intensive models take hours – rather than weeks or months – to train.

2020-12-04 13:31:00 Read the full story…
Weighted Interest Score: 3.6284, Raw Interest Score: 2.0378,
Positive Sentiment: 0.1456, Negative Sentiment 0.0000

‘Biggest Leap’: Qualcomm Introduces Range Of AI Capabilities In New Snapdragon Processor

n the first day of the Snapdragon Tech Summit Digit, Qualcomm has announced the release of the Snapdragon 888, which is its latest 5G-equipped flagship smartphone processor. This, according to the company, would set the benchmark for flagship smartphones in 2021.

This new smartphone processor offers industry-leading mobile innovations in 5G for improved enterprise mobility, video telephony, console-quality cloud gaming etc. Of special interest is the artificial intelligence offered by this processor, which uses the 6th generation Qualcomm AI Engine with Hexagon 780 processor, which provides performance improvement by up to 50%.

Enhanced AI Capabilities : Qualcomm describes Snapdragon 888 as its ‘biggest leap in architecture and performance in years’. With the introduction of the new Hexagon 780 processor, Qualcomm removes the distance between the accelerators and combines them to form the fused AI accelerator architecture, as opposed to a separate scalar, vector, and tensor accelerators used for earlier models. As a matter of fact, the performance per watt on the Hexagon 780 processor is three times higher than the previous generation.

2020-12-07 04:30:00+00:00 Read the full story…
Weighted Interest Score: 3.5084, Raw Interest Score: 1.4855,
Positive Sentiment: 0.3875, Negative Sentiment 0.0215

Microsoft unveils Azure Purview for data governance, Azure Synapse Analytics hits general availability

Microsoft today unveiled Azure Purview, a new data governance solution in public preview. Additionally, the company announced that Azure Synapse Analytics is now generally available.

Azure Purview automates the discovery of data and cataloging while minimizing compliance risk. Purview helps businesses map all their data, no matter where it resides, and provides an end-to-end view of their data estate. Azure Synapse Analytics meanwhile leverages on-demand or provisioned resources to ingest, prepare, manage, and serve data for business intelligence. Azure Synapse Analytics changes how enterprises store data and gain insight by bringing together data warehousing, big data, data integration, and AI.

Businesses are increasingly leveraging data as a strategic asset, which makes data services critical. Data needs to not only be stored and managed, but also discovered and analyzed at ever-growing volumes. Having designed services that do exactly that for itself, Microsoft is comfortable selling access to them.

2020-12-03 00:00:00 Read the full story…
Weighted Interest Score: 3.3692, Raw Interest Score: 1.7840,
Positive Sentiment: 0.1768, Negative Sentiment 0.1768

SEC Announces Office Focused on Innovation and Financial Technology

The Securities and Exchange Commission today announced that the SEC’s Strategic Hub for Innovation and Financial Technology, commonly referred to as FinHub, will become a stand-alone office. Valerie A. Szczepanik will continue to lead FinHub as its first director and will report directly to the SEC Chairman.

Established within the Division of Corporation Finance in 2018, FinHub has spearheaded agency efforts to encourage responsible innovation in the financial sector, including in evolving areas such as distributed ledger technology and digital assets, automated investment advice, digital marketplace financing, and artificial intelligence and machine learning. Through FinHub, market and technology innovators as well as domestic and international regulators have been able to engage with SEC staff on new approaches to capital formation, trading, and other financial services within the parameters of the federal securities laws.

2020-12-04 12:34:41-05:00 Read the full story…
Weighted Interest Score: 3.3555, Raw Interest Score: 1.7906,
Positive Sentiment: 0.6373, Negative Sentiment 0.0000

AI will explain it to you

Explainable artificial intelligence (XAI) removes the last barrier in entrusting transaction monitoring to algorithms: the skepticism about its results.

At the outset of my professional career, I worked as an analyst in the anti-money laundering (AML) department of a global financial institution. I still recall boundless open spaces packed with analysts trying to determine how risky a cooperation with a particular entity actually is. Every couple of weeks we would hit another employment level… 100, 200, 300 people aboard. Back then, we were only armed with internet access to decide if a given risk can be mitigated or reported further up the chain of command.

Today, 15 years later, while I think we had a great time, virtually following our clients to Caymans, Belize or at least Wyoming, it strikes me how inefficient and fallible we were. As the current numbers show, we were also quite expensive, as the complete know your customer (KYC) process costs banks around $141,000 per FTE a year, according to LexisNexis.

2020-12-02 13:30:53+00:00 Read the full story…
Weighted Interest Score: 3.1380, Raw Interest Score: 1.3297,
Positive Sentiment: 0.1922, Negative Sentiment 0.6408

Best structure for a data team

Some thoughts for setting data science teams for success

Recently I read a post on who should Data Science report to. This is really a hot topic for established companies and startups alike, since everybody wants to have a well functioning data science team, and the reporting line of data seems to be one of the unknowns in the equation. I believe that these discussions completely miss the point, and I’ll try to explain why.

Understand the company strategy : First of all, the most important thing is to know what your company wants to achieve. Is your company expanding to newer cities? Opening new verticals? Are you focus on monetising your current user base? Once you know this, you need to understand the value are you going to add to your company from Data Science.

2020-12-07 12:44:01.230000+00:00 Read the full story…
Weighted Interest Score: 2.9976, Raw Interest Score: 1.8067,
Positive Sentiment: 0.2235, Negative Sentiment 0.1304

We can reduce gender bias in natural-language AI, but it will take a lot more work

Thanks to breakthroughs in natural language processing (NLP), machines can generate increasingly sophisticated representations of words. Every year, research groups release more and more powerful language models — like the recently announced GPT-3, M2M 100, and MT-5 — that are able to write complex essays or translate text into multiple languages with better accuracy than previous iterations. However, since machine learning algorithms are what they eat (in other words, they function based on the training data they ingest), they inevitably end up picking up on human biases that exist in language data itself.

This summer, GPT-3 researchers discovered inherent biases within the model’s results related to gender, race, and religion. Gender biases included the relationship between gender and occupation, as well as gendered descriptive words. For example, the algorithm predicted that 83% of 388 occupations were more likely to be associated with a male identifier. Descriptive words related to appearance, such as “beautiful” or “gorgeous” were more likely to be associated with women.

When gender (and many other) biases are so rampant in our language and in the language data we have accumulated over time, how do we keep machines from perpetuating them?

2020-12-06 00:00:00 Read the full story…
Weighted Interest Score: 2.9197, Raw Interest Score: 1.3693,
Positive Sentiment: 0.1920, Negative Sentiment 0.1920

Predictiv AI on track to profit from a more predictable future using artificial intelligence

• Recently rebranded to stress its commitment to artificial intelligence innovation
• Focused on profitable fundamental-growth technology
• Has launched ThermalPass fever detection system to help fight coronavirus

What Predictiv AI does: Toronto-based Predictiv AI Inc (CVE:PAI) (OTCMKTS:INOTF), formerly Internet of Things Inc, is committed to using its expertise to accelerate artificial intelligence (AI) innovation as it advances AI and machine learning solutions. The company’s AI Labs Inc subsidiary is its research and development business arm, which uses artificial intelligence sensor-based technology solutions to solve real-world problems.

Predictiv AI also owns data-science company Weather Telematics Inc, which uses a vehicle-mounted mobile Internet of Things (IoT) sensor network and artificial intelligence system to generate historical, current and forecasted weather conditions for road hazard risk alerts.

2020-12-02 00:00:00 Read the full story…
Weighted Interest Score: 2.9179, Raw Interest Score: 1.6844,
Positive Sentiment: 0.2323, Negative Sentiment 0.1549

The Top Trends in Data Management for 2021 : register for Expert Panel

The Top Trends in Data Management for 2021

THURSDAY, DECEMBER 10, 2020 – 11:00 am PT / 2:00 pm ET

From the rise of hybrid and multicloud architectures, to the impact of machine learning and automation, the business of data management is constantly evolving with new technologies, strategies, challenges and opportunities. The demand for fast, wide-range access to information is growing. At the same time, the need to effectively integrate, govern, protect and analyze data is also intensifying. All the while, data environments are increasing in size and complexity — traversing relational and non-relational databases, transactional and analytical systems, and on-premises and cloud sites.
2020-12-10 00:00:00 Read the full story…
Weighted Interest Score: 2.7460, Raw Interest Score: 1.7571,
Positive Sentiment: 0.0764, Negative Sentiment 0.0764

Mindtree and Databricks Collaborate to Bring Users Advanced, Cloud-Based Data Intelligence

Mindtree, a leading digital transformation and technology services company, is partnering with Databricks, the data and AI company, to help customers implement cloud-based data platforms for advanced analytics. This service will support use of the Databricks platform from implementation throughout the entire customer journey.

“Companies are accelerating their digital transformation, boosting demand for our open, cloud-based platform,” said, Michael Hoff, SVP of business development and partners, Databricks. “This partnership with Mindtree will bring together the right skills and technologies to help organizations advance their digital adoption journey and drive far-reaching business impact for our customers.”
2020-12-01 00:00:00 Read the full story…
Weighted Interest Score: 2.7165, Raw Interest Score: 1.7584,
Positive Sentiment: 0.5672, Negative Sentiment 0.0000

How Self-Supervised Text Annotation Works In TagTog

TagTog is an AI startup company making NLP modelling easier with its text analytics, visualization and annotation system democratized by subject matter experts bringing in domain-specific insights. It can annotate text, pdf, source code, or web URLs manually, using semi-supervised learning, and automation. It was launched in October 2017. Founders Jorge Campos Prieto and Dr Juan Miguel Cejuela during their PhD research in text mining applied biomedical in the University of Munich. Dr Cejuela along with some colleagues had represented a paper-based on TagTog. TagTog is based in Munich (Germany) and Gdansk (Poland).

TagTog helps in generating high-quality text datasets for training NLP algorithms with moderation and customization. The platform uses ML assisted models in learning from pre-annotated data to quickly annotate new data and put through the relevant information in the text. Manually annotation services are also provided following customer’s guidelines. TagTog specializes in text classification and annotation, entity extraction, entity normalisation, concept search ( Discover patterns in unstructured text, identify problems, realize solutions), Big Texts, annotated corpus, semantic search, text mining, business intelligence, and CRM data enrichment. Its automatic review annotations help in saving costs and time.

2020-12-07 11:30:23+00:00 Read the full story…
Weighted Interest Score: 2.6373, Raw Interest Score: 1.5355,
Positive Sentiment: 0.0649, Negative Sentiment 0.1081

Is Data Science for you? Read these books to find out.

How do you know if Data Science is right for you? Here are the best Data Science books for beginners. No coding involved.

Following my article on Switching Career to Data Science in your 30s, many readers asked me for more details. Especially on how to get started. Despite the sea of information, it can be daunting to navigate through hundreds of videos, courses, and articles. As a result, you feel stuck and might lose your motivation. Data science, machine learning and artificial intelligence are exciting topics, but we need to walk before we can run.

Finding someone who understands where you are in your journey is not easy. The majority of authors and YouTubers assume that all you want is something as straightforward as a Python tutorial. However, as beginners, I believe most of us have two kinds of concerns when learning Data Science:

2020-12-07 12:46:53.989000+00:00 Read the full story…
Weighted Interest Score: 2.4828, Raw Interest Score: 1.3994,
Positive Sentiment: 0.3137, Negative Sentiment 0.3016

Tune your models on Kubernetes the correct way

Let Katib handle the mundane work of HP optimization for you.
In Machine Learning, a hyperparameter is a user-defined value that is kept fixed during training. Examples of hyperparameters are the value of k in k-means clustering, the learning rate, the batch size, or the number of hidden nodes in neural networks.

To deploy a Machine Learning system in a production setup, we have to support the needs of both Data Scientists and DevOps Engineers. This isn’t easy because these two users of the environment rarely speak the same language. Data Scientists are interested in developing the most accurate ML model possible; thus, to tune the hyperparameters of a model, they work as follows:

2020-12-07 12:44:57.009000+00:00 Read the full story…
Weighted Interest Score: 2.4347, Raw Interest Score: 1.1355,
Positive Sentiment: 0.1323, Negative Sentiment 0.0992

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AI & Machine Learning News. 14, December 2020

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?

Hyundai Buys Boston Dynamics In $1.1B Deal

The South Korean automaker Hyundai Motors has announced that it’s reached a deal to buy an 80% stake in the robotics company Boston Dynamics from its owner SoftBank. The acquisition is reported as a $1.1 billion deal, implying that Hyundai is paying in the ballpark of $880 million for its 80% stake, while the remaining 20% will be held by SoftBank and its affiliates.

With an 80% stake, Hyundai has control of Boston Dynamics, which has drawn fame for its autonomous legged robots; the two-legged humanoid-like Atlas and the four-legged dog-like Spot.

In a press statement, Hyundai notes that it’ll make use of Boston Dynamics to improve its in-house manufacturing capabilities while providing more funding for the company to grow and sell its products to other customers. Hyundai itself says it’s looking to expand into the humanoid robot market over time, an area where Boston Dynamics’ expertise proves very useful.

CloudQuant Thoughts : In 2013 Google bought Boston Dynamics, selling it to SoftBank three years ago. BD had always said they were not interested in military applications, and the Netflix Black Mirror MetalHead episode served to make most people grateful for that statement. However, defense department contractor Ghost Robotics offers a very similar model!

Sizing The AI Software Market: Not As Big As Investors Expect But Still $37 Billion By 2025

How big is the market for artificial intelligence software? In our new report, “The AI Software Market Will Grow To $37 Billion Globally By 2025,” Forrester forecasts that the size of the AI software market will approach $37 billion by 2025. That’s a large number but still smaller than many investors and other analysts have projected. We believe our projection is more realistic, however, for two reasons. First, most business applications are adding AI functions without monetizing them. Second, the custom-built AI applications that businesses create for their own use don’t generate market revenues. Investors, vendors, and buyers who wish to understand and/or invest in the AI software market must understand Forrester’s four segments: AI maker platforms, AI facilitator platforms, AI-centric applications, and AI-infused applications.
2020-12-10 14:49:43-05:00 Read the full story…
Weighted Interest Score: 7.3077, Raw Interest Score: 2.4268,
Positive Sentiment: 0.0000, Negative Sentiment 0.1776

CloudQuant Thoughts : The market for AI will be huge and it will take away a lot of jobs. For an example of its impact on an industry you may not have considered, watch this excellent Blender Conference presentation.

Survey Shows Increased Need for Data Skills in Finance

• 90% of respondents are going to increase their data consumption over the next twelve months
• Over half (52%) stated generating meaningful insights from data is a strategic priority for their firm, with an extra 33% stating their firm is seeking to enhance their data intelligence
• Nearly half of respondents (41%) are anticipating increased demand for data science skills from their business over the next 12 months

There is an industry-wide need for specialist data science skills to match the growing appetite for meaningful data insights and greater data consumption, according to a survey by SIX among 113 representatives from buy-side and sell-side firms, exchanges, regulatory bodies and other organisations. The survey was conducted from 28^thSeptember to 02^nd November 2020  to check the pulse of the industry with regards to their views on data consumption, management, and analytics.

2020-12-09 09:59:10-05:00 Read the full story…
Weighted Interest Score: 4.0329, Raw Interest Score: 1.8630,
Positive Sentiment: 0.1644, Negative Sentiment 0.0822

CloudQuant Thoughts : “Respondents ranked data management and data analytics as the first and second most important initiatives at their firm.”

Yet Another Library for Deep Learning You Should Know About – SciKit-Learn

Yet Another Library for Deep Learning You Should Know About

PyTorch and TensorFlow aren’t the only Deep Learning frameworks in Python. There’s another library similar to scikit-learn.

SciKit-Learn is my first choice when it comes to classic Machine Learning algorithms in Python. It has many algorithms, supports sparse datasets, is fast and has many utility functions, like cross-validation, grid search, etc.

2020-12-14 13:58:50.280000+00:00 Read the full story…
Weighted Interest Score: 3.2632, Raw Interest Score: 1.7962,
Positive Sentiment: 0.1239, Negative Sentiment 0.0619

CloudQuant Thoughts : A number of our researchers use Scikit-Learn.

Forbes Insights: Medical Imaging’s Next Frontier: AI And The Edge

Medical imaging is facing a problem:

“There’s a worldwide shortage of radiologists,” says Prashant Shah, global head of AI for Intel’s Health and Life Sciences group. “At the same time, the number of [radiology] studies is increasing at an unprecedented rate.”

To keep pace, the average radiologist interpreting computed tomography (CT) and magnetic resonance imaging (MRI) examinations would need to read an image every three-to-four seconds of an eight-hour workday, according to one study.

What’s more, says Shah, “if you burden the radiologist with more scans and ask them to read them faster, you can introduce life-critical errors.”

The solution? Many clinicians are finding it in artificial intelligence (AI) and edge computing. Thanks to these technologies, healthcare providers can process, store and analyze complex imaging data on-premises, speeding diagnosis, improving clinician workflows and saving time—and potentially lives.

2020-12-09 00:00:00 Read the full story…
Weighted Interest Score: 2.6073, Raw Interest Score: 1.1238,
Positive Sentiment: 0.2593, Negative Sentiment 0.2737

CloudQuant Thoughts : And there you go, worldwide shortage of Radiologists.. AI can help.. in steps SalesForce.

Salesforce claims its AI can spot signs of breast cancer with 92% accuracy

Salesforce today peeled back the curtains on ReceptorNet, a machine learning system researchers at the company developed in partnership with clinicians at the University of Southern California’s Lawrence J. Ellison Institute for Transformative Medicine of USC. The system, which can determine a critical biomarker for oncologists when deciding on the appropriate treatment for breast cancer patients, achieved 92% accuracy in a study published in the journal Nature Communications.

Breast cancer affects more than 2 million women each year, with around one in eight women in the U.S. developing the disease over the course of their lifetime. In 2018 in the U.S. alone, there were also 2,550 new cases of breast cancer in men. And rates of breast cancer are increasing in nearly every region around the world.

2020-12-10 00:00:00 Read the full story…
Weighted Interest Score: 2.6111, Raw Interest Score: 1.1304,
Positive Sentiment: 0.1758, Negative Sentiment 0.1256

CloudQuant Thoughts : SalesForce/Breast Cancer Research, that is not a combination I thought I would see, but all power to them if they are helping to identify Breast Cancer.

EU AI Watchdog

EU watchdog warns of using AI in predictive policing, medical diagnoses, and targeted advertising

The European Union’s rights watchdog has warned of the risks of using artificial intelligence in predictive policing, medical diagnoses, and targeted advertising as the bloc mulls rules next year to address challenges posed by the technology. While AI is widely used by law enforcement agencies, rights groups say it is also abused by authoritarian regimes for mass and discriminatory surveillance. Critics also worry about the violation of people’s fundamental rights and data privacy rules.

In a report issued on Monday, the Vienna-based EU Agency for Fundamental Rights (FRA) urged policymakers to provide more guidance on how existing rules apply to AI and ensure that future AI laws protect fundamental rights. “AI is not infallible, it is made by people — and humans can make mistakes. That is why people need to be aware when AI is used, how it works, and how to challenge automated decisions,” FRA director Michael O’Flaherty said in a statement.

2020-12-14 00:00:00 Read the full story…
Weighted Interest Score: 4.3327, Raw Interest Score: 1.4963,
Positive Sentiment: 0.0499, Negative Sentiment 0.3491

EU rights watchdog warns of pitfalls in use of AI

The European Union’s rights watchdog has warned of the risks of using artificial intelligence in predictive policing, medical diagnoses and targeted advertising as the bloc mulls rules next year to address the challenges posed by the technology.

While AI is widely used by law enforcement agencies, rights groups say it is also abused by authoritarian regimes for mass and discriminatory surveillance. Critics also worry about the violation of people’s fundamental rights and data privacy rules.

The Vienna-based EU Agency for Fundamental Rights (FRA) urged policymakers in a report issued on Monday to provide more guidance on how existing rules apply to AI and ensure that future AI laws protect fundamental rights.

2020-12-14 05:08:44+00:00 Read the full story…
Weighted Interest Score: 3.9430, Raw Interest Score: 1.3783,
Positive Sentiment: 0.0475, Negative Sentiment 0.3802

VMware exec: AI’s two Achilles’ heels keep me up at night

The sudden switch to remote working during the COVID-19 pandemic left a huge gap of visibility for cybersecurity attacks. In some cases, on-premise security tools couldn’t immediately extend to the cloud or into home-working environments.  This meant between March and May, teams scrambled to render their technology into a risk-free format. Microsoft’s CEO Satya Nadella said his company has seen two years’ worth of digital transformation in just two months as a result of the pandemic.

AI – a force for good and evil : Against this backdrop, Tom Kellermann, cybersecurity strategy head at major US software firm VMware, points out the particular threat of artificial intelligence (AI).  “AI has two Achilles heels,” he explains at a roundtable attended by FinTech Futures. One is that timestamps and data can be manipulated, he says. The other is that the technology can be “turned against its mission”.

2020-12-09 07:30:09+00:00 Read the full story…
Weighted Interest Score: 3.9175, Raw Interest Score: 1.3625,
Positive Sentiment: 0.2890, Negative Sentiment 0.5367

2021 Predictions: Focus on People; See Nuances with Emotion AI; Turn to RPA Bots

AI experts are predicting 2021 will bring: more focus on people, reinforcement of analytic core, turn to RPA bots, coping with ‘Zoom fatigue’
By AI Trends Staff

We have heard from a range of AI practitioners for their predictions on AI Trends in 2021. Here are predictions from a selection of those writing.

2020-12-10 22:28:57+00:00 Read the full story…
Weighted Interest Score: 3.5265, Raw Interest Score: 1.3664,
Positive Sentiment: 0.1314, Negative Sentiment 0.3022

Accern launches AI Marketplace with over 400 ready-made use cases

Accern, a no-code AI platform, has launched the Accern AI Marketplace, which dramatically increases the speed enterprises can deploy and begin reaping the benefits of AI across their businesses.

Accern’s AI Marketplace allows data scientists and business analysts to empower their business functions with over 400 ready-made AI use cases to automate manual workflows and enhance decisions. Accern says the result is a 24x gain in productivity for financial teams.

Use cases include but are not limited to insights on Credit Risk, ESG Behaviours, Covid-19, Anti-Money Laundering Analytics, Mergers and Acquisitions, and more. These ready-made use cases are backed by AI and adaptive NLP (natural language processing) to allow financial services teams to quickly research, summarise, and extract data, and gain investment insights and manage risk.

2020-12-14 00:00:00 Read the full story…
Weighted Interest Score: 6.5084, Raw Interest Score: 2.9401,
Positive Sentiment: 0.3769, Negative Sentiment 0.1131

TD Securities Makes Strategic Investment in Data Services and Analytics to Accelerate its Digital Transformation Journey

Bloomberg selected to help enable data strategy

High-quality, comprehensive and integrated data that is accessible, shareable and utilized effectively across our TD Securities organization is critical to supporting the evolving needs of our clients.

TD Securities today announced an investment in data services and analytics using Bloomberg Enterprise Data content and services. Bloomberg is a global leader in providing business and financial data, news and insights. Access to its extensive catalog of comprehensive, market-leading datasets and robust data management tools will help strengthen TD Securities’ advanced analytics, AI and machine learning platforms.

“The importance of data quality and its management is revolutionizing capital markets and has become increasingly more critical in all aspects of how we operate and serve our clients,” says Rajesh Tolani, Head of Business Innovation and Chief Data Office for TD Securities.

2020-12-10 08:41:30-05:00 Read the full story…
Weighted Interest Score: 6.2765, Raw Interest Score: 2.8196,
Positive Sentiment: 0.4240, Negative Sentiment 0.1272

Workshop: From Privacy to Fairness in AI – 9th January 2021

“Responsible AI and AI governance also become a priority for AI on an industrial scale” according to Gartner 2020 hypecycle.

Machine learning(ML) and Artificial intelligence(AI) solutions are getting deeper in our day to day life. Currently, AI is empowering our convenience at the cost of our privacy. In the last few years, we have heard the news about big techs and startups facing lawsuits because of not compiling with new data governance laws. AI implementation in business has been facing several issues and challenges to solve them.

The aim of this workshop to empower ML/AI researches and industry leaders in understanding and mitigating the risk of AI. The workshop also provides a platform to exchange ideas and discuss some of the open problems in privacy, ethics and fairness surrounding AI.

2020-12-11 09:59:15+00:00 Read the full story…

2020-12-11 12:30:00+00:00 Read the full story…
Weighted Interest Score: 6.2171, Raw Interest Score: 2.3231,
Positive Sentiment: 0.2112, Negative Sentiment 0.2112

20 Core Data Science Concepts for Beginners

With so much to learn and so many advancements to follow in the field of data science, there are a core set of foundational concepts that remain essential. Twenty of these ideas are highlighted here that are key to review when preparing for a job interview or just to refresh your appreciation of the basics.
2020-12-20 00:00:00 Read the full story…
Weighted Interest Score: 5.5455, Raw Interest Score: 2.3596,
Positive Sentiment: 0.1265, Negative Sentiment 0.1925

IBM Commercialises Its AI FactSheets. Could It Become An Industry Standard?

IBM has recently announced the commercialisation of its AI FactSheets, which were first introduced in 2018. In an official release, the company wrote that it plans to “commercialise key automated documentation capabilities from IBM Research’s AI FactSheets methodology into Watson Studio in Cloud Pak for Data throughout 2021”. This fact sheet will provide businesses with a framework to define how AI is to be used, to measure a model’s performance, and to generate reports on internal and external transparency. Can such an AI fact sheet from IBM become an industry standard?

What Is IBM FactSheet & Why Is It Required? Some of the most important factors to consider while achieving trust in AI are — fairness, safety, explainability, reliability, and accountability. Apart from these factors, it must be accompanied by having a parameter against which the models are measured. A lot of this could be attributed to the increasing usage of AI models and services, even in high-stakes situations such as financial risk assessments, medical diagnosis, talent acquisition, policing, and governance.

2020-12-14 12:30:00+00:00 Read the full story…
Weighted Interest Score: 4.9550, Raw Interest Score: 1.7350,
Positive Sentiment: 0.1803, Negative Sentiment 0.1577

DataRobot, Snowflake Expand AI Collaboration

DataRobot announced more late-round investments this week along with an expanded partnership with big data leader Snowflake Inc. that would extend the reach of its enterprise AI platform.

A Series F funding round announced last month and led by Altimeter Capital raised $270 million. Boston-based DataRobot said Salesforce Ventures and Hewlett Packard Enterprises (NYSE: HPE) have since added strategic investments, raising that total to $320 million.

The enterprise AI and MLOps specialist has so far raised about $750 million in venture funding, and claims a market valuation of $2.8 billion.

Along with the new funding, DataRobot said Wednesday (Dec. 9) it is expanding collaboration with Snowflake “through deep product integration” and product marketing to their joint customers. The collaboration includes integration of Snowflake’s cloud data warehouse and its emerging data marketplace with DataRobot’s AI platform.

2020-12-09 00:00:00 Read the full story…
Weighted Interest Score: 4.2495, Raw Interest Score: 1.7478,
Positive Sentiment: 0.2399, Negative Sentiment 0.1028

Deutsche Bank expands Google cloud partnership

Deutsche Bank and Google have revealed further enhancements to a cloud partnership signed in July this year.

The bank will utilise Google Cloud’s AI and data analytics to “deliver new capabilities quicker and cheaper”.

The lender also plans to “co-innovate” by making its products available on the Google Cloud for the first time.

Co-innovation use cases include new lending products, one retail bank interface and enhancements to the Autobahn pla…
2020-12-09 08:30:27+00:00 Read the full story…
Weighted Interest Score: 4.1294, Raw Interest Score: 2.2727,
Positive Sentiment: 0.5510, Negative Sentiment 0.2755

AWS Announces Amazon Redshift ML, A Cloud-based Service For Data Scientists To Use ML Technologies

Recently at the AWS re:Invent event, the e-commerce giant announced the launch of Amazon Redshift Machine Learning (Amazon Redshift ML). According to its developers, with Amazon Redshift ML data scientists can now create, train as well as deploy machine learning models in Amazon Redshift using SQL.

Amazon Redshift is one of the most widely used cloud data warehouses, where one can query and combine exabytes of structured and semi-structured data across a data warehouse, operational database, and data lake us…
2020-12-14 06:30:00+00:00 Read the full story…
Weighted Interest Score: 4.0794, Raw Interest Score: 2.3022,
Positive Sentiment: 0.1788, Negative Sentiment 0.0447

AI for Credit Risk Management: Banking and Finance

A strong credit risk management system in combination with AI and ML technologies can not only mitigate financial risks but also level up the effectiveness of decision-making processes, increasing a company’s profit.

According to Statista, the number of only FDIC-insured commercial banks in the U.S. over the last 20 years reduced by half. It suggests that there is strong competition in the market. Banks and lending organizations need to check ev…
2020-12-14 12:41:52 Read the full story…
Weighted Interest Score: 4.0668, Raw Interest Score: 2.2567,
Positive Sentiment: 0.3056, Negative Sentiment 0.5407

C3.ai Soars 138% From IPO Price to Start Trading at $100

The IPO for C3.ai, the artificial-intelligence-software provider, was priced at $42 a share.

C3.ai (AI) – Get Report, an artificial intelligence software provider, began trading Wednesday on the New York Stock Exchange at $100 a share, up 138% from its initial public offering price of $42.

At last check, the stock was at $96.48, up 129%.

With the company issuing 15.5 million Class A shares, C3.ai raised $651 million. The $42 share price put the company’s value at $4.05 billion.

2020-12-09 13:48:20+00:00 Read the full story…
Weighted Interest Score: 3.9054, Raw Interest Score: 1.9412,
Positive Sentiment: 0.1109, Negative Sentiment 0.2219

How digital labour helps financial firms cope with market volatility

intelligence (AI), helps financial firms effectively manage increases in operational workload due to market volatility. It covers trends in digital labour, as well as governance and the need to put appropriate solutions in place to manage exceptions and risk, while considering the interplay between human and digital labour.

Recent market volatility caused by the COVID-19 outbreak resulted in an unprecedented surge in trading v…
2020-12-14 00:01:32+00:00 Read the full story…
Weighted Interest Score: 3.7593, Raw Interest Score: 1.7550,
Positive Sentiment: 0.1856, Negative Sentiment 0.3037

How artificial intelligence can improve software development process?

How Artificial Intelligence can improve Software Development Process?

Today, Artificial intelligence dominates technology trends. It has impacted retail, finance, healthcare, and many industries around the world. In fact, by 2025, the global AI market is expected to reach an impressive $60 million.

2020-12-08 14:55:07+00:00 Read the full story…
Weighted Interest Score: 3.6506, Raw Interest Score: 2.0975,
Positive Sentiment: 0.3056, Negative Sentiment 0.1945

Microsoft initiative will use AI to sniff out bribes, theft and other government corruption

Microsoft unveiled an initiative to use artificial intelligence to detect government corruption, calling it “an urgent global issue that can and must be solved.”

The new Microsoft Anti-Corruption Technology and Solutions initiative “will leverage the company’s investments in cloud computing, data visualization, AI, machine learning, and other emerging technologies to enhance transparency and to detect and deter corruption” over the next decade, said Dev Stahlkopf, Microsoft general counsel, in a post announcing the plan.

Microsoft made the announcement in conjunction with the United Nations’ International Anti-Corruption Day. Stahlkopf described the initiative as increasingly important given the events of the past year.

2020-12-09 18:44:00+00:00 Read the full story…
Weighted Interest Score: 3.6039, Raw Interest Score: 1.4634,
Positive Sentiment: 0.0976, Negative Sentiment 0.7805

New venture capital firm looks to invest at intersection of 5G, edge networks and AI

A new venture capital firm is taking shape in Seattle with a name suitable for this rain-soaked city.

Cloud City Venture Capital was formed earlier this year by tech veterans Jim Brisimitzis and Kevin Ober, and is in the process of raising a new fund, GeekWire has learned.

Ober is well known in venture capital circles. He co-founded Seattle venture capital firm Divergent Ventures, and before that spent seven years with Vulcan Ventures, the vent…
2020-12-11 18:09:00+00:00 Read the full story…
Weighted Interest Score: 3.5891, Raw Interest Score: 1.6134,
Positive Sentiment: 0.2305, Negative Sentiment 0.0659

5 Major Benefits of Big Data in Financial Trading Industry

Big data is making a significant impact on the financial world. The market for big data in the banking industry alone is projected to reach over $14.8 million by 2023.

The impact it’s making is much more of a grandiose splash rather than a few ripples. This is primarily due to the fact the technology in the space is scaling to unprecedented levels at such a fast rate. The exponentially increasing complexity and generation of data are dynamically…
2020-12-12 15:06:36+00:00 Read the full story…
Weighted Interest Score: 3.5888, Raw Interest Score: 1.8178,
Positive Sentiment: 0.4016, Negative Sentiment 0.1057

2/10 Recruiters are prepared for the deployment of AI in hiring

Artificial intelligence (AI) has numerous applications in today’s world – from website chatbots to healthcare and supply chain management, it is revolutionising the way that we work in numerous industries. But with AI being used increasingly in HR and recruitment, what do the professionals think about the technology?

RS Components has surveyed recruiters and HR professionals from the UK, to get an industry perspective on how AI could impact the way we hire and are hired ourselves, for jobs in the future. You can see the full piece here.

2020-12-08 16:06:59+00:00 Read the full story…
Weighted Interest Score: 3.5256, Raw Interest Score: 1.3743,
Positive Sentiment: 0.0916, Negative Sentiment 0.0000

Designing systems for real-time risk management

Financial organisations rely on risk management systems to assess strategic, compliance and operational risks. However, according to a pre-COVID-19 survey of more than 800 audit committee and board members conducted by KPMG, the top challenge for companies is maintaining a highly effective risk management program, due to fast changing regulations and volatility in the business environment. Almost half of the survey respondents reported that their…
2020-12-08 01:01:23+00:00 Read the full story…
Weighted Interest Score: 3.5145, Raw Interest Score: 2.1433,
Positive Sentiment: 0.1715, Negative Sentiment 0.3772

Top 10 AI Collaborations Between Indian Govt. & Tech Giants In 2020

The pandemic has forced organisations, businesses and academia around the globe to make a technological shift. Joining the efforts, the Government of India has been doing a lot of advancement in the case of emerging technologies. As a matter of fact, the increasing cases of COVID pandemic leading to a massive economic downfall has pushed the government of the country to take more such AI initiatives in order to grow the economy and businesses.

Below here, we have curated a list of top 10 AI collaborations, in no particular order, between the Government of India and tech giants in 2020.

2020-12-09 Read the Full Story…

AI Weekly: NeurIPS 2020 and the hope for change

Following a keynote address Wednesday, Microsoft Research Lab director Chris Bishop was asked if Big Tech companies’ monopoly on infrastructure and machine learning talent is stifling innovation. He responded by arguing that cloud computing allows developers to rent compute resources instead of undertaking the more expensive task of buying the hardware that powers machine learning.

The tension between corporate interests, human rights, ethics, and power could be seen at workshops throughout the week. At the Muslim in AI workshop on Tuesday, participants exp…
2020-12-11 00:00:00 Read the full story…
Weighted Interest Score: 3.3810, Raw Interest Score: 1.4538,
Positive Sentiment: 0.1869, Negative Sentiment 0.2492

An end-to-end machine learning project with Python Pandas, Keras, Flask, Docker and Heroku

Predicting sport scores, from data wrangling to model deployment

At the time of the Rugby World Cup in 2019 I did a small data science project to try and predict rugby match results, which I wrote about here. I’ve expanded this into an example end-to-end machine learning project to demonstrate how to deploy a machine learning model as an interactive web app.

Goal : To provide a high-level overview of the key steps needed in going from raw data to a live deployed machine learning app.

Once you’ve gone through this — pick a topic that you’re interested in, find some data, get your hands dirty and aim to build your own machine learning app, from data preparation to deployment.
2020-12-09 13:01:07.764000+00:00 Read the full story…
Weighted Interest Score: 3.3411, Raw Interest Score: 2.5098,
Positive Sentiment: 0.0000, Negative Sentiment 0.0000

FPGA chips are coming on fast in the race to accelerate AI

AI is hungry, hyperscale AI ravenous. Both can devour processing, electricity, algorithms, and programming schedules. As AI models rapidly get larger and more complex (an estimated 10x a year), a recent MIT study warns that computational challenges, especially in deep learning, will continue to grow.

But there’s more. Service providers, large enterprises and others also face unrelenting pressures to speed up innovation, performance, and rollouts of neural networks and other low-latency, data-intensive applications, often involving exascale cloud and High-Performance Computing (HPC). These dueling demands are driving technology advances and adoption of a growing universe of Field Programmable Gate Arrays (FPGAs).

Early leader gains a new edge : In the early days of exascale computing and AI, these customer-configurable integrated circuits played a key role. Organizations could program and reprogram FPGAs onsite to handle a range of changing demands. As time went on, however, their performance and market growth got outpaced by faster GPUs and specialized ASICs.

2020-12-10 00:00:00 Read the full story…
Weighted Interest Score: 3.2571, Raw Interest Score: 1.4770,
Positive Sentiment: 0.3296, Negative Sentiment 0.1099

How Quantum Computing Works at Goldman Sachs and JPMorgan

As we’ve reported before, banks have been busy building teams of quantum computing researchers. The heads of those teams at Goldman Sachs and JPMorgan recently presented their work at the Q2B Practical Quantum Computing conference.

Goldman Sachs has assembled a “full team dedicated to quantum computing,” William Zeng, head of quantum research at Goldman Sachs, told the virtual audience. In a subsequent session, Marco Pistoia, managing director and head of the FLARE (Future Lab for Applied Research and Engineering) at JPMorgan, said quantum computing is what FLARE is “particularly interested in.”

Zheng said Goldman sees three broad use cases for quantum computing in finance: simulation (e.g., for the statistical simulations of stochastic processes used in derivative pricing); optimization (e.g., portfolio optimization in the context of regulatory and tax constraints); and machine learning (which remains a “nascent” field in finance).

2020-12-14 00:00:00 Read the full story…
Weighted Interest Score: 3.2407, Raw Interest Score: 1.3607,
Positive Sentiment: 0.2027, Negative Sentiment 0.2316

A New Trend Of Training GANs With Less Data: NVIDIA Joins The Gang

Following MIT, researchers at NVIDIA have recently developed a new augmented method for training Generative Adversarial Networks (GANs) with a limited amount of data. The approach is an adaptive discriminator augmentation mechanism that significantly stabilised training in limited data regimes.

Machine learning models are data-hungry. As a matter of fact, in the past few years, we have seen that models that are fed with silos of data produce outstanding predictive outcomes.

Alongside, with significant growth, Generative Adversarial Networks have been successfully used for various applications including high-fidelity natural image synthesis, data augmentation tasks, improving image compressions, etc. From emoting realistic expressions to traversing the deep space, and from bridging the gap between humans and machines to introduce new and unique art forms, GANs have it all covered.

2020-12-13 05:30:00+00:00 Read the full story…
Weighted Interest Score: 3.2104, Raw Interest Score: 1.3238,
Positive Sentiment: 0.1953, Negative Sentiment 0.2170

Teachable AI will help Alexa users set up preferences

Alexa called Teachable AI that will enable the assistant to ask questions to fill gaps in its understanding. First announced during the company’s September virtual press event, Teachable AI leverages machine learning to determine whether a request can be a trigger for a teachable moment. If Alexa makes this determination, it will ask a customer for information to help it learn.

Amazon says Teachable AI will become available in the next few months for smart home devices before expanding to other areas.

Scientists at Amazon’s Alexa AI research division have long pursued semi-supervised and unsupervised learning techniques, in which AI systems learn to make predictions without ingesting gobs of annotated data. Semi-supervised and unsupervised learning have their limitations, but both promise to supercharge Alexa and other voice assistants’ capabilities by imbuing them with a humanlike capacity for inference.
2020-12-11 00:00:00 Read the full story…
Weighted Interest Score: 3.1390, Raw Interest Score: 1.1967,
Positive Sentiment: 0.2244, Negative Sentiment 0.1122

Google Wades Into Controversy with Dismissal of AI Ethicist Timnit Gebru

By John P. Desmond, Editor, AI Trends

Google ignited a firestorm around its ethics program last week when it let go a prominent AI ethicist, Timnit Gebru, apparently over contents of an email where she expressed her feelings, following a request by Google that a paper on large language models she had submitted to an industry conference be withdrawn.

Gebru had sent an email saying she felt “constantly dehumanized” at the company, according to an account in The Washington Post. She had been the co-leader of Google’s Ethical AI Team, where she was researching the fairness and risks associated with Google’s technology.

Of Ethiopian descent, Gebru was a rarity in the Silicon Valley culture known for its racial homogeneity. She became known in a senior role at Google for critically examining bias in the technology and its repercussions. She co-founded the Blacks in AI advocacy group that has pushed for more Black roles in AI development and research.

2020-12-10 23:07:24+00:00 Read the full story…
Weighted Interest Score: 3.0487, Raw Interest Score: 1.7498,
Positive Sentiment: 0.0866, Negative Sentiment 0.3292

Data Sourcebook 2020 – Downloadable PDF

Now, more than ever, the ability to pivot and adapt is a key characteristic of modern companies striving to position themselves strongly for the future. Download this year’s Data Sourcebook to dive into the key issues impact enterprise data management today and gain insights from leaders in cloud, data architecture, machine learning, data science and analytics.

2020-12-10 00:00:00 Read the full story…
Weighted Interest Score: 3.0137, Raw Interest Score: 2.2039,
Positive Sentiment: 0.2755, Negative Sentiment 0.0000

Einblick Introduces Visual Data Computing Platform, Announces $6 Million in Seed Funding

Einblick, a visual data computing platform provider, based on years of research at MIT and Brown University, announced it has secured $6 million in Seed funding along with launching its visual data computing platform. The funding round was lead investor Amplify Partners with participation from top-tier investors Flybridge and Samsung Next.

Additionally, Sunil Dhaliwal, general partner at Amplify Partners, joins Einblick’s board of directors.

The funding will be used to continue investing in building a world-class engineering team, as well as expanding its sales and marketing capacity.

“Organizations are challenged by making data-driven decisions to achieve the best business outcome,” said Tim Kraska, founder and CEO at Einblick. “With Einblick’s visual data computing platform, business analysts and data scientists in organizations of all sizes can analyze past data, build predictive models, and simulate scenarios all in the same platform to quickly make data-driven decisions.”

2020-12-09 00:00:00 Read the full story…
Weighted Interest Score: 2.9345, Raw Interest Score: 1.6554,
Positive Sentiment: 0.3386, Negative Sentiment 0.0376

ML Deployment Woes Persist

Despite greater spending on staffing and use cases, investors in machine learning have so far reaped few returns as they struggle with life cycle issues related to data governance, security and auditing requirements.

An annual assessment released this week by Algorithmia on enterprise trends in machine learning found that machine learning investments are up, but adopters continue to struggle to reach production due to regulatory requirements. The survey released on Thursday (Dec. 10) found that 67 percent of the more than 400 executives polled said they must comply with multiple regulations covering data used in machine learning models.

Still, 83 percent of organizations said they are pressing on with ML development, increasing budget and hiring more data scientists. Staffing increased 76 percent over the previous year, Seattle-based Algorithmia reported.

2020-12-10 00:00:00 Read the full story…
Weighted Interest Score: 2.8799, Raw Interest Score: 2.2185,
Positive Sentiment: 0.1664, Negative Sentiment 0.2219

Study: Expert Network Industry Tops $1.5 Billion Despite Muted Growth During Pandemic • Integrity Research

Expert networks were not immune from the effects of Covid-19 yet continued to grow double-digits in 2020, bringing the total industry size over $1.5 billion, according to analysis released by Integrity Research and Inex One, an expert network marketplace. The study, which includes detailed revenue estimates for the top 40 expert networks over the last five years, provides an in-depth perspective on the expert network industry, which continues to have opportunities for future growth.

Based on firm-level bottom-up analysis, 2020 Expert Network Market Sizing estimates that aggregate revenues for the industry slowed to low double digits in 2020 as Covid-19 dampened client activity in several key customer segments. Nevertheless, industry revenues exceeded $1.5 billion. Expert networks have had a strong trajectory of revenue growth even as the number of providers has increased.

2020-12-07 05:15:00+00:00 Read the full story…
Weighted Interest Score: 2.7397, Raw Interest Score: 1.7078,
Positive Sentiment: 0.3000, Negative Sentiment 0.0923

Web Data Scraping with AI is More Important than Ever

The latest innovation in the proxy service market makes every data gathering operation quicker and easier than ever before. Since the market for big data is expected to reach $243 billion by 2027, savvy business owners will need to find ways to invest in big data. Artificial intelligence is rapidly changing the process for collecting big data, especially via online media.

The Growth of AI in Web Data Collection: An entire generation of software engineers, data scientists, and even technical executives working in web data reliant industries are familiar with the pains of web data gathering, also known as web scraping. In brief, ineffective information retrieval, collection of incomplete or low-quality data, and complex data treatment operations are causing the most difficulties.

In this climate, the latest innovation in the industry – Next-Gen residential proxies are quickly gaining popularity among web-scraping professionals. The new web data gathering tool, powered by AI and machine learning (ML) algorithms, promises a staggering 100% success rate for scraping sessions, among many other advantages.

2020-12-07 18:49:00+00:00 Read the full story…
Weighted Interest Score: 2.7269, Raw Interest Score: 1.3983,
Positive Sentiment: 0.3122, Negative Sentiment 0.4073

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