AI & Machine Learning News. 14, September 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?


CloudQuant to release results of latest disruptive data set analysis at The Trading Show – Chicago – September 15th 2020

CloudQuant is all about CONTENT at this year’s Trading Show.

We are participating in 3 panels and will release our latest research paper.

Register here.

Stop by our virtual booth at the show to learn more… FIRST!

Alternatively fill in the form to your right or Register for a Demo and we will contact you directly!

2020-09-14  Read the full story…

How Amazon is using machine learning to eliminate 915,000 tons of packaging

Amazon’s 2019 Climate Pledge calls for a commitment to net zero carbon across their businesses by 2040. Since then, the company has reduced the weight of their outbound packaging by 33%, eliminating 915,000 tons of packaging material worldwide, or the equivalent of over 1.5 billion shipping boxes. With less packaging used throughout the supply chain, volume per shipment is reduced and transportation becomes more efficient. The cumulative impact across Amazon’s enormous network is a dramatic reduction in carbon emissions.

To make this happen, the customer packaging experience team partnered with AWS to build a machine learning solution powered by Amazon SageMaker. The primary goal was to make more sustainable packaging decisions, while keeping the customer experience bar high.

“When we make packaging decisions, we think about the end-to-end supply chain, working backward from the customer in terms of the waste they get on their doorstep, but we are also really cognizant of how our decisions in packaging impacts speed to fulfillment,” says Justine Mahler, Senior Manager, Packaging at Amazon.

2020-09-14  Read the full story…

CloudQuant Thoughts : Fascinating that Amazon has already managed to reduce the use of boxes from 69% to 42%. Most interesting was that the ML was learning which toys were likely to be thought of as collectibles and so need more packaging!

AI Ruined Chess. Now, It’s Making the Game Beautiful Again

A former world champion teams up with the makers of AlphaZero to test variants on the age-old game that can jolt players into creative patterns.

CHESS HAS A reputation for cold logic, but Vladimir Kramnik loves the game for its beauty.

“It’s a kind of creation,” he says. His passion for the artistry of minds clashing over the board, trading complex but elegant provocations and counters, helped him dethrone Garry Kasparov in 2000 and spend several years as world champion.

Yet Kramnik, who retired from competitive chess last year, also believes his beloved game has grown less creative. He partly blames computers, whose soulless calculations have produced a vast library of openings and defenses that top-flight players know by rote. “For quite a number of games on the highest level, half of the game—sometimes a full game—is played out of memory,” Kramnik says. “You don’t even play your own preparation; you play your computer’s preparation.”

Wednesday, Kramnik presented some ideas for how to restore some of the human art to chess, with help from a counterintuitive source—the world’s most powerful chess computer. He teamed up with Alphabet artificial intelligence lab DeepMind, whose researchers challenged their superhuman game-playing software AlphaZero to learn nine variants of chess chosen to jolt players into creative new patterns.

2020-09-09  Read the full story…

CloudQuant Thoughts : Regular readers will know we loved the AlphaGo Documentary. The AIs ability to play in a style never before seen was amazing. However, I can understand that these AI/ML based Chess games are programmed not to meet you at your level and challenge you but to beat you at all costs. So it was a pleasure to read about this effort to train an AI to coax a player along.

Boost Liquidity Capture With Dynamic Liquidity Awareness

A critical component that determines an algorithm’s success in sourcing liquidity is how it rebalances—or decides which venues to route to based on where it already sent orders and received fills. Many algorithms claim to “intelligently” source liquidity but still rely on static previous-fills heatmap data. However, if you use a genuinely dynamic Liquidity Awareness Signal, it’s possible to achieve a nearly 500% improvement in hit rates for midpoint orders*.

In a previous blog post, we outlined the attributes you should look for in an algorithm’s rebalancing logic and demonstrated that prior performance does not necessarily predict future results. Not all venues or order types behave the same, so relying on one-size-fits-all previous fills as the only input might, in fact, harm overall fill rates. We also discussed a quantitative research project undertaken to better understand market conditions, venue microstructure, and symbol liquidity throughout the day. Those results showed that the rate at which liquidity decays—or how fast liquidity is exhausted—varies dramatically across venues and order types. Thus, you risk missing out on liquidity capture if rebalance logic does not factor in various venues’ liquidity dynamics.

2020-09-14 04:36:13+00:00 Read the full story…
Weighted Interest Score: 5.6365, Raw Interest Score: 1.8152,
Positive Sentiment: 0.3241, Negative Sentiment 0.0648

CloudQuant Thoughts : An excellent article and, if what they suggest is true, a real boon to both manual and algorithmic traders!

Buy these 17 ‘superstar’ stocks poised to soar as they use AI technology to drive market-beating growth, UBS says

UBS analyst Paul Winter says artificial intelligence technologies are giving the world’s largest and most profitable companies a big advantage.

He says that because those companies can spend more money in AI, they benefit more than smaller competitors do — adding to their sales and hiring and enabling even more investment.

Winter names a group of companies as “superstars” that will continue to benefit from that pattern.

2020-09-13 00:00:00 Read the full story…
Weighted Interest Score: 3.6474, Raw Interest Score: 1.7791,
Positive Sentiment: 0.6378, Negative Sentiment 0.0671

CloudQuant Thoughts : There is one name in there that I would bet my house on!

$26 billion Coatue is down one of its top alternative-data buyers after the firm’s quant fund that relied heavily on the unique datasets was rocked by market volatility earlier this year

Coatue — the long-running hedge fund of billionaire Philippe Laffont that manages $25.8 billion in assets — has lost one of its top people in charge of buying the data many consider to be the lifeblood of equity-focused hedge funds.

Dave Schwartz, a vice president focused on data acquisition and strategy, is no longer at the firm, sources tell Business Insider. It is not clear if Schwartz was dismissed by Laffont or if he left on his own accord. Coatue declined to comment, while Schwartz did not immediately return requests for comment. Schwartz’s role, which nearly all funds Coatue’s size now have, is to vet and bring in alternative data streams that will help portfolio managers and analysts project market moves before more traditional numbers, like earnings and jobs reports, are released. The multi-billion alternative data space has been even more important during the ongoing pandemic, as investors are scouring data feeds for a sign of life returning to normal.

Coatue’s data science team, led by Alex Izydorcyzk, is well-regarded in the industry, with more than two dozen people on it. But it ran into some speed bumps this year when the team’s young quant fund was unable to keep up with the market volatility caused by the coronavirus in the spring.
2020-09-11 00:00:00 Read the full story…
Weighted Interest Score: 5.0079, Raw Interest Score: 2.0485,
Positive Sentiment: 0.0000, Negative Sentiment 0.3152

Building a Strong Data Management Foundation for Scalable ML and AI (Register to download PDF)

A strong data management foundation is essential for effectively scaling AI and machine learning programs to evolve into a core competence of the business. Download this special report for the key steps to success.
2020-09-09 00:00:00 Read the full story…
Weighted Interest Score: 4.6296, Raw Interest Score: 2.8037,
Positive Sentiment: 0.9346, Negative Sentiment 0.0000

Top Stocks To Short As Choppy Trading Continues For Markets

In choppy trading to start the final trading day of the week, all indexes rose as the Nasdaq once again tried to rally after a rough day – and an even rougher week. While the Dow is down 1.7% this week, and the S&P 500 is down 2.56%, and the Nasdaq is down 3.5%, which is the tech-heavy index’s worst week since March. This will also be the S&P’s second straight weekly loss for the first time since May. This morning, however, the Dow traded 183 points higher, or 0.7%, the S&P 500 climbed 0.5%, and the Nasdaq NDAQ +1.5% traded marginally higher at 0.2%. The big tech names were up and down to start the day-Facebook, Netflix NFLX +0.4%, Alphabet and Microsoft MSFT +1.9% were up slightly, while Apple AAPL +2% and Amazon AMZN +1.7% dropped 1.1% and 0.4%, respectively. The biggest movers of the day were Peloton and Oracle ORCL +5.4%, who had blowout quarterly earnings which crushed estimations.

After rising double digits after-hours, Peloton in morning trading gained more than 4% while Oracle rose 3.6%. In economic news, the U.S. Consumer Price Index also was a catalyst as consumer prices rose in August due to higher costs for a variety of goods. This shows that the economy and demand for goods is rebounding and recovering from the COVID-induced downturn earlier this year. Despite the rally, all indices are on pace for down weeks in the Labor Day shortened-week. For investors looking to make sense of a volatile and potentially overheated market, the deep learning algorithms at Q.ai have crunched the data to give you a set of Top Shorts. Our Artificial Intelligence (“AI”) systems assessed each firm on parameters of Technical, Growth, Momentum Volatility, and Quality Value to find the best short plays.
2020-09-11 00:00:00 Read the full story…
Weighted Interest Score: 4.3275, Raw Interest Score: 1.8072,
Positive Sentiment: 0.1070, Negative Sentiment 0.2140

EQUITY X joins BT Radianz Cloud

EQUITY X, an equity valuation software and alternative data provider, has joined BT Radianz Cloud.

The move is part of EQUITY X’s ambition to deliver the best possible experience to users of its new peer search engine, powered by machine learning, and a new feature valuing public shares based on alternative data.

Its new peer search engine analyses target company similarity from a pool of approximately 44,000 public companies. It displays a list of comparable companies in ascending order, significantly reducing time and effort needed to objectively identify and select a peer group.

Automation of fundamental analysis, consistent formatting and excellent coverage of small and mid-caps help sell-side and buy-side firms solve data availability issues, compare reports and find alpha signals in a timely manner.

2020-09-14 00:00:00 Read the full story…
Weighted Interest Score: 4.0825, Raw Interest Score: 1.5567,
Positive Sentiment: 0.4916, Negative Sentiment 0.1639

Using Orange to Build a Machine Learning Model

Orange is an open-source, GUI based platform that is popularly used for rule mining and easy data analysis. The reason behind the popularity of this platform is it is completely code-free. Researchers, students, non-developers and business analysts use platforms like Orange to get a good understanding of the data at hand and also quickly build machine learning models to understand the relationship between the data points better.

Orange is a platform built on Python that lets you do everything required to build machine learning models without code. Orange includes a wide range of data visualisation, exploration, preprocessing and modelling techniques. Not only does it become handy in machine learning, but it is also very useful for associative rule mining of numbers, text and even network analysis.

2020-09-14 05:30:37+00:00 Read the full story…
Weighted Interest Score: 4.0476, Raw Interest Score: 1.6868,
Positive Sentiment: 0.1432, Negative Sentiment 0.1591

How To Implement ML Models With Small Datasets

Machine learning is now being implemented in several different applications today. People these days are figuring out how they can use the power of machine learning in their domain. But they often come across the problem of lack of data. The data is not sufficient to build a predictive model over it. Also, when we build predictive models over this amount of data, often the model is overfitted and does not perform well. But what to do in these situations? How to build a model over a data set that has only 100-200 rows of data.

Through this article, we will explore and understand ways how we can tackle this problem and build a model on even small datasets. We will also understand how to tackle the over-fitting situation. For this experiment, we will use the Iris data set that has three different classes of species in which we have to classify the flower. The dataset is publicly available on Kaggle for download.

2020-09-13 10:30:07+00:00 Read the full story…
Weighted Interest Score: 3.8862, Raw Interest Score: 1.6553,
Positive Sentiment: 0.0556, Negative Sentiment 0.1530

Data Quality in Machine Learning.

We regularly see and hear phrases like “data is the life blood of an organisation” or “the world’s most valuable resource is no longer oil, but data”. There is no denying that data is an incredibly valuable resource. But a theme that is overlooked in many articles or only mentioned in passing is the importance of data quality.

Technology by itself is not a panacea. You can have any technology you like, and you can have much data as you like but if you don’t have high quality data you are taking an immense risk.

This short paper starts by looking at different types of data: quantitative, qualitative, and then looks the challenges of using this data in Machine Learning applications.

2020-09-07 11:10:02 Read the full story…
Weighted Interest Score: 3.8817, Raw Interest Score: 2.0438,
Positive Sentiment: 0.3371, Negative Sentiment 0.4003

Gary Marcus: COVID-19 should be a wake-up call for AI

The global pandemic has been cited as a “wake-up call” for many things — the environment, economic and social rights, and general global inequalities. However, scientist, author, and entrepreneur Gary Marcus thinks that the COVID-19 crisis should also be considered a wake-up call for AI.

Speaking at the virtual Intelligent Health AI conference yesterday, Marcus lamented decades of missed opportunities to build a more robust artificial intelligence, arguing that too much attention has been placed on AI technologies that don’t really help the world in any meaningful way.

“We would like AI that could read and synthesize the vast, quickly growing medical literature, for example, about COVID-19,” he said. “We want our AI to be able to reason causally, we want it to be able to weed out misinformation. We want to be able to guide robots to keep humans out of dangerous situations, care for the elderly, deliver packages to the door. With AI having been around [for] 60 years, I don’t think it’s unreasonable to wish that we might have had some of these things by now. But the AI that we actually have, like playing games, transcribing syllables, and vacuuming floors, it’s really pretty far away from the things that we’ve been promised.”

2020-09-11 00:00:00 Read the full story…
Weighted Interest Score: 3.8402, Raw Interest Score: 1.7664,
Positive Sentiment: 0.2650, Negative Sentiment 0.2429

Ex-Uber AI Chief Scientist Zoubin Ghahramani Joins Google Brain Leadership Team

Former Uber Chief Scientist and VP for AI Zoubin Ghahramani has joined Google Research as part of the Google Brain team leadership. “In addition to my ongoing academic position, I’m really excited to now be part of GoogleAI and its machine learning community,” he tweeted.

Ghahramani announced his departure from Uber on On September 1, tweeting, “Stay tuned for my next steps.” Those steps have now landed the respected 50-year-old British-Iranian researcher at Google AI.
2020-09-13 00:00:00 Read the full story…
Weighted Interest Score: 3.7284, Raw Interest Score: 1.9025,
Positive Sentiment: 0.2670, Negative Sentiment 0.1001

Data Strategy & Insights: Come For The Insight, Stay For The Impact

We have only about five weeks until our Data Strategy & Insights live virtual event on October 14-15, and I’m excited to share a glimpse of what’s on our program across our six keynotes and three main tracks. Our theme this year is “Insight To Impact,” and as a data and analytics leader, it’s your time to shine.

Over the course of two days, our keynotes will let you peer into a crystal ball of what the future of data and AI might look like, which, in turn, will help you reimagine and plan for the future of work and AI-led augmentation. We will also show you how to prioritize your insights efforts — especially at a time when what you thought you knew about your business and customers was put to the ultimate test this year — all while continuing to shore up on data literacy across your organization. ​A panel discussion with industry data and analytics leaders will demonstrate how organizations are pivoting or staying on course with their data and analytics efforts and will give you pointers on your own planning efforts.

We also have 18 deep dive sessions across three main tracks:

  • Drive Your Digital Business With Data
  • Amplify Intelligence With AI And Analytics​
  • Deepen Customer Relationships With Smarter Insights​

2020-09-10 17:10:49-04:00 Read the full story…
Weighted Interest Score: 3.7024, Raw Interest Score: 1.8705,
Positive Sentiment: 0.1079, Negative Sentiment 0.0719

Join This Full-Day Workshop On Natural Language Processing From Scratch

The Association of Data Scientists, the premier global professional body of data science & machine learning professionals, has announced a full-day workshop on Natural Language Processing (NLP) on the 26th of September, Saturday.

Over the last few years, the applications around NLP have increased tremendously, with use cases ranging from review analysis to intelligent chatbots in various industries. The workshop by AdaSci aims to take the participants on a learning ride with hands-on exposure to implementing NLP techniques in Python from scratch.

2020-09-14 10:39:07+00:00 Read the full story…
Weighted Interest Score: 3.4002, Raw Interest Score: 1.6010,
Positive Sentiment: 0.1298, Negative Sentiment 0.0000

A ‘Breakout Year’ for ModelOps, Forrester Says

The rapid maturation of machine learning operations (ModelOps) tools is leading to a “breakout year” for ModelOps, Forrester says in a recent report.

The ML lifecycle is a potential nightmare for many organizations, write Forrester analysts Mike Gualtieri and Kjell Carlsson in an August report, titled “Introducing ModelOps to Operationalize AI.”

“This process takes too long and is fraught with technical and business challenges, just with one model,” the analysts write. “What about a dozen use cases and models? A hundred? A thousand?”

The answer, of course, is ModelOps (also known as MLOps), which Forrester defines as “tools, technology, and practices that enable cross-functional AI teams to efficiently deploy, monitor, retrain, and govern AI models in production systems.”

Gualtieri and Carlsson identify three core ModelOps capabilities that organizations must have if they’re going to succeed with AI at scale.

2020-09-11 00:00:00 Read the full story…
Weighted Interest Score: 3.3343, Raw Interest Score: 1.6269,
Positive Sentiment: 0.1964, Negative Sentiment 0.0561

High-Performance Data Science—Laptops to Supercomputers

When talking about data science, most people feel as if they are in one of two camps as far as data size. The first is really small data—hundreds of megabytes to a few gigabytes. The second is gigabytes to terabytes. Notice I didn’t say “big data,” nor did I say “petabytes.” The source datasets may start at petabyte-scale, but keep in mind that data is often very raw, and most of it is ignored. This is the case, even in the typical data analytics (warehousing) workloads that may operate over these datasets to perform large-scale aggregations. The vast majority of data science-related workloads are using 10 terabytes or less of that data. In truth, more than 95% of these problems are smaller than 100 gigabytes. While there is certainly a lot of work that goes into cleaning up, aggregating, and reducing the data down to relevant datasets that are useful for each use case, the typical working set of data for data science workloads is not petabyte-scale.
2020-09-11 00:00:00 Read the full story…
Weighted Interest Score: 3.3242, Raw Interest Score: 1.6735,
Positive Sentiment: 0.2063, Negative Sentiment 0.1834

5 Powerful Networking Technologies That Are Disrupted By AI

Artificial intelligence is changing the Internet in ways we never expected. Savvy technology evangelists recognize the importance of AI in the 21st Century, especially as Internet technology continues to evolve.

AI is especially important in shaping the future of networking. An article in CISCOMAG talks about the benefits of using AI in improving network security. However, there are other applications of AI for networking, which include greater efficiency and better customer service.

2020-09-11 00:30:00+00:00 Read the full story…
Weighted Interest Score: 3.2707, Raw Interest Score: 1.3930,
Positive Sentiment: 0.5065, Negative Sentiment 0.0844

Congress probes how AI will impact U.S. economic recovery

AI has the potential to improve human lives and a company’s bottom line, but it can also accelerate inequality and eliminate jobs during the worst U.S. recession since the Great Depression. This dual promise and peril led members of the House Budget Committee to hold a hearing today to discuss the impact of AI on economic recovery, the future of work, and the federal budget.

Expert witnesses recommended approaches that ranged from giving people lifelong upskilling accounts to creating regional investment districts and portable benefits.

MIT professor and economist Daron Acemoglu warned the committee about the dangers of excessive automation. Acemoglu recently found that every robot replaces 3.3 human jobs in the U.S. In a working paper published by the National Bureau of Economic Research, Acemoglu detailed how excessive automation looks for ways to replace workers with machines or algorithms but produces few new jobs. Companies are currently incentivized by a U.S. tax code that taxes capital at a lower rate than human labor, policy he said incentivizes companies to replace humans with automation. In practice, this can be as simple as replacing a McDonald’s worker with a touchscreen. He argues automation has been a drag on the U.S. economy, potentially slowed market productivity, and failed to lead to higher wages for low- and middle-class workers.

2020-09-10 00:00:00 Read the full story…
Weighted Interest Score: 3.1236, Raw Interest Score: 1.7419,
Positive Sentiment: 0.2311, Negative Sentiment 0.3910

18 Open-Source Computer Vision Projects

Open source computer vision projects are a great segway to landing a role in the deep learning industry. Start working on these 18 popular and all-time classic open source computer vision projects.

Computer vision applications are ubiquitous right now. I honestly can’t remember the last time I went through an entire day without encountering or interacting with at least one computer vision use case (hello facial recognition on my phone!).

But here’s the thing – people who want to learn computer vision tend to get stuck in the theoretical concepts. And that’s the worst path you can take! To truly learn and master computer vision, we need to combine theory with practiceal experience.

And that’s where open source computer vision projects come in. You don’t need to spend a dime to practice your computer vision skills – you can do it sitting right where you are right now!

2020-09-18 00:00:00 Read the full story…
Weighted Interest Score: 3.0961, Raw Interest Score: 1.3028,
Positive Sentiment: 0.1263, Negative Sentiment 0.0861

Research into AI, Neuroscience, Psychology Aims to Make AI Less Artificial

Research at the intersection of AI, psychology, and neuroscience is attracting interest and investment. The study of the nervous system is called by some the “ultimate challenge” of the biological sciences.

The trend is exemplified in the experience of Irina Rish, now an Associate Professor in the Computer Science and Operations Research department at the Université de Montréal (UdeM),and a core member of Mila – the Quebec AI Institute.

Rish was 14 years old and going to high school in the central Asian city of Samarkand, Uzbekistan, when she first came across the notion of artificial intelligence. “I saw a book, translated from English into Russian, the cover was black with yellow letters, and the title was ‘Can Machines Think?’” Rish recalled in a recent article in Mirage.

2020-09-10 21:52:12+00:00 Read the full story…
Weighted Interest Score: 2.9955, Raw Interest Score: 1.4770,
Positive Sentiment: 0.1377, Negative Sentiment 0.1627

Learn different ways to Treat Overfitting in CNNs

Overfitting or high variance in machine learning models occurs when the accuracy of your training dataset, the dataset used to “teach” the model, is greater than your testing accuracy. In terms of ‘loss’, overfitting reveals itself when your model has a low error in the training set and a higher error in the testing set. You can identify this visually by plotting your loss and accuracy metrics and seeing where the performance metrics converge for both datasets.

Overfitting indicates that your model is too complex for the problem that it is solving, i.e. your model has too many features in the case of regression models and ensemble learning, filters in the case of Convolutional Neural Networks, and layers in the case of overall Deep Learning Models. This causes your model to know the example data well, but perform poorly against any new data.

This is annoying but can be resolved through tuning your hyperparameters, but first, let’s start by making sure our data is divided into well-proportioned sets.

2020-09-07 13:53:36+00:00 Read the full story…
Weighted Interest Score: 2.9912, Raw Interest Score: 1.2337,
Positive Sentiment: 0.1134, Negative Sentiment 0.2127

AI Put to Work to Help Assess Structural Integrity of Bridges

AI is being applied to assess the health of civil infrastructure through systems that test the integrity of bridges.

A civil engineering assistant professor at The University of Texas at Arlington is working to better understand a bridge’s structural health by combining machine learning with traditional monitoring measurements, according to a press release from the University of Texas at Arlington (UTA).

The 18-month, $122,000 grant to Dr. Suyun Ham of the Civil Engineering department is part of UTA’s membership in the Transportation Consortium of South-Central States (Tran-SET), a U.S. Department of Transportation Center administered by Louisiana State University. He will test his models in Dallas and Fort Worth.

The systems in place to monitor bridges today are weight-in-motion systems with sensors that measure vibrations, strain, and deflection. Measuring the bridge’s response to those elements present a picture of the bridge’s structural health. But the sensors do not take into account different types of trucks, multiple lanes, times of day and traffic congestion.

2020-09-10 21:41:24+00:00 Read the full story…
Weighted Interest Score: 2.9214, Raw Interest Score: 1.5223,
Positive Sentiment: 0.1582, Negative Sentiment 0.4350

What to Do When Your Data Is Too Big for Your Memory?

When we are working on any data science project, one of the essential steps to take is to download some data from an API to the memory so we can process it.

When doing that, there are some problems that we can face; one of these problems is having too much data to process. If the size of our data is larger than the size of our available memory (RAM), we might face some problems in getting the project done.

So, what to do then? There are different options to solve the problem of big data, small problems. These solutions either cost time or money :

  • Money-costing solution: One possible solution is to buy a new computer with a more robust CPU and larger RAM that is capable of handling the entire dataset. Or, rent a cloud or a virtual memory and then create some clustering arrangement to handle the workload.
  • Time-costing solution: Your RAM might be too small to handle your data, but often, your hard drive is much larger than your RAM. So, why not just use it? Using the hard drive to deal with your date will make the processing of it much slower because even an SSD hard drive is slower than a RAM.

2020-09-13 23:15:29.636000+00:00 Read the full story…
Weighted Interest Score: 2.8966, Raw Interest Score: 1.1427,
Positive Sentiment: 0.0399, Negative Sentiment 0.1329

How Analytics Is Being Used In Data Journalism

The field of journalism over the past decade or so has been witnessing continuous change. Today, journalism is influenced by big data and new computational tools. Data and visualisation have become the latest techniques for telling stories in media, thanks to intersections between journalism and computation.

One of the many things that AI is doing for journalism is to make it easier and faster to analyse the data and also synthesise the data into stories. When we mention automatic story writing tools, they use Natural Language Understanding and Processing, to synthesise the stories. We also see the use of AI to help generate imagery and videos.

Major news publications are struggling with budgets to maintain strong reporting staff. In such times, media houses have been exploring data and related computational tools to keep the expense of public accountability journalism economical, while presenting fact-based news reporting.
2020-09-14 08:30:21+00:00 Read the full story…
Weighted Interest Score: 2.8343, Raw Interest Score: 1.2936,
Positive Sentiment: 0.0727, Negative Sentiment 0.1890

Tamr on Azure Provides Flexible Approach to Data Mastering

Tamr, Inc., the provider of cloud-native data mastering solutions, is offering its cloud-native capabilities on Microsoft Azure, allowing companies to master their enterprise data using Tamr while taking advantage of the flexibility, scalability, and security of Microsoft Azure.

Tamr integrates with Azure’s data services, including Azure Synapse Analytics, Azure Databricks, Azure HDInsight, Azure Data Catalog, Azure Data Lake Storage, and Azure Data Factory.

These capabilities give enterprises the option to take a hybrid approach to data mastering by starting on-premises and then moving the process and their clean data to the Azure cloud.

“Whether beginning a migration to the cloud or looking to expand the scale of their data mastering projects, Tamr and Microsoft customers can now leverage a cloud-native solution that combines an innovative, machine learning-driven approach to data mastering with the power, security, and flexibility of the Azure platform,” said Anthony Deighton, chief product officer at Tamr. “Tamr is complementary with the existing Azure’s data services portfolio, and with its flexible deployment architecture, Tamr is especially useful for customers who are migrating their workloads to the Azure platform and want to ensure that the cloud-housed data is comprehensive, accurate, and current.”

2020-09-09 00:00:00 Read the full story…
Weighted Interest Score: 2.7795, Raw Interest Score: 1.6718,
Positive Sentiment: 0.1238, Negative Sentiment 0.0619

New Analytics Tools Predict COVID-19 Patient Mortality

One of the most urgent needs in the care of patients with COVID-19 is a better understanding of which patients will require more intensive treatment and attention. Now, researchers from Oklahoma State University’s Center for Health Systems Innovation (CHSI) are applying big data analytics to build predictive models of COVID-19 patient risk that could help physicians better manage patient care during the pandemic.

Zhuqi Miao (the health data science program manager at CHSI) and Meghan Sealey (a doctoral student studying statistics at Oklahoma State) worked with anonymized data from nearly 19,000 COVID-19 patients from healthcare IT firm Cerner’s COVID datasets. Using this data, they developed two tools for modeling mortality risk: one based on patient data at time of admission, and one based on patient data from the first data of hospitalization.

“The models identified a similar set of medical conditions suggested by the Centers for Disease Control and Prevention as the essential risk factors for death, such as history of diabetes, respiratory disorders and hypertension, and onset of respiratory or kidney failures,” Miao said, “but we also found some unique ones.”

2020-09-08 00:00:00 Read the full story…
Weighted Interest Score: 2.6678, Raw Interest Score: 1.3769,
Positive Sentiment: 0.3873, Negative Sentiment 0.2582

AI Removes Guesswork in An Uncertain World

It’s been a tumultuous year. In just the span of a few weeks, COVID-19 emerged unexpectedly and abruptly altered almost every corner of the commercial insurance space. Stock market and GDP forecasts have whipsawed as economists and investors have tried to make sense of frequently shifting news. And now, we’re headed straight into what is likely to be a contentious and unpredictable election cycle.

Divining the future is always a challenge, but lately, it’s become especially difficult. During periods of intense change, traditional patterns and precedents lose their predictive power. Regression style tools that provide data extrapolations become a useless blur. Take the insurance industry, for example, the average workers comp claim duration of 2019 will look very different than it is in 2020. Litigation and fraud may emerge in new forms, with most new types passing undetected by screens developed from prior period data.

One approach that can help companies navigate the uncertainty is artificial intelligence (AI), which is highly sensitive to new data and tends to react immediately, creating a dynamically updated vision of the future. While much of the world has been focused on the pandemic and the related economic challenges, the underlying technologies behind AI have continued to accelerate in speed, efficiency, and predictive accuracy. Integrating machine learning, natural language processing, and other AI techniques into organizations’ operations is helping companies become more resilient.

2020-09-11 00:00:00 Read the full story…
Weighted Interest Score: 2.6632, Raw Interest Score: 1.1997,
Positive Sentiment: 0.2742, Negative Sentiment 0.3256

Is Straight Through Processing the silver bullet for reducing false positives?

“How can we reduce false positives?” is the million-dollar question facing the banking industry. Costly and potentially harmful from a customer service perspective when a legitimate account is frozen unnecessarily, the silver bullet is yet to be found.

But this problem lies in the very nature of fraud detection engines used during the KYC process – the technology is designed to surface every single false positive hit to show that the proper compliance processes were followed and, therefore, avoid allowing any possible criminal attempts to slip through the net, as missing any alerts can lead to substantial regulatory fines. Hence, the reduction in false positives is very difficult to achieve.

However, the route to real efficiency gains within financial crime could be achieved via Straight Through Processing (STP) of these false positives.

2020-09-11 13:51:02 Read the full story…
Weighted Interest Score: 2.6359, Raw Interest Score: 1.4749,
Positive Sentiment: 0.4978, Negative Sentiment 0.4978

Fidelity salaries revealed: What the money management behemoth pays for tech-focused roles, from software engineers to data scientists

A Business Insider analysis of public visa data sheds light on some Fidelity employees’ base salaries.

Traditional asset management firms like Fidelity, the Boston-based behemoth, are not immune to the war for talent playing out between Silicon Valley and Wall Street.

Competition for roles in high demand like software engineers and cloud technologists is fierce as companies look to upgrade their tech tools. It’s an acute goal for big legacy financial services firms like Fidelity, which have started competing with a crop of new money-management and brokerage startups in recent years.

Fidelity, which reported earlier this summer $8.3 trillion in assets under administration, $3.3 trillion of which is managed on a discretionary basis, has rolled out its own slate of new tech-focused features and products this year.

2020-09-11 00:00:00 Read the full story…
Weighted Interest Score: 2.6352, Raw Interest Score: 1.5826,
Positive Sentiment: 0.0195, Negative Sentiment 0.0586

The Top Trends in Data Management for 2021 (Panel – Registration required)

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: 2.5974, Raw Interest Score: 1.6729,
Positive Sentiment: 0.0929, Negative Sentiment 0.0929

Snowflake, Unity Lead Off Busy Tech IPO Season

A few months after the pandemic sidelined many IPOs, a crop of new tech names are due to make their public debuts in September.

Among them are several multibillion-dollar firms working in software, data, cloud infrastructure and related high-growth sectors. Here’s a breakdown of who is listing when:

  • Snowflake
  • JFrog
  • Sumo Logic
  • Unity
  • Palantir
  • Asana

2020-09-12 11:00:00+00:00 Read the full story…
Weighted Interest Score: 2.5805, Raw Interest Score: 1.4998,
Positive Sentiment: 0.1544, Negative Sentiment 0.2206

Modern Data Warehousing: Enterprise Must-Haves (Register for Round Table Webinar)

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

How Can MLflow Add Value To Machine Learning Lifecycle And Model Management

One of the major concerns around machine learning is deploying it. Running a large number of deployment tools and environments, and migrating a model to a production environment can be extremely challenging.

There are countless independent tools from data preparation to model training, and software tools that cover every stage of the machine learning life cycle. Machine learning developers need to use and deploy dozens of libraries while in a production environment. There is no standard way to migrate models from any library to any of these tools, so that every time a new deployment is made, new risks are created.

What Are The Challenges With ML Workflow?

2020-09-12 07:30:00+00:00 Read the full story…
Weighted Interest Score: 2.4979, Raw Interest Score: 1.5261,
Positive Sentiment: 0.0832, Negative Sentiment 0.1942

Big Data 50—Companies Driving Innovation in 2020

The COVID-19 crisis has presented some new hurdles—but they are ones that many innovative companies are actively working to overcome. Forward-looking companies aren’t sitting the year out waiting for the business climate to improve. They are actively seeking ways to expand their reach and take advantage of new opportunities.

Two recent surveys conducted by CFO Research in conjunction with Vistra found that 92% of multi-national corporations ($100 million-plus) with plans for acquisitions and takeovers before the pandemic are pushing ahead with those plans, despite continued volatility in the global economy. As they enter a new phase of global business, the research found, organizations are aware of the need to overcome evolving hurdles, including turbulence stemming from the public health and economic crises, as well as difficulties related to supply chains, evolving global mobility requirements, and tightening regulations.

2020-09-10 00:00:00 Read the full story…
Weighted Interest Score: 2.4742, Raw Interest Score: 1.0722,
Positive Sentiment: 0.5361, Negative Sentiment 0.3299

New Technologies Shaping Today’s Big Data World

Big Data has been around in one form or another for a long time, but lately, due to current events and intensified pressure, there has been greater attention focused on data-driven approaches to manage operations and understand customers. Recognizing that value has shifted to the digital realm, businesses have been looking to technologies that will take them to the next level. To explore this mass movement in more detail, we asked a number of leading industry experts and solution providers to describe what they see as the most impactful technologies shaping today’s big data world.
2020-09-11 00:00:00 Read the full story…
Weighted Interest Score: 2.4622, Raw Interest Score: 1.4175,
Positive Sentiment: 0.2913, Negative Sentiment 0.1748

From Modeling to Scoring: Finding an Optimal Classification Threshold based on Cost and Profit

Wheeling like a hamster in the Data Science cycle? Don’t know when to stop training your model?

Model evaluation is an important part of a Data Science project and it’s exactly this part that quantifies how good your model is, how much it has improved from the previous version, how much better it is than your colleague’s model, and how much room for improvement there still is.

In this series of posts, we review different scoring metrics: for classification, numeric prediction, unbalanced datasets, and other similar more or less challenging model evaluation problems.

2020-09-11 07:30:09+00:00 Read the full story…
Weighted Interest Score: 2.4620, Raw Interest Score: 1.4775,
Positive Sentiment: 0.2208, Negative Sentiment 0.4076

How Financial Institutions Must Resolve Internal Squabbles Over Data

Huge opportunities await banks and credit unions that can move beyond the head-butting that often accompanies increasing gathering and use of data. Resolving the friction of implementation is the first step to realizing these advantages.

Which one of the following strategic priorities do you think produces the most conflict at banks and credit unions: branch initiatives, advocacy initiatives, mobile banking initiatives, data utilization initiatives, or AI-driven initiatives?

The answer is data utilization initiatives. A survey of industry leaders at a mix of financial institutions ranging from less than $500 million in assets to more than $10 billion found that respondents overwhelmingly said such initiatives produce the most conflict in their organization. This is among finding in the “Ultimate Guide to AI, Data, and Personalized Financial Automation.”

What’s particularly surprising is just how much more these initiatives around data utilization produced conflict compared to the other options: More than 20 percentage points higher than conflict around branch initiatives and nearly 40 percentage points higher than mobile banking initiatives.

2020-09-08 00:01:53+00:00 Read the full story…
Weighted Interest Score: 2.4536, Raw Interest Score: 1.2575,
Positive Sentiment: 0.2454, Negative Sentiment 0.3680

How the Trevor Project is using AI to prevent LGBTQ suicides

Over the past three years, the nation’s largest suicide prevention organization for LGBTQ youth has undergone a major tech overhaul, most recently using machine learning to assess high-risk outreach.

In 2017, when John Callery joined the Trevor Project, an LGBTQ suicide prevention organization, as its director of technology, he had a galvanizing, if not daunting, mandate from the newly appointed CEO, Amit Paley: “Rethink everything.”

“I think my computer had tape on it when I started on the first day,” says Callery, who’s now the Trevor Project’s VP of technology. “In a lot of nonprofits, the investments are not made in technology. The focus is on the programmatic areas, not on the tech as a way of driving programmatic innovation.”

2020-09-09 07:00:38 Read the full story…
Weighted Interest Score: 2.4366, Raw Interest Score: 1.0894,
Positive Sentiment: 0.2294, Negative Sentiment 0.3870

UK sees tech jobs recovery as vacancies grow by third

Vacancies in the tech sector have grown by more than a third over the past two months as restrictions on hiring begin to ease, new figures show.

In the months before lockdown there were more than 150,000 jobs in the industry advertised each week, according to data from jobs site Adzuna. With job ads plummeting during lockdown and other restrictions some recovery has been cited in the tech sector. By August 9, tech job ads had increased by 36pct.
2020-09-07 00:00:00 Read the full story…
Weighted Interest Score: 2.4267, Raw Interest Score: 1.5132,
Positive Sentiment: 0.1892, Negative Sentiment 0.0315

DuckieNet lets developers test autonomous vehicle systems using toy cars

Robotics research has a reproducibility problem, owing in part to robots’ myriad interacting components. These components tend to be complex, only partially observable, and trained with AI techniques where performance varies greatly across environments. In an effort to address some of the challenges specific to the autonomous driving domain, researchers at ETH Zurich, the Toyota Technological Institute, Mila in Montreal, and NuTonomy developed what they call the Decentralized Urban Collaborative Benchmarking Network (DuckieNet), a setup built using the open source Duckietown platform. DuckieNet provides a framework for developing, testing, and deploying both perception and navigation algorithms, and the researchers claim it’s highly scalable but inexpensive to construct.
2020-09-10 00:00:00 Read the full story…
Weighted Interest Score: 2.3947, Raw Interest Score: 1.1292,
Positive Sentiment: 0.1652, Negative Sentiment 0.2203

These ‘superstar’ stocks are disrupting their industries and have momentum, UBS says

The Wall Street Bull (The Charging Bull) is seen during Covid-19 pandemic in New York, on May 26, 2020.

The rise of artificial intelligence will mostly benefit “superstar” companies and investors should adjust their strategies, UBS said in a new note.

Companies that have invested more in artificial intelligence in recent years have seen bigger gains in sales and employment, according to UBS. That mirrors other trends, such as the top 10% of companies have been growing their profits at a faster rate than other companies, and that trend has accelerated over the past decade, the note said.

2020-09-10 00:00:00 Read the full story…
Weighted Interest Score: 2.3256, Raw Interest Score: 1.8663,
Positive Sentiment: 0.3110, Negative Sentiment 0.0000

Tech Behind Nasa’s ML Model To Predict Hurricane Intensity

a way to predict and analyse these hurricane patterns. Thus in an attempt to forecast future hurricane intensity, scientists at NASA’s Jet Propulsion Laboratory in Southern California have proposed a machine learning model that claims to predict rapid-intensification events of the future accurately.

The critical factor in understanding the intensity of a hurricane is the wind speed. Traditionally it has been a challenge to predict the severity of storms or hurricanes while it’s brewing. However, NASA’s new ML model can improve the accuracy of the prediction and provide better results.

Developed via surfing through years of satellite data, this model claims to predict the hurricane’s strength, with more accurate forecasting. This allows people to prepare the way before the storm actually hits. When asked, Hui Su, an atmospheric scientist at JPL said that such a prediction is critical to get right because of the potential harm hurricanes and storms can do to people and property.
2020-09-14 07:30:32+00:00 Read the full story…
Weighted Interest Score: 2.3204, Raw Interest Score: 1.4093,
Positive Sentiment: 0.1649, Negative Sentiment 0.2399

Why GPUs are more suited for Deep Learning?

Since the past decade, we have seen GPU coming into the picture more frequently in fields like HPC(High-Performance Computing) and the most popular field i.e gaming. GPUs have improved year after year and now they are capable of doing some incredibly great stuff, but in the past few years, they are catching even more attention due to deep learning.

As deep learning models spend a large amount of time in training, even powerful CPUs weren’t efficient enough to handle soo many computations at a given time and this is the area where GPUs simply outperformed CPUs due to its parallelism. But before diving into the depth lets first understand some things about GPU.

What is the GPU?
A GPU or ‘Graphics Processing Unit’ is a mini version of an entire computer but only dedicated to a specific task. It is unlike a CPU that carries out multiple tasks at the same time. GPU comes with its own processor which is embedded onto its own motherboard coupled with v-ram or video ram, and also a proper thermal design for ventilation and cooling.

2020-09-09 13:01:47+00:00 Read the full story…
Weighted Interest Score: 2.2510, Raw Interest Score: 1.4581,
Positive Sentiment: 0.2573, Negative Sentiment 0.0214

VC Ben Horowitz Dishes on Hadoop, AI, and Data Culture

Don’t mistake Ben Horowitz as big fan of Hadoop. “The product was just never good,” the noted venture capitalist said today in a wide-ranging fireside chat with Sisu CEO Peter Bailis during the Future Data Conference.

There’s no denying that Horowitz has had an outside influence on tech startups with Andreessen Horowitz, the Menlo Park, California investment firm that he co-founded with Marc Andreessen, the co-author of Mosaic and the founder of Netscape. The list of current investments and exits on the venture capital company’s website is simply ridiculous.

The storied Sandhill Road firm is currently invested in Sisu, which shows promise as a next-gen analytics system that uses machine learning to help people ask better questions of the data. Andressen Horowitz, which has $12 billion under management, has helped fund a variety of ecosystem tool players featured in these pages, like Alluxio, Anyscale, Cazena, Databricks, and Fivetran. And that’s just the first six letters of the alphabet (this may be an online publication, but we don’t have that much space).

2020-09-09 00:00:00 Read the full story…
Weighted Interest Score: 2.2281, Raw Interest Score: 1.2604,
Positive Sentiment: 0.3151, Negative Sentiment 0.2395


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