AI & Machine Learning News. 17, August 2020

The Artificial Intelligence and Machine Learning Newsletter by CloudQuant

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


Vid2Player: Controllable Video Sprites that Behave and Appear like Professional Tennis Players

We present a system that converts annotated broadcast video of tennis matches into interactively controllable video sprites that behave and appear like professional tennis players. Our approach is based on controllable video textures, and utilizes domain knowledge of the cyclic structure of tennis rallies to place clip transitions and accept control inputs at key decision-making moments of point play. Most importantly, we use points from the video collection to model a player’s court positioning and shot selection decisions during points. We use these behavioral models to select video clips that reflect actions the real-life player is likely to take in a given match play situation, yielding sprites that behave realistically at the macro level of full points, not just individual tennis motions. Our system can generate novel points between professional tennis players that resemble Wimbledon broadcasts, enabling new experiences such as the creation of matchups between players that have not competed in real life, or interactive control of players in the Wimbledon final. According to expert tennis players, the rallies generated using our approach are significantly more realistic in terms of player behavior than video sprite methods that only consider the quality of motion transitions during video synthesis.

2020-08-15 Read the full story…

CloudQuant Thoughts : This is amazing!

NeRF in the Wild – Neural Radiance Fields for Unconstrained Photo Collections

We present NeRF-W, a system for 3D reconstruction of landmarks from unconstrained, “in-the-wild” photo collections. Given a set of posed photos, NeRF-W is able to disentangle the shared, underlying 3D geometry from transient objects and photometric variations, producing a consistent, photorealistic scene representation that can be rendered from novel viewpoints.

2020-08-11 Read the full story…

CloudQuant Thoughts : Very reminiscent of Microsoft PhotoSynth but very impressive none the less.

The future of farming is one giant A/B test on all the crops in the world at once.

John Deere subsidiary Blue River Technology is using computer vision and machine learning to make the farmers of tomorrow more efficient.

John Deere has been making farming equipment for more than 180 years, adapting and evolving with the times. But as the world’s population continues to balloon and the number of farms in the U.S. falls, farmers need to be able to do more with less to maximize the amount of crops they can harvest. For years that’s meant more efficient motors, tillers and even GPS systems, but the next frontier lies in automation.

Deere acquired Blue River Technology, an agricultural AI and robotics company, back in 2017, to help it on that mission. At the time, Blue River was primarily focused on trying to make lettuce growing more efficient, but Deere shifted its focus onto some of the most lucrative crops in the country — soy and cotton. Deere’s goal, at least for now, isn’t to replace the farmer with autonomous robots (although it is making its machines much easier to pilot), but rather to make its equipment as effective as possible to help farmers increase their yields, according to Chris Padwick, Blue River’s director of computer vision and machine learning.
2020-08-11 Read the full story…

CloudQuant Thoughts : Do y’all know your food history. Do you know about the Potato Famine? Do you know we are about to lose bananas? This is crazy!

What makes a data analyst excellent?

Before we dissect the nature of analytical excellence, let’s start with a quick summary of three common misconceptions about analytics from Part 1:

  • Analytics is statistics. (No.)
  • Analytics is journalism/marketing / storytelling. (No.)
  • Analytics is decision-making. (No!)

It’s all about Speed…

  • Speed of getting data that’s promising and relevant. (Domain knowledge.)
  • Speed of getting data ready for manipulation. (Software skills.)
  • Speed of getting data summarized. (Mathematical skills.)
  • Speed of getting data summaries into their own brains. (Data visualization skills.)
  • Speed of getting data summaries into stakeholders’ brains. (Communication skills.)
  • Speed of getting the decision-maker inspired. (Business acumen.)

2020-08-15 15:04:44.933000+00:00 Read the full story…
Weighted Interest Score: 2.5604, Raw Interest Score: 1.0746,
Positive Sentiment: 0.3317, Negative Sentiment 0.0929

CloudQuant Thoughts : This is a very interesting article, how to measure the quality of a data scientist? Give them a known dataset and see what they can find and how quickly? Byteboard now have a test for data scientists!

Biggest AI Acquisitions For The Year 2020, So Far

Although the year 2020 started with a crisis, the technology landscape has drastically gained its momentum, and artificial intelligence has played a crucial role amid this pandemic. And that’s why the majority of companies are trying to get their hands on this technology, either by hiring AI and analytics experts or by acqui-hiring AI startups.

Every year, many AI companies get acquired, especially the promising startups that get gobbled up by large tech companies in order to expand their AI capabilities. Similarly, this year also saw many exciting mergers and acquisition in the AI space, despite the COVID pandemic.

Here are the top AI acquisitions of 2020, so far — in random order:

2020-08-04 Read the full story…

CloudQuant Thoughts : You may have noticed that we were away last week. I did not try to get news from the missed week but there were one or two articles that stood out for highlighting and this was one. Even in the midst of a pandemic, the big players are still picking up promising AI/ML companies left, right and center.

ICE just signed a contract with facial recognition company Clearview AI

The contract comes after months of scrutiny of Clearview’s privacy practices. Immigration and Customs Enforcement (ICE) signed a contract with facial recognition company Clearview AI this week for “mission support,” government contracting records show (as first spotted by the tech accountability nonprofit Tech Inquiry). The purchase order for $224,000 describes “clearview licenses” and lists “ICE mission support dallas” as the contracting office.

ICE is known to use facial recognition technology; last month, The Washington Post reported the agency, along with the FBI, had accessed state drivers’ license databases — a veritable facial recognition gold mine, as the Post termed it — but without the knowledge or consent of drivers. The agency has been criticized for its practices at the US southern border, which has included separating immigrant children from their families and detaining refugees indefinitely. “Clearview AI’s agreement is with Homeland Security Investigations (HSI), which uses our technology for their Child Exploitation Unit and ongoing criminal investigations,” Clearview AI CEO Hoan Ton-That said in an emailed statement to The Verge. “Clearview AI has enabled HSI to rescue children across the country from sexual abuse and exploitation.”
2020-08-14 Read the full story…

Data Analyst, Python Lead U.S. Job Searches

The data science skills gap has been well documented here and elsewhere. However, there appears to be a mismatch between talent supply and demand.

Curiously, U.S. job hunters are most interested in data analyst positions that many companies claim they are having trouble filling. Furthermore, recent research indicates the programming language most potential recruits intend to learn is also the dominant language for data science: Python.

Of the most in-demand technology positions turning up in job searches, the IT consulting firm Prolifics Testing found data analyst drawing the most interest—with data scientist not far behind.
2020-08-13 00:00:00 Read the full story…
Weighted Interest Score: 4.5002, Raw Interest Score: 2.9692,
Positive Sentiment: 0.1466, Negative Sentiment 0.1100

Hedge Funds Must Embrace AI or Die

Renaissance Technologies famed hedge fund, Medallion, along with other AI-driven funds including Citadel, D.E. Shaw and Two Sigma, are on the verge of facing off against a new generational hedge fund fueled by the latest AI technologies with one key difference: a 100 percent model-driven, alpha-learning, AI algorithm designed to pinpoint market demand projections while actively applying real-time data analysis insights without human interruptions. The new hedge fund is Project One.
The brainchild of Andrew Sobko and Rami Jachi, the Project One hedge fund, targets $1B under management by 2021 and projects an average of 60 percent annualized returns, surpassing the success and performance of the Medallion predecessor. Interested investors must meet the minimum $1M threshold.
2020-08-12 12:58:47+00:00 Read the full story…
Weighted Interest Score: 9.5030, Raw Interest Score: 3.0654,
Positive Sentiment: 0.0938, Negative Sentiment 0.3754

Trading Up: The Shocking Evolution Of Data Analytics In Online Trading

Data analytics is playing a major role in online trading in 2020. Here’s what to know.

Data analytics technology is becoming more integral to the future of most industries. The online trading industry is one of the sectors where data analytics will be particularly important.

Advances in analytics technology are – in part at least – behind some of the biggest leaps forward in business and commerce. Trading the global markets is no different. In recent years, we have seen an evolution in platforms and solutions that have made trading quicker, simpler and much more accessible than ever before. And that evolution is perhaps still only in its early stages, as we have not even begun to see the transformation that data analytics will have on the sector.

The effect of this evolution so far, however, is clear. To look at the global currency market, the average daily trading volume scaled new highs in 2019, according to figures from the Bank for International Settlements. With this greater integration of technology, investors are benefiting from larger volumes of data, dynamic pricing and instant communication. Is it just the start?
2020-08-11 23:24:39+00:00 Read the full story…
Weighted Interest Score: 3.8491, Raw Interest Score: 1.7255,
Positive Sentiment: 0.3552, Negative Sentiment 0.0254

Can Machine Learning Models Accurately Predict The Stock Market?

Artificial intelligence is viewed as the Holy Grail of technology. It’s being investigated as a way of solving many of the complex problems that face mankind. What makes artificial intelligence attractive is that it combines unbelievably fast computing power with an intuitiveness that was previously only available from human involvement.

Artificial intelligence is being used in the financial markets. Many believe that …
2020-08-16 21:41:12+00:00 Read the full story…
Weighted Interest Score: 2.6061, Raw Interest Score: 1.8701,
Positive Sentiment: 0.2843, Negative Sentiment 0.2244

How to Improve Your Training Data for Vastly Better Machine Learning

Your machine learning models are only as good as the data you’re using to train and test them. So, how can you improve your datasets? This guide breaks down simple strategies to acquire better data and quick approaches and methods to fine-tune and manipulate your existing data will get you better testing results and insights (REGISTER FOR DOWNLOAD).
2020-08-11 00:00:00 Read the full story…
Weighted Interest Score: 5.3731, Raw Interest Score: 2.1021,
Positive Sentiment: 1.2012, Negative Sentiment 0.6006

Machine Learning Practices And The Art of Research Management

“Allegro AI offers the first true end-to-end ML / DL product life-cycle management solution with a focus on deep learning applied to unstructured data.”

Machine learning projects involve iterative and recursive R&D process of data gathering, data annotation, research, QA, deployment, additional data gathering from deployed units and back again. The effectiveness of a machine learning product depends on how intact the synergies are between data, model and various teams across the organisation.

In this informative session at CVDC 2020, a 2 day event organised by ADaSci, Dan Malowany of Allegro.AI presented the attendees with the best practices to imbibe during the lifecycle of an ML product—from inception to production.
2020-08-16 12:30:00+00:00 Read the full story…
Weighted Interest Score: 4.7887, Raw Interest Score: 2.4671,
Positive Sentiment: 0.2350, Negative Sentiment 0.1645

Singapore’s MAS pours $182m into second fintech fund

The Monetary Authority of Singapore (MAS) has committed SGD 250 million ($182.2 million) in its ongoing efforts to accelerate technology adoption in the country’s financial sector.

Over the next three years, the regulator will pour the capital into its existing Financial Sector Technology and Innovation Scheme (FSTI 2.0).

The scheme is also designed to promote large-scale innovation projects and strengthen Singapore’s fintech pipeline.
2020-08-17 06:30:44+00:00 Read the full story…
Weighted Interest Score: 4.1018, Raw Interest Score: 1.1673,
Positive Sentiment: 0.3537, Negative Sentiment 0.0000

10 Use Cases for Privacy-Preserving Synthetic Data

This article presents 10 use-cases for synthetic data, showing how enterprises today can use this artificially generated information to train machine learning models or share data externally without violating individuals’ privacy.

Fast-evolving data protection laws are constantly reshaping the data landscape. The organizational ability to overcome sensitive data usage restrictions while safeguarding customer privacy will be a key driver of tomorrow’s successful businesses. This blog presents ten concrete applications for privacy-preserving synthetic data that could help businesses maintain a competitive advantage:

  • Cloud migration
  • Internal data sharing
  • Data retention
  • Data analysis
  • Data testing
  • AI/ML model training
  • 3rd party data sharing
  • Product development
  • Data monetization
  • Data publication

With the appropriate privacy guarantees, privacy-preserving synthetic data is a type of anonymized data. Thus, it falls out of the scope of personal data protection laws. This, in turn, reduces for organizations the restrictions associated with the use of sensitive data while safeguarding individuals’ privacy. It’s particularly valuable in heavily regulated industries, as we’ll see through the following use-cases.
2020-08-10 00:00:00 Read the full story…
Weighted Interest Score: 3.7439, Raw Interest Score: 2.0579,
Positive Sentiment: 0.2884, Negative Sentiment 0.2490

Why An End To End AI Platform Is Needed For A Unified AI Strategy

End to end AI platforms can not only provide businesses with a unified AI strategy but also provide integrated tools for managing data annotation projects of any size. In this talk of Computer Vision DevCon 2020, Matthew Zeiler, Founder and CEO of Clarifai, an independent artificial intelligence (AI) company, talked about common challenges companies face while deploying AI, and how Clarifai’s complete AI ecosystem can help companies achieve their AI goals.

As a founder of Clarifai, Zeiler works on simplifying the complex challenges related to image and video recognition and making it accessible to all. With Zeiler’s tremendous experience in the field, he has built Clarifai’s problem-solving AI ecosystem which has been explained further in his talk.

2020-08-17 04:30:00+00:00 Read the full story…
Weighted Interest Score: 3.7141, Raw Interest Score: 1.6678,
Positive Sentiment: 0.2312, Negative Sentiment 0.1321

PyFlux Guide – Python Library For Time Series Analysis And Prediction

mes it’s lower. Similarly, we see that stock prices are always changing.

Although it is not easy to predict the time series data due to various factors on which it depends still Python has different machine learning models that can be used to analyze and predict the time-series data.

PyFlux is a library for time series analysis and prediction. We can choose from a flexible range of modeling and inference options, and use the output for forecasting. PyFlux has most of the time series prediction models such as ARIMA, Garch, etc. predefined we just need to call the model we need to analyze.
2020-08-17 10:30:28+00:00 Read the full story…
Weighted Interest Score: 3.5780, Raw Interest Score: 2.0007,
Positive Sentiment: 0.0367, Negative Sentiment 0.0551

Elyra reaches 1.0.0

Building on a Jupyter Notebooks foundation, the de facto tool for data scientists, machine learning engineers and AI developers, Elyra is an open-source project that provides a set of AI-centric extensions to JupyterLab aiming to help users through the model development life cycle complexities, making JupyterLab even better for AI practitioners.

Elyra is proud to announce its 1.0.0 Release. This release brings usability enhancements and bug fixes for existing features, such as enhanced inline user documentation and validation capabilities for the Pipeline Editor, improved performance for pipeline submission to Kubeflow Pipelines runtime. It also provides new capabilities such as a new reusable Code Snippets extension and the ability to configure runtimes directly on the JupyterLab user interface.
2020-08-10 15:55:20.214000+00:00 Read the full story…
Weighted Interest Score: 3.5160, Raw Interest Score: 1.3350,
Positive Sentiment: 0.3034, Negative Sentiment 0.0850

AIoT: When Artificial Intelligence Meets the Internet of Things

The Internet of Things (IoT) is a technology helping us to reimagine daily life, but artificial intelligence (AI) is the real driving force behind the IoT’s full potential.

From its most basic applications of tracking our fitness levels, to its wide-reaching potential across industries and urban planning, the growing partnership between AI and the IoT means that a smarter future could occur sooner than we think.

This infographic by TSMC highlights the breakthrough technologies and trends making that shift possible, and how we’re continuing to push the boundaries.
2020-08-12 08:56:47-07:00 Read the full story…
Weighted Interest Score: 3.4960, Raw Interest Score: 1.9711,
Positive Sentiment: 0.3066, Negative Sentiment 0.1752

Key Takeaways from Data Summit Connect 2020

The annual Data Summit conference went digital earlier this year, becoming Data Summit Connect. The online event in June featured live presentations by executives from leading IT organizations who engaged attendees with compelling presentations and spirited discussions on a variety of topics including data analytics and privacy, knowledge graphs, and AI and machine learning.

The following are some key points distilled from the 3-day webinar series which was preceded by a day of workshops. Full videos of Data Summit Connect 2020 presentations are available at www.dbta.com/DBTA-Downloads/WhitePapers.

Join us again October 20-22 for Data Summit Connect Fall 2020. The call for speakers is now open.
2020-08-11 00:00:00 Read the full story…
Weighted Interest Score: 3.3610, Raw Interest Score: 1.6267,
Positive Sentiment: 0.2484, Negative Sentiment 0.1863

The Digital Transformation of Compliance & Reporting in Business

Digital transformation has been a trend in the news for a while now, and recently it got me thinking: about how we work, what this means for businesses, and where technologies like AI, machine learning, and cloud software can ease the burden on employees and finance organisations, allowing them more time to work on meaningful things.

These types of considerations are all part of what I call the Fourth Industrial Revolution, during which corporate compliance and reporting needs to transform in order to face unforeseen circumstances, most notably COVID-19 and its impact on global markets. There is a lot of pressure on government agencies and regulatory authorities to reduce the high costs of compliance and to create better economic conditions for business growth and also capital allocation, for the reduction of operating expenses that are typically funded by treasury departments who ideally, need to do more with less.
2020-08-14 16:18:28 Read the full story…
Weighted Interest Score: 3.3456, Raw Interest Score: 1.6628,
Positive Sentiment: 0.3002, Negative Sentiment 0.1386

HK virtual bank WeLab opens 10,000 accounts in first ten days; Livi goes live

WeLab’s new virtual bank in Hong Kong has picked up 10,000 new accounts within ten days of opening to the public.

WeLab was the first first homegrown applicant to be granted one of Hong Kong’s new virtual banking licenses back in 2019. The mobile lender raised US$156 million of Series C strategic financing in December last year to build out its banking proposition, using AI, machine learning and big data to create a fully-functioning app-based service.

So far, more than 60% of new customers are using two or more WeLab Bank products, with the firm’s innovative GoSave time deposit account proving particularly popular. GoSave harnesses the power of the community to pay higher interest rate as more people join each group.
2020-08-13 09:27:00 Read the full story…
Weighted Interest Score: 3.2787, Raw Interest Score: 2.0669,
Positive Sentiment: 0.1824, Negative Sentiment 0.0608

R and Python: The Data Science Dynamic Duo

The language R is in the midst of a sizzling resurgence this summer. One might hypothesize that this growth is coming at the expense of Python, by far the dominant language for data science. But some evidence suggests that data scientists are increasingly using both.

“Rather than R versus Python, we focus on R and Python,” says Lou Bajuk, director of product marketing for RStudio, the Boston, Massachusetts-based provider of commercial and open source R software.
2020-08-11 00:00:00 Read the full story…
Weighted Interest Score: 3.1250, Raw Interest Score: 2.2599,
Positive Sentiment: 0.4708, Negative Sentiment 0.1695

Manufacturers need to maximise the competitive opportunity of data

The emergence of technologies such as AI and machine learning, along with sophisticated analytics, offers opportunities for smart manufacturers to transform their businesses radically — to create new product and service offerings while maximising the efficiency of supply chains and processes.

Contemporary computing models — such as Cloud and, increasingly, Edge computing — release huge amounts of sensor- and device-related data, to help with dec…
2020-08-09 16:38:35+00:00 Read the full story…
Weighted Interest Score: 3.0548, Raw Interest Score: 1.6482,
Positive Sentiment: 0.3296, Negative Sentiment 0.1798

IIT Madras Invites Applications For Post-Doctoral Fellowship In Data Science & AI

The Robert Bosch Centre for Data Science and Artificial Intelligence (RBC DSAI) at IIT Madras has invited applications for its Post-Doctoral Fellowship. It is open to candidates across the country with PhD Degrees in Research Topics related to Data Science, Artificial Intelligence or allied application domains.

The areas of research include Deep Learning, Network Analytics, Theoretical Machine Learning, Reinforcement Learning and Multi-armed Bandits, Natural Language Processing, AI on the edge, System Architecture for Data Science and AI, Ethics, Fairness and Explainability in AI, Systems Biology and Healthcare, Smart Cities and Transportation, and Financial Analytics.
2020-08-17 08:55:14+00:00 Read the full story…
Weighted Interest Score: 3.0471, Raw Interest Score: 1.8661,
Positive Sentiment: 0.1647, Negative Sentiment 0.0549

How to improve AI economics by taming the long tail of data

As the CTO of one late-stage data startup put it, AI development often feels “closer to molecule discovery in pharma” than software engineering.

This is because AI development is a process of experimenting, much like chemistry or physics. The job of an AI developer is to fit a statistical model to a dataset, test how well the model performs on new data, and repeat. This is essentially an attempt to reign in the complexity of the real world.

The long tail – and the work it creates – turn out to be a major cause of the economic challenges of building AI businesses.
2020-08-14 00:00:00 Read the full story…
Weighted Interest Score: 3.0428, Raw Interest Score: 1.7176,
Positive Sentiment: 0.2089, Negative Sentiment 0.3482

Would You Rather Be an NLP or Computer Vision Data Scientist?

A closer look into these popular Data Scientist roles.

When applying for a position as a Data Scientist, you may see a variety of skills required in the job description section. You scroll down and then see even the education required is different between postings. Most importantly, you see an overview that summarizes the role, and although the title of the position is the same, …
2020-08-17 03:21:39.767000+00:00 Read the full story…
Weighted Interest Score: 3.0101, Raw Interest Score: 1.7140,
Positive Sentiment: 0.1880, Negative Sentiment 0.0663

A bankers guide to AI Part 3. Does the AI have more than one purpose? What is the roadmap?

This is the third in a 5 part series (published weekly) written by guest author Amber Sutherland a banker who understands technology who currently works for Silent Eight an AI-based name, entity and transaction adjudication solution provider to financial institutions. Click here for Index and Part 1.

Many financial institutions have the dueling mandates to be both innovative and transform digitally, but also to rationalize vendors. So, when cons…
2020-08-12 00:00:00 Read the full story…
Weighted Interest Score: 2.9326, Raw Interest Score: 1.3206,
Positive Sentiment: 0.1467, Negative Sentiment 0.0734

Balancing Data Integration with Data Governance

The proliferation of data sources, types, and stores is increasing the challenge of combining data into meaningful, valuable information.

The need for faster and smarter data integration capabilities is growing. At the same time, to deliver actual value, people need information they can trust—now more than ever during this COVID-19 pandemic—balancing data governance is absolutely essential.

DBTA recently held a webinar with Quinn Lewis, consult…
2020-08-11 00:00:00 Read the full story…
Weighted Interest Score: 2.8478, Raw Interest Score: 1.9338,
Positive Sentiment: 0.2622, Negative Sentiment 0.2950

XBRL News: IFRS webcast, Muscat and nowcasting

Here is our pick of the 3 most important XBRL news stories this slow summertime week.

In this webcast, Ann Tarca, a member of the International Accounting Standards Board (Board), and Vivek Baid, a member of the technical staff, provide a short introduction to the IFRS Taxonomy 2020 and highlight the key changes from the IFRS Taxonomy 2019.

A quick and efficient way to catch up both with the technical changes in the IFRS taxonomy as well as (im…
2020-08-13 00:00:00 Read the full story…
Weighted Interest Score: 2.8401, Raw Interest Score: 1.1452,
Positive Sentiment: 0.0458, Negative Sentiment 0.0916

AI in Cybersecurity Helping with Threat Hunting, Reducing Attack Vectors

By John P. Desmond, AI Trends Editor

The cybersecurity landscape is looking at higher than ever threat levels, data volumes quadrupling every 36 months, computing power and data transfer speeds increasing just as fast, and a diversity of IoT devices ushering in a new era of automation.

To get a grip on this, more organizations are exploring how AI can help. The Next-generation security operations center (SOC) incorporates automation and orchest…
2020-08-13 21:30:37+00:00 Read the full story…
Weighted Interest Score: 2.8123, Raw Interest Score: 1.3493,
Positive Sentiment: 0.2076, Negative Sentiment 0.4300

DBTA 100 2020: The Companies That Matter Most in Data

Today, there is a constantly evolving list of data management issues that organizations are contending with. In addition to pressures of exploding data volumes, there is urgent demand for real-time, data-driven insights as well as more widespread data access. Expanding regulatory mandates also demand greater data quality and governance, as do cybersecurity threats.

The myriad, and sometimes conflicting, requirements facing data managers were hig…
2020-09-09 00:00:00 Read the full story…
Weighted Interest Score: 2.6779, Raw Interest Score: 1.6080,
Positive Sentiment: 0.2297, Negative Sentiment 0.3063

How much product managers are paid at enterprise giants like Oracle, Cisco, VMware, SAP, ServiceNow and Workday — and how the job is evolving

the lead in planning, troubleshooting and rolling out new products.

Their job has evolved dramatically with the rapid growth of cloud computing, and the emergence of new technologies, such as AI and big data.

Here’s how much Oracle, Cisco, SAP, Workday, ServiceNow and VMware pay product managers, based on disclosure data for permanent and temporary workers filed with the US Office of Foreign Labor Certification in 2019.

Product managers play such an important role in tech that Silicon Valley investor Ben Horowitz once argued that “a good product manager is the…
2020-08-16 00:00:00 Read the full story…
Weighted Interest Score: 2.6708, Raw Interest Score: 1.8012,
Positive Sentiment: 0.0621, Negative Sentiment 0.2174

The Top Trends in Data Management for 2021 Webinar

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

Is it Human or is it Animal? Target Classification with Doppler-Pulse Radar and Neural Networks

How humans and animals leave different doppler-pulse footprints and MAFAT’s latest data science prize for creating a model that can distinguish between them.

As you can see in the above diagram we start with the 126×32 I/Q matrix. This matrix, along with 15 others, are aligned, and the first convolution of training happens, of which the result is altered and resized to a different dimensionality. Eventually, the model concludes with a single value, a number somewhere between 0 and 1 where the closer to 0 the more likely the signal is an animal, and the closer to 1 the more likely the signal is human. …
2020-08-17 03:29:44.524000+00:00 Read the full story…
Weighted Interest Score: 2.5516, Raw Interest Score: 1.2475,
Positive Sentiment: 0.1721, Negative Sentiment 0.1721

8 Categorical Data Encoding Techniques to Boost your Model in Python!

  • Understand what is Categorical Data Encoding
  • Learn different encoding techniques and when to use them

The performance of a machine learning model not only depends on the model and the hyperparameters but also on how we process and feed different types of variables to the model. Since most machine learning models only accept numerical variables, preprocessing the categorical variables becomes a necessary step. We need to …
2020-08-13 17:02:12+00:00 Read the full story…
Weighted Interest Score: 2.5440, Raw Interest Score: 1.2001,
Positive Sentiment: 0.1257, Negative Sentiment 0.1194

A Model for Creating a Data-Driven Culture

Over the past decade, firms have taken the plunge to become data driven. They have amassed data, invested in technologies, and paid handsomely for analytical talent. Yet for many, a strong, data-driven culture remains elusive and data is not universally used for decision making. Too often, this plunge has not yet paid off.

Why is it so hard?

In his recent Harvard Business Review article, “10 Steps to Creating a Data-Driven Culture,” David Walle…
2020-08-19 16:00:00+00:00 Read the full story…
Weighted Interest Score: 2.4885, Raw Interest Score: 1.2443,
Positive Sentiment: 0.0655, Negative Sentiment 0.1310

SimCorp Offers ‘Dimension as a Service’ on Microsoft Azure

SimCorp completes next phase in cloud transformation, offering SimCorp Dimension as a Service, on Microsoft Azure

SimCorp, a leading provider of integrated, front-to-back, multi-asset investment management solutions and services to the world’s largest buy-side institutions, today announces a new integration of its front-to-back investment management platform, with Microsoft Azure. The move significantly benefits SimCorp clients with a highly sca…
2020-08-12 17:56:16+00:00 Read the full story…
2020-08-12 00:00:00 Read the full story…
Weighted Interest Score: 2.3139, Raw Interest Score: 1.4538,
Positive Sentiment: 0.6082, Negative Sentiment 0.0148

Covid-19 AI Update: NIH Developing Imaging Tools

Among the latest developments around the use of AI to battle the Covid-19 pandemic, the National Institutes for Health (NIH) has launched the Medical Imaging and Data Resource Center (MIDRC), an effort to combine AI and medical imaging.

Led by the National Institute of Biomedical Imaging and Bioengineering unit of NIH, the effort aims to create new tools physicians can use for early detection and personalized therapies for Co…
2020-08-13 21:30:16+00:00 Read the full story…
Weighted Interest Score: 2.4517, Raw Interest Score: 1.1748,
Positive Sentiment: 0.1767, Negative Sentiment 0.1663

How to Transform into a Data-Driven Organization?

It is a journey to ensure the alignment of analytics initiatives to organizational objectives, combined with consistent and effective coordination of activities across all business units.

The road from a pile of raw data to insights and from insights to action is paved with strategic goals. More often than not, organizations spend the majority of their time going from raw data to insights, whi…
2020-08-14 07:35:10+00:00 Read the full story…
Weighted Interest Score: 2.4477, Raw Interest Score: 1.3012,
Positive Sentiment: 0.2506, Negative Sentiment 0.1349

How Community-Driven Analytics Promotes Data Literacy in Enterprises

Using the term “community” to describe technology innovation for analytics and business intelligence (A&BI) may seem an unlikely pairing. But consider the discussion around data culture and data collaboration that has been circulating for years without a solution that gives business users real power to act on their data questions.

Just as many technology innovations took off when developers were invited to the table to be a part of the business …
2020-08-11 00:00:00 Read the full story…
Weighted Interest Score: 2.4459, Raw Interest Score: 1.4480,
Positive Sentiment: 0.3816, Negative Sentiment 0.2544

10 Powerful Data Science Channels on YouTube

Here is a list of Top 10 Data Science Channels on YouTube. Feel free to add more.

  • sentdex
  • 3Blue1Brown Grant Sanderson (@3blue1brown)
  • freeCodeCamp.org
  • StatQuest!!! An epic journey through statistics and machine learning with John Starmer
  • Krish Naik
  • Python Programmer
  • Corey Schafer (@CoreyMSchafer)
  • Tech with Tim
  • Brandon Foltz
  • 365 Data Science

2020-08-10 00:00:00 Read the full story…
Weighted Interest Score: 2.4352, Raw Interest Score: 1.6761,
Positive Sentiment: 0.1809, Negative Sentiment 0.0724

How PyTorch And AWS Come To The Rescue Of ML Models In Production

Today, more than 83% of the cloud-based PyTorch projects happen on AWS. So, it is crucial to address these challenges. This is where TorchServe comes in handy. TorchServe, a PyTorch model-serving library that makes it easy to deploy trained models at scale without writing custom code. TorchServe was developed by AWS in partnership with Facebook. TorchServe addresses the difficulty of deploying PyTorch models.

Model serving is the process of situating a trained ML model within a system so that it can take new inputs and return inferences to the system. TorchServe allows users to expose webAPI for their model that can be accessed directly or via application.

2020-08-15 07:30:00+00:00 Read the full story…
Weighted Interest Score: 2.3256, Raw Interest Score: 1.4859,
Positive Sentiment: 0.1238, Negative Sentiment 0.1981

Qlik Is Now The Official Analytics Partner Of Fortune Magazine

Qlik, a leading data analytics & data integration solutions provider launched a “History of the Fortune Global 500” interactive data analytics site in partnership with Fortune Magazine. It comes at the 30th anniversary of the Fortune Global 500 list. It is a first-of-its-kind partnership as Fortune teams up with Qlik to help users explore and understand data like never before.

As the official analytics partner, Qlik has developed the visual experience leveraging data storytelling and interactive visualisations to showcase various data points of these companies. For instance, industry sector status, the economic trends across geographies, historical events that shaped those changes and more.

Qlik and Fortune delivered a similar visual experience for the Fortune 500 earlier this year as part of a multi-year partnership.

2020-08-13 06:12:28+00:00 Read the full story…
Weighted Interest Score: 2.3202, Raw Interest Score: 1.1932,
Positive Sentiment: 0.1989, Negative Sentiment 0.0663


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