AI & Machine Learning News. 03, 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?


AI against birds

Big Tech CEOs Avoid the Big Question

Members of Congress grilled four big tech CEOs yesterday. While there were some verbal fireworks and accusations of political bias, the hearing largely failed to address the elephant in the room: the significant competitive advantages tech giants have built using big data and AI.

The fearsome foursome–Amazon’s Jeff Bezos, Facebook’s Mark Zuckerberg, Apple’s Tim Cook, and Google’s Sundar Pichai–appeared virtually yesterday in the House Judiciary Committee, which has been investigating the companies and their business practices for years.

But the line of questioning largely left out one big topic: data and AI. Outside of one exchange with Bezos about Amazon’s purported use of third-party seller data to inform an Amazon-branded product, there was scant discussion about whether the FANG companies (minus Netflix and Microsoft) were abusing monopolistic power through the collection and analysis of vast amounts of data generated by consumers.

The big tech firms have huge treasure troves of information about consumers all over the world, and they use it to power sophisticated AI systems that give them a huge competitive advantage. There’s nothing illegal about using data and AI to build a competitive advantage, and clearly it has been good for Facebook, Apple, Amazon, and Google, which have grown tremendously in the past decade and are emulated by thousands of firms that are seeking to build their own AI systems powered by big data.

But whether it’s currently illegal is not the point. The whole purpose of the Judiciary subcommittee’s bipartisan investigation is to determine whether existing competition and anti-trust laws are adequate for regulating tech giants as they currently exist, or whether new laws and regulations are required. It’s becoming to look increasingly that they are not.

2020-07-30 00:00:00 Read the full story…
Weighted Interest Score: 2.1645, Raw Interest Score: 0.9314,
Positive Sentiment: 0.2361, Negative Sentiment 0.5116

CloudQuant Thoughts : Will science fiction’s prediction, that we will hit a tipping point where those in power achieve an unassailable level of power where the population cannot topple them, come to pass? Or will this period just be another in a long line of historical rises and falls.

Artificial intelligence identifies prostate cancer with near-perfect accuracy

Prostate biopsy with cancer probability (blue is low, red is high). This case was originally diagnosed as benign but changed to cancer upon further review. The AI accurately detected cancer in this tricky case. Credit: Ibex Medical Analytics
A study published today in The Lancet Digital Health by UPMC and University of Pittsburgh researchers demonstrates the highest accuracy to date in recognizing and characterizing prostate cancer using an artificial intelligence (AI) program.

“Humans are good at recognizing anomalies, but they have their own biases or past experience,” said senior author Rajiv Dhir, M.D., M.B.A., chief pathologist and vice chair of pathology at UPMC Shadyside and professor of biomedical informatics at Pitt. “Machines are detached from the whole story. There’s definitely an element of standardizing care.”

To train the AI to recognize prostate cancer, Dhir and his colleagues provided images from more than a million parts of stained tissue slides taken from patient biopsies. Each image was labeled by expert pathologists to teach the AI how to discriminate between healthy and abnormal tissue. The algorithm was then tested on a separate set of 1,600 slides taken from 100 consecutive patients seen at UPMC for suspected prostate cancer.

2020-07-27 Read the full story…

CloudQuant Thoughts : “During testing, the AI demonstrated 98% sensitivity and 97% specificity at detecting prostate cancer”, again ML steps up to improve our lives!

Google claims its new TPUs are 2.7 times faster than the previous generation

Google’s fourth-generation tensor processing units (TPUs), the existence of which weren’t publicly revealed until today, can complete AI and machine learning training workloads in close-to-record wall clock time. That’s according to the latest set of metrics released by MLPerf, the consortium of over 70 companies and academic institutions behind the MLPerf suite for AI performance benchmarking. It shows clusters of fourth-gen TPUs surpassing the capabilities of third-generation TPUs — and even those of Nvidia’s recently released A100 — on object detection, image classification, natural language processing, machine translation, and recommendation benchmarks.

Google says its fourth-generation TPU offers more than double the matrix multiplication TFLOPs of a third-generation TPU, where a single TFLOP is equivalent to 1 trillion floating-point operations per second. (Matrices are often used to represent the data that feeds into AI models.) It also offers a “significant” boost in memory bandwidth while benefiting from unspecified advances in interconnect technology. Google says that overall, at an identical scale of 64 chips and not accounting for improvement attributable to software, the fourth-generation TPU demonstrates an average improvement of 2.7 times over third-generation TPU performance in last year’s MLPerf benchmark.

2020-07- 29 Read the full story…

CloudQuant Thoughts : With all the recent fuss around GPT-3, and this consistent improvement of AI hardware, it is only a matter of time before we see AI pass the Turing test.

Gatling Exploration to use artificial intelligence to identify possible gold targets at the Larder project in Ontario

Windfall Geotek will use their advanced Computer Aided Resource Detection System to mark targets using pattern recognition and machine learning.

Gatling Exploration Inc announced Thursday it will employ artificial intelligence (AI) to identify possible gold targets at the Larder gold project in Ontario.

The company said AI experts with Windfall Geotek will use their advanced Computer Aided Resource Detection System (CARDS) to mark targets which will be evaluated and explored using traditional exploration techniques in upcoming programs.

Gatling’s Larder Gold project occupies 3,370 hectares along the Cadillac Larder Lake Break, a prolific structural gold trend. The property hosts three high-grade deposits along the main break, as well as two additional, underexplored gold trends, recently discovered 6 kilometers north.
2020-07-30 00:00:00 Read the full story…
Weighted Interest Score: 2.8152, Raw Interest Score: 1.3529,
Positive Sentiment: 0.0000, Negative Sentiment 0.1176

CloudQuant Thoughts : “There’s gold in them thar hills”, you just need AI to get it out! What would the Gold Rush pioneers of 100 years ago make of today’s AI/ML Gold Rush?

A.I. Helped Uncover Chinese Boats Hiding in North Korean Waters

A combination of technologies helped scientists discover a potentially illegal fishing operation involving more than 900 vessels.

The researchers trained a convolutional neural network to identify pair trawlers, which have a distinctive fishing pattern and comprise the largest portion of foreign vessels in the region. They used the neural network to identify the location of the fleet, and then used satellite imagery to further verify the vessels they identified as pair trawlers, and to verify the location and size of the fleet. They also used the technology to identify 3,000 smaller artisanal wooden vessels with dimmer lights, which are believed to be a North Korean fleet fishing in Russian waters in 2018.

2020-07-25 Read the full story…

CloudQuant Thoughts : As China’s needs grow, it’s unified CCP approach and obvious lack of concern for the environment will put more and more of its neighbors at risk.

News Popularity Prediction: Weekend Hackathon #14

Weekend Hackathons are becoming more competitive, so we are back with a tougher one this time. In this weekend hackathon, we are providing an open UCI dataset but the target has been predicted by our machine learning model. Yes, you heard it right, In this weekend hackathon, we are challenging all the MachineHackers to design a machine learning model to predict the popularity of a news article provided various statistics associated with the raw text from news articles. The goal is to predict the news article’s popularity as close as possible.

The challenge will start on July 31st Friday at 6 pm IST.
2020-07-31 05:19:01+00:00 Read the full story…
Weighted Interest Score: 2.7947, Raw Interest Score: 1.1869,
Positive Sentiment: 0.2130, Negative Sentiment 0.2435

CloudQuant Thoughts : This looks like fun!

NumPy Fundamentals for Data Science and Machine Learning

It is no exaggeration to say that NumPy is at the core of the entire scientific computing Python ecosystem, both as a standalone package for numerical computation and as the engine behind most data science packages.

In this document, I review NumPy main components and functionality, with attention to the needs of Data Science and Machine Learning practitioners, and people who aspire to become a data professional. My only assumption is that you have basic familiarity with Python, things like variables, lists, tuples, and loops. Advance Python concepts like Object Oriented Programming are not touched at all.

The resources I used to build this tutorial are three:

  • NumPy documentation
  • A few miscellaneous articles from the Internet
  • My own experience with NumPy

2020-07-28 00:00:00 Read the full story…
Weighted Interest Score: 3.1088, Raw Interest Score: 2.0942,
Positive Sentiment: 0.0000, Negative Sentiment 0.0000

CloudQuant Thoughts : Most Numpy articles are little more than intros, or at best cheat sheets. This article goes into quite a lot of detail and would be very useful for someone starting out on their journey.

Google’s TF-Coder tool automates machine learning model design

Researchers at Google Brain, one of Google’s AI research divisions, developed an automated tool for programming in machine learning frameworks like TensorFlow. They say it achieves better-than-human performance on some challenging development tasks, taking seconds to solve problems that take human programmers minutes to hours.

Emerging AI techniques have resulted in breakthroughs across computer vision, audio processing, natural language processing, and robotics. Playing an important role are machine learning frameworks like TensorFlow, Facebook’s PyTorch, and MXNet, which enable researchers to develop and refine new models. But while these frameworks have eased the iterating and training of AI models, they have a steep learning curve because the paradigm of computing over tensors is quite different from traditional programming. (Tensors are algebraic objects that describe relationships between sets of things related to a vector space, and they’re a convenient data format in machine learning.) Most models require various tensor manipulations for data processing or cleaning, custom loss functions, and accuracy metrics that must be implemented within the constraints of a framework.
2020-07-30 00:00:00 Read the full story…
Weighted Interest Score: 3.1791, Raw Interest Score: 1.7261,
Positive Sentiment: 0.2547, Negative Sentiment 0.3679

Low Code ML Library PyCaret Launches 2.0 Release

PyCaret- the open source low-code machine learning library in Python has come up with the new version PyCaret 2.0. The latest release aims to reduce the hypothesis to insights cycle time in a ML experiment, and enables data scientists to perform end-to-end experiments quickly and efficiently. Some major updates in the new release of PyCaret include:

  • Logging back-end: Integrates MLFlow backend to track experiments (metrics, model parameters, artifacts, visuals etc.)
  • Modular Automation: PyCaret 2.0 is an end-to-end workflow automation tool. You can use it to build automated machine learning workflows or even a front-end ML software.
  • Command Line Interface (CLI): Optimize to work in Non Notebook environment such as Spyder, PyCharm, VS Code.
  • GPU enabled training: Now you can train xgboost and catboost model using GPU.
  • Parallel Processing: Supports parallel processing for almost all algorithms.
  • Utility: Many new util functions introduced to help developers leverage more out of PyCaret.

2020-08-03 10:39:04+00:00 Read the full story…
Weighted Interest Score: 3.1314, Raw Interest Score: 1.8797,
Positive Sentiment: 0.2392, Negative Sentiment 0.0684


AI Bias Section

What the Fintech? Podcast – Episode 10 – Diversity & inclusion: AI bias

Despite the UK beginning to reopen, the team veered on the side of caution, continuing to bring you What the Fintech? as a remotely recorded podcast. On this episode, we welcome Theodora Lau, founder of the boutique fintech consultancy firm, Unconventional Ventures.

We examine the lack of diversity within the financial services industry in the wake of the Black Lives Matters movement. Lau also provides her take on artificial intelligence (AI), specifically its discriminatory tendencies and the consequences this has for marginalised groups, individual rights and corporations.

Tune in to find out which exciting buzzword Lau nominated for sentencing in our ‘Fintech Jail’!

2020-07-29 15:00:30+00:00 Read the full story…
Weighted Interest Score: 8.0439, Raw Interest Score: 1.8298,
Positive Sentiment: 0.1830, Negative Sentiment 0.2745

Why artificial intelligence models are often biased, according to the Google exec who heads Alphabet’s internal tech incubator Jigsaw

Yasmin Green, director of research and development at Jigsaw, a unit of Google parent company Alphabet, spoke about one particularly complex hurdle in modern society: the difficulty of programming artificial intelligence without bias. The problem with training AI on humans, Green said, is that humans are biased, and when the data that feeds AI is biased, then the AI becomes biased itself.

Green detailed an experiment that demonstrated this unconscious bias in AI. She and her team created the same fake professional profile for a woman and a man and browsed online job sites as each of these imaginary people. In the end, they found that men were five times as likely to see ads for higher-paying jobs than women. This, she said, was because women believe they must fulfill 100% of the requirements before they apply to a job, whereas men believe they only need to meet at least 60% of the requirements before they apply to the job.

“So at the same skill level, we [women] are clicking on jobs that are less senior and less well paid,” Green said. “But if we click that way, then the internet is going to learn and that’s what we’re going to see.”
2020-08-03 00:00:00 Read the full story…
Weighted Interest Score: 3.1915, Raw Interest Score: 1.0937,
Positive Sentiment: 0.0591, Negative Sentiment 0.2069


How EQT Ventures’ Motherbrain uses AI to find promising startups

Since Sweden’s EQT Ventures embraced AI to drive the way it makes investments, the company has learned that reaping the benefits of algorithms is a journey full of detours that involve experimenting, fine-tuning, and adaptation to achieve the promised efficiencies and insights.

Following the firm’s launch in 2016, a team there developed Motherbrain, an AI-driven system whose goal is to help EQT Ventures spot the hidden gems that no one else sees and back them early. So far, Motherbrain has directly led to investments in 7 startups out of the 50 the firm has made.

That may seem like a disappointment. But according to Henrik Landgren, the EQT Ventures partner who took the lead on developing the system, the practical value so far has been the ability to make partners more productive by prioritizing which companies are worth spending time getting to know.

“Leveraging data has been one of our core pillars,” Landgren said. “We wanted to create a different fund.”

2020-07-26 Read the full story…

One of the Best Market Neutral Funds Is Run by a Robot

Castle Ridge’s AI-powered hedge fund racked up double-digit gains at a time when market-neutral peers are struggling.

A market-neutral strategy powered by new artificial intelligence techniques has beaten its human peers by multiple return and risk measures in this year’s market downturn, one of the most volatile environments ever for stocks.

Castle Ridge Asset Management’s market-neutral strategy is up 12.1 percent, before fees, year-to-date through the end of June, according to a letter obtained by Institutional Investor. That compares with the Standard & Poor’s 500 stock index, which lost 4 percent over the same period, after recovering from a historic drop of 30 percent.

Since inception in October 2019, the Castle Ridge strategy returned 16.3 percent before fees, beating SPX, an ETF tracking the S&P 500, which delivered 4.2 percent. It also outperformed the HFRI Equity Market Neutral Index, which lost 5.7 percent from October through June.

2020-07-29  Read the full story…

The 24 quant power players driving the future of hedge funds, from well-known billionaire founders to under-the-radar data chiefs

Quants have gone from a niche practice to a dominant player — the largest and most important hedge funds in the world are heavily influenced by, or completely committed to, computer-run strategies.

The future of quantitative investing is under question, as a growing group of experts have been calling for more machine-learning techniques to be incorporated in a move away from the models that made so many people successful.
2020-07-31 00:00:00 Read the full story…
Weighted Interest Score: 5.3562, Raw Interest Score: 2.1659,
Positive Sentiment: 0.2641, Negative Sentiment 0.2641

How to learn Python for finance – Cuemacro

The question I get asked most is, what is your favourite burger joint? The answer.. well, you’ll have to ask me! The second question I get asked a lot, particularly in recent months, is how can I learn Python if I’m working in finance? I will endeavour to answer that question, updating and adding to articles I’ve written before.

If you work in finance there are lots of good reasons to learn Python. It can help to automate all those boring Excel spreadsheet updates. It’s also a good transferable skill that is useful in any industry where you’re working with data. Python is also becoming more of a requirement for many roles in finance. When I started working in markets in 2005, lots of people coded, but mostly it was IT and quants like myself. These days you’ll likely find folks coding in many desks of a bank or on the buy side.
2020-08-01 00:00:00 Read the full story…
Weighted Interest Score: 4.6820, Raw Interest Score: 2.0222,
Positive Sentiment: 0.1216, Negative Sentiment 0.0760

Global Artificial Intelligence Conference – 2020 Sep 16th – 18th – Seattle – WA

Global Big Data Conference’s vendor agnostic Global Artificial Intelligence Conference is held on Sep 16th – 18th 2020 on all industry verticals(Finance, Retail/E-Commerce/M-Commerce, Healthcare/Pharma/BioTech, Energy, Education, Insurance, Manufacturing, Telco, Auto, Hi-Tech, Media, Agriculture, Chemical, Government, Transportation etc.. ). It will be the largest vendor agnostic conference in AI space. The Conference allows thought leaders & practitioners to discuss AI through effective use of various techniques.

You Get To Meet : You get to meet technical experts, Senior, VC and C-level executives from leading innovators in the AI space (Executives from startups to large corporations will be at our conference.)

Who Should Attend : CEO, EVP/SVP/VP, C-Level, Director, Global Head, Manager, Decision-makers, Business Executives responsible for AI Intiatives, Heads of Innovation, Heads of Product Development, Analysts, Project managers, Analytics managers, Data Scientist, Statistian, Sales, Marketing, human resources, Engineers, AI & Software Developers, VCs/Investors, AI Consultants and Service Providers, Architects, Networking specialists, Students, Professional Services, Data Analyst, BI Developer/Architect, QA, Performance Engineers, Data Warehouse Professional, Sales, Pre Sales, Technical Marketing, PM, Teaching Staff, Delivery Manager and other line-of-business executives.
2020-09-16 00:00:00 Read the full story…
Weighted Interest Score: 4.4335, Raw Interest Score: 2.1565,
Positive Sentiment: 0.3697, Negative Sentiment 0.0000

Hacking AI: Exposing Vulnerabilities in Machine Learning

A military drone misidentifies enemy tanks as friendlies. A self-driving car swerves into oncoming traffic. An NLP bot gives an erroneous summary of an intercepted wire. These are examples of how AI systems can be hacked, which is an area of increased focus for government and industrial leaders alike.

As AI technology matures, it’s being adopted widely, which is great. That is what is supposed to happen, after all. However, greater reliance on automated decision-making in the real world brings a greater threat that bad actors will employ techniques like adversarial machine learning and data poisoning to hack our AI systems.

What’s concerning is how easy it can be to hack AI. According to Arash Rahnama, Phd., the head of applied AI research at Modzy, AI models can be hacked by inserting a few tactically inserted pixels (for a computer vision algorithm) or some innocuous looking typos (for a natural language processing model) into the training set. Any algorithm, including neural networks and more traditional approaches like regression algorithms, is susceptible, he says.
2020-07-28 00:00:00 Read the full story…
Weighted Interest Score: 4.4078, Raw Interest Score: 1.7007,
Positive Sentiment: 0.1529, Negative Sentiment 0.5733

Explorium Platform Billed as App Store for Predictive Models

As the need for more reliable model training grows, so also does investor interest in funding data science startups seeking to leverage automation to track down and match the appropriate data sets to a given model and application.

Among the platform developers gaining investors’ attention is Explorium, the Bay Area startup that this week announced a $31 million Series B funding round. So far, the three-year-old company has raised over $50 million. Lead investors include Zeev Ventures, 01 Advisors and Dynamic Loop.

One backer likens the platform to a storefront for predictive analytics.
2020-07-29 00:00:00 Read the full story…
Weighted Interest Score: 4.2726, Raw Interest Score: 2.5967,
Positive Sentiment: 0.2546, Negative Sentiment 0.2546

Report: The future of digital wealth management

The wealth management industry is facing a period of unprecedented change. Economic turmoil, regulatory variation, customer experience demands, and digital transformation are all shaking the foundations of how firms operate.

Wealth managers stand on the edge of a fundamental change in the way they do business.

High-net-worth individuals (HNWIs) are getting younger by the year, and as their median age dips, their expectations rise.

New customers demand better personalisation. They need niche portfolios tailored to their interests and ways to invest sustainably.
2020-07-27 11:23:04+00:00 Read the full story…
Weighted Interest Score: 4.0958, Raw Interest Score: 2.2684,
Positive Sentiment: 0.3151, Negative Sentiment 0.3781

UCL launches new £100m start-up investment fund

University College London has launched a new £100m investment fund to back university spin-outs developing medical research as well as artificial intelligence (AI) projects.

The new UCL Technology Fund is backed by British Patient Capital, as well as UCL itself and other investors from the US and Asia. It will be managed by London-headquartered investment firm AlbionVC.

“There will definitely be a biomedical focus,” said Anne Lane, the chief executive of UCL’s commercialisation company. The fund will also back AI businesses as well as companies devel…
2020-08-03 00:00:00 Read the full story…
Weighted Interest Score: 3.9839, Raw Interest Score: 1.7659,
Positive Sentiment: 0.1514, Negative Sentiment 0.3532

Object Detection in 6 steps using Detectron2

Have you ever tried training an object detection model using a custom dataset of your own choice from scratch?

If yes, you’d know how tedious the process would be. We need to start with building a model using a Feature Pyramid Network combined with a Region Proposal Network if we opt for region proposal based methods such as Faster R-CNN or we can also use one-shot detector algorithms like SSD and YOLO.

Either of them is a bit complicated to work with if we want to implement it from scratch. We need a framework where we can use state-of-the-art models such as Fast, Faster, and Mask R-CNNs with ease. Nevertheless, it is important to try building a model at least once from scratch to understand the math behind it.
2020-08-03 06:45:05.267000+00:00 Read the full story…
Weighted Interest Score: 3.8726, Raw Interest Score: 1.4384,
Positive Sentiment: 0.0000, Negative Sentiment 0.0625

Oxford University Introduces New Commission to Address AI Governance in Public Policy

A new commission has been formed by Oxford University to advise world leaders on effective ways to use Artificial Intelligence (AI) and machine learning in public administration and governance.

The Oxford Commission on AI and Good Governance (OxCAIGG) will bring together academics, technology experts and policymakers to analyse the AI implementation and procurement challenges faced by governments around the world. Led by the Oxford Internet Institute, the Commission will make recommendations on how AI–related tools can be adapted and adopted by policymakers for good governance now and in the near future.
2020-08-03 10:55:42+00:00 Read the full story…
Weighted Interest Score: 3.8224, Raw Interest Score: 1.7594,
Positive Sentiment: 0.2853, Negative Sentiment 0.2853

Going Deeper with Data Science and Machine Learning

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.

However, the challenges are numerous, from selecting data sets and data platforms, to architecting and optimizing data pipelines, and model training and deployment.

In response, 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.
2020-07-31 00:00:00 Read the full story…
Weighted Interest Score: 3.8043, Raw Interest Score: 1.9885,
Positive Sentiment: 0.3528, Negative Sentiment 0.1604

How Synthetic Data Sets Can Improve Computer Vision Models

In recent years, deep learning models have produced a substantial amount of advances in various areas, including computer vision. Computer vision typically usually works by analysing images that have been captured using the physical camera sensor, followed by a human-in-the-loop process that requires annotators to label things of interest. It’s important to note that the more sophisticated the annotation is, the more laborious labelling can be. But it provides for a much richer analysis of the image itself.

For example, for spotting a tiny detail within an image, a simple bounding box around the object might suffice. But once you start looking to get a robot to grasp something, you might need a segmentation mask to flesh out the fine contours of the object. Once this data is collected and labelled, we can then train this algorithm, following which it can be incorporated into an edge device such as a smart camera, to be sold to consumers or businesses.

For practitioners in modern computer vision, the greatest bottleneck throughout this whole process has often been data. The first two steps, collecting and annotating data, usually takes several months. Another reason why data is the real bottleneck is that algorithms these days are a dime a dozen and the hundreds of new ones pop up regularly.
2020-08-01 07:30:00+00:00 Read the full story…
Weighted Interest Score: 3.6969, Raw Interest Score: 1.6477,
Positive Sentiment: 0.1831, Negative Sentiment 0.3112

AI is struggling to adjust to 2020 – TechCrunch

2020 has made every industry reimagine how to move forward in light of COVID-19: civil rights movements, an election year and countless other big news moments. On a human level, we’ve had to adjust to a new way of living. We’ve started to accept these changes and figure out how to live our lives under these new pandemic rules. While humans settle in, AI is struggling to keep up.

The issue with AI training in 2020 is that, all of a sudden, we’ve changed our social and cultural norms. The truths that we have taught these algorithms are often no longer actually true. With visual AI specifically, we’re asking it to immediately interpret the new way we live with updated context that it doesn’t have yet.

Algorithms are still adjusting to new visual queues and trying to understand how to accurately identify them. As visual AI catches up, we also need a renewed importance on routine updates in the AI training process so inaccurate training datasets and preexisting open-source models can be corrected.
2020-08-02 00:00:00 Read the full story…
Weighted Interest Score: 3.6074, Raw Interest Score: 0.9903,
Positive Sentiment: 0.0671, Negative Sentiment 0.2685

Best Practices for Preparing Data Centers for AI, ML and DL

The intensive demands of artificial intelligence, machine learning and deep learning applications challenge data center performance, reliability and scalability–especially as architects mimic the design of public clouds to simplify the transition to hybrid cloud and on-premise deployments.

The intensive demands of artificial intelligence, machine learning and deep learning applications challenge data center performance, reliability and scalability–especially as architects mimic the design of public clouds to simplify the transition to hybrid cloud and on-premise deployments.

GPU (graphics processing unit) servers are now common, and the ecosystem around GPU computing is rapidly evolving to increase the efficiency and scalability of GPU workloads. Yet there are tricks to maximizing the more costly GPU utilization while avoiding potential choke points in storage and networking.

In this edition of eWEEK Data Points, Sven Breuner, field CTO, and Kirill Shoikhet, chief architect, at Excelero, offer nine best practices on preparing data centers for AI, ML and DL.
2020-07-30 00:00:00 Read the full story…
Weighted Interest Score: 3.4473, Raw Interest Score: 1.8164,
Positive Sentiment: 0.2980, Negative Sentiment 0.1703

ComplyAdvantage raises $50m in Series C funding

Financial crime, risk and detection firm, ComplyAdvantage, has raised $50 million in a Series C funding round. The firm aims for an international expansion across the United States, Europe, and the Asia-Pacific region. The round was led by Ontario Teachers’ Pension Plan Board through its Teachers’ Innovation Platform (TIP). Existing investors Index Ventures and Balderton Capital also participated in the round.

ComplyAdvantage claims to use machine learning to help clients manage risk obligations and prevent financial crime.
2020-07-31 09:00:08+00:00 Read the full story…
Weighted Interest Score: 3.0758, Raw Interest Score: 2.0316,
Positive Sentiment: 0.1505, Negative Sentiment 0.5267

Researchers examine the ethical implications of AI in surgical settings

A new whitepaper coauthored by researchers at the Vector Institute for Artificial Intelligence examines the ethics of AI in surgery, making the case that surgery and AI carry similar expectations but diverge with respect to ethical understanding. Surgeons are faced with moral and ethical dilemmas as a matter of course, the paper points out, whereas ethical frameworks in AI have arguably only begun to take shape.

In surgery, AI applications are largely confined to machines performing tasks controlled entirely by surgeons. AI might also be used in a clinical decision support system, and in these circumstances, the burden of responsibility falls on the human designers of the machine or AI system, the coauthors argue.

Privacy is a foremost ethical concern. AI learns to make predictions from large data sets — specifically patient data, in the case of surgical systems — and it’s often described as being at odds with privacy-preserving practices. The Royal Free London NHS Foundation Trust, a division of the U.K.’s National Health Service based in London, provided Alphabet’s DeepMind with data on 1.6 million patients without their consent. Separately, Google, whose health data-sharing partnership with Ascension became the subject of scrutiny last November, abandoned plans to publish scans of chest X-rays over concerns that they contained personally identifiable information.
2020-07-31 00:00:00 Read the full story…
Weighted Interest Score: 3.0710, Raw Interest Score: 1.3073,
Positive Sentiment: 0.1473, Negative Sentiment 0.4235

Artificial Intelligence in Medical Imaging Diagnostics

Deep learning has revolutionized image recognition and analysis, making unprecedented performance leaps between 2010-2014. These rapid advancements enabled the development of automated, accurate, accessible, and cost-effective medical diagnostics. Over 60 entities including 40 new firms globally have set out to capitalize on these technological advances, seeking to commercialize AI-based diagnostics services in fields such as cancer and cardiovascular disease (CVD). IDTechEx forecasts the market for AI-enabled image-based medical diagnostics to exceed $3 billion by 2030.

In this article, IDTechEx examines the future market for image recognition AI in medical diagnostics. The article considers the progress thus far and assesses how each segment of the market is likely to evolve. Next, it considers the competitive landscape, examining investment patterns by disease area, company readiness levels by application, and the trends in focus areas. Finally, it provides an outlook about the future of this market.
2020-07-29 15:28:35+00:00 Read the full story…
Weighted Interest Score: 2.8505, Raw Interest Score: 1.2579,
Positive Sentiment: 0.2342, Negative Sentiment 0.0868

Getting Data Scientists and Data Engineers on the Same Page

Like cats and dogs, data engineers and data scientists often seem like two incompatible species. Scientists love probabilities and experimentation, while engineers live for repeatability and efficiency. They have different responsibilities and dissimilar mindsets, but getting these two personas to work together is a critical step for any organization that wants to succeed with data.

Data scientists emerged as the rock stars of the 2010s thanks to their ability to use machine learning algorithms to detect small differences in big data sets and exploit them for business advantage. As data continued to grow bigger and more complex, the work became more specialized and data engineers emerged as a critical cog in the big data machine.

In today’s larger organizations, you will often find a mix of data scientists and data engineers working with data (and perhaps other related positions, such as the machine learning engineer, which blends characteristics of both). While engineers and scientists, ostensibly, have the same end goal–their organization’s successful exploitation of data–their paths to achieve that goal could not be more different.
2020-07-27 00:00:00 Read the full story…
Weighted Interest Score: 2.8272, Raw Interest Score: 1.5382,
Positive Sentiment: 0.2588, Negative Sentiment 0.2444

ETHICA, An AI Framework By Wipro Might Soon Be Available To Its Clients

Reports suggest that Wipro might offer its artificial intelligence framework, ETHICA to clients as a part of their new strategy to revamp digital tech-based solutions under the leadership of the newly appointed CEO, Thierry Delaporte.

Wipro appointed Delaporte as the Chief Executive Officer and Managing Director of the company, effective from July 6, 2020. He was the Chief Operating Officer of Capgemini Group and a member of its Group Executive Board, prior to this.

Wipro had shared in a report that ETHICA is essentially a part of HOLMES and stands for Explainability, Transparency, Human-first, Interpretability, Common sense, and Auditability. It was developed to help companies ensure that their consumer-facing solutions are transparent, ethical and unbiased.
2020-07-28 09:51:17+00:00 Read the full story…
Weighted Interest Score: 2.7325, Raw Interest Score: 1.0072,
Positive Sentiment: 0.2878, Negative Sentiment 0.0480

Financial Marketers Overlooking Data and AI in Their Growth Strategies

Artificial intelligence is one of the most powerful tools available to financial marketers. While there is almost universal consensus on the potential of using data and advanced analytics for targeting, offer development, creative design and automation, financial institutions have little confidence in their ability to the use AI tools, says new research from the Digital Banking Report.

Over the last seven years of research by the Digital Banking Report on the “State of Financial Marketing,” the banking industry has moved from talking about the power of data and advanced analytics to actually beginning to use AI-powered tools in day-to-day tasks. Although the technology is still rather new, the list of tasks it can complete is growing steadily.

Based on the research, AI will usually augment, as opposed to replace, traditional marketing functions. But it will still have a disruptive effect on the industry. We are already seeing shifts in media used and marketing talent being sought as organizations try to find ways to drive costs down and performance up through AI-powered solutions. These shifts have only accelerated as a result of COVID-19.
2020-07-27 00:05:01+00:00 Read the full story…
Weighted Interest Score: 2.6091, Raw Interest Score: 1.3052,
Positive Sentiment: 0.3399, Negative Sentiment 0.2311

Stratifyd’s New Analytics Platform Simplifies Data Science Needs

Stratifyd, a technology company that democratizes data science and artificial intelligence (AI) through self-service data analytics, is releasing its next generation platform, a powerful analytics engine that was re-designed from the ground up.

The Stratifyd platform now provides the functionality to meet the demanding data science needs of an organization, but is specifically designed to be easy to use for those with limited data analytics experience.

It empowers users of all skill levels to connect data sources to the platform, perform in depth analysis and data modeling, and discover insightful stories faster and more easily than previously possible.

2020-07-30 00:00:00 Read the full story…
Weighted Interest Score: 2.5393, Raw Interest Score: 1.6329,
Positive Sentiment: 0.5141, Negative Sentiment 0.0907

Stratifyd launches ‘next generation’ data analytics platform

Self-service data analytics specialist Stratifyd has launched its new ‘next generation’ platform.

Stratifyd says the analytics engine was re-designed from the ground up to be intuitive and easy-to-use, enabling business users – regardless of education, skill, or job function – to harness the power of proprietary and third-party data to easily reveal and understand hidden stories represented within the data, thus delivering the benefits of a data science team to every organisation.

The Stratifyd platform now provides the functionality to meet the data science needs of an organisation, but is specifically designed to be easy to use for those with limited data analytics experience. It aims to allow users of all skill levels to connect data sources to the platform, perform in depth analysis and data modelling, and discover insightful stories faster and more easily than previously possible. Through a graphical user interface, pre-built and customisable data analytics models, and simplified dashboards, the platform enables business users to extract insights (ie, stories) that are hidden in the data and essential in helping companies improve customer service, better understand customer requirements, deliver product enhancements that address gaps in the market, solve problems experienced by customers, rollout new product and service offerings that deliver a competitive advantage, and more.

2020-07-31 00:00:00 Read the full story…
Weighted Interest Score: 2.4719, Raw Interest Score: 1.5169,
Positive Sentiment: 0.4869, Negative Sentiment 0.1124

3 Trends Driving Growth in the Wealth Business: Capgemini

The growth of robo-advising may have given technology a bit of a bad rap in the traditional advisory space. However, the hyper-personalization that I described above requires that firms embrace new technologies, not shy away from them.

Artificial intelligence (AI) and analytics can help firms create, for example, more tailored risk profiles, personalized portfolio construction and advice, and customized client reports, according to Capgemini.

And that’s not all. Advisors’ work processes benefit from these technologies as well.

For instance, firms can use Application Programming Interfaces (APIs) to improve the advisor desktop, so that it provides a more comprehensive view of a client’s investments, rather than an advisor having to move between different dashboards to track a single client’s full portfolio, the report notes.

2020-07-30 00:00:00 Read the full story…
Weighted Interest Score: 2.5335, Raw Interest Score: 1.3613,
Positive Sentiment: 0.1885, Negative Sentiment 0.1047

Top 8 Machine Learning Libraries In Kotlin One Must Know

ccording to the Stack Overflow Developer survey report, Kotlin is one of the most loved programming languages among professional developers. It secured the 4th position among 25 programming languages. As per the official documentation, Kotlin claims to be a preferred choice for building data pipelines, productionising machine learning models, among others.

In this article, we list down the top 8 machine learning libraries in Kotlin.

  1. Kotlin Statistics
  2. Krangl
  3. Koma
  4. KMath
  5. Lets-Plot
  6. Kravis
  7. SimpleDNN
  8. LinguisticDescription

2020-08-03 05:30:00+00:00 Read the full story…
Weighted Interest Score: 2.4793, Raw Interest Score: 1.4471,
Positive Sentiment: 0.1296, Negative Sentiment 0.0216

Scaling A.I. While Navigating the Current Uncertainty

The amount of uncertainty and complexity the recent economic difficulties have introduced into the business landscape has left many businesses reeling. While trying to adjust to the new normal, businesses are pressured to find new efficiencies and discover previously untapped sources of economic opportunity, making A.I. and machine learning models more important than ever to making critical and often timely business decisions.

The time for A.I. experimentation is over. We have arrived at the point where A.I. has to produce results and drive real revenue, while safeguarding the business from all of the potential risks that can jeopardize the bottom line. This expectation only becomes more challenging at a time when data is changing by the hour and previous historical patterns are not reliable. Furthermore, the complexities compound as businesses decide to rely more on A.I. in these trying times as a way to stay ahead of the competition.
2020-07-31 00:00:00 Read the full story…
Weighted Interest Score: 2.4784, Raw Interest Score: 1.4179,
Positive Sentiment: 0.1575, Negative Sentiment 0.2757

AI Will Overtake Humans In Five Years: Elon Musk

In yet another warning against artificial intelligence, Elon Musk said that AI is likely to overtake humans in the next five years. He said that artificial intelligence will be vastly smarter than humans and would overtake the human race by 2025.

“But that doesn’t mean that everything goes to hell in five years. It just means that things get unstable or weird,” Musk said in an interview with the New York Times. He also said that things will be weird when computers are way smarter than humans.

He expressed that his top concern is Google’s DeepMind. “Just the nature of the AI that they’re building is one that crushes all humans at all games,” he said in the interview.
2020-07-29 06:16:51+00:00 Read the full story…
Weighted Interest Score: 2.4670, Raw Interest Score: 0.8611,
Positive Sentiment: 0.0574, Negative Sentiment 0.2870

With sports (and everything else) cancelled, this data scientist decided to take on COVID-19 – interview

When his hobbies went on hiatus, Kaggler David Mezzetti made fighting COVID-19 his mission.

David Mezzetti is the founder of NeuML, a data analytics and machine learning company that develops innovative products backed by machine learning. He previously co-founded and built Data Works into a 50+ person well-respected software services company. In August 2019, Data Works was acquired and Dave worked to ensure a successful transition.

David: My technical background is in ETL, data extraction, data engineering and data analytics. I spent over a decade of my career developing large-scale data pipelines to transform both structured and unstructured data into formats that can be utilized in downstream systems. I also have experience in building large-scale distributed text search and Natural Language Processing (NLP) systems.
2020-07-29 19:39:26.208000+00:00 Read the full story…
Weighted Interest Score: 2.4622, Raw Interest Score: 1.2246,
Positive Sentiment: 0.2271, Negative Sentiment 0.2173

MIT CSAIL’s system can defer to experts when making predictions

A new study from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) proposes a machine learning system that can examine X-rays to diagnose conditions, including lung collapse and an enlarged heart. That’s not especially novel — computer vision in health care is a well-established field — but CSAIL’s system can novelly defer to experts, depending on factors like the person’s ability and experience level.

Despite its promise, AI in medicine is fraught with ethical challenges. Google recently published a whitepaper that found an eye disease-predicting system was impractical in the real world, partially because of technological and clinical missteps. STAT reports that unproven AI algorithms are being used to predict the decline of COVID-19 patients. And companies like Babylon Health, which claim their systems can diagnose diseases as well as human physicians can, have come under scrutiny from regulators and clinicians.
2020-07-31 00:00:00 Read the full story…
Weighted Interest Score: 2.4021, Raw Interest Score: 1.4439,
Positive Sentiment: 0.3702, Negative Sentiment 0.3332

AI Will Power a Safe Return to the Workplace. Here’s how

The pandemic isn’t over yet. How can you safely welcome employees back to the workplace?

Just imagine your first day back to the office after months of isolation: Not only are you potentially exposed to the virus on your morning commute, but you’re then presented with crowded elevators. As you enter the floor, you notice door handles that have likely been touched by dozens of others right before you, and confined workspaces that make it too easy to breach social distancing protocols. It’s hardly a situation that would put your mind at ease, let alone one that would help you to get back into the swing of working in the office.

That’s why it’s vital that organizations take strict and cautious measures when welcoming their teams back into the workplace. What many businesses don’t realize is how artificial intelligence (AI) can power more general health and safety protocols to new heights.
The technology can allow teams to gain the benefits of in-person collaboration in the safest possible way. Here’s how…
2020-08-03 02:43:21.944000+00:00 Read the full story…
Weighted Interest Score: 2.2543, Raw Interest Score: 1.0501,
Positive Sentiment: 0.1462, Negative Sentiment 0.1994

The Changing Role of the Data Storage Manager

Storage-specific roles are changing due to the rise of cloud, edge computing, advanced analytics, AI and machine learning. According to Gartner, by 2025, 40% of workloads will reside in the public cloud, 30% at the edge and 30% on-premise—compared to the 80% on-premise in 2019.

Predictive analytics, AI and ML are enhancing IT infrastructures to proactively address problems, meaning storage admins don’t have to spend as much time managing. In addition, the increasing use of public clouds is causing a shift from building servers and loading applications to tasks such as migrating data to the cloud and ensuring data remains secure in a hybrid environment.

Security threats are also impacting storage managers. As shown by the 97 percent increase in ransomware attacks over the past two years, defending data against malicious software that locks up files until a ransom is paid is now a pressing concern for enterprises. With a new organization set to fall victim to ransomware every 11 seconds by 2021, storage managers must ensure they’re prepared, as storage is the last line of defense when other measures fail.
2020-07-30 00:00:00 Read the full story…
Weighted Interest Score: 2.2524, Raw Interest Score: 1.3180,
Positive Sentiment: 0.2338, Negative Sentiment 0.4252

Enova to buy OnDeck for $90m

OnDeck, the online lender to small businesses, is being acquired by rival Enova in a cash and stock deal worth around $90 million. The $90 million – $8 million of which will be in cash – is a 90% premium on OnDeck’s closing price on Monday.

Founded in 2006, OnDeck was a pioneer of the alternative lending market, using data analytics and digital technology to make real-time lending decisions. The firm went public in 2014 and joined the unicorn club. However, it has struggled in recent times. Last year JPMorgan Chase ended a four-year collaboration with OnDeck to provide online loans to small businesses. The US bank has brought processing inhouse to offer similar services from its own platform, a decision which sent shares in OnDeck tumbling.
2020-07-29 16:12:00 Read the full story…
Weighted Interest Score: 2.2481, Raw Interest Score: 1.5504,
Positive Sentiment: 0.3876, Negative Sentiment 0.2326

Can behavioural banking drive financial literacy and inclusion?

As the global markets respond to the coronavirus (COVID-19) pandemic, we find ourselves in challenging financial times. Just 12 years after the 2008 markets signaled an impending financial crisis, we find ourselves in the midst of the deepest global recession in decades, according to the World Bank.

We knew pandemics happen every 100 years or so, but much of the world was still caught off guard with COVID-19. The resulting economic downturn is less unique and less surprising. Even in good times of strong financial market performance experts in banking know that the next recession, or downturn, is right around the corner. Perhaps that’s why during one of the strongest global economies we’ve ever experienced, Discovery Bank was focused on the financial health of its customers, many being Gen Z or millennial customers of their digital bank.

Gen X had to grapple with the 1987 stock market crash as they were entering the workforce and came of age during the 2001 internet bubble bursting. Millennials experienced the 2008 financial crisis. Now, this potential global recession may impact those two generations plus Gen Z just which is just entering the workforce.
2020-07-29 00:00:23+00:00 Read the full story…
Weighted Interest Score: 2.2238, Raw Interest Score: 1.1985,
Positive Sentiment: 0.6135, Negative Sentiment 0.2283


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