Data Science newsletter – September 25, 2018

Newsletter features journalism, research papers, events, tools/software, and jobs for September 25, 2018

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Data Science News



Exploring the ruins of a Toys R Us, discovering a trove of sensitive employee data

Boing Boing, Cory Doctorow


from

When the private equity raiders who took over Toys R Us, saddled it up with debt, extracted $200,000,000 and then crashed it, they took the employee severance fund with them, but that wasn’t the final indignity the titans of finance inflicted on the workforce before turning them out on the unemployment line.

Hackaday’s Tom Nardi went on an urban exploration adventure through an abandoned Toys R Us, checking out the fixtures and fittings that remained after its inventory had been sold off and the auctioneer had come and gone.


The Future is Tiny

Medium, Bryan Costanich


from

Microcontrollers will transform IoT, and our lives.

Ten years ago, Steve Jobs stood on stage with the iPhone and we got a glimpse of the future. A supercomputer in every pocket, everywhere in the world. Now there are almost as many smartphones as humans.¹

And yet, by the numbers, the smartphone pales in comparison to the hardware revolution that comes next; by 2025, there will be ten times more non-mobile phone connected devices in the world than humans.


The Thriving AI Landscape In Israel And What It Means For Global AI Competition

Forbes, Gil Press


from

China is beating the US in AI and Europe’s AI experts are challenging both US and China, but a new contender for AI leadership is rapidly emerging. The startup nation of Israel is targeting today’s hottest tech sector by deploying its expertise in cutting-edge data analysis, software and hardware engineering talent, and proven entrepreneurial skills.

According to analyst Daniel Singer, Israeli AI startups (using technologies such as machine learning, deep learning, computer vision, natural language processing, robotics, and speech recognition) have raised close to $2 billion in 2017, an increase of 70% over 2016, and have already raised $1.5 billion this year. An average of 140 startups have been created annually over the last 5 years and there are now over 950 active Israeli startups utilizing or developing AI technologies. (See here for an interactive version of the infographic above).

It’s good to be a member of this exclusive club.


Official Google Canada Blog: Improving Search for the next 20 years

Google, Official Canada Blog, Ben Gomes


from

As Google marks our 20th anniversary, I wanted to share a first look at the next chapter of Search, and how we’re working to make information more accessible and useful for people everywhere. This next chapter is driven by three fundamental shifts in how we think about Search:

  • The shift from answers to journeys: To help you resume tasks where you left off and learn new interests and hobbies, we’re bringing new features to Search that help you with ongoing information needs.
  • The shift from queries to providing a queryless way to get to information: We can surface relevant information related to your interests, even when you don’t have a specific query in mind.
  • And the shift from text to a more visual way of finding information: We’re bringing more visual content to Search and completely redesigning Google Images to help you find information more easily.

  • Sloan Kettering’s Cozy Deal With Start-Up Ignites a New Uproar

    The New York Times, Charles Ornstein and Katie Thomas


    from

    An artificial intelligence start-up founded by three insiders at Memorial Sloan Kettering Cancer Center debuted with great fanfare in February, with $25 million in venture capital and the promise that it might one day transform how cancer is diagnosed.

    The company, Paige.AI, is one in a burgeoning field of start-ups that are applying artificial intelligence to health care, yet it has an advantage over many competitors: The company has an exclusive deal to use the cancer center’s vast archive of 25 million patient tissue slides, along with decades of work by its world-renowned pathologists.

    Memorial Sloan Kettering holds an equity stake in Paige.AI, as does a member of the cancer center’s executive board, the chairman of its pathology department and the head of one of its research laboratories. Three other board members are investors.


    Canadian firms fall behind in adoption of artificial intelligence

    Ottawa Citizen, Canadian Press


    from

    In spite of Canada’s reputation as a hotbed of artificial intelligence, AI deployment has not yet been a “real success” for the country’s companies, a new report has found.

    Canada ranked last out of 10 countries, with just 31 per cent of adopters of the technology claiming successful AI deployment, compared with 59 per cent in India and 58 per cent in Germany, according to the study by Forbes Insights.

    Canadian companies also came last for full deployment throughout their firm, and encountered the most resistance from employees due to concerns about job security, the report states.


    Harvard Business School to Expand Case Studies Related to Artificial Intelligence

    Harvard Business School, News


    from

    Harvard Business School (HBS) announced today a $5 million gift from alumnus Stephen A. Schwarzman (MBA 1972), chairman, CEO, and co-founder of Blackstone, a multinational private equity alternative asset management and financial services firm, to support the development of case studies and other programming that explore the implications of artificial intelligence (AI) on industries, business, and markets. The Schwarzman Research Fund will spur and extend the efforts of HBS faculty members across a range of disciplines who are researching and writing case studies on the use of AI.


    Harnessing the GPS Data Explosion for Interdisciplinary Science

    Eos; Geoffrey Blewitt, William C. Hammond, and Corné Kreemer


    from

    Thanks to a series of innovations and exponential growth over the past 3 decades, GPS has become an important tool for geodesy and geophysics, pushing forward the science and precise measurement of the Earth’s various active processes on land, water, and ice and in the atmosphere. GPS now forms an integral component of the newest generation of Earth science and natural hazard assessment capabilities for monitoring and understanding earthquakes, tsunamis, volcanoes, mountain growth, aquifers, sea level, glaciers, ice sheets, mantle flow, terrestrial water storage, and water vapor, to name a few.

    The field of geodesy—which measures the size, shape, gravitational field, and spin of the Earth and how they all change over time—was the first to use GPS for science. In fact, geodesy had been instrumental in improving GPS data analysis that enables pinpoint positioning using high-precision equipment. But now, the scope of applications is broadening rapidly.

    We are witnessing an exponential explosion in the number of geodetic-quality GPS stations around the globe, in the amount of data collected, and in the quantity and variety of data products for scientific applications. This explosion is both a cause and an effect of scientists starting to use GPS data from many traditionally nongeodetic disciplines. Moreover, feedback from multiple disciplines leads to improved models of geodetic observables, thus improving GPS data products for all.


    An interview with Robert Fink, Architect of Foundry, Palantir’s open data platform Part One: Open Data Architectures

    CTOvision.com, Bob Gourley


    from

    This post is the first of a series of three capturing the result of recent interview/discussions I had with Robert Fink of Palantir. The conversation was wide ranging, hitting on topics of design, development environments and a bit on the philosophy of enterprise tech. Several common themes emerged in those topic areas, including ways that Palantir has been leveraging open approaches to data architectures, system design and even developer environments. This first post focuses on open data architectures.


    Chip ramps up artificial intelligence systems’ performance

    Princeton University, School of Engineering and Applied Science


    from

    Princeton researchers, in collaboration with Analog Devices Inc., have fabricated a chip that markedly boosts the performance and efficiency of neural networks—computer algorithms modeled on the workings of the human brain.

    In a series of tests, the Princeton chip performed tens to hundreds of times better than other advanced, neural-network chips.

    The researchers believe that with further development, the chip could help advance image recognition and numerous other neural-network applications, including artificial intelligence systems in autonomous vehicles and robots.


    Taking Algorithms To Court – Current Strategies for Litigating Government Use of Algorithmic Decision-Making

    Medium, AI Now Institute


    from

    This blog post summarizes the AI Now Litigating Algorithms workshop, held on June 22, 2018 in collaboration with NYU Law’s Center on Race, Inequality, and the Law and the Electronic Frontier Foundation with the support of the Macarthur Foundation.


    Reducing false positives in credit card fraud detection

    MIT News


    from

    MIT researchers have developed an “automated feature engineering” approach that extracts more than 200 detailed features for each individual transaction — say, if a user was present during purchases, and the average amount spent on certain days at certain vendors. By doing so, it can better pinpoint when a specific card holder’s spending habits deviate from the norm.

    Tested on a dataset of 1.8 million transactions from a large bank, the model reduced false positive predictions by 54 percent over traditional models, which the researchers estimate could have saved the bank 190,000 euros (around $220,000) in lost revenue.

    “The big challenge in this industry is false positives,” says Kalyan Veeramachaneni, a principal research scientist at MIT’s Laboratory for Information and Decision Systems (LIDS) and co-author of a paper describing the model, which was presented at the recent European Conference for Machine Learning. “We can say there’s a direct connection between feature engineering and [reducing] false positives. … That’s the most impactful thing to improve accuracy of these machine-learning models.”


    Universities and the conglomerate challenge

    McKinsey & Company; Bernard T. Ferrari and Phillip H. Phan


    from

    Business leaders in the United States and across the world spend countless hours in the boardrooms of major research universities. For many institutions and trustees, those meetings have become more challenging due to some well-documented threats. Rapidly rising tuition, shifting demographics, the growing popularity of online learning, pressure on research funding, volatile endowment earnings, and parental and graduate dissatisfaction with employment opportunities: all are trends that pose significant risks for university departments, colleges, and central administrations.

    Lurking beneath the surface, and making those trends more ominous, is an issue that corporate executives have been wrestling with for years. It’s what we call the “conglomerate challenge” of today’s research universities. In short, today’s research universities mirror corporate conglomerates in structure, but without the degrees of freedom enjoyed by their corporate counterparts. We believe that by better understanding the realities and the limits of their corporate conglomerate–like structures, university leaders can increase their odds of successfully addressing the many threats they face.

    The theory of the case is straightforward: from a strategic-management and corporate-finance perspective, a university can be viewed as a diversified conglomerate of independent strategic business units (SBUs): colleges, divisions, and schools. Each of these SBUs has a business-level strategy that is driven by its intellectual traditions, educational objectives, and professional disciplinary norms. The corporate strategy of a university supports these strategic intents by serving as a platform for attracting and allocating resources across its academic units.


    Harvard Admissions Suit Fuels Effort to Rein In Alumni Legacies

    Bloomberg Government, Emily Wilkins


    from

    Longtime opponents of legacy college admissions that give preference to the children of alumni say they may have their best shot in almost 15 years to push Congress to act on the issue.Congress, as in 2004, is looking to update the Higher Education Act, which underpins most federal government dealings with colleges. At the same time, a high-profile affirmative action lawsuit—this time involving Harvard instead ofthe University of Michigan—is winding its way through the judicial system. Additionally, populist anger at economic inequality, which critics see as fostered by things like legacy admissions, is fueling some Democrats’ drive for changes.

    “You have a similar confluence of events between now and 2004,” said Michael Dannenberg, a director for strategic initiative and policy at Democratic-leaningEducation Reform Now. “Then, there was a strong policy argument for more fair access and representation for working-class and low-income students. That case is as great now.”


    Government Would Gain Power to Shoot Down Drones in FAA Bill

    Bloomberg Government, Michaela Ross


    from

    Drones that are deemed a threat could be tracked and taken down by the U.S. Department of Homeland Security and Department of Justice under legislation unveiled Sept. 22.

    The provision was included as part of a larger bill (H.R. 302) to reauthorize the Federal Aviation Administration. The House is slated to vote on the measure as early as Wednesday, with a Senate vote expected next week as well as lawmakers seek to have the FAA bill enacted before the current authorization expires Sept. 30.

     
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