Data Science newsletter – November 1, 2016

Newsletter features journalism, research papers, events, tools/software, and jobs for November 1, 2016

GROUP CURATION: N/A

 
 
Data Science News



Big Data in Washington — We’re Not Listening

Medium, CUSP, Civic Analytics & Urban Intelligence blog, Danny Fay


from October 31, 2016

I remember reading “data science: sexiest job of the 21st century” and beginning to research the vast applications of this new field, ranging from movie recommendations to mapping the human genome. The private sector has jumped on the big data revolution, however Washington is another story.


Education’s Response to the Big Data Skills Demand

KDnuggets, Rick Delgado


from October 31, 2016

Every business wants to harness Big Data – but even those with the bank account to finance experts with six-digit figures a year can’t get hold of the necessary manpower. The experts with Big Data skills are few and far between, and bringing up the next generation with those talents is a task the world is struggling with. There just aren’t enough professionals.

The gap needs to be closed, and education has a response to the demand. What are universities and colleges doing to make these skills easier to obtain, and how are they speeding up the educational process to get these people into the workforce faster?


How to Teach Computational Literacy/Thinking: Wolfram’s Language and Code.org’s Response

Mark Guzdial, Computing Ed blog


from October 31, 2016

Stephen Wolfram has published an essay arguing for a programming language as key to teaching computational literacy. He says computational thinking — I think he means the same thing as I do with CL instead of CT. I agree with him, and made a similar argument in my book. He goes on to argue that Wolfram Language (and the Mathematica infrastructure behind it) is particularly good for this.


The Rise Of Artificial Intelligence And How It Affects Economists

Fortune, Jonathan Vanian


from October 28, 2016

According to professor Ajay Agrawal of the University of Toronto, humanity should be pondering how the ability of cutting edge A.I. techniques like deep learning—which has boosted the ability for computers to recognize patterns in enormous loads of data—could reshape the global economy.

Making his comments at the Machine Learning and the Market for Intelligence conference this week by the Rotman School of Management at the University of Toronto, Agrawal likened the current boom of A.I. to 1995, when the Internet went mainstream. Gaining enough mainstream traction, the Internet ceased to be seen as a new technology. Instead, it was a new economy where businesses could emerge online.


How the Role of the Data Center Will Change in the Future

Bloomberg


from October 28, 2016

Venkata “Murthy” Renduchintala, president of Intel’s Client and Internet of Things businesses and Systems Architecture Group, discusses the future of the data center with Bloomberg’s Cory Johnson on “Bloomberg Technology.”


How AI Is Shaking Up the Chip Market

WIRED, Business


from October 28, 2016

In less than 12 hours, three different people offered to pay me if I’d spend an hour talking to a stranger on the phone.

All three said they’d enjoyed reading an article I’d written about Google building a new computer chip for artificial intelligence, and all three urged me to discuss the story with one of their clients. Each described this client as the manager of a major hedge fund, but wouldn’t say who it was.

The requests came from what are called expert networks—research firms that connect investors with people who can help them understand particular markets and provide a competitive edge (sometimes, it seems, through insider information). These expert networks wanted me to explain how Google’s AI processor would affect the chip market. But first, they wanted me to sign a non-disclosure agreement. I declined.

These unsolicited, extremely specific, high-pressure requests—which arrived about three week ago—underscore the radical changes underway in the enormously lucrative computer chip market, changes driven by the rise of artificial intelligence. Those hedge fund managers see these changes coming, but aren’t quite sure how they’ll play out.


Mark Zuckerberg Is Funding a Facebook for Human Cells

MIT Technology Review, Antonio Regalado


from October 31, 2016

Stephen Quake’s laboratory at Stanford University looks like biology’s version of Thomas Edison’s famous New Jersey workshop. Roll-down curtains cast shadows across odd devices buzzing and clicking in the aisles. You half expect to find Quake, author of 135 patents and rarely seen wearing anything other than a faded polo shirt, sleeping on one of the benches, just as the Wizard of Menlo Park was known to.

In September, Quake was named co-president of the BioHub, a new $600 million center funded by Facebook billionaire Mark Zuckerberg. BioHub has as its premier project helping to create a vast directory of human cells, which it calls a “cell atlas.” Quake and BioHub are also part a consortium of researchers around the globe who say mapping the millions of cells in the human body is a feat that could help drugmakers and scientists find new ways to treat disease.


12 Observations About Artificial Intelligence From The O’Reilly AI Conference

Forbes, Gil Press


from October 31, 2016

At the inaugural O’Reilly AI conference, 66 artificial intelligence practitioners and researchers from 39 organizations presented the current state-of-AI: From chatbots and deep learning to self-driving cars and emotion recognition to automating jobs and obstacles to AI progress to saving lives and new business opportunities. There is no better place to imbibe the most up-to-date tech zeitgeist than at an O’Reilly Media event as has been proven again and again ever since the company put together the first Web-related meeting (WWW Wizards Workshop in July 1993).

The conference was organized by Ben Lorica and Roger Chen, with Peter Norvig and Tim O’Reilly acting as honorary program chairs. Here’s a summary of what I heard there, embellished with a few references to recent AI news and commentary.


Facebook’s AI director outlined his hiring tactics

Business Insider


from October 31, 2016

Yann LeCun, the director of Facebook AI Research and one of the world’s most prominent AI academics, told Business Insider last week that he employs certain tactics to get people to come and work for him.

“There’s various things … but a lot of it is nurturing relationships with academic laboratories that have a track record of producing interesting students,” said LeCun, who is also a professor at New York University.

He went on to say that allowing scientists to publish their work — something that Apple does not do — is also key.


Physicists Leapfrog Accelerators with Ultrahigh Energy Cosmic Rays

NYU News


from October 31, 2016

An international team of physicists has developed a pioneering approach to using Ultrahigh Energy Cosmic Rays (UHECRs)—the highest energy particles in nature since the Big Bang—to study particle interactions far beyond the reach of human-made accelerators. The work, outlined in the journal Physical Review Letters, makes use of UHECR measurements by the Pierre Auger Observatory (PAO) in Argentina, which has been recording UHECR data for about a decade.

The study may also point to the emergence of some new, not-yet-understood physical phenomenon at an order-of-magnitude higher energy than can be accessed with the Large Hadron Collider (LHC), where the Higgs particle was discovered.


The Camera That Doesn’t Let You Lie

Wall Street Journal


from October 24, 2016

MACHINES MAY NOT FEEL EMOTIONS, but they’re starting to read ours. A whole field of computer science has sprung up to analyze whether we’re giddy or glum, chill or chafed. SoftBank Group’s Pepper robot is programmed to gauge how people are feeling and respond with sympathy, and the Japanese information and telecommunications giant is working with Honda to create emotion-sensing cars. Some researchers have gone further by attempting to decode the brief, involuntary facial movements that can indicate dishonesty—someone’s “tell,” in the parlance of poker. Imagine an app that can confirm whether your opponent is bluffing or if your child is simply playing sick.

A lie-detecting app isn’t here yet, but its forerunner is. Designed by Russians, it has a name that sounds like something James Bond (or Austin Powers) might come up against: Fraudoscope.

 
Events



The speaker lineup for this data science and engineering conf at New Lab is impressive



New York, NY Fresh off the heels of defense organization MD5‘s hackathon, New Lab hosting DataEngConf, a two-day data conference organized by New York- and San Francisco-based company Hakka Labs, on November 3 and 4. The goal of the event, per its website, is to bridge the gap between data scientists and engineers.

Building the evidence: Evaluating the data on government programs



Washington, DC Do policymakers rely on scientific evidence of what works when they create laws and government programs? Friday, November 4, starting at 8:30 a.m., Brookings Institution (1755 Massachusetts Ave NW)
 
NYU Center for Data Science News



NYU, Sports and Data

Claudio Silva, director of the NYU Center for Data Science, has an active applied research program in sports that combines data science, computer graphics and human-computer interaction.

NVIDIA recently profiled Dr. Silva’s work in baseball. And Dr. Silva’s group published a paper (also, video) on the Statcast Dashboard in the current IEEE Computer Graphics and Application journal special section devoted to sports.

NYU also has an comprehensive sports management education program which regularly offers courses in sports data analysis. Keep up with the Tisch Institute for Sports Management, Media and Business via http://twitter.com/NYUTischSports.


StatCast Dashboard: Exploration of Spatiotemporal Baseball DataStatCast Dashboard: Exploration of Spatiotemporal Baseball Data

IEEE Computer Graphics and Applications


from October 08, 2016

Major League Baseball (MLB) has a long history of providing detailed, high-quality data, leading to a tremendous surge in sports analytics research in recent years. In 2015, MLB.com released the StatCast spatiotemporal data-tracking system, which has been used in approximately 2,500 games since its inception to capture player and ball locations as well as semantically meaningful game events. This article presents a visualization and analytics infrastructure to help query and facilitate the analysis of this new tracking data. The goal is to go beyond descriptive statistics of individual plays, allowing analysts to study diverse collections of games and game events. The proposed system enables the exploration of the data using a simple querying interface and a set of flexible interactive visualization tools.

 
Tools & Resources



Jupyter Notebook Cloudera CSD

Daniel Rodriguez


from October 29, 2016

“Running a Jupyter Notebook in a non-standard environment (a Hadoop cluster) is basically the same. It requires more of sys admin experience (not much), but problems really start when you don’t have admin access to the cluster nodes.”


Jupyter Notebook Tutorial

Medium, Den Kasyanov


from October 29, 2016

“I want to share some concepts and ideas about using Jupyter Notebook that I would have liked to have known when I started.”


Deep Learning papers reading roadmap for anyone who are eager to learn this amazing tech!

GitHub – songrotek


from October 29, 2016

If you are a newcomer to the Deep Learning area, the first question you may have is “Which paper should I start reading from?”

Here is a reading roadmap of Deep Learning papers!

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