NYU Data Science newsletter – March 31, 2016

NYU Data Science Newsletter features journalism, research papers, events, tools/software, and jobs for March 31, 2016

GROUP CURATION: N/A

 
Data Science News



Containers Are Entering the Mainstream

Datamation


from March 30, 2016

Container technologies, like the white-hot Docker platform, are quickly becoming the norm among enterprise IT environments according to a new study by the Web server and application specialists at NGINX.

In the short three years since its release, Docker has taken IT departments and the developer community by storm. The lightweight app containerization technology has also spurred interest in competing container platforms like Rocket and LXD.

 

1-2-3 Let’s Have Fun: Getting Started With Hockey Analytics

Corsica


from March 29, 2016

Hockey analytics are for anyone who wants them.

First things first: you can enjoy a hockey game with or without analytics. Knowing your team is getting horrendously outshot does not mean you can’t enjoy a gorgeous breakaway goal, or no-look pass, or a clean hip-check along the boards.

Analytics answer questions. They don’t necessarily tell us who will win, but they help us understand why a team might. If the top scorer in the league will get that many goals next season. Who ought to stay on a roster and who ought to go. Whether or not you should pick that guy up in your keeper league. It’s a place to begin.

 

#icss2016 Twitter NodeXL SNA Map and Report for Monday, 28 March

NodeXL Graph Gallery, Marc Smith


from March 28, 2016


The graph represents a network of 109 Twitter users whose recent tweets contained “#icss2016”, or who were replied to or mentioned in those tweets, taken from a data set limited to a maximum of 18,000 tweets. The network was obtained from Twitter on Monday, 28 March 2016 at 14:18 UTC.

 

Microsoft’s developer conference highlights machine learning

American Public Media, Marketplace


from March 28, 2016

Microsoft Build, the company’s annual conference with software developers, gets underway Wednesday.

Microsoft will be talking about its plans for the year, and encouraging developers to think up new applications for its products.

Two of the most important things Microsoft is highlighting are artificial intelligence and machine learning. If you use Microsoft products, chances are you already experience machine learning, said spokesman Frank Shaw, who gave the example of Microsoft virtual assistant Cortana. [audio, 1:24]

 

How Google Plans to Solve Artificial Intelligence

MIT Technology Review


from March 31, 2016

Padded walls, gloomy lighting, and a ceiling with floral wallpaper. It doesn’t look like a place to make groundbreaking discoveries that change the trajectory of society. But in these simulated, claustrophobic corridors, Demis Hassabis thinks he can lay the foundations for software that’s smart enough to solve humanity’s biggest problems.

“Our goal’s very big,” says Hassabis, whose level-headed manner can mask the audacity of his ideas. He leads a team of roughly 200 computer scientists and neuroscientists at Google’s DeepMind, the London-based group behind the AlphaGo software that defeated the world champion at Go in a five-game series earlier this month, setting a milestone in computing.

It’s supposed to be just an early checkpoint in an effort Hassabis describes as the Apollo program of artificial intelligence, aimed at “solving intelligence, and then using that to solve everything else.”

 

New data science major to launch in fall

Penn State University, Daily Collegian


from March 28, 2016

With the growing demand for data scientists, Penn State is now offering an undergraduate major in data science based in three different colleges.

David Hunter, professor and head of the Department of Statistics, said the major will become available fall 2016 and will consist of three options, each housed in a different college.

 

Coursera launches Data Science Masters program for a fraction of university prices

The Next Web


from March 30, 2016

Higher education degrees are appealing, but involve significant financial sacrifice: in America, an opportunity for a higher paying job also comes with a sentence of paying more student loans for longer. That’s why Coursera is busy establishing a host of masters courses, today announcing a new Data Science program in partnership with the University of Illinois at Urbana-Champaign.

 

Barbara Cohn

Government Technology


from March 23, 2016

New York’s first chief data officer isn’t releasing data just for the sake of it.

“We want to change the conversation from quantity to quality,” said Barbara Cohn, who started working for the state in 2012 and quickly turned around impressive accomplishments, including a much-lauded Open Data Handbook and the OpenNY portal. Cohn credited executive sponsorship from Gov. Andrew Cuomo and a great team with allowing work on the state’s open data efforts to move along so quickly.

 

Texas Hold’em: AI is almost as good as humans at playing poker

Wired UK


from March 30, 2016

Poker playing artificial intelligence has already “approached the performance” of human experts and can use “state-of-the-art methods” in its gameplay.

Researchers from University College London – including a staff member from DeepMind’s Go defeating team – have created a series of reinforcement algorithms that are able to play Texas Hold’em and a simplistic Leduc poker.

 

Algorithms may save us from information overload, but are they the curators we want?

New Statesman, Barbara Speed


from March 30, 2016

We’ve entered the age of the algorithm.

In a way, it was inevitable: thanks to the rise of smartphones and social media, we’re surrounded by vast, unfiltered streams of information, dripped to us via “feeds” on sites like Facebook and Twitter. As a result, we needed something to tame all that information, because an unfiltered stream is about as useful as no information at all. So we turned to a type of algorithm which could help separate the signal from the noise: basically, a set of steps which would calculate which information should be prioritised, and which should be hidden.

Facebook or Twitter are apt demonstrations of this effect. Both collected users and incentivised them to post as much as possible, only to realise that the wash of information was turning off users. So both, at different times and in different ways, introduced curation algorithms to give us what they hoped we wanted.

 

From Alexa to Watson, Boston Children’s Hospital Ups Digital Focus

Xconomy


from March 30, 2016

John Brownstein spends a lot of time thinking about how to build the hospital of the future. One thing the chief innovation officer of Boston Children’s Hospital is sure of: digital technologies will be a cornerstone.

“Brick and mortar will always be incredibly important,” Brownstein says, referring to treating patients within a hospital’s walls. But at the same time, “so much of that interaction’s going to happen virtually,” he says.

That’s due to a mix of coalescing factors: hospitals’ adoption of electronic medical records systems; the proliferation of wearable health-tracking devices; patient demand for online medical information and virtual doctor visits; and the healthcare industry’s shift from a fee-for-service model to value-based care reimbursement.

 
Events



Conference: Unlocking the Black Box: The Promise and Limits of Algorithmic Accountability in the Professions



The increasing power of big data and algorithmic decision-making—in commercial, government, and even non-profit contexts—has raised concerns among academics, activists, journalists and legal experts. Three characteristics of algorithmic ordering have made the problem particularly difficult to address: the data used may be inaccurate or inappropriate, algorithmic modeling may be biased or limited, and the uses of algorithms are still opaque in many critical sectors.

Saturday, April 2, starting at 8:15 a.m., Yale Law School (127 Wall Street, New Haven)

 

CDS & PRIISM Talk: Mediation: From Intuition to Data Analysis, Ilya Shpitser



Modern causal inference links the “top-down” representation of causal intuitions and “bottom-up” data analysis with the aim of choosing policy. Two innovations that proved key for this synthesis were a formalization of Hume’s counterfactual account of causation using potential outcomes (due to Jerzy Neyman), and viewing cause effect relationships via directed acyclic graphs (due to Sewall Wright). I will briefly review how a synthesis of these two ideas was instrumental in formally representing the notion of “causal effect” as a parameter in the language of potential outcomes, and discuss a complete identification theory linking these types of causal parameters and observed data, as well as approaches to estimation of the resulting statistical parameters.

Wednesday, April 6, starting at 11 a.m. in the CDS open space (726 Broadway, 7th Floor)

 

5th Annual Conference Human Capital Innovation in Technology & Analytics



Using Games to Win the War for Talent in the Workplace From Design to AI & Big Data

Speakers at this Conference include serious game and gamification experts who will discuss how games are being used in the workplace, best practice design and what we’re learning from the data the games generate. This is a valuable opportunity to hear from and network with leading practitioners and game technology vendors.

Tuesday, April 12, starting at 12 noon, 5 MetroTech Center, Pfizer Auditorium.

 

2016 NYU Tandon School of Engineering Research Expo



The highly interactive expo will showcase dozens of the most promising and exciting research projects from every academic department, as well as exhibits from the school’s Center for K-12 STEM Education, which is dedicated to boosting science, technology, engineering, and math education in New York City’s public schools.

Wednesday, April 27, starting at 1 p.m., in Brooklyn.

 

ASEE Annual Conference, U471D SUNDAY WORKSHOP: What’s Missing in the Technical? Rendering the Social Visible by Integrating Social Justice Where It Matters Most—Engineering Problem Definition and Solution



This workshop first addresses why the technical myth persists and how engineering ideologies and mindsets in engineering contribute to that persistence, and then focuses on a central question:

What classroom tools can help students integrate social and technical dimensions in ways that prepare them for the rigors of engineering practice, promote better learning in engineering classrooms, and enhance diversity of problem-definition and problem-solving approaches?

Sunday, June 26, starting at 1 p.m. in the New Orleans Convention Center, Room 266.

 
Deadlines



Big Data Day LA 2016 Tech Talk Proposal

deadline: subsection?

Big Data Day LA will take place on Saturday, July 9.

The deadline to propose a 20-40 minute talk is April 30th, 2016

 

Join BuzzFeed Open Lab! The Open Lab is now accepting applications for 2016-2017 fellows.

deadline: subsection?

Open Lab for Journalism, Technology, and the Arts is a workshop in BuzzFeed’s San Francisco bureau. We offer fellowships to artists, programmers, and storytellers to spend a year making new work in a collaborative environment. Read more about the lab.

We seek applications for 2016-2017 fellows. We are particularly interested in proposals that incorporate augmented reality, virtual reality, machine learning, and artificial intelligence.

Open Lab Fellowships are year-long appointments based in San Francisco. Fellows receive full benefits and a salary of $100,000 during the fellowship year.

Deadline to apply is May 1.

 

2016 Financial Stability Conference – Innovation, Market Structure, and Financial Stability

deadline: subsection?

Washington, DC The Federal Reserve Bank of Cleveland and the Office of Financial Research invite the submission of research and policy-oriented papers for the 2016 Financial Stability Conference to be held December 1-2, 2016, in Washington, D.C. The objectives of this conference are to highlight research and advance the dialogue on financial market dynamics that affect financial stability, and to disseminate recent advances in systemic risk measurement and forecasting tools that assist in macroprudential policy development and implementation.

The deadline for submissions is July 31.

 
Tools & Resources



ml-notebook: Dockerfile for multiple machine learning tools.

GitHub – kylemcdonald


from January 04, 2016

This project is aimed at providing an accessible and reproducible environment for a variety of machine learning toolkits, with a focus on deep learning toolkits. Instead of asking you to follow a set of complex setup instructions, ml-notebook asks you to wait while a tested, pre-built image is installed.

 

Activity Net

Fabian Caba Heilbron and Juan Carlos Niebles


from October 15, 2015

A Large-Scale Video Benchmark for Human Activity Understanding

Our benchmark aims at covering a wide range of complex human activities that are of interest to people in their daily living. We illustrate three scenarios in which ActivityNet can be used to compare algorithms for human activity understanding: global video classification,trimmed activity classification and activity detection.

 

Dataproofer: A proofreader for your data

GitHub – dataproofer


from March 28, 2016

Before you can make use of any data, you need to know if it’s reliable. Is it weird? Is it clean? Can I use it to write or make a viz? … Data proofer is built to automate this process of checking a dataset for errors or potential mistakes.

 

Leave a Comment

Your email address will not be published.