NYU Data Science newsletter – September 11, 2015

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Apple has ramped up its hiring of artificial intelligence experts, recruiting from PhD programs, posting dozens of job listings and greatly increasing the size of its AI staff, a review of hiring sites suggests and numerous sources confirm.

The goal is to challenge Google in an area the Internet search giant has long dominated: smartphone features that give users what they want before they ask.

Many commentators have pointed to disturbances in Ferguson and elsewhere over the past year as proof that economic inequality leads to tensions and even violence. But new research out from Yale University suggests that it’s not the presence of inequality that causes problems, but rather the visibility of that inequality.

“Making wealth visible was a very corrosive force. It resulted in the rich exploiting the poor,” said Nicholas A. Christakis, the co-director of Yale Institute for Network Science and one of the senior authors of the study.

Macroeconomic data can and should be purged of the effects of bad weather to help policymakers and markets have a more accurate sense of the health of the economy. Unusual weather is not accounted for by applying “seasonal adjustment,” and the new research shows that the effects of unusual weather can be responsible for swings of as much as 100,000 jobs monthly. Michael Boldin of the Federal Reserve Bank of Philadelphia and Jonathan H. Wright of Johns Hopkins University find that unusual weather effects are important and are not reflected in the conventional seasonal adjustment that the Bureau of Labor Statistics currently uses. Adjusting for unseasonal weather (snowstorms, low temperature and snowfall) also impacts GDP data, with growth in the first quarter of 2015 increasing from 0.6 percentage points at an annualized rate to 1.4 percentage points, while the estimate of growth in the second quarter drops from 3.7 to 2.8 percentage points.

With the return of the NFL, it also marks the return of the universally popular fantasy aspect of the game. Millions of fans across the country scramble to outsmart one another, looking for patterns in the sea of football statistics. And one Somerville-based company is using its unique capacity to simplify big data for the betterment of your fantasy roster.

Nutonian, a data science company founded in 2011, uses the Eureqa software tool to help its impressive list of clients harness massive amounts of data in an easy to read, convenient format. Despite applying most of its time to aiding the manufacturing or finance industries (along with many others), Nutonian recently had a little fun with its program and created a model for fantasy football quarterback evaluation.

We are very pleased to announce that Microsoft will not only continue the Revolution Analytics’ tradition of supporting R user groups worldwide, but is expanding the scope of the user group program. The new 2016 Microsoft Data Science User Group Sponsorship Program is open to all user groups that are passionate about open-source data science technologies. If your group is focused on R, Python, Apache Hadoop or some other vital data science technology you may qualify for the Microsoft program.

An air of mystery has long surrounded student debt. We know the total number of borrowers and their combined debt — 40 million people owe $1.2 trillion — but beyond these headline numbers, the data has been frustratingly thin. Who borrows? Who defaults? Why are so many borrowers in distress? The answers have been unclear, leaving analysts and policy makers to prescribe remedies without an accurate diagnosis of the disease.

But now the picture has become significantly sharper.

The Knight News Challenge on Data is now open for ideas through 5 p.m. ET Sept. 30. This News Challenge, our 14th, reflects Knight Foundation’s ongoing support for projects that use data for good to inform and empower people to make decisions about their lives, communities and democracy.

In collaboration with Data & Society and the Open Society Foundations, we are seeking projects that provide an answer to the question: How might we make data work for individuals and communities?

Deadline for Submissions: Wednesday, September 30

We invite researchers to submit their recent work on scalable non-parametric methods, including, for example, Gaussian processes, Dirichlet processes, Indian buffet processes, and support vector machines. Full details appear below, in the workshop overview. Submissions will take place in the form of 2-4 page abstracts (unlimited references), in the NIPS style (available here). Author names do not need to be anonymized. Accepted papers will be presented as posters or contributed talks.

Deadline for Paper Submission: Monday, October 12