NYU Data Science newsletter – July 12, 2016

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Early iterations saved soldiers’ lives, debunked myths about slavery and helped Americans settle the frontier

… This year’s conference comes at a time when AI is playing an increasingly important role in the world. AI has been a hotbed of research and development and the fruits of these efforts will have numerous benefits for people and society. Along with the enthusiasm have come questions about potential challenges and rough edges, and a rise of concerns—with some people expressing worries that resonate with themes portrayed for decades in science fiction. It’s been great to see AI experts as well as the greater public engaged in discussions about short-term and long-term AI futures.

… We advocated a community-based strategy of monitoring and travel limitations [14]. Rather than focusing on the very sick individuals that come to hospitals and trying to track down all of their contacts, the intervention would take place on the level of communities. Each member of a community would be screened daily for fever and isolated if they show these early symptoms (Fig. 4). Catching infections as the first symptoms develop nearly eliminates the chances for transmission. Travel restrictions would help prevent community to community transmission. Whether or not to implement travel restrictions became a political rather than scientific discussion [15,16], but for the Ebola response it was secondary to screening of people within the community, so we focused on that aspect.

Setting up this approach requires having neighborhood teams do daily monitoring. In collaboration with the Army Corps of Engineers, experts in managing large projects, we put together a specific plan for such an effort.

Each year, tens of millions of people around the world are diagnosed with diabetes, high blood pressure, or depression. You’d expect that by now, doctors would have settled on a few standard ways to treat these diseases. But you’d be wrong.

A new analysis of common treatments for these three conditions, using a database of 250 million patients’ records from four countries, has found that at least one in 10 patients received a course of drugs that no other patient with the same condition did. In other words, more than 25 million people were essentially being treated by guesswork.

Neural networks are changing the Internet. Inspired by the networks of neurons inside the human brain, these deep mathematical models can learn discrete tasks by analyzing enormous amounts of data. They’ve learned to recognize faces in photos, identify spoken commands, and translate text from one language to another. And that’s just a start. They’re also moving into the heart of tech giants like Google and Facebook. They’re helping to choose what you see when you query the Google search engine or visit your Facebook News Feed.

All this is sharpening the behavior of online services. But it also means the Internet is poised for an ideological confrontation with the European Union, the world’s single largest online market.

In part one we learned about data and how it can be used to find knowledge or meaning. Part two explained the term Big Data and showed how it became an industry mainly in response to economic forces. This is part three, where it all has to fit together and make sense — rueful, sometimes ironic, and occasionally frightening sense. You see our technological, business, and even social futures are being redefined right now by Big Data in ways we are only now coming to understand and may no longer be able to control.

Mike Williams, Friederike Schuur, and Miriam Shiffman represented Fast Forward Labs at the event, and reported these highlights:

Roy Austin of the White House Policy Council underscored that the government has a long way to go to improve data collection practices.

ICML is too big for me to “review” it per se, but I can provide a myopic perspective.

The heavy hitting topics were Deep Learning, Reinforcement Learning, and Optimization; but there was a heavy tail of topics receiving attention. It felt like deep learning was less dominant this year; but the success of deep learning has led to multiple application specific alternative venues (e.g., CVPR, EMNLP), and ICLR is also a prestigious venue; so deep learning at ICML this year was heavyweight in either the more theoretical or multimodal works.

A few years ago, Elizabeth Jaffee, a professor of oncology in the Johns Hopkins School of Medicine, probably wouldn’t have imagined that she would team up with an aerospace engineer to advance her research on cancer therapies.

Advancements in mapping out genetic sequences had already given biomedical researchers a wealth of information to study tumors and cells — but they didn’t have existing tools to handle that data. So Jaffee reached out to a professor in the department of physics, thinking that the computer system used to analyze complex data from outer space might be able to accommodate the huge trove of information she had on her hands.

These days, not only is IBM back in Cambridge, but the enthusiasm and funding for AI companies in Boston and across the country have returned — accompanied by a raft of buzzphrases suggesting that computers are getting brainier, from “deep learning” to “synaptic intelligence.”

But unlike the 1980s, when technology fresh from the academic lab stumbled out into the world looking for problems to solve, many of today’s companies are focused on specific business problems.

The MS in Business Analytics Program is a one-year, part-time program divided into five on-site class sessions (modules). Two of the five modules occur outside of NYU Stern in global rotating locations, allowing you to expand your international network of valuable peers and contacts.

Deadline to apply for the program beginning in May, 2017 is August 1.

We are pleased to announce the release of this Request for Proposals (RFP). This RFP will help serve as a means to identify approximately six proof-of-principle Demonstration Projects to advance precision medicine in California.

Deadline for concept proposals is Monday, August 8.

This doctoral consortium aims to bring together students and faculty mentors across NLP and the social sciences, to encourage interdisciplinary collaboration and cross-pollination.

Austin, TX The November 6 consortium event is part of a workshop at EMNLP, one of the top conferences in natural language processing

Deadline for submissions is Friday, August 12.

TalkingData, the leading Big Data and analytics company in China, is launching its “Global Data Science Competition” beginning today, July 11, 2016 and ending September 5, 2016. The event will being held on Kaggle and is working in partnership with Turi, the leading machine learning platform.

Deadline to participate in the Kaggle competition is Monday, September 5.

We are pleased to announce the release of IPython 5.0 LTS (or Long Term Support). IPython is the Python kernel for Jupyter and the interactive Python shell; it provides a rich set of features for fluid interactive computation in Python at the terminal, in the Jupyter Notebook and across all other clients that support the Jupyter architecture.

Microsoft has made Project Malmo, a platform that uses the world of Minecraft as a testing ground for advanced artificial intelligence research, available for novice to experienced programmers on GitHub via an open-source license.

github.com/Microsoft/malmo (July 11, GitHub – Microsoft)

Paper: http://science.sciencemag.org/content/353/6295/163

Data and code: http://snap.stanford.edu/higher-order/

Altair is developed by Brian Granger and Jake Vanderplas in close collaboration with the UW Interactive Data Lab.

With Altair, you can spend more time understanding your data and its meaning. Altair’s API is simple, friendly and consistent and built on top of the powerful Vega-Lite JSON specification. This elegant simplicity produces beautiful and effective visualizations with a minimal amount of code.

We develop a computer-assisted (as opposed to fully automated) statistical approach that suggests keywords from available text without needing structured data as inputs.

Over the years, we’ve shared several posts on using the ScaleR package to import, process, visualize and analyze large data sets with R. Until now, you needed to have access to a Microsoft R Server license to take advantage of the package. Now, you can use all of the capabilities of ScaleR free of charge with Microsoft R Client for Windows, which is available for download now.