NYU Data Science newsletter – November 9, 2015

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As the volume, variety and velocity of data grow, data analysts struggle with asking and answering big questions of the data – even with the availability of increasingly sophisticated data visualization tools. It takes far too long for analysts to get from visualization to answers or discoveries. Think of it as the “World Wide Wait” but for complex analytics.

Through a number of research projects, the ISTC for Big Data is “imagineering” the possibilities of faster, more-interactive and more-accessible data visualization. Three project teams presented papers describing their work at the recent VLDB 2015 and IEEE VIS 2015 conferences.

neural-storyteller is a recurrent neural network that generates little stories about images. This repository contains code for generating stories with your own images, as well as instructions for training new models.

Drew Conway is a leading expert in the application of computational methods to help solve large-scale social and behavioral problems. Conway is the founder and CEO of Alluvium. Alluvium delivers fully integrated analytics products that empower teams to perfect their production by interpreting and simplifying mechanical and operational systems. He was formerly the Head of Data at Project Florida, a NYC-based hardware/software startup that worked to harness an expansive breadth of data to improve outcomes for patients, and help people better understand their own health.

Conway started his career in the U.S. intelligence community as a Computational Social Scientist researching how human networks have evolved and changed over time, and how organizations that don’t have a traditional command and control structure make personnel choices.

Conway is currently working on multiple projects for startups in the data analysis and data security space. As part of the expert interviews for our Future of Safety & Security report, PSFK spoke with Conway about how the rules of safety and security for consumers are evolving.

The White House Office of Science and Technology Policy is laying the groundwork to host a governmentwide open data exposition slated for “a year from now,” a policy advisor to the U.S. chief technology officer announced Friday.

The technology office envisions bringing together agencies, industry and developers “in a big-tent way” to showcase how openly accessible government data and APIs are being used across all industry sectors and federal agencies in new and valuable ways, said Kristen Honey, policy advisor at OSTP.

In the last few decades, we have witnessed major technological innovations such as personal computers and the internet finally reach the mainstream. And with mobile devices and social networks on the rise, we’re now more connected than ever. So what’s next? When is it coming? And how will it change our lives? Today I’ll tell you that the next big advance is well underway and it’s being fueled by a recent technique in the field of Artificial Intelligence known as Deep Learning.

The authors of a much-discussed study on mortality rates among middle-aged whites have released additional data that rebuts part of an argument made by a critic of their study.

The original paper — by Anne Case and Angus Deaton, both economists at Princeton — found that mortality rates for middle-aged white Americans had risen since 1999, in contrast to the patterns for every other racial group and for residents of virtually every other affluent country. Rising substance abuse, including alcohol-related disease and painkiller overdose, was the main cause of the disturbing trend.

The mood was fes­tive as Pres­i­dent Joseph E. Aoun, net­work sci­ence lumi­naries, deans, stu­dents, and friends gath­ered on Wednesday evening on the 11th floor of 177 Hunt­ington Ave. to cel­e­brate the launch of Northeastern’s Net­work Sci­ence Insti­tute, home of the nation’s first doc­toral pro­gram in net­work science.

The Net­work Sci­ence Insti­tute brings together an inter­dis­ci­pli­nary team of renowned scholars from across the uni­ver­sity to dis­cover and inspire new ways to mea­sure, model, and pre­dict mean­ingful inter­ac­tions in social, phys­ical, bio­log­ical, and tech­no­log­ical sys­tems. Its theme, “Solving Prob­lems in an Inter­con­nected World,” speaks to its com­mit­ment to develop inter­ven­tion strate­gies that will improve the health and secu­rity of people around the world.

Toyota is serious about artificial intelligence: The Japanese automaker will establish a new $1 billion A.I. research and development unit in Silicon Valley.

“Toyota believes artificial intelligence has significant potential to support future industrial technologies and the creation of an entirely new industry,” the company said in a statement. “To underscore this belief, it is making an initial investment of $1 billion over the next five years.”

If you are wondering whether exposure to some chemical could increase your chances of getting colon cancer, you could easily find supportive evidence from animal experiments. You might then discover that epidemiological studies tell a different story.

There have never been more options when it comes to measuring factors relevant to health. We can sequence our entire genomes and those of our bacteria, viruses and tumours. In principle, every visit to the doctor can be tracked from electronic medical records. Information on physiology, behaviours, diets, movements and interactions with others can be extracted from wearable devices, smartphone apps and social-networking sites1. And thanks to the open-access movement and a shift in data-sharing norms, more data are being made publicly available.

Yet sifting through the information to find answers to questions about health is becoming increasingly difficult, even for the experts. The data exist in disparate domains, are generated using different methods, and are stored in different infrastructures — from the private servers of hospitals to global platforms, such as dbGaP, an open database of genotypes and clinical information.

For last several decades, large-scale computing, whether for massive supercomputers or distributed enterprise systems, has been engaged in a never-ending game of whack-a-mole. Less colloquially, of chasing down one bottleneck bubble, only to find it revived elsewhere in the system with equal force.

That endless game is still being played, but as the scale of systems grows, those bubbles are spurting up at ever lower levels of the system. One generation’s hardware bottleneck bubbles have become this generation’s set of profound software challenges. But just as new processor, interconnect, and memory development sprung from necessity then, so too are some clever solutions emerging now. This time they’re not from the national supercomputing labs or the vendor community. It’s from the folks who know scale the best—and they don’t even need named here, do they?

On Tuesday 3 November, Imperial welcomed data experts from around the UK for the launch of the College’s Data Science Hub and the KPMG Data Observatory– two new facilities for the College’s Data Science Institute.

he thing to know about Yann LeCun, the director of Facebook AI Research (FAIR) and a professor at New York University, is that he was right. LeCun was an early developer and proponent of deep learning during the 1980s, which is a machine-learning technique that builds and combines different types of artificial intelligence so they are standardized to all learn new things the same way. When this and other neural network techniques were unpopular during the 1990s and early 2000s, LeCun continued his work. And now he has emerged victorious, with a pretty sweet gig at Facebook. “I was basically given carte blanche to create a research lab in A.I.,” he said.