NYU Data Science newsletter – November 4, 2015

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What I love about working at Google is the opportunity to harness cutting-edge machine intelligence for users’ benefit. Two recent Research Blog posts talked about how we’ve used machine learning in the form of deep neural networks to improve voice search and YouTube thumbnails. Today we can share something even wilder — Smart Reply, a deep neural network that writes email.

I get a lot of email, and I often peek at it on the go with my phone. But replying to email on mobile is a real pain, even for short replies. What if there were a system that could automatically determine if an email was answerable with a short reply, and compose a few suitable responses that I could edit or send with just a tap?

… Cities are complex systems. Increasingly, scientists studying cities have gone from thinking about “cities as machines”, to approaching “cities as organisms”. Viewing cities as complex, adaptive organisms – similar to natural systems like termite mounds or slime mould colonies – allows us to gain unique insights into their inner workings. Here’s how.

Complex organisms are characterised by individual units that can be driven by a small number of simple rules. As these relatively simple things live and behave, the culmination of all their individual interactions and behaviours generate more widespread aggregate phenomena.

The eScience Institute is pleased to announce our participation with University of Washington as one of three West Coast co-leads selected by the National Science Foundation as part of four Big Data Regional Innovation Hubs the NSF is establishing around the country.

Technology makes some types of jobs obsolete and creates other types of jobs?—?that’s been true since the stone age. While in the past, machines have replaced people in jobs that require physical labor, we’re increasingly seeing traditionally white collar jobs augmented by machines: financial analysts, online marketers, and financial reporters, just to name a few. Of course, these advances also create new jobs. The electronic computers that we know today, for example, replaced human beings performing the actual calculations, but in the process created all kinds of new types of work.

Artificial intelligence seems like it might work the same way, creating jobs for artificial intelligence researchers and slowly displacing all other kinds of knowledge work. And while this might be where we end up a century from now, the path to get there won’t quite look the way people think. We can see where we’re going from AI design patterns used at Google, Facebook and other companies investing heavily in artificial intelligence. In the most common design patterns, AI can actually increase demand for exactly the kind of work that it is automating.

Overdevelopment in China has created urban regions known as ghost cities that are more or less uninhabited. Nobody knew how bad the problem was until Baidu used its Big Data Lab to find out.

A prestigious academic journal has just experienced the closest thing to outright mutiny: All six editors and the entire editorial board of the well-respected linguistics journal Lingua resigned en masse last week. And the reason says a lot about the ongoing disruption taking place in the formerly sleepy world of academic publishing.

This summer, alongside Prof. Stephen Hawking and an esteemed board of scientists, Milner announced an unprecedented $100 million global Breakthrough Initiative to reinvigorate the search for life in the universe.

The initiative, called “Breakthrough Listen,” seeks to answer three fundamental questions:

How did life begin?

Where else is it in the galaxy and universe?

What’s the future of life here on Earth?

The National Science Foundation announced Monday that it is awarding a handful of universities more than $5 million to establish four regional hubs devoted to furthering the ability of organizations in fields ranging from agriculture to healthcare to make better use of big data.

The awards are the next step in advancing the National Big Data Research and Development Initiative that’s been in the works for a couple of years now.

The Big Data Regional Innovation Hubs are coordinated by data scientists at Columbia University (Northeast Hub), Georgia Tech and the University of North Carolina (South Hub), the University of Illinois at Urbana-Champaign (Midwest Hub) and the University of California, San Diego the University of California, Berkeley and the University of Washington (West Hub). More than 250 organizations nationwide, including from municipalities and big companies, will work with the schools to speed research discoveries into practice.

… For healthcare providers to realize the promise of mHealth, they must find a way to expand self-service mobile access while meeting significant responsibilities – moral, ethical and legal – to protect data privacy and governance. mHealth applications can and should provide security protections, but relying on them to enforce complex authorization logic surrounding healthcare data privacy is often a non-starter. Such efforts make applications brittle, thicker and more complex and expensive to develop, especially in an mHealth arena where speed and flexibility are core expectations.

There’s a smarter way to handle this problem. Using a Zero Trust Data approach, organizations can automate data privacy and governance for mHealth while making mobile devices and apps more secure.

… When did you start to incorporate data science into your research?

From my early days as a physicist, I would write simulations to generate data that I would analyze separately. However, the real transition to data science was during my doctoral studies. I started working on complex networks using the “raw” data generated by web-servers to look at how users navigate through web pages.

How does data science connect with the projects you are currently working on?

Right now I’m using geolocated online data sets from sites like Wikipedia and Twitter to study how human mobility and social behavior are interrelated. And with some of the same data sets, I’m using natural language processing tools to look at how the use of language changes regionally, and how language evolves over time.