NYU Data Science newsletter – September 15, 2016

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The New York Digital Health Accelerator has once again announced six new startups that will participate in its accelerator program, co-run by the State of New York, nonprofit New York e-Health Collaborative (NYeC), and the Partnership Fund for New York City.

The program runs for four months and during that time, startups receive mentorship, attend workshops, and receive about $100,000 in capital investment.

Microsoft has chosen the 10 machine learning and data science companies that will make up the fourth class of its Seattle Accelerator.

The startups hail from all over the U.S. and focus on a variety of industries. All are later stage companies, and Microsoft said they have a collective average of $5.3 million in funding and $3 million in annual recurring revenue.

If you visited Iceland and asked someone what they called the smelling organ in the middle of their face, they’d tell you, nev. In Japan, it’s hana. To Sar speakers in southern Chad it’s kon, and among the Zuni tribe of the southwestern United States. it’s noli. In fact, you could go to more than 1,400 places around the world, question speakers of more than 1,400 different languages, and hear 1,400 words that contain the sound “n.” But all of them mean the same thing: nose.

That’s one of the findings of a sweeping study in the Proceedings of the National Academy of Sciences Monday, which found evidence of strong associations between the sounds in words and the ideas they represent in completely unrelated languages from all corners of the world. Despite a long-standing assumption in linguistics that the sounds we pick to signify certain concepts are arbitrary, the researchers argue that at least some associations are more universal than you’d think.

An overview of Indeed.com’s 2016 Tech Salary Report, and summary details of top cities for data-centric professions vis-a-vis adjusted salary.

SRI International, the Silicon Valley research lab where Apple’s virtual assistant Siri was born, is working on a new generation of virtual assistants that respond to users’ emotions. … “[Humans] change our behavior in reaction to how whoever we are talking to is feeling or what we think they’re thinking,” says William Mark, who leads SRI International’s Information and Computing Sciences Division. “We want systems to be able to do the same thing.”

The first catalogue of more than a billion stars from ESA’s Gaia satellite was published today – the largest all-sky survey of celestial objects to date.

On its way to assembling the most detailed 3D map ever made of our Milky Way galaxy, Gaia has pinned down the precise position on the sky and the brightness of 1142 million stars.

At TechCrunch Disrupt SF 2016 today, I had a chance to chat with DJ Patil, the nation’s deputy chief technology officer for data policy and chief data scientist. We talked about what he’s been up to at the White House lately, the criminal justice system and a recently-launched data justice initiative.

Pixelation has long been a familiar fig leaf to cover our visual media’s most private parts. Blurred chunks of text or obscured faces and license plates show up on the news, in redacted documents, and online. The technique is nothing fancy, but it has worked well enough, because people can’t see or read through the distortion. The problem, however, is that humans aren’t the only image recognition masters around anymore. As computer vision becomes increasingly robust, it’s starting to see things we can’t.

Researchers at the University of Texas at Austin and Cornell Tech say that they’ve trained a piece of software that can undermine the privacy benefits of standard content-masking techniques like blurring and pixelation by learning to read or see what’s meant to be hidden in images—anything from a blurred house number to a pixelated human face in the background of a photo. And they didn’t even need to painstakingly develop extensive new image uncloaking methodologies to do it. Instead, the team found that mainstream machine learning methods—the process of “training” a computer with a set of example data rather than programming it—lend themselves readily to this type of attack.

A Japanese startup is entering the equity-research business in a bid to challenge the dominance of securities firms by using computers to crunch vast troves of information and predict companies’ earnings.

Nowcast Inc., a financial-technology venture formed last year out of the University of Tokyo, will begin providing automated earnings estimates of consumer goods makers as soon as October by analyzing millions of transactions at retail stores, Chief Executive Officer Ryota Hayashi said.

In recent years, there has been a huge increase in the number of bots online, varying from Web crawlers for search engines, to chatbots for online customer service, spambots on social media, and content-editing bots in online collaboration communities. The online world has turned into an ecosystem of bots. However, our knowledge of how these automated agents are interacting with each other is rather poor. In this article, we analyze collaborative bots by studying the interactions between bots that edit articles on Wikipedia. We find that, although Wikipedia bots are intended to support the encyclopedia, they often undo each other’s edits and these sterile “fights” may sometimes continue for years. Further, just like humans, Wikipedia bots exhibit cultural differences. Our research suggests that even relatively “dumb” bots may give rise to complex interactions, and this provides a warning to the Artificial Intelligence research community.

The most precise map of the night sky ever assembled is taking shape.

Astronomers working on the Gaia space telescope have released a first tranche of data recording the position and brightness of over a billion stars.

And for some two million of these objects, their distance and sideways motion across the heavens has also been accurately plotted.

How Media Lab and HafenCity University researchers are tackling the refugee crisis in Hamburg, using algorithms and LEGO bricks.

  Lots of Upcoming Data Science Talks at NYU

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  There’s no advice like data
  
  
  on September 11
  One of the most compelling reasons to enter into a Master of Science in Data Science program is the host of career opportunities available upon graduation. At the Center for Data Science, we pride ourselves on being able to give our students access to the plethora of data science opportunities that New York City has to offer. To kick off our fall semester company information sessions, we invited PwC to stop by our new office space, and discuss how data science plays a role in their auditing, consulting, and tax departments.