NYU Data Science newsletter – August 11, 2016

GROUP CURATION: N/A

Google is losing three key executives in its self-driving car unit, including Chris Urmson, the chief technology officer and technical lead.

Urmson was, for many, the face of Google’s self-driving car. He spoke at most events, indicated what the unit was focused on, and is a well respected figure in the self-driving industry.

Brewster Kahle, the Internet’s most famous librarian and an inductee of the Internet Hall of Fame, believes that the time has come to build a better Web.

The problem, he says, is that the Internet is no longer either private or secure. So his argument, summarized in a white paper entitled “Locking the Web Open: A Call for a Decentralized Web”, says that we now need to rebuilt the Web to ensure that it guarantees both privacy and security for its users.

The database market today, then, looks very different than the database market of a decade ago. The traditional relational databases are all still around, but they are increasingly one of many databases employed in a given business rather than the database employed.

Just as it was clear a decade ago that the market would be expanded, however, it is equally apparent today that the database market is poised for change. Functionally, we will continue to see a steady, even accelerating evolution of new approaches – fueled in large part by the release or replication of technologies developed at companies occupying the bleeding edge of web scale. Strategically, however, the available evidence suggests we should look for two major shifts in market.

Many academics I have talked to are unhappy about the dominance of deep learning right now, because it is an approach which works well, maybe even too well, but doesn’t bring us much closer to really understand how the human mind works.

I also think the fundamental problem remains unsolved. How do we understand the world? How do we create new concepts? Deep learning stays an imitation on a behavioral level and while that may be enough for some, it isn’t for me.

Data Science for Social Good (DSSG) Atlanta is living up to its name. The summer intern fellowship program hosted by Georgia Tech recently held its annual student showcase.

Nearly 70 people attended the event, held July 14 at Ponce City Market, as three student teams presented results of their data-driven projects benefiting communities across Atlanta.

Incredibly, we might actually get a chance at a do-over?—?of our cities, our fossil fuel dependence, and the social contract with labor?—?thanks to the impending advent of autonomous cars. Yes, their arrival is inevitable, but how they will impact us is yet to be determined.

Kevin Munger at New York University is interested in group identity on the internet. Offline, we signal which social groups we belong to with things like in-jokes, insider knowledge, clothes, mannerisms and so on. When we communicate online, all of that collapses into what we type. “Basically, the only way to affiliate yourself is with the words you use,” says Munger.

The award-winning author has agreed to answer your questions about the unnerving parallels between his science-fiction work and the deeply divided world we’re living in…and just about anything else you want to ask him.

Research focused on artificial intelligence, robotics and autonomous driving at the University of Michigan will get a major boost thanks to an initial $22 million commitment from the Toyota Research Institute.

Can computer scientists — the people who think about the foundations of computing and programming — help us to solve human problems such as having too many things to do, and not enough time in which to do them?

That’s the premise of Algorithms to Live By a book by Brian Christian and Tom Griffiths. It’s an appealing idea to any economist. We tend to think of everyday decisions as a branch of applied mathematics, which is what computer science is.

To find out what happened and why the effects were so widespread, The Times turned to industry experts Jan Brueckner, an economics professor at UC Irvine; Mark Gerchick, an author and former chief counsel at the Federal Aviation Administration; and Sam Kidd, an account manager at Zerto, a Boston-based data disaster recovery software company. Here are edited excerpts from those interviews.

We invite researchers and designers who have been involved in one or more design-oriented project(s) involving the study of collaborative activities around all sorts of interactive surfaces and devices. Those wishing to attend will be required to submit a work-in-progress or position paper. Wherever appropriate, submitting a short companion video describing the work is strongly encouraged. Please provide a Youtube or Vimeo URL at the author’s convenience.

Deadline for workshop paper submissions is Wednesday, September 28. Workshop will on Sunday, November 6, preceding ISS 2016 in Niagara Falls, Ontario, Canada.

“There is no such thing as a new idea. It is impossible. We simply take a lot of old ideas and put them into a sort of mental kaleidoscope…”

All that to say, here’s some of the places we go to fill our mental kaleidoscopes.

“Is the correlative result (without an interpretive story) useful for anything, independently of whether it’s causal? Causality is usually much harder to establish than correlation (usually through a controlled experiment). Causality is also much more powerful. If there’s a direct causal relationship between college and income, then we can act on it: you send more people to college, and they’ll make more money. If the relationship is due to unobserved common causes, then sending more people to college won’t have an effect on income. Causation is hard to find, but very powerful. Correlation is easier to find, but less powerful.”

“In this post, we’re going to look in-depth at when correlation gives you the right answer, and when it fails to give the right answer.”

We think data science is going through a similar phase of evolution and maturation, so we thought it would be helpful to write something like the Joel Test for assessing the maturity of your data science program. It’s our “highly irresponsible sloppy test to rate the quality of a data science team.”

Here’s our first draft, let us know what you think.