NYU Data Science newsletter – January 13, 2016

GROUP CURATION: N/A

In today’s lookit-me culture of selfies, Twitter, Facebook and (ahem) endless blogging, the notion of anonymity is about as welcome as a case of hemorrhoids. But Paul Hanel thinks it may be key to correcting some fundamental problems in science.

Hanel, a psychologist at Cardiff University in the United Kingdom, posted a manuscript recently calling for anonymity in science articles. More than that, Hanel suggests stripping identifiers from virtually all academic output: doing away with name-based citations, CVs on researchers’ web sites, author names on book chapters, titles on academic journals, and more.

Toyota wasn’t the only automaker to suffer during the Great Recession. But when the volume of Toyota customers behind on car payments spiked to record levels in 2009, the company decided to overhaul its approach to collections using big data analytics.

If you go into a Toyota dealership today to buy a car, it’s likely that you’ll be introduced to Toyota Financial Services, the auto giant’s banking arm. With more than 4 million customers and an $80-billion portfolio of car loans and leases, the company is sizable in its own right.

Mobileye Co-founder, CTO and Chairman Amnon Shashua discusses the future of autonomous driving and road mapping at his 2016 CES press conference.

… [Jaclyn] Smolinsky has gone from tracking migratory songbirds at stopover sites in the field to following their activity and departures at stopover sites using weather radar.

“It’s staying with the same birds and using the radar technology so it’s really related to what I used to do except I don’t see the birds and put little tags on them any more, I just use the radar to study them. It’s sort of transitioned from actual birds to dots on a screen. But they’re still birds,” said Smolinsky. “I’m drawn to this side of it because there are so many cool technologies available now.”

… Driving itself is changing. Between electric and self-­driving vehicles, ubiquitous sensors, network connectivity, and new kinds of transportation companies, everything is in flux: cars, how we feel about them, even roads and cities. This isn’t just hypothetical; you can use these things today. A radical phase shift is redrawing the map, literally and metaphorically.

It’s a shift we need. The fact is, too many people own too many cars—an estimated 1.2 billion vehicles globally. Congestion in many cities is already untenable, and it’s only getting worse. And the existence of so many cars is both environmentally disastrous and reliably lethal (to the tune of more than 32,000 driving deaths a year in the US). If current trends hold and places like China and India make personal vehicle ownership a hallmark of middle-class achievement like the US has, the number of vehicles goes up to 2 billion by 2040.

Last month I shook hands with the future. Well, it was more of a pincer than a hand, but it was definitely the future. This pincer belonged to BRETT, the Berkeley Robot for Eliminating Tedious Tasks, created by the UC Berkeley People and Robots Initiative. I interrupted BRETT tying knots in a thick piece of red rope. His real trick though, is to do the laundry.

While laundry may be an annoying chore for us fleshy humans, for a silicon-brained robot, it’s insanely difficult, comprising a complex series of steps in a messy real-world environment. Watching BRETT in action – it takes about ten minutes to fold a single towel – is sure to dispel any fears of a Terminator-style uprising any time soon. But BRETT is part of a new approach to artificial intelligence known as ‘deep learning’, where instead of tasks being pre-programmed, the system is able to train and improve itself based on experience.

TensorFlow is Google’s new framework for implementing machine learning algorithms using dataflow graphs. Nodes/vertices in the graph represent operations (i.e., mathematical operations, machine learning functions), and the edges represent the tensors, (i.e., multidimensional data arrays, vectors/matrices) communicated between the nodes. Special edges, called control dependencies, can also exist in the graph to denote that the source node must finish executing before the destination node starts executing. Nodes are assigned to computational devices and execute asynchronously and in parallel once all the tensors on their incoming edges becomes available.

It seems like the dataflow model is getting a lot of attention recently and is emerging as a useful abstraction for large-scale distributed systems programming. I had reviewed Naiad dataflow framework earlier. Adopting the dataflow model provides flexiblity to TensorFlow, and as a result, TensorFlow framework can be used to express a wide variety of algorithms, including training and inference algorithms for deep neural network models.

Mansion Maniac, a whimsical online toy created by the New York Public Library, may seem like envy bait for the real-estate have-nots. With the help of a Pac-Man-like icon, users can explore the floor plans of some of the city’s most extravagant early-20th-century residences, culled from the library’s archives.

But the game is what you might call a marketing teaser for a major redistribution of property, digitally speaking: the release of more than 180,000 photographs, postcards, maps and other public-domain items from the library’s special collections in downloadable high-resolution files — along with an invitation to users to grab them and do with them whatever they please.

Digitization has been all the rage over the past decade, as libraries, museums and other institutions have scanned millions of items and posted them online. But the library’s initiative (nypl.org/publicdomain), which goes live on Wednesday, goes beyond the practical questions of how and what to digitize to the deeper one of what happens next.

While officers raced to a recent 911 call about a man threatening his ex-girlfriend, a police operator in headquarters consulted software that scored the suspect’s potential for violence the way a bank might run a credit report.

The program scoured billions of data points, including arrest reports, property records, commercial databases, deep Web searches and the man’s social- media postings. It calculated his threat level as the highest of three color-coded scores: a bright red warning.

The man had a firearm conviction and gang associations, so out of caution police called a negotiator. The suspect surrendered, and police said the intelligence helped them make the right call — it turned out he had a gun.