NYU Data Science newsletter – March 22, 2016

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Where is humanity going, and what will we be like when we get there? Do we really have less than 15 years before computers match the intellectual and emotional capabilities of humans, and less than 30 years before artificial intelligence surpasses humanity? Join us for the Season 7 premiere of StarTalk Radio as Neil deGrasse Tyson examines futurist Ray Kurzweil’s predictions about “the Singularity” with the help of guest neuroscientist Dr. Gary Marcus and co-host Chuck Nice. Find out why Ray thinks that we’ll be able to directly link our neocortexes to the cloud, yielding an increase in brainpower the likes of which we haven’t seen since humans developed our frontal cortex millions of years ago. Ponder the possibilities of nanobot computers the size of blood cells, preloaded with information, that can enter our brains through capillaries and make us smarter. Throughout the episode, Prof. Marcus plays devil’s advocate, reminding us that Ray’s predictions are often at odds with mainstream projections of scientific and technological achievement. Finally, explore the potential benefits of advancements in biotechnology and find out about a company that is already using 3D printing to create human organs that are being “installed” into animals with some measure of success. [audio, 54:56]

… There are some people who say, “Twitter absolutely needs to increase monthly active users,” others who say, “Twitter should be happy just being the size it is and figure out the content strategy.” What is your philosophy about how Twitter should grow?

I think as anything grows, you get in this mode of paying more attention to the folks you don’t have instead of the folks you do have. And we have a mindset of making sure that we’re building a stronger tool and a more powerful tool for the people we do have. And when you do that, when you have that focus, and when you’re really listening to your customers, it tends to grow.

In the past, when people heard about Twitter, they assumed that the way to use it was you had to tweet about something. I think more and more people are seeing it as, “I can just see what’s happening in the world. I can see what’s happening about any event.” And the faster we make it for people to realize that, we grow this amazing daily audience around any particular event around the globe.

Then our work is to connect them to people they want to follow long term, and then our work is to convince them that actually you should talk about it, you should share something.

… A team of researchers, led by computer scientist Arkaitz Zubiaga of the University of Warwick, wanted to track the life cycle of a social media rumor. … What the researchers learned was that “rumors that are ultimately proven true tend to be resolved faster than those that turn out to be false.”

Brain science draws legions of eager students to the field and countless millions in dollars, euros and renminbi to fund research. These endeavors, however, have not yielded major improvements in treating patients who suffer from psychiatric disorders for decades.

The languid pace of translating research into therapies stems from the inherent difficulties in understanding mental illness. “Psychiatry deals with brains interacting with the world and with other brains, so we’re not just considering a brain’s function but its function in complex situations,” says Quentin Huys of the Swiss Federal Institute of Technology (E.T.H. Zurich) and the University of Zurich, lead author of a review of the emerging field of computational psychiatry, published this month in Nature Neuroscience. Computational psychiatry sets forth the ambitious goal of using sophisticated numerical tools to understand and treat mental illness.

So far in 2016, the quantitative hiring market has been vibrant! Q1 historically brings new clients and job openings, but this year feels different: this year there is a marked sense of urgency to find talent. With more companies jumping on the data-driven bandwagon, companies have been creating new roles and new data science and analytics teams. With such an increase in the number of quantitative jobs available, no wonder there has been such an increase in churn and talent movement!

Twitter, the popular real-time social network, turns 10 on Monday. To celebrate, the company has unveiled an interactive feature showing how big stories — everything from the raid on Osama bin Laden’s compound to the breakup of the band One Direction — spread on the platform.

The company profiles users so it can sell against your “ethnic affinity.”

[And the followup story: Facebook explains that it is totally not doing racial profiling]

The IoT already produces massive amounts of data. It’s time to start dealing with it. Is Fog and Edge Computing inevitable?

What happens when the cloud isn’t enough? This is a modern problem if there ever was one. Experts are saying 2016 will mark the rise of a new system: fog computing. Fogging involves extending cloud computing to the edge of a network. It helps end devices and data centers work together better.

Fog computing is one answer to several questions. In fact, the term “fog computing” is a recent creation of Cisco, and is often interchanged with “edge computing”. The “edge” simply refers to points nearer where data is produced than to the database and centralized processing centers. This means the edge of a network, or even access-providing devices like routers.

A single standard for smart homes and other connected devices sounds great, but some of the biggest tech firms don’t seem interested.

Sadly, we see two things today when it comes to APIs: either the closed “partner-only’’ API model, where a company announces with some fanfare that it now has APIs, but you can’t use them unless you’re very important, or the mildly less-depressing situation where a company launches a technically great API, but does so with no developer business model. … The closed-API model ends up missing the big opportunities because, unlike the “Biz Dev 2.0’’ approach of APIs, it relies on hand-crafted partnerships that inevitably try to pick winners and losers before a line of code has been written.

The Data Science Summer School (DS3) is an intensive, eight-week hands-on introduction to data science for college students in the New York City area. As we are committed to increasing diversity in computer science, we strongly encourage women, minorities, and individuals with disabilities to apply.

Deadline to apply is April 15.

We have a collection of nearly two million images that play very prominent roles in helping members pick what to watch. This blog describes how we use computer vision algorithms to address the challenges of focal point, text placement and image clustering at a large scale.

Ibis 0.7.0 has been released! The biggest new feature in the release is Impala-Kudu integration. This is great timing, because Kudu’s Python client went beta officially in its recent 0.7.0 release.