NYU Data Science newsletter – October 27, 2015

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… Nearly every speaker and panelist touched on the importance of humans throughout the data science process, no matter how “smart” the machines get.

Brainstorm makes working with neural networks fast, flexible and fun.

It combines lessons from previous projects with new design elements. It is written completely in Python, and has been designed to work on multiple platforms with multiple computing backends.

What do Lisbeth Salander, Chloe O’Brien and Elliot Alderson have in common? They are all expert computer programmers or hackers, and (like most fictional portrayals of people with their skills), they’re all, well, rather odd and socially awkward. In other words, they all conform to the commonly held stereotype of the IT guy (or girl) – which must be one of the most stereotyped occupations in the world – as good with machines and programming code, but lousy with people and emotions. Is this stereotype fair? A new meta-analysis published in the Journal of Research in Personality, combining data from 19 previous studies involving nearly 1700 people, suggests the answer is (mostly) “No”.

Most kids learn the grade school civics lesson about how a bill becomes a law. What those lessons usually neglect to show is how legislation today is often birthed on a lobbyist’s desk.

But even for expert researchers, journalists, and government transparency groups, tracing a bill’s lineage isn’t easy—especially at the state level. Last year alone, there were 70,000 state bills introduced in 50 states. It would take one person five weeks to even read them all. Groups that do track state legislation usually focus narrowly on a single topic, such as abortion, or perhaps a single lobby groups.

Computers can do much better.

Hillary Clinton followed up a strong debate with the other Democratic candidates, last week on October 13, with strong debate with the Select Committee on Benghazi, this week on October 22. Consequently, she has moved from really likely to almost certainly the Democratic nominee for president. Meanwhile, in the fight for the Republican nomination Jeb Bush downsized his payroll by 40% on October 23, but he was in a freefall starting October 17 as he struggled with questions tying him to his brother, George W. Bush. Equally important is that Donald Trump refuses to go away, increasing the probability that the Republican establishment coalesces around an establishment candidate soon; and, Marco Rubio has similar establishment positions and is now perceived to be more electable than Bush.

Before I decided to make the jump from my postdoc in particle physics to a new career in the data science space, I had a few doubts about whether or not I’d be a good fit outside of academia. These doubts were compounded by the fact that most of my colleagues and mentors, while supportive, were not in a position to offer unbiased advice to help me understand if I was making the right decision, as they had never worked outside the university/lab setting themselves. This resulted in developing a few pre-conceived notions of what I thought data science was, which I slowly realized were not accurate after speaking to more data scientists and leaders in industry.

After my first 10 months as a Program Director at Insight with a bird’s-eye view of the data science scene here in Silicon Valley, I’ve gotten a pretty unique view of what it means to be a data scientist.

When Google-parent Alphabet Inc. reported eye-popping earnings last week its executives couldn’t stop talking up the company’s investments in machine learning and artificial intelligence.

For any other company that would be a wonky distraction from its core business. At Google, the two are intertwined. Artificial intelligence sits at the extreme end of machine learning, which sees people create software that can learn about the world. Google has been one of the biggest corporate sponsors of AI, and has invested heavily in it for videos, speech, translation and, recently, search.

For the past few months, a “very large fraction” of the millions of queries a second that people type into the company’s search engine have been interpreted by an artificial intelligence system, nicknamed RankBrain, said Greg Corrado, a senior research scientist with the company, outlining for the first time the emerging role of AI in search.

… This fall, you’re teaching an introductory course for the statistical and mathematical methods needed for data science. Are you enjoying it?

Yes, I’m enjoying teaching very much. The point of the course is to give students a background in probability, statistics, linear algebra and optimization so they can understand more advanced algorithms in machine learning and data science. Most of the students are going for their master’s so even though in the beginning the material is not that complicated, I try to treat it at a more rigorous level than what they have probably seen in undergrad. I give them a practical viewpoint of the subjects but also tell them about the more theoretical aspects so they will know both.

… What did you study in school?

Originally, I was a physicist with a dash of computer science. I spent my undergraduate days studying statistical physics, a branch of physics that focuses on understanding how microscopic behaviors result in macroscopic phenomena.

How does your background in physics apply to your work in the social sciences?

For me, the common denominator between physics and the social sciences has been connecting macro and micro elements to solve problems.

Data sharing will reduce the experiments needed in the lab and will increase the speed of knowledge generation by decreasing the time spent on the generation of equivalent datasets.