NYU Data Science newsletter – October 30, 2015

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With millions of research papers published every year, there is a huge information overload in scientific literature search. Semantic Scholar leverages our AI expertise to help researchers find the most relevant information efficiently. We utilize methods from data mining, natural-language processing, and computer vision to create powerful new search and discovery experiences. Starting with Computer Science in 2015, we plan to scale the service to additional scientific areas over the next few years in support of AI2’s mission of “AI for the Common Good”.

The first in a series of blog posts that: outline the basic strategy for integrating Python and R, run through the different steps involved in this process; and give a real example of how and why you would want to do this.

The press loves to talk about Watson as if it’s some AI panacea, but how advanced are the algorithms it runs? As far as I know, it’s just information retrieval with a ton of computation thrown at the queries (there’s no next-gen NLP going on there).

You rarely hear about Watson in ML circles, is its prominence in the media mostly hype? [40 comments as of Oct 30]

More than 50 years ago, John Tukey called for a reformation of academic statistics. In ‘The Future of Data Analysis’, he pointed to the existence of an as-yet unrecognized science, whose subject of interest was learning from data, or ‘data analysis’. Ten to twenty years ago, John Chambers, Bill Cleveland and Leo Breiman independently once again urged academic statistics to expand its boundaries beyond the classical domain of theoretical statistics.

A recent and growing phenomenon is the emergence of “Data Science” programs at major universities, including UC Berkeley, NYU, MIT, and most recently the Univ. of Michigan, which on September 8, 2015 announced a $100M “Data Science Initiative” that will hire 35 new faculty.

This paper reviews some ingredients of the current “Data Science moment”, including recent commentary about data science in the popular media, and about how/whether Data Science is really different from Statistics.

I envision a system that enables successful collaborations between humans and machine learning models by harnessing the relative strength to accomplish what neither can do alone. Machine learning techniques and humans have skills that complement each other — machine learning techniques are good at computation on data at the lowest level of granularity, whereas people are better at abstracting knowledge from their experience, and transferring the knowledge across domains. The goal of my research is to develop a framework for human-in-the-loop machine learning that enables people to interact effectively with machine learning models to make better decisions using large datasets, without requiring in-depth knowledge about machine learning techniques.

In this talk, I present the Bayesian Case Model (BCM), a general framework for Bayesian case-based reasoning (CBR) and prototype classification and clustering. [video, 1:14:48]

It’s Pro Bono Week, a global celebration of the pro bono ethic reaching across all professions, and the perfect time to announce our newest supporter, Bloomberg.

While data science can help mission-driven organizations expand their impact, most don’t have the staff or budget to leverage it the same way companies do. Thankfully, data science is one of the newest professions to embrace the pro bono ethic, with a growing interest among data scientists to apply their skills for the greater good. However, to strengthen pro bono data science, bonds between the data science and social sectors need to be strengthened as well.

… given the state of IoT today, that might be a bumpy tenancy if certain issues aren’t ironed out now. Security, privacy, and reliability concerns are the main barriers to a sudden arrival of some singularity where we all live as happy cogs in an IoT machine world. So how will the human social order take to a world of persistent networked everything?

Donald Trump falls somewhere between “Adventures of Huckleberry Finn” and the fairy tales of Hans Christian Andersen. Ben Carson resembles A.W. Tozer’s “The Pursuit of God.” Marco Rubio has a lot in common with “Journey to the Center of the Earth,” while Jeb Bush is a little simpler and sunnier — closer to “The Life-Changing Magic of Tidying Up,” Marie Kondo’s tribute to decluttering.

These comparisons aren’t about the candidates’ policy platforms. Mr. Trump has not advocated a great rafting trip down the Mississippi, at least not yet.

Well-known businesses and organizations in Massachusetts and beyond are lavishing both time and money on the new Center for Data Science at the University of Massachusetts Amherst.

The center, established earlier this year, has received more than $1.5 million worth of computer hardware, cloud computing infrastructure, data sets and research funding from companies including Amazon, Google, MassMutual, Microsoft Research, Oracle Labs, Thomson Reuters and Yahoo, according to Andrew McCallum, director of the new center.