NYU Data Science newsletter – June 4, 2015

GROUP CURATION: N/A

We are trying something a little new here and doing an interview with Google Hangouts on Air. The interview will be live at 11:30am EST. I have some questions lined up for Chris, but if you have others you’d like to ask, you can tweet them @simplystats and I’ll see if I can work them in. After the livestream we’ll leave the video on Youtube so you can check out the interview if you can’t watch the live stream. I’m embedding the Youtube video here but if you can’t see the live stream when it is running go check out the event page: https://plus.google.com/events/c7chrkg0ene47mikqrvevrg3a4o. [video, 24:46]

A group of researchers at the Chinese web services company Baidu have been barred from participating in an international competition for artificial intelligence technology after organizers discovered that the Baidu scientists broke the contest’s rules.

The competition, which is known as the “Large Scale Visual Recognition Challenge,” is organized annually by computer scientists at Stanford University, the University of North Carolina at Chapel Hill and the University of Michigan.

Stephanie and I just finished up teaching the third year of the Peer-led Biocomputing Working Group at the University of Texas at Austin, supported by our Center for Computational Biology and Bioinformatics. I wanted to share a little about why we are teaching this class, how the class is structured, and discuss some of the triumphs and obstacles we’ve experienced along the way. … First, why is learning computing so important for biologists? Within the last decade we’ve gone from data files that are one kilobyte to hundreds of gigabytes in size (that’s six orders of magnitude!). Computation is often a roadblock for biologists, who can interpret the biological meaning of data but cannot sift through these huge files manually to grab the information they need.

All too often, businesses store their data in separate tech silos and never think to draw connections, re-use the data, or drive business value from it. Storage is a depreciating asset, but data is the core of your business and an appreciating asset. That means your company is missing important opportunities.

Both Python and R are popular open source languages for performing data science tasks. As a consequence, we at DataCamp often get questions from our students on whether they should use one or the other for their statistical chores.

This post describes some of the basic differences between the two languages and the places they occupy in the world of data science, based on the research we did for our recent infographic “Data Science Wars: R vs Python.”

The immense problems of the 21st century invite innovative thinking from students, academic researchers, business research managers, and government policy makers. Hopes for raising quality in healthcare delivery, securing community safety, expanding food production, improving environmental sustainability, and much more depend on pervasive application of research solutions.

This forthcoming book (The New ABCs of Research: Achieving Breakthrough Collaborations, to be published by Oxford University Press in January 2016) recognizes the unbounded nature of human creativity, the multiplicative power of teamwork, and the catalytic effects of innovation. Contemporary research teams get a further boost from fresh ways of using the Web, social media, and visual communications tools that amplify collaborations.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for computing numeric representations of words. One of the methods includes obtaining a set of training data, wherein the set of training data comprises sequences of words; training a classifier and an embedding function on the set of training data, wherein training the embedding function comprises obtained trained values of the embedding function parameters; processing each word in the vocabulary using the embedding function in accordance with the trained values of the embedding function parameters to generate a respective numerical representation of each word in the vocabulary in the high-dimensional space; and associating each word in the vocabulary with the respective numeric representation of the word in the high-dimensional space.

Both social apps have the announcements in posts to their respective blogs. Instagram says its new ad strategy will bring “new formats, increased relevance, and broader [ad] availability.” Pinterest is more direct; the title of its post is “Coming soon: Buyable Pins!” Here’s a breakdown.

James Andrew, Founder of PixelRouter VR and creator of Z0NE, explores the challenges virtual reality developers face when trying to transcend the boundaries of users’ physical space for the purposes of virtual reality. He shares his experiences at NYU’s MAGNET ‘Holodeck’ and some of his research into creating ‘extra-dimensional’ VR space.