NYU Data Science newsletter – May 6, 2015

GROUP CURATION: N/A

T-tests? That’s for amateurs! In this post we’re going to build a Voight-Kampff Test! If you haven’t watched Blade Runner or read Do Androids Dream of Electric Sheep? you may be unfamiliar with this famous test. The purpose of the Voight-Kampff test is identify replicants. In the film replicants are androids that are nearly indistinguishable from humans. The only way to detect a replicant is by testing their reaction to a series of questions intended to invoke an emotional response. When humans are asked these question they uncontrollably respond with “capillary dilation or the so called ‘blush response’, fluctuation of the pupil, involuntary dilation of the the iris.” Using the Voight-Kampff machine an interviewer can measure these responses.

New York University’s (NYU) Center for Data Science (CDS) is joining forces with Nvidia in an effort to improve deep learning applications and algorithms for large-scale GPU-accelerated systems.

The goal of the deep learning process is to improve algorithms and obtain better computer technologies, so that computers can identify patterns and objects with even greater efficiency than humans. GPUs hasten the improvements by eliminating much of the effort expended on training digital neutral networks. In fact, GPUs can reduce the training time by months.

Last year in a post on interesting R topics presented at the JSM I described how data scientists in Google’s human resources department were using R and predictive analytics to better understand the characteristics of its workforce. Google may very well have done the pioneering work, but predictive analytics for HR applications is going mainstream. In the still below from a Predictive Analytics Times video on Data Science for Work Force Optimization Pasha Roberts, Chief Scientists at Talent Analytics, describes using survival analysis for modeling employee retention.

Honeycreepers, small birds inhabiting the Hawaiian Islands, have a rich assortment of beak shapes. Some species have long, thin beaks suited to plucking insects from leaves. Others possess thick beaks good for cracking open tough seeds. According to the classic view of evolution, natural selection drove the development of these different species. Each variant adapted to suit a different ecological niche. But Blair Hedges, a biologist at Temple University in Philadelphia, has proposed a provocative alternative: Adaptation had little to do with it. It was simply a matter of chance and time.

This controversial proposal stems from efforts by Hedges and collaborators to build the world’s most comprehensive tree of life — a chart plotting the connections among 50,000 species of Earth’s vast menagerie.

Jacob Kaplan-Moss is known for his work on Django but, as he would describe in his PyCon 2015 keynote, many think he had more to do with its creation than he actually did. While his talk ranged quite a bit, the theme covered something that software development organizations—and open source projects—may be grappling with: a myth about developer performance and how it impacts the industry. It was a thought-provoking talk that was frequently punctuated by applause; these are the kinds of issues that the Python community tries to confront head on, so the talk was aimed well.

Did you know that using phrases like “a proven track record” on job postings result in more male applicants, whereas “a passion for learning” attracts female applicants?

These findings are according to the startup Textio that launched last year and recently raised $1.5 million for its software that promises to spot gender bias in job descriptions and performance reviews. Companies like Textio are becoming a big business, particularly in Silicon Valley, where the percentage of underrepresented minorities is so low, employers shouldn’t trust their own judgment anymore.

Apple has quietly added a new page to its support site that details its first Web crawler.

The 2015 New England Symposium on Statistics in Sports will be a meeting of statisticians and quantitative analysts connected with sports teams, sports media, and universities to discuss common problems of interest in statistical modeling and analysis of sports data. The symposium format will be a mixture of invited talks, a poster session, and a panel discussion.

Abstracts Deadline: June 15