NYU Data Science newsletter – April 22, 2015

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Kentaro Toyama calls himself “a recovering technoholic”—someone who once was “addicted to a technological way of solving problems.” Five years in India changed him. After getting his PhD in computer science and working on machine vision technologies at Microsoft, Toyama moved to Bangalore in 2004 to help lead the company’s new research center there. He and his colleagues launched dozens of projects that sought to use computers and Internet connectivity to improve education and reduce poverty. But early successes in pilot projects often couldn’t be replicated; in some schools, computers made things worse. In a book being released this spring, Geek Heresy: Rescuing Social Change from the Cult of Technology, Toyama argues that technologists undermine efforts at social progress by promoting “packaged interventions” at the expense of more difficult reforms. Toyama, who is now an associate professor in the School of Information at the University of Michigan, spoke to MIT Technology Review’s deputy editor, Brian Bergstein.

In Montréal this time of year, the city literally stops and everyone starts talking, thinking and dreaming about a single thing: the Stanley Cup Playoffs. Even most of those who don’t normally care the least bit about hockey transform into die hard fans of the Montréal Canadiens, or the Habs like we also call them.

Below is a Youtube clip of the epic goal celebration hack in action. In a single sentence, I trained a machine learning model to detect in real-time that a goal was just scored by the Habs based on the live audio feed of a game and to trigger a light show using Philips hues in my living room.

… Recently Wichita State University’s National Institute for Aviation Research (NIAR) and the Kansas State University Applied Aviation Research Center on the Salina campus conducted radiated susceptibility testing on a gasoline-powered, fixed-wing aircraft weighing 50 pounds with an 11-foot wingspan in a reverberation chamber at NIAR. … The tests are part of a larger effort by K-State Salina to validate the ASTM F38 Committee’s standards for small unmanned aircraft systems.

Team Driving It took second place in the hugely popular AXA Driver Telematics competition. This blog shares their experience working as a team to build a “telematic fingerprint” for drivers, making it possible to distinguish if a given driver was behind the wheel.

Data science and machine learning have long been interests of mine, but now that I’m working on Fuzzy.io I need to keep on top of all the news in both fields.

My preferred way to do this is through listening to podcasts. I’ve listened to a bunch of machine learning and data science podcasts in the last few months, so I thought I’d share my favorites.

1. Value simplicity of analysis over fancy algorithms

If you can’t explain your analysis to a 5 year old, then you’ll have a tough time selling it to others. The focus for product data analysis is not the analysis — don’t get me wrong, you need the analysis, but it’s the story you tell and your recommendations based on that data that really matter.