CUNY Graduate Center / Data & Society
Cloudera Fast Forward Labs
The successes of technology companies that rely on data to drive their business hints at the potential of data science and machine learning (DS/ML) to reshape the corporate world. However, despite the headway made by a few notable titans (e.g., Google, Amazon, Apple) and upstarts, the advances that are advertised around DS/ML have yet to be realized on a broader basis. The authors examine the tension between the spectacular image of DS/ML and the realities of applying the latest DS/ML techniques to solve industry problems. The authors discern two distinct ways, or modes, of thinking about DS/ML woven into current marketing and hype. One mode focuses on the spectacular capabilities of DS/ML. It expresses itself through one-off, easy-to-grasp marketable projects, such as DeepMind’s AlphaGo (Zero). The other mode focuses on DS/ML’s potential to transform industry. Hampered by an emphasis on tremendous but as of yet unrealized...
by SIMON ROBERTS, Stripe Partners
Today I turned left out of London Bridge station. I usually turn right and take the Tube but instead I went in the other direction and took the bus. I can’t explain why I did that.
Perhaps I was responding to a barely discernible change in crowd density or the fact that it was a bit warm today and I didn’t want to ride the Tube. Either way, I was trusting instincts that I am not able to translate into words.
Often when I travel around London I reach for the CityMapper app. I rely on it to tell me how best to get from A to Z but I don’t really know how it makes the recommendations it does. Likely it has access to information about the performance of the Tube today or real time knowledge of snarl-ups on London’s medieval roads. It’s clever and I love it. It knows more than I do about these things and what to do about them.
The workings of CityMapper are a mystery to me—but so are the workings of my brain. Even if I had a sophisticated understanding of neuroscience, physiology...