Rand Hindi once put on more than 70 pounds just to see if data could help him take the weight off. He tracked every aspect of his life—what he ate and drank, how long he slept—and fed the results into software that determined which behaviors were bad for him. Sure enough, after heeding the software’s advice, he lost the weight.
Now what Hindi wants to reduce is the “friction” of urban life. In 2012 he founded a Paris-based company called Snips, which analyzes data in hopes of making city living more efficient. For example, Snips partnered with France’s national railway to create an app that predicts up to three days in advance how crowded different trains will be. By mining such sources as weather information, historical passenger counts, and real-time check-ins from users of the app, it can advise people to stay away from particular stations or guide them to trains with more seats available. Now Snips is developing ways to use an urbanite’s context—location, weather, interests—and deliver useful information before he or she even asks for it.
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