Space scientists are abandoning the heavens to help you decide what to wear and watch and listen to. Whether it's stars or Stitch Fix, it's all about machine learning. Wired: Chris Moody knows a thing or two about the universe. As an astrophysicist, he built galaxy simulations, using supercomputers to model the way the universe expands and how galaxies crash into one another. One night, not long after he'd finished his PhD at UC Santa Cruz, he met up with a few other astrophysicists for beers. But that night, no one was talking about galaxies. Instead, they were talking about fashion. A couple of Moody's astrophysicist pals had recently left academia to work for Stitch Fix, the online personal styling company now valued at $2 billion. Moody gawked at them. "They were like, 'You don't think this is an interesting problem?'" he says. Indeed, he did not. But when his friends described the work they were doing -- sprinkling in phrases like "Bayesian models" and "Poincare space" -- predicting what clothes someone might like started to sound eerily like the work he'd done during his PhD. Quantifying style, he discovered, "turns out to have really close analogues to how general relativity works." Four years later, Moody works for Stitch Fix too. He belongs to a growing group of astrophysicist deserters, who have stopped researching the cosmos to start building recommendation algorithms and data models for the tech industry. They make up the data science teams at companies like Netflix and Spotify and Google. And even at elite universities, fewer astrophysics PhDs go on to take postdoctoral fellowships or pursue competitive professorships. Now, more of them go straight to work in Silicon Valley. To understand what's driving astrophysicists into consumer product startups, consider the recent explosion of machine learning. Astrophysicists, who wrangle massive amounts of data collected from high-powered telescopes that survey the sky, have long used machine learning models, which "train" computers to perform tasks based on examples. Tell a computer what to recognize in one intergalactic snapshot and it can do the same for 30 million more and start to make predictions. But machine learning can also be used to make predictions about customers, and around 2012, corporations started to staff up with people who knew how to deploy it.
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