How Google is Remaking Itself as a “Machine Learning First” Company
More and more, the machine learning efforts from those engineers began appearing in Google’s popular products. Since key machine learning domains are vision, speech, voice recognition, and translation, it’s unsurprising that ML is now a big part of Voice Search, Translate, and Photos. More striking is the effort to work machine learning into everything.
One example is Smart Reply in Gmail, launched in November 2015. [...] What if the team used machine learning to automatically generate replies to emails, saving mobile users the hassle of tapping out answers on those tiny keyboards? “I was actually flabbergasted because the suggestion seemed so crazy,” says Corrado. “But then I thought that with the predictive neural net technology we’d been working on, it might be possible. And once we realized there was even a chance, we had to try.”
Google boosted the odds by keeping Corrado and his team in close and constant contact with the Gmail group, an approach that is increasingly common as machine learning experts fan out among product groups. “Machine learning is as much art as it is science,” says Corrado. “It’s like cooking — yes, there’s chemistry involved but to do something really interesting, you have to learn how to combine the ingredients available to you.”
When the team began testing Smart Reply, though, users noted a weird quirk: it would often suggest inappropriate romantic responses. “One of the failure modes was this really hysterical tendency for it to say, ‘I love you’ whenever it got confused,” says Corrado. “It wasn’t a software bug — it was an error in what we asked it to do.” The program had somehow learned a subtle aspect of human behavior: “If you’re cornered, saying, ‘I love you’ is a good defensive strategy.” Corrado was able to help the team tamp down the ardor.
Smart Reply, released last November, is a hit — users of the Gmail Inbox app now routinely get a choice of three potential replies to emails that they can dash off with a single touch. Often they seem uncannily on the mark. Of responses sent by mobile Inbox users, one in ten is created by the machine-learning system. “It’s still kind of surprising to me that it works,” says Corrado with a laugh.
[F]or years, the joke in academia was that Google hires top students even when it doesn’t need them, just to deny them to the competition. (The joke misses the point that Google does need them.) “My students, no matter who, always get an offer from Google.” says Domingos. And things are getting tougher: just last week, Google announced it will open a brand new machine-learning research lab in Zurich, with a whole lot of workstations to fill.
“Machine learning,” she says, “is huge here.”
Machine learning killed the algorithm star.