An Infusion of AI Makes Google Translate More Powerful Than Ever

An Infusion of AI Makes Google Translate More Powerful Than Ever

An Infusion of AI Makes Google Translate More Powerful Than Ever
Last March, a computer built by a team of Google engineers beat one of the world’s top players at the ancient game of Go. The match between AlphaGo and Korean grandmaster Lee Sedol was so exhilarating, so upsetting, and so unexpectedly powerful, we turned it into a cover story for the magazine. On a Friday in late April, we were about an hour away from sending this story to the printer when I got an email.

According to the email, Lee had won all five matches—and all against top competition—since his loss to AlphaGo. Even as it surpasses human talents, AI can also pull humans to new heights—a theme that ran through our magazine story. After playing AlphaGo, Lee said the machine opened his eyes to new ways of playing the ancient game, and indeed, it had. We had to get his latest wins into the story.

But we had a problem: the source of this news was in Korean, and no one in our office spoke the language. We ran it the through Google Translate, but it spat out some English that didn’t quite make sense. We had to find a second source.

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We did, just in time. And today, as Google rolls out a new incarnation of its translation software, it comes with a certain irony. Online translation couldn’t help our story on the new wave in artificial intelligence, but the new wave in artificial intelligence is improving online translation. The technology that underpinned AlphaGo—deep neural networks—is now playing a very big role on Google Translate.

Modeled after the way neurons connect in the human brain, deep neural networks are the same breed of AI technology that identifies commands spoken into Android phones and recognizes people in photos posted to Facebook, and the promise is that it will reinvent machine translation in much the same way. Google says that with certain languages, its new system—dubbed Google Neural Machine Translation, or GNMT—reduces errors by 60 percent.

For now, it only translates between English and Chinese—perhaps a key language in Google’s larger ambitions. But the company plans to roll it out for the more than 10,000 languages now handled by Google Translate. “We can train this whole system in an end-to-end fashion. That makes it much easier for [Google] to focus on reducing the final error rate.” says Google engineer Mike Schuster, one of the lead authors on the paper Google released on the tech today and a member of the Google Brain team, which oversees the company’s AI work. “What we have now is not perfect. But you can tell that it is much, much better.”

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All the big Internet giants are moving in the same direction, training deep neural nets using translations gathered from across the Internet. Neural nets already drive small parts of the best online translation systems, and they all know that deep learning is the way to do it all. “We’re racing against everyone,” says Peter Lee, who oversees AI work at Microsoft Research. “We’re all on the verge.”

They’re all moving to this method not only because they can improve machine translation, but because they can improve it in a much faster and much broader way.

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