This happens by feeding large sets of data through layers of interconnected processors. Neural machine translation has been a latecomer to the game of deep learning, a method of making predictions about everything from effective marketing pitches to potential drug candidates. 'This … demonstrates like never before the power of neural machine translation,' says Yoshua Bengio, a computer scientist at the University of Montreal in Canada, who helped invent one of the critical components of the new system several years ago, but who was not involved in the current work. The new system, a deep learning model known as neural machine translation, effectively trains itself- and reduces translation errors by up to 87%. Today, Quoc and his colleagues at Google rolled out a new translation system that uses massive amounts of data and increased processing power to build more accurate translations. Right now, it's less about finding the perfect one-to-one translation and more about 'avoiding embarrassment.'īut that may all change soon. But together, they tell a larger story: 'Translation is not a solved problem,' he says. Most errors are tiny-not important enough to remember. Whenever the Google research scientist in Mountain View, California, visits his native Vietnam, he laughs with his parents over mistranslations in the very system he is helping shape, the 10-year-old online service Google Translate. Quoc Le is no stranger to the indignity of translation.

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