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Machine learning boosts Google’s translation capabilities to near human levels



The technology behind Google’s AlphaGo is about to give Google Translate a major upgrade and it’s within a whisker of being as good at it as humans are.


Last year, a computer system built by a team of Google engineers beat one of the world’s top Go players and the wins that AlphaGo racked up against Korean grandmaster Lee Sedol were so unexpected that the story turned into a media frenzy.  Now the same DeepMind AI that that was behind AlphaGo is at the heart of Google’s latest incarnation of its online translation service “Google Translate”.


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Modelled 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 the same technology that recognises people in photos posted to Facebook. Now 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 and given the fact that Google’s previous iteration of its translation system was 92 percent accurate reducing the number of errors by this amount is a significant improvement and takes it almost to human levels of competence.


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For now this latest incarnation only translates from Chinese into English but the company plans to roll it out for the more than 10,000 language pairs now handled by Google Translate.

“We can train this whole system in an end-to-end fashion and that makes it much easier for us to focus on reducing the final error rate,” said Google engineer Mike Schuster, one of the lead members 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.”

All the big internet giants, from Facebook to Microsoft, from LinkedIn to Alibaba, are moving in the same direction, training deep neural nets using translati

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