Google Translate gets a boost from deep neural networks

Many of the technological nightmares we bring to life on film revolve around artificial intelligence gone wrong, usually somewhere around the year 2000. 2016 is almost over, though, and we don't live in a post-apocalyptic future—at least, not yet. AI still struggles to do things humans do with ease, like forming coherent sentences and playing games millions of people play easily. But Google has been making big strides in both of those areas this year. Case in point is a recent boost to Google Translate's performance thanks to deep neural networks.

Earlier this year, one of Google's deep neural networks tackled one of the toughest puzzles in artificial intelligence research when it defeated Lee Sedol, a Korean grandmaster in the game of Go. Go is relatively simple to learn, but difficult to master thanks to an emphasis on intuition and strategies that can take many turns to play out. Compared to the eight-by-eight chess board, Go uses a 19x19 grid. Until this year, AI could beat only the most amateur of Go players.

Now, Google has turned that same neural network technology toward language translation. Google says its technology—called Google Neural Machine Translation, or GNMT—reduces errors by 60 percent in Chinese translation while climbing even higher in other languages.

If you're feeling brave, you can check out the complete paper Google published on the subject, but here's a short breakdown. As Science Magazine explains, the deep neural net uses a technology that Google's team calls vectors that seem to  give it more room to understand context. "Cat" is more likely to be associated with "Dog" than to "Car," for example. The system is trained on pairs of translated sentences, builds the vectors and compares the input against them to come up with a set of likely translations. 

In contrast, Science Magazine notes that the translation method for most of the languages Translate offers today works based off of phrases. It takes user input and breaks it down into isolated chunks before it stitches together its best effort. This technique can create some truly outlandish results that are unusable at best and dangerous at worst, but it's the state of the art today.

According to Science Magazine's report, the Google Brain team chose Chinese as its first language to work with, not only because a big part of the team is Chinese, but also because the language is considered one of the most difficult to machine translate. While the 60% improvement statistic above is for the tech's attempts to translate from English to Chinese, Google says results in translation from English to Spanish is 87% more accurate with the new system, and French to English is 83% better. Google tells Science Magazine that it's using GNMT to perform Chinese to English translation now, and it'll gradually roll out the technology to other languages in the future.

We're not yet in the age of A Hitchhiker's Guide to the Galaxy's Babel Fish or Doctor Who's TARDIS. Google's Mike Schuster, an engineer on the project and one of the lead authors on the paper Google released this week, acknowledged when speaking to Wired that what the team has now isn't perfect, but added that "it is much, much better" than existing tech. Google hasn't set a date on when we'll see the tech rolling out to other languages, but maybe the Babel Fish future isn't so far off.

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