Google's AI is not just better at grasping languages like Mandarin, but can now translate between two languages it hasn't even trained on. In a research paper, Google reveals how it uses its own "interlingua" to internally represent phrases, regardless of the language. The resulting "zero-shot" deep learning lets it translate a language pair with "reasonable" accuracy, as long as it has translated them both into another common language. The company recently switched its Translate feature to the deep-learning Google Neural Machine Translation (GNMT) system. That's an "end-to-end learning framework that learns from millions of examples," the company says, and has drastically improved translation quality.

Google has previously taught its artificial intelligence to play games, and it's even capable of creating its own encryption. Now, its language translation tool has used machine learning to create a'language' all of its own. In September, the search giant turned on its Google Neural Machine Translation (GNMT) system to help it automatically improve how it translates languages. The machine learning system analyses and makes sense of languages by looking at entire sentences – rather than individual phrases or words. Following several months of testing, the researchers behind the AI have seen it be able to blindly translate languages even if it's never studied one of the languages involved in the translation.

What does the Chinese Room problem attempt to demonstrate?

All right, don't panic, but computers have created their own secret language and are probably talking about us right now. Well, that's kind of an oversimplification, and the last part is just plain untrue. But there is a fascinating and existentially challenging development that Google's AI researchers recently happened across. You may remember that back in September, Google announced that its Neural Machine Translation system had gone live. It uses deep learning to produce better, more natural translations between languages. Following on this success, GNMT's creators were curious about something.

Google just made a major upgrade to its Translate app. The company is now using a new technology called neural machine translation -- which aims to make computer-generated translations more similar to those done by humans -- to power its translations in seven new languages. Google says the update should make translations in those languages much more accurate and easier to understand. The company previously rolled out this technology for Chinese to English translations in September. Now, Google is using the same technology to power translations to and from English in French, German, Spanish, Portuguese, Japanese, Korean and Turkish.

All right, don't panic, but computers have created their own secret language and are probably talking about us right now. Well, that's kind of an oversimplification, and the last part is just plain untrue. But there is a fascinating and existentially challenging development that Google's AI researchers recently happened across. You may remember that back in September, Google announced that its Neural Machine Translation system had gone live. It uses deep learning to produce better, more natural translations between languages. Following on this success, GNMT's creators were curious about something.

The Google Cloud, Google's eponymous artificial intelligence platform, is quite the capable little set of services. Its algorithms can handle everything from language translation to the identification of objects and landmarks. On Tuesday, Google Cloud chief Diane Greene announced the formation of a new team, the Google Cloud Machine Learning group, that will manage the Mountain View, California-based company's cloud intelligence efforts going forward. The group will be helmed by Jia Li, former head of research at Snapchat and pioneer behind the feature that lets you attach emojis to real-world objects, and Fe-Fei Li, former director of AI at Stanford. They will oversee a slew of upgrades to Google's cloud services in the coming months, much of which will involve Google Cloud's hardware infrastructure.

The Google Neural Machine Translation paper (GNMT) describes an interesting approach towards deep learning in production. The paper and architecture are non-standard, in many cases deviating far from what you might expect from an architecture you'd find in an academic paper. Emphasis is placed on ensuring the system remains practical rather than chasing the state of the art through typical but computationally intensive tweaks. To understand the model used in GNMT we'll start with a traditional encoder decoder machine translation model and keep evolving it until it matches GNMT. The GNMT evolution seems primarily motivated by improving accuracy while maintaining practical production speeds for both training and prediction. The encoder decoder architecture started the recent neural machine translation trend.

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Google says its Translate app now spits back more natural translations. Google said Tuesday it has vastly improved its Google Translate app, available on phones and the web. The search giant said it's now incorporating "neural machine translation" into the software, which means it can translate whole sentences at a time, instead of breaking the text down to smaller chunks and translating those pieces. The result is translations coming out more natural, with better syntax and grammar, Google said. "It has improved more in one single leap than in 10 years combined," Barak Turovsky, the product lead for Google Translate, said during a press event at Google's San Francisco office.