Google’s neural network learns to translate languages it hasn’t been trained on • The Register

The gap between human and machine translators could be narrowing as researchers find a new way to improve the learning capabilities of Google Translate’s neural network.

On the same day that Google announced its translation services were now operating with its Neural Machine Translation (NMT) system, a team of researchers released a paper on arXiv showing how its NMT could be pushed one step further.

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This AI Gets Smarter by Surfing the Internet – Robotics Trends

Of the vast wealth of information unlocked by the Internet, most is plain text. The data necessary to answer myriad questions – about, say, the correlations between the industrial use of certain chemicals and incidents of disease, or between patterns of news coverage and voter-poll results – may all be online. But extracting it from plain text and organizing it for quantitative analysis may be prohibitively time consuming.

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Has Parsey McParseface solved one of the world’s biggest language problems?

Google has just created possibly the world’s best computer program designed to understand the English language but this cutting-edge technology may just be the tip of the iceberg.

A new theory could be about to unlock the secrets of how natural human language really works, putting an end to a question that has troubled linguists for more than half a century.

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Deep learning alone will never outperform natural language understanding | VentureBeat 

Google, Microsoft, IBM, Apple, and 885 other players in the A.I. market have all been spinning their wheels in the wrong direction.

Using brute force in machine learning and natural language processing (NLP) with advanced statistics, bots such as Siri, Echo, Viv, Hound, Skype and others fall off a cliff the moment they receive a command that is not an exact match for the engine.

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