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Google teaches machines to become more fluent translators

While Google Translate can be a lifesaver for any foreign traveler or lazy student, the tool still remains far from ideal.

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Alphabet Inc (NASDAQ:GOOGL) recently held an event during which the company’s management announced the opening of a new Machine Learning and Artificial Intelligence based Cloud Computing Group.

Google also improved a host of its other machine learning APIs, and launched a new page where users can experiment with the technology. This improves the translation significantly according to Google as context is better understood which in turn produces better translated sentences.

Using Google Cloud and its open-sourced AI platform, TensorFlow, it was possible to combine data science with existing systems.

Machine learning with documents, sales and business systems can help companies “save 10 seconds over and over again” he told the WIRED Retail 2016 conference at London’s British Museum. Turovsky also declared that in only a couple of day, the company managed to innovate more in terms of accurate translations than it did in the last decade.

You can try neural machine translation now with Google Translate on the web, Android, and iOS. Over time, Google also plans to bring Neural Machine Translation to all 103 languages which Translate now supports.

Thing Translator: In this feature, you can take a picture of something, and you can also hear how to say it different languages. Part of that feedback will still come from users who participate in the Translate Community, which Google is still banking on despite having a potentially smarter Google Translate. The search giant, Google prioritized them first because they represent the one-third of world’s population and getting 35% of more than Google queries.

Thankfully, translation services – such as Google Translate – removes barriers, allowing people to more easily communicate despite language differences.

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The system interprets the whole sentence, and the decoder begins to translate each word by looking at the weighted distribution over the encoded vectors and matching them up to the most relevant words in the target language. As you keep making your doodle, it keeps guessing the drawing.

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