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Google's Neural Machine Translation (NMT) system works by leveraging artificial neural networks to learn patterns in both the source language and the target language. The data is broken down into bite-sized chunks called "tokens" and fed into the neural networks. The neural networks then use those tokens to create a statistical model of the source language, which is then converted into the target language. This is done by comparing the words and phrases in the source language to the words and phrases in the target language, allowing the translation to remain faithful to the context of the original sentence. The result is a translation that is faster and more accurate than traditional machine translation methods.