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2015 was a milestone year for AI--"deep learning" was validated in a very public way with AlphaGo. However, at the time, the question was raised: "What else is deep learning good for?"

5 years later, I want to gauge:

  • How is deep learning applied to real world problems in 2020? What real world applications is it currently used for?
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Deep learning is used to perform language translation in Google Translate [1]. Specifically, Google Translate now uses transformers and RNNs rather than the original GNMT system (proposed in 2016), which was also based on neural networks. Deep learning is also used in DeepL (though I cannot find a good resource to cite apart from Wikipedia [2] given that the system is closed-source), a strong alternative to Google Translate. However, note that, in general, machine translation is still far from perfect and it is probably not adopted to perform serious translations.

Tesla's autopilot also uses neural networks [3].

Built on a deep neural network, Tesla Vision deconstructs the car's environment at greater levels of reliability than those achievable with classical vision processing techniques.

DeepFakes are also developed using deep learning techniques [4].

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