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It’s late 2019 and the hype around Machine Learning has grown to unreasonable proportions. It seems like every week a new state of the art result is reported, a slicker Deep Learning library surfaces on GitHub, and OpenAI releases a GPT-2 model with more parameters. With the mind-bending results we’ve seen so far, it’s hard not to get swept up in the hype.

Others, however, warn that Machine Learning has overpromised and underdelivered. They worry that such continued action could cause research funding to dry up, leading to another Artificial Intelligence winter. …


Eugen Hotaj

Machine Learning at Facebook AI. Previously at Google Research.

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