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I think his point is that it's very easy to create a lossless input representation of the Go board, and the ultimate loss function is obvious. We're then left with a large sequential prediction task. Previous learning algorithms were stumped by the non-linearities, but this is exactly the situation where deep learning shines.

The problem changes dramatically when the AI is supposed to take arbitrary input from the world. Then the AI needs to determine what input to collect, and the path length connecting its decisions to its reward grows enormously.

I still agree with your take though: there's an important milestone here.




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