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It seems to me that images and sounds are 'alike' in a way that doesn't (on its obvious face) expand to include Atari game strategies and evaluating Go positions. In which case generalizing across the latter gap is more impressive than a single algorithm working well for both images and sounds.

The difference isn't easy to describe, but one such difference would be that a single extra stone can change a Go position value much more than a single pixel changes an image classification.




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.


> The difference isn't easy to describe, but one such difference would be that a single extra stone can change a Go position value much more than a single pixel changes an image classification.

A CNN can still distinguish extremely subtle differences of various animal breeds, exceeding human performance in such tasks. Why was that advance not a warning sign? The rotational-translational invariance prior of the convolutional neural network probably helps because, by default, local changes of the patterns can massively change the output value without the need to train that subtle change for all translations. Also, AlphaGo does a tree search all the way to the games end, which can probably easily detect such dramatic changes of single extra stones. Reality is likely much too unconstrained to to able to efficiently simulate such things.




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