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serge_cell t1_jbwt0s9 wrote

Reply to comment by currentscurrents in [N] Man beats machine at Go in human victory over AI : « It shows once again we’ve been far too hasty to ascribe superhuman levels of intelligence to machines. » by fchung

It's a question of training. AlphaGo was not trained agains adversarial attacks. If it was the whole family of attacks wouldn't work, and new adversarial traning would be order of magnitude more difficult. It's a shield and sword again.

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Excellent_Dirt_7504 t1_jbwwi8v wrote

If you train against one attack, you remain vulnerable to another. There is no evidence of a defense that is robust to any adversarial attack.

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suflaj t1_jbx9h57 wrote

But there is evidence of a defense by taking as many adversarial attacks as possible and training against them. Ultimately, the ultimate defense is generalization. We know it exists, we know it is achievable, we only don't know HOW it's achievable (for non-trivial problems).

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OptimizedGarbage t1_jbxticv wrote

It kinda was though? It was trained using self-play, so the agent it was playing against was adversarially searching for exploitable weaknesses. They actually cite this as one of the reasons for it's success in the paper

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ertgbnm t1_jbyocgi wrote

Isn't alphago trained against itself? So I would consider it adversarial training.

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serge_cell t1_jc1to7o wrote

There was a paper about it. There was a find - specific set of positions not encountered or pooply represented during self-play. Fully trained AlphaGo was failing on those positions. However then they were explicitly added to the training set the problem was fixed and AlphaGo was able to play them well. This adversarial traning seems just an automatic way to find those positions.

PS fintess landscape is not convex it separated by hills and valleys. Self-play may have a problem in reaching all important states.

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