Those that follows are random spurts of ideas that emerged when thinking at AlphaGo. I make no claim of either validity, soundness or even sanity. But they are random interesting directions that are fun for me to investigate, and they might turn out interesting for you too:
AlphaGo uses two deep neural networks to prune the enormous search tree of a Go position, and it does so unsupervised.
lol no. The pruning ('policy') network is entirely the result of supervised learning from human games. The other network is used to evaluate game states.
Your other ideas are more interesting, but they are not related to AlphaGo specifically, just deep neural networks.
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