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.
• Information geometry allows us to treat information theory as geometry.
• Neural networks allows us to partition high-dimensional data.
• Pruning a search tree is also strangely similar to dual intuitionistic logic.
• Deep neural networks can thus apply a sort of paraconsistent probabilistic deduction.
• Probabilistc self-reflection is possible.
• Deep neural networks can operate a sort of paraconsistent probabilistic self-reflection?