Math research shares those qualities, except small input size if you include the body of all the already-known theorems as an input. I don't know if we'll see much smarter proof assistants soon, but it doesn't seem absurd to me as a possible development.

Actually, just converting all the already-known theorems into a form that can be computationally verified (not just convince a skilled human) would be an interesting starting point. This would really help the metamath project, and perhaps make peer review of mathematical research papers easier:

http://us.metamath.org/mpeuni/mmset.html

It still uses MCTS as its search algorithm. It no longer uses random rollouts as part of the evaluation, though. (Previously it was rollouts/2 + value_network/2)

Random rollouts are what the MC in MCTS stands for.

Without that, it is simply a tree search.

Excerpt from the paper:

> [AlphaGo Zero] uses a simpler tree search that relies upon this single neural network to evaluate positions and sample moves, without performing any Monte-Carlo rollouts.

Does this mean it learns what to search? I wonder why they thought it was a good idea. I thought the whole point of MC was that pruning algorithms like the ones in chess wouldn't work for a larger search space.

The policy network is a function from board states to a scoring of moves. The policy network with the greedy heuristic, ie pick the highest rated move with no explicit look ahead method, plays at a high amateur level.

This was... unexpectedly good.

It effectively reduces the branching factor of Go from the number of moves available, to the number of moves actually worth considering.

That's what the policy is. Given a board state, the policy gives you a distribution over all available moves.

Except it is still using MCTS (heavily).

Are you sure the new version is not using MCTS ? I saw it mentioned in the new paper