Yeah they seem to forget that Alpha-Go is looking deep into the future. I have not read the Nature paper but I assume it's playing out possible moves way into the future.
At some point it figured that the Ko fight at the bottom was already won. Hence that white move at the top which nobody saw coming.
Another interesting moment was when Michael Redmond said "A human would typically not spend too much time thinking on this obvious move". This was the move on the right-hand side somewhere. What this tells me is that human players rush through some moves because they seem obvious but since Alpha-Go is a machine, it does not care about obvious and non-obvious. It's calculating the entire board through to the end and is not interested in "local fights".
He was talking about a pretty much forced move. But it makes sense for AlphaGo to still think it through, after all it's pretty young and experimental, and the Monte Carlo is good at catching blunders.
Also, that forced move it's very obvious to us that it's forced, but AlphaGo might not have this concept.
> I have not read the Nature paper but I assume it's playing out all possible moves.
To some relatively small depth, right? I hear the estimate that all possible moves in a Go game probably can't be physically represented in the universe (unless we learn much more about the structure of games' evolution).
No, it plays deep but only so broad. It uses a neural net (which playing by itself without MCTS already beats Pachi with like 80% probability) to sketch out the best moves until the end and then rates each move on its chance of winning.
This objective function is why Go playing AI jumped hugely in the last 10 years.
They didn't provide the exact depth of the search tree in the paper, but IIRC it was mentioned somewhere that it evaluates ~20 moves deep before terminating with the value net.
It plays out thousands of moves all the way until the end of the game using its neural net to quickly sketch optimal play. That's the big advantage! Humans read moves until they feel an outcome is favorable. AlphaGo reads out till endgame and plays moves that optimize for a win.
Also I'm not entirely sure how AlphaGo's time management works, but it's doing the same thing for every move—populating the game tree as deeply and intelligently as it can. It may just look for "30 seconds" on every move and then take the best bet meaning it's a more thorough and exhaustive reader than any human.
