What I'm curious about is how much this reflects an attempt by AlphaGo to conserve computational resources.
If I understand correctly, at least according to the Nature paper, it doesn't explicitly optimize for this. Game-playing software is often perceived as playing "conservatively", this is a general property of minimax search, and in the limit the Nash equilibrium consists of maximally conservative strategies.
but I was still surprised by the amount of thought that went into some of the moves.
Maybe these obvious moves weren't so obvious at that level.
Maybe these obvious moves weren't so obvious at that level.
Sure. And I'm pretty low as amateurs go--what I found surprising was that there were ~6 moves where I thought "obviously play X," and 이 immediately played X in half of them and spent 2 minutes to play X in the other half of them. It wasn't clear to me if 이 was precomputing something he would need later, or was worried about something I wasn't, or so on.
Most of the time I was thinking something like "well, I would play Y, but I'm pretty unconfident that's the right move" and t...
There have been a couple of brief discussions of this in the Open Thread, but it seems likely to generate more so here's a place for it.
The original paper in Nature about AlphaGo.
Google Asia Pacific blog, where results will be posted. DeepMind's YouTube channel, where the games are being live-streamed.
Discussion on Hacker News after AlphaGo's win of the first game.