howeman

Interesting, I've heard it said that Cho Chikun thinks it would be a fair fight against the perfect player with Cho getting four stones.

I'm not on the deep mind team, but I have read their nature paper closely.

AlphaGo does not explicitly know anything about opening theory, shapes, or tesuji (with an important caveat I'll get to in a minute). AlphaGo roughly thinks about moves the way players do. It decides on a few moves to consider, and reads out the likely variations. AlphaGo evaluates positions quite differently than a human.

AlphaGo used human games to learn which moves to consider and partly to decide if positions are good. Neither of those necessarily need to be learned from human games. It could be learned by playing itself (and in fact, it did do quite a bit of learning by playing itself). I have heard rumors that the DeepMind team is planning on doing this.

The caveat I mentioned is that is does have some important features built in to it. For example, it is told explicitly if a stone is in a ladder, and if that ladder is winning or losing. It is also told if a group of stones are in atari. In theory those features could be relaxed as well, but it would be much more difficult to build an effective system.

To Michael: I was extremely impressed on your commentary of the game. I felt I got a lot from your commentary (9k ish), and others I know who don't play Go also learned a lot. I am also a researcher who works with AI, and I was most impressed by how well you described her actions. My question to you is -- how were you able to speak so well about it? Were you coached, or have you had an interest in Go AI for a long time?

In our games, the spies win a lot. The plot cards help, but it also takes away from the cleanness and purity of the original game. We were thinking of trying to balance it by saying if there are multiple spies on the mission, and the mission passes, the spies lose because they are dysfunctional. Do you have other favorite ways to balance?

To add, it's a fine line between what's AI, what's statistics, and what's just a good program. The use of the phrase "trained" in your question is generally how people decide between AI and a program.