I think the emphasis on personalized and frequent analysis actually supports the idea that part of the rise in novelty is the ability for players to get feedback on moves that they have played. It’s not that I think that there is a conscious decision to try out novel moves in a serious game (which is surely where our game records come from) because it can be evaluated later. But I do think that the access to feedback will encourage experimentation in practice sessions, which will be reflected in games. (This is not to say that post game evaluation doesn’t have any impact at all.)
Here’s my theoretical framework for how access to AI may not only allow for better moves, but allow for increased novelty in play. I also used this as an excuse to form an alliterative list, because I like playing with words almost as much as I like playing go.
- First, the player must have conviction that a move that is superior to traditional wisdom exists. While it may seem obvious that stronger moves are out there, I think there is a huge difference between thinking there is likely a better move and being sure of it. Consider the difference between looking at a life and death position on the board during a game and feeling like there is probably a move that will kill your opponent’s group and facing a similar position in a tsumego problem where you know that there is a right answer. I, at least, am typically more willing to devote energy to finding a solution in the second situation. With access to superhuman AI, professionals know for certain that the traditional wisdom is not always the best way to play.
- The player must also have the creativity to think up novel moves. There have been many conversations on this forum about how people filter out possible candidate moves. If your way of thinking, reinforced by training methodologies, eliminates candidates that are outside of the received wisdom, you will never find those moves which are both novel and good. Historically, only a few players in each generation were truly creative in their approach to the game. Most were content to try to do as well as possible within the framework they had received from their teachers. Access to strong AI opponents provides many more possibly candidate moves that players can consider in their own games, so novel ideas are not so dependent upon individual genius.
- Next, the player must have the confidence to be willing to try new moves in a game situation. This means, possibly, going against the received wisdom of one’s teachers and being wiling to lose a few (or many!) games trying out a new idea. I think that few people have typically had the combination of conviction, creativity, and confidence to truly be willing to push the game in a new direction. This is where access to AI feedback during practice makes a huge difference—players get many, many opportunities to refine their ideas before trying them out in a game in a situation where there is no possible negative judgment from others. I think this is likely a significant factor in players’ willingness to try out new ideas.
- But it’s not only the feedback that players get in practice that helps them improve. I also think that the post-game criticism offered by AI evaluation after the game is important. In the past, the only way to really test whether your ideas were good was to look at your win rate. But with AI, you can find out that the novel move you tried was a good idea (or at least playable), even if you made a mistake in the follow-up. Being able to have effective, correct mid-game evaluation helps to push the experimental horizon a little further each game. As a small example in my own games, AI has showed me that I can try a wide variety of openings with little difference in the quality of my play. Very rarely do I lose a game in the opening; it’s almost always an error in middle game fighting that does me in. This type of feedback gives me a willingness to experiment with a wide variety of openings that I didn’t have in the past, which is part of what’s driving my own improvement.
- Of course, all of this assumes a certain level of competence in play. AI also helps in the ability to quickly evaluate games and hasten the iterative process of refining what works and what doesn’t. All professional players have a basic level of competence, of course, but some are better able to apply what they learn more consistently.
I’m certainly not confident that this adequately explains the results of the paper! I just find it fun to think about how we think about the game, and how our methods of gaining knowledge show up on the goban.