| No, the reason was that the search space is insanely large. > It's just a 19x19 board, so it was always known if you could read all the possible outcomes you could see all the possibilities and win. All possible outcomes is not something you can iterate over in our universe. > There was a certain assumption in the question for AI researchers academically that it would be an understood algorithm as an AI agent like a Prolog application, not a brute forced model. I never heard this, and the result is really not brute forced. You can't brute force go. > That doesn't make AlphaGo any less impressive or any less practical, but it even has its own readout issues. It can't even read ladders without hard coding it in, for instance, because it becomes a long enough depth search. This is one of the first things a newbie would learn. Only in early versions, AlphaZero didn't have any built in knowledge and can learn different games and the later developments in MuZero went further to make it more generalised as a learner. Removing the hard coded logic and removing even seeing how humans plan, it got better. It found strategies and ways of playing that experts had missed in an ancient game. > This is just looking at all possible outcomes and picking the best one, not knowing how to play. "It's just doing X, it doesn't really know how to Y" is a common refrain. It looks at options, and explores "what if" scenarios in a guided sense with a feeling about how good any particular potential board is. I find it hard to say that it doesn't "know" how to play. |