| The reason for Go was that much of the research was based on AI algorithms like Monte-Carlo. What was "solved" with AlphaGo was using deep learning machine learning which are effectively black boxes. 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. That's still not the case that we have a "solved" strategy and all we can do is watch it play as if it is a deaf mute player. So there still is no "tic-tac-toe" known winning strategy to Go or anything. 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. 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. This is just looking at all possible outcomes and picking the best one, not knowing how to play. Creating models of data that is 2, 3, or even 4+ dimensions is always possible, just depends on how much computing power you can throw at it. The created models are essentially aggregate simplifications to play quicker. Generalized intelligence is so much different. You have to define the problems themselves that you are trying to solve, figure out what the variables are, and solve it. Then you have to operate and run the machinery to create those experiments. Outside of a scenario that you've taken actual physical territory as an intelligence, I can't see how it would get there (think Terminator or BSG, doesn't have to be malicious but they'd have to be in control of the physical area autonomously). But the hardest part is defining the problems independently given the sheer number of problems they'd need to define second to second just to solve basic tasks, and they'd likely have millions of variables with millions of possible values. |
> 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.