Also, the specific cycle attack doesn’t work against other engines I think? In the paper their adversary doesn’t transfer very well to LeelaZero, for example. So it’s more one particular AI having issues, than a fact about Go itself.
Sure, but refutations don’t transfer to different openings either, right? I feel like most game-winning insights are contingent in this sense, rather than being fundamental to the game.
EDIT: also, I think if you got arbitrary I/O access to a Magnus simulator, and then queried it millions of times in the course of doing AlphaZero style training to derive an adversarial example, I’d say it’s pretty borderline if it’s you beating beating him. Clearly there’s some level of engine skill where it’s no longer you playing!
This is a really interesting hypothetical, but I see it differently.
If the engine isn’t feeding me moves over the board (which I certainly agree would be cheating), then it has to give me something I can memorize and use later. But I can’t memorize a whole dense game tree full of winning lines (and the AI can’t calculate that anyway), so it has to give me something compressed (that is, abstract) that I can decompress and apply to a bunch of different board positions. If a human trainer did that we’d call those compressed things “insights”, “tactics”, or “strategies”, and I don’t think making the trainer into a mostly-superhuman computer changes anything. I had to learn all the tactics and insights, and I had to figure out how to apply them; what is chess, aside from that?
Also, I wouldn’t expect that Carlsen has any flaws that a generally weak player, whether human or AI, could exploit the way the cyclic adversary exploits KataGo. It would find flaws, and win consistently, but if the pattern in its play were comprehensible at all it would be the kind of thing that you have to be a super-GM yourself to take advantage of. Maybe your intuition is different here? In limit I’d definitely agree with you: if the adversarial AI spit out something like “Play 1. f3 2. Kf2 and he’ll have a stroke and start playing randomly”, then yeah, that can’t really be called “beating Carlsen” anymore. But the key point there, to me, isn’t the strength of the trainer so much as the simplicity of the example; I’d have the same objection no matter how easy the exploit was to find.
Sure, but refutations don’t transfer to different openings either, right? I feel like most game-winning insights are contingent in this sense, rather than being fundamental to the game.
This is a really interesting hypothetical, but I see it differently.
If the engine isn’t feeding me moves over the board (which I certainly agree would be cheating), then it has to give me something I can memorize and use later. But I can’t memorize a whole dense game tree full of winning lines (and the AI can’t calculate that anyway), so it has to give me something compressed (that is, abstract) that I can decompress and apply to a bunch of different board positions. If a human trainer did that we’d call those compressed things “insights”, “tactics”, or “strategies”, and I don’t think making the trainer into a mostly-superhuman computer changes anything. I had to learn all the tactics and insights, and I had to figure out how to apply them; what is chess, aside from that?
Also, I wouldn’t expect that Carlsen has any flaws that a generally weak player, whether human or AI, could exploit the way the cyclic adversary exploits KataGo. It would find flaws, and win consistently, but if the pattern in its play were comprehensible at all it would be the kind of thing that you have to be a super-GM yourself to take advantage of. Maybe your intuition is different here? In limit I’d definitely agree with you: if the adversarial AI spit out something like “Play 1. f3 2. Kf2 and he’ll have a stroke and start playing randomly”, then yeah, that can’t really be called “beating Carlsen” anymore. But the key point there, to me, isn’t the strength of the trainer so much as the simplicity of the example; I’d have the same objection no matter how easy the exploit was to find.
Curious what you make of this.