Aji is the concept of essentially making lemonaid from lemons by using the existence of the dead stones to put pressure on the surrounding pieces and claw back some of your losses.

Because they haven’t been captured yet they reduce the safety (liberties) of nearby stones. And until those are fully settled an incorrect move could rescue them, and the effort put into preventing that may cost points in the defense.

The statement that the flaw remains is wrong. We included a few cyclical groups in the training data and the flaw disappeared rather quickly. It is even less of an issue after the move to a slightly larger DCNN.

The current consensus in the community is, as far as I can tell, that there will always be some (other) flaws. But finding and exploiting them is becoming so difficult that it is perhaps impossible for human ingenuity. Automated approaches could succeed, but it is not clear that their effort is less than "simply" training a second, inherently stronger DCNN instead.

Thank you. So the attack somehow sets up a situation where AlphaGo/KataGo is the dead man walking? It doesn't realise at move 30 it has a group that is dead, and continues not to realise that until (close to the time that?) the group is formally surrounded at move 150?

I still don't really understand, because this makes it sound as if AlphaGo/KataGo is just not very good at Go!

This is not a reasonable summary. The adversarial AI is not finding some weird position that relies on KataGo not understanding the status. It's relying, supposedly, on KataGo not understanding the ruleset which uses area scoring and doesn't include removing dead stones (because in area scoring you can always play it out without losing points, so this is a simple way to avoid disputes between computers, which don't get bored of it).

I assume that KataGo still has this "flaw" after adversarial training simply because it doesn't overcome the training it has in environments where taking dead stones off the board (or denying them space to make two eyes if you passed every move) isn't expected.

See https://boardgames.stackexchange.com/questions/58127 which includes an image of a position the adversarial AI supposedly "won" which even at your level should appear utterly laughable. (Sorry, I don't mean to condescend - I am only somewhere around 1dan myself.)

(ELO is sometimes used in Go ranking, but I don't think it can fairly be compared to chess ranking nor used as a metric for "level of effort".)

The last one, for sure won't happen. The two with the crazy pawn chains are unlikely, but these extremely locked structures do occasionally occur. And the first one is actually pretty plausible. The situation with the king on f1 and the rook stuck in the corner is fairly thematic in some opening.It's just not well suited for engine analysis and fairly trivial for humans because we can eliminate large swathes of game tree via logic.

I.e. Assuming the black bishop and knight never move, we can see the kingside pawns will never move either. And the king will only ever be able to shuffle between f1 and g1. Therefore we can deduce the rook can never make a useful move. Now the only pieces that can make meaningful moves are the two connected passed pawns on the queenside, and the light-square bishop. Assume there was no bishop. The king can simply shuffle between b6 and c5, and the pawns are contained. Can the white bishop change any of this? No, because those two squares are dark squares, and in fact all of the black pieces are on dark squares. So the white bishop is useless. Ergo, no progress can be made. We've eliminated all the possible continuations based on a very shallow search using constraint based reasoning and basic deduction.

Engines can't do any of this. No one has found a generalised algorithm to do this sort of thing(it's something I spend a silly amount of time trying to think up, and I've gotten nowhere with it). All they can do is explore paths to future possible positions, assign them a heuristic evaluation. And choose the best path they find.

Although, I haven't actually tried to analyse position 1 with stockfish. I feel like on sufficient depth, it should find a forced repetition. Or the 50 move rule. Though it might waste a ton of time looking at meaningless bishop moves. Naïvely, I'd expect it to do 49 pointless bishop moves and king shuffles, then move a pawn, losing it, then another 49 moves, lose the other pawn. Then finally another 50 moves until running into 50 move rule. So back of the envelope, it would need to search to 150ply before concluding it's a draw. Although pruning and might actually mean it gets there significantly faster.

I think you'd have to translate the statement into the appropriate space. Here, the unconventional moves would refer to pro-level trick play. These doubtlessly exist, and have for a long time (e.g. secret house moves in joseki 100 years ago). Once made public, they either lose their surprise advantage and become poor moves, or become standard moves themselves.

So I'd say it is correct that there is a benefit to unconventional moves where punishing them requires understanding of unusual patterns.

On the same note, it is certainly true that playing unconventional moves against an opponent who does now how to punish them would only bring defeat quicker :)

Just in case someone thinks Magnus comes up with those openings on the spot.

No he has a team that uses computers to find out those plays based on what other player played as all past matches are available.

Source: I watched interview with a guy that was hired as a computer scientist consulting gig by Magnus team.

It does not take away how good he is as I don’t think many people could learn to remember weird openings and win from that against grand master level players anyway.

Fwiw this is normal in chess nowadays. There was some brief era in chess where everybody was just going down the most critical lines and assuming they could outprepare their opponents, or outplay them if that didn't work out. Kasparov and Fischer are the typical examples of this style.

But computers have made this less practical in modern times simply because it's so easy to lose in these sort of positions to the endless number of comp-prepped novelties which may be both objectively mediocre, but also nary impossible to play against without preparation against a prepared opponent.

So a lot of preparation now a days is about getting positions that may not be the most critical test of an opening, but that lead to interesting positions and where the first player to spring a novelty isn't going to just steamroll the other guy.

So in this brave new world you see things like the Berlin Defense becoming hugely popular while the Najdorf has substantially declined in popularity.

It is true that Magnus usually prefers offbeat lines to get out of the opponent's preparation. However, they're rarely very sharp or otherwise tactically complicated; on the contrary, he excels at slow maneuvering in strategic positions (and, as you say, the endgame).

> And it's not clear why such a simple pattern is impossible for deep neural nets to figure out.

Maybe solving ladders is iterative? Once they make chain-of-thought version of AlphaZero it might figure them out.

>It seems like neural networks will never figure out ladders (!!!!!). And it's not clear why such a simple pattern is impossible for deep neural nets to figure out.

this is very interesting (i dont play go) can you elaborate - what is the characteristic of these formations that elude AIs - is it that they dont appear in the self-training or game databases.

Some of our neutral networks learned ladders. You forgot the "a" standing for artificial. Even so amended, "never"? Good luck betting on that belief.

I think this is a misuse of the term hallucination.

When most people talk about AI hallucinating, they're referring to output which violates some desired constraints.

In the context of chess, this would be making an invalid move, or upgrading a knight to a queen.

In other contexts, some real examples are fabricating court cases and legal precedent (several lawyers have gotten in trouble here), or a grocery store recipe generator recommending mixing bleach and ammonia for a delightful cocktail.

None of these hallucinations are an attempt to reason about anything. This is why some people oppose using the term hallucination- it is an anthropomorphizing term that gives too much credit to the AI.

We can tighten the band of errors with more data or compute efficiency or power, but in the search for generic AI, this is a dead end.

I don't think I see them as a win, but they're easily dealt with. AI will need analysts at the latter stage to evaluate the outputs but that will be a relatively short-lived problem.

Well, that does not apply to future Go AIs for all of time.

It's not the same rule set though. The rule set they evaluated the AI on isn't one of the ones that it supports.

Edit: This is confusing for some people because there are essentially two rule sets with the same name, but Tromp-Taylor rules as commonly implemented for actual play (including by Katago) involves dead stone removal, where as Tromp Taylor rules as defined for Computer Science research doesn't. One might argue that the latter is the "real" Tromp Taylor rules (whatever that means), but at that point it is obvious that you are rules lawyering with the engine authors rather than doing anything that could reasonably be considered adversarial policy research.

What is "cyclic" about the adversarial strategy, exactly? Is it depending on a superko rule? That might potentially be interesting, and explanatory. Positions where superko matters are extremely rare in human games, so it might be hard to seed training data. It probably wouldn't come up in self-play, either.

Future payoffs are almost always discounted, even if for no other reason than the future has a greater deal of uncertainty. I.e even if it was not explicit which it almost always is, it would still be implicit.

Their conservative style is usually due to having a better fitness function. Humans tend to not be able to model uncertainty as accurately and this results in more aggressive play, a bird in the hand is worth two in the bush.

Doesn't the board get filled up with stones? I could see how a go player might think a win is a win so it doesn't mater how many stones you win by, but I don;t see how you would go about delaying winning.

Example: https://lichess.org/study/kPWZgp6s/nwqy2Hwg

When I was a child, I didn't understand that episode as Data demonstrating his superiority at the game by deliberately keeping it evenly-matched, or that the alien opponent somehow realized that Data could win at any time and simply chose not to.

Rather, I figured Data had come up with some hitherto-unknown strategy that allowed for making the game arbitrarily long; and that the alien had a choice between deliberately losing, accidentally losing (the way the game is depicted, it gets more complex the longer you play) or continuing to play (where an android wouldn't be limited by biology). (No, I didn't phrase my understanding like that, or speak it aloud.)

It is possible to commit no mistakes and still lose.

That's not a weakness.

That's life.

Q: If the AIs are trained on adversarial policies, will this strategy also start to fail in these game-playing scenarios?

EDIT: Discussed later on https://news.ycombinator.com/item?id=42503110