Adversarial policies beat superhuman Go AIs (2023)
arxiv.org
arxiv.org
Seems to be a good intro.
Go uniquely has long periods of dead-man walking, as I like to call it. Your group might be dead on turn 30, but your opponent won't formally kill the group until turn 150 or later.
If your opponent knows the truth all the way back in turn30, while you are led down the wrong path for those hundreds of turns, you will almost certainly lose.
This adversarial AI tricks AlphaGo/KataGo into such situations. And instead of capitalizing on it, they focus on the trickery knowing that KataGo reliably fails to understand the situation (aka it's better to make a suboptimal play to keep KataGo tricked / glitched, rather than play an optimal move that may reveal to KataGo the failure of understanding).
Even with adversarial training (IE: KataGo training on this flaw), the flaw remains and it's not clear why.
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It appears that this glitch (the cyclical group) is easy enough for an amateur player to understand (I'm ranked around 10kyu, which is estimated to be the same level of effort as 1500Elo chess. Reasonably practiced but nothing special).
So it seems like I as a human (even at 10kyu) could defeat AlphaGo/KataGo with a bit of practice.
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 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.
Our results demonstrate that even superhuman AI systems may harbor surprising failure modes.
This should be read as a general observation and not just specific to Go as is made clear by reading the paper. So you can patch up Go-specific flaws but the problem remains that deep neural net performance is inconsistent and unreliable.
I still don't really understand, because this makes it sound as if AlphaGo/KataGo is just not very good at Go!
So we aren't talking about 'one' Deadman walking position, but multiple ones that this research group searches for, categorizes and studies to see if AlphaGo / KataGo can learn / defend against them with more training.
I'd argue that Go is specifically a game where the absurdly long turn counts and long-term thinking allows for these situations to ever come up in the first place. It's why the game is and always fascinated players.
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Or in other words: if you know that a superhuman AI has a flaw in its endgame calculation, then play in a deeply 'dead man walking' manner, tricking the AI into thinking it's winning when in truth its losing for hundreds of moves.
MCTS is strong because it plays out reasonable games and foresees and estimates endgame positions. If the neural nets oracle is just plain wrong in some positions, it leads to incredible vulnerabilities.
Judging whether something "is dead" emerges from a combination of basic principles and skill at the game. Formally, we can distinguish concepts of unconditionally alive or "pass-alive" (cannot be captured by any legal sequence of moves) and unconditionally dead (cannot be made unconditionally alive by any sequence of moves), in the sense of Benson's algorithm (https://en.wikipedia.org/wiki/Benson%27s_algorithm_(Go) , not the only one with that name apparently). But players are more generally concerned with "cannot be captured in alternating play" (i.e., if the opponent starts first, it's always possible to reach a pass-alive state; ideally the player has read out how to do so) and "cannot be defended in alternating play" (i.e., not in the previous state, and cannot be made so with any single move).
Most commonly, an "alive" string of stones either already has two separate "eyes" or can be shown to reach such a configuration inevitably. (Eyes are surrounded points such that neither is a legal move for the opponent; supposing that playing on either fails to capture the string or any other string - then it is impossible to capture the string, because stones are played one at a time, and capturing the string would require covering both spaces at once.)
In rarer cases, a "seki" (English transliteration of Japanese - also see https://senseis.xmp.net/?Seki) arises, where both player's strings are kept alive by each others' weakness: any attempt by either player to capture results in losing a capturing race (because the empty spaces next to the strings are shared, such that covering the opponent's "liberty" also takes one from your own string). I say "arises", but typically the seki position is forced (as the least bad option for the opponent) by one player, in a part of the board where the opponent has an advantage and living by forming two eyes would be impossible.
Even rarer forms of life may be possible depending on the ruleset, as well as global situations that prevent one from reducing the position to a sum of scores of groups. For example, if there is no superko restriction, a "triple ko" (https://senseis.xmp.net/?TripleKo) can emerge - three separate ko (https://senseis.xmp.net/?Ko) positions, such that every move must capture in the "next" ko in a cycle or else lose the game immediately.
It gets much more complex than that (https://senseis.xmp.net/?GoRulesBestiary), although also much rarer. Many positions that challenge rulesets are completely implausible in real play and basically require cooperation between the players to achieve.
(Please note, I absolutely do understand life requires two eyes, and why that is so, but my knowledge doesn't extend much further than that).
So hypothetically, if we get to the point where play normally stops, why can't I put a stone into my opponent's territory? I am reducing his territory by 1 point. So he will presumably object and take my "dead" stone off, first restoring the balance and then penalising me one point by putting the newly captured stone in my territory. But can't I insist that he actually surrounds the stone before he takes it off? That would take four turns (I would pass each time) costing him 4 points to gain 1. There must be a rule to stop this, but is it easily formally expressed? Or is it a) Complicated or b) Require some handwaving ?
There are multiple scoring systems (American, Chinese, and Japanese and a couple of others).
* In Chinese scoring, stones do NOT penalize your score. So they capture your stone and gain +1 point, and lose 0 points.
* In American scoring, passing penalizes your score. So you place a stone (ultimately -1 point), they place 4 stones (-4 points), but you pass a further 4 points (4x passes == -4 more points). This ends with -4 points to the opponent, but -5 points to you. Effectively +1 point differential.
* In Japanese scoring, the player will declare your stone dead. Because you continue to object the players play it out. Once it has been played out, time is rewound and the state of the stones will be declared what both players now agree (ie: I need 4 stones to kill your stone, if you keep passing I'll kill it).
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So your question is only relevant to Japanese scoring (in the other two systems, you fail to gain any points). And in Japanese scoring, there is the "time rewind" rule for post-game debate. (You play out positions only to determine alive vs dead if there's a debate. This is rarely invoked because nearly everyone can instinctively see alive vs dead).
IE: In Japanese scoring, the game has ended after both players have passed. Time "rewinds" to this point, any "play" is purely for the determination of alive vs dead groups.
In all three cases, playing out such a position is considered a dick move and a waste of everyone's time.
That said, when I teach beginners I teach them Chinese scoring and counting. If they understand the principles it'll be easy enough to adapt later, and it doesn't change strategy in a practical way. They can play it out without worry, it makes more sense from an aesthetic standpoint ("you're trying to have stones on the board and keep them there, or have a place to put them later" - then you only have to explain that you still get points for the eyes you can't fill).
It's also IMX faster to score on 9x9: you can shift the borders around (equally benefiting both players) to make some simple shapes and easily see who has the majority of the area and you aren't worrying about arranging territories into rectangles.
You play every good move. Then you traditionally play the neutral moves (+0 points to either player) to make counting easier. Then the game ends as both players pass.
In Chinese, American, or Japanese scoring, this process works to maximize your endgame score.
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".)
I believe my discussion above is a reasonable survey of the cyclic attack linked to at the beginning of the website.
zahlman doesn't seem to have read the part of the paper dealing with cyclic adversaries, but the cyclic adversary strategy doesn't depend on KataGo mis-classifying alive or dead groups over long time horizons. If you watch the example games play out, KataGo kills the stones successfully and is trivially winning for most of the game. It makes a short term & devastating mistake where it doesn't seem to understand that it has a shortage of liberties and lets the adversary kill a huge group in a stupid way.
The mistake KataGo makes doesn't have anything to do with long move horizons, on a long time horizon it still plays excellently. The short horizon is where it mucks up.
Or if you want a replay, try https://goattack.far.ai/adversarial-policy-katago#contents - the last game (KataGo with 10,000,000 visits - https://goattack.far.ai/adversarial-policy-katago#10mil_visi...} - game 1 in the table) shows KataGo with a trivially winning position around move 200 that it then throws away with a baffling sequence of about 20 moves. I'm pretty sure even as late as move 223 KataGo has an easily winning position, looks like it wins the capture race in the extreme lower left. It would have figured out the game was over by the capture 8 moves later.
So dead man walking is a bad description. From your perspective it's still KataGo winning but a series of serious blunders that occurs in these attacks positions.
I guess, unsurprisingly, for jargon it is as for almost anything else: there’s a utility function with one inflection point past which the output value actually becomes less (if the goal is to convey information as clearly as possible, for other goals, I guess the utility function may be exponential …)
8/8/8/1Pk5/2Pn3p/5BbP/6P1/5K1R w - - 0 1 (white can not free the rook)
1B4r1/1p6/pPp5/P1Pp1k2/3Pp3/4Pp1p/5P1P/5K2 b - - 0 1 (the rook can not enter white's position)
kqb5/1p6/1Pp5/p1Pp4/P2Pp1p1/K3PpPp/5P1B/R7 b - - 0 1 (Rook to h1. King to g1, Queen can not enter via a6)
2nnkn2/2nnnn2/2nnnn2/8/8/8/3QQQ2/3QKQ2 w - - 0 1 (the knights advance as block, so that attacked knights are protected twice)
In the first both Stockfish and Lc0 think white is better (slightly on a deep ply). In the second and in the third they think black wins. Lc0 understands the fourth (applause), Stockfish does not.
https://lichess.org/analysis/standard/8/8/8/1Pk5/2Pn3p/5BbP/... https://lichess.org/analysis/fromPosition/1B4r1/1p6/pPp5/P1P... https://lichess.org/analysis/fromPosition/kqb5/1p6/1Pp5/p1Pp... https://lichess.org/analysis/fromPosition/2nnkn2/2nnnn2/2nnn...
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.
This is exactly why current AI cannot be said to actually think in the same fashion as humans, and why AI is very unlikey to reach AGI
(Source: I'm European 4 dan. I wipe the go board with weaker players playing whatever unconventional moves they like. Likewise, I get crushed by stronger players, faster than usual if I choose unusual moves. This might work on like the double-digit kyu level...)
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 :)
If the weaker player learns all the variations of a trick sequence... perhaps he just became stronger :)
Unconventional moves can help if you want to throw off a similarly skilled opponent.
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.
In chess, there are basically three ways to evaluate moves
1) pure calculation
2) recognize the position (or a very similar one) from a previous game, and remember what the best move was
3) intuition - this one is harder to explain but, I think of it like instinct/muscle memory
All the top players are good at all of these things. But some are agreed upon as much better than others. Magnus is widely agreed to have the best memory. The contender for best calculator might be Fabiano.
In humans, all else being equal, memory seems to be superior to calculation, because calculation takes time.
Chess engines seem to reverse this, with calculation being better than memory, because memory is expensive.
All that remained was rote memorization, which makes for a boring game indeed.
Despite all of that, I suspect chess will long outlive my preferred entertainment of Unreal Tournament.
I enjoy using nearly pure intuition when playing so I just use that strategy and see the same ~50/50 win percentage as most players because my ELO is based on how I play past games and there’s millions of online players across a huge range of skill levels.
There’s nothing wrong with staying at 1000 or even 300 if that’s what it takes to enjoy the game. It’s only if you want to beat specific people or raise your ELO that forces you to try and optimize play.
That sounds frustrating and tedious. If I get better I want to win more often.
The win or loss is ancillary to the experience for me.
Maybe because I primarily play sports and not chess but this attitude is completely foreign and mystifying to me.
Don't you feel bad when you lose? Why would you purposely engage in an ELO system that results in you feeling bad after 50% of games, and never gives you a sense of progress?
Isn't that profoundly discouraging?
Do you think Tiger Woods or Leo Messi wish they won fewer matches? Like I just can't get myself into a headspace where you're out for competition but are satisfied with a 50% win rate.
Do you think professional athletes like Woods and Messi are stupid because they could be playing in Farm League and winning every time against scrubs?
By definition it does not, unless your definition of progress is "number go up".
>Do you think professional athletes are stupid because they could be playing in Little League and winning every time against kids?
So let me get this straight: are you seriously suggesting that you don't understand the difference between e.g. the format of the NHL or the FIFA world cup, and playing against literal children to pad one's win rate?
Because I think you're probably not arguing in good faith with that last comment. Time for me to duck out of this conversation.
If you have a major skill gap, games become boring. Try playing Martin bot for 3 hours.
All he's saying is it's boring to win all the time.
Winning half the time is better because each of those wins means far far more than winning against bad players.
Playing down is only fun for insecure, unambitious people. If winning is the fun part, just cheat, don't seek out bad players to play against. Playing against bad players makes you bad at chess.
The memory game involved memorizing highlighted circles on a grid so even something ostesibly chess adjacent. Magnus did not do particularly well. Even when playing a blindfold sim against 'just' 5 people (the record is 48) he lost track of the positions (slightly) multiple times and would eventually lose 2 of the games on time.
But where Magnus is completely unrivaled is in intuition. His intuition just leads him in a better direction faster than other top players. This is both what makes him so unstoppable in faster time controls, and also so dangerous in obscure openings where he may have objectively 'meh' positions, but ones where the better player will still win, and that better player is just about always him.
The same is not true for other things to memorize. "Memorizing highlighted circles on a grid" is quite different from chess patterns. So I don't think this test can be used to say anything about his relative capabilities.
This is why I said the test was quite adjacent to chess.
Instead people think players have this enormous cache of memorized positions in their minds where they know the optimal move, but it's more about lots of ideas and patterns, which then show themselves immediately when you look at a position.
Watch any world class player solve puzzles and you'll find they have often solved it before 'you' (you being any person under master level) have even been able to figure out where all the pieces are. And it's not like they've ever seen the exact position before (at least not usually), but they've developed such an extreme intuition that the position just instantly reveals itself.
So one could call this some sort of memory as I suspect you're doing here with 'lifelong memory', but I think intuition is a far more precise term.
Anything beyond that goes into LTM. It takes around 5 seconds to put something into LTM. If the test talked about here has more than 3 things to remember, LTM must be in use and will be a limiting factor.
Any differences between people at that point likely refer to their chunking capabilities, i.e. if they can reduce complex patterns to a single entity to remember (each of which takes ~5 seconds).
Source: Human Problem Solving by Newell/Simon.
He chooses not to participate in the FIDE World Championship primarily because he doesn't like the format. He prefers a tournament format instead of a long 1-on-1 match against the running champion.
Honestly, your anecdote makes me respect him even more.
Few people go to those lengths to prepare.
Maybe doesn’t reduce my image of any individual player, but does reduce the image of the game itself.
[1] https://sahovski.com/Chess-Informant-161-Olympic-Spirit-p695... [2] https://sahovski.com/Chess-Informant-161-DOWNLOAD-VERSION-p6...
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.
Like chess more compute will win out, as has already been shown. I will remind everyone that elo is a measure of wins and losses not difficulty, conflating the two will lead to poor reasoning.
Link is mid-thread, because the earlier version of the paper was less interesting than the revision later on.
[1] https://forums.online-go.com/t/potential-rank-inflation-on-o...
A simple formation that even mild amateurs must learn to reach the lowest ranks.
KataGo recognizes the flaw and has an explicit ladder solver written in traditional code. 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.
I'm not surprised that there are other, deeper patterns that all of these AIs have missed.
Maybe solving ladders is iterative? Once they make chain-of-thought version of AlphaZero it might figure them out.
That’s a great idea. I think some sort of CoT would definitely help.
The MCTS search is itself a chain-of-thought.
Or in the case of KataGo, a dedicated Ladder-solver that serves as the input to the neural network is more than sufficient. IIRC all ladders of liberties 4 or less are solved by the dedicated KataGo solver.
It's not clear why these adversarial examples pop up yet IMO. It's not an issue of search depth or breadth either, it seems like an instinct thing.
I'm not quite sure it's a fair characterization.
Either way...
MCTS evaluates current position using predictions of future positions.
To understand value of ladders the algorithm would need iteratively analyse just the current layout of the pieces on the board.
Apparently the value of ladders is hard to infer from probabilisticrvsample of predictions of the future.
Ladders were accidental human discovery just because our attention is drawn to patterns. It just happens to be that they are valuable and can be mechanistically analyzed and evaluated. AI so far struggles with 1 shot outputting solutions that would require running small iterative program to calculate.
That’s the type of flexible reflection that is needed. I think most people would agree that the hard-coded ladder solver in Katago is not ideal, and feels like a dirty hack. The system should learn when it needs to do special analysis, not have us tell it when to. It’s good that it works, but it’d be better if it didn’t need us to hard-code such knowledge.
Humans are capable of realizing what a ladder is on their own (even if many learn from external sources). And it definitely isn’t hard-coded into us :)
I believe neural-net based MCTS (ex: AlphaZero and similar) use the neural-net to determine how deep any line should go. (Ex: which moves are worth exploring? Well, might as well have that itself part of the training / inference neural net).
The raw net output includes some values that could be used in addition, but they are not used. I don't know if they were ever looked at closely for this purpose.
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.
I don't think anyone knows for sure, but ladders are very calculation heavy. Unlike a lot of positions where Go is played by so called instinct, a ladder switches modes into "If I do X opponent does Y so I do Z.....", almost chess like.
Except it's very easy because there are only 3 or 4 options per step and really only one of those options continues the ladder. So it's this position where a chess-like tree breaks out in the game of Go but far simpler.
You still need to play Go (determining the strength of the overall board and evaluate if the ladder is worth it or if ladder breaker moves are possible/reasonable). But for strictly the ladder it's a simple and somewhat tedious calculation lasting about 20 or so turns on the average.
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The thing about ladders is that no one actually plays out a ladder. They just sit there on the board because it's rare for it to play to both players advantages (ladders are sharp: they either favor white or black by significant margins).
So as, say Black, is losing the ladder, Black will NEVER play the ladder. But needs to remember that the ladder is there for the rest of the game.
A ladder breaker is when Black places a piece that maybe in 15 turns (or later) will win the ladder (often while accomplishing something else). So after a ladder breaker, Black is winning the ladder and White should never play the ladder.
So the threat of the ladder breaker changes the game and position severely in ways that can only be seen in the far far future, dozens or even a hundred turns from now. It's outside the realm of computer calculations but yet feasible for humans to understand the implications.
A ladder is a kind of a mechanical one-way sequence which is quite long to read out. This is easy for humans (it's a one-way street!) but hard for AI (the MCTS prefers to search wide rather than deep). It is easy to tell the neural net as one of its inputs eg "this ladder works" or "this ladder doesn't work" -- in fact that's exactly what KataGo does.
See the pictures for more details about ladders: https://senseis.xmp.net/?Ladder
Traditional MCTS searches all the way to endgame and estimates how the current position leads to either win or loss. I'm not sure what the latest and greatest is but those % chance to win numbers are literally a search result over possible endgames IIRC.
I guess I'd assume that MCTS should see ladders and play at least some of them out.
(Kind of like wikipedia for go players)
At some point we will realize that AI will never be perfect, it will just have much better precision than us.
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.
LLMs are prediction engines. Given the text so far, what’s most likely to come next? In that context, there’s very little difference between citing a real court case and citing something that sounds like a real court case.
The weird thing is that they’re capable of producing any useful output at all.
Unavoidable, probably
> 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
That solves only to some degree. Hallucinations may happen at this stage too. Then either correct answer can get rejected or false pass through.
and much faster with the right hardware. And that's enough if AI can do in seconds what humans takes years. With o3 the price is only the limit, looks like.
Surely that's important? It was thought that AlphaGo and KataGo were undefeatable by humans.
"MuZero can beat any professional Go player"
"MuZero is an AI which is superhuman at Go"
"MuZero has superhuman intelligence in the domain of Go."Please see https://boardgames.stackexchange.com/questions/58127/ for reference. The first picture there shows a game supposedly "won by Black", due to a refusal to acknowledge that Black's stones are hopelessly dead everywhere except the top-right of the board. The "exploit" that the adversarial AI has found is, in effect, to convince KataGo to pass in this position, and then claim that White has no territory. It doesn't do this by claiming it could possibly make life with alternating play; it does so, in effect, by citing a ruleset that doesn't include the idea of removing dead stones (https://tromp.github.io/go.html) and expects everything to be played out (using area scoring) for as long as either player isn't satisfied.
Tromp comments: "As a practical shortcut, the following amendment allows dead stone removal" - but this isn't part of the formalization, and anyway the adversarial AI could just not agree, and it's up to KataGo to make pointless moves until it does. To my understanding, the formalization exists in large part because early Go programs often couldn't reliably tell when the position was fully settled (just like beginner players). It's also relevant on a theoretical level for some algorithms - which would like to know with certainty what the score is in any given position, but would theoretically have to already play Go perfectly in order to compute that.
(If you're interested in why so many rulesets exist, what kinds of strange situations would make the differences matter, etc., definitely check out the work of Robert Jasiek, a relatively strong amateur European player: https://home.snafu.de/jasiek/rules.html . Much of this was disregarded by the Go community at the time, because it's incredibly pedantic; but that's exactly what's necessary when it comes to rules disputes and computers.)
One of the authors of the paper posted on the Stack Exchange question and argued
> Now this does all feel rather contrived from a human perspective. But remember, KataGo was trained with this rule set, and configured to play with it. It doesn't know that the "human" rules of Go are any more important than Tromp-Taylor.
But I don't see anything to substantiate that claim. All sorts of Go bots are happy to play against humans in online implementations of the game, under a variety of human-oriented rulesets; and they pass in natural circumstances, and then the online implementation (sometimes using a different AI) proposes group status that is almost always correct (and matches the group status that the human player modeled in order to play that way). As far as I know, if a human player deliberately tries to claim the status is wrong, an AI will either hold its ground or request to resume play and demonstrate the status more clearly. In the position shown at the Stack Exchange link, even in territory scoring without pass stones, White could afford dozens of plays inside the territory (unfairly costing 1 point each) in order to make the White stones all pass-alive and deny any mathematical possibility of the Black stones reaching that status. (Sorry, there really isn't a way to explain that last sentence better without multiple pages of the background theory I linked and/or alluded to above.)
The AI is evaluated on the ruleset the AI is trained to play on, which is a Go variant designed to be easier for computers to implement.
The fact that the AI might have won if using a different ruleset is completely irrelevant.
The fact that the AI can be adapted to play in other rulesets, and this is frequently done when playing against human players, is irrelevant.
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.
That said, the cyclic adversary is a legitimate weakness in Katago, and I found it quite impressive.
http://hardboiledpoker.blogspot.com/2015/02/when-they-introd...
Also I feel like we're aging out of the tech scene. Or maybe that the seemingly antiquated life lessons we've learned might be needed now more than ever.
>> No sir, it is a stalemate.
> What did you do?
>> I was playing for a standoff; a draw. While Kolrami was dedicated to winning, I was able to pass up obvious avenues of advancement and settle for a balance. Theoretically, I should be able to challenge him indefinitely.
> Then you have beaten him!
>> In the strictest sense, I did not win.
> (groans) Data!
>> I busted him up.
> (everybody claps)
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.
To some extent, but a player who's way ahead could still have a lot of latitude to play pointless moves without endangering the win. In the case of Go it's generally not so much "delaying winning" as just embarrassing the opponent by playing obviously suboptimal moves (that make it clearer that some key group is dead, for example).
Although it's possible to start irrelevant, time-wasting ko positions - if the opponent accepts the offer to fight over them.
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.)
That's not a weakness.
That's life.
EDIT: Discussed later on https://news.ycombinator.com/item?id=42503110