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Go grandmaster Shin defeats AI KataGo with a two-stone handicap

467 points by gmays 1mo ago

shric 1mo ago
For those who don’t follow human vs AI Go (I don’t), this is with a 2 stone handicap in favor of the human which is apparently standard.
- leethargo 1mo ago
  Yes, so calling it a "defeat" is improper to me.
  - kadoban 1mo ago
    The headline is misleading, but this is still huge. 2 stones against katago is insane, I'd never have guessed we'd see that, ever.
    - bulletmarker 1mo ago
      Annoyingly misleading. I read it that the human player was handicapped.
      - kuboble 1mo ago
        Like many random headlines they may be targeted at people who know something about a topic. If you know anything about go or computer go from last 10 years then it is obvious which direction the handicap goes.
hirshi 1mo ago
I'm struggling to believe it's not good enough to beat a mere mortal.
- noknownsender 1mo ago
  This was with a two-stone advantage for the strongest-ever human player, apparently.
- gear54rus 1mo ago
  Thinking machines were finally defeated and will now be prohibited.
dlevine 1mo ago
Apparently 2 stones is a huge advantage. An estimate is that the computer is roughly 4-600 ELO stronger on an even match. Also, the human played a strategy tailored to that huge initial advantage. He said that the AI did not handle this particularly well, and played high probability moves instead of trying to lure him into a mistake. Also, even though this was the best Go engine, it was not running on a supercomputer, and had a relatively limited amount of time per move. So, this was an important victory for a human, but not a sign that humans are now stronger than AIs at Go.
- kadoban 1mo ago
  > Also, the human played a strategy tailored to that huge initial advantage. He said that the AI did not handle this particularly well, and played high probability moves instead of trying to lure him into a mistake. Yeah, katago's training is not really focused at all on handicap games, because it's by nature learning from even games against similar-strength opponents. It doesn't have specific training from playing in a way to exploit a weaker player. In a handicap game you have to give your opponent opportunities to fuck up if you want to play optimally. If a move loses 0.0005 points if the opponent plays optimally, katago won't play it even if there's ~zero chance a weaker player would play it right. There have been go AIs that tried to train more directly on uneven opponents, one called "sai" comes to mind, but katago has huge advantages otherwise and won out over the others (for very good reason, it's a great project).
  - rudi-c 1mo ago
    While AlphaGo originally only had win rate as a metric, modern Go AIs have more knobs, including an evaluation of "complexity". Just stating this off the top of my head so I could be misremembering, but I heard that the KataGo settings used were tweaked to favor complexity. This was most apparent in Game 1 which Shin Jinseo lost, where the AI had an unusual opening. However, the last game was quite plain leading me to wonder whether that setting was present in the last game (or at all).
    - foota 1mo ago
rao-v 1mo ago
It’s worth understanding that Shin Jinse has been significantly stronger than his nearest human opponents for a while now, more so than Magnus was even at his very peak. In go ELO like scoring he’s something like 120 points over the next strongest player. No other player has ever broken a 3800 rating let alone 3850. Ke Jie (the previous long time champion) peaked at 3755. Shin Jinseo’s strength graph is the most absurd straight line. https://www.goratings.org/en/ https://www.goratings.org/en/ 2 stones is historically the gap between a 9P ranked and a 1P ranked professional player (very roughly the gap between super grandmasters and an almost grandmaster) That is to say it’s shocking that Katago (almost certainly significantly stronger than AlphaGo) is a mere 2 stones stronger than Shin Jinseo. I suspect it would be 3-4 stones vs any other human pro.
- ninju 1mo ago
  Deep link to Shin Jinseo's strength graph https://www.goratings.org/en/players/1313.html https://www.goratings.org/en/players/1313.html
  - avadodin 1mo ago
    It would be interesting to find out what insight he discovered about the game to consistently rise like that. It can't be just play like AI. Any other Korean on the Korean Go program could have done the same. In fact, many did when AlphaGo was the pinnacle of AI.
- saucymew 1mo ago
  For a non-Go player, do you think this trend will persist, or is it more of a dead-cat/human bounce?
  - rudi-c 1mo ago
    On one hand, Shin Jinseo is an outlier player of this generation. On the other hand, the newest generation of new pros will have exclusively learned by using the AI to tell them what the best move is, so there's reason to believe that peak human level has yet to be reached.
dombiscoff 1mo ago
What I found interesting was that he adapted and shifted to a very unconventional strategy of play, opposed to the AI who primarily seems to play high probability moves. Does this not demonstrate the human edge against AI in novel / unconventional thinking?
- fn-mote 1mo ago
  > a very unconventional strategy Correction: a very conservative strategy, so as not to lose the advantage he started with.
thangalin 1mo ago
In Go, there are exchanges of plays called "joseki". Professionals consider the outcome of joseki to be an equal result for both players. Most joseki are only a handful of moves, but some, such as the "flying knife" joseki have variations that continue for upwards of 50 moves. A traditional 19x19 go board has 361 intersections. Shin's genius was to play out a complex variation of the flying knife joseki that was, in essence, a one-way path to reach an equal board position that occupied about 1/4 of the board. Due to the 2-stone handicap, the position favoured black with the game ~25% complete. KataGo could not have played any other way, where a human may have tried to foil the plan by introducing further complications. What was truly incredible was how Shin held the advantage from that point on.
- mtlmtlmtlmtl 1mo ago
  I have some questions as a chess player who barely even understands the rules of go. First these josekis sound like what in chess is called a forced tactical sequence. When you say Shin played out a long complex joseki, how does he do that? Does he have to read/calculate it out over the board(50 moves seems crazy to me unless the search tree is highly constrained by geometry/deduction/very few candidate moves, which does occasionally happen in chess endgames), or is the joseki more of a fixed sequence of moves which he's memorised, only needing to read to "punish" if the opponent diverges? Second, if it is a fixed sequence, how position independent is it? In chess, tactical sequences end up depending on the entire board state to work when they get sufficiently long. I guess what I'm asking is, could a player with some capacity for stategic thinking recognise this idea and take steps to make the flying knife impossible? I really should spend some more time learning go, it's such a fascinating game.
  - EchoAce 1mo ago
    You can think of joseki as “local opening”. Like, in a vacuum, this is known by study / AI to be an even result for black and white. It’s just like a chess opening, there’s no calculation up to a certain point. And it doesn’t exist in the midgame, it’s not similar to forced sequences which exist in both games; it’s much more like choosing French closed vs open or gambit/gambit declined. The one thing is (and this is huge), since Go board is very big, existing stone formations on other parts of the board influence the value of joseki and make certain ones more advantageous for black or white. To my knowledge this doesn’t really exist in chess, because the opening is already the entire board. However, when Shin executed the 50 move flying knife, the board was pretty much empty. So there is really no need for calculation, both Shin and the AI know it’s locally optimal. But getting to play a very long locally optimal sequence is good for the weaker player, so they have less “real” moves to lose EV on. Notably Shin probably can’t open with the flying knife in one corner past a certain point in the game, even if that corner were completely empty - the rest of the board positions would change the end values of the variants. If the AI could know this, they might play a variant that ends 30 moves sooner but is 0.01 pts worse. Then they would have more time to mess Shin up through organic new moves (which the AI will be better at of course). (disclaimer: only ranked 1 dan)
AceJohnny2 1mo ago
The fact that it's news that a human beat an AI, post-AlphaGo, demonstrates the current norm.
bluecalm 1mo ago
KataGo isn't very good at exploiting weaker opponents. In chess people were convinced a grandmaster can never be beaten with a knight odds. It's just too easy to simplify the position and win. It was very easy (for a grandmaster) vs already super human Stockfish. It was still kinda easy (for a strong GM) vs 200+ ELO stronger NNUE Stockfish. And then someone made a net optimized for exploiting humans. Its games are amazing and it beats GMs with knight odds with ease. It's unreal how good it is at setting traps, playing lines that don't work in theory but the refutation is based on precise tactical sequence a few moves deep. Playing vs it feels like playing vs a spider that slowly weaves a net around you till you can't move anymore. I predict the same thing is going to happen in Go once the engines catch up. (You can play those chess bots on Lichess for free. Challenge LeelaQueenOdds or LeelaRookOdds if you are master level or stronger)
- logicallee 1mo ago
  can the grandmasters still win with rook odds?
  - 317070 1mo ago
    they can win with any odds. But, they can only barely win even with queen odds: https://lqo.leumon.com/ https://lqo.leumon.com/
    - Davidzheng 1mo ago
      That's a bit misleading. I'm pretty sure grandmasters can win almost all games with queen odds at classical time controls
  - bluecalm 1mo ago
    Sometimes, especially at long blitz/rapid but they usually lose. I am master level player (FM) and it took me almost 300 games of slow blitz to beat it with N odds and about 150 games to beat it with rook odds. I was beating it at Q odds (meaning more than 50%) but I suspect it might be even better now than it used to be.
lunchbucket 1mo ago
We're so back
adamrezich 1mo ago
For all four of you that are like me and understand Dota 2 a lot better than Go, and are wondering what impact a “two-stone handicap” has and what it means, ChatGPT Pro claims that to analogize this scenario to a professional team playing against OpenAI Five: > The professional human team begins from a legal eight-to-ten-minute game state in which it has decisively won the laning stage: roughly a 6,000–8,000 team-net-worth lead, a 4,000–6,000 team-XP lead, two enemy Tier 1 towers destroyed, the third badly damaged, and all three friendly Tier 1 towers standing.
- retr0rocket 1mo ago
  [dead]
- avadodin 1mo ago
  I don't get it, can you explain in StarCraft II?
  - KingMob 1mo ago
    It's like 1.5x zerglings rushing 20s earlier.
xhevahir 1mo ago
> "This series taught me that rather than trying to imitate AI, it is far more important to build the board according to my own style." It's never made sense to me that so many go players study AI go play in the hope of emulating it in a human game. We're not machines. We can't do thousands of Monte Carlo tree searches per second.
- shric 1mo ago
  They study it because it has done things that humans had long assumed were bad until AI proved otherwise. The conceptual knowledge has been valuable at the top level.
- geocar 1mo ago
  > It's never made sense to me that so many go players study AI go play in the hope of emulating it in a human game. > We're not machines. We can't do thousands of Monte Carlo tree searches per second. You sound so sure of that, but I have seen people catch a ball, and I am not so sure that any artificial person should be any more aware of the tremendous maths they are "solving" From the perspective of a game with far fewer rules, "trying to imitate AI" might not mean anything like what you are thinking.
  - Garlef 1mo ago
    > but I have seen people catch a ball from what i remember, dogs sometimes run in curves so that the perceived trajectory of the object they try to catch is more linear so not everything might be in-brain math but also good trickery
    - geocar 1mo ago
      What exactly do you think the difference is? If you have an LLM add 2+2, it is doing a tremendous number of additions and multiplications it doesn't have to, same as us, and I am not sure the LLM can answer any more questions about that process than we can about ourselves.
- helloplanets 1mo ago
joelthelion 1mo ago
Is there an equivalent of LeelaKnightOdds for Go? That might be harder to tackle.
- Shorel 1mo ago
  This would be exactly that. The handicap stones in go allow it, it also works between humans of different strength levels. Now, if you mean a NN specifically trained to play at disadvantage, that would be extremely interesting and I hope they make it!
  - joelthelion 1mo ago
    That's exactly what I meant . LeelaKnightOdds is specifically trained for that, and it makes a world of difference.
PenanceAU 1mo ago
The headline is a bit misleading, though perhaps not intentionally. Shin took a 2-stone handicap from KataGo which means that Shin is the weaker of the two. But to give that more context, Shin is also the strongest human player to have ever lived in raw strength terms by a good margin, and is known as replicating AI move-for-move more closely than anyone else. If they were to play even then there’s no chance any human could win (and pretty much all pros agree with that). Lee Sedol beating AlphaGo in game 4 of that series is largely considered the last time a human beat a modern AI in an even game, which is why it was so amazing. RE the game, Katago was set to use the strongest available model and ran on a 3x 3090 GPU system, which is a lot for KataGo. 20 seconds might sound like a handicap, but that’s over 100,000 play out variations which is essentially infinite for modern KataGo models (anything over 10,000 is overkill). Shin played well in all games, but his strategy was to avoid complexity. KataGo reads out complex fighting like an absolute monster, so Shin was trying to play very very solid and very very calm so as to not give KataGo an in. The 2-stone handicap could be thought of as roughly 10-15 points of ‘buffer’. That’s massive in professional games, and that’s what Shin used to win. He played so overly solid that it sometimes cost a point or two, but it removed an opening for Katago to fight. He did this at the key opening and middle-game sections and never burnt through the full buffer of handicap points in the last two games. That’s why those games look kinda ‘boring’, it’s because Shin wanted them to be that way. Also note that KataGo probably could have won if its ’variance’ was tuned higher (basically it taking risks). Standard KataGo won’t take risks, it just wins with brute force. For handicap games though you can tune its willingness to start fights higher to prevent people from just playing ultra solid (like Shin did). Shin did an absolutely amazing job and he deserves all the recognition. Katago routinely beats professionals giving them 3-4 stones of handicap, so the win by Shin highlights to me how strong he is, but also just how well he understands how the AI ‘thinks’.
- jasonfarnon 1mo ago
  naive question--does " He did this at the key opening and middle-game sections and never burnt through the full buffer of handicap points in the last two games. " imply in retrospect he could have won with a smaller handicap? or was having the rest of that buffer in reserve guiding strategy?
bamboozled 1mo ago
Neo...
ggm 1mo ago
I recommend "The Master of Go" by Kawabata, 1951. It's a beautiful story of the change of power at the apex of the Go ranking system. I read it the same year as I read "The Glass Bead Game" by Hesse published in 1943 and its hard not to believe a relationship but in truth I think they are completely independent stories of the same situational tension.
thataccount 1mo ago
I don't know what the fascination with these AI versus human tournaments is. I'm old enough to remember the whole "Deep Blue" vs Kasparov exhibition, and I didn't really understand the fascination with all that either. That humans can make sufficiently strong calculators has never been a dispute in my mind. If the human wins over the calculator, good, but if the calculator wins, okay. What does that tell us exactly? The human had a bad day? The machine had a good one?
- kadoban 1mo ago
  > That humans can make sufficiently strong calculators has never been a dispute in my mind. Within our lifetimes (unless you're quite young) it was doubtful if a go ai would ever beat a decent human. Same was true for chess a generation or two earlier. It's news because it's the handoff of man to machine being the best at a particular thing. This current news is news from the other way, this human did _exceptionally_ well.
  - thataccount 1mo ago
    >It's news because it's the handoff of man to machine being the best at a particular thing. That's a way to see it. Another way to see it is that for any given event in a zero sum game, one of two outcomes, where two players are involved, will occur. Man only had to beat machines for what, 200-400 years only for it to beat man how many times for it to be "best"?
- japanoise 1mo ago
  It's all narrative. Abstract strategy games with perfect information (like chess and go) look at a casual glance like they should be difficult for computers, but in fact they're rather ideal. Before AlphaGo, belligerent go players used to smugly dismiss chess as a 'solved game' (not what that means) because chess engines have been better than the best humans for years.
  - Davidzheng 1mo ago
    Surely we didn't think they were ideal before alphago--they were huge challenges back then no? Just that now we can solve them.
ernsheong 1mo ago
Okay, now remove the 2-stone handicap
andsoitis 1mo ago
"The 26-year-old South Korean grandmaster became the first human to win an official series against a state-of-the-art Go engine under a two-stone handicap, a margin considered the absolute boundary for human competition against modern AI." What a powerful story. Humans have a chance of remaining superior because emotions are the fuel for our intellect and wisdom.
- nswango 1mo ago
  A strange take. Everything indicates that while the best human chess and Go players will continue to be incrementally better than all previous humans, computer players will improve much faster and leave all humans behind in very short order. As with chess, roughly equal matches between computers and human players are probably happening for the very last few times.
mellosouls 1mo ago
The article is hilariously triumphalist given the AI had a handicap (which it only mentions in passing unlike the title here). Obviously its an impressive human intellectual feat but the hubris is instructive perhaps for our wider interactions with AI as its abilities accelerate around us.
ctchocula 1mo ago
Given all the interest in sudden interest in go in this thread, anyone have a sure-fire method to improve at go from double-digit kyu to single-digit kyu and amateur dan level?
- hakuseki 1mo ago
  At double-digit kyu level, you should just play every day until you feel you are stuck. After you get stuck, you should pick one thing at a time to focus on improving. But the one thing should probably be related to tesuji or life and death.
  - ctchocula 1mo ago
    Thanks! One follow-up: Any recommended go servers to play on? OGS, Fox, Pandanet?
    - Alpha3031 1mo ago
      Probably Fox would be the server with the most players but at DDK basically any would work and I think getting exposure to different playing styles would be worthwhile.
    - amcoy37 1mo ago
      OGS (online-go.com) is the predominant server for players in the Western hemisphere, and provides a modern browser based UI. Fox has a larger player base, but that player base is almost entirely in Asian timezones. Fox requires downloading a not so modern client whose UI is lacking English translation. I get the feeling that Fox has a lot of bot players in the lower ranks. It can take a few minutes to get a match on OGS. When that happens, I take the opportunity to warm up with a few tsumego drills on goproblems.com
- KingMob 1mo ago
  Binge Hikaru No Go and pay close attention to whatever Sai does. /s
- jrimbault 1mo ago
eru 1mo ago
I'm glad that the best humans are still good enough at Go that holding these matches at all is still viable.
tasuki 1mo ago
Great, now next match against blue spot[0], starting even and adjusting the handicap each game. Blue spot is an adversarial ai and it's managed to beat average professionals on five handicaps, which is absolutely insane. [0]: https://codenamebluespot.com/ https://codenamebluespot.com/
- spuz 1mo ago
  What does "on five handicaps" mean? The human received a 5-stone handicap?
  - tasuki 1mo ago
    Yes. More info here: https://codenamebluespot.com/open-test https://codenamebluespot.com/open-test (including commentary of the games)
noobermin 1mo ago
This is the bed all you AI hypers made. Now all manner of disparate tools fall under the banner of AI, with something like this that are not LLMs or transformer type tool, they happen to get lumped with all the rest of them.
- lunchbucket 1mo ago
  Not sure what you're talking about. Playing games has been a research topic in AI for much longer than transformer models have existed.
alkyon 1mo ago
Only two stone handicap against the machine with much deeper tree searching capabilities. This means that KataGo is lacking in strategy compared to Shin and he could leverage this to achieve this significant win.
rhaps0dy 1mo ago
Kellin Pelrine already beat KataGo in 2022 (https://www.reddit.com/r/baduk/comments/13at6s5/how_many_blind_spots_like_the_one_found_by_kellin/ https://www.reddit.com/r/baduk/comments/13at6s5/how_many_bli...). Why is this different? Is it meaningful that Shin is also extremely good at beating professional humans?
- EchoAce 1mo ago
  That kind of blind spot is pretty brilliant but more of a technical hack from a Go perspective. Shin is world #1 and won “the normal way”, i.e. playing good moves
bumbledraven 1mo ago
Deep Blue vs Garry Kasparov (1997) and AlphaGo vs Lee Sedol (2016) marked the first defeats of a world champion by a machine in a chess or Go match. Coincidentally, both machines played an iconic move on Move 37 of Game 2: • Deep Blue played Be4, declining a pawn capture to achieve a long-term positional advantage and fueling Kasparov’s suspicions of human intervention. • AlphaGo played P10, a brilliant 5th-line shoulder hit that live commentators initially dismissed as a blunder or misclick. https://franky07724-57962.medium.com/amazing-coincidence-in-the-matches-of-deep-blue-and-alphago-move-37-game-2-76524f3d68c1 https://franky07724-57962.medium.com/amazing-coincidence-in-...
lackoftactics 1mo ago
I read it wrong, I really hoped that he gave AI a handicap :)
animanoir 1mo ago
[dead]
mark_l_watson 1mo ago
I love this. I watched every game years ago between Lee Sedol, even though it was late at night. I love AI and I love Go. Off topic, but I wrote the first commercial Go program for the Apple II in the late 1970s.
- thangalin 1mo ago
  Here's likely the first computer Go algorithm (in FORTRAN) and I believe the SGF is the first ever recorded human-computer game: https://repo.autonoma.ca/repo/historical-computer-go https://repo.autonoma.ca/repo/historical-computer-go
  - mark_l_watson 1mo ago
    I should have said first commercial Go program because I actively marketed it. There were several previous Go programs.
- ahazred8ta 1mo ago
  The software industry is such a young field that we can still sit at the feet of the giants whose shoulders we stand on.
ApolloFortyNine 1mo ago
Last time I read about these handicapped games in Go I saw that the model isn't trained to play from behind, so while in a normal game of Go, those 50 move Joseki's or any large move combo that's an 'even trade' will inevitably let the AI claw a lead at some point, the AI is already behind. It's similar in chess but there's a model specifically trained to play a knight down, and it's pretty cool to see the insane tactics it'll try. Though a knight down is considerably more than a 2 stone handicap in Go.
- sincerely 1mo ago
  If anyone wants to know more about the chess model: https://lczero.org/blog/2024/12/the-leela-piece-odds-challenge-what-does-it-take-you-to-win-against-leela/ https://lczero.org/blog/2024/12/the-leela-piece-odds-challen... You can choose which piece it's missing. It's humbling to lose starting up a queen

467 points by gmays 1mo ago

shric 1mo ago

For those who don’t follow human vs AI Go (I don’t), this is with a 2 stone handicap in favor of the human which is apparently standard.

leethargo 1mo ago
Yes, so calling it a "defeat" is improper to me.
- kadoban 1mo ago
  The headline is misleading, but this is still huge. 2 stones against katago is insane, I'd never have guessed we'd see that, ever.
  - bulletmarker 1mo ago
    Annoyingly misleading. I read it that the human player was handicapped.
    - kuboble 1mo ago
      Like many random headlines they may be targeted at people who know something about a topic. If you know anything about go or computer go from last 10 years then it is obvious which direction the handicap goes.

hirshi 1mo ago

I'm struggling to believe it's not good enough to beat a mere mortal.

noknownsender 1mo ago
This was with a two-stone advantage for the strongest-ever human player, apparently.
gear54rus 1mo ago
Thinking machines were finally defeated and will now be prohibited.

dlevine 1mo ago

Apparently 2 stones is a huge advantage. An estimate is that the computer is roughly 4-600 ELO stronger on an even match. Also, the human played a strategy tailored to that huge initial advantage. He said that the AI did not handle this particularly well, and played high probability moves instead of trying to lure him into a mistake. Also, even though this was the best Go engine, it was not running on a supercomputer, and had a relatively limited amount of time per move. So, this was an important victory for a human, but not a sign that humans are now stronger than AIs at Go.

kadoban 1mo ago
> Also, the human played a strategy tailored to that huge initial advantage. He said that the AI did not handle this particularly well, and played high probability moves instead of trying to lure him into a mistake. Yeah, katago's training is not really focused at all on handicap games, because it's by nature learning from even games against similar-strength opponents. It doesn't have specific training from playing in a way to exploit a weaker player. In a handicap game you have to give your opponent opportunities to fuck up if you want to play optimally. If a move loses 0.0005 points if the opponent plays optimally, katago won't play it even if there's ~zero chance a weaker player would play it right. There have been go AIs that tried to train more directly on uneven opponents, one called "sai" comes to mind, but katago has huge advantages otherwise and won out over the others (for very good reason, it's a great project).
- rudi-c 1mo ago
  While AlphaGo originally only had win rate as a metric, modern Go AIs have more knobs, including an evaluation of "complexity". Just stating this off the top of my head so I could be misremembering, but I heard that the KataGo settings used were tweaked to favor complexity. This was most apparent in Game 1 which Shin Jinseo lost, where the AI had an unusual opening. However, the last game was quite plain leading me to wonder whether that setting was present in the last game (or at all).
  - foota 1mo ago

rao-v 1mo ago

It’s worth understanding that Shin Jinse has been significantly stronger than his nearest human opponents for a while now, more so than Magnus was even at his very peak. In go ELO like scoring he’s something like 120 points over the next strongest player. No other player has ever broken a 3800 rating let alone 3850. Ke Jie (the previous long time champion) peaked at 3755. Shin Jinseo’s strength graph is the most absurd straight line. https://www.goratings.org/en/ https://www.goratings.org/en/ 2 stones is historically the gap between a 9P ranked and a 1P ranked professional player (very roughly the gap between super grandmasters and an almost grandmaster) That is to say it’s shocking that Katago (almost certainly significantly stronger than AlphaGo) is a mere 2 stones stronger than Shin Jinseo. I suspect it would be 3-4 stones vs any other human pro.

ninju 1mo ago
Deep link to Shin Jinseo's strength graph https://www.goratings.org/en/players/1313.html https://www.goratings.org/en/players/1313.html
- avadodin 1mo ago
  It would be interesting to find out what insight he discovered about the game to consistently rise like that. It can't be just play like AI. Any other Korean on the Korean Go program could have done the same. In fact, many did when AlphaGo was the pinnacle of AI.
saucymew 1mo ago
For a non-Go player, do you think this trend will persist, or is it more of a dead-cat/human bounce?
- rudi-c 1mo ago
  On one hand, Shin Jinseo is an outlier player of this generation. On the other hand, the newest generation of new pros will have exclusively learned by using the AI to tell them what the best move is, so there's reason to believe that peak human level has yet to be reached.

dombiscoff 1mo ago

What I found interesting was that he adapted and shifted to a very unconventional strategy of play, opposed to the AI who primarily seems to play high probability moves. Does this not demonstrate the human edge against AI in novel / unconventional thinking?

fn-mote 1mo ago
> a very unconventional strategy Correction: a very conservative strategy, so as not to lose the advantage he started with.

thangalin 1mo ago

In Go, there are exchanges of plays called "joseki". Professionals consider the outcome of joseki to be an equal result for both players. Most joseki are only a handful of moves, but some, such as the "flying knife" joseki have variations that continue for upwards of 50 moves. A traditional 19x19 go board has 361 intersections. Shin's genius was to play out a complex variation of the flying knife joseki that was, in essence, a one-way path to reach an equal board position that occupied about 1/4 of the board. Due to the 2-stone handicap, the position favoured black with the game ~25% complete. KataGo could not have played any other way, where a human may have tried to foil the plan by introducing further complications. What was truly incredible was how Shin held the advantage from that point on.

mtlmtlmtlmtl 1mo ago
I have some questions as a chess player who barely even understands the rules of go. First these josekis sound like what in chess is called a forced tactical sequence. When you say Shin played out a long complex joseki, how does he do that? Does he have to read/calculate it out over the board(50 moves seems crazy to me unless the search tree is highly constrained by geometry/deduction/very few candidate moves, which does occasionally happen in chess endgames), or is the joseki more of a fixed sequence of moves which he's memorised, only needing to read to "punish" if the opponent diverges? Second, if it is a fixed sequence, how position independent is it? In chess, tactical sequences end up depending on the entire board state to work when they get sufficiently long. I guess what I'm asking is, could a player with some capacity for stategic thinking recognise this idea and take steps to make the flying knife impossible? I really should spend some more time learning go, it's such a fascinating game.
- EchoAce 1mo ago
  You can think of joseki as “local opening”. Like, in a vacuum, this is known by study / AI to be an even result for black and white. It’s just like a chess opening, there’s no calculation up to a certain point. And it doesn’t exist in the midgame, it’s not similar to forced sequences which exist in both games; it’s much more like choosing French closed vs open or gambit/gambit declined. The one thing is (and this is huge), since Go board is very big, existing stone formations on other parts of the board influence the value of joseki and make certain ones more advantageous for black or white. To my knowledge this doesn’t really exist in chess, because the opening is already the entire board. However, when Shin executed the 50 move flying knife, the board was pretty much empty. So there is really no need for calculation, both Shin and the AI know it’s locally optimal. But getting to play a very long locally optimal sequence is good for the weaker player, so they have less “real” moves to lose EV on. Notably Shin probably can’t open with the flying knife in one corner past a certain point in the game, even if that corner were completely empty - the rest of the board positions would change the end values of the variants. If the AI could know this, they might play a variant that ends 30 moves sooner but is 0.01 pts worse. Then they would have more time to mess Shin up through organic new moves (which the AI will be better at of course). (disclaimer: only ranked 1 dan)

AceJohnny2 1mo ago

The fact that it's news that a human beat an AI, post-AlphaGo, demonstrates the current norm.

bluecalm 1mo ago

KataGo isn't very good at exploiting weaker opponents. In chess people were convinced a grandmaster can never be beaten with a knight odds. It's just too easy to simplify the position and win. It was very easy (for a grandmaster) vs already super human Stockfish. It was still kinda easy (for a strong GM) vs 200+ ELO stronger NNUE Stockfish. And then someone made a net optimized for exploiting humans. Its games are amazing and it beats GMs with knight odds with ease. It's unreal how good it is at setting traps, playing lines that don't work in theory but the refutation is based on precise tactical sequence a few moves deep. Playing vs it feels like playing vs a spider that slowly weaves a net around you till you can't move anymore. I predict the same thing is going to happen in Go once the engines catch up. (You can play those chess bots on Lichess for free. Challenge LeelaQueenOdds or LeelaRookOdds if you are master level or stronger)

logicallee 1mo ago
can the grandmasters still win with rook odds?
- 317070 1mo ago
  they can win with any odds. But, they can only barely win even with queen odds: https://lqo.leumon.com/ https://lqo.leumon.com/
  - Davidzheng 1mo ago
    That's a bit misleading. I'm pretty sure grandmasters can win almost all games with queen odds at classical time controls
- bluecalm 1mo ago
  Sometimes, especially at long blitz/rapid but they usually lose. I am master level player (FM) and it took me almost 300 games of slow blitz to beat it with N odds and about 150 games to beat it with rook odds. I was beating it at Q odds (meaning more than 50%) but I suspect it might be even better now than it used to be.

lunchbucket 1mo ago

We're so back

adamrezich 1mo ago

For all four of you that are like me and understand Dota 2 a lot better than Go, and are wondering what impact a “two-stone handicap” has and what it means, ChatGPT Pro claims that to analogize this scenario to a professional team playing against OpenAI Five: > The professional human team begins from a legal eight-to-ten-minute game state in which it has decisively won the laning stage: roughly a 6,000–8,000 team-net-worth lead, a 4,000–6,000 team-XP lead, two enemy Tier 1 towers destroyed, the third badly damaged, and all three friendly Tier 1 towers standing.

retr0rocket 1mo ago
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avadodin 1mo ago
I don't get it, can you explain in StarCraft II?
- KingMob 1mo ago
  It's like 1.5x zerglings rushing 20s earlier.

xhevahir 1mo ago

> "This series taught me that rather than trying to imitate AI, it is far more important to build the board according to my own style." It's never made sense to me that so many go players study AI go play in the hope of emulating it in a human game. We're not machines. We can't do thousands of Monte Carlo tree searches per second.

shric 1mo ago
They study it because it has done things that humans had long assumed were bad until AI proved otherwise. The conceptual knowledge has been valuable at the top level.
geocar 1mo ago
> It's never made sense to me that so many go players study AI go play in the hope of emulating it in a human game. > We're not machines. We can't do thousands of Monte Carlo tree searches per second. You sound so sure of that, but I have seen people catch a ball, and I am not so sure that any artificial person should be any more aware of the tremendous maths they are "solving" From the perspective of a game with far fewer rules, "trying to imitate AI" might not mean anything like what you are thinking.
- Garlef 1mo ago
  > but I have seen people catch a ball from what i remember, dogs sometimes run in curves so that the perceived trajectory of the object they try to catch is more linear so not everything might be in-brain math but also good trickery
  - geocar 1mo ago
    What exactly do you think the difference is? If you have an LLM add 2+2, it is doing a tremendous number of additions and multiplications it doesn't have to, same as us, and I am not sure the LLM can answer any more questions about that process than we can about ourselves.
helloplanets 1mo ago

joelthelion 1mo ago

Is there an equivalent of LeelaKnightOdds for Go? That might be harder to tackle.

Shorel 1mo ago
This would be exactly that. The handicap stones in go allow it, it also works between humans of different strength levels. Now, if you mean a NN specifically trained to play at disadvantage, that would be extremely interesting and I hope they make it!
- joelthelion 1mo ago
  That's exactly what I meant . LeelaKnightOdds is specifically trained for that, and it makes a world of difference.

PenanceAU 1mo ago

The headline is a bit misleading, though perhaps not intentionally. Shin took a 2-stone handicap from KataGo which means that Shin is the weaker of the two. But to give that more context, Shin is also the strongest human player to have ever lived in raw strength terms by a good margin, and is known as replicating AI move-for-move more closely than anyone else. If they were to play even then there’s no chance any human could win (and pretty much all pros agree with that). Lee Sedol beating AlphaGo in game 4 of that series is largely considered the last time a human beat a modern AI in an even game, which is why it was so amazing. RE the game, Katago was set to use the strongest available model and ran on a 3x 3090 GPU system, which is a lot for KataGo. 20 seconds might sound like a handicap, but that’s over 100,000 play out variations which is essentially infinite for modern KataGo models (anything over 10,000 is overkill). Shin played well in all games, but his strategy was to avoid complexity. KataGo reads out complex fighting like an absolute monster, so Shin was trying to play very very solid and very very calm so as to not give KataGo an in. The 2-stone handicap could be thought of as roughly 10-15 points of ‘buffer’. That’s massive in professional games, and that’s what Shin used to win. He played so overly solid that it sometimes cost a point or two, but it removed an opening for Katago to fight. He did this at the key opening and middle-game sections and never burnt through the full buffer of handicap points in the last two games. That’s why those games look kinda ‘boring’, it’s because Shin wanted them to be that way. Also note that KataGo probably could have won if its ’variance’ was tuned higher (basically it taking risks). Standard KataGo won’t take risks, it just wins with brute force. For handicap games though you can tune its willingness to start fights higher to prevent people from just playing ultra solid (like Shin did). Shin did an absolutely amazing job and he deserves all the recognition. Katago routinely beats professionals giving them 3-4 stones of handicap, so the win by Shin highlights to me how strong he is, but also just how well he understands how the AI ‘thinks’.

jasonfarnon 1mo ago
naive question--does " He did this at the key opening and middle-game sections and never burnt through the full buffer of handicap points in the last two games. " imply in retrospect he could have won with a smaller handicap? or was having the rest of that buffer in reserve guiding strategy?

bamboozled 1mo ago

Neo...

ggm 1mo ago

I recommend "The Master of Go" by Kawabata, 1951. It's a beautiful story of the change of power at the apex of the Go ranking system. I read it the same year as I read "The Glass Bead Game" by Hesse published in 1943 and its hard not to believe a relationship but in truth I think they are completely independent stories of the same situational tension.

thataccount 1mo ago

I don't know what the fascination with these AI versus human tournaments is. I'm old enough to remember the whole "Deep Blue" vs Kasparov exhibition, and I didn't really understand the fascination with all that either. That humans can make sufficiently strong calculators has never been a dispute in my mind. If the human wins over the calculator, good, but if the calculator wins, okay. What does that tell us exactly? The human had a bad day? The machine had a good one?

kadoban 1mo ago
> That humans can make sufficiently strong calculators has never been a dispute in my mind. Within our lifetimes (unless you're quite young) it was doubtful if a go ai would ever beat a decent human. Same was true for chess a generation or two earlier. It's news because it's the handoff of man to machine being the best at a particular thing. This current news is news from the other way, this human did _exceptionally_ well.
- thataccount 1mo ago
  >It's news because it's the handoff of man to machine being the best at a particular thing. That's a way to see it. Another way to see it is that for any given event in a zero sum game, one of two outcomes, where two players are involved, will occur. Man only had to beat machines for what, 200-400 years only for it to beat man how many times for it to be "best"?
japanoise 1mo ago
It's all narrative. Abstract strategy games with perfect information (like chess and go) look at a casual glance like they should be difficult for computers, but in fact they're rather ideal. Before AlphaGo, belligerent go players used to smugly dismiss chess as a 'solved game' (not what that means) because chess engines have been better than the best humans for years.
- Davidzheng 1mo ago
  Surely we didn't think they were ideal before alphago--they were huge challenges back then no? Just that now we can solve them.

ernsheong 1mo ago

Okay, now remove the 2-stone handicap

andsoitis 1mo ago

"The 26-year-old South Korean grandmaster became the first human to win an official series against a state-of-the-art Go engine under a two-stone handicap, a margin considered the absolute boundary for human competition against modern AI." What a powerful story. Humans have a chance of remaining superior because emotions are the fuel for our intellect and wisdom.

nswango 1mo ago
A strange take. Everything indicates that while the best human chess and Go players will continue to be incrementally better than all previous humans, computer players will improve much faster and leave all humans behind in very short order. As with chess, roughly equal matches between computers and human players are probably happening for the very last few times.

mellosouls 1mo ago

The article is hilariously triumphalist given the AI had a handicap (which it only mentions in passing unlike the title here). Obviously its an impressive human intellectual feat but the hubris is instructive perhaps for our wider interactions with AI as its abilities accelerate around us.

ctchocula 1mo ago

Given all the interest in sudden interest in go in this thread, anyone have a sure-fire method to improve at go from double-digit kyu to single-digit kyu and amateur dan level?

hakuseki 1mo ago
At double-digit kyu level, you should just play every day until you feel you are stuck. After you get stuck, you should pick one thing at a time to focus on improving. But the one thing should probably be related to tesuji or life and death.
- ctchocula 1mo ago
  Thanks! One follow-up: Any recommended go servers to play on? OGS, Fox, Pandanet?
  - Alpha3031 1mo ago
    Probably Fox would be the server with the most players but at DDK basically any would work and I think getting exposure to different playing styles would be worthwhile.
  - amcoy37 1mo ago
    OGS (online-go.com) is the predominant server for players in the Western hemisphere, and provides a modern browser based UI. Fox has a larger player base, but that player base is almost entirely in Asian timezones. Fox requires downloading a not so modern client whose UI is lacking English translation. I get the feeling that Fox has a lot of bot players in the lower ranks. It can take a few minutes to get a match on OGS. When that happens, I take the opportunity to warm up with a few tsumego drills on goproblems.com
KingMob 1mo ago
Binge Hikaru No Go and pay close attention to whatever Sai does. /s
jrimbault 1mo ago

eru 1mo ago

I'm glad that the best humans are still good enough at Go that holding these matches at all is still viable.

tasuki 1mo ago

Great, now next match against blue spot[0], starting even and adjusting the handicap each game. Blue spot is an adversarial ai and it's managed to beat average professionals on five handicaps, which is absolutely insane. [0]: https://codenamebluespot.com/ https://codenamebluespot.com/

spuz 1mo ago
What does "on five handicaps" mean? The human received a 5-stone handicap?
- tasuki 1mo ago
  Yes. More info here: https://codenamebluespot.com/open-test https://codenamebluespot.com/open-test (including commentary of the games)

noobermin 1mo ago

This is the bed all you AI hypers made. Now all manner of disparate tools fall under the banner of AI, with something like this that are not LLMs or transformer type tool, they happen to get lumped with all the rest of them.

lunchbucket 1mo ago
Not sure what you're talking about. Playing games has been a research topic in AI for much longer than transformer models have existed.

alkyon 1mo ago

Only two stone handicap against the machine with much deeper tree searching capabilities. This means that KataGo is lacking in strategy compared to Shin and he could leverage this to achieve this significant win.

rhaps0dy 1mo ago

Kellin Pelrine already beat KataGo in 2022 (https://www.reddit.com/r/baduk/comments/13at6s5/how_many_blind_spots_like_the_one_found_by_kellin/ https://www.reddit.com/r/baduk/comments/13at6s5/how_many_bli...). Why is this different? Is it meaningful that Shin is also extremely good at beating professional humans?

EchoAce 1mo ago
That kind of blind spot is pretty brilliant but more of a technical hack from a Go perspective. Shin is world #1 and won “the normal way”, i.e. playing good moves

bumbledraven 1mo ago

Deep Blue vs Garry Kasparov (1997) and AlphaGo vs Lee Sedol (2016) marked the first defeats of a world champion by a machine in a chess or Go match. Coincidentally, both machines played an iconic move on Move 37 of Game 2: • Deep Blue played Be4, declining a pawn capture to achieve a long-term positional advantage and fueling Kasparov’s suspicions of human intervention. • AlphaGo played P10, a brilliant 5th-line shoulder hit that live commentators initially dismissed as a blunder or misclick. https://franky07724-57962.medium.com/amazing-coincidence-in-the-matches-of-deep-blue-and-alphago-move-37-game-2-76524f3d68c1 https://franky07724-57962.medium.com/amazing-coincidence-in-...

lackoftactics 1mo ago

I read it wrong, I really hoped that he gave AI a handicap :)

animanoir 1mo ago

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mark_l_watson 1mo ago

I love this. I watched every game years ago between Lee Sedol, even though it was late at night. I love AI and I love Go. Off topic, but I wrote the first commercial Go program for the Apple II in the late 1970s.

thangalin 1mo ago
Here's likely the first computer Go algorithm (in FORTRAN) and I believe the SGF is the first ever recorded human-computer game: https://repo.autonoma.ca/repo/historical-computer-go https://repo.autonoma.ca/repo/historical-computer-go
- mark_l_watson 1mo ago
  I should have said first commercial Go program because I actively marketed it. There were several previous Go programs.
ahazred8ta 1mo ago
The software industry is such a young field that we can still sit at the feet of the giants whose shoulders we stand on.

ApolloFortyNine 1mo ago

Last time I read about these handicapped games in Go I saw that the model isn't trained to play from behind, so while in a normal game of Go, those 50 move Joseki's or any large move combo that's an 'even trade' will inevitably let the AI claw a lead at some point, the AI is already behind. It's similar in chess but there's a model specifically trained to play a knight down, and it's pretty cool to see the insane tactics it'll try. Though a knight down is considerably more than a 2 stone handicap in Go.

sincerely 1mo ago
If anyone wants to know more about the chess model: https://lczero.org/blog/2024/12/the-leela-piece-odds-challenge-what-does-it-take-you-to-win-against-leela/ https://lczero.org/blog/2024/12/the-leela-piece-odds-challen... You can choose which piece it's missing. It's humbling to lose starting up a queen