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.<p>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.<p><a href="https://www.goratings.org/en/" rel="nofollow">https://www.goratings.org/en/</a><p>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)<p>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.
I read this comment before looking at the article and thought that the grandmaster beat the AI even giving the AI 2 stones. Too bad. But this way around is of course more realistic.<p>And of course you would need to take into consideration the scale of go ratings and chess ratings when making that comparison. With top chess ratings being around 2800, being 1000 less than the top go ratings, one would have to apply a factor of roughly 3/4.
If you scaled Shin Jinseo to 2800, you would have players with extremely negative ratings. This page shows ratings of European players on a roughly aligned scale: <a href="https://europeangodatabase.eu/EGD/createalleuro3.php?country=**&dgob=false" rel="nofollow">https://europeangodatabase.eu/EGD/createalleuro3.php?country...</a>. It still has negative numbers on it, and this only contains players who have attended a tournament (though it's more common for beginners to play tournaments in the west, since it's hard to find times to play).<p>It's not a comparison of the worth of the games (I play both, though I'm better at Go, and prefer it), but the dynamic range of Go is larger.<p>That said, any cross-game/sport comparisons of this kind are pretty tough to do properly.
Depends on what chess rating you're talking about. Online at fast time controls, where many many more games get played at than OTB classical, the top super GMs are rated in the 3400-3600 range.
You're right that Shin Jinseo is a generational talent, and more dominant than anyone since Lee Changho (peaked in the 90s and was strong into the early-mid 2000s).<p>However, you can't compare goratings over time, the top ranks are not nearly stable enough. <a href="https://www.goratings.org/en/history/" rel="nofollow">https://www.goratings.org/en/history/</a> (I think it's believable Shin Jinseo is better than Lee Changho, but not that there has been steady progress since the days of Lee Changho, so that there are now 20 players stronger than him).
Deep link to Shin Jinseo's strength graph<p><a href="https://www.goratings.org/en/players/1313.html" rel="nofollow">https://www.goratings.org/en/players/1313.html</a>
Important to note that KataGo was double-handicapped. 20 seconds per move maximum; it couldn’t read deep. Against an amateur, it doesn’t matter, but against a historically strong pro it matters a lot.
> Katago (almost certainly significantly stronger than AlphaGo)<p>Interesting KataGo is an open sourced Go program written primarily by David Wu in C++ and recently heavily vibe coded by Claude. It's running on four Nvidia RTX-3090 GPUs with 96GB VRAM. [1]<p>[1] <a href="https://github.com/lightvector/KataGo" rel="nofollow">https://github.com/lightvector/KataGo</a>
I had to read your comment five times thinking "how it is written in C++ if it's a go program?" Do'h!
It significantly predates Claude, and has been one of, if not the best engine in the world for many years.
I don't think it's solely a matter of raw strength, but as Shin said, a willingness not to play to the program's strengths. I mean, one thing that rankled me about original Lee Sedol match was that Lee had no access to the program's "record" while the machine by the nature of the AI training process had effectively studied Lee's games in great detail.<p>I recall a while back someone came up with a set of "anti-computer" strategies that allowed even an amateur to defeat a strong go program. These moves weren't anything like ordinary go moves (and perhaps the "loophole" has been closed now) but imo, their existence suggests that a study of programs may reveal other unexpected weakness.
Probably a better comparison from the chess world(in reasonably modern times, though perhaps players like Capablanca and Lasker could be mentioned as well. Alas, I don't think FIDE rating existed back then) is Bobby Fischer. In the july 1972 FIDE rating list he held a rating of 2785, the highest in history at the time, with Spassky in second sitting at a "measly" 2660, and only 13 players being above 2600 even.
For a non-Go player, do you think this trend will persist, or is it more of a dead-cat/human bounce?
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.
Could someone sufficiently motivated invest in training Katago to be able to beat Shin Jinseo with 3 stones of handicap? Unfortunately - probably yes.<p>This in no way detracts from how absurd and remarkable it is that Shin Jinseo can beat KataGo (it gets a LOT of training and architecture refinements <a href="https://katagotraining.org/#eloGraphButtons" rel="nofollow">https://katagotraining.org/#eloGraphButtons</a>) with 2 stones of handicap.
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.<p>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.<p>What was truly incredible was how Shin held the advantage from that point on.
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?<p>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?<p>I really should spend some more time learning go, it's such a fascinating game.
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.
The headline is a bit misleading, though perhaps not intentionally.<p>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.<p>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.<p>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).<p>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.<p>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.<p>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).<p>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’.
> "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."<p>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.
Apparently 2 stones is a huge advantage. An estimate is that the computer is roughly 4-600 ELO stronger on an even match.<p>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.<p>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.<p>So, this was an important victory for a human, but not a sign that humans are now stronger than AIs at Go.
> 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.<p>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.<p>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.<p>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.<p>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).
While AlphaGo originally only had win rate as a metric, modern Go AIs have more knobs, including an evaluation of "complexity".<p>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).
> So, this was an important victory for a human, but not a sign that humans are now stronger than AIs at Go.<p>Another way to look at this: Go's handicap system gives us a genuinely interesting metric for the distance between a human and a machine at this specific game. Instead of just "computers beat humans" we get a quantified gap.
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.
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?
Neo...
Is there an equivalent of LeelaKnightOdds for Go? That might be harder to tackle.
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.<p>I predict the same thing is going to happen in Go once the engines catch up.<p>(You can play those chess bots on Lichess for free. Challenge LeelaQueenOdds or LeelaRookOdds if you are master level or stronger)
The fact that it's news that a human beat an AI, post-AlphaGo, demonstrates the current norm.
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:<p>> 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.
I'm struggling to believe it's not good enough to beat a mere mortal.
We're so back