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- Hacker News
- 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?by dombiscoff
- > a very unconventional strategy
Correction: a very conservative strategy, so as not to lose the advantage he started with.
by fn-mote - 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.by ggm
- 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.
- If anyone wants to know more about the chess model: 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
by sincerely - 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.by shric
- Yes, so calling it a "defeat" is improper to me.by leethargo
- > "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.
by xhevahir - > 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.
by geocar - At least when it comes to chess, Magnus Carlsen has stated multiple times that he's been inspired by AlphaZero and adjusted his own style of play after studying its games.by helloplanets
- 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.by shric
- 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-...
by bumbledraven - 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.
- The software industry is such a young field that we can still sit at the feet of the giants whose shoulders we stand on.by ahazred8ta
- Here's likely the first computer Go algorithm (in FORTRAN) and I believe the SGF is the first ever recorded human-computer game:by thangalin
- 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.
by dlevine - I'm sorry, but Elo is not an acronym for Electronic Light Orchestra. You don't write ELO, but simply Elo.by tucnak
- The question of whether machines or humans are stronger is moot, isn't it?
In any intellectual contest between human and machine, all the machine winning implies is that the endeavor is algorithmic.
The machine can be given practically unlimited memory and compute; we consider it cheating if the human would use memory aids. The machine could be implemented as many agents cooperating; we'd think it's not right if thousands of humans collaborated to face the machine, etc.
So statements like "not a sign that humans are now stronger than AIs at Go" are pretty meaningless, IMO
by prmph - > So, this was an important victory for a human, but not a sign that humans are now stronger than AIs at Go.
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.
by wslh - > 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).
by kadoban - 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)
by bluecalm - can the grandmasters still win with rook odds?by logicallee
- I follow some chess channels on YouTube (among them the usual suspects, Gotham, Chessbrah, Eric Rosen) but do you know of any video content that does game reviews of what you describe?by Leptonmaniac
- 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’.
by PenanceAU - 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?by jasonfarnon
- 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.
by thangalin - In odds chess bots, the bots would willingly take more disadvantageous positions which are more complicated--probably the bots in GO which are trained for odds do similar? Why does it not avoid such a joseki & play a worse response which it believes the human cannot read?by Davidzheng
- 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.
by mtlmtlmtlmtl - 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.
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.
by rao-v - A small note - it's Elo scoring, not ELO. It's named after Arpad Elo, it's not an acronym of any kind.by simiones