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

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

#41

Post by Ntrails »

joe_the_user wrote: post #29
rao-v wrote: post #5

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 absu…

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. I recall a while back someone came up with a set of "anti-computer…

I saw the same things when the OpenAI Dota bots could eviscerate humans 1v1 - even pros lost!

Until a more average player confuses the AI with an unseen behaviour (pulling creeps between the towers etc) to get an advantage.

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

#42

Post by mtlmtlmtlmtl »

thangalin wrote: post #16

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…

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.

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

#43

Post by retr0rocket »

adamrezich wrote: post #22

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…

[dead]

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

#45

Post by prmph »

dlevine wrote: post #4

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…

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

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

#46

Post by avadodin »

adamrezich wrote: post #22

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…

I don't get it, can you explain in StarCraft II?

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

#47

Post by jasonfarnon »

PenanceAU wrote: post #35

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 huma…

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?

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

#48

Post by EchoAce »

mtlmtlmtlmtl wrote: post #42
thangalin wrote: post #16

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…

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…

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)

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

#49

Post by avadodin »

ninju wrote: post #11
rao-v wrote: post #5

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 absu…

Deep link to Shin Jinseo's strength graph https://www.goratings.org/en/players/1313.html

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.

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

#50

Post by shric »

xhevahir wrote: post #23

> "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.

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.
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