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?
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.
Thanks!
One follow-up: Any recommended go servers to play on? OGS, Fox, Pandanet?
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. 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 fa…
>Go grandmaster Shin defeats AI KataGo with a two-stone handicap English is not my first language but for clarity perhaps the title should be: Go grandmaster Shin with a two-stone handicap defeats AI KataGo
Idiomatically a handicap is a disadvantage not an advantage. So if Shin had two extra stones, KataGo had a two stone handicap.
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?
Binge Hikaru No Go and pay close attention to whatever Sai does. /s
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…
Joseki are akin to book openings in chess, eg, we routinely see players going 20+ moves entirely from AI prep.
The trouble is that in chess, if one player deviates significantly that can often still mean they have a playable (perhaps slightly worse) position which has to either be memorised or understood on the spot, which can be really difficult. The main line is not the only playable line. Is this also the case for Go's joseki? Or is any deviation easy to punish?
It is the same. If you are not a pro punishing deviations is quite difficult (but can occasionally be easy like I'm sure it can be in chess with hanging pieces). The most obvious difficult punish example is just finding unintuitive ways to capture or suppress their stones (analogy: multi move trick captures or getting a better position). But it can be more subtle, sometimes the answer to a deviation is just to not directly punish, but just accept that their shape is slightly inefficient. But if you know they deviated incorrectly, you can be tempted to try to attack hard to directly punish when the line doesn't exist (and then you overplay). Another example of a difficult punish is to just ignore their move and make progress on a different of the board, challenging them to prove their move was actually a threat. You can kind of do that in chess with parallel pawnstorms, but it is much more common in Go.
"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.
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.
>Go grandmaster Shin defeats AI KataGo with a two-stone handicap English is not my first language but for clarity perhaps the title should be: Go grandmaster Shin with a two-stone handicap defeats AI KataGo
Idiomatically a handicap is a disadvantage not an advantage. So if Shin had two extra stones, KataGo had a two stone handicap.
Handicaps can be positive, e.g. in fixed distance racing one way to handicap is to "give" a starting distance to the racers who are slower than the scratch racer; in this system a higher handicap is more of an advantage.
Since the AI was playing scratch (the normal, unmodified play style), it seems odd to say it had a handicap. Rather, the human had a positive handicap of two stones.
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…
Sounds like the counter is a strategy Magnus Carlsen has been known for in chess: get out of theory, probably on a bit weaker foot than the other player, to then crush them on raw power.
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.
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.