91–100 of 214 posts
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A perhaps more accurate way to say it is that AlphaGo's models its opponents as a copy of itself.
So do human players, with rare exceptional ocassions
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There is already a wide range of strength among 9p players. In addition, once a player reaches 9p, they never lose it, so it includes players in their prime as well as players who have declined.
Yes, the ranking system you refer to is the one based on total wins over a player career and doesn't consider loses like ELO or other rating systems do. http://senseis.xmp.net/?NihonKiInNewPromotionSystem Fernando Aguilar, a 6 dan amateur from Argentina ( http://senseis.xmp.net/?FernandoAguilar ) defeated 2 Nihon Kiin 9 professional dans (Hasegawa Sunao and Yo Kagen), which is unexpected given the substantial rank di…
1) The two 9p professionals you mentioned achieved 9p status before the new promotion system you linked to. The new standards are much more stringent (though still based on lifetime achievement).
2) The standards differ by country. All the major countries now give out 9p ranks sparingly, but I think China especially may be quite difficult.
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Human moves tend to fit a narrative and be explainable, although often for very concrete reasons. For example: "I am sketching out territory while attacking an opponent group." "I am making my group safe so that I will not have to worry about its life while I accomplish other strategic goals." "I am making a very strong group so that I can use it to make it harder for my opponent to accomplish anything." Master, on t…
When playing against a much stronger player, it's always very hard to figure out why they play tenuki (make a move somewhere else on the board, apparently uncorrelated to the current fight). When AI gets strong enough (and it seems like it has already), it will just tenuki everyone all the time, while winning. Sounds like exactly what's happening already. It's past the event horizon for human understanding.
AlphaGo creators could "rewind" the whole program state to that move and inspect the tree search probabilities according to the board states it looks through to find a list of board states that generate a cumulative highest probability of wining by doing a move in that exact odd spot.
My guess would be that while humans tend to put stones with a single, double, or sometimes triple "reason", or in AI words, "high probability of local effectiveness in upcoming several turns", AlphaGo, with his ability to see further, can see past the local effectiveness into more global effectiveness and higher probability of winning further down the road.
In other words, those tenuki moves might actually be past the event horizon of human understanding, but only if inspected by looking at them and thinking ourselves. If we use AlphaGo itself, it should be possible to find out the reason for every single tenuki it will ever do.
Wonder when we will see AI tutors appear? Seems like there is a massive potential to train an AI super player then have it teach you. Does anyone know of such tools yet? Guess there are probably many AI augmented things we use all the time (Google Search) but just don't know it. Fascinating to think.
There was a recent post here about a new LiChess feature that identifies mistakes in your (or someone else's) games, prompts you to look for a better move, and tells you the best move if you can't figure it out. I think this is a form of AI tutoring. https://news.ycombinator.com/item?id=13241669
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> I would not have guessed that human strategy was that deficient. With as much freedom as Go allows I think it would be surprising if humans had stumbled upon an optimal strategy (or Master for that matter, I'm sure there is still much to be improved!).
The fact that Go commentators talk in terms of local strategy and narrative and anything other than the end-game from the very beginning made me feel fairly confident that Go was not a game that humans would ever reach optimal strategy levels at.
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> I would not have guessed that human strategy was that deficient. With as much freedom as Go allows I think it would be surprising if humans had stumbled upon an optimal strategy (or Master for that matter, I'm sure there is still much to be improved!).
The fact that Go commentators talk in terms of local strategy and narrative and anything other than the end-game from the very beginning made me feel fairly confident that Go was not a game that humans would ever reach optimal strategy levels at.
So if it requires some experience just to recognize a winner, and as far as I know sometimes even professionals can't tell for sure who is wining, it's pretty safe to say Go game is just too complex of a game for humans to come to optimal strategies in any reasonable amount of time.
We know that Master can figure out what it would play. We also know that its predecessor had a model for what moves a human professional would be likely to play. What I would find truly fascinating is if Master could divide moves that it plays, which professionals wouldn't, into groups based on a similar internal categorization of the moves. And then see if human minds can look at any of groups and come up with a hum…
At some point, if this method would be used, it would probably require a second AI that would help understand the main AI, because the primary "explanation" would still be too complex and/or subtle for us to comprehend.
Wonder when we will see AI tutors appear? Seems like there is a massive potential to train an AI super player then have it teach you. Does anyone know of such tools yet? Guess there are probably many AI augmented things we use all the time (Google Search) but just don't know it. Fascinating to think.
For example Leela (a free program) does this, and can display it with a "heatmap" style: https://sjeng.org/leela.html
Crazy Stone Deep Learning ($80 USD) has some similar analysis/hint features: http://www.unbalance.co.jp/igo/eng/
It would be good to start experimenting with handicap stones to understand how many stones stronger the bot is. http://senseis.xmp.net/?Handicap Also experimenting with a new ranking system beyond 9 dan.
I would also like to see how the strength is affected by computing resources. For example, one server, versus 10 servers, versus a room full of servers, versus an entire datacenter. I wonder how close this is to playing a perfect game. I would also like to know if two perfect players would always end up with a draw, or if they would each win 50% of the games.
Go rules prevent draws by giving Black a non-integer score bonus (this is called Komi, http://senseis.xmp.net/?Komi); By definition of "perfect play", a Black perfect player either always wins games, or a White perfect player always beats him.