81–90 of 282 posts
It doesn't seem to like the Sicilian Defense (1.e4 c5), which is the most popular opening by human players. I wonder if this will change opening theory?
Queen's gambit is there.
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Now I'm really intrigued. SF plays really odd moves when left to its own devices for a time. As does this AI. So maybe chess looks really weird with play significantly better than the best humans. It's actually really disturbing.
I think being able to play tactically perfect chess over 20 or so moves will often look weird to human strategic sensibilities. The computer sees every tiny exception to the patterns and heuristics you've incorporated into your gut feel about positions. In a way these moves are right just because they're right, and that's what's jarring - there's no _principle_ behind them that can be learned and generalised, which i…
While this sounds impressive, I'll believe it when AlphaZero wins TCEC.
This is an incredible demonstration that the AG Zero expert iteration method is a general method. If you go back to the discussions of AG Zero lo a month ago, there was a lot of skepticism that NNs would ever challenge Stockfish et al - they are just too good, too close to perfection, and chess not well suited for MCTS and NNs. Well, it turns out that AG Zero doesn't work as well in chess: it works better as it only…
Let's break this down and consider things carefully. To informed researchers, what is most surprising here is not that the AlphaGo Zero algorithm beat stockfish but that MCTS managed to outperform Alpha-beta search. I'll venture a hypothesis as to why this was. Informed skepticism would have discounted MCTS against alpha-beta search but wouldn't have put much stock into the idea that Neural Networks couldn't learn be…
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Shogi, chess and Go are "perfect information games", meaning you can see the whole game state. It's a whole different thing to be able to solve games where you don't see everything (based on uncertainty).
You mean, like poker? https://www.cmu.edu/news/stories/archives/2017/january/AI-be... Actually machines can have an even higher advantage in those cases, because they can be much better at estimating probabilities than humans. Think of card counting, for example.
Furthermore, techniques like monte-carlo tree search used in AlphaGo don't work very well for poker - You can't just try and find the "best move" from the current game state, or you will end up playing a highly-exploitable strategy. You essentially have to solve the entire game every time (or completely in advance) to make sure you are playing a balanced strategy.
Only the Counter-Factual Regret Minimization algorithm has been able to achieve this level of play in Heads Up, and right now it looks hard to scale to poker games with more players, like the full-ring games you see at the World Series of Poker, for example. We still have a ways to go in Poker AI.
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It briefly became popular in the otaku culture from an anime called Hunter X Hunter.
I'm curious to see if "San Gatsu no Lion" (the Lion of March) will spark interest. I highly recommend it to anyone interested in more slice-of-life/drama kinds of things. It's quite a beautiful anime/manga, even if the shogi isn't quite centre stage.
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> which would never be considered by ... a superhuman How would you know?
I don’t see it in my database, and it’s never been played on Lichess, even in bullet games.
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I don’t see it in my database, and it’s never been played on Lichess, even in bullet games.
Which, of course, is not evidence that a superhuman wouldn't consider such a move. AlphaGo also made unusual moves that looked like mistakes, but turned out to be insights.