This really reminds me of the web as I remember it from the mid-to-late 90's; I feel like I'm just a click away from the old deoxy.org, if anyone remembers that. (Don't go there now; the domain appears to have been long-ago hijacked.)
AFAIK chess is has been "solved" for a few years in the sense that Stockfish running on modern laptop with 1 minute per move is unbeatable from the starting position.
This is not true. Stockfish is not unbeatable by another engine, or another copy of Stockfish.
Chess engines have been impossible for humans to beat for well over a decade.
But a position in chess being solved is a specific thing, which is still very far from having happened for the starting position. Chess has been solved up to 7 pieces. Solving basically amounts to some absolutely massive tables that have every variation accounted for, so that you know whether a given position will end in a draw, black win or white win. (https://syzygy-tables.info)
You can run Stockfish single threaded in a deterministic manner by specifying nodes searched instead of time, so in principle it is possible to set some kind of bounty for beating Stockfish X at Y nodes per move from the start position, but I haven't seen anyone willing to actually do so.
I'm no expert on chess engine development, but it's surprising to me that both lc0 and stockfish use SPSA for "tuning" the miscellaneous magic numbers which appear in the system rather than different black box optimization algorithms like Bayesian optimization or evolutionary algorithms. As far as I am aware both of these approaches are used more often for similar tasks in non-chess applications (ex. hyperparameter optimization in ML training) and have much more active research communities compared to SPSA.
Is there something special about these chess engines that makes SPSA more desirable for these use cases specifically? My intuition is that something like Bayesian optimization could yield stronger optimization results, and that the computational overhead of doing BO would be minimal compared to the time it takes to train and evaluate the models.
I know a fair deal about the subject of chess AI, but when I was reading this and I didn't understand. I was polarized, was I reading a mastermind that was way above my level? Or someone way too confident that learned enough buzzwords through an LLM to briefly delude someone else other than themselves?
A quick visit at the homepage suggests that it's probably the latter. I don't want to be rude, not posting out of malice, but if someone else was reading this and was trying to parse it, I think it might be helpful to compare notes and evaluate whether it's better to discard the article altogether.
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[ 4.6 ms ] story [ 41.4 ms ] threadResponse from the author of Viridithas, there is a link to this engine in her webpage.
Chess engines have been impossible for humans to beat for well over a decade.
But a position in chess being solved is a specific thing, which is still very far from having happened for the starting position. Chess has been solved up to 7 pieces. Solving basically amounts to some absolutely massive tables that have every variation accounted for, so that you know whether a given position will end in a draw, black win or white win. (https://syzygy-tables.info)
Is there something special about these chess engines that makes SPSA more desirable for these use cases specifically? My intuition is that something like Bayesian optimization could yield stronger optimization results, and that the computational overhead of doing BO would be minimal compared to the time it takes to train and evaluate the models.
A quick visit at the homepage suggests that it's probably the latter. I don't want to be rude, not posting out of malice, but if someone else was reading this and was trying to parse it, I think it might be helpful to compare notes and evaluate whether it's better to discard the article altogether.
See the main page https://girl.surgery/
And there's:
> Here's a video of me burning off my pubic hair in the alley.