And in 2020 Stockfish 12 adding some NN evaluation. And then in 2023 Stockfish 16 entirely removing the classical position evaluation code.
> I would also be VERY slow to claim that general-purpose models will never be competitive at chess.
This is not the claim. The claim is that for the same amount of compute, a general-purpose language model will never beat a Chess model. I'm dubious, but allow for the possibility that a language model could eventually compete at a top level against humans with enough compute. However, it will never compete with a dedicated Chess model with similar resources. Training a model for a specific task with the same amount of compute will outperform training a general-purpose model with the same amount of compute. This should be common sense, right? The bitter lesson was only about compute over human algorithms, not at throwing compute at a generalised domain over throwing compute at a specific domain.
You made arguments against two claims that I did not make (that I was trying to refute the bitter lesson or that I claimed that LLMs could never be competitive against humans at Chess), so I'd like to ask you read my statements a little more carefully this time.
I see arguments like this all of the time in ML. There is a large branch of statistical theory that can cast some doubt on this.
Stein's paradox: when three or more parameters are estimated simultaneously, there exist combined estimators more accurate on average (that is, having lower expected mean squared error) than any method that handles the parameters separately.
This goes deeply against "common sense", but is mathematically proven, empirically proven, etc.
If you are just taking a chess model vs. an LLM, you are right.
But if you want a chess model, a medical diagnosis model, and customer service chat model, at a high level the intuition of Stein's paradox is that one combined model will get you better performance than three task-specific models.
Most practitioners don't get this because what amounts to graduate-level stats theory is often brushed aside, and we keep relearning it the hard way.
In the latter case, the chess example would tend to support the Bitter Lesson, rather than refute it.
I would also be VERY slow to claim that general-purpose models will never be competitive at chess. It wasn't so long ago that transformers couldn't add two-digit numbers reliably without resorting to tool use. They are now as good at "mental arithmetic" as any human savant. It wouldn't surprise me at all to see someone come up with a model that just happens to be really, really good at absorbing the portions of its general training data having to do with chess.
In fact you could argue that AGI demands such a model, if we are to assume that LLMs are a guidepost in that direction.