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People misunderstand this paper. The point is not to create a strong chess engine. We already have those. It's also not to train it in a smart way. It's just su
by thomasahle 3y ago
People misunderstand this paper. The point is not to create a strong chess engine. We already have those. It's also not to train it in a smart way. It's just supervised learning.
The whole paper is just an argument against people who still think of transformers as "stochastic parrots".
Probably it's even a mainly internal Deep Mind debate, since people outside have long ago accepted that transformers obviously learn algorthms and do something akind to planning.
You can see this undertone many places in the paper, like when they emphasize the systems plays strongly without _explicit_ search. Or if you read the conclusion.
- helloplanets 3y agoWould be interesting to see how well it would fare in situations where you have to prove your knowledge of patterns regarding specific pieces outside the context of traditional chess. What I mean by this is playing Chess960 or solving a tactics problem that starts from a traditionally unreachable position, but otherwise adheres to the original rules. Even doing these things at an average level would be pretty mind blowing.
- Certhas 3y agoDid you read the blog post? The whole point of the blog post is that the model does not demonstrate that it's learning in interesting ways (as strongly as people make it out).
- jacquesm 3y ago> Did you read the blog post? Guidelines meditation for you. https://news.ycombinator.com/newsguidelines.html https://news.ycombinator.com/newsguidelines.html "Please don't comment on whether someone read an article. "Did you even read the article? It mentions that" can be shortened to "The article mentions that". "
- Certhas 3y agoFair, too late to edit it now.
- suddenlybananas 3y agoIt's much less impressive when its given an explicit internal model (stockfish move probabilities).
- deleted 3y ago[deleted]
- jebarker 3y agoCan you explain why you think the paper is evidence against stochastic parroting? To me this result seems even less sophisticated than predicting the next move directly GPT style (and that’s its charm since it’s much more data efficient than training that model would be). Isn’t this model just simply predicting the most likely next move that stockfish would play? I actually think their conclusion is mis-stated. Instead of saying this shows transformers aren’t mere statistical pattern recognizers but are powerful algorithm approximators they should say that it shows statistical pattern recognition trained on enough samples is sufficient for approximating sophisticated algorithms.