suckerpinch
8 HN recommenders · recommended in 9 months, 2019–2026
42:36 · 965K views · Jul 15, 2019
Tom Scott-style absurdist tour by Tom7 of SIGBOVIK chess projects: a tournament of 30 weird chess algorithms used to rate a color- and piece-blind chess program. Topics include mirrors, arithmetic encoding, perverted game tree search and a diluted Stockfish.
HN commenters call it a must-watch for its deadpan, absurdly elaborate engineering and cite it for ideas like diluting strong engines with random moves.
13 comments from Hacker News
Tom7 is brilliant for anyone that hasn't seen him before. His Reverse Emulation video ( [LINK] ) and Weird Chess Algorithms ( this video ) are a must watch. His dead pan delivery coupled with the ridiculous amount of effort he puts into things that don't _really_ matter is honestly inspiring.
It's unknown what role if any BPE plays. I was surprised to discover that the final probability of a move is equal to the probability of each token from the root prompt, i.e. even though "1.Nf3 e5" is encoded as ['1', '.', 'N', 'f', '3', ' e', '5'] the probability of e5 seems unaffected by the fact that Nf3 is 3 tokens as opposed to one. You're right that coming up with a token mapping could help things. It's a bit tricky to do that right now. Your options for fitting a custom vocab seems to be "use sentencepiece to fit a vocab, then modify the gpt-2 codebase to use the sentencepiece library for decoding". I am honestly not sure if the output of sentencepiece is compatible with traditional encoders. What I mean is, it doesn't seem to generate an encoder.json + vocab.bpe file. It seemed to be some other kind of format. So I'm not sure if the tooling that has evolved around OpenAI's encoder format would be applicable there. I really don't know, though. According to this slatestarcodex comment, someone got superior results on solely algebraic notation (which looks like g1f3 instead of Nf3): [LINK] Another extension that might help is to periodically inject the full FEN board state. This was the format we were going to try next, which injects the full FEN after every move: [LINK] I'm so happy to get to work with GPT-2 1.5B. It's been a lot of fun to train. By the way, if you like this kind of thing, you'll love Elo World. this video
Suckerpinch (Tom) recently had a video[1] about predicting the position of the oponents pieces from knowning just white. I don't think he's aware of this. Great video anyway. He trained an NN to predict the pieces and then used Stockfish on the resulting position. [1] this video
This reminded me of Tom7's video where he made a bunch of ridiculous engines and pitted them against each other (and against "diluted" versions of Stockfish): this video [LINK]
Anything that catches your eye is good but my favorites are: 30 Weird Chess Algorithms: ELO World this video Reverse Emulating the NES to give it SUPER POWERS! [LINK]