leela-zero VS KataGo

https://github.com/leela-zero/leela-zero but it is not user friendly imo

Yeah, see https://github.com/leela-zero/leela-zero/pull/883 for the discussions near the origin of this idea, which Leela Zero was the first to use many years ago. KataGo's implementation is a bit different in minor ways, but still based on the same mathematical idea. https://github.com/lightvector/KataGo/blob/master/cpp/search/searchhelpers.cpp#L482

Totally agree. I don't even know what benefit they'd get at this point from keeping some parts locked up.

Anyway if you want something runnable Leela has a nice reimplementation: https://github.com/leela-zero/leela-zero

See https://github.com/leela-zero/leela-zero/issues/2445 for an example with Leela Zero failing to see an atari, even *with* tons of search. This is a similar issue - neural nets have a hard time perceiving things that depend sensitively on large areas when unusual shapes are involved.

(https://github.com/leela-zero/leela-zero/issues/2273)

I think it's Leela https://github.com/leela-zero/leela-zero

There is Leela Zero

Yeah, it looks like I need to recompile Leela Zero with a 9x9 board size? https://github.com/leela-zero/leela-zero/pull/928 (and https://github.com/leela-zero/leela-zero/issues/2613)

> KataGo was trained with more knowledge of the game (feature engineering and loss engineering), so it trained faster.

Not really important to your point, but it's not really just that it uses more game knowledge. Mostly it's that a small but dedicated community (especially lightvector) worked hard to build on what AlphaGo and LeelaZero did. Lightvector is a genius and put a lot of effort into KataGo. It wasn't just add some game knowledge and that's it. https://github.com/lightvector/KataGo?tab=readme-ov-file#tra... has a bunch of info if you're interested.

Immediately recognise the author as the genius behind KataGo: https://github.com/lightvector/KataGo

(also, if you want to donate GPU time, https://katagotraining.org/ would be happy to have more people contributing to training as well!)

> Kellin Pelrine, an American player who is one level below the top amateur ranking, beat the machine by taking advantage of a previously unknown flaw that had been identified by another computer. But the head-to-head confrontation in which he won 14 of 15 games was undertaken without direct computer support.

My take: what Kellin Pelrine really exploited is that the AI can't learn and adapt. Even GPT can't learn or adapt to anything beyond its context window. It took a computer to find and teach him the winning strategy, and it probably took a lot longer than AlphaGo did to train. But once he learned, he had the advantage; meanwhile AlphaGo never adapted and learned to counter the strategy itself, because it can't.

One thing to note is that he beat KataGo [1] and Leela Zero [2], but not AlphaGo or AlphaZero, because the AlphaGos aren't public. So it's possible he wouldn't actually beat the real AlphaZero with this strategy. But considering the strategy he used works in theory work against any model with AlphaGo/AlphaZero's design (he beat Leela Zero which has the exact same model), and Leela Chess and Stockfish are apparently better than AlphaZero now; I think he would still win.

[1] https://github.com/lightvector/KataGo

[2] https://github.com/leela-zero/leela-zero

Experimentally, KataGo did also try some limited ways of using external data at the end of its June 2020 run, and has continued to do so into its most recent public distributed run, "kata1" at https://katagotraining.org/. External data is not necessary for reaching top levels of play, but still appears to provide some mild benefits against some opponents, and noticeable benefits in a useful analysis tool for a variety of kinds of situations that don't occur in self-play but that do occur in human games and games that users wish to analyze.

I've been using ChatGPT since launch and constantly seeking out examples of how others have been using it. A few years ago I started using KataGo with Sabaki to improve my go-playing abilities. I've known about token embeddings in neural networks before ChatGPT was a twinkle in OpenAI's eye. I was there, but I haven't seen everything you've seen, so please show me. If the truth is that ChatGPT has canned responses to some prompt or set of prompts, then I want to believe that it does. If I have misconceptions about anything, I want to break those misconceptions. As long as your beliefs and mine contradict one another, one of us has the opportunity to learn.

For some stuff besides LCB, see https://github.com/lightvector/KataGo/blob/master/docs/KataGoMethods.md for a summary of a few more recent other things KataGo added that hadn't been done in earlier bots.

I'd suggest KataGo, which is much stronger and more actively developed than Leela Zero https://github.com/lightvector/KataGo