igoki
KataGo
igoki | KataGo | |
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13 | 49 | |
158 | 3,246 | |
- | - | |
2.0 | 9.3 | |
10 months ago | 12 days ago | |
Clojure | C++ | |
Eclipse Public License 1.0 | GNU General Public License v3.0 or later |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
igoki
- Here is my setup to play online with a go board.
- Picture to SGF android app?
- Just ordered this on a whim
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Contributing to open-source go projects?
There's a neat project that allows players to use ogs with a real board. It's one I hope will get some more attention. https://github.com/CmdrDats/igoki
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Physical go board projects
I can recommend u/cmdrdats' program Igoki.
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An article on an AI goban device that looks like a real go board and can be played on with normal stones, but at the same time has a built-in AI automatically recording your games, offering various levels of opposition and providing many other features.
You could also try using a program like igoki with a projector to play on OGS against a bot.
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A new Python package to play Go online from a physical board.
A: Yeah, sort of. I've seen that igoki is back under development by /u/cmdrdats and it seems like kifusnap is popular as well. There are any number of github projects that can read the current state of a go board. Those projects do all kinds of things this project can't do, such as reading boards from weird lighting or interfacing cleanly with OGS.
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igoki 0.7 update - now with manual screen capture
Release page: https://github.com/CmdrDats/igoki/releases/tag/0.7
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igoki pre-release 0.6 published (play on OGS with physical game board)
and done. https://github.com/CmdrDats/igoki/releases/tag/0.7 - it works pretty amazingly! Thank you for the suggestion!
- igoki - play on OGS on a physical board, or review/record physical game.
KataGo
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After AI beat them, professional Go players got better and more creative
> 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.
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Monte-Carlo Graph Search from First Principles
Immediately recognise the author as the genius behind KataGo: https://github.com/lightvector/KataGo
- Request for help getting two specific outputs from the Katago AI engine
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KataGo should be partially resistant to cyclic groups now
(also, if you want to donate GPU time, https://katagotraining.org/ would be happy to have more people contributing to training as well!)
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Man beats machine at Go in human victory over AI
> 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.
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I wonder if these ChatGPT answers will every get nuked
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.
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Human Go players beat top Go AIs using a "trick"
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.
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DeepMind has open-sourced the heart of AlphaGo and AlphaZero
I'd suggest KataGo, which is much stronger and more actively developed than Leela Zero https://github.com/lightvector/KataGo
- KataGo changes training framework from TensorFlow to PyTorch
What are some alternatives?
Sabaki - An elegant Go board and SGF editor for a more civilized age.
alpha-zero-boosted - A "build to learn" Alpha Zero implementation using Gradient Boosted Decision Trees (LightGBM)