I have explored trying to make global state CA systems, with no success. If anyone has examples of trainable global state CA systems please share, I'd love to learn more.
My idea was that there would be global information but local rules. And the local rules depended on the global information. However I struggled to train such a system and gave up. I would like to go back to it though and some expert perspective might be enough to push me back to it.
I find CA's interesting because they are both continuous and could conceptually vary the amount of compute attributed to a particular problem. NN's can't really do either of these things.
(I appreciate LLM's vary the amount of compute based on the question. But just running the same model over and over seems like an immature method, there must be a better way.)
My idea was that there would be global information but local rules. And the local rules depended on the global information. However I struggled to train such a system and gave up. I would like to go back to it though and some expert perspective might be enough to push me back to it.
I find CA's interesting because they are both continuous and could conceptually vary the amount of compute attributed to a particular problem. NN's can't really do either of these things.
(I appreciate LLM's vary the amount of compute based on the question. But just running the same model over and over seems like an immature method, there must be a better way.)