[theGnuMe]

Thanks for the reply.

How would CAs vary the amount of compute? Don't you have to compute everything state-wise every iteration?

Right now my understanding is that in neural cellular automata people replace the update rule with a DNN. And this DNN is trained on small inputs. Basically a cell's neighborhood input vs a "pixel" vs token level input... a cellular neighborhood here is basically patches which aligns with DNNs anyway.

A good example is: https://distill.pub/2020/growing-ca/

The examples remind of inpainting though in some sense.

You can apply transformers to this to get a shared memory (people have done this I believe).

Too be honest I feel like neural CAs are a trick but I am probably wrong.

[bestatsiege_01]

My hypothesis is that CA's could have input and output cells, then just run the CA, pass a single or continuous input and then parse the outputs when available.

But - I haven't been able to get such a concept to work. I maybe missing some fundamental theory / understand which prevents such a structure (or at least limits is value).

One major challenge is how do you train an unconstrained process?

Ill take a look at Neural CA's. thanks for sharing.

Thanks for sharing Neural CA's. I'll spend some time on them, much easier to train a DNN.

[theGnuMe]

Wolfram talks about mobile automata.. This is where you calculate one cell at a time.

https://en.wikipedia.org/wiki/Mobile_automaton and https://www.wolframscience.com/nks/p71--mobile-automata/

I'm confused as to how you actually train a cellular automaton? How does one do it? Is it just rule search to match a pattern at time t given input state from time t-1 (and repeated?).. it seems like you'd have a bunch of boolean equations and then look for and extract its solutions.