I can see why Mordvintsev et al are up to what they are doing, but to be honest I'm struggling with understanding the point of using a neural-net to 'emulate' CAs like OP seems to be doing (and as far as I can gather, only totalistic ones too?).
It sounds a bit like swatting a fly using an H-bomb tbh, but maybe someone who knows more about the project can share some of the underlying rationale?
I think the biggest advantage NNs have over CA is the fact that most CA only provide localized computation. It can take a large number of fixed iterations before information propagates to the appropriate location in the 1d/2d/3d/etc. space. Contrast this with arbitrary NN topology where instant global connectivity is possible between any elements.
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[ 2.5 ms ] story [ 20.6 ms ] threadhttps://www.neuralca.org/
https://google-research.github.io/self-organising-systems/di...
https://google-research.github.io/self-organising-systems/is...
I can see why Mordvintsev et al are up to what they are doing, but to be honest I'm struggling with understanding the point of using a neural-net to 'emulate' CAs like OP seems to be doing (and as far as I can gather, only totalistic ones too?).
It sounds a bit like swatting a fly using an H-bomb tbh, but maybe someone who knows more about the project can share some of the underlying rationale?
I clicked in the hope that it would tell me something about how CAs can be 'trained' and 'used' to make useful predictions somehow.
Instead, I got a neural network which is trained to predict the t+3 step of a CA based on an initial state.
Am I missing something?