[ablatt89]

Can you quantitatively say that some cellular automata compute framework would give a broad or more accurate output than a typical linear compute framework? I fail to see how embedding some complex computation into a cellular automata framework with over-provisioned resources gives unique compute insight and it almost seems synonymous with some ML auto-scaler or some ML controller that dynamically scales compute when needed.

What specific problems are not tractable via traditional autoscaling methods that cellular automata can compute more efficiently or accurately? I understand you think stochastic type/life type computations are better suited for this, but that would be more of a hunch than verifiable proof.

[livcomp]

I accept that lots of folks don't and won't get this, but one specific problem I think is intractable via traditional means is: Actual computer security.

[ablatt89]

I think if you can write some pseudocode that summarizes the computation, it would help. I think I and others have seen the Github repo, but generally don't know where to start to analyze what it's doing.

For example, for computer security, if you write a stochastic algorithm pseudocode such that the cellular automata are essentially doing a "search" for something, an that the cellular automata replicate and scale for the purpose of this stochastic search, I think that would help people understand your computational model better. At least for me!