this post was submitted on 09 Sep 2024
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Yeah, but since Neural networks are really function approximators, the farther you move away from the training input space, the higher the error rate will get. For multiplication it gets worse because layers are generally additive, so you'd need layers = largest input value to work.
hear me out: evolving finite state automaton (plus tape)
Is that a thing? Looking it up I really only see a couple one off papers on mixing deep learning and finite state machines. Do you have examples or references to what you're talking about, or is it just a concept?
just a slightly seared concept
though it's just an evolving turing machine