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rootatixww3

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an LLM can't access its high dimensional vectors any more than we can access whatever the brain is doing at a low level

all kind of math structures were found in mammals brains - fourier transforms (well, not exactly), ballistic equations, Gabor filters

who knows how exactly we approximate the magnitude of a math operations, maybe we also use helices

my point is that we dont know if what we discover the neural networks doing (helical manifolds) is actually the same thing a brain converges on, or not

and there is an implicit bias here - evolution created language, and we forced neural networks to also evolve to be good at it. so it wouldn't be surprising to find some convergence, this particular kind of language turned out to work well (words, linear sentences, grammar)