2026-10-22 15:44 UTC
Linear algebra + sigmoids are enough to approximate any function from R to R; in particular, a small set of a few layers of LLM neurons can connect and interact in pretty novel ways. There are some things they can’t do, and those things aren’t computable. If our brains can do non-computable things, then our brains are super-turing (and this, given the evidence, seems pretty unlikely right?).
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@dream_weasel@sh.itjust.works 2026-10-22 16:18
I’m not a neurologist or a philosopher so I think this may not be something I can speak intelligently about, but it seems to me that the question doesn’t really live in the function approximation, but in the gaps. Doing a single train of thought is the easy part. I think what you need is an “always online” feature to keep thinking and some way to be stateful (make memories) without constantly retraining the whole network or whole sets of layers. This is, I think, the benefit of the parallelism and asynchrony of the “individual neuron” discussed in the book.