2026-10-22 16:18 UTC
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.
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@Artisian@lemmy.world 2026-10-23 06:42
(book? The linked article is like 4 pages. References are somewhat larger, but nothing over 60 afaict?) They claim that silicon is essentially incapable of doing what is needed; if you disagree with this, then computationally everything you can build with a circuit board + SSD + RAM you can build with an LLM with a deep layer or 7. In particular, you can do online learning of a sort. I note that wasps and some beetles don’t seem to have memory whatsoever, and plasticity seems awfully low. The authors, and I think most people, would say they are probably a little conscious.