The wall confronting large language models

We know the healthy brain is unpredictable. We suspect error minimization and prediction are not central tenets. We know the brain creates memory via differences in sharp wave ripples. That it's oscillatory. That it neither uses symbols nor represents. That words are wholly external to what we call thought. The authors deal with molecules which are neither arbitrary nor specific. Yet tumors ARE specific, while words are wholly arbitrary. Knowing these things should offer a deep suspicion of ML/LLMs. They have so little to do with how brains work and the units brains actually use (all oscillation is specific, all stats emerge from arbitrary symbols and worse: metaphors) that mistaking LLMs for reasoning/inference is less lexemic hallucination and more eugenic.

"That words are wholly external to what we call thought." may be what we should learn, or at least hypothesize, based on what we see LLMs doing. I'm disappointed that AI isn't more of a laboratory for understanding brain architecture, and precisely what is this thing called thought.

What do you think about the idea that LLMs are not reasoning/inferring, but are rather an approximation of the result? Just like you yourself might have to spend some effort reasoning, on how a plant grows, in order to answer questions about that subject. When asked, you wouldn't replicate that reasoning, instead you would recall the crystallized representation of the knowledge you accumulated while previously reasoning/learning. The "thinking" in the process isn't modelled by the LLM data, but rather by the code/strategies used to iterate over this crystallized knowledge, and present it to the user.

The question is how to model the irreducible. And then to concatenate between spatiotemporal neuroscience (the oscillators) and neural syntax (what's oscillating) and add or subtract what the fields are doing to bind that to the surroundings.

This is toughest part. We need some kind of analog external that concatenates. It's software, but not necessarily binary, it uses topology to express that analog. It somehow is visual, ie you can see it, but at the same time, it can be expanded specifically into syntax, which the details of are invisible. Scale invariance is probably key.

We don't know those things about the brain. I don't know why you keep going around HN making wildly false claims about the state of contemporary neuroscience. We know very very little about how higher order cognition works in the brain.

Of course we know these things about the brain, and who said anything about higher order cognition? I'd stay current, you seem to be a legacy thinker. I'll needle drop ONE of the references re: unpredictability and brain health, there are about 30, just to keep you in your corner. The rest you'll have to hunt down, but please stop pretending you know what you're talking about.

Your line of attack which is to dismiss from a pretend point of certainty, rather than inquiry and curiosity, seems indicative of the cog-sci/engineering problem in general. There's an imposition based in intuition/folk psychology that suffuses the industry. The field doesn't remain curious to new discoveries in neurobiology, which supplants psychology (psychology is being based, neuro is neural based). What this does is remove the intent of rhetoric/being and suggest brains built our external communication. The question is how and by what regularities. Cog-sci has no grasp of that in the slightest.

https://pubmed.ncbi.nlm.nih.gov/38579270/

Your writing reminds me of a schizophrenic.