> More broadly, the predictive brain model would suggest that all of the brain, not just the neocortex, is dedicated to prediction. What would you say makes LLMs similar to the neocortex, rather than the basal ganglia or Broca's area?

The whole brain is most definitely not dedicated to prediction, and I don't think "prediction" is a very useful model anyways. You could say the hippocampus is for "prediction" if you really squint, but that's underselling what it's doing. And the basal ganglia operates off of prediction error, but it's more about regulating short timescale feedback loop learning than learning itself - speaking somewhat loosely because it's a particularly ancient structure in the brain and things get muddied.

LLMs obviously don't have functional equivalents to either of those - sure they can remember facts, but they can't intake new facts the way we can, the hippocampus is set up completely differently. But higher reasoning is humans is the neocortex, and that LLMs can do, and there's even structural parallels - LLMs and the neocortex are both implemented in layers, and you can even (with a shit ton of analysis) watch how LLMs walk the geometry across layers. The neocortex needs a lot fewer layers than LLMs, but that's because it's not pure feedforward across layers; the neocortex already does what the "looped LLM" people are trying to do.

> Similarly, if you agree with the Manifold Hypothesis, then all machine learning models operate on manifolds. I agree it's an exciting thought, but then I don't know what would distinguish an LLM from a VAE or SVM in terms of operating over a low-dimensional manifold embedded in high dimensional spaces - maybe just scale?

I view things more mathematically, and in math and physics it frequently turns out that there's really only one solution to a problem, or correct way to model something - and then everything else is just isomorphisms (which may be interesting objects in their own right!).

My interpretation/guess (informed by research, but with a lot of still unanswered questions) is that the manifold is fundamentally the structure that emerges from natural language: natural language has a computational model, just like Church/Turing machines have a computational model. Reimannian manifold is also underselling it a bit, it's also Finsler, and RoPE adds additional structure to that manifold (and when we get to RoPE it wouldn't surprise me if the neocortex and LLMs do diverge significantly in how they compute, but does the structure change? dunno yet).

Basically, I'm fairly confident in what I say about machine learning models that understand natural language; for non-NL machine learning, maybe the same structure emerges with enough complexity, but I haven't really pondered that.