alt Hacker NewsThat's fundamental to how anything that compresses/understands the world has to work, in the Kolmogeravian sense. That's why people denigrate LLMs as being just "next token predictors" - they're not wrong, but they're missing the point.
Because to do that kind of prediction out in the world you have to build up an accurate model of reality - a model that includes yourself! Which is why we and LLMs are self aware.
For the "how", it's been known for some time that LLMs operate on a Reimannian manifold - the semantic manifold - and that's a good place to start if you want to learn how they actually work; how a Reimannian manifold (plus some extra structure on top) can represent natural language in a form you can do work with is the part I find particularly beautiful. At a high level, the neocortex and LLMs appear to compute on the manifold in basically the same way - though a lot of the details are different; both are more sophisticated in some areas and less in others.
I'm afraid the precise connection you're making isn't totally obvious to me.
As far as prediction - I mean sure the cortex and LLMs do prediction, but then so can RNNs or diffusion models or any other generative model. Really any ML architecture is learning to compress its environment in pursuit of modelling. 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?
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?