The models we use nowadays operate on discrete tokens. To overly reduce the process of human learning, we take a constant stream of realtime information. It never ends and it’s never discrete. Nor do we learn in an isolated “learn” stage in which we’re not interacting with our environment.
If you try taking reality and breaking into discrete (ordered in the case of LLMs) parts, you lose information.
Having said that, I think this picture is missing something: when we teach each new generation what we know, part of that process involves recapitulating the steps by which we got to where we are. It is a highly selective (compressed?) history, however, focusing on the things that made a difference and putting aside most of the false starts, dead ends and mistaken notions (except when the topic is history, of course, and often even then.)
I do not know if this view has any significance for AI.
Is human knowledge really derived in a similar manner though? That reduction of biological processes to compression algorithms seems like a huge oversimplification.
It's almost like saying that all of of human knowledge derives from Einstein's Field Equations, the Standard Model Lagrangian, and the Second Law of Thermodynamics (what else could human knowledge really derive from?) and all we have to do to create artificial intelligence is just to model these forces to a high enough fidelity and with enough computation.