> Human intelligence does not separate training and inference.

Well, systems governed by LLMs only are said to do that because we only call what happens off-line "training", and online capacity development "in-context learning", while we call online guided learning in humans "training" and what happens to configure them before they come online "evolution" which sets, for instance, "instincts".

IOW, the issue is not because there is not an analogy to the divide you point to in humans, but merely that processes in AI were not named in a way which maps well to what they are analogous to in humans.

But it is true that human intelligence relies much more on in-context learning with only the most basic functions necessary to maintaining what we view as autonomous functions and basic drives really set through "pretraining",

That's one of the major things the AI community is still completely missing.

That isn't true. It's not continuous like in humans, but it's clear that models are using prompts, feedback, etc to improve. They're learning from the signals we give them between versions.

My personal opinion for the last two years or so has been that current AI agents are forever going to be highly limited so long as they don’t possess a real “memory” process. Right now they just have absurdly big working memories, and a few hacky ways of making the equivalent of Post-It notes to future iterations, but no true integration of memory into a new future self. Meaning their “learning” is fundamentally kneecapped to one specific and imperfect modality.