As best as I’ve been able to research, it’s still under active exploration and there are hypotheses but no real answers. I believe research has basically been circling around the recent understanding that in addition to being part of how the brain is wired, it is also an active, deliberate (if unconscious) mechanism that takes place in the background and is run “at a higher priority” during sleep (sort of like an indexing daemon running at low priority during waking hours then getting the bulk of system resources devoted to it during idle).
There are also studies that show “data” in the brain isn’t stored read-only and the process of accessing that memory involves remapping the neurons (which is how fake memories are possible) - so my take is if you access a memory or datum sequentially start to finish each time the brain knows this is to be stored verbatim for as-is retrieval but if you access snapshots of it or actively seek to and replay a certain part while trying to relate that memory to a process or a new task, the brain rewires the neural pathways accusingly. Which implies that there us an unconscious part that takes place globally plus an active, modifying process where how we use a stored memory affects how it is stored and indexed (so data isn’t accessed by simple fields but rather by complex properties or getters, in programming parlance).
I guess the key difference from how machine learning works (and I believe an integral part of AGI, if it is even possible) is that inference is constant, even when you’re only “looking up” data and you don’t know the right answer (i.e. not training stage). The brain recognizes how the new query differs from queries it has been trained on and can modify its own records to take into account the new data. For example, let’s say you’re trying to classify animals into groups and you’ve “been trained” on a dataset that doesn’t include monotremes or marsupials. The first time you come across a platypus in the wild (with its mammaries but no nipples, warm-blooded but lays eggs, and a single duct for waste and reproduction) you wouldn’t just mistakenly classify it as a bird or mammal - you would actively trigger a (delayed/background) reclassification of all your existing inferences to account for this new phenomenon, even though you don’t know what the answer to the platypus classification question is.
imo, it amounts to revisiting concepts once more general principles are found — and needed. For instance, you learn the alphabet, and it's hard. the order is tricky. the sounds are tricky, etc. but eventually, it get distilled to a pattern. But you still have to start from A to remember what letter 6 is, until you encounter that problem many times, and then the brain creates a 6=F mapping. I think of it in economic terms: when the brain realizes it's cheaper to create a generalization, it does so on the fly, and that generalization takes over the task.
Somtimes it's almost like creating a specialist shard to take over the task. Driving is hard at first, with very high task overload, lots to pay attention to. With practice, it becomes a little automated part of yourself takes care of those tasks while your main general intelligence can do whatever it likes, even as the "driver" deals with seriously difficult tasks.
There are also studies that show “data” in the brain isn’t stored read-only and the process of accessing that memory involves remapping the neurons (which is how fake memories are possible) - so my take is if you access a memory or datum sequentially start to finish each time the brain knows this is to be stored verbatim for as-is retrieval but if you access snapshots of it or actively seek to and replay a certain part while trying to relate that memory to a process or a new task, the brain rewires the neural pathways accusingly. Which implies that there us an unconscious part that takes place globally plus an active, modifying process where how we use a stored memory affects how it is stored and indexed (so data isn’t accessed by simple fields but rather by complex properties or getters, in programming parlance).
I guess the key difference from how machine learning works (and I believe an integral part of AGI, if it is even possible) is that inference is constant, even when you’re only “looking up” data and you don’t know the right answer (i.e. not training stage). The brain recognizes how the new query differs from queries it has been trained on and can modify its own records to take into account the new data. For example, let’s say you’re trying to classify animals into groups and you’ve “been trained” on a dataset that doesn’t include monotremes or marsupials. The first time you come across a platypus in the wild (with its mammaries but no nipples, warm-blooded but lays eggs, and a single duct for waste and reproduction) you wouldn’t just mistakenly classify it as a bird or mammal - you would actively trigger a (delayed/background) reclassification of all your existing inferences to account for this new phenomenon, even though you don’t know what the answer to the platypus classification question is.