[concinds]

The brain is composed of distinct regions that specialise in specific tasks. It's reasonable to assume AGI would be the same.

So the goal should be: we've created a "language module" (LLMs) and a "visual perception module" (computer vision), but we also need to add a "logic module", a "reasoning module", an "empathy module", etc, while continuing to improve each.

I just don't see how you could get an LLM, no matter how advanced, to recognize a car. Even if it can describe cars (wheels, windshield, doors) it doesn't know what any of those components look like. It's like that old joke about philosophers being unable to define a chair beyond "I'll know it when I see it".

[avereveard]

it's even more foundamental than that, the brain is constantly learning and retaining new knowledge and solve problems with a mix of old knowledge and knowledge learned in the context of the problem, either by experimentation or research

these net can replicate at most the first step for now, even tuning by reingofrcement learning is more of a set up batch than an ongoing thing, and certainly not something they will be able to do in the context of a single problem but as part of a retraining

agi is still a fair bit away, I'm unsure if these super large architecture will ever get to replicate the second part of our brain, the flexibility while on the job, because of their intrinsic training mechanism.