I'm with you that LLM's don't work like the human brain. They were built for a very specific task. But that's a model architecture problem (and being gimped by having only two dimension of awareness, arguably two if you count "self attention" another limiting factor in it's depth of understanding, see my post history if you want). I wouldn't bet against us making it to agi however we define it through incremental improvements over the next decade or two.

Yup, broadly agreed. I’m not saying “give it everything”. I’m sure regions would develop to simplify processing via filtering.

Actually, most models are already doing some form of filtering AFAIK, but I don't know how comparable it is to our sensory system. CNN's, for example, work the way our eyes work. The short of it is image data goes through a few layers, each node in the next layer collecting the aggregate data of several from the last (usually a 3x3) grid. Each of these layers has filters to determine the output of that node, which need to be trained to collectively recognize specific patterns in the data, like a dog. Source: lecture notes and homework from my applied neural networks class