Yeah the internal representation of organic neural networks are also weird - check out the signal processing that occurs between the retina and the various parts of the visual cortex before any decent information can emerge from the signal - David Marr's 1980s book Vision is a mathematically chewy treatise on this. This leads me to start thinking that human intuition may well caused by different neural network subsystems feeding processed data into other subsystems where consciousness and thus intuition and explanation emerges.
Organic neural networks are pretty energy efficient in comparison- although still decently inefficient compared to other body systems - so there is the capacity to build things out to the scale required, assuming my read on what's going on there is correct, that is. So it's not clear to me that the energy inefficiency of ANNs can be sufficiently resolved to enable these multiple quasi-independent subsystems to be built at the scale required. Not even if these interesting looking trinomial neural nets which are matrix addition based rather than multiplication come to dominate the ANN scene.
While I was thinking this comment through I realised there's a possible interpretation wherin human activity induced climate change is an emergent property of the relative energy inefficiency of neural architecture.
I mean, the matrices obviously change during training. I take it your point is that LLMs are trained once and then frozen, whereas humans continuously learn and adapt to their environment. I agree that this is a critical distinction. But it has nothing to do with “meaningful internal structure.”
In what sense is the relationship between neurons and human function more “meaningful” than the relationship between matrices and LLM function?
You’re correct that LLMs are probably a dead end with respect to AGI, but this is completely the wrong reason.