Last time I read about this the main practical difficulty was model transferability. The very thing that makes it so powerful and efficient is also the thing that make it uncopiable, because sensitivity to tiny physical differences in the devices inevitably gets encoded into the model during training. It seems intuitive this is an unavoidable, fundamental problem. Maybe that scares away big tech, but I quite like the…
Well, the brain is a physical neural network, and evolution seems to have figured out how to generate a (somewhat) copiable model. I bet we could learn a trick or two from biology here.
There's indeed a nice trick to be learned from cognitive science focused in biological cognition: the mind is embodied and embedded. Which means, roughly, that it is not portable. It doesn't store things like "glass at position x,y" but only "glass is at a small movement of the hand towards the right". Consequently, whatever gets encoded only makes sense within a given body and only inasmuch as it relied on its environment (with humans, that includes social environments). The good news is that, despite being not portable, this reliance on physical properties might be a step in the right direction, after all.