A pretty obvious difference is that these models are still nowhere near as large or complex as a human brain. This network has 15 billion parameters, whereas a human brain is estimated to have 60 trillion neuronal connections. Additionally each neuron, of which a human brain has around 90 billion, can fulfill many more roles than a "neuron" in a language model.
Apples to oranges, but there's a pretty obvious complexity gap.
The neurons in Wernicke's area however is a very small subset of this, so since these models aren't doing anything related to taste or smell, etc that number isn't as relevant as you may think it is. The number of neurons more dedicated towards proprioception for example is quite vast, and often almost completely undiscussed by the AI community.
So you're not making quite the argument that you think you are; although the general idea that there's still a difference is obviously true (birds v planes, yada yada).
Yep. Also of note is the fact that human learning is fundamentally a more flexible process in that it can lay down new neuronal connections and in fact new neurons too.
I'm sure there are (evolutionary?) NN models that try to do things like this but I have no idea how successful they've been.
A pretty obvious difference is that these models are still nowhere near as large or complex as a human brain. This network has 15 billion parameters, whereas a human brain is estimated to have 60 trillion neuronal connections. Additionally each neuron, of which a human brain has around 90 billion, can fulfill many more roles than a "neuron" in a language model.
Apples to oranges, but there's a pretty obvious complexity gap.