> Its really just a matter of degrees. There are 1 million, 1 million, 1 trillion parameter LLMs... and you keep scaling those parameters and you eventually get to humans.

It isn’t because humans and current LLMs have radically different architectures

LLMs: training and inference are two separate processes; weights are modifiable during training, static/fixed/read-only at runtime

Humans: training and inference are integrated and run together; weights are dynamic, continuously updated in response to new experiences

You can scale current LLM architectures as far as you want, it will never compete with humans because it architecturally lacks their dynamism

Actually scaling to humans is going to require fundamentally new architectures-which some people are working on, but it isn’t clear if any of them have succeeded yet