alt Hacker NewsI agree, but maybe for different reasons. I think Karpathy is right. We need models that reason, not models that memorize.
Karpathy calls it a "Cognitive Core", and it's essentially a small model that learns to reason and look up the data it needs as opposed to a giant model that memorizes all the data in the world and tries to process large chunks of it all at once with every thought. I think it will be based on the thing that grokking, the lottery ticket hypothesis, and the universal weight subpspace hypothesis all point to.
Eventually someone will figure out how to build it and the entire economy that we've now built on top of the wacky idea that nothing can possibly ever get more efficient will collapse overnight.
Sometimes I wonder how much Nvidia would pay someone not to release a thing like that, and then I wonder if that's already happened.
The theorem you want to pay attention to is the no free lunch theorem. The important thing to understand there is that the larger models give you "free lunch" in the sense that you can approximate more different systems accurately at the cost of model size. If there was a Karpathy style universal solver, it wouldn't be very smart unless we scaled it up.
This isn't to say that there aren't a fair amount of wasted parameters in current LLMs, but then we already kinda knew that since you can quantize models down to 3-4 bits per weight often times with minimal loss.