Skip to content

Comment on State-space models can learn in-context by gradient descentparent

Comments

One reason to believe there's even new low hanging fruit (that doesn't even require new math) is how simple and trivial the "Attention Heads" structure of the Transformer architecture really is. It's not advanced at all. It was just a great ideal that panned out that pretty much any creative AI researcher could've thought up after smokin' a joint. lol. I mean someone could do trivial experiments with different Perceptron network structuring and end up revolutionizing the world.

I think things are gonna get interesting real quick once LLMs themselves start "self experimenting" with writing code for different architectures.

AboutSource Built by g1lg1l

Hackerly is an independent reader for Hacker News, built on the public HN API. Not affiliated with Y Combinator.