Dwarkesh Patel

I personally have tried to get LLMs to be useful in domains which are just pure language in, language out.

Like rewriting transcripts, like coming up with clips based on transcripts, etc.

And you might say, well, it's very plausible that I didn't do every single possible thing I could do.

I put a bunch of good examples in context, but maybe I should have done some kind of fine-tuning, whatever.

So our mutual friend, Annie Matuszak, told me that

He actually tried 50 billion things to try to get models to be good at writing spaced repetition prompts.

Again, very much language in, language out tasks, the kind of thing that should be dead center in the repertoire of these LLMs.

And he tried in-context learning, obviously, with a few short examples.

He tried, I think, he told me a bunch of things, like supervised fine-tuning and retrieval, whatever.

And he just could not get them to make cards to a satisfaction.

So I find it striking that even in language out domains, it's actually very hard to get a lot of economic value out of these models separate from coding.

And I don't know what explains it.

How do you think about superintelligence?

Do you expect it to feel qualitatively different from normal humans or human companies?

roughly speaking.

I guess automation includes the things humans can already do and super intelligence supplies things to humans.

Yeah.

But I guess maybe less abstractly and more sort of like qualitatively, do you expect something to feel like, okay, because this thing can either think so fast or has so many copies or the copies can merge back in themselves or is quote unquote much smarter, any number of advantages an AI might have.

It will qualitatively, the civilization in which these AIs exist will just feel qualitatively different from human civilization.

Let me probe on that a bit.