Andrej Karpathy: belief

Transcript context

…I’m not sure that alone explains it. I personally have tried to get LLMs to be useful in domains which are just pure language-in, language-out, like rewriting transcripts, coming up with clips based on transcripts. 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. Our mutual friend, Andy Matuschak, told me that he 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. He tried in-context learning with a few-shot examples. He tried supervised fine-tuning and retrieval. He could not get them to make cards to his satisfaction. So I find it striking that even in language-out domains, it’s very hard to get a lot of economic value out of these models separate from coding. I don’t know what explains it. That makes sense. I’m not saying that anything text is trivial. I do think that code is pretty structured. Text is maybe a lot more flowery, and there’s a lot more entropy in text, I would say. I don’t know how else to put it. Also code is hard, and so people feel quite empowered by LLMs, even from simple knowledge. I don’t know that I have a very good answer. Obviously, text makes it much, much easier, but it doesn’t mean that all text is trivial. How do you think about superintelligence? Do you expect it to feel qualitatively different from normal humans or human companies?…

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