So this is what I’m excited about in AI.

LLMs are statistical machines that simply output reasonable sequences of tokens. Useful! Not particularly smart, but it approximates language. I think it proves that a great majority of what humans do is learned sequences of behaviors.

But now we’re working on corralling that statistical language into workflows that improve the reasoning of the output. These are the first experiments into what makes thinking actually work. Is it iteratively refining a rough concept (like we’re seeing in this paper)? Or is it subdividing tasks into more easily solved problems (like the Atom of Thoughts paper)?

Once we find something that works, a real theory of intelligence seems much more likely to emerge. If that happens, I wouldn’t be surprised to see LLMs die out in favor of something far simpler and more efficient.

Very similar to chain of draft but seems more thorough