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  • Of course AGI is possible, human brains can’t violate P=NP any more than silicon can.

    Our current approach may be flawed for sure, but there’s nothing special about nature compared to technology, other than the fact it’s had a billion times longer to work on its tech.

  • Right, simply scaling won’t lead to AGI, there will need to be some algorithmic changes. But nobody in the world knows what those are yet. Is it a simple framework on top of LLMs like the “atom of thought” paper? Or are transformers themselves a dead end? Or is multimodality the secret to AGI? I don’t think anyone really knows.

  • 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.

  • Or in other words, “free will” is a macroscopic effect arising from the fundamental laws of the universe. Like most everything else we deal with.

    Like… temperature doesn’t really exist, it’s really just an average of kinetic energy of particles. But that doesn’t stop it from being a useful concept!