New paper: "Large Language Models & Emergence: A Complex Systems Perspective" (D. Krakauer, J. Krakauer, M. Mitchell). We look at claims of "emergent capabilities" & "emergent intelligence" in LLMs from perspective of what emergence means in complexity science. ⬇️

Hmm, I find current reasoning LLMs incredibly useful as math and programming and research assistants. However the limitations that this Apple paper notes have been obvious to me from the start of my interaction with them. The way I would put it conceptually is: these models are amazing and amazingly useful, but if the complexity intervening between their knowledge and your problem is too much, they will fail. E.g. they are good at doing some quite complex math so long as it's basically in the domain of math they already know. Same with programming. But if you give them math or programming in a too far out of the mainstream domains they're trained on, they will flail around. So they tend to mess up e.g. with paraconsistent logic or MeTTa programming because these things are a bit weird compared to the bulk of their training data. But they're pretty good at category theory, information theory etc. as there is a lot of relevant training data there. In terms of reasoning LLMs as a component of AGI systems, what this means is they may be great helpers for domains that are already fairly well known to the AGI systems but not for exploring wild unknown territory. I.e. they are for exploitation (in the AI sense, though yeah not only..) not so much exploration. Anyway there is a LOT of useful reasoning to be done close to the spheres of accumulated knowledge. But for reasoning about highly novel situations in games, or about new forms of math or programming, we will need systems that can actually reason in a more GI-ish way.... Hybridizing these reasoning models with other systems like oh say Hyperon's Probabilistic Logic Networks that can actually do full-on logical reasoning -- regardless of how far it leaps ahead of prior knowledge, seems more promising as an AGI strategy... but we (or well my colleagues and I) already knew that... In short -- modern reasoning LLMs as potential central hubs for AGI systems... -- NAY ... modern reasoning LLMs as reasoning helpers to humans or AGIs dealing with familiar domains where there's loads of training data -- YAY

Microsoft Flight Simulator in VR is an absolutely breathtaking experience!

I don't wanna say "I told you so", but I told you so. Quote: "Ilya Sutskever, co-founder of AI labs Safe Superintelligence (SSI) and OpenAI, told Reuters recently that results from scaling up pre-training - the phase of training an AI model that uses a vast amount of unlabeled data to understand language patterns and structures - have plateaued." ... threads.net/@yannlecun/post/…

As OpenAI and Meta introduce LLM-driven searchbots, I'd like to once again remind people that neither LLMs nor chatbots are good technology for information access. A thread, with links: >>

I asked an AI to show me how Egyptian pyramids were built, and now I'm pretty sure I'll have nightmares for life 🤯

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