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> AI has, multiple times already over multiple different era's definitions of what counts as "AI", been used to invent new things that humans have not been able

> AI has, multiple times already over multiple different era's definitions of what counts as "AI", been used to invent new things that humans have not been able to. Can you give a few specific examples? > They can be superhuman one moment and amateurish-to-childlike in the next. Even the smartest humans are like that. No one is best at everything. Which brings me to another question: Is a know-it-all ASI actually possible? If so what's the bottleneck? The current approach, the data or the compute? PS: These questions are kinda pointless. I understand that we don't know how to answer them yet hence I'm not expecting them to be answered. Just your general thoughts.

ben_w 2mo ago
> Can you give a few specific examples? Early AI, the "Symbolic AI/Expert systems" era: https://en.wikipedia.org/wiki/Dendral https://en.wikipedia.org/wiki/Dendral Evolutionary computation era: https://en.wikipedia.org/wiki/Evolved_antenna https://en.wikipedia.org/wiki/Evolved_antenna Deep learning: https://en.wikipedia.org/wiki/AlphaFold https://en.wikipedia.org/wiki/AlphaFold Generative AI: https://openai.com/index/model-disproves-discrete-geometry-conjecture/ https://openai.com/index/model-disproves-discrete-geometry-c... but also I've lost track of how many Erdős problems have been solved in the last year. > Is a know-it-all ASI actually possible? If so what's the bottleneck? The current approach, the data or the compute? It depends what you mean by "know-it-all". All human knowledge? Clearly possible. (There's a theoretical case for "everything" which provably isn't, but I regard this as a purely mathematical objection and not something that applies to the real world). There are several bottlenecks; one big one is that we don't know what we're doing, another is that even the biggest models don't have as many parameters as even lower-bound estimates for a single healthy human brain. (The best guess I know of, and it is still a guess and I'm not a biologist, would place state of the art models at around the same complexity (if one parameter is one synapse) as the brain of a rat).

ben_w 2mo ago

> Can you give a few specific examples? Early AI, the "Symbolic AI/Expert systems" era: https://en.wikipedia.org/wiki/Dendral https://en.wikipedia.org/wiki/Dendral Evolutionary computation era: https://en.wikipedia.org/wiki/Evolved_antenna https://en.wikipedia.org/wiki/Evolved_antenna Deep learning: https://en.wikipedia.org/wiki/AlphaFold https://en.wikipedia.org/wiki/AlphaFold Generative AI: https://openai.com/index/model-disproves-discrete-geometry-conjecture/ https://openai.com/index/model-disproves-discrete-geometry-c... but also I've lost track of how many Erdős problems have been solved in the last year. > Is a know-it-all ASI actually possible? If so what's the bottleneck? The current approach, the data or the compute? It depends what you mean by "know-it-all". All human knowledge? Clearly possible. (There's a theoretical case for "everything" which provably isn't, but I regard this as a purely mathematical objection and not something that applies to the real world). There are several bottlenecks; one big one is that we don't know what we're doing, another is that even the biggest models don't have as many parameters as even lower-bound estimates for a single healthy human brain. (The best guess I know of, and it is still a guess and I'm not a biologist, would place state of the art models at around the same complexity (if one parameter is one synapse) as the brain of a rat).