> I don't think I agree with your characterization of the second definition. Time scales matter. It's not much use to be able to solve human-scale problems if i

> I don't think I agree with your characterization of the second definition. Time scales matter. It's not much use to be able to solve human-scale problems if it takes millennia. And it only takes months to train an LLM to the level that it can solve cutting-edge math problems. Aye, for practical purposes; but this gets you crystallised intelligence. I'd be happy to say e.g. the Chinese Room has crystallised intelligence. But humanity invented fire before reaching the anatomically modern form, and even anatomically modern humans collectively took hundreds of thousands of years to invent durable writing with which the room in the Chinese Room thought experiment could be filled. It was around a million (or so) years from fire to having enough shared cultural knowledge to be able to formulate the cutting-edge math problems that LLMs can now solve. Human fluid intelligence means we can pick up deep shards of this accumulation of wisdom, find new avenues of novel research to poke at, all within 40 years, even despite the depth and breadth of work from all the other humans who came before. (Though this also points at another way to be "superhuman": breadth. Many hands make light work, as the saying goes, and a lot of different humans solving different puzzles at the same time is part of how we got so good so recently even though ~10% of all humans who ever lived are currently still alive; and the same for AI was (accidentally) also part of how the OpenAI-HuggingFace incident went down). AI (not only, but also, LLMs) are very useful, and I'm getting value from using them. But the fluid intelligence of machine learning* is very poor, and the only way they have to make up for this is by being very fast**, but when there's not enough to train the AI on, they get stuck at a very low plateau. * possibly the architectures, but I suspect the process by which AI weights and biases are set, and again I don't mean just LLMs ** the speed difference between a transistor and a synapse is about the same as the speed difference between a jogger and continental drift