My take is that you can only ideate with AI (and brains) but knowledge comes from the contact of those ideas with the world. Making AI better does not make feedback cheaper, faster or more plentiful, it is domain specific. And intelligence does not carry from one domain to another - I might be a good heart surgeon, that does not make me a good investor or AI researcher.

Einstein was forgetful, Ramanujan and Godel could not manage simple things like diet. Godel's fear of being poisoned made eating dependent on Adele tasting his food. We all know someone could be a genius in some domain and below average in many other domains.

Why does intelligence not simply apply across all domains? Why are our PhD's hyper specialized to their domains and not generalists? Why can't a brilliant scientist simply cure their own dyslexia and still struggle - if intelligence was portable to any domain?

The explosion story needs intelligence to be one substance that gets bigger and flows into any domain, I deny intelligence is general.

https://www.rameznaam.com/p/471bbae4-1163-4048-944b-18f8b0bf...

The ultimate representation for an AI model is an ordinary computer program. Ideally, as a linear tape of instructions. Once we have that kind of a model at the frontier, I think the RSI monster becomes much more plausible.

Imagine you are evaluating what the machine should do when it is improving itself. It does a bunch of work and returns with "I supervaluated the liminal overdecomposition from the previous homological calibulation pass. It shows us that subtransitory mulutination will underspecify the tensor of stermullification. Where do you want to go from here?"

It will be like when you are reading a Wikipedia about a topic you don't understand. You follow the links, and you get more questions with more links. Your whole day is taken up following links, to the point where you forgot the original question.

Except this time, all the words come from the AI's work. You can't refer to an external authority who has already been there and can tell you what to do.

The AI needs you to tell it whether it is more intelligent than it was before, but you don't know, because you can't follow its reasoning any more. It's like an ordinary person trying to hire a math professor, there's just no way to do it.

But whereas a human math prof can evaluate another one, a machine intelligence can't evaluate another one, by construction. Because it's still usefulness to humans that is the evaluation criterion.