Comment by vasco
11 hours ago
He is right if model intelligence stalls. If model intelligence continues to improve soon there's no need for the prompter to understand anything or for any workshop as a mathematician will just be able to ask the model to explain how the proof works and models will do a good job at walking them through it step by step.
There will be no gap in understanding. Now there is because the models are discovering things at the edge of what they can do and so suck at explaining it. There's nothing particularly special about a newly solved problem in terms of learning it.
If we accept AI can explain all of existing math nicely, why shouldn't it be able to explain new proofs?
>if model intelligence stalls. If model intelligence continues to improve
So much in AI is dependent on which of these two outcomes occur.
I am not so sure that's true. Even if AI intelligence were to plateau at today's levels, there are still many gains to be had in speeding up today's intelligences. ASICs with burned in weights could become economical to invest in as they would retain usefulness longer than 18 months, and I feel we have only scratched the surface on possible usecases of local AI and what it means for almost any technology product or interface.
If the mathematician doesn't understand anything, are they even a mathematician?
That doesn't make sense to me. Terence Tao is great at explaining things, but he could spend months explaining some of his proofs to me without me understanding it. Even if the AI had a superhuman ability to explain things it's no guarantee that it could make a human understand.
It couldn't make every human understand. But it could make Tao and other mathematicians understand. You're not going to become knowledgeable at everything suddenly, even though I doubt what you say. Many months of 1-1 tutoring and hard work from a student with a top mathematician would make you understand a lot. Just not sit down and read it first day.