The comment under “Future Directions” at the end of that paper would seem to apply to the use of AI in software development, business management, and other areas as well:
“For AI[-assisted mathematics] research, improving research judgement and research-state representation may require training and evaluation on records of mathematics as a process, including failed approaches, strategic decisions, and evolving assessments of evidence. For mathematical practice, while these limitations persist, human expertise is likely to remain most valuable in these global functionalities: deciding when to persist or reframe a direction, and maintaining an accurate representation of accumulated progress.”
a2ff6eeb0 3 hours ago [-]
Yeah, the end goal is that you can run the economy autonomously, at higher levels of complexity than people could understand or reasonably make decisions about.
pestatije 2 hours ago [-]
AI will tell us how the world works and we won't be able to understand what it means
danabramov 7 hours ago [-]
Thanks for posting. I'm doing some AI vibemathing and running into exactly the difficulties they describe.
nh23423fefe 8 hours ago [-]
The section where the Research-state representation is assessed to be fragile is the work I want to see more of.
I've already accepted that models know everything and will only get smarter, its cope to pretend hallucination is achilles heel. I want to understand how to build/use harnesses that converge on goal states and allow me to contribute human expertise like intuition and taste.
The components they call bulletin, session report, and especially the curated summary are the parts that make their work go forward.
throwaway81523 4 hours ago [-]
Interesting. I wonder if there has also been any recent progress on the Legendre constant. Kidding.
semiquaver 7 hours ago [-]
[flagged]
aleph_minus_one 7 hours ago [-]
> This is nothing, wait until they see my proof of the Flibknopf Conjecture!
You mean the (Tait) Flyping Conjecture from knot theory?
“For AI[-assisted mathematics] research, improving research judgement and research-state representation may require training and evaluation on records of mathematics as a process, including failed approaches, strategic decisions, and evolving assessments of evidence. For mathematical practice, while these limitations persist, human expertise is likely to remain most valuable in these global functionalities: deciding when to persist or reframe a direction, and maintaining an accurate representation of accumulated progress.”
I've already accepted that models know everything and will only get smarter, its cope to pretend hallucination is achilles heel. I want to understand how to build/use harnesses that converge on goal states and allow me to contribute human expertise like intuition and taste.
The components they call bulletin, session report, and especially the curated summary are the parts that make their work go forward.
You mean the (Tait) Flyping Conjecture from knot theory?
> https://mathworld.wolfram.com/FlypingConjecture.html
> https://en.wikipedia.org/wiki/Tait_conjectures#Flyping
This one has already been proved.