I take it you're not a mathematician. This is an achievement, regardless of whether you like LLMs or not, so let's not belittle the people working on these kinds of problems please.
And now you're belittling me. Yeah, good one, that'll convince people.
> out of control chatbot that can't comprehend basic arguments
I don't see how it is out of control. It is a tool. It is being used for a job. For low-level jobs it often succeeds. For tougher jobs, it is succeeding sufficiently often to be interesting. I don't care if it understands worldview semantics, that's for humans to do.
> we've spent trillions collectively on ai
The economics around AI do not suggest that continuing to perform large training runs is sustainable. That's also not relevant to the discussion. Once the training is done, further costs are purely on inference, and that is the comparison I was making.
> Inference costs are heavily subsidised
Even if you pay to run inference on your own hardware, economics of scale dictate that it is still cheaper than students.
> It's been active research but the problem estimates only 5-10 people are even aware that it is a problem.
That sounds about right for most pure math problems. Were you expecting more?
Let's not pretend that society would have invested that kind of money into pure mathematics research. It is extraordinarily difficult to get funding for that kind of work in most parts of the world. Mathematicians are relatively cheap, yes, but the money coming into AI was from blind VCs with a sense of grandeur. It wasn't to do maths research. If it's here anyway, and causing nightmares for actually teaching new students, may as well try to make some good of it. It has only recently crossed the edge of being useful. Most researchers I know are only now starting to consider it, mostly as a search engine, but some for proof assistance. Experiences a year ago were highly negative. They're a lot more positive now.
I'm trying to give a perspective from someone who actually does do math research at a senior level, who actually does have a half dozen math PhD students to supervise, to say that your blind attitude toward this is not sensible or helpful. Your comments about the problem being trivial do belittle the actual effort people have put into the problem without success. If they could easily have discovered this without AI, they would have already done so. Researchers do not have unlimited time and there are many more problems than students, especially good ones (hence my random comment).
Source? This sounds like hyperbole. The entire US GDP is low tens of trillions.
Comparing total ai spend to the value added of producing a few new maths/sciences proofs is unfair since ai is doing more than maths proofs, but for comparison one can estimate the total spent to date on mathematicians and associated costs (buildings, experiments etc). I would very roughly estimate that the total cost of all mathematics to date since 1600 is less than what we've spent on ai to date, and the results from investment in mathematicians are incomparable to a few derivative extensions of well-established ideas. For less than a few trillion we have all of mathematics. For an additional 2T dollars, we have trivial advancements that no one really cares about.
I don't think PhD students are sitting around and solving one problem for a year. Also PhD students are way cheaper
How are they cheaper? Your average grant where I am can pay for a couple of PhD students. I could afford to pay for inference costs out of my own salary, no grant needed. Completely different economic scales here. I like students better of course, but funding is drying up these days.
I guess it's different in somewhere like Europe. But in Canada, most of the PhD students are paid for doing TAships, not primarily through grant. Average salary is 25k/year. Take 6-10k out for tuition, that's 15-19k/year. You get a student doing so many things for less pay. I guess, if your job only requires research then you can do it.
This is the most baffling and ironic aspects of these discussions. Human exceptionalism is what drives these arguments but the machines are becoming so good you can no longer do this without putting down even the top percenter humans in the process. Same thing happening all over this thread (https://news.ycombinator.com/item?id=47006594). And it's like they don't even realize it.
