It's not sarcasm. If I had to pay $25 to perform my work from an ever-growing pile of projects, I would be losing money. That's not "cheap intelligence" for me. It certainly is for my employer, though, because for the mere price of $25 extra per day they can get 4*X the product per period of time. In Scrum terms my personal velocity grows by a factor of four or more with access to agentic AI workload, but if it means…
I'll simplify it for you: AI is cheap for those who pay for it.
So if you dig down a bit it turns out the author had been trying to solve that problem for a year with GPT 5.4 and 5.5 and he fed all that information to the prompt he gave to Sol Pro which may or may not had direct access to the author's chat history. So the claimed "148 minutes" was really "a year plus 148 minutes". Moreover, it seems the prompt included the technique used to solve the problem: https://old.reddit.c…
I (author of the original post and paper) can add a few things here:
1. My previous approaches with GPT 5.5 were really not very sophisticated in terms of my input. I threw the problem at it, and just kept encouraging it to go iterate through ideas without any success.
2. The approaches that are in the prompt, though they will seem cryptic to someone not in the field, are relatively natural ideas. In fact, the construction that worked was something that even 5.5 initially looked at but was just too weak to see how to make it work. From my view, I would have never gotten this result myself. Imagine you are telling a contractor to build the empire state building, and you say: "You should explore approaches that can include building materials like steel, wood, concrete, or clay, and any combinations of those. You can use arcs, columns, supportive beams, and anything else you can think of to solve load-bearing issues. Do not stop until you've completed a viable plan to construct the empire state building." And then the contractor shows you the finished empire state building using reinforced concrete and steel beams with all kinds of crazy ways of making everything stable; that's kinda how I feel.
Genuinely asking... How do you get chatgpt to work for 148 mins when I can't get gemini to think for even a minute at a time?
clear specifications for what counts as completing the tasks, and an explicit list of what does not count as completing the task, and clearly stating to not return until the task has been completed. I've had agents run for almost 12h!
Genuine question: If you still or did think LLMs are just stochastic parrots that just summarize everything and have no form of creativity, what do you think after seeing results like this? I'm very curious how people reconcile their fear/hatred of AI with actual objective reality. This is actually what interests me most about the whole AI thing. How we tell ourselves what we tell ourselves.
I had to create an account to respond to this because I am quite convinced these math problems they are "solving" are pure marketing. Why is it only GPT doing this, why not Claude? Why does Terrance Tao do marketing for OpenAI? I suspect OpenAI has hired math researchers to solve obscure problems and put them in their training set, purely for marketing reasons. There was a good comment on the Pelican bicycle svg yest…
I (author of the paper) can't speak for others, but I have no affiliation whatsoever with OpenAI and have not received anything from them (I even pay for my subscription lol). I've thrown this problem at each model with new model releases, and 5.6 was the first one to solve it, so that's my side of that story.
This is all a depressing and bleak future that I don’t look forward to. One solution is to ban LLM’s, to artificially create a demand for human thought, that just feels like living in an artificially constructed zoo. Another solution is humans don’t do anything that AI can do better , / doesn’t need the human touch. So I suppose we will all become artists, sportsmen or politicians, the only jobs that will remain exce…
> This is all a depressing and bleak future that I don’t look forward to.
Or, for the vast majority of humanity who aren't professional mathematicians, it's a liberating and empowering advancement.
I'm sorry if the automated digital theorem looms are eliminating the market for your handwoven proofs.
Maybe we should also burn books and lobotomize ourselves to make science more fun and challenging. The ego here sickens me. I don't care about your ego. I want accelerated material science, medical science, energy, better outcomes for people across the world.
> I want accelerated material science, medical science, energy, better outcomes for people across the world. There will be people benefiting from advances in these fields. Neither you nor I will be one of them.
Ah, so you're saying none of us will benefit from the discovery of new mathematical knowledge?
You've just condemned funding math, even human-produced math.
Either math is useful, in which case AI math is useful, or math is not useful, in which case we can save money by having mathematicians sell shoes or clean toilets.
Nobody knows. It's not a zero sum game. You can have AI "senior engineers" working under humans building bigger things than we've been able to. We also don't know where the capabilities of current AIs will plateau. The benchmarks aren't really telling the entire story. From my perspective of using the models there are certain axis where they're not making a lot of progress, like being able to have large accurate cont…
> It's not a zero sum game. Resources are, though. The planet cannot support a race of digital super-people, and us, and an continually growing economy. It's the height of folly to think that, as things are going, we are going anywhere "good".
Economic growth doesn't mean using up resources faster, it means trading things faster.
AI is a threat to everyone. People who claim that AI will never be able to do X have consistently been proven wrong. The only people who are safe are those whose jobs depend in some way on their humanity. e.g. yoga teachers, bouncers, etc
Your first two sentences were correct. The last one is already being proven false. It's a threat to everyone. UBI is the only way.
Productivity improvements tend to increase employment. AI will not reduce employment.
(Also, the US budget deficit is way too high to afford a UBI.)
How would this be different from taking a yoga class from a video? I would be willing to be proven wrong, but I doubt the ability of LLMs to give useful corrections in yoga much more than their ability to write useful code.
Probably that you'd be doing it in a group, which is a lot less lonely than doing it from home.
I think there's a lot of opportunity for computer vision in exercise coaching.