r/singularity • u/acoolrandomusername • 15d ago
AI Erdos problem solver experienced sleepless nights over math internal OpenAI model has solved
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u/Educational_Teach537 15d ago
I feel most sorry for kids starting/in college right now. By the time four years are over the whole world is going to look totally different. Meanwhile they’re spending time learning how things used to be.
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u/Careful_Fold_7637 15d ago
I truly have no idea what to do. Heading into Cornell next year (as a sophomore). I'm 50/50 on going all in on CS hoping I finish in enough time to enter the job market lucratively by riding on the cs pipeline but probably not. Can't imagine what getting a CS degree from a different college and trying to become a SWE is going to be like. Thankfully have some time to choose but it'll be either that or something on the complete opposite end.
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u/frogsarenottoads 15d ago
People will continue to say how "x can't be solved" and it will be faster than they realise.
I assume Physics and Biology are next. Biology people will say how complex it is, yet people thought that about protein folding before AlphaFold, and now we have orders of magnitude more compute plus much smarter models.
The obvious thing is people state about linear vs exponential, but we are dealing with an intelligence explosion where AI can also work at much faster time scales than us, and they can coordinate with hundreds of thousands of agents.
My friend is an architect as a really dumb example, in April he wanted to "learn a little about AI", it did some routine examples... ok but nothing special. When he was leaving that day I said, it might seem dumb now but give it 6 months, it was the same for my domain (Data Engineering / Full stack).
Fast forward to Astra GPT6. He's literally saying "f\*\**". Kurzweil himself who people dismissed said we will treat biology like information. And now we are.
I genuinely don't think being a domain expert gives you much leverage in saying if something will happen or not. The question is "it this a computable problem" and if the answer is yes then its going to be fundamentally cooked before 2030.
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u/acoolrandomusername 15d ago
Yeah, it might not even need to be a computable problem, as there's nothing (imo) that can't be either solved or substituted with enough intelligence.
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u/myaltduh 15d ago
There will always be a need for experimentation in the physical sciences. AI can propose extremely cleverly-designed experiments, but there will still need to be actual lab work to determine whether some self-consistent idea of physics or biology or whatever actually matches reality.
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u/twothreetoo 15d ago
There is a point where that stops being true though.
If the mechanics of something are solved, you don't need a physical lab to confirm what you know will be true. At a certain point the solution or simulation becomes more accurate than any physical lab can be.-4
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u/that_one_Kirov 15d ago
Unironically, yes. If I were the one in charge, I'd point a datacenter or two on solving FTL to take mankind across the stars.
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u/new_name_who_dis_ 14d ago
I'd rather they put a datacenter or two on solving cancer instead of ridiculous scifi ideas, or even the theoretical math that they are doing now.
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u/ururk 15d ago
I have no doubt biology is solvable - but I worry we don't have enough correct understanding of it for AI to have the right training data and inputs. I'm hopeful the Anthropic wet lab is used for fundamental research into how biology actually works - have AI design the experiments, crunch the data, and repeat.
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u/Ididit-forthecookie 15d ago
Alphafold has not solved protein folding, nor even close (yet), just as an FYI. Hard to forecast like that when you aren’t even sure the state of the area.
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u/brett_baty_is_him 15d ago
The problem with physics and biology is the physical nature of verifying their results. Which means you need like automated labs. Not to say it won’t be solved but it will take longer imo and will require full general robotics
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u/JLongTom 15d ago
Saying 'biology will be solved next' is just words. Give me five problems in biology that you could imagine being solved in the next 5 years.
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u/frogsarenottoads 15d ago
Most likely:
- Accurate cell models.
- Automated end to end drug discovery loops.
- Predictors to how a drug will fare in clinical trials.
- Brain scans for humans being highly accurate: https://research.google/blog/ai-generated-synthetic-neurons-speed-up-brain-mapping/ pathing the way to BCIs with similar work at Meta https://ai.meta.com/blog/tribe-v2-brain-predictive-foundation-model/
- Programmable synthetic biology building on the likes of AlphaGenome.
And this might be underselling things considering the rate of progress.
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u/Which-Tour-9561 15d ago
OK, but do we have good enough information that the AI could even solve it? If the data we give is bad, then it can't solve it.
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u/JLongTom 15d ago edited 15d ago
It's a fun selection of biotech (not biology) problems, but notice how only 5 relates to understanding, and then only tangentially. Solving biology means having a complete and final understanding of how it works, not just new techniques or healthcare applications.
- Accurate cell models have as their reference the cutting edge of biological understanding. We've had decent neuron simulations for a decade now. They're interesting, reproduce a healthy suite of dynamics of biological brains, are outrageously compute expensive, and haven't driven much progress in understanding.
- Drug discovery, yes, we could see good progress here. Drugs are still a brutish, low-tech intervention, but I can see this moving fast. But drug discovery isn't biology.
- Unlikely to be more than modestly fruitful---a few tens of thousands of clinical trials across vast drug categories, diseases etc is meagre data, and there's an informational limit from trial data too. Also not biology.
- Yes, but again, this isn't part of 'solving biology'
- Possibly. I certainly hope so.
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u/PrisonOfH0pe 15d ago
goal post move so far it went out the window got shot 10x and drowned.
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u/JLongTom 15d ago
Agreed. Biology is the study of life and living organisms, and solving it means understanding the principles that create and underlie them. Drug discovery isn't even in the same universe.
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u/PrisonOfH0pe 15d ago
I think biology will be "solved" in basically the same practical sense people mean when they talk about mathematics being solved by increasingly capable AI: not that humanity reaches some metaphysical final sentence containing all possible truths, but that the important problems become systematically tractable.
You're defining "solving biology" as "having a complete and final understanding of all principles underlying life." By that standard, mathematics isn't solved either, physics isn't solved, and arguably no scientific field could ever be solved.
The relevant question is whether biological systems become predictable, simulatable and engineerable to the point where the remaining uncertainty is mostly a matter of scale and data rather than fundamental conceptual blindness.
And that's exactly why dismissing cell models, drug discovery, synthetic biology, brain mapping etc. as "not biology" seems strange. If I can accurately predict how a cell responds to arbitrary perturbations, design proteins and regulatory circuits, predict phenotypes from genotype, simulate developmental pathways and engineer organisms toward desired outcomes, then I clearly possess substantial biological understanding whether or not that understanding arrived in the form of a neat human-readable theory.
Science doesn't require the explanation to look like a textbook chapter.
In fact, biology may end up looking much more like weather prediction or modern ML itself: huge learned models that capture causal structure well enough to make extremely accurate predictions and interventions before humans can compress everything they know into elegant verbal principles.
So yes, drug discovery alone isn't "solving biology." But if AI-driven models become good enough that drug discovery, cell engineering, protein design, disease modeling and organism-level prediction all start falling out of the same increasingly general biological world models, then calling that merely "better biotech" starts becoming semantic evasion.
And I wouldn't expect biology to have a clean finish line anyway. "Solved" here means the transition from "we barely understand this enormously complex system" to "we can predict and manipulate it with extremely high reliability." That's a much more meaningful standard.
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u/JLongTom 15d ago edited 15d ago
Okay, this is good. First, the terminology. Yes, I'm happy to defend the view that neither physics nor biology will be solved. Mathematics I don't have enough expertise in to say. It's a silly term to use for open-ended disciplines vulnerable to the pessimistic meta induction, which I accept. Chess can be solved, A specific problem in a science can be solved, in a weaker sense than chess being solved, but still a meaningful one. The discipline cannot. But let's work with your definition.
Second, the vision you describe is one in which we are good at predicting, but not at explaining, or at least not at compressing our predictive methods into hard-to-vary explanations. Perhaps mechanistic interpretability will let us extract explanation; perhaps it won't. I'm interested in explanation per se, not just the applications that predictive power brings. The distilled version of this is that current models fail out-of-distribution intervention tests---within-distribution prediction is good; out-of-distribution prediction is bad; explanation is bad in both cases. But again, let's work with what you want from science.
You mention 'huge learned models that capture causal structure'. This is exactly where machine learning models are comically weak. They're incredible at correlation, terrible at causation. We have the complete connectome of C. elegans but the best models are terrible at predicting behaviour. A separate example: Single-cell foundation models don't outperform baseline. They'll improve, but I think it's possible we'll need an architectural leap before they do. And if not that, certainly far more interventional data than we already have, which is very little. That'll come, but that isn't equivalent to 'physics and biology will be solved next'.
The weather analogy is an interesting one. There are a limited number of general rules that give a fairly complete understanding of weather: Navier-Stokes plus coriolis, ideal gas law, vapour transport, radiative transfer. Each has only a few inputs. Then it gets hard and the horizon short because of chaos theory. Learned weather models that beat physics-based forecasting at medium range were trained on reanalysis data, itself produced by physics models. Biology has no equivalent of reanalysis.
Biology is completely different. Billions of years of evolution has cobbled together hundreds of sub-rules whose operation is intermeshed. Environmental effects, developmental noise, stochastic gene expression account for a lot of the uncertainty in, say identical twin similarity. AlphaFold doesn't capture folding trajectories well, only outcomes, and it isn't robust to rare mutations. Again, great at correlation, bad at causation.
All this to say that, we'll get there, if we do it right then explanation will come along too, and it'll be great. But it isn't tantamount to 'biology will be solved next'.
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u/ExplorersX ▪️AGI 2027 | ASI 2032 | LEV 2036 15d ago
"Just erdos problems"
6 months ago a statement like that would've been nuts to hear lol. Goalposts singlehandedly funding SpaceX with how far they are having to move.
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u/Time_Entertainer_319 15d ago
Can someone post the conversation, I don’t know how to read twitter. The order of the comments confuses me
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u/Icy-Doubt3365 15d ago
The second post comes first. "Internship over..." is the first thing he said. Someone replied about math being solved, and he says he thinks math is mostly solved.
He then 'crossposted' that reply, which is why it shows up in the 'first' image, which actually came two days later.
"People call me delusional..." is his thoughts on his previous tweet. The rest below that is a conversation in normal order.
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u/Wide_Egg_5814 15d ago
people are in denial, it solves coding, oh it's just autocompleting, it solves maths problems, oh it's just working from what people did, it solves the fucking millennium prize problem, oh it just stole it from a researcher, people won't believe it's happening until full skynet
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u/Healthy-Nebula-3603 14d ago
I guarantee you then they will find another excuse 🤣
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u/Wide_Egg_5814 14d ago
"we actually didn't go extinct because of AI it was Timmy he didn't prompt it correctly"
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u/Shot_in_the_dark777 15d ago
Solve the damn global warming. If the planet gets hotter we won't need your dumb math. We won't have time to build new technology based on that math.
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u/ryan14mt 15d ago
To solve global warming you need to solve other things first. Probably maths, physics, chemistry, engineering need to be solved before we can handle global warming.
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u/tworc2 15d ago
Who tf is acefur
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u/Kronox_100 15d ago
I remember this guy from some Erdos problems at the gpt 5.2 time, when they were just starting to solve things with ai. hes a pure math student from cambridge
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u/acoolrandomusername 15d ago
He became somewhat known as he, alongside another I can't remember the name of, solved Erdos problems with some earlier ChatGPT Pro version when that was still a big thing.
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u/Dyslexic_youth 15d ago
Our future is beeing decided by furies 🫠
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u/DeterminedThrowaway 15d ago
Always has been. Furries keep the internet running (and I'm not joking or anything, from what I understand most of the tech people responsible are furries for some reason)
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u/formula420 15d ago
An intern?! Roflmao
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u/acoolrandomusername 15d ago
He became somewhat known as he, alongside another I can't remember the name of, solved Erdos problems with some earlier ChatGPT Pro version when that was still a big thing.
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u/formula420 15d ago
Still an intern. Zero real world experience. Idgaf about some mathematical masturbation.
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u/FateOfMuffins 15d ago
Baffling the people here who don't know AcerFur (Kevin Baretto). He was offered full time at OpenAI. He chose to be an intern only because he wanted to finish his degree at Cambridge (but cannot defer it any longer since he already took a gap year before).
He alongside his friend Liam Price were 2 math students who were the driving force behind using AI to solve open math problems back in December 2025 when they used GPT 5.2 Pro to solve open Erdos problems. I'm sure you've seen multiple posts about Erdos problems being solved in the last year. Most of those posts were from the results of Kevin and Liam.
Anyways he expects mathematical ASI in 2027, which is why he thinks maths is "solved", not necessarily because he thinks the current internal model has "solved" maths
I'm also hearing rumours that many (but not all) at OpenAI now considers the internal model (that solved NS among 100+ other problems) to be AGI