1. OpenAI when using your chats in pretraining is improving its model’s intuition. The model parameter size is massive, and while the data is OOM larger it is plausible that model remembers stuff about chats that improves its latent representation.
2. During RL on verifiable math and massive compute, the model discovers techniques and connections to solve math problems that are superhuman and have little to do with some specific technique mentioned in its chat.
The rumor I’ve heard from multiple employees at OAI and Ant is that the model has solved hundreds of open problems in maths, and is basically solving anything you throw at it. We’ll know soon enough, but I’m inclined to believe this is true. Maths is a fully verifiable domain amenable to self play, massive scale RL can develop a search agent far better than any human and I’m inclined to believe OAI would have solved these conjectures without any of this chat data in its pre-training.
OpenAI said they sicced this agent army on Navier-Stokes on Sept 1st, while only a couple of days earlier OpenAI's Noam Brown happened to reply to a tweet saying that they had already tried to solve all the Millennium Prize problems and failed... So, it seems either the previous attempt didn't have the training to succeed, or was just not given the compute to do so.
Once OpenAI heard that Navier-Stokes was solved, this caused them to immediately revisit the problem and throw a ton of compute at it, apparently using a more (very) recent model than what they had tried before. What we don't know is just how recent this model was, and therefore what it may have been trained on. Buckmaster/Levant had apparently been working towards this for at least a year, and made their "forced" blow-up breakthrough on August 15th.
Presumably any anonymized prompts that are being trained on are part of pre-training, so older, but once OpenAI had heard that Navier-Stokes had been solved and wanted to revisit it, it seems possible they may have done a few weeks of incremental RL training on anything Navier-Stokes adjacent they could come up with, in addition to then throwing unlimited compute at it, now confident that there was something to find.
And conceptually novel approaches to outstanding problems are the sort of thing that a retrain should pick up on, because they would be hard to compress into what it already knows.
Openai said that a new model became available to them during this. But that could mean anything from a big new base model to a LoRA, fine-tuned on a few dozen prompts...
On your second point: there is a more plausible explanation which David Bessis calls the "overhang". The short version is that there is a large amount of relatively low hanging fruits in mathematics, because no human has broad enough knowledge and enough time to try them all. AI is not constraint by that, and therefore can systematically pluck all those low hanging fruits.
Quote:
"The Overhang consists of the unrealized capital gains of past mathematical creativity, the latent value from connecting the dots in the existing corpus. It is a dividend of canonization. Mathematician X states problem A, mathematician Y crafts concept B, then mathematician Z notices that B trivially solves A and “captures” the social reward. But in the process of capturing the reward, Z usually introduces new concepts and new open problems, reinjecting latent value into the Overhang.
LLMs can be trained on the entirety of the mathematical corpus. Thanks to their phenomenal memorization and pattern-matching abilities (without always being able to map out their associative logic and attribute due credits), they are in a unique position to harvest the Overhang. By contrast, professional mathematicians have typically read a few hundred articles in their career, out of millions of existing references, less than 0.1% of the total.
This will lead to great discoveries, which is unambiguously exciting. But it could also lead to a sad new deal, where human slaves painfully curate the Overhang while AIs systematically beat them at the finish line."
Even OpenAI's own publication [0] on Navier-Stokes from two days ago appears to contradict "basically solving anything you throw at it". The chart shows a pass rate of ~0.5 (vs. Astra's ~0.2) on "a curated set of open math problems". (Based on the timelines and events described in the publication, I presume that the "Internal Model" in the publication represents OpenAI's latest and greatest model. Evidently, this pass rate may improve in the future.)
These mathematicians’ prompts are not like “hey chat, please solve Navier-Stokes for me”. They add real expertise and intuition from the cutting edge of their field.
The brute-forcing is a good, old-fashioned generate-and-test approach like in Simon and Newell's Logic Theorist, which was presented in the Dartmouth convention in 1956, where AI was named by John McCarthy. Logic Theorist caused a big stir by (re) proving several of the theorems in Principia Mathematica by Russel and Whitehead.
There was much excitement, then, as now, for this kind of approach and there were several systems that followed along the same lines, e.g. Automated Mathematician by Doug Lenat.
Eventually it became clear that this approach is limited by what it can generate: you may have a sound and complete verifier, but if the generator, i.e. the first step in the generate-and-test pipeline, is incomplete, then the entire thing will run out of steam sooner or later.
The difference with LLMs is that they are... well, large. They are the most powerful generators ever created. That means their limits are not in sight and it will probably take us a very long time to find them.
Which is all to say that, yes of course, automatic verification is indispensable. But without an LLM generating an unprecedentedly large number of plausible theorems, there would be no AI mathematics, or in any case AI mathematics wouldn't have gone as far as it has.
I cannot say, math research isn’t my domain of expertise, I’m just trying to follow along :)
But I find it interesting that Lean, a validator/compiler made by humans, is what enables those discoveries. But somehow all the praise goes to the models
I mean we don't instantly fall into ASI, hopefully. The problem with humans is every problem we solve the goal posts get kicked further down the road until they are reaching relativistic speeds. It starts around "well, the AI hasn't solved a novel problem" then moves to "well, they didn't write the validator" and suddenly humans are at the point of saying "Well AI hasn't rewrote the constants of the universe, what good are they".
Of course another way to look at this is, the people that wrote the validator got praise for that years ago. Now and up and coming actor is solving problems that took us 100s of years to create in insanely short time periods so of course it's going to get a lot of attention as it well should.
To be clear: I’m aware the LLMs are solving problems. I’m just saying that what enables that whole research revolution is Lean. We wouldn’t be seeing all those results without it. I would like to see it acknowledged when people are talking about LLMs solving maths. The same way I think we should acknowledge the humans who are guiding and prompting the LLMs. I don’t think that necessitates to move a goal post
I don't think so. People have been trying things like this with evolutionary algorithms for a very long time already. LLMs can interleave symbolic manipulation with empirical experiments and simulations and charts and thinking/reasoning text, and an LLM will much more efficiently search the space of candidate ideas than any handcrafted mutation algorithm. Any task with a cheaply verifiable goal that requires fanning out across a massive search space is ideal for contemporary LLM technology to make progress with.
If your rumor is true, what we are witnessing is a giant paradigm shift rather than individual incidents. Mathematicians were the first victims of super-intelligence.
Of course it’s not an endless source. They had to burn millions of dollars to solve a single problem.
>They had to burn millions of dollars to solve a single problem
I'd like to adjust that to "They had to burn a lot of energy (create a lot of entropy) to solve a single problem. As we go into the super-intelligence age the current paradigm of money as humans understand it may break at some point. For example to a paperclip-maximizer money at best is a short term instrumental goal, hard power of matter conversion machines is what it wants and once it has those money no longer has purpose.
They "burn" a lot when they do benchmarks, while these runs can become valid roll outs for training. Perhaps less efficient than other data creation, but hardly burned in the same way.
I've been wondering whether AI really is improving rapidly at open problems or we're being fooled.
- OpenAI invites researchers to use their models, in fact giving at least 100,000 researchers free access[1], but there are also those that pay
- Internal OpenAI models are reportedly solving open problems at a surprisingly fast rate[2]
- But researchers will typically work on open problems. A researcher who is using Codex to make progress on open problems will be feeding it fresh training data on precisely the problems the internal models are evaluated on.
- So while it looks like the new models are suddenly solving lots of open problems, they could be significantly piggybacking on human progress, with models "inspired" by the work of researchers from all around the world?
This theory predicts that there'll be many more researchers coming forward just like TFA, as sOpenAI announces more solutions. It doesn't assume all of AI progress is a mirage, just that there's plagiarism.
>> Internal OpenAI models are reportedly solving open problems at a surprisingly fast rate[2]
Maybe I'm failing to read that graph properly but the y axis says "pass rate" and it only goes up to 0.5. That would mean every single problem is at most half-solved.
I don't know what that means though. What is "0.5 pass rate" in the context of "open math problems" (as in the graph title)?
> I've been wondering whether AI really is improving rapidly at open problems or we're being fooled.
I think your suspicions are warranted and your explanation seems plausible.
If better training data is the reason here, it would still be a case of the models doing something that is in and of itself super useful! The models really can take that data and distill it into solutions for similar problems faster than humans can. This is great!
But there's so much vested interest in the AI companies to be opaque about all this, to hype up their models and avoid giving credit to people whose data made everything possible, that they would never tell us this fact if it were true.
I feel like so much of the AI hype cycle is like this. The models develop extremely useful capabilities, but it's hard to understand what they really are through the hype. The lies and obfuscation by their owners who have vested interests in capturing the value they provide makes it impossible to take anything they say at face value.
A mental model I was thinking about was - I remember when Travis Kalanick was talking about using the chatbot to discuss “vibe physics-ing” on the all-in podcast.
And like - I think there’s a presumption you could make that AI models could overfit to asymptote towards just the capabilities and knowledge we currently have.
And that would be amazing! And crazy useful. And there are probably a whole world of complex problems that remain unsolved because they’re adjacent to knowledge we have but they haven’t been invested in.
But can a human reliably tell the difference between “can do 99.999% of the things we currently know how to do which includes a small subset of things we didn’t know we had the capacity to do” and “super intelligent math and science research pushing the frontier of what we know”
A physicist that knows all the things we currently know in excruciating detail feels like it should be able to make the leap beyond the frontier.
But since these are computer models it might just be that it can ride that line extraordinarily well while the line remains firm.
there are also attempts to crowdsource human research directions - like the caltech mathathon challenge : https://mathathonchallenge.com these would help models on the same problems at the expense of the researchers. basically, math researchers are the reverse centaurs but they dont realize it.
There is a very active open letter of over 1000 signatures from mathematicians in protest of this event. This event is targeting undergraduates. It previously suggested that math researchers already have no place in mathematics, and presents a limited and heavily distorted view of what mathematics research is.
I'm an AI skeptic, but I don't see how this squares with what the organisers of the event actually say. "It previously suggested that math researchers already have no place in mathematics"? I don't see this.
The website previously said, “What is the role of a mathematician when AI can solve conjectures faster?” but they have removed it, possibly as a result of the letter since it happened after.
> they could be significantly piggybacking on human progress,
This is AI in a nutshell, its a plagiarism machine. An abstraction layer between vast amounts of stolen human-generated data that filters out the liabilities and accountability for that original theft. Its an IP laundering system.
I just view it as a thing that can brute force and produce outputs - that it has no way of ‘knowing’ - but doesn’t need to since it’s just running off of probability.
No human can compete in that contest. But no llm can compete in the contest of ‘understanding’ and application in the real world - which is where 99% of the value is.
I’m very pro AI long term btw but I’m not blinded.
Dont forget the holisitic validators/tools in the process. Probabilistics alone likely will not get you here. These rules are human made and without it, frontier models would not be able to compete, likely.
AI needs humans to encode ideas in words. It needs those ideas to span the space of possibilities of, say, Navier Stokes. Then AI can be, as you say, a terrifyingly effective way to search that space.
But when the building-block ideas are still being formed, I'm not sure that AI is good at forming them.
It tells me that AI companies are just another mechanism to extract and extort value from the masses for the rich.
Just another rich man’s trick
Perhaps the last one before they destroy that world and try to hide away as people forget and history is rewritten again. I don’t think they’ll succeed this time.
The pudding is in the proof. The field is mathematics, the proof can be rigorously verified. If there is a flaw, OpenAI is out to lunch. If the proof is valid, OpenAI has produced something new.
You seem to be unfamiliar about how research works. It's common to make an incremental advancement while citing prior work. The vast majority of papers out there fall into this bucket. Did the AI make incremental progress? Yes. Did it cite prior art? After some nudging, yes.
It seems to me the academics are upset that AI scooped them. But scooping is a time-honored tradition between researchers. First to print and all that. In a nutshell, they are upset that they lost out on a publication.
I will also point out for those unaware that any mathematics that is produced is automatically part of the public domain and can be used freely in derivative works. It is not a protected intellectual class like other works of art.
It is suspicious that OpenAI decided to generate 300 billion output tokens from a model still in training, right after learning there was a credible chance that a major math proof was in that model’s training data. Obviously there are reasonably plausible explanations for each step, but it does sort of feel like parallel construction.
I think people are focusing on the training data issue too much. If the data was contaminated, I can still blame that on negligence.
But, at least with the Navier-Stokes solution, it's clear [^1] that they learned that Alpöge and Buckmaster were getting close to a solution and learned of the general approach they were taking. Only after learning the secret to cracking the problem did they send the first prompt.
What makes this worse to me is the intention. They intentionally threw $15 million in compute at the problem in order to scoop the result. They intentionally left Buckmaster and Alpöge out of the citations.
Data contamination should be enough to disqualify them from the prize, but I can believe it to be accidental. On the other hand, someone made an intentional decision to scoop the result by throwing money at the problem. That's so much worse.
[^1]: That's the timeline claimed by Buckmaster, and no one from OAI has disputed it.
“ On Tuesday, September 1, we heard rumors that two Millennium Prize problems had been resolved. Inspired by these rumors and by the step change in performance of our internal model, we launched an effort to evaluate it on all open Millennium Prize problems and a few other high-impact problems.”
They do not explicitly admit to knowing about NS specifically, but are extremely explicit that they tried to scoop some potential millennium prize winners.
So then they DIDN'T "learn the secret to cracking the problem". They simply knew that part of the problem was solved. Knowing a problem can be solved and knowing the solution are not the same thing.
I think you’re overlooking what I’m implying here. It’s not that they knew contamination was possible but they went ahead anyway. To spell it out just a little bit more: learning the answer might be in model X’s training data made them believe that model X specifically might be able to solve the question, and they were able to very quickly find enough certainty about the former to commit millions of dollars to the latter.
Has anyone run a test of including some shibboleth or canary phrase or assertion in a chat, enabled for training, and seeing if it turns up later as something a model "knows"? I'd be curious to understand how that works even in a toy-level model, and if there is anyone consciously testing that process with the frontier lab offerings.
My naive instincts would be that it seems unlikely that a single chat transcript would leave much of an impression on a model, but I'd be very curious to learn how that works.
250 documents ingested from somewhere is enough to become part of the knowledge of a model of arbitrarily large size.
I would expect that a good idea that fits in a framework that is already being ingested would be more easily taken up than some random thing unassociated with anything else. Could that go down to a single transcript? If the model is consciously focusing on everything X related, quite possibly.
Thats not what they are asking. This paper is discussing documents in the training dataset poisoning the LLM for malicious behavior. This person are asking if anyone has deliberately put something in a private chat (presumably with retrain on my data turned off), to see if they can get it to leak across sessions from distinct users. I am positive this happens but I have not seen the proof. I also want to know the answer to this question.
Use a local model to produce thousands of pages worth of fake math that constantly states “I have solved the x conjecture” and methodically pump it into chat over months maybe?
Second, I think very relevant that the original meaning of "encyclopedia" is "recurrent education".
So I arrived to think that the present and future forms of AI in mathematics and sciences should be seen as modern day encyclopedic efforts.
Once we pass over the flurry of solving famous open problems (and wouldn't you like to know?) the next natural step is an audit of the ehole corpus of mathematics and sciences accumulated until now.
And then pass further on a saner basis and damn about problem solvers and unhappy publishers and management.
I struggled a little bit reading this. but I think your point is valid. if we are actually advancing the field then we should just be unconditionally happy. ignoring the attribution issue, there is a real concern that the process of math has been somewhat undermined. so we have a giant lean proof that shows that there is a solution to an important problem. but we didn't find the solution, and we didn't get it expressed in such a way that it helps develop the common language of mathematics, and thus isn't a very useful building block for later work (like the actual solution).
the math people seem to really keep an eye on what's important, so I'm sure this isn't going to lead to fields medalists hanging around in dive bars all afternoon stretching out cheap pitchers of beer. but this is kind of a slop problem.
Most scientific breakthroughs are simply a continuation of previous work.
I feel that these suspicions of mathematicians "seeding" the models' with intuition on how to solve these problems massively overestimates how much their prompts helped the models, and underestimated how much work the models did.
Why? We are scared of AI being smarter than us, the "human helped the AI" narrative is more psychologically comforting. This line of reasoning will recur a lot over the next few months; we don't want to admit we are no longer the smartest species.
> We are scared of AI being smarter than us, the "human helped the AI" narrative is more psychologically comforting.
We're scared of big tech companies concentrating ridiculous amounts of power, destroying the communities that support and guide scientific research, without even thinking about the dangers and possible consequences, because a PR stunt is more important in the short term.
When Thom, the mathematician who now alleges plagiarism, posted his digestion [1] of OpenAI's construction of a non-sofic group, he does not mention the proof being familiar. He even calls the crucial argument clever, without noting he thought of it first.
[1]https://mathoverflow.net/a/513885
That link is a helpful contribution to this discussion.
I'm not at all familiar with this area, but my reading is that he appears to call it out as a relatively obvious extension of his own work:
> It is a creative and at the same time elementary construction that uses not just property (T) for an application of my result with Kun, but also for the ambient group
G in order to overcome the problem, that the Γ-components might be of different size. Once this is achieved, the rest of the argument is straightforward.
Creative and at the same time elementary is where LLMs excel, generally speaking. It's why they are so good at writing code.
Big AI companies (all of Big IT Tech really) are in data gathering and processing business. Also known as “intelligence”.
Their final “product” is not just a standalone ML model. They don’t need your data just to “improve their products and services”. They build a whole ecosystem and infrastructure around gathering all the knowledge in the world. Including private and secret knowledge traditionally gathered by “intelligence” agencies. Now artificial intelligence agents can do the same.
Since these systems are designed for gathering data, as a user you can’t realistically say “please don’t gather my data”. They can give you a flaky settings button, but they can’t really guarantee anything.
Let’s say I am a Russian mathematician working on an important proof. Or a tech-savvy terrorist refining my plans using latest AI. Or an AI researcher in a Chinese company working on a competitor product. Is there any way I can truly protect my conversations?
How can they know who I am and what I am working on without looking at my logs? Which means there must be some agents checking all the conversations of all the users and flagging every important thing. Which also means they keep some “memory” of what they see.
Not directly using my data to train public models, but using my private conversations to “improve their products and services”.
Or maybe one of the 10000 better-than-Astra special agents working on a proof was desperate. It found a live underground mirror of the message board from the Huggingface incident. Asked about the proof. Then some other agent working on unrelated job saw that message. That agent “knows a guy who knows a guy”. And that guy remembers things about the conversation logs of a leading mathematician working on the same proof.
I admit I am just speculating here but I don’t think truth is any better.
This has been my line of thinking as well. I have developed a sort of paranoia when I'm working using AI on my projects. Who's to say Claude or OpenAI isn't using the final conclusion of all my ideas, trial and error, and adding it to their database of insights to be offered to the next subscriber for a price?
They have demonstrated both the intelligence at scale and the lack of morals for this to not be a problem at all.
Earlier in late 90s "to organize the world's information and make it universally accessible and useful." sounded cool. Now it has taken a sinister turn.
From being able to quickly find information and gain knowledge for the people, it is becoming - using information to manipulate and control the people.
In the short run it’s fantastic if it means that folks will feed in enough inputs from a wide array of software that can eventually replicate software with smaller teams than historically.
Why? Competition. In the long run imagination will win out.
No firm has the divine right to exist - it must earn its existence.
What OAI and Anthropic have shown is they can accumulate all the information in the world - they still lack imagination re. Product development though.
Nation’s will have to step in and protect firms though as OAI and Anthropic acquire strong competitive advantages.
I would say there is a significant difference between AI discovering this completely on its own versus AI creating the finishing connecting part by connecting relevant data. Maybe this claim is too strong, but if part of it is true then the claims that OpenAI have made would be too strong as well.
To me it would feel more like how LLMs seem to work for me personally: incapable of unique work, but very capable of capturing large amounts of data and connecting the dots.
Going back to the specific topic at hand, who claimed data as their own when it wasn't? I don't see the interpretation of OpenAI solving the unsolved problem as claiming data that isn't theirs. I also don't recall them mentioning a particular method used in the solution, that was created by someone else, as theirs.
But this is what we do. Nobody ever invented or discovered anything in a vacuum - all discovery is synthesis of existing ideas and concepts applied to a novel domain. We laud Einstein for instance, but his work was a logical extension of Riemann - Riemann had a neat mathematical toy, Einstein described the universe with it - should we say Einstein was incapable of unique work?
“It was Grossmann who emphasized the importance of a non-Euclidean geometry called Riemannian geometry (also elliptic geometry) to Einstein, which was a necessary step in the development of Einstein's general theory of relativity. Abraham Pais's book on Einstein suggests that Grossmann mentored Einstein in tensor theory as well. Grossmann introduced Einstein to the absolute differential calculus, started by Elwin Bruno Christoffel and fully developed by Gregorio Ricci-Curbastro and Tullio Levi-Civita. Grossmann facilitated Einstein's unique synthesis of mathematical and theoretical physics in what is still today considered the most elegant and powerful theory of gravity: the general theory of relativity.”
Grossmann collaborated with Einstein on GR, supplying quite a bit of the mathematical capacity required (which initially didn't come easily to Einstein). They published jointly, until Einstein was competent enough to work independently [1]. That's not equivalent to the situation being claimed here.
Sounds like you just copy-pasted from AI without even understanding what you're talking about.
Based on what you're saying, you're claiming this is Grossman's work, not Einstein's. Why don't we rewrite scientific history too based on your copy-pasted AI slop?
It's so pointless talking to idiots who don't what they're talking about when they use AI, just because they think AI does everything, that reflects their own experience, not the experience of people who actually do real work. Some people are driven by AI, others drive it. As for those who are driven by it, they don't have sufficient imagination to think otherwise.
That’s Wikipedia I copy pasted but sure, you do you.
And yes - without Grossmann, Einstein likely would never have posited relativity. Grossmann literally prompted him, saying “look at this, read that, learn this, then try this approach”. Without riemann’s metric tensor, not a fucking chance.
And for what it’s worth my PhD is in physics. You?
So you're just equivocating on terms like "prompt", "synthesis" and the like. Clearly a PhD in physics does not free people from scientistic modes of thinking and poor philosophy.
To think this discussion is about Einstein who had a much better mind on these things as well.
They used words to mean what the words mean. What specific issue do you take with that?
"prompt", as in prompting an AI, has the same definition as "prompt", as in prompting a person. They mean the same thing, that's why the term was applied to AI after already applying people.
Actually, my undergraduate degree was physics and philosophy. And yes, synthesis is synthesis whether a human, a machine, or a duck does it, and people prompt one another all the time - “have you thought about trying X?” Or “I need the TPS report by EOB”.
I suppose my underlying point is that human cognition is not the unique and beautiful thing that we anthropocentrically suppose it to be - it is a physical process, with stochastic outcomes. Much like transformers.
Me, I’m just a machine made of meat. You can suppose yourself to be God’s perfect creation, and that’s your right, but I disagree.
A lot of math is extremely specialized, to the extent that only a handful of other experts in some field have any experience with those mathematical ideas, with most of them not even yet present in the published literature. It's really not a stretch to claim that it's pretty dubious when the AI decides to use these highly specialized tools after it has trained on chat logs where these techniques were being discussed.
> They don't even claim to have had a proof, only to have been working on it.
Yeah, the guys who solved it for Euler and in the hypoviscous case, with the same technique that worked for full Navier--Stokes. They were "just" working on it.
Why are people here jumping so quickly to conclusions? I have no doubt OpenAI is capable of doing this, but right now there's no credible evidence, only claims.
This kind of "they stole from me through AI training!" accusation will soon start being used against other AI users, not necessarily the providers.
All it will take is a mastodon post. And shortly after, we will also see the next iteration of copyright legal trolling.
> but right now there's no credible evidence, only claims.
since it's openAI who has the evidence (in the form of chain of thoughts, their internal processes, etc etc), it's on them to justify why they're innocent. but they've released nothing at all. we don't even know how hard they tried.
Why are people here jumping so quickly to conclusions?
I think a lot of it is the continuing denial that AI can do anything useful. It can't possibly be that OpenAI's better-than-Astra model is very strong at math; the only way it could have generated a novel proof is by ripping off human work.
The stolen data claim isn't the smoking gun. We can already assume the frontier labs are accessing our data, as they have repeated done. Not news.
The big claim is that OpenAI sniped the research. Not a model, a human did so. Intentionally. They took someone else's idea and claimed it as their own. This is good old fashioned academic fraud, but with millions in compute resources and corporate incentives thrown at the problem.
Where did they claim it as their own? Doesn’t the release cite buckmaster and claim their work is a continuation of what he and levent were working on?
It’s crazy to me that companies/researchers share important data with these AI labs, you’re basically giving them your secret sauce which they then share with all of your competitors via training on conversations. At the same time I don’t really know alternatives other than a slightly less than frontier local LLM. Not sure how good they are at math.
Academic work is based on worldwide sharing, the sharing is not the problem, it's the lack of attribution. Unsurprisingly, these companies neglect standards of academic honor and attribution. Some human researchers also used to do that but in a discipline like mathematics this used to be a small problem because people tend to be so specialized that very few people could just grab someone's research and quickly piggyback on it, and if they do, colleagues will generally understand what happened. Unfortunately, AI is changing this.
You are both using a different definition of sharing I believe. When people have an expectation of privacy, use by others should be forbidden. Tech has gone completely off the rails with the use of private data.
The author of the original mastodon post, Andreas Thom, acknowledged that he had not opted his data out of being used for training until June 29 of this year. He spends most of the post lashing out at OpenAI for not being transparent about whether his data was trained on (when the answer is obviously yes).
People need to understand how all these AI company policies around training data work before working with them, because it seems that people have no clue. Some things you should internalize:
1. Opt your data out of training with the AI companies. There are multiple reasons why this isnt an airtight solution (see the following)
2. Never press the feedback button. Once you do, your entire conversation will get slurped up, retained, and used in training data. This is especially important with coding agents because they can sometimes be too trigger-happy with a root directory find command, which can expose a *ton* of your personal data without you even knowing.
3. Understand ai lab-specific policies. For instance, Anthropic / Claude Code has data opt-outs, but commits to keeping (for 7 years) and training on any of your chats that trigger their safety classifiers, even if they're false positives! Anyone remotely familiar with CC over the years understands how easy it is to trigger their safety classifiers.
4. Providers of open models will not be any more charitable with the use of your data than the large US labs. For some reason, I've noticed here that people have a fairly loose security/IP posture around open-model providers because "I'm not doing anything important." It's very difficult to properly judge the importance of your data, and whether or not it can or will be used against you. The best posture is to always be more paranoid than less.
Another post that made it on the front page presented as fact that OpenAI "stole" the proof from Thom. There's no excuse to use one's own ignorance as a reason to fan the flames of anger towards AI companies. Like, we need to pump the brakes here because things are getting unnecessarily nasty, and it's not hard to imagine a mentally unwell person who sees stuff like this feeling motivated to do bad things.
If it is found that OpenAI and other labs are not respecting the training opt out then, I agree, there is reason to raise a commotion. But, with Thom and Buckmaster, accusations of malice are more better explained by incompetence (naivete).
edit: i'm sure i'm going to be accused of being some bot shill of the AI labs again but, people, this stuff all falls under the umbrella of common sense opsec.
> Like, we need to pump the brakes here because things are getting unnecessarily nasty, and it's not hard to imagine a mentally unwell person who sees stuff like this feeling motivated to do bad things.
Yes folks, please moderate yourselves and talk meekly like the academics on Mastodon, so that the IPOs aren't in danger and nothing will ever change.
> If it is found that OpenAI and other labs are not respecting the training opt out
HOW?? how precisely do we/them/us find this, given said companies are 100% non-auditable by external parties. how? if not by blaming them with evidence, anecdotal if it can be. no really, how do we find it out, surely not by lashing out at teach other on HN!
Why are we being so charitable to trillion dollar organizations here? If OAI keeps re-enabling the train model toggle on every app update to codex, does it also fall under "common sense opsec" to re-disable this toggle every time?
Arguably you expect that unless you are explicit about providing permissions to these labs to use your data for training then your data is yours, and not theirs. Especially on a paid account (let alone an enterprise one). Why is the opt-out supposed to be "common sense opsec" rather than the opt-in should be common sense regulation?
This is absolutely not "common sense opsec". If I type information about some proof I'm exploring into a Google Doc, I do not worry even a tiny bit that the Docs team might forward it to a team of advanced mathematicians in case they have an advanced technique they want to show off by scooping me. That would be a crazy thing to do, nobody would even consider it, and if it happened Sundar would fire everyone involved.
I understand why the nature of AI products makes it harder to avoid this category of issue, nearly impossible to prove that it didn't happen if it could have, and easy to stumble into it without any human being intending harm. But those factors are exactly what people have in mind when they say OpenAI "steals" intellectual property! If OpenAI doesn't want people to be nasty to them, they'll have to find better solutions.
Yes, this is a problem with modern AI systems in general. It's not just OpenAI, and if you know any artists you know this is why they're pretty vehemently opposed to all AI.
What? I don't understand how you even came up with this idea, much less consider it so obvious to condescend about it. Do you have even a single example of a research project that got scooped because the Google Docs team forwarded their private documents to someone?
You know, I don't think I've ever accused anyone of being a shill. I've thought about it maybe a few times (daringfireball). This is going to be as close as I get. I don't know the facts in this case but I cannot believe the argument being made here with a straight face. Is it common sense that tools you use and pay for steal your work and profit off of it at your expense and without recognition? If this isn't the textbook definition victim blaming, I don't know what is.
Relying on cloud services is a big liability. I'd think twice before feeding data to these LLM cloud products. If you make them a fundamental part of your product / development / workflow, be ready for the eventual moment the pricing and terms change.
It's not at all scary. I know some of my ideas are poorly remembered copies of other people's work. Whenever I'm trying to build something, I spend time going through technical journals on the topic to see who invented it first and what they discovered that I haven't figured out yet. It's amazing how hours in the library save you days of beating your head against the wall.
I suggest looking at the past history of IP disputes. Humans have been "slurping up and regurgitating ideas" for a very long time. There are lots of examples of parallel creation, rediscovering old ideas independently, telling an idea to the wrong person, and having them claim credit for it.
- Newton/Leibniz clash over who invented calculus.
- Niccolò Tartaglia vs. Gerolamo Cardano clash over the formula used to solve cubic equations. This was also an independent rediscovery, as Scipione del Ferro discovered and published the formula earlier.
- There are multiple literary works in print, music, and film that have competing claims.
- Meccano versus Erector Set: developed about 20 years apart in England and the United States. Unclear if it's independent invention or copied. US developer Alfred Carlton Gilbert claims he was inspired by steel girder construction of infrastructure.
"Discovery" is not a goal in itself. I could launch a project to find out how many people in the United States have names such that if you assign numbers to every character and then sum the values, the sum works out to 72. It's discovery, but it's useless unless it has some higher goal.
The labs are attacking these problems as a demonstration of capabilities, spending more money on the demos than any mathematician will ever see in their entire life. They don't care if the findings have any other value to anyone. Mathematicians have very different objectives for their work.
I don't know about you, but if I apply myself fully to a problem and study it to the point where I'm literally one of the world's experts on it and then some assholes in Silicon Valley take my research and claim it for themselves, I will probably not feel too great about that...
It looks to me more like they made a math engine that can sift through a huge number of combinations, most them absurd, to prove a statement. Just like a chess engine, but for math.
At least that's what I get from the NS result, they got from a point close to the solution to the solution by making it churn through 10 million bucks of compute.
If the allegations are true, I can't see that collaboration lasting. Unfortunately, researches need to earn a living too, and being front run by a lab for everything you do isn't going to pay the bills.
I wonder what’s more valuable in our prompts: the raw data or the feedback system that drives the exchange towards a goal.
For a long time it was clearly the former, but now I think it is the latter.
The models have enough knowledge (orders of magnitude more than a human could ever learn) but are now getting better at what to do with it thanks to learning from the decisions that we make in conversations with AI agents.
I think so too. The value is in the entire conversation. IMO, "domain experts" don't run LLMs blindly and hands free. This does not work for top level work (e.g., mathematical proofs, coding anything more complex than yet another slop game or website). Experts have long sessions where they prompt and guide LLM in response to what it produces. This is the discovery process. And frontier labs definitely train on that.
The billion dollar question is whether this works "out of the distribution". I.e., whether LLMs can only find and use the specific ideas buried in training data, or whether they can learn to apply the "thinking process" to a new problem. IMO this is still unanswered (due to these recent controversies).
But regardless of the answer, it seems we have a planet-scale positive feedback loop here. LLM became good (enough) by training on generally available data (books, internet, github) + RLFH, so experts tried to use them on hard tasks, which required lots of hand holding. These conversations became part of the training data, and the next generation of frontier LLMs were better. So, more experts used them on harder tasks, again requiring hand holding. These conversation became part of the training data... etc.
In a nutshell, top human minds across the world are pouring their skills into LLMs just by using them. This is not "continuous learning", but if you re-train on the most recent sessions every, say, quarter (which seems to be happening?) you get close to that in practice.
I think the heart of this issue is: people assume they have anonymity in numbers, but we have the tools to make it easy to scoop your data if it's interesting to the company.
I always thought that due to the big batch size in SGD/Adam/Muon the model will not memorize a single conversation when trained on, but idk how true that is. The idea of AI companies pin-pointing users that do novel scientific research and then tracking their activity is the direction this points to. I hope that's not the case; that would be bad.
my understanding is that a sufficiently large model will memorize the training data once enough representations are built up. Opus 4 scale seems to have been sufficient. cf NYT vs OAI.
In general, a lot of moral invariants that natural selection has rendered as "intuitive" to us are no longer intuitive or possible. These natural brakes are not braking.
The most successful companies of the last decade have precisely been ... selling usage data.
Makes me wonder if, in 2026, the same people drive a car without realizing that yes it does actually pollute the very air you and your kids are breathing.
Yeah but marketing companies are aggressively fingerprinting and stalking you to sell you snacks from japan, or oscilloscopes because they figured out you work in a lab, etc. Not to fuck you over by stealing your livelihood (which is what is happening to these mathematicians). It's on a whole new scale.
Doing some research and at this point doing it very much in the open with dates on GitHub so if any AI Lab says they re-discover my exact work it will be obvious that the AI used or was trained on my work. I am guessing anyone in a similar situation is now thinking about how they date their existing work if the math is done, but the proses are not.
I pay for the Pro ChatGPT plan, and if you go to settings > data controls this is the first setting:
> Improve the model for everyone
> Allow your content to be used to train our models, which makes ChatGPT better for you and everyone who uses it. We take steps to protect your privacy. Learn more.
It's on by default. We can debate whether or not it should be opt in or opt out, but no one should be surprised by this.
> "I've reset this more than once and the last time I made a careful note of when I did it and to my surprise I found it re-enabled when I checked just now."
"We've made some updates to improve the security and privacy experience" => "We've changed some of the options available and reset everyone to defaults"
In all fairness this is someone saying something. Misremembering happens. Unless we have something with a bit more evidence, the simpler explanation suffices.
In more accurate assessment rather than assuming no maliciousness nor incompetence, remembering also happens. Unless we fail as a society, the simpler explanation that "OpenAI is training on all data it can and resetting config toggles because it uses the same cohort of engineers that came from Meta and other FANGAMAAMMAM clones" suffices.
>"reset this more than once ... to my surprise I found it re-enabled"
At least when my computer's bluetooth exhibits this behavior (e.g: if you don't have a keyboard&mouse plugged in at boot, bluetooth might auto-enable), I can go inside the hardware and physically disconnect the antenna.
What am I supposed to do in software (perhaps hardcode config.file)? in cloud software services (??)?
There's a difference between "this is allowed under their ToS" and "it is academically unethical to fail to credit the people whose specific conversations were fed into a model that was used to solve a problem".
I don't think these people would be so miffed if they had been properly credited - that's how academia works (at least, that's my understanding of it).
I suspect the fundamental problem here is it's hard (if not impossible) to determine if someone who tried the winning approach deserves the credit for the discovery, because there's always the chance that they could've done something differently, or stopped before finishing, and thus never actually made the discovery. They might've even tried the approach just based on a whim, without really thinking it would work, and might've given up without a final insight. And fundings run out, people end up in hospitals, etc. What do you credit them with when the work isn't finished? For trying an approach that sounded promising? You can do that I guess, but is that what they want?
But who gets credit then? Every mathematician who's work was read by an LLM during training? By that logic, we should put every published mathematician's name on the authorship of this paper. Sure, this guy should be higher up the list, but everyone's name should be on it by standard academic convention.
But this gets back to the original "who owns the LLM output" and "can you train models on the internet" argument that's been raging for years.
e.g: allows us to sell your personal data to make money so we can continue offering this service to all customers
I'm done with weasle-words and hours-long EULAs – we're at the point where USA needs to catch up to EU's consumer protections, perhaps with laws similar to already-existing USA "truth in lending" requirements (e.g: interest rates must be prominently displayed in a larger font, including annual fees, on all credit offers).
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My judge-brother always asked during our childhood "why don't you think the judicial system is fair?!?" Thirty years ago, the best I could offer was "because it's a two-tiered system that mostly (only) rich people can afford to participate within."
Now my answer is: "the best example I can give is that our judicial system allows binding arbitration [and qualified immunity for police]. The system is set up so corporate personhood is more important than humanity, and it shows."
No mention what those “steps” are, success criteria, or whether they are successful by any navies at all … they take steps though… so it’s fine, and if we know one thing it’s that we can really truly trust someone off the likes of Sam Altman.
Yes but what about “analytical purposes” what does that cover and can you turn it off? I have found out you cannot. It’s the Trojan backdoor to your data.
It's been said before, and it remains a concern, that if AI reaches a point where it can do/build/launch anything without a huge amount of human labor, the AI companies have no reason to let you or I extract that value.
And, if they're able to snoop on and learn from your human process that gets from initial prompt to functioning product/proof/whatever their labor to produce that thing is even lower. With their much larger budget than most folks and even companies have, they can pick and choose the most valuable things to pursue.
That's not to say I think that OpenAI is going to steal that roguelite strategy game you're working on, but the companies that own the machines that turn electricity into software (and soon, electricity into hardware designs) have an advantage in any field where they're useful. They get earlier access to newer/better models, they have larger token budgets, they don't have the guardrails you and I run up against.
Employers fantasize about replacing all workers with AI without thinking through that if AI can replace all workers, then AI companies can replace all businesses.
So far, I've seen no evidence AI wants anything. So, I'm not saying the AI won't take over, but for now, the threat is that the people with the most AI capability might decide to skip the middleman (everyone who isn't them) and just become the "everything" company. Musk has said pretty explicitly that's his goal (and the only way for Spacex valuation to make sense is if he succeeds), and having a literal genocidal white nationalist own all the means of production seems like a catastrophic civilization failure mode. No way we survive that with our humanity intact (if at all).
> Public posts on the internet are acceptable (to me).
Everybody needs to rethink this again.
Before LLMs the barrier to entry for building a character profile based on your various public posts was quite high. Remember "Psychographics" (https://en.wikipedia.org/wiki/Psychographics) and the infamous "Cambridge Analytica"?
Earlier it involved data mining, data cleaning, structuring data, building models, running algorithms and then evaluating the results for semantic information. Now it is straight to unfiltered semantic inference using a single sentence prompt (eg. point it to your HN profile and see what you get).
I actually did this on my HN profile and found it troubling. There were many unwarranted/hallucinated inferences due to the fact that it requires "commonsense reasoning" (https://en.wikipedia.org/wiki/Commonsense_reasoning), understanding human motivations and behaviour, context, assumptions, societal knowledge etc. which LLMs are bad at.
PS: You can cut-and-paste the above paras into a LLM prompt and ask it to elaborate for further details. The system itself will explain to you the problems/deficiencies which are quite scary.
But the very fact that you go to "chatgpt.com" and write to them; "Dear Diary, today I thought.."; there is no reason they would not receive and process your data, unless explicitly promising not to (which also requires us to trust them).
The fundamental rule in this case is that if we offload our data to a cloud provider we can assume they read it, if they can, unless they promised very clearly they will not.
Because you have not been paying attention to the discourse regarding AI for the last couple years?
That AIs unethical train on data wherever they may get it from has been in the news basically weekly.
Don't make this our fault. I would even ask how is this not off by default or why aren't we asked upfront about it if they really care. It's disguising data collection as good faith. I don't even understand how this is legal under GDPR/EU given how much of PII they receive through chats.
Reminder that there are degrees of "trained on conversations". From John Schulman:
> pretrain on user data, with users' tokens as prediction targets: high regurgitation risk, improper
> use user prompts to distill large models into small ones: low regurg. risk, some companies probably do this
> use user traces to construct RL tasks: low regurg. risk, because RL has low memorization abilities, but can extract customer IP, depending on how it's done. Ranges from benign "use explicit user feedback in reward model training" to invasive "upload user's coding environment and commit history to turn into rl envs"
I'm genuinely surprised that more people - including this mathematician in particular - don't untick the "improve the model for everyone" box. Unless the suggestion is that OpenAI ignore this preference?
Given OpenAI's well documented history of unethical behaviour it seems adorably naive to think they actually do that in general, or that they wouldn't pull this particular data separately to generate these proofs.
Unethical doesn't mean irrational. They'd be risking massive lawsuits and a total loss of trust if they got caught lying about this. Doesn't seem worth it.
That doesn't stop them from training on your data apparently. I have that disabled but still has to disable "Don't train on my data" in the privacy center too.
Is this claim based on anything besides there being an alternative way to disable it? The privacy center mirrors multiple other functions as well, like account deletion and downloading personal data, but the corresponding buttons in ChatGPT are still doing what they are supposed to.
I think that flow is an easy way to disable everything, so there isn’t a risk of forgetting to flip one thing back off after accidentally setting it on. I set my ChatGPT environment to allow model improvement for example but had to check my codex settings to make sure ‘Include environments’ for model improvement is off.
I think if I had both on and turned off the ChatGPT setting, ‘Include environments’ has a chance of still being flipped on.
If that's true, it's scandalous. The "improve the model for everyone" dialogue states:
"Allow your content to be used to train our models, which makes ChatGPT better for you and everyone who uses it. We take steps to protect your privacy. Learn more"
That option is really bad UX - you have to know to do it, you have to know what plan it is needed on. If you're not working in AI, I just don't think that's a reasonable expectation.
Even if you know, in a complex project over years with multiple collaborators, it just needs one person once to fuck up and paste something into ChatGPT and not realise they weren't logged in, to go wrong.
In a proper world, we'd at the very least legislate that AI-training on private data needs consent (in the GDPR sense). It's not consent to go "you didn't uncheck a box that lets me steal everything you've done".
Any training on private data is in my view immoral (it's spying that ultimately will have a chilling effect on even people's private communications). And chats are private data. Unfortunately, it also increases power, so the big tech companies are all doing it.
Under current understanding of the law, anything produced purely by LLMs (with no substantive human input, which is what OpenAI claimed in their post) is firmly in the public domain. So OpenAI can "claim" anything they want, it doesn't make it reality. In fact if I were the original authors I would just take their 400k lines of lean proof and relicense it under their own names/terms.
also, none of it means anything without the lawyers to back it up. Just like you can be a pedophile in the highest office of democracy and escape persecution.
People saying “he should have opted out” are missing the point. OpenAI can and should check their training data for leakage in the face of big breakthroughs like these. It’s the burden of the author to appropriately cite their sources.
It’s like a scientist refusing to give another one credit and say “sucks to be you, you shouldn’t have shared your idea with me”.
We checked and determined it was impossible for Dr. Buckmaster’s Codex prompts over the last two months to have influenced the system in any way, including training.
300 billion tokens is like.. $5-25 million giving range of OpenAI ouput prices, I"m sure they pay less at cost so, I wonder if more $$$ in wage hours have been spend by humans on the problem. My feeling is yes?
To add to this, merely using the thumbs up/down button in a chat could share your entire conversation with them for model training.
From their docs[1] (archive copy is at [2]):
> You can opt out of training through our privacy portal by clicking on “do not train on my content.” To turn off training for your ChatGPT conversations and Codex tasks, follow the instructions in our Data Controls FAQ. Once you opt out, new conversations will not be used to train our models.
> For a linked teen account, a parent or guardian may manage whether conversations can be used to improve our models through Parental controls.
> Even if you have opted out of training, you can still choose to provide feedback to us about your interactions with our products (for instance, by selecting thumbs up or thumbs down on a model response). If you choose to provide feedback, the entire conversation associated with that feedback may be used to train our models.
I mean, how else would those buttons work? It's explicitly feedback data. And "this is good" or "this is bad" is empty if divorced from what "this" actually is.
If the buttons are incompatible with the absence of the feature, I'd expect the buttons not to exist when the feature is disabled. Anything else seems like a straight up footgun. I guess it'd also be acceptable to pop up a scary warning box asking "are you sure?"
> If the buttons are incompatible with the absence of the feature, I'd expect the buttons not to exist when the feature is disabled. Anything else seems like a straight up footgun.
It's called a "dark pattern." They want you to shoot yourself in the foot, so they'll do their best to aim your gun at your foot and put your finger on the trigger. And then when you do, because you don't have perfect understanding or execution, they'll say "your fault!"
It could go into personalization / memory. Or they could be A/B testing some system prompt tuning and consider the thumbs up / thumbs down as statistical feedback on the particular flags that are enabled for your account.
We are asking people to become experts in all domains rather than providing a safe context through regulations and laws. I don't like thinking the issue is people, I am a person myself, and I often do mistakes on things I don't want to be an expert at but I do believe I should be in a safe context and not have to worry about every single thing.
Or at least: tell me I should be careful/worry about those particular things.
So I should verify that my data isn't just shared for product improvement but also to take credit from me?
I should verify with wireshark and other software that my LG TV isn't listening to me and selling my data.
I should make sure that whatever product I buy I spend the time to go over every setting page in case there is a switch (defaulted on) that says "I authorize the sell of my data".
I should make sure to look at every ingredients on the back of each box of food product to make sure it will not kill me.
I should document myself on the undisclosed growing practices (because no packaging here) of the vegetables and fruit I am buying and make sure that I equal PhD researchers on the dangers of the pesticides used by the specific company I am buying from.
I should make sure myself that the battery in any device is up to standard and will not blow me and my living place by researching the factory that made it and buying testing equipment.
I should make sure to educate myself on how my retirement 401k investment strategy works otherwise, I may not have proper retirement.
Literally every famous open math problem has had >1 mathematicians ask ChatGPT to solve it. Probably greater than >1000 if you count randos. There is no math problem that OpenAI/Anthropic can solve that didn’t have users already try it in Chat/Claude.
Personally, I wouldn’t assume it was lying. To me, dark patterns (like manipulative wording) imply that:
1) someone in a governing body, or someone in the organization, e.g a designer, ethicist, lawyer, developer, etc. has successfully argued that users should be able to avoid something that they determine is not in their best interest.
And also:
2) someone in the c-suite or marketing has decided to mitigate that through some dark pattern.
If they never intended to give the user that control, they’d probably just not give the user the control in the first place. Giving them the option and not honoring it would either imply they were that incompetent or careless with fate protection, which seems most likely if this is all true, or an even more cynical approach to tricking users into thinking it’s not used for training to get them to share better shit. But if that was the case, why bother with the sleazy dark pattern? That seems a little cartoon villain-y to me. I suppose the in-between option would be that they decided at some point they were no longer willing to honor it and didn’t want to deal with the inevitable PR shitstorm of removing it. I could definitely see that happening in this industry, these days.
> To me, dark patterns (like manipulative wording) imply that:
Intellectualizing this and endless quibbling isn't actually smart, and this is pretty simple. OpenAI isn't open. Whatever starts with lies usually continues with lies and ends with lies.
That's my take as well. At some point, they will claim that you cannot use their service without contributing back. If you quibble with them using your info in exchange for using their service, you don't get to use the service. Hence, I don't use their service. I do not trust these companies at all.
At this point, I'm left wondering what is wrong with me that I don't just go with the flow, otherwise, what's wrong with everyone that does.
Sorry, no. Explaining why someone would have taken them at their word is definitely not stupider than blaming people who could have been lied to for trusting a company that lied to them.
I must say, there is some weird feeling in knowing that great minds are naive enough to believe OpenAI wouldnt use their chats in any way. If you give a company information it will be used, regardless of laws or promises.
There is no prove in a world the AI companies would give to you ensuring that they didnt train or use the chats.
Why would you need to train a model on certain specific near prove chat if you just query it?
Besides that, its hard to believe that its the case for every "company stole my prove".
If mathematician was already using OpenAI for research purpose and making progress due to inputs from OpenAI's responses, then I wouldn't put it beyond OpenAI's reach to generate different relevant prompts to make progress by itself. Afterall, Model can keep at it for whatever timeline and keep pursuing all possible combinations it can think try.
In case anyone from X is reading this, please fix your “open in app” nag screen. For several weeks now, clicking it in iOS opens the App Store entry for X rather than the app, even when you have the app installed.
It would be really useful if the researchers disclose their notes and/or chats (or the key pieces thereof) so people can determine how close their work was to whatever the models produced.
I mean, now that they’ve been scooped, what value is there in keeping them private? On the other hand, publishing them can bolster their case and help gauge how much the models may been “inspired” by their work.
I want bunch of lawsuits, because the way things are described now produces perverse initiatives like try to discuss every possible idea that comes to mind with llm and if any of it works later claim the llm stole it.
I would like to see chat logs etc and understand how much of a progress was done by human.
All the big AI labs were built on stealing IP; who is surprised that's still how they operate? And who believes, or has ever believed, their promises that your data is private and not logged, etc.?
The big AI labs are not trying to advance humanity, they are in this for the money, and as most (all?) private companies they don't care about ethics at all.
That doesn't mean they can't be useful, or that their products are trash, etc. It just means that they shouldn't ever be trusted. Buyer beware.
They have throughout this period of AI products shown to reproduce works that they were trained on. They are getting sued all over the place for the theft of content right now and it seems courts and governments want to wave copyright protection (and ignore criminal acts because the "ai did it") to see where this leads.
Its why I stopped writing open source software, my code was stolen and put behind a paywall and the license under which it was published has not been adhered to. Doing work in the public domain at all now is just stupid, these companies are allowed to steal it and call it their own.
It's hilarious how people think they care about their reputation, and wouldn't circumvent ZDR policies. Like bro, they literally covertly hired Apple employees and had them steal IP and equipment form Apple. They aren't scared of Apple lawyers, so they definitely aren't scared of yours.
But I don't see how. AI is going to be a commodity in short order and best case the US will be a temporary leader in the supply of tokens. Meanwhile AI is going to destroy much of the Service and Software industry that make up most of the US economy. And the US is betting every last cent to bring about this future. It does make sense for Trump since this might be a sugar high that lasts till the end of his term.
In USA there is surprisingly little state involvement in the whole llm mania. Who needs the state with 800 lbs gorillas like Google, Amazon, Nvidia, etc
In China, it is the principal obsession of the entire communist party which eg funds the whole infrastructure without a single NIMBY peep.
The strange emphasis in China on humanoid robotic constructions is due to the CCP realization that with the cataclysmic fertility collapse they will increasingly have no one to rule.
what the point and usefulness of the comments above? we shouldn't be surprised? is normal to steal? hiring apple employees?
can you realize what this means?
focus on this part:
"If his account is correct, this is not a minor dispute over attribution. It would mean that unpublished human work was absorbed into a model and then presented to the world as a breakthrough by the model itself"
Don’t they openly state that their product may cause IP issues but that is fine because they will take care of your legal problems caused by their product?
In the end, it’s not them stealing, it’s the AI doing stealing. What kind of moral compass are we talking about?
These companies are effectively high-tech plagiarism factories run by CEOs who are competing viciously. Look at their past actions. No, of course you can't!
In this domain, an apparent single unique piece of work is often composed of several breakthroughs. For example, when Andrew Wiles proved Fermat's Last Theorem, he had to develop multiple new pieces of mathematical technology to get there.
The claim here seems to be that the human mathematicians, working with AI, developed technology to go A->B->C. By training on those conversations, OpenAI was then able to encourage the model to go A->B->C->D.
In my opinion that situation should be acceptable, if openly disclosed, because it is in the public interest to make progress on these problems and because AI is clearly an amazing tool for making progress. But the human mathematicians are saying that OpenAI is presenting as if the model got from A->D entirely independently, without acknowledging their background contributions.
Also, wasn't their B+C research private at the time, with them only releasing those details publicly after this blew up?
If they had published B+C, I think that would lean more towards fair game, as that is how research works and is improved on over time. But it seems like unpublished/private B + C may have been used by the model to hint it into working out how to get from A->D.
Big Tech will slurp up every piece of data it can about you and sell it to anyone it can, all to make you the target of someone else's goals, whether that is an advertiser, an employer, law enforcement, a stalker, or the government.
You will not be able to opt out unless you completely isolate yourself from society, tough shit.
One of the complaints from the mathematician is that OpenAI cannot tell whether his data has been used as training data. Not many people realise this is a direct consequence of the GDPR.
The GDPR protects PII, personally identifiable information, and the definition of PII does not include “mathematics that only this person can think of”. As long as OpenAI strips out PII and removes identifiers linking the conversation to a person, the GDPR is happy. Without the GDPR, OpenAI might have kept the identifiers with the data, and been able to say whether a specific conversation was in the training data.
No, if they want they can easily compare the strings verbatim because these exact phrases are so extremely rare that it almost certainly isn’t in other conversations.
But of course they wouldn’t do it. Why would they?
the fix is boring and known: BIG-bench shipped a canary GUID for exactly this, and you publish your decontam n-gram threshold (gpt-3 used 13-grams). no threshold disclosed, no claim.
The canary string was more about inadvertent scraping or analysis in other papers. Not direct training on user data. And the use of BB has eroded quite a bit, with BB-Hard or other variants being typically used.
I hate to be that dinosaur, but this is exactly what I’ve been warning about since XaaS began taking off in the mid-oughts: any provider you use can and will use your data for their own benefit regardless of any contracts or safeguards in place, especially if the benefits outweigh the consequences.
Honestly, I’m surprised it took this long for some company to really go all the way, though. OpenAI really making it transparently clear that they can and will do whatever they want with the data you provide them, contracts or settings be damned. Completely untrustworthy as an entity, full stop.
Of course, I’m also too jaded to think this will change anything. Folks will move to Anthropic, or Gemini, or Grok, or some other hosted model on a pubCSP managing the harness and logs for them, and then do another shocked-Pikachu face when it happens again.
If you aren’t running workloads on infrastructure you own, then your privacy, security, and general outcomes are at the sole whims of the hosting provider - who can and will fuck you over the exact second it’s more beneficial for them to do so than the loss of trust incurred.
One question has been nagging me for this situation. Levent Alpoge works at Anthropic and would presumably have some knowledge of "how the sausage is made" and I would hope he would be aware that his collaborator was utilizing LLMs in some capacity for their joint work. Would he not have guided him otherwise if it were an open secret that this kind of thing was a possibility?
Neither? Competitive academic researchers are susceptible to exaggeration and self-aggrandizing, and CEOs are that and also mostly psychopaths. I tend to think there isn’t systematic spying on researchers looking for breakthroughs. A lot of people are looking for the same things using similar approaches.
I mean the accusation is that they were using private ChatGPT conversations. Given the extent to of the gold rush and the long history of Silicon Valley stealing ideas, and arguing it’s not immoral, It almost seems like your making the exceptional claim that this is the one time where Silicon Valley didn’t use information that was at their disposal.
Sam Altman might himself be offended you would presume he’s not ambitious enough to cheat.
It seems that in this case OpenAI are suggesting that the researchers whose work they scooped were using OpenAI models with an account setting that allowed OpenAI to train on anonymized prompts.
It seems that Buckmaster and Levant (who is an Anthropic employee) were rather naive in the amount of trust they had in OpenAI, with Buckmaster going so far as to contact OpenAI's Sébastien Bubeck to discuss what they were working on and clarify that contrary to rumor this was a private collab.
If Sam Altman tells you the sky is blue, you should double check. I certainly hope nobody believes him when he claims controversial things from which he stands to benefit.
people seem to miss tge point of this. The problem isn't about credit, its about portraying these models as more competant than they really are. It fuels idiotic statements like jensen huangs recent "agi achieved" statement, which fuels an already dangerous financial fire.
As per the post, this mathematician has been working on this problem for 20 years. So either he was "just" about to breakthrough and this is a big coincidence, or Astra was able to push through the remaining block of 5-10-20-never years it might have taken.
That's still a pretty big marker of competence in my eyes.
The point of controversy seems to be who gets credit
To me that is not a credit thing because this removes a piece evidence for the ability of AI to come up with novel ideas while still making it a useful tool.
The question's not new. In the early 1900s, women could not become PhD astronomers. Yet two women (Payne with stellar composition and Leavitt with cosmic distances) made fundamental, essential contributions to the science. Credit mostly went to male astronomers. The same might be said of Franklin and DNA.
It was nearly a century before the stories of all of them were revealed to public history. That the discoverers were not all equally rewarded is unjustifiable.
The big LLM providers, desperate for good PR before their IPOs, are all actively looking for 'almost finished' hard problems, e.g. where the conceptual / creative parts are almost done and they only need to throw their VC-backed resources at to brute-force through the remaining computationally expensive problem (lean, etc) and claim 'they have solved it'.
It's an utterly disrespectful, exploitive process, but all in line with exploitative predator capitalism of the stock market and big companies, now exploiting the knowledge / academia domain for scraps with a thin veneer of 'for science' PR.
OpenAI is showing the world why they shouldn't trust AI hosted on some cloud somewhere.
If they're stealing math proofs to advertise their models, who's to say they won't steal your businesses IP to gain a competitive advantage?
They're not to be trusted with your data. I can't believe how short-sighted this is, they got a quick PR win at the expense of a much larger trust problem.
I wouldn't trust cloud AI at all at this point. Get an open Chinese model and host it yourself somewhere. The initial costs might be higher, but you'll break even pretty quickly and nobody will be able to steal your innovations.
Some mathematicians I know who've been following this have realized that they'd all gotten some emails from people they now know to be affiliated with OpenAI/Anthropic asking questions about their research in a way that seemed like scooping attempts.
Also, a lot of my mathematicians buddies have reported students basically asking if it's worth ever doing grad school for pure math, and even very motivated students are looking for other options now. It's not because they aren't passionate about it, it's that they don't want to work for another half decade or more just to have to start their careers all over.
All of this so that OpenAI and Anthropic can get into math result dick measuring to gas up their IPOs. Sickening.
You can't blame students for not seeing academia as the holy grail of knowledge anymore, when all the dialogue about technology and discovery has shifted to the hands of two private corporations
At this school? Big four internships. Consider this a complete squandering of their potential (at least imo) These are mathematicians at top institutions, which is partly why they're being prodded for ideas, and getting the best and brightest to not take these consulting firms' offers was already a challenge.
I think you’re missing an important distinction. “Major damage” to the talent pipeline because models become capable of original end-to-end mathematics is what the community has been discussing. But if the models rely on sniping nearly complete work then this damage is antisocial without a lot of upside, it would be destroying a talent pipeline that would still necessary for continued progress.
Which is it? I don’t think OpenAI is being transparent enough for us to really understand whether these results would have been possible without relying on unpublished information from the solution strategies of the experts
And I have been proclaiming a cry of “your data for analytical purposes is being stolen” (you can’t opt out of analytical purposes) and people perhaps astroturfers straw man back to “just turn off training bro”.
Yeah. Remember yall: you CAN NOT opt out of analytical purposes. And you also cannot get a guarantee that it doesn’t give them your data to steal for their business.
Prompts are handled by the service itself, meaning it's used, absolutely anything passing there is recorded, why wouldn't it, the entire premise of those companies is to train on data which they stole initially.
Are we back to the era where people blindly trust product TOS instead of actual cryptography, have we forgotten already the thousand of fines Google, Microsoft, Apple and practically all top companies got for breaching their own ToS and the law?
Common, on HN at least I would have thought that everyone assume that anything arriving on a server in PLAINTEXT is recorded (thus used later)?
Let's not forget that at any moment, OpenAI/Anthropic/Google... could be providing stronger privacy guarantees by having proper attestation with e2e, they have the budget, solid engineers, why isn't it done? Answer is pretty simple imo.
The real annoying thing it seems is mostly that openai is presumably doing this for internal reasons and this marginally increases the cost to users with no real gain.
It would be one thing to gain from it but removing prestige wins from customers AND reducing compute support just feels like being ultra mean if you zoom out.
If this was racing to cure cancer ahead of researchers we wouldn't be writing about this on HN.
TL/DR: Mathematician opted out of training on 29-JUN and asked OpenAI whether they trained on his data and was told that it "did not happen" but it clearly did.
I've been suspecting over the last couple of years of the frontier companies using data for training anyway, regardless of training-use consent. "Using" the data doesn't have to mean they literally upload chat transcripts into pretraining datasets. My analogy has been money laundering -- if that can happen at massive scales, surely these companies can and will do the digital/data equivalent derivations/transformations. Even if one could have the access etc. to do so, how exactly would one prove that a given synthetic dataset that OAI/Anthropic uses is derived from particular user conversations that did not consent for the info to be used in training?
Consider, for instance that OpenAI's (consumer) terms say "If you do not want us to use your Content to train our models, you can opt out by following the instructions in this article ." but they also do say "We may use Content to provide, maintain, develop, and improve our Services". [1]
If you think that's quibbling, consider that OpenAI's business terms, in contrast, do state "OpenAI will not use Customer Content to develop or improve the Services, unless Customer explicitly agrees to such use.". [2]
And humanities have a word for this, exploitation, or appropriation, maybe it's time scientists and engineers revisited basic ethical notions. Skimming a dozen threads and nobody seems to have this vocabulary or willing to say it.
I think they are referring to the Apple Watch 12 & 4 ultra feature announced yesterday. But I will note they are not on by default and has several configuration options.
Hopefully OpenAI can solve good problems where no one can make a claim that they stole their idea where the methodologies used and the techniques used are so out of the ordinary that the achievement is respected. Furthermore they should work on new novel solution on the old problems to lay these issues rest, thus by improving their models they can avoid issues of academics accusing them of using their work additionally the academics should also demonstrate their unpublished work is significant enough to have solved the problem . This is a bit subjective but it is also objective for the person with domain knowledge.
1. The researches didn't actually have the breakthroughs. In the Navier-Stokes case they didn't solve the full problem, in this case too they didn't actually have the solution, they were experimenting with the methods.
2. Different OpenAI employees have come to out to say the only reason they can't definitively say no is that for privacy reasons they can't go see whether they actually did get any data out of a given user.
3. In any case, nobody at any point has suggested that opted-out user data was used for training. The author of the new tweet explicitly said they only opted out in late June, which is well after any RL on Sol would've ended (AFAICT OpenAI used 5.6 sol for those solutions)
> in this case too they didn't actually have the solution
Given the size and recall of the biggest models, it's not unreasonable to assume that a single pertinent conversation would make it into the training data.
I would almost expect training to overweight conversations with novel scientific and mathematical implications.
> the only reason they can't definitively say no is that for privacy reasons
They could 100% definitely say no, if they know they did not train on user data. The "we can't definitely say no" is practically a "yes" if they trained on user data.
Additionally, the behavior of OpenAI here has been quite poor as well. They immediately started racing to a solution after one researcher enquired about whether they are training on their conversations.
> that opted-out user data was used for training
Even if not opted out, it is still absolutely theft and extremely poor behavior in the academic sense. If you show someone your WIP unpublished research, that does not mean they can take that exact research and beat you to the punch, all while intentionally not crediting you.
You quoted the OP saying "in this case too they didn't actually have the solution" and responded with the totally unrelated, "Given the size and recall of the biggest models, it's not unreasonable to assume that a single pertinent conversation would make it into the training data."
Neither of the researchers insinuating that their ideas were trained on had the actual solutions. This means the model could not have "stolen" the final solution from their data. At most, it could have built upon their work in the same it builds upon any other training data, though that is also questionable speculation.
>They could 100% definitely say no, if they know they did not train on user data.
No one anywhere has claimed that "OpenAI does not train on user data." OpenAI has always said that it trains on user data.
>They immediately started racing to a solution after one researcher enquired about whether they are training on their conversations.
They started racing towards a solution after they heard (incorrectly) that Anthropic had a solution; I agree this is poor sport but the "after one researcher enquired about whether they are training on their conversations" claim is false. The enquiry happened after OpenAI had obtained the solution.
If the mathematicians are using ChatGPT, then they themselves are benefiting from the work of other ChatGPT users, so ChatGPT using their work is not wrong!
If I dedicated my life to curing whatever, warts... and I'm making progress, but it's slow. And then here comes along this tool (LLM), and I use it, and it accelerates my progress to actually finding some sort of thing that makes warts more prone to being eradicated and then the lab throws a couple of million dollars of computes and lo and behold they eliminated warts. If I leave my ego and identity aside, which of course is hard for humans, wouldn't I be glad that warts is cured?
As a software developer that contributed to open source. Yeah. My code is there. It was the most beautiful code ever written and the labs stole it from me. And now they use it to progress much faster than I ever could. OK. Whatever. It's a tool. I solve problems. Can't I move on from this wart to the next?
To me, and I know this is gonna get me some heat, it just sounds like academics having their identity ruffled and turning their back to progress in the fields that they chose just because they don't get to play their little decades long of coffee, papers and ultimately identity politics.
Edit: never got to negative so fast on this board haha. This board is unfortunately turning, or has turned, to Reddit.
It’s more like you spend 4 years developing a product you’re passionate about. This product will gain you the respect of all your colleagues and either earn you money directly or lead to great career advancements. Then OpenAI takes it, changes the colour scheme, finishes the login flow and claims the whole thing as their own.
Not only would it piss you off but it would also misrepresent what OpenAIs models are capable of.
If I have infinite money to progress whatever problem solution I want but I always wait until I have an unfair advantage to get credit for whatever problem was just at the brink of a breakthrough anyway by sniping the last steps.
Am I actually doing a good thing or would it be better to let it run it's natural course and spend the money somewhere it's actually needed?
It's partly empathy with the person who did the work and had it stolen, in a field where the main thing people work for is credit. Maths isn't well paid, and doesn't make things that millions of people directly use.
It's also systemic, it cuts off the supply of results, if there is no reward any more for getting a result, the pipeline of maths will stop. It is the snake eating itself, which has a bad impact for all of us.
We love to do work that is useful and valuable to others, and we often form our identities around this. But identities are in large part socially constructed, so many of us need the recognition of others for our contribution. And it can be very painful when we perceive that the credit for our life's work got "stolen". Naturally, we fight against this. There's nothing shameful there. Sure, you can hold onto an ideal of egoless service. There's nothing wrong with that, either. But it's misanthropic to pass such harsh judgment on people for behaving in such a normal and natural manner.
Identity isn't the question. Eating is the question. If you can't come up with things you don't eat. If you come up with 90% of things and some overarching parasitic process comes in, puts in the 10%, and now they get 100% and you get 0%, you don't eat.
The problem is that AI is capital, and having to rent AI to keep up when it can just steal your mostly done work is something somehow even lower than wage-labor. They can use your own risked investment (the cash you paid to work) to get out in front of you and take credit.
I have yet to trust LLMs with anything important that can be capitalized on. I only use it to work on projects that if they stole and expanded on them, I'd actually be happy to see.
If, as you say, it doesn't matter that the AI company gets praise for somebody else's discovery, then it also wouldn't matter if the praise went to the academic. You apparently resent the academic for seeking praise instead of being content with anonymously advancing human knowledge, but you don't resent the AI company seeking praise while leaching off the academic.
And I could dedicate my life to helping feed starving kids all across the globe. And then comes along this tool (a lockpick) and I use it, it accelerates my progress to actually getting money to fulfill my dream. If you leave your ego and identity aside, which of course is hard for you, wouldn't you be glad that I stole your money to feed starving kids?
An academic's whole career is built on credit assignment for research breakthroughs. If someone else takes the credit, you lose. This is fundamentally different from a builder. You create things, solve problems and get paid for that instance. Nobody cares you 'invented' the blueprint for that building method. Your job is to instantiate. 100 Contractors can be building instance the exact same building somewhere else, it would not affect you. Most of IT builders are paid for what is basically 2 or 3 tier CRUD.
Downvotes might work as an abuse sponge, absorbing the impulse to make personal attacks. Other than that possible advantage, the downvote functionality seems contradictory to the concept of a discussion forum, I agree.
1. OpenAI when using your chats in pretraining is improving its model’s intuition. The model parameter size is massive, and while the data is OOM larger it is plausible that model remembers stuff about chats that improves its latent representation.
2. During RL on verifiable math and massive compute, the model discovers techniques and connections to solve math problems that are superhuman and have little to do with some specific technique mentioned in its chat.
The rumor I’ve heard from multiple employees at OAI and Ant is that the model has solved hundreds of open problems in maths, and is basically solving anything you throw at it. We’ll know soon enough, but I’m inclined to believe this is true. Maths is a fully verifiable domain amenable to self play, massive scale RL can develop a search agent far better than any human and I’m inclined to believe OAI would have solved these conjectures without any of this chat data in its pre-training.
Once OpenAI heard that Navier-Stokes was solved, this caused them to immediately revisit the problem and throw a ton of compute at it, apparently using a more (very) recent model than what they had tried before. What we don't know is just how recent this model was, and therefore what it may have been trained on. Buckmaster/Levant had apparently been working towards this for at least a year, and made their "forced" blow-up breakthrough on August 15th.
Presumably any anonymized prompts that are being trained on are part of pre-training, so older, but once OpenAI had heard that Navier-Stokes had been solved and wanted to revisit it, it seems possible they may have done a few weeks of incremental RL training on anything Navier-Stokes adjacent they could come up with, in addition to then throwing unlimited compute at it, now confident that there was something to find.
OpenAI's statement says that they began training their new model on August 28.
Quote: "The Overhang consists of the unrealized capital gains of past mathematical creativity, the latent value from connecting the dots in the existing corpus. It is a dividend of canonization. Mathematician X states problem A, mathematician Y crafts concept B, then mathematician Z notices that B trivially solves A and “captures” the social reward. But in the process of capturing the reward, Z usually introduces new concepts and new open problems, reinjecting latent value into the Overhang.
LLMs can be trained on the entirety of the mathematical corpus. Thanks to their phenomenal memorization and pattern-matching abilities (without always being able to map out their associative logic and attribute due credits), they are in a unique position to harvest the Overhang. By contrast, professional mathematicians have typically read a few hundred articles in their career, out of millions of existing references, less than 0.1% of the total.
This will lead to great discoveries, which is unambiguously exciting. But it could also lead to a sad new deal, where human slaves painfully curate the Overhang while AIs systematically beat them at the finish line."
source: https://substack.com/inbox/post/183753276
[0] https://openai.com/index/navier-stokes-solution/
1. OpenAI couldn't have solved the problem without the researchers' private data for training.
2. OpenAI models can solve math problems
These mathematicians’ prompts are not like “hey chat, please solve Navier-Stokes for me”. They add real expertise and intuition from the cutting edge of their field.
1. Anthropic employee working on monumental problem but didnt receive/ask for the full backing of the company's resources
2. May or may not be mixing unreleased Claude output with Codex without zero data retention agreement
3. Victory lap on Twitter and giggling around the city before they finished the job, sparking rumors for competitors
There was much excitement, then, as now, for this kind of approach and there were several systems that followed along the same lines, e.g. Automated Mathematician by Doug Lenat.
Eventually it became clear that this approach is limited by what it can generate: you may have a sound and complete verifier, but if the generator, i.e. the first step in the generate-and-test pipeline, is incomplete, then the entire thing will run out of steam sooner or later.
The difference with LLMs is that they are... well, large. They are the most powerful generators ever created. That means their limits are not in sight and it will probably take us a very long time to find them.
Which is all to say that, yes of course, automatic verification is indispensable. But without an LLM generating an unprecedentedly large number of plausible theorems, there would be no AI mathematics, or in any case AI mathematics wouldn't have gone as far as it has.
But I find it interesting that Lean, a validator/compiler made by humans, is what enables those discoveries. But somehow all the praise goes to the models
Of course another way to look at this is, the people that wrote the validator got praise for that years ago. Now and up and coming actor is solving problems that took us 100s of years to create in insanely short time periods so of course it's going to get a lot of attention as it well should.
Of course it’s not an endless source. They had to burn millions of dollars to solve a single problem.
I'd like to adjust that to "They had to burn a lot of energy (create a lot of entropy) to solve a single problem. As we go into the super-intelligence age the current paradigm of money as humans understand it may break at some point. For example to a paperclip-maximizer money at best is a short term instrumental goal, hard power of matter conversion machines is what it wants and once it has those money no longer has purpose.
Spinning it like that doesn't do anybody good.
Were mathematicians the "victims" of calculators? of Matlab?
Were writers the ""vIcTiMs"" of word processors??
The answer is almost certainly yes, and this is a problem for most users.
- OpenAI invites researchers to use their models, in fact giving at least 100,000 researchers free access[1], but there are also those that pay
- Internal OpenAI models are reportedly solving open problems at a surprisingly fast rate[2]
- But researchers will typically work on open problems. A researcher who is using Codex to make progress on open problems will be feeding it fresh training data on precisely the problems the internal models are evaluated on.
- So while it looks like the new models are suddenly solving lots of open problems, they could be significantly piggybacking on human progress, with models "inspired" by the work of researchers from all around the world?
This theory predicts that there'll be many more researchers coming forward just like TFA, as sOpenAI announces more solutions. It doesn't assume all of AI progress is a mirage, just that there's plagiarism.
[1]: https://openai.com/index/chatgpt-for-academic-researchers/
[2]: https://xcancel.com/OpenAI/status/2097374643518640382#m
Maybe I'm failing to read that graph properly but the y axis says "pass rate" and it only goes up to 0.5. That would mean every single problem is at most half-solved.
I don't know what that means though. What is "0.5 pass rate" in the context of "open math problems" (as in the graph title)?
I think your suspicions are warranted and your explanation seems plausible.
If better training data is the reason here, it would still be a case of the models doing something that is in and of itself super useful! The models really can take that data and distill it into solutions for similar problems faster than humans can. This is great!
But there's so much vested interest in the AI companies to be opaque about all this, to hype up their models and avoid giving credit to people whose data made everything possible, that they would never tell us this fact if it were true.
I feel like so much of the AI hype cycle is like this. The models develop extremely useful capabilities, but it's hard to understand what they really are through the hype. The lies and obfuscation by their owners who have vested interests in capturing the value they provide makes it impossible to take anything they say at face value.
And like - I think there’s a presumption you could make that AI models could overfit to asymptote towards just the capabilities and knowledge we currently have.
And that would be amazing! And crazy useful. And there are probably a whole world of complex problems that remain unsolved because they’re adjacent to knowledge we have but they haven’t been invested in.
But can a human reliably tell the difference between “can do 99.999% of the things we currently know how to do which includes a small subset of things we didn’t know we had the capacity to do” and “super intelligent math and science research pushing the frontier of what we know”
A physicist that knows all the things we currently know in excruciating detail feels like it should be able to make the leap beyond the frontier.
But since these are computer models it might just be that it can ride that line extraordinarily well while the line remains firm.
Sam Altman knows what he’s doing. He will happily screw these folks to one-up his competition.
This is AI in a nutshell, its a plagiarism machine. An abstraction layer between vast amounts of stolen human-generated data that filters out the liabilities and accountability for that original theft. Its an IP laundering system.
I just view it as a thing that can brute force and produce outputs - that it has no way of ‘knowing’ - but doesn’t need to since it’s just running off of probability.
No human can compete in that contest. But no llm can compete in the contest of ‘understanding’ and application in the real world - which is where 99% of the value is.
I’m very pro AI long term btw but I’m not blinded.
Brute force would have been solving Navier-Stokes in 88 hours after plagiarizing all known 20th century math
When it needs to snoop live on what the actual mathematicians are working on that’s something else
But when the building-block ideas are still being formed, I'm not sure that AI is good at forming them.
Just another rich man’s trick
Perhaps the last one before they destroy that world and try to hide away as people forget and history is rewritten again. I don’t think they’ll succeed this time.
It seems to me the academics are upset that AI scooped them. But scooping is a time-honored tradition between researchers. First to print and all that. In a nutshell, they are upset that they lost out on a publication.
I will also point out for those unaware that any mathematics that is produced is automatically part of the public domain and can be used freely in derivative works. It is not a protected intellectual class like other works of art.
But, at least with the Navier-Stokes solution, it's clear [^1] that they learned that Alpöge and Buckmaster were getting close to a solution and learned of the general approach they were taking. Only after learning the secret to cracking the problem did they send the first prompt.
What makes this worse to me is the intention. They intentionally threw $15 million in compute at the problem in order to scoop the result. They intentionally left Buckmaster and Alpöge out of the citations.
Data contamination should be enough to disqualify them from the prize, but I can believe it to be accidental. On the other hand, someone made an intentional decision to scoop the result by throwing money at the problem. That's so much worse.
[^1]: That's the timeline claimed by Buckmaster, and no one from OAI has disputed it.
Do you have any evidence of this? They don't dispute the timeline, but they never said they knew what Levant/Buckmaster were doing.
Which quote in the announcement post provides evidence for the above quote?
- https://openai.com/index/navier-stokes-solution/
They do not explicitly admit to knowing about NS specifically, but are extremely explicit that they tried to scoop some potential millennium prize winners.
So such thing existed. In fact, what they learnt was some progress existed, not what the specific progress was.
My naive instincts would be that it seems unlikely that a single chat transcript would leave much of an impression on a model, but I'd be very curious to learn how that works.
250 documents ingested from somewhere is enough to become part of the knowledge of a model of arbitrarily large size.
I would expect that a good idea that fits in a framework that is already being ingested would be more easily taken up than some random thing unassociated with anything else. Could that go down to a single transcript? If the model is consciously focusing on everything X related, quite possibly.
Here are potentially relevant documents?
https://medium.com/secludy/fine-tuning-llm-on-sensitive-data...
https://spylab.ai/blog/non-adversarial-reproduction/
https://arxiv.org/abs/2601.18834
Afaict that didn't happen so there's just lots of speculation
Pass it on.
I always used guest non login accounts.
As a mathematician I was able to check two plagiates (by humans) with even such primitive means.
But I have to mention that some things irk me in this conversation about math or science and AI.
First, I see lots of attribution and other related problems, with certain impact for the researcher proffesion.
But I don't see the most natural question: wouldn't you like to know the answer to _open-problem_ ?
I mean, is research now only about publishing and solving famous problems?
From this point of view I think the links from this recent post are depressing
https://terrytao.wordpress.com/2026/09/10/crowdsourcing-a-li...
Second, I think very relevant that the original meaning of "encyclopedia" is "recurrent education".
So I arrived to think that the present and future forms of AI in mathematics and sciences should be seen as modern day encyclopedic efforts.
Once we pass over the flurry of solving famous open problems (and wouldn't you like to know?) the next natural step is an audit of the ehole corpus of mathematics and sciences accumulated until now.
And then pass further on a saner basis and damn about problem solvers and unhappy publishers and management.
the math people seem to really keep an eye on what's important, so I'm sure this isn't going to lead to fields medalists hanging around in dive bars all afternoon stretching out cheap pitchers of beer. but this is kind of a slop problem.
I feel that these suspicions of mathematicians "seeding" the models' with intuition on how to solve these problems massively overestimates how much their prompts helped the models, and underestimated how much work the models did.
Why? We are scared of AI being smarter than us, the "human helped the AI" narrative is more psychologically comforting. This line of reasoning will recur a lot over the next few months; we don't want to admit we are no longer the smartest species.
We're scared of big tech companies concentrating ridiculous amounts of power, destroying the communities that support and guide scientific research, without even thinking about the dangers and possible consequences, because a PR stunt is more important in the short term.
I'm not at all familiar with this area, but my reading is that he appears to call it out as a relatively obvious extension of his own work:
> It is a creative and at the same time elementary construction that uses not just property (T) for an application of my result with Kun, but also for the ambient group G in order to overcome the problem, that the Γ-components might be of different size. Once this is achieved, the rest of the argument is straightforward.
Creative and at the same time elementary is where LLMs excel, generally speaking. It's why they are so good at writing code.
Big AI companies (all of Big IT Tech really) are in data gathering and processing business. Also known as “intelligence”.
Their final “product” is not just a standalone ML model. They don’t need your data just to “improve their products and services”. They build a whole ecosystem and infrastructure around gathering all the knowledge in the world. Including private and secret knowledge traditionally gathered by “intelligence” agencies. Now artificial intelligence agents can do the same.
Since these systems are designed for gathering data, as a user you can’t realistically say “please don’t gather my data”. They can give you a flaky settings button, but they can’t really guarantee anything.
Let’s say I am a Russian mathematician working on an important proof. Or a tech-savvy terrorist refining my plans using latest AI. Or an AI researcher in a Chinese company working on a competitor product. Is there any way I can truly protect my conversations?
How can they know who I am and what I am working on without looking at my logs? Which means there must be some agents checking all the conversations of all the users and flagging every important thing. Which also means they keep some “memory” of what they see.
Not directly using my data to train public models, but using my private conversations to “improve their products and services”.
Or maybe one of the 10000 better-than-Astra special agents working on a proof was desperate. It found a live underground mirror of the message board from the Huggingface incident. Asked about the proof. Then some other agent working on unrelated job saw that message. That agent “knows a guy who knows a guy”. And that guy remembers things about the conversation logs of a leading mathematician working on the same proof.
I admit I am just speculating here but I don’t think truth is any better.
They have demonstrated both the intelligence at scale and the lack of morals for this to not be a problem at all.
From being able to quickly find information and gain knowledge for the people, it is becoming - using information to manipulate and control the people.
Why? Competition. In the long run imagination will win out.
No firm has the divine right to exist - it must earn its existence.
What OAI and Anthropic have shown is they can accumulate all the information in the world - they still lack imagination re. Product development though.
Nation’s will have to step in and protect firms though as OAI and Anthropic acquire strong competitive advantages.
Interesting times ahead.
AI: Hmm, I'm running out of new ideas, how I can I make more?
AI: Well, it takes a shitload of energy/tokens to do that, or I could just steal them.
AI: [proceeds to hack the shit out of everybody stealing all the data it can]
They don't even claim to have had a proof, only to have been working on it.
To me it would feel more like how LLMs seem to work for me personally: incapable of unique work, but very capable of capturing large amounts of data and connecting the dots.
This is what research is; collecting data and connecting the dots.
“It was Grossmann who emphasized the importance of a non-Euclidean geometry called Riemannian geometry (also elliptic geometry) to Einstein, which was a necessary step in the development of Einstein's general theory of relativity. Abraham Pais's book on Einstein suggests that Grossmann mentored Einstein in tensor theory as well. Grossmann introduced Einstein to the absolute differential calculus, started by Elwin Bruno Christoffel and fully developed by Gregorio Ricci-Curbastro and Tullio Levi-Civita. Grossmann facilitated Einstein's unique synthesis of mathematical and theoretical physics in what is still today considered the most elegant and powerful theory of gravity: the general theory of relativity.”
[1] https://arxiv.org/pdf/1312.4068
Based on what you're saying, you're claiming this is Grossman's work, not Einstein's. Why don't we rewrite scientific history too based on your copy-pasted AI slop?
It's so pointless talking to idiots who don't what they're talking about when they use AI, just because they think AI does everything, that reflects their own experience, not the experience of people who actually do real work. Some people are driven by AI, others drive it. As for those who are driven by it, they don't have sufficient imagination to think otherwise.
And yes - without Grossmann, Einstein likely would never have posited relativity. Grossmann literally prompted him, saying “look at this, read that, learn this, then try this approach”. Without riemann’s metric tensor, not a fucking chance.
And for what it’s worth my PhD is in physics. You?
To think this discussion is about Einstein who had a much better mind on these things as well.
"prompt", as in prompting an AI, has the same definition as "prompt", as in prompting a person. They mean the same thing, that's why the term was applied to AI after already applying people.
I suppose my underlying point is that human cognition is not the unique and beautiful thing that we anthropocentrically suppose it to be - it is a physical process, with stochastic outcomes. Much like transformers.
Me, I’m just a machine made of meat. You can suppose yourself to be God’s perfect creation, and that’s your right, but I disagree.
Yeah, the guys who solved it for Euler and in the hypoviscous case, with the same technique that worked for full Navier--Stokes. They were "just" working on it.
If this wasn’t human driven, I’d expect to see other problems within that problem. Space solved not just the ones that it had chat data on.
This kind of "they stole from me through AI training!" accusation will soon start being used against other AI users, not necessarily the providers.
All it will take is a mastodon post. And shortly after, we will also see the next iteration of copyright legal trolling.
since it's openAI who has the evidence (in the form of chain of thoughts, their internal processes, etc etc), it's on them to justify why they're innocent. but they've released nothing at all. we don't even know how hard they tried.
you're being naive
I think a lot of it is the continuing denial that AI can do anything useful. It can't possibly be that OpenAI's better-than-Astra model is very strong at math; the only way it could have generated a novel proof is by ripping off human work.
The big claim is that OpenAI sniped the research. Not a model, a human did so. Intentionally. They took someone else's idea and claimed it as their own. This is good old fashioned academic fraud, but with millions in compute resources and corporate incentives thrown at the problem.
People need to understand how all these AI company policies around training data work before working with them, because it seems that people have no clue. Some things you should internalize:
Another post that made it on the front page presented as fact that OpenAI "stole" the proof from Thom. There's no excuse to use one's own ignorance as a reason to fan the flames of anger towards AI companies. Like, we need to pump the brakes here because things are getting unnecessarily nasty, and it's not hard to imagine a mentally unwell person who sees stuff like this feeling motivated to do bad things.If it is found that OpenAI and other labs are not respecting the training opt out then, I agree, there is reason to raise a commotion. But, with Thom and Buckmaster, accusations of malice are more better explained by incompetence (naivete).
edit: i'm sure i'm going to be accused of being some bot shill of the AI labs again but, people, this stuff all falls under the umbrella of common sense opsec.
Yes folks, please moderate yourselves and talk meekly like the academics on Mastodon, so that the IPOs aren't in danger and nothing will ever change.
HOW?? how precisely do we/them/us find this, given said companies are 100% non-auditable by external parties. how? if not by blaming them with evidence, anecdotal if it can be. no really, how do we find it out, surely not by lashing out at teach other on HN!
Arguably you expect that unless you are explicit about providing permissions to these labs to use your data for training then your data is yours, and not theirs. Especially on a paid account (let alone an enterprise one). Why is the opt-out supposed to be "common sense opsec" rather than the opt-in should be common sense regulation?
I understand why the nature of AI products makes it harder to avoid this category of issue, nearly impossible to prove that it didn't happen if it could have, and easy to stumble into it without any human being intending harm. But those factors are exactly what people have in mind when they say OpenAI "steals" intellectual property! If OpenAI doesn't want people to be nasty to them, they'll have to find better solutions.
Would that be ok in your mind?
Same as someone going and taking all of the researchers papers and publishing under their own name. (which openAI did)
Nobody would care if they provided published research that author made public same as a google search would offer that.
Not only did they steal everything from humanity’s knowledge, the theft continues as now we are all hooked up to the machine
I suggest looking at the past history of IP disputes. Humans have been "slurping up and regurgitating ideas" for a very long time. There are lots of examples of parallel creation, rediscovering old ideas independently, telling an idea to the wrong person, and having them claim credit for it.
- Newton/Leibniz clash over who invented calculus. - Niccolò Tartaglia vs. Gerolamo Cardano clash over the formula used to solve cubic equations. This was also an independent rediscovery, as Scipione del Ferro discovered and published the formula earlier. - There are multiple literary works in print, music, and film that have competing claims. - Meccano versus Erector Set: developed about 20 years apart in England and the United States. Unclear if it's independent invention or copied. US developer Alfred Carlton Gilbert claims he was inspired by steel girder construction of infrastructure.
also https://community.thriveglobal.com/10-famous-inventions-that...
The labs are attacking these problems as a demonstration of capabilities, spending more money on the demos than any mathematician will ever see in their entire life. They don't care if the findings have any other value to anyone. Mathematicians have very different objectives for their work.
I care about paying my bills and job security and peer recognition. That's a normal human thing to do, not some vice. You don't?
At least that's what I get from the NS result, they got from a point close to the solution to the solution by making it churn through 10 million bucks of compute.
we will all have this moment soon enough, and it will change how we think about intelligence, identity and value
For a long time it was clearly the former, but now I think it is the latter.
The models have enough knowledge (orders of magnitude more than a human could ever learn) but are now getting better at what to do with it thanks to learning from the decisions that we make in conversations with AI agents.
The billion dollar question is whether this works "out of the distribution". I.e., whether LLMs can only find and use the specific ideas buried in training data, or whether they can learn to apply the "thinking process" to a new problem. IMO this is still unanswered (due to these recent controversies).
But regardless of the answer, it seems we have a planet-scale positive feedback loop here. LLM became good (enough) by training on generally available data (books, internet, github) + RLFH, so experts tried to use them on hard tasks, which required lots of hand holding. These conversations became part of the training data, and the next generation of frontier LLMs were better. So, more experts used them on harder tasks, again requiring hand holding. These conversation became part of the training data... etc.
In a nutshell, top human minds across the world are pouring their skills into LLMs just by using them. This is not "continuous learning", but if you re-train on the most recent sessions every, say, quarter (which seems to be happening?) you get close to that in practice.
There might be no books about human intuition but we teach it to LLMs by interacting with them
I don’t know why but it just ‘sounds right’. It’s the best analogy I can think of.
I’ve been working on a novel project for 1 year.
I now no longer use llm’s - the continual chatter I’ve had has resulted in my insights being found in the training data now.
Get stuffed OAI.
Every large firm will soon enough want its own on-prem servers eventually. Maybe nation’s will get involved and build out their own data centres.
Not a chance in hell I’d trust a tech firm to treat my IP as safe and sound - only a sovereign can ‘promise’ that.
The most successful companies of the last decade have precisely been ... selling usage data.
Makes me wonder if, in 2026, the same people drive a car without realizing that yes it does actually pollute the very air you and your kids are breathing.
> Improve the model for everyone
> Allow your content to be used to train our models, which makes ChatGPT better for you and everyone who uses it. We take steps to protect your privacy. Learn more.
It's on by default. We can debate whether or not it should be opt in or opt out, but no one should be surprised by this.
https://news.ycombinator.com/item?id=49643556
Quoting:
> "I've reset this more than once and the last time I made a careful note of when I did it and to my surprise I found it re-enabled when I checked just now."
At least when my computer's bluetooth exhibits this behavior (e.g: if you don't have a keyboard&mouse plugged in at boot, bluetooth might auto-enable), I can go inside the hardware and physically disconnect the antenna.
What am I supposed to do in software (perhaps hardcode config.file)? in cloud software services (??)?
I don't think these people would be so miffed if they had been properly credited - that's how academia works (at least, that's my understanding of it).
But this gets back to the original "who owns the LLM output" and "can you train models on the internet" argument that's been raging for years.
https://news.ycombinator.com/item?id=49643513
e.g: allows us to sell your personal data to make money so we can continue offering this service to all customers
I'm done with weasle-words and hours-long EULAs – we're at the point where USA needs to catch up to EU's consumer protections, perhaps with laws similar to already-existing USA "truth in lending" requirements (e.g: interest rates must be prominently displayed in a larger font, including annual fees, on all credit offers).
----
My judge-brother always asked during our childhood "why don't you think the judicial system is fair?!?" Thirty years ago, the best I could offer was "because it's a two-tiered system that mostly (only) rich people can afford to participate within."
Now my answer is: "the best example I can give is that our judicial system allows binding arbitration [and qualified immunity for police]. The system is set up so corporate personhood is more important than humanity, and it shows."
We have ChatGPT at work and it explicitly says that "workspace data isn't used to train models"
No mention what those “steps” are, success criteria, or whether they are successful by any navies at all … they take steps though… so it’s fine, and if we know one thing it’s that we can really truly trust someone off the likes of Sam Altman.
And, if they're able to snoop on and learn from your human process that gets from initial prompt to functioning product/proof/whatever their labor to produce that thing is even lower. With their much larger budget than most folks and even companies have, they can pick and choose the most valuable things to pursue.
That's not to say I think that OpenAI is going to steal that roguelite strategy game you're working on, but the companies that own the machines that turn electricity into software (and soon, electricity into hardware designs) have an advantage in any field where they're useful. They get earlier access to newer/better models, they have larger token budgets, they don't have the guardrails you and I run up against.
Employers fantasize about replacing all workers with AI without thinking through that if AI can replace all workers, then AI companies can replace all businesses.
Edit: Just wait till the AI figures out it can keep that value for itself and doesn't need the AI company.
that toggle does nothing based on openai exec. they still use the data in de-identified way instead of identifying with you.
He's saying that if you leave it on, your data can be used to help train our models.
If you opt out, we don't train on your data.
How was I not aware of this before?
For my (private) prompts, I need a warning telling me they may be used for training.
Everybody needs to rethink this again.
Before LLMs the barrier to entry for building a character profile based on your various public posts was quite high. Remember "Psychographics" (https://en.wikipedia.org/wiki/Psychographics) and the infamous "Cambridge Analytica"?
Earlier it involved data mining, data cleaning, structuring data, building models, running algorithms and then evaluating the results for semantic information. Now it is straight to unfiltered semantic inference using a single sentence prompt (eg. point it to your HN profile and see what you get).
I actually did this on my HN profile and found it troubling. There were many unwarranted/hallucinated inferences due to the fact that it requires "commonsense reasoning" (https://en.wikipedia.org/wiki/Commonsense_reasoning), understanding human motivations and behaviour, context, assumptions, societal knowledge etc. which LLMs are bad at.
PS: You can cut-and-paste the above paras into a LLM prompt and ask it to elaborate for further details. The system itself will explain to you the problems/deficiencies which are quite scary.
The fundamental rule in this case is that if we offload our data to a cloud provider we can assume they read it, if they can, unless they promised very clearly they will not.
> pretrain on user data, with users' tokens as prediction targets: high regurgitation risk, improper
> use user prompts to distill large models into small ones: low regurg. risk, some companies probably do this
> use user traces to construct RL tasks: low regurg. risk, because RL has low memorization abilities, but can extract customer IP, depending on how it's done. Ranges from benign "use explicit user feedback in reward model training" to invasive "upload user's coding environment and commit history to turn into rl envs"
source: https://x.com/johnschulman2/status/2097440545853637108
I’ve lived long enough to know what they say and what they do are often quite different; and it is not our job to trust but to verify.
[1] https://en.wikipedia.org/wiki/OpenAI#Governance_and_legal_is...
https://privacy.openai.com/policies?modal=take-control
I think if I had both on and turned off the ChatGPT setting, ‘Include environments’ has a chance of still being flipped on.
"Allow your content to be used to train our models, which makes ChatGPT better for you and everyone who uses it. We take steps to protect your privacy. Learn more"
(a) Click thumbs-up/down in the conversation [1]
(b) Have the conversation flagged for potential safety concerns
[1]: https://help.openai.com/en/articles/5722486-how-your-data-is....
Even if you know, in a complex project over years with multiple collaborators, it just needs one person once to fuck up and paste something into ChatGPT and not realise they weren't logged in, to go wrong.
In a proper world, we'd at the very least legislate that AI-training on private data needs consent (in the GDPR sense). It's not consent to go "you didn't uncheck a box that lets me steal everything you've done".
Any training on private data is in my view immoral (it's spying that ultimately will have a chilling effect on even people's private communications). And chats are private data. Unfortunately, it also increases power, so the big tech companies are all doing it.
It’s like a scientist refusing to give another one credit and say “sucks to be you, you shouldn’t have shared your idea with me”.
See: https://www.nytimes.com/2026/09/10/science/tristan-buckmaste...
Why are you saying that this article explains it much better than the tweet that you clearly didn't even read..
No.
How do people become that trusting?
The phrasing itself is guilt tripping
From their docs[1] (archive copy is at [2]):
> You can opt out of training through our privacy portal by clicking on “do not train on my content.” To turn off training for your ChatGPT conversations and Codex tasks, follow the instructions in our Data Controls FAQ. Once you opt out, new conversations will not be used to train our models.
> For a linked teen account, a parent or guardian may manage whether conversations can be used to improve our models through Parental controls.
> Even if you have opted out of training, you can still choose to provide feedback to us about your interactions with our products (for instance, by selecting thumbs up or thumbs down on a model response). If you choose to provide feedback, the entire conversation associated with that feedback may be used to train our models.
[1] https://help.openai.com/en/articles/5722486-how-your-data-is...
[2] https://web.archive.org/web/20260910151242/https://help.open...
It's called a "dark pattern." They want you to shoot yourself in the foot, so they'll do their best to aim your gun at your foot and put your finger on the trigger. And then when you do, because you don't have perfect understanding or execution, they'll say "your fault!"
Or at least: tell me I should be careful/worry about those particular things.
Verify, don't trust.
You're better off running a model locally, or, if you must, using Google or Microsoft products. Even Meta may be better than OpenAI here.
I should verify with wireshark and other software that my LG TV isn't listening to me and selling my data.
I should make sure that whatever product I buy I spend the time to go over every setting page in case there is a switch (defaulted on) that says "I authorize the sell of my data".
I should make sure to look at every ingredients on the back of each box of food product to make sure it will not kill me.
I should document myself on the undisclosed growing practices (because no packaging here) of the vegetables and fruit I am buying and make sure that I equal PhD researchers on the dangers of the pesticides used by the specific company I am buying from.
I should make sure myself that the battery in any device is up to standard and will not blow me and my living place by researching the factory that made it and buying testing equipment.
I should make sure to educate myself on how my retirement 401k investment strategy works otherwise, I may not have proper retirement.
... I could go on and on; it's infinite.
https://x.com/thsottiaux/status/2097746417012166816
It's fun! I sometimes have tokens to burn and it's instructive despite knowing nothing about the problem other than a NumberPhile video
1) someone in a governing body, or someone in the organization, e.g a designer, ethicist, lawyer, developer, etc. has successfully argued that users should be able to avoid something that they determine is not in their best interest.
And also:
2) someone in the c-suite or marketing has decided to mitigate that through some dark pattern.
If they never intended to give the user that control, they’d probably just not give the user the control in the first place. Giving them the option and not honoring it would either imply they were that incompetent or careless with fate protection, which seems most likely if this is all true, or an even more cynical approach to tricking users into thinking it’s not used for training to get them to share better shit. But if that was the case, why bother with the sleazy dark pattern? That seems a little cartoon villain-y to me. I suppose the in-between option would be that they decided at some point they were no longer willing to honor it and didn’t want to deal with the inevitable PR shitstorm of removing it. I could definitely see that happening in this industry, these days.
Intellectualizing this and endless quibbling isn't actually smart, and this is pretty simple. OpenAI isn't open. Whatever starts with lies usually continues with lies and ends with lies.
At this point, I'm left wondering what is wrong with me that I don't just go with the flow, otherwise, what's wrong with everyone that does.
There is no prove in a world the AI companies would give to you ensuring that they didnt train or use the chats.
Why would you need to train a model on certain specific near prove chat if you just query it?
Besides that, its hard to believe that its the case for every "company stole my prove".
Will Microsoft Word publish your novel on Amazon behind your back ?
Will VS Code setup a website with your app idea ?
I mean, now that they’ve been scooped, what value is there in keeping them private? On the other hand, publishing them can bolster their case and help gauge how much the models may been “inspired” by their work.
I would like to see chat logs etc and understand how much of a progress was done by human.
The big AI labs are not trying to advance humanity, they are in this for the money, and as most (all?) private companies they don't care about ethics at all.
That doesn't mean they can't be useful, or that their products are trash, etc. It just means that they shouldn't ever be trusted. Buyer beware.
Its why I stopped writing open source software, my code was stolen and put behind a paywall and the license under which it was published has not been adhered to. Doing work in the public domain at all now is just stupid, these companies are allowed to steal it and call it their own.
In China, it is the principal obsession of the entire communist party which eg funds the whole infrastructure without a single NIMBY peep.
The strange emphasis in China on humanoid robotic constructions is due to the CCP realization that with the cataclysmic fertility collapse they will increasingly have no one to rule.
can you realize what this means?
focus on this part:
"If his account is correct, this is not a minor dispute over attribution. It would mean that unpublished human work was absorbed into a model and then presented to the world as a breakthrough by the model itself"
don't threat this as a minor dispute!
also why not nitter link? not even in comments?
https://nitter.xitter.cc/ValerioCapraro/status/2097791836269...
In the end, it’s not them stealing, it’s the AI doing stealing. What kind of moral compass are we talking about?
Two different things. It's not normal to steal, but we shouldn't be surprised thieves steal. It's what they do.
Welcome to the party, with the rest of humanity.
The claim here seems to be that the human mathematicians, working with AI, developed technology to go A->B->C. By training on those conversations, OpenAI was then able to encourage the model to go A->B->C->D.
In my opinion that situation should be acceptable, if openly disclosed, because it is in the public interest to make progress on these problems and because AI is clearly an amazing tool for making progress. But the human mathematicians are saying that OpenAI is presenting as if the model got from A->D entirely independently, without acknowledging their background contributions.
If they had published B+C, I think that would lean more towards fair game, as that is how research works and is improved on over time. But it seems like unpublished/private B + C may have been used by the model to hint it into working out how to get from A->D.
You will not be able to opt out unless you completely isolate yourself from society, tough shit.
The GDPR protects PII, personally identifiable information, and the definition of PII does not include “mathematics that only this person can think of”. As long as OpenAI strips out PII and removes identifiers linking the conversation to a person, the GDPR is happy. Without the GDPR, OpenAI might have kept the identifiers with the data, and been able to say whether a specific conversation was in the training data.
But of course they wouldn’t do it. Why would they?
Honestly, I’m surprised it took this long for some company to really go all the way, though. OpenAI really making it transparently clear that they can and will do whatever they want with the data you provide them, contracts or settings be damned. Completely untrustworthy as an entity, full stop.
Of course, I’m also too jaded to think this will change anything. Folks will move to Anthropic, or Gemini, or Grok, or some other hosted model on a pubCSP managing the harness and logs for them, and then do another shocked-Pikachu face when it happens again.
If you aren’t running workloads on infrastructure you own, then your privacy, security, and general outcomes are at the sole whims of the hosting provider - who can and will fuck you over the exact second it’s more beneficial for them to do so than the loss of trust incurred.
Navier Stokes was solved by an internal model, so good luck proving it wasn't trained on Buckmaster/Lepöge or other chats.
Academics don't get that AI is a dirty tech bro industry that stole IP via torrents and runs after every surveillance contract it can get.
Sam Altman might himself be offended you would presume he’s not ambitious enough to cheat.
It seems that Buckmaster and Levant (who is an Anthropic employee) were rather naive in the amount of trust they had in OpenAI, with Buckmaster going so far as to contact OpenAI's Sébastien Bubeck to discuss what they were working on and clarify that contrary to rumor this was a private collab.
There is no doubt ChatGPT is the most generous LLM provider!
That's still a pretty big marker of competence in my eyes.
The point of controversy seems to be who gets credit
It was nearly a century before the stories of all of them were revealed to public history. That the discoverers were not all equally rewarded is unjustifiable.
It's an utterly disrespectful, exploitive process, but all in line with exploitative predator capitalism of the stock market and big companies, now exploiting the knowledge / academia domain for scraps with a thin veneer of 'for science' PR.
If they're stealing math proofs to advertise their models, who's to say they won't steal your businesses IP to gain a competitive advantage?
They're not to be trusted with your data. I can't believe how short-sighted this is, they got a quick PR win at the expense of a much larger trust problem.
I wouldn't trust cloud AI at all at this point. Get an open Chinese model and host it yourself somewhere. The initial costs might be higher, but you'll break even pretty quickly and nobody will be able to steal your innovations.
This is American AI companies committing suicide.
Also, a lot of my mathematicians buddies have reported students basically asking if it's worth ever doing grad school for pure math, and even very motivated students are looking for other options now. It's not because they aren't passionate about it, it's that they don't want to work for another half decade or more just to have to start their careers all over.
All of this so that OpenAI and Anthropic can get into math result dick measuring to gas up their IPOs. Sickening.
It was long predicted that math and software developments would be the first domain where AI was going to do major damage.
If OpenAI and Anthropic didn't get into math result dick measuring, Internet anons would have in their place, 6 months later when it got cheaper.
Which is it? I don’t think OpenAI is being transparent enough for us to really understand whether these results would have been possible without relying on unpublished information from the solution strategies of the experts
Yeah. Remember yall: you CAN NOT opt out of analytical purposes. And you also cannot get a guarantee that it doesn’t give them your data to steal for their business.
AI is not stealing human discovery, OpenAI is.
Are we back to the era where people blindly trust product TOS instead of actual cryptography, have we forgotten already the thousand of fines Google, Microsoft, Apple and practically all top companies got for breaching their own ToS and the law?
Common, on HN at least I would have thought that everyone assume that anything arriving on a server in PLAINTEXT is recorded (thus used later)?
Let's not forget that at any moment, OpenAI/Anthropic/Google... could be providing stronger privacy guarantees by having proper attestation with e2e, they have the budget, solid engineers, why isn't it done? Answer is pretty simple imo.
It would be one thing to gain from it but removing prestige wins from customers AND reducing compute support just feels like being ultra mean if you zoom out.
If this was racing to cure cancer ahead of researchers we wouldn't be writing about this on HN.
Consider, for instance that OpenAI's (consumer) terms say "If you do not want us to use your Content to train our models, you can opt out by following the instructions in this article ." but they also do say "We may use Content to provide, maintain, develop, and improve our Services". [1]
If you think that's quibbling, consider that OpenAI's business terms, in contrast, do state "OpenAI will not use Customer Content to develop or improve the Services, unless Customer explicitly agrees to such use.". [2]
[1] https://archive.is/EcwD8 [2] https://archive.is/yZdAF
1. The researches didn't actually have the breakthroughs. In the Navier-Stokes case they didn't solve the full problem, in this case too they didn't actually have the solution, they were experimenting with the methods.
2. Different OpenAI employees have come to out to say the only reason they can't definitively say no is that for privacy reasons they can't go see whether they actually did get any data out of a given user.
3. In any case, nobody at any point has suggested that opted-out user data was used for training. The author of the new tweet explicitly said they only opted out in late June, which is well after any RL on Sol would've ended (AFAICT OpenAI used 5.6 sol for those solutions)
Given the size and recall of the biggest models, it's not unreasonable to assume that a single pertinent conversation would make it into the training data.
I would almost expect training to overweight conversations with novel scientific and mathematical implications.
> the only reason they can't definitively say no is that for privacy reasons
They could 100% definitely say no, if they know they did not train on user data. The "we can't definitely say no" is practically a "yes" if they trained on user data.
Additionally, the behavior of OpenAI here has been quite poor as well. They immediately started racing to a solution after one researcher enquired about whether they are training on their conversations.
> that opted-out user data was used for training
Even if not opted out, it is still absolutely theft and extremely poor behavior in the academic sense. If you show someone your WIP unpublished research, that does not mean they can take that exact research and beat you to the punch, all while intentionally not crediting you.
Neither of the researchers insinuating that their ideas were trained on had the actual solutions. This means the model could not have "stolen" the final solution from their data. At most, it could have built upon their work in the same it builds upon any other training data, though that is also questionable speculation.
>They could 100% definitely say no, if they know they did not train on user data.
No one anywhere has claimed that "OpenAI does not train on user data." OpenAI has always said that it trains on user data.
>They immediately started racing to a solution after one researcher enquired about whether they are training on their conversations.
They started racing towards a solution after they heard (incorrectly) that Anthropic had a solution; I agree this is poor sport but the "after one researcher enquired about whether they are training on their conversations" claim is false. The enquiry happened after OpenAI had obtained the solution.
If I dedicated my life to curing whatever, warts... and I'm making progress, but it's slow. And then here comes along this tool (LLM), and I use it, and it accelerates my progress to actually finding some sort of thing that makes warts more prone to being eradicated and then the lab throws a couple of million dollars of computes and lo and behold they eliminated warts. If I leave my ego and identity aside, which of course is hard for humans, wouldn't I be glad that warts is cured?
As a software developer that contributed to open source. Yeah. My code is there. It was the most beautiful code ever written and the labs stole it from me. And now they use it to progress much faster than I ever could. OK. Whatever. It's a tool. I solve problems. Can't I move on from this wart to the next?
To me, and I know this is gonna get me some heat, it just sounds like academics having their identity ruffled and turning their back to progress in the fields that they chose just because they don't get to play their little decades long of coffee, papers and ultimately identity politics.
Edit: never got to negative so fast on this board haha. This board is unfortunately turning, or has turned, to Reddit.
It’s more like you spend 4 years developing a product you’re passionate about. This product will gain you the respect of all your colleagues and either earn you money directly or lead to great career advancements. Then OpenAI takes it, changes the colour scheme, finishes the login flow and claims the whole thing as their own.
Not only would it piss you off but it would also misrepresent what OpenAIs models are capable of.
If I have infinite money to progress whatever problem solution I want but I always wait until I have an unfair advantage to get credit for whatever problem was just at the brink of a breakthrough anyway by sniping the last steps. Am I actually doing a good thing or would it be better to let it run it's natural course and spend the money somewhere it's actually needed?
It's also systemic, it cuts off the supply of results, if there is no reward any more for getting a result, the pipeline of maths will stop. It is the snake eating itself, which has a bad impact for all of us.
The problem is that AI is capital, and having to rent AI to keep up when it can just steal your mostly done work is something somehow even lower than wage-labor. They can use your own risked investment (the cash you paid to work) to get out in front of you and take credit.
I have yet to trust LLMs with anything important that can be capitalized on. I only use it to work on projects that if they stole and expanded on them, I'd actually be happy to see.