Because the core of the issue is that it may well not have solved it, but instead plagiarised the significant step of the result from other researchers
That's why nobody's talking about how impressive this is, because its not nearly as impressive of a piece of work to simply cobble together other peoples' work that didn't know you were doing it. I could have republished relativity from einstein's notes, but people would correctly not be impressed with my ability
Until the plagiarism scandal is sorted out, its not a meaningful result at all, because nobody knows how much genuine innovation these models are displaying
Turning a bunch of vague research directions and exploratory prompts into a formalized proof is quite impressive on its own. OpenAI would have no incentive to taint its first math announcement of this magnitude if it knew it were "plagiarizing" another person's work.
People are grasping at straws it seems to dismiss the power of this new model they may have. Hate OpenAI for any reason you want, but denying the capabilities of models has been a losing game for the past 5 years.
It's perfectly reasonable to assume that the result itself is legit and that OpenAI behaved unethically.
Even by their own account, they decided to throw an unpublished model and millions of dollars in compute at this particular problem simply because they had heard rumours that other people were making progress and wanted to snatch the prize from them.
I really truly honestly am not sure what to make of this result from $20M in compute, 10K+ parallel agents (smells like brute force), and a pre-existing approach that was already bearing fruit. I know the models are good---I use them every day and continue to be impressed---but how much better than the benchmark of the best publicly available models is this supposed to be? It seems impossible to say.
But if it happened, they didn't know. Also OAI has demonstrated that they aren't big on understanding what they create, that their AI can get out of their control.
It's very simple really user data can be used to train future models, so maybe or definitely some users helped in solving the problem, there's no scenario were it is impossible this happened, as it would have been in a haskell or virtualized type of system where the model has absolutely no knowledge of the user data dataset in question (and even if virtualized the models can break virtualization anyways)
> Because the core of the issue is that it may well not have solved it, but instead plagiarised the significant step of the result from other researchers
It's also true however that I haven't seen a single write up trying to discern what did more of the work in those AI chats - the prompts or the responses - bubble to the surface, also since we don't have access to them.
For example, if I prompt Codex with "Make me a website about strawberry cake" and nothing else, and OpenAI announces they have the best strawberry cake minutes before I launch, I'm not sure they plagiarized anything.
We just don't know if this is quibbling over "who prompted first" or if the researchers came up with anything strikingly original by themselves.
The researchers apparently spend a year or so working on this, and it builds off significant previous work, so it seems like it was a pretty significant amount of work that OpenAI may have trained on
I'd love to see an in depth analysis of how much OpenAI actually did, but I suspect we'll never see that because it would indicate at least some plagiarism which undermines a lot of what OpenAI is putting out in public
The American Mathematical Society credits the Spanish researchers Diego Córdoba and Luis Martínez‑Zoroa with the breakthroughs that eventually led to this solution, and which were published from ~2023 onwards.
This is a good summary:
> In broad outline, the pair’s technique relies on creating an infinite sequence of “layers,” each of which is a non-singular solution to the equation they are studying. (They’ve applied similar techniques to both the Euler and Navier-Stokes equations, as well as to other related systems.) They then combine those solutions in what Martínez-Zoroa calls an “infinite cascade” to produce a new solution.
>
> That new solution, they showed, contains the desired singularity. However, even though each individual layer relies on a smooth forcing function, combining them together can cause the forcing function to have undesirable mathematical properties. That’s why their solution fell short of satisfying the Millennium Prize criteria. The remaining hurdle was to figure out how to create a similar infinite cascade that resulted not only in a singularity, but also in a smooth forcing function.
>
> That’s the step that both competing AI groups appear to have had success with.
The question is whether OpenAI started out from that published and well known research exclusively, or they also had some insight into the ongoing work of Tristan Buckmaster and Levent Alpöge.
On the one hand, OpenAI have already admitted that they only launched their massive effort after hearing rumours that this particular problem had been solved.
On the other, progress in mathematics research has accelerated significantly over the past months thanks to the availability of newer and more capable AI models. Alpöge himself presented a counterexample to the Jacobian conjecture on July, found with Claude Fable. So if model capability was a bottleneck, that gives credibility to the idea that an even more powerful unreleased model with massive compute would be able to make even faster progress.
It's worth noting that the case is that your input is being used to train their AI, and that's more important than whether it materially contributed, it cannot be denied or attributed accurately, it cannot be said with certainty which way it happened, and that's what's important.
It is very unlikely to be plagiarized, and claims of plagiarism are largely unfounded and show a lack of understanding of the situation. They fall apart when reviewing the timeline, and what was actually solved.
In late August, OpenAI completed a pretrain of its latest internal model. A model derived from this pretrain, built after August 28, found a solution to 3D incompressible Euler without forcing and Navier-Stokes with forcing. https://openai.com/index/navier-stokes-solution/
Tristan + Levent: 3D incompressible Euler with forcing
OpenAI: 3D incompressible Euler without forcing
OpenAI: Navier-Stokes with forcing
No one: Navier-Stokes without forcing
Euler equations = Navier-Stokes without viscosity. Forcing means external force. Absence of viscosity and presence of external force make blowup easier to construct.
Tristan+Levent ticked the weakest case, OpenAI ticked the two next weakest, then the final case is unsolved. Only the last two are eligible for the Millennium Prize. The Navier-Stokes general case remains unsolved.
Buckmaster disabled model training long before the August 15 breakthrough results, so these chats were not used as training data for OpenAI's model which solved Navier-Stokes.
Additionally, Tristan and Levent only solved the easiest version of the problem and did not have the key insights to solve the harder versions of the problem required for the Millennium Prize.
"We can say categorically that it is impossible for Dr. Buckmaster’s Codex prompts over the last two months to have influenced the system in any way, including training."
> And OpenAI directly addressed these plagiarism claims, and called them impossible
Funny, you were telling me two days ago that on the contrary, "it’s genuinely impossible to know how much of Buckmaster’s Codex data is in OpenAI’s training set":
Which is still a true statement, and you're being deceptive in your framing here. You're conflating two completely different things.
First, that OpenAI statement is in response to Buckmaster's plagiarism accusations regarding his August 15 breakthrough proof. Those accusations are unfounded because Buckmaster disabled data sharing on June 29. The model could not have seen or trained on his proof. Additionally, the model that found a solution to NS completed pre-training around August 25, and models take several months to train. The model very likely began its training prior to June, and would not be trained on any data from after that point.
Second, it's still genuinely impossible to know how much of Buckmaster's pre-June 29 data persists in OpenAI's systems. That includes all chats (which are anonymized then trained on), any (thumbs up/thumbs down) chat ratings used as RLHF feedback (which are anonymized), any synthetic data derived from said anonymized chats and RLHF feedback, and any downstream models derived from said synthetic data.
In short, Buckmaster's data has been anonymized, chopped into pieces, used to generate synthetic training data, then future models were trained on said synthetic data. There is no traceable chain of what happened to it. Buckmaster’s Codex data from prior to June 29 has been mixed and completely laundered, in a similar manner to a crypto mixer.
I’m unsure or not if this is true but I did see some people saying that that checkbox when off only anonymizes your data, but it still may be trained on. Someone correct me if I am wrong
Even if it does use your data with or without anonymization, it doesn't have to be intentional, it could just be a glitch, or a bug, or something we'll catch in the next update, it's all good man, just a normal computer error.
It doesn't seem like you're familiar with how mathematical research is done. Taking 6 weeks between a major breakthrough on a huge proof, and making your proof public, is not unusual.
It takes a lot of time to finish a proof and figure out the best way to present it. I would personally be surprised if Buckmaster had not gotten it mostly cracked before June 29th.
The timeline here does not support your argument. Quoting from Buckmaster's statement:
For most of the past year progress was slow. We worked through the literature and upgraded various preliminary results, up to obtaining finite time blow up for the Incompressible Porous Media equation (with smooth forcing). This was until about a month ago, when we had real progress: on August 15th, we obtained the blow up results, with smooth forcing, for both Boussinesq and Euler.
I can say the first LLM generated proof Levent sent me was the most horrendous I have ever read; we verified it on Lean on August 22nd. Since this point, we have been working around the clock to understand this proof and turn it into something readable.
Specifically: "For most of the past year progress was slow ... until about a month ago, when we had real progress: on August 15th"
And you avoided addressing the critical issue: they weren't even solving the same problem. Buckmaster solved a simplified and easier version of Navier-Stokes. OpenAI solved a harder version eligible for the Millennium prize. Buckmaster did not.
who cares about plagiarism? the biggest issue, as described by Terence Tao, is that AI companies don't understand the math they are publishing and do not devote any resources to answering questions about their methods after publishing results and getting a headline. they miss the whole point of mathematics. they do not contribute to the improvement of human understanding of math, perhaps because they are unable to.
"The route to the Clay problem through a smooth force, options c and d in Fefferman’s statement of the problem, is the route Luis and Diego opened and the one Levent and I had quietly chosen to attack. Almost nobody else I know of was working on it. It is not the direction one arrives at in a few days by giving a model the problem statement. When I heard “forced,” it was a bright red flag."
We know that OpenAI trained on their prompts, plagiarism is incredibly likely. The only thing we don't know is whether or not it was deliberate plagiarism yet
OpenAI have admitted their new model they used was trained on prompts at around the time that researcher was working on it, so it seems self evident that it was used as part of the millennium solution
We're having to rediscover in real time the extremely hard way, why enabling mass theft is so incredibly damaging to society. This is literally why we need a functional copyright system
If theft becomes more profitable than genuine creation, then nobody will create anything. Then there's nothing to steal, at which point all progress collapses
1. using copyrighted material to train LLMs is fair use, not theft
2. The topic we are dissussing concerns LLMs being trained on logs from previous LLM chats. If you're prompting a model and it spits out some unique mathematical insight, you do not have copyright on that.
> why enabling mass theft is so incredibly damaging to society. This is literally why we need a functional copyright system
What is interesting is that LLM's do not directly violate copyright. The settlements we have seen are for how the works were acquired (that was a copyright violation) not the use of the works.
The vectors of a book, or a paper, are not the paper. They are, for all intents, facts about the work itself, and more generally writing. You can not copyright a fact.
It also means that the weights, the things that (mostly) matter can not be copyrighted either.
> Copyright maximalism is a bad look on a site called "Hacker News." Perhaps other sites beckon.
Frankly it's more of an insult to the "hacker" name to be apologising for big companies profiting off of frontrunning existing work for PR purposes, if the claims about piggybacking on human-directed efforts/prompting are true.
Being pro-copyright in order to protect the work of an individual from being reconstituted into the corporate machine is VERY hackery. Novel use for an existing tool, to fight the dominant system.
(Of course, we're on a so-called "hacker" site hosted by a company run by squarely-establishment individuals acting in an extremely un-hackery-field (investing), so the irony here has been at least one layer deep since the start.)
In my mind being a hacker is something very anarchist/left libertarian coded. Hackers don't try and get laws passed to achieve their goals, they do it themselves no matter if it's legal or not. Activists fight for right to repair, hackers jailbreak their device and publish the keys. We need both to fight big tech effectively.
Copyright is not a hacker thing because it restricts peoples freedom to share and hack on anything they see. Now abusing a bad system to do something good, as you said, would be hackery. But that's not the same thing as supporting copyright as it is right now or wanting to expand it. It needs to be in the spirit of "we turn their own tools against them". The ultimate goal is still eliminating IP laws as far as they limit individuals free speech.
It would be different if it was an asymmetric system. Protect creators from companies trying to make money off their work, but not the companies from users taking their profits. That would mean switching to a form of copyright that only applies to commercial use and abolishing work for hire / any form of rights assignment as opposed to a license. That's one idea I can get behind.
"Corporate machine," yadda, yadda, whatever, go sell it on Reddit. The model running on the box in my basement is almost as good as the one we're talking about here, and may in fact be just as good by this time next year... and it couldn't have existed under your proposed regime.
Yes, OpenAI is likely to be found to have acted like a slimeball in this instance, or at least the employee in question may have. But you can't fix that without making laws that will make everything else worse... and only here in the US.
Sure, but unless you’ve got some exceptionally deep pockets, congress has seemingly no interest in turning the fact that it’s ethically bankrupt into any practical recourse.
Ai companies got where they are by stealing all of the intellectual property from human history. It seems entirely likely that their goal is to purloin everything produced going forward as well.
If you don't trust the labs directly, you can always use AWS Bedrock or Azure Foundry which should have a much stronger incentive to not train. They make money from asset rental, not selling models. I'd be shocked if they were training.
They kind of did though, they were hoping to keep the fact that they may well have plagiarised these researchers unpublished work quiet. They did not want this to turn into a scandal about the fact that they appear to be training on prompts without consent
It makes a certain amount of sense. The internet data is too polluted with AI usage now to be useful, so the only AI free new data source is the prompts people feed into ChatGPT. The only problem is that its clearly plagiarism
Edit:
OpenAI have admitted to training on prompts at the time the breakthrough was made:
OpenAI claims the data contamination issue only surfaced after they proactively reached out to Buckmaster and Alpöge to coordinate a joint release. They also say that even if there was some contamination, the underlying proofs diverge substantially:
> Our effort began on September 1st after hearing a rumor which we later realized was related to Levent Alpöge, an Anthropic employee, and Tristan Buckmaster, a math professor at NYU. After the completion of our full project and Lean verification (on September 6th), believing from the rumor they also had a solution of Navier–Stokes, we reached out to them to offer a concurrent release of our result and to recognize their priority in a joint announcement. At that point we found out that they had a resolution of the forced Euler problem. In these discussions we offered them visibility into all of the prompts we used and later to see the proof. We recognize the priority of their work on forced Euler and congratulate them on their remarkable mathematical achievement.
The biggest issue we aren't talking about is, of course, that those two researchers were not the only two using ChatGPT to work on the problem at the time
That's why nobody's talking about how impressive this is, because its not nearly as impressive of a piece of work to simply cobble together other peoples' work that didn't know you were doing it. I could have republished relativity from einstein's notes, but people would correctly not be impressed with my ability
Until the plagiarism scandal is sorted out, its not a meaningful result at all, because nobody knows how much genuine innovation these models are displaying
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