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.
They sugggested a cooperation with the other guy, using OpenAI's resources and OpenAI's solution of NS to work on and publish NS proof (that those other guys didn't have). Of course, OpenAI can decide whom to work with and that giving resources to their competitor's employee would be weird for both companies.
No. Euler solution would have bith names, this was not up to debate. The discussion was about the solution for NS, which was solved by OpenAI, but not by B&A. OpenAI proposed B a cooperation on NS (were super-nice and threw him a bone, really) using OpenAI's findings and resources. It would be weird to have A, an Anthropic employee, as part of the OpenAI research and project.
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Even by their own account, they decided to throw an unpublished model and millions of dollars in compute at this particular problem simply because they had heard rumours that other people were making progress and wanted to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.
2. to be able to say "you came with the proof, but our model can do this too"
3. to verify the result. This is also a great thing for the math.
Of course, it makes sense to test your new model on the problem that is solvable at all, but not solvable by you just yet. It makes no sense trying to test your model by throwing resources into an unsolvable problem.
Well, it turns out the rumors were incorrect, NS was not solved by other guys, and OpenAI became the first one.
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)
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.
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.