comments (10)

  • I think it's a useful analogy to compare OpenAI to a human collaborator. These researchers willingly collaborated with an OpenAI model, giving it ideas, and OpenAI provided useful replies. Then, OpenAI goes ahead and publishes work along the lines of this collaboration, without attributing the researchers. If OpenAI was in fact a human researcher, this would be highly unethical.

    Now, OpenAI is claiming that the model it used to generate the result was not trained on these collaborative communications with the researcher. This is a technical argument that is impossible to verify as an OpenAI outsider, and probably difficult to verify even for internal OpenAI employees. Provenance is hard to track - you would hope OpenAI has very good tools for this, but a full data trail of all inputs is difficult to trace through.

    Another interesting thing to consider is if instead of OpenAI doing this, it was another research mathematician A using an OpenAI model just like the internal group at OpenAI did to publish these results. What if the model A used was trained with unpublished communications with other researchers B who were working on the same problem? Should researcher A technically include B as coauthors? How could they do this when they do not know the communications B had with OpenAI? In this scenario OpenAI, as a middle man, has laundered information from B to A, stripping out attribution. A scooped B without even knowing it!

    nezi

  • 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.

    [1]: https://openai.com/index/chatgpt-for-academic-researchers/

    [2]: https://xcancel.com/OpenAI/status/2097374643518640382#m

    bertonvv

  • Both things can be true:

    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.

    sashank_1509

  • 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.

    fwlr

  • 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

    bamb008

  • This is the second wake up call.

    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.

    thaway7388

  • This is a really weak claim. The evidence they offer is just "someone somewhere says they had a discussion with AI about the topic at some point".

    They don't even claim to have had a proof, only to have been working on it.

    Legend2440

  • 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.

    aaronharnly

  • 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.

    mlazos

  • Most people here are missing the forest for the trees.

    We live in a society where phones and internet providers and websites all collect an incredible amount of data about everywhere you go, what you do, and what you think. In the US, we have very few digital rights.

    We are building a society where a trillion dollar company can aggregate all this data and just yoink your shiny new idea away from you at the finish line.

    This is double plus ungood.

    nautikos2