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More questions about whether researchers can trust OpenAI with unpublished math

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Re: More questions about whether researchers can trust OpenAI with unpublished math

#311

Post by pixl97 »

ozgung wrote: post #293
sashank_1509 wrote: post #266

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…

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.

Re: More questions about whether researchers can trust OpenAI with unpublished math

#312

Post by irthomasthomas »

HarHarVeryFunny wrote: post #303
sashank_1509 wrote: post #266

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…

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, th…

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...

Re: More questions about whether researchers can trust OpenAI with unpublished math

#313

Post by auntienomen »

HarHarVeryFunny wrote: post #303
sashank_1509 wrote: post #266

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…

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, th…

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.

Re: More questions about whether researchers can trust OpenAI with unpublished math

#314

Post by ndiddy »

HarHarVeryFunny wrote: post #303
sashank_1509 wrote: post #266

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…

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, th…

> What we don't know is just how recent this model was, and therefore what it may have been trained on.

OpenAI's statement says that they began training their new model on August 28.

Re: More questions about whether researchers can trust OpenAI with unpublished math

#315

Post by pixl97 »

dgellow wrote: post #297
iamgopal wrote: post #292

Earlier quoted context omitted.

True, but could humans cross pollinating lean x prolog x A* ( or any search algorithm) could have solved such math problems with super computer ?

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.

Re: More questions about whether researchers can trust OpenAI with unpublished math

#316

Post by convolvatron »

encyclopediai wrote: post #307
aaronharnly wrote: post #249

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 wou…

I run such tests since a long time at chorasimilarity open notebook. 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…

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.

Re: More questions about whether researchers can trust OpenAI with unpublished math

#317

Post by bwfan123 »

bertonvv wrote: post #130

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 Co…

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.

Re: More questions about whether researchers can trust OpenAI with unpublished math

#318

Post by amelius »

glitchc wrote: post #295
bertonvv wrote: post #130

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 Co…

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.

Did you read what they said? The question is now if OAI produced something new or just stole the researchers' good ideas.

Re: More questions about whether researchers can trust OpenAI with unpublished math

#319

Post by dgellow »

pixl97 wrote: post #315
dgellow wrote: post #297

Earlier quoted context omitted.

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 go…

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
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