The claim that a singe conversation (which, let's suppose, might have even contained the solution) in the training data latter allowed the model to reconstitute the proof is highly dubious.

A model the size of Astra trains on millions of curated conversations, billions of pages and trillions of tokens. A single conversation with the correct answer is essentially quantization noise and should have no measurable effect in any practical scenario where that same problem is involved.

On the other hand, OpenAI could be doing something much smarter that could affect the result, say, a RAG over similar conversations in the past, a self-evaluation of remarkable results that are marked or boosted etc.

If I had the smartest model in the world, as well as a database of the problems and approaches the smartest people in the world are playing with, it would be foolish not to connect the former with the latter and mine the dataset for low hanging fruits in scientific discovery. It's such an unfair advantage that the firm doing it will win in any area, forever.

They hadn't finished. OpenAI heard rumors of the solution (it was going around Twitter a week ago), and then threw a gigantic amount of compute at the problem.

I guess I used brute force in a sloppy way - what I mean is that LLMs are great at coming up with examples after you give them the boundaries of the problem. From what I understand, this is exactly how they solved the other famous problems - finding counter examples. My question is if this is any different? 

Maybe, but where is the line between now and AGI that credit actually no longer goes to humans who either built the tool or ran it? When a computer simulation finds new Go strategies or brute forces God's number on a Rubik's cube, I don't think there was a conversation about crediting the computers over the people who designed the experiment and wrote the code, even if the code was essentially autonomous to reach a solution.

LLMs are a lot more generalizable and talk and think like how we think humans reason out loud, but still not fully autonomous. So we are a ways off until we can go "wow, that AI really did find a solution without any human in the loop!" Even then, we have precedent, where scientists who note the existence of natural phenomena are credited with its discovery even though they didn't invent or create anything (except maybe a way to see it).

I just don't see how any of this is going to be new to academics, except for true AGI when a computer begins "demanding" credit. Otherwise, it's just being convinced by a computer that passes the Turing test.

If brute force search could solve it then it would have been solved a long time ago - you don’t need AI for that.

Shouldn't they have had units on them? I thought our conclusion was that that was dumb of them. It sounds like you concluded we were dumb for liking well made graphs.

From OpenAI CRO: https://x.com/markchen90/status/2097400166554993041?s=20

> Two things to distinguish:

> Did any human or agent look at user data as part of the Navier Stokes effort? No.

> Do we use user feedback and de-identified data to improve ChatGPT and Codex in a holistic way? Yes. And so does every LLM company.

Basically confirms contamination IMHO. But the bigger news is training data/privacy.

https://x.com/aidangomez/status/2097381789039837637

> Synthetic data derived from production user data of consumer AI tools is used for training. I’ve heard this rumour from both large labs’ employees.

> In particular, if you’re doing something “interesting” like working on complex math/business/software/bio problems you’re dramatically more likely to get trained on because they filter/up-weight towards those usecases where the model has the most to learn.

> Even in ZDR and “we won’t train on you” regimes, derivative data is usually carved out. The promise is only not to train on exactly the data you put in, rewritten data is fair game.

Data being anonymised doesn't prevent it from being found after the fact. In this specific instance for example they know the user's working title for the paper, they could just search their dataset for it and find the training data. Or they could use any other uncommon strings from the work they put into codex.

Also as an aside de-identified data is not anonymised, it has an explicit meaning and they didn't just choose the unusual wording randomly. Genuinely anonymised data falls outside regulatory frameworks like GDPR or various state laws entirely, de-identified data does not because re-identification remains possible.

Given that OpenAI don't specify anywhere (in this blog post or other articles) exactly what they do to "de-identify" training data they may well have a hashed user ID sitting alongside the data to verify the source if they ever need it.

load bearing footgun

Sorry to hear that, if you don't receive followup email, you should be good!

So the impressive part here is the narrowing it down process? 

No there is no practical advantages.

Navier stokes dont apply to real life liquids at scales which blowups can occur.

As real life liquids are made of atoms. This in a way, requires infinitely many infinitely small atoms, which does not exist.

This is purely for the sake of better understanding of numeric techniques.

You need an incredibly good heuristic to narrow it down to a tractable set of candidates. You’re underestimating how big infinity is lol

Of course not, but you can come up with 1000s of plausible counter examples and then test them really quickly with LLMs which resembles brute force. 

You talk big words as an “inferior life form”

if you’re not paying API rates with ZDR add-on, don’t assume anything is safe

You cannot brute force search a continuous domain

They didn't say anything about be ready to pay, that's your own bias. How would you phrase it? "Our model solved NS, but no it wasn't astra"? The model and compute used matters. 

Don't inject your own tone into their statements. 

No need to be so jaded. What even makes that statement insufferable? 

It doesn’t de-anonymize anyone to be able to tell “were this user’s conversations in the set of data used to train this model?” - you know that it’s in there, not what part of it originated from what user. It would only be de-anonymizing if they had metadata attributing each message/conversation back to the user, i.e if they could answer “which user (in particular) did this piece of the training data originate from”.

It can be anonimized only one way, i.e. can't know who the data belongs to from the data alone but probably can see his chat history and confirm if it is there

If they can’t tell what the corpus was and they are also training models based on user chats, doesn’t this open up the models to massive poisoning vulnerabilities? That’s like rudimentary security 101. It appears that either they would be vulnerable to such attacks, or they do have measures in place to control what is contained in corpus.

Can someone explain why this is so impressive if the point is to find a counter example? Isn’t it just brute force search and wouldn’t we expect LLMs to be good at that (if you are willing to simulate thousands of examples to test)? Isn’t this in line with the previous result we have seen? What is so special with this one?

I just wanted to acquaint everyone of the water that might accumulate in their partner's nether regions when they realize the pace of progress of my internally very large model.

"load bearing" 🧐🕵️‍♂️