That could be a result of a failed exposure stack. Phone sensors and lenses suck for light gathering so they have to take multiple photos and merge them together to create one that's less noisy. Occasionally you get artifacts where the merge failed.
It's still distinct from actual AI generation imo.
This is correct (from someone who has worked on software to do this). Processing images for the 3D walkthroughs in real estate sometimes does this too.
“relying on” is misleading here relative to what the researchers have said.
If I write a book and pass it through a spelling and polish checker, I still wrote the book and its core IP. I didn’t “rely on” the tool to create the IP.
it’s much more like you come up with the premise and someone else writes the book. the released prompts for other foundational problems (like unit distance) prove that.
The tools the researchers used though was much more than an spellchecker, because spellcheckers don't come up with chains of reasoning for the arguments in the book. The LLMs did in the case of the Navier-Stokes problem.
that's not how AI was used in this case. It's more like a professor with assistants.
Professor says the assistants - why don't you dig in this direction, I have a hunch it might produce something valuable. And AI assistant does just that, proving or disproving a hunch. This would take the professor a lot of time if doing by themselves.
Yes, and keyboards also save a lot of time over handwriting. Numerical methods and proof engines save even more time. LLMs are just another tool in the kit.
Keyboards don't suggest chains of reasoning or words to type. When I press the K key, I know exactly what will happen. It's just a translation layer that gives an output known ahead of time and thus does not impinge upon the creativity of putting words together.
A better example would be playing chess against a player slightly stronger than me and using a chess computer to suggest some good moves. I could win, but it certianly wouldn't be just my brain that wins. It would be an amalgamation of my brain with a machine that suggests good moves.
This is straight up misleading. When you press your K key on a touchscreen, your keyboard program may decide you meant to press the neighboring L key (by dynamically inflating the collision geometry on it) because it was statistically far more likely that you meant to press the L key next.
This likely doesn't happen exactly on an analog keyboard, but then many text-processing environments that do the same thing in post. My keyboard just edited 'yuor' to 'your' even though I successfully input the prior string.
The researchers were driving prompts and trying to actually do math.
The OpenAI effort was a pure brute force attempt. I'm not even sure an LLM was actually involved. I think they just used their hardware to run the matrix multiplies required by the search for a counter example. Perhaps some clever approach guided the search but that seems to be about it.
The models are trained on the conversations of hundreds of millions of people. ChatGPT has several billion conversations every day. I estimate that the model that solved Navier–Stokes was trained on data from nearly a trillion conversations.
It's unknowable and not possible to prove if any one specific conversation was the key to solving Navier–Stokes.
>It's unknowable and not possible to prove if any one specific conversation was the key to solving Navier–Stokes.
If the conversation was in the training set, there's a high likelihood that the small set of conversations related to solving Navier-Stokes was used by the model. I get Astra to still quote some of my friends' books or blogposts nearly verbatim on certain niche issues.
Much more importantly, we _can_ determine whether a conversation was used in the training data. And if it was, it gives us a great idea whether that logic was captured in reasoning for a novel problem never yet solved.
Given that you don't see any of this as below the belt according to your other comments, maybe your contribution here is more for yourself than a fair conversation about attribution.
The flaw with this line of reasoning is that Buckmaster and Alpöge only had a partially completed proof of a weaker version of the Navier-Stokes problem. OpenAI's internal model solved the full, harder problem. This means the key information needed to bridge the gap was not present in Buckmaster and Alpöge's chat history.
You might retort that ChatGPT used the training data to copy their approach, but the approach Buckmaster and Alpöge chose was already published by Luis and Diego in 2023 and in every frontier model's training set.
This argument proves too much. By this standard, it wouldn't have counted as copying their approach if the researchers had just fed in Levent & Buckmaster's paper verbatim as a prompt into the swarm.
I don't think the person describing the paper by Buckmaster as the same as the 2023 paper by Córdoba and Martínez-Zoroa is really discussing this in good faith fwiw. There are some massive advancements within it and if the person was participating wasn't just regurgitating something to "win the argument" in their eyes, they wouldn't describe it that way.
I don't believe people are denying that the model is impressive. The problem is that learning someone else is making progress on a topic using method X and then rushing to scoop them borders on academic misconduct. If, on top of this, their private conversations about X were used in the proof, I really don't see how its defensible...
The rumor going around X was that Anthropic had solved a Millennium Prize problem weeks ago and was sitting on the solution, waiting to release it right before their IPO to maximize hype.
If I were at OpenAI, I'd naturally want to snipe that from them. I am completely unsurprised they formed a crack team to steal Anthropic's glory, and do so in just five days.
Between companies, direct malevolent competition is OK.
Between academics, there are other rules to the game.
When you go into a boxing match, you agree to get punched in the face.
All this to say, trust is important, and grounded in social convention.
So I do agree with you, but also disagree.
Whenever this is OK or not really depends on how the breakthrough is contextualized, and how there people at play, here, agree to contextualize it.
In my view, in the blog post, there is much discussion about who will be publishing the paper. If instead it was just a blog post that said "oops, we beat you to it, our model is the best", it would have been different.
I’m pretty sure you think you are doing a good job of defending your employer and you probably believe “Open”AI are the good guys here. I also acknowledge that they butter your bread so your financial future currently depends on their success.
However the way you are conducting yourself in public, while announcing yourself as an OpenAI employee is doing enormous harm to the greater and magnanimous aim of your organisation. Take a step back and read the temperature of the room. Being the smartest guy in the room will never protect you from alienating the rest of the room into a baying mob. Right now you are Icarus flying straight into the sun.
Do you not log the training data? Seems like you should be able to just check what was in the training data. To not keep track is just sloppy work and certainly unprofessional science.
The burden of proof is on Buckmaster and Alpöge to reveal if they had the "Improve the model for everyone" setting enabled or disabled. OpenAI shouldn't be expected to reveal private user configuration data. You're asking them to perform a user privacy violation.
If the reason that OpenAI is unable to state whether they trained on this data is because they (as policy) do not reveal whether a given member has turned on/off the "Improve the model for everyone" setting, they can at least say so.
FWIW, publicly facing OAI docs are very unclear about whether this setting even applies to Codex conversations.
it is exceptionally unlikely that anything they ever did made it into any part of training, and the chances are zero if they have opted out (likely). it would be a terrible precedent to break the the PII-scrubbing boundary to go and round it down to 0, and we won’t do it
I agree that it is not possible to prove if any one specific conversation (or derived RL tasks) was key to solving Navier-Stokes (at least without massive resource expenditure).
I don't really understand how the quantity of training data/rollouts used in training is relevant to the question of whether or not it was trained on these conversations.
I also don't really believe that whether or not this model was trained on these conversations is unknowable information.
> ChatGPT has several billion conversations every day. I estimate that the model that solved Navier–Stokes was trained on data from nearly a trillion conversations.
How many of those trillion conversations were about Navier-Stokes you reckon?
Sorry but this is a misconception: these models are both capable of complete novelty and of plagiarism. For a concrete example, image diffusion models have been shown to reproduce many existing images nearly 100% exactly, yet clearly, they can also create new ones.
A model being trained on lots of irrelevant information does not mean relevant information was not used.
We looked into it further and can confirm it was impossible for Dr. Buckmaster’s Codex prompts over the last two months to have influenced the system in any way, including training.
If prompts were submitted earlier than that and training was not opted out, there's a chance they made their way into our training pipeline in some form. But this would be a droplet in an ocean and unlikely to have made any difference, imo.
That is “the people” isn’t it? It is the consequence of democratization and openness.
I am not sure if superstars were good representatives of what most people wanted. Often, I am sorry to say, it was the lowest common denominator. Now common people can actually enter the arena and represent their corner of the population.
But, almost by definition, if you are part of “the people”, you don’t get to stand out much.
> And it’s not like non-meat is fine - the corn industry uses a blinding amount of water, as do almonds, etc.
Switching from meat to non-meat unequivocally lowers your GHG and water footprint and there's really no room for quibbling on this unless you are switching to a diet of exclusively almonds
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