Read the article, that's not what this is about. Title is clickbait, they fired contractors for using AI to label data when the whole point of labeling data is to distill human knowledge into weights, not distill weights into weights.
You're replying to a joke, and yes, that's all it's about. For all the tales of rapid self-improvement, all labs are real careful not to taint their precious training sets with anything that comes out of their systems. Website reputation modeling, fingerprints in generated text, firing contractors that rely on AI.
I've been told on HN about a year ago that the era of scraping is over and that it's all AI-training-AI now. My web server logs and stories like that disagree.
There's a difference between shoving LLM output back into the input and RSI more broadly. Using synthetic training data didn't work super well, that would have been one path to RSI, but it doesn't work. The main path towards RSI people are talking about today is using the models to do research and experimentation for training new models. Machine learning is a very empirical field, you need to run tons of experiments, tune hyperparameters, and try different architectures. It's something agents are extremely good at doing, just because one path to RSI doesn't work doesn't mean it won't work at all.
I've been told on HN that 1+1=3, so, yes, skepticism is required on broad claims.
There are more subtle truths on this. Things that can be proven algorithmically are much more apt to be in recursive AI loops now. Hence things like programming and hacking keep improving steadily over time with much less human training data being added.
Think of labeling as establishing a ground truth. It's more important than training, but it's far less complex than training at an individual level. A kid can tell you what an ice cream cone looks like, but they cannot tell you the best algorithm to use to get the best model with the least power usage.
Labeling is more like working a checkout at Walmart. Just about anyone can do it with the smallest amount of training, but you have to ensure your labelers are not just scanning one item multiple times and bagging up the rest as your dataset can skew from reality since AI cannot just capture this data fully reliably at this point (well in many fields it can or can do even better than humans, but it's still lumpy as to where and why).
Bubble bursting behavior in action - AI company that pushes everyone to use AI, replace everything and everyone with AI, fires people for using AI to make the AI.
Earlier this year it was crazy to see so many companies explaining how they are going to replace their engineers with AI from both OpenAI and Anthropic, while both companies were instead (and are still) hiring all the human talent they can
Typically, for AI training, you need human feedback. If AI were enough, then OpenAI would do it themselves. Though we all know how bad AI slop is, running it in a loop can lead to unreadable sentences.
They hired contractors with the condition to provide human feedback without AI; those people broke the rules, so their contracts ended prematurely.
This is more a failure of journalistic understanding than poor lab behavior.
"Percentage of humans that read headlines" > "Percentage of humans that read articles"
Time and time again I see headlines that are designed to catch eyeballs and are totally refuted by the article. The problem is meat statocastic parrots read the headline and hallucinate their version of the story that is most likely.
“Suppose the government puts a certain drug in the water supply … A couple of conspiracy nuts say it makes your fingers fall off one by one, but the government says that’s ridiculous … However, government employees are all observed drinking bottled water exclusively, and if anyone suggests that government employees might also want to take the completely innocuous drug, they freak out … If by chance you manage to slip a little bit of tap water into a government employee’s drink, and he finds out about it, he runs around shrieking like a banshee and occasionally yelling “AAAAAAH! MY FINGERS! MY PRECIOUS FINGERS!”. At some point you might start to wonder whether the government was being entirely honest with you.”
What is it, exactly, that makes AI-generated text so poisonous to AIs but totally harmless to humans? What is mode/model collapse, and why can it only happen to AIs with too much AI text in their training data and not to, say, human students with too much AI text in their textbooks? The people who know the most about this phenomenon seem much more careful about contamination than they are encouraging us to be.
Because this isn't the case, training AI on its own output is more akin to what you get from pointing a camera at a TV showing its own output. Its a feedback loop that causes noise to blow up out of proportion and leaves nothing meaningful behind but a self-recursive loop. This is "what makes ai generated text poisonous to ais".
> they fired contractors for using AI to label data
Is your objection to the word “train” in headline? Otherwise, you’re just restating the headline while calling it clickbait (which, btw, it’s not, perhaps you meant to say it is misleading, which is a different thing.)
labeling is more specific than training, and changes the implied situation.
Its the same difference between "I was arrested for having liquid in my car while driving" and "I was arrested for holding an open bottle of whiskey while driving"
Yeah, that's a pretty big difference. The people "training" OpenAI models are getting paid like pro football players, have PhDs in the field, and are the superstars of the company. The people "labeling" data are low level contractors that get paid basically nothing to read chat responses and grade them, anyone who can type can do it.
I don't know if it's deliberately misleading or if the journalist doesn't understand the difference, but the output is functionally the same.
Why exactly it is clickbaity? It makes perfect sense to me, I understand title as: people told not use AI fired for using AI - without any need for additional context.
Mechanical Turk was shut down for exactly same reason, it was always a race to the bottom and using LLMs was cheaper than even third world gig workers. Verifying if task were done by humans is probably harder than doing them in the first place.
Really? It’s clickbait because it paints OpenAI as hypocritical: “look they’re marketing AI as valuable, and they won’t even let their own people use it”. When in fact the job description is to not use AI.
Please reread. I am not making the claim that OpenAI is hypocritical.
I am making the claim that the article headline is clickbait because it paints OpenAI as hypocritical for firing workers for using AI to do their job, which is a mischaracterization because the whole job is to give human input.
I don’t know if I’d call if clickbate but it does have a Fox News “pretending to be incredulous at something perfectly sensible because you think your readers are sufficiently ignorant to go along with your feigned incredulity” vibe.
There's data in verifiable domains where a model can learn through the environment (e.g., math or coding), but some data is necessarily human experience. For instance, "what is the best ice cream in Malaga?" no matter how capable current AIs are they are not capable to get this information, there's no embodiment or simulation of human experience so far, if it's not in the labeled data they will just hallucinate the answer.
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[ 0.22 ms ] story [ 2.8 ms ] threadI've been told on HN about a year ago that the era of scraping is over and that it's all AI-training-AI now. My web server logs and stories like that disagree.
There are more subtle truths on this. Things that can be proven algorithmically are much more apt to be in recursive AI loops now. Hence things like programming and hacking keep improving steadily over time with much less human training data being added.
Labeling is more like working a checkout at Walmart. Just about anyone can do it with the smallest amount of training, but you have to ensure your labelers are not just scanning one item multiple times and bagging up the rest as your dataset can skew from reality since AI cannot just capture this data fully reliably at this point (well in many fields it can or can do even better than humans, but it's still lumpy as to where and why).
The more you think about it, the worse it gets.
They hired contractors with the condition to provide human feedback without AI; those people broke the rules, so their contracts ended prematurely.
This is more a failure of journalistic understanding than poor lab behavior.
Time and time again I see headlines that are designed to catch eyeballs and are totally refuted by the article. The problem is meat statocastic parrots read the headline and hallucinate their version of the story that is most likely.
What is it, exactly, that makes AI-generated text so poisonous to AIs but totally harmless to humans? What is mode/model collapse, and why can it only happen to AIs with too much AI text in their training data and not to, say, human students with too much AI text in their textbooks? The people who know the most about this phenomenon seem much more careful about contamination than they are encouraging us to be.
Is your objection to the word “train” in headline? Otherwise, you’re just restating the headline while calling it clickbait (which, btw, it’s not, perhaps you meant to say it is misleading, which is a different thing.)
Its the same difference between "I was arrested for having liquid in my car while driving" and "I was arrested for holding an open bottle of whiskey while driving"
I don't know if it's deliberately misleading or if the journalist doesn't understand the difference, but the output is functionally the same.
1. That system isn't broken in a difficult to tell way.
2. The systems implementation is incorrect.
3. That the theory that particular implementation uses isn't incorrect.
There is nothing hypocritical at all about that.
I am making the claim that the article headline is clickbait because it paints OpenAI as hypocritical for firing workers for using AI to do their job, which is a mischaracterization because the whole job is to give human input.