labs invest multiple billion dollars a year each in private data, and that number is growing. internet training data is not where frontier capabilities come from, this view is outdated
This is a misleading statement. The "private data" is still largely publicly produced data that has been curated through private agreements instead of scraping, such as reddit posts/comments (this is the "third-party data agreements" that companies like OpenAI mention). And yes, there is still a lot of processing done on this data, which is the norm for preparing training data.
This is doubly misleading. A lot of private data is sourced through providers like e.g. Mercor, who pay experts to answer questions and write out their reasoning. (E.g. paying a software engineer to write a project from scratch and recording every keystroke, paying a Chem PhD to answer hard Chem questions, etc.). A second source of private data comes from custom RL environments with fine-grained intermediate rewards for e.g. software engineering, financial modeling, etc.. Also, imagine the amount of usage data recorded by Claude Code, etc.
Pretraining is mostly curated public data, post-training is increasingly private expert data and tests.
Well since you work at a lab you should know that most capabilities arise in pretraining, not posttraining or mid training, and the latter two mostly function to bring out the hidden intelligence in these models more than anything else.
No, it isn't. The private data is largely private data, created by highly-specialized, highly-paid contracted teams of experts for domains finance, swe, consulting, etc.
Reddit data is just not that interesting, that deal is worth like $60m/year. Labs spend 10x as much on computer-use RL environments.
When did they start doing so? We all know that they DID train on all the available public information, so at what point did they stop? Is the public information still in the training set? If so, they should STILL release ALL the data as public, as they are including training data that was acquired without permission.
Why are the leading models capable of regurgitating full copyrighted works such as "Harry Potter" and "On the Road"? Did they hire someone to type those out for them?
Okay that's fine, then make the law say they must provide publicly owned models off of publicly obtained data. To think that such a baseline of critical information isn't is the literal foundation of everything they will do, both now in the future, is just exposing what their end game is: control.
There no reason to not to otherwise outside of the poor little billion dollar corporations not wanting to provide a public utility they stolen from the public.
Anything that removes control from American big tech is a good thing for American citizens and the world writ large.
Didn't the famous "Textbooks are all you need" paper already proof that point three years ago?
Sure, we ask a lot more of modern models, but private training data also got a lot better. You would loose out on a lot of long-tail knowledge, but that can be fixed with web search tools. You'd limit the styles, dialects and colloquial phrases the model understands and can use, but for many use cases that would be fine
But why would any frontier lab do that? Throwing in more training data still leads to better results in pretraining. And showing that they don't need to hoover up the internet and Anna's Archive only empowers regulators to prevent them from doing that
> internet training data is not where frontier capabilities come from
We 100% would not be at the current progress without it, though. And it's not like they only train on this once. They keep training on all the internet data PLUS the private data. Private data only (probably) wouldn't work, as learning the base regularities of language takes a lot of weights.
Define "come from". Could they have gotten those frontier capabilities, or any capabilities, without internet training data?
It seems to me that without the private data, you might get a slightly less competitive model, but without the CommonCrawl-style data piles used in "pretraining", you get no model at all.
Even accepting the copying-as-theft framing, if I go to a village, steal some vegetables from everyone's gardens and ham from their sheds, and then add some prohibitively expensive spices I bought myself to make soup, do I get to claim it as mine and punish the villagers for trying to take it?
Does this private data come from places like Reddit, Twitter, etc., where it’s contributed by users? I think it is unethical for these companies to accept payment for user-contributed data.
it is certainly possible and being done all over the place.
there's a black market that chinese labs use to buy frontier american llm trajectories by the millions through US intermediaries. they're not even particularly shy about it, i have been offered $0.7 per opus 4.8 call
there's also a market for chinese labs sending checkpoints to US companies to be trained on US compute and sent back
i'm surprised that so many people take chinese tech reports about how they train their models at face value tbh
The benchmarks are now the equivalents of SAT/ACT/other standardized exams for humans. They are directionally quite predictive, but with plenty of outcome variance on the margins
You could just look it up on their website leaderboard? The newest Claude model makes over $10k profit over a simulated year of operation, after starting with $500
They've never translated it to the real world though. So saying the problem is "too easy" when they have no public (as far as I know) demonstration that they've solved that problem is a stretch.
Yes, they did. You could also find this information easily.
A company like Andon creates value by exposing interesting AI failure modes, so it makes perfect sense for them to move on to harder problems when the previous ones get saturated.
I think you're just being overly cynical.
Can you point me to an example then? It's not linked in the article as far as I can tell and it's not easy to find on their website if it's there. I don't count simulations because I used to work with simulations regularly and they often fail to translate to the real world.
I don't think this was a simple assumption. LLMs used to be much dumber!
GPT-3 era LLMS were not good at grep, they were not that good at recovering from errors, and they were not good at making followup queries over multiple turns of search. Multiple breakthroughs in code generation, tool use, and reasoning had to happen on the model side to make vector-based RAG look like unnecessary complexity
It's a construct that long predates AI. And using it with such intensity and frequency is more likely a sign that this _wasn't_ AI generated, since AI writing tends to _not_ repeat things quite so often.
I doubt a human would use it repetitively, even if it is common. This was most likely written paragraph-by-paragraph by AI, causing the repetition, if I had to guess.
I can't wait for the EU AI Act to require mandatory labelling for AI-generated content.
I hope so. Random accusations of "this feels like AI" don't add anything to the conversation and are genuinely harmful to those accused when there is no AI involved.
AI has it's demons, for sure, but there is an awful lot of jumping at ghosts these days.
I would rather people who don't currently have a voice due to language barriers or simply poor communications skills be able to use LLMs than try to gatekeep them.
And I'm certainly weary of "someone used an em-dash, must be GPT" low-value comments.
I certainly hope we gatekeep them. "I just need my hallucinatory text generator to translate for me" -> "I just need my hallucinatory text generator to refine my thoughts for me" -> "I just need my hallucinatory text generator to generate my comment for me". This is a damn near antithesis of this place.
I had the same reaction. The disclaimer at the bottom doesn't mention AI, but I have a feeling this was generated from a prompt to consolidate the 24 human submissions into a single essay.
Tangentially, I really look forward to the day "Not X but Y" stops being so overused by LLMs. It's a valid and useful construction in a vacuum, one which we should be able to use, but its overuse has gone past semantic satiety into something like semantic emesis.
Because "I haven't seen this literary tool" or "I wouldn't think to use it" or "It doesn't match my perception of human literary tools", it must be artificial.
Kinda funny how we went full circle with you calling me ignorant and illiterate on the basis of not using your preferred terminology, as opposed to the actual, obvious meaning of the subject
one underrated approach more and more people are finding success with:
apple watch ultra as a primary device (optionally with a case for a more phone-like factor)
you can do most things an iphone does, but you can't doom scroll. you don't have to eject out of apple ecosystem, you get payments, 2fa, navigation, notifications. your iphone can remain as a backup that's always in sync for when you need it (e.g. traveling)
> In nearly every case of RTO, there was no recorded dip in productivity associated with the move to remote work.
I am extremely skeptical of this. On the contrary there is a mountain of direct evidence that people barely work when working from home. People have been openly bragging both on the internet and in person about how they do laundry and watch netflix and mow their lawns while looking productive
All you need to do is look at the crowds in the park or lines at the grocery store on any given friday to gauge how much work is being done on wfh days
And I am extremely skeptical of your "direct evidence" (source?). Most jobs are not measurable, they are more like N amount of employees collectively moving a target / goal. And just like in school projects there are the lazies of course, but again, direct measurability is mostly an illusion sold by consultants that get sweet money by lying to executives and telling them what they want to hear.
Consider that you might be in the bubble of yes-people.
Work from home allowed many people to find their exact productive schedule, motivators and rhythm. But we can't have that, 8h or leave!
