The irony of the whole thing is brutal.
And when other mediums have been saturated with AI? Books, music, radio, podcasts, movies -- what then? Do we need a (curated?) unadulterated stockpile of human content to avoid the enshittification of everything?
Either that, or a human level AI.
That said… are content creators collectively (all media, film and books as well as web) a thin tail or a fat tail?
I could easily believe most of the actual culture comes from 10k-100k people today, even if there's, IDK, ten million YouTubers or something (I have a YouTube channel, something like 14 k views over 14 years, this isn't "culturally relevant" scale, and even if it had been most of those views are for algorithmically generated music from 2010 that's a literal Markov chain).
I’m trying to work on monetization for my product now. The “personal Google” idea is really just an accidental byproduct of solving a much harder task. Not sure if people would pay for that alone.
Common crawl alone is only a few hundred TB, I have more content than that on a NAS sitting in my office that I built for a few grand (Granted I’m a bit of a data hoarder). The fears that we have “used all the data” are incredibly unfounded.
> The fears that we have “used all the data” are incredibly unfounded.
The problem isn't whether we used all the real data or not, the problem is that it becomes increasingly difficult to distinguish real data from previous LLM outputs.
I don't know about that. If you scraped the same data and ran a search engine I think people would generally say you're fine. The copyright issue isn't the scraping step.
Even refined web runs about 2TB once loaded into Postgres with TS vector columns, and that’s a substantially smaller dataset than common crawl.
It’s not just a dumping a to of zip files on your NAS, it’s making the data responsive and usable.
YMMV depending on the value of "you" and your budget.
If you're Google, Amazon or even lower tier companies like Comcast, Yahoo or OpenAI, you can scape a massive amount of data (ignoring the "allowed" here, because TFA is about OpenAI disregarding robots.txt)
Meta is happily training their own models with this data, so it isn't going to waste.
However, Microsoft has been flying under the radar. If they gave all Hotmail and O365 data to OpenAI I’d not be surprised in the slightest.
What do you think the NSA is storing in that datacenter in Utah? Power point presentations? All that data is going to be trained into large models. Every phone call you ever had and every email you ever wrote. They are likely pumping enormous money into it as we speak, probably with the help of OpenAI, Microsoft and friends.
A buffer with several-days-worth of the entire internet's traffic for post-hoc decryption/analysis/filtering on interesting bits. All that tapped backbone/undersea cable traffic has to be stored somewhere.
encrypted with an algorithm currently considered to be un-brute-forcible. If you presume we'll be able to decrypt today's encrypted transmissions in, say, 50-100 years, I'd record the encrypted transmission if I were the NSA.
But is it big enough to store 50 years worth of encrypted transmissions?
Far cheaper to simply have spies infiltrate the ~3 companies that hold the keys to 98% of internet traffic.
If they actually worked, that is.
They would almost be failing in their purpose if they were not doing this.
On the other hand, this is an incredibly tough signal to noise problem. I am not sure we really understand what kind of scaling properties this would have as far as finding signals.
My thoughts - I teach myself all the time. Self reflection with a loss function can lead to better results. Why can't the LLMs do the same (I grasp that they may not be programmed that way currently)? Top engines already do it with chess, go, etc. They exceed human abilities without human gameplay. To me that seems like the obvious and perhaps only route to general intelligence.
We as humans can recognize botnets. Why wouldn't the LLM? Sort of in a hierarchal boost - learn the language, learn about bots and botnets (by reading things like this discussion), learn to identify them, learn that their content doesn't help the loss function much, etc. I mean sure, if the main input is "as a language model I cannot..." and that is treated as 'gospel' that would lead to a poor LLM, but i don't think that is the future. LLMs are interacting with humans - how many times do they have to re-ask a question - that should be part of the learning/loss function. How often do they copy the text into their clipboard (weak evidence that the reply was good)? do you see that text in the wild, showing it was used? If so, in what context "Witness this horrible output of chatGPT: <blah>" should result in lower scores and suppression of that kind of thing.
I dream of the day where I have a local LLM (ie individualized, I don't care where the hardware is) as a filter on my internet. Never see a botnet again, or a stack overflow q/a that is just "this has already been answered" (just show me where it was answered), rewrite things to fix grammar, etc. We already have that with automatic translation of languages in your browser, but now we have the tools for something more intelligent than that. That sort of thing. Of course there will be an arms race, but in one sense who cares. If a bot is entirely indistinguishable from a person, is that a difference that matters? I can think of scenerios where the answer is an emphatic YES, but overall it seems like a net improvement.
What makes you think this is true? Yes, it's likely that the internet will have more AI generated content than real content eventually (if it hasn't happened already), but why do you think AI companies won't realize this and adjust their training methods?
It looks less like an ouroboros and more like a bootstrapping problem.
This problem can't really be avoided once we begin using AI to write, understand, explain, and disseminate information for us. It'll be writing more than blogs and SEO pages.
How long before we start readily using AI to write academic journals and scientific papers? It's really only a matter of time, if it's not already happening.
From “known good” sources of knowledge, we can generate an infinite amount of content. We can add more “known good” knowledge to the model by generating content about that knowledge and training on it.
I agree there will be many issues keeping up with what “known good” is, but that’s always been an issue.
That's my entire point -- AI only generates content right now, but it will also be the source of content for training purposes soon. We need a "known good" human knowledge-base, otherwise generative AI will degenerate as AI generated content proliferates.
Crawling the web, like in the case of the OP, isn't going to work for much longer. And books, video, and music are next.
That is training on content.
The future will have models pre-trained on content and tuned on corpuses of knowledge. The knowledge it is trained on will be a selling point for the model.
Think of it this way - if you want to update the model so it knows the latest news, does it matter if the news was AI generated if it was generated from details of actual events?
“It’s ok bro, another model will fix, just please, one more ~layer~ ~agent~ model”
It’s all fun and games until you can’t reliably generate your base models anymore, because all your _base_ data is too polluted.
Let’s not forget MS has a $10bn stake in the current crop of LLM’s turning out to be as magic as they claim, so I’m sure they will do anything to ensure that happens.
My point is about the inevitable future when _those_ models start to struggle.
The phi approach doesn’t seem like breaking the ouroboros, it just feels like inserting another model/snake into the loop.
I don’t think that we are going to get big improvements in LLMs without architecture improvements that need less data, and the current generation of models appears to be good enough at creating content from data/knowledge to train any future architectures we have with better synthetic datasets. Fortunately we have already seen examples of both of these “in the lab” and will probably see commercially sized models using some of the techniques in the coming months.
It's funny whenever people bring this up, they think AI companies are some mindless juggernauts who will simply train without caring about data quality at all and end up with worse models that they'll still for some reason release. Don't people realize that attention to data quality is the core differentiating feature that lead companies like OpenAI to their market dominance in the first place?