Artificial intelligence systems like ChatGPT could soon run out of what keeps making them smarter — the tens of trillions of words that people have written and shared online.
Artificial intelligence systems like ChatGPT could soon run out of what keeps making them smarter — the tens of trillions of words people have written and shared online.
A new study released Thursday by research group Epoch AI projects that tech companies will exhaust the supply of publicly available training data for AI language models by roughly the turn of the decade -- sometime between 2026 and 2032.
Comparing it to a “literal gold rush” that depletes finite natural resources, Tamay Besiroglu, an author of the study, said the AI field might face challenges in maintaining its current pace of progress once it drains the reserves of human-generated writing.
In the short term, tech companies like ChatGPT-maker OpenAI and Google are racing to secure and sometimes pay for high-quality data sources to train their AI large language models – for instance, by signing deals to tap into the steady flow of sentences coming out of Reddit forums and news media outlets.
In the longer term, there won’t be enough new blogs, news articles and social media commentary to sustain the current trajectory of AI development, putting pressure on companies to tap into sensitive data now considered private — such as emails or text messages — or relying on less-reliable “synthetic data” spit out by the chatbots themselves.
It is the new crypto. The same people went Crypto -> NFT -> Metaverse -> AI without ever changing the way they talk about it. AI is slightly different because it has some utility in the world but it's still the same unstable, over hyped garbage.
It is in how corporations want to use them, but just like after the .com bubble, after the bubble bursts, I feel we will trully see the true future of AI
I've seen this suggested elsewhere, this isn't my idea:
Journalism is falling apart and now is reduced to clickbait journalism because they can't find the money to pay for real, good investigative journalism.
LLMs need endless amounts of quality text to learn from.
AI companies need to make working deals with journalism outfits to be able to train off of all new journalistic writing. (We won't get into older writing because that's more complicated, just make the deal for anything written after the deal)
You've just solved two problems. Now good journalism is well-funded and LLMs have a legal, endless path to training. There will always be more news to write about. Win-win.
Personally, I think this would solve the copyright issue as well, because this is actually paying people a good wage for their copyrighted material.
The first AI company to come to an agreement with the New York Times or the Wall Street Journal will likely have a very safe position in the market.
Nice idea, but does all journalism combined supply enough data (and varied data) to meet the needs for training the models? Also, why pay a special rate when only a few subscriptions would be required and most of the rest is free?
Well, for one, the whole publishing industry is struggling and writers in general are struggling to make money. They could start financing the arts they're ostensibly taking so much from to train their models. In doing so they could create an explosion of new, quality literature and journalism. It could change the face of a dying industry and revitalize and reshape it. However, it would require the "benevolence" (*slight retch) of tech moguls to accept that perhaps the people producing the content they train their AI on should be properly compensated.
The reason to pay a special rate would be to support the arts and produce new, quality text of all varieties to train on. Available internet training data is easily and cheaply accessible, but less quality, and quickly being diluted by websites auto-generated by AI, causing AI to be trained on AI. By "financing the arts" you would produce quality PR for your company as well as making a short pathway to a constant churn of new content for your models. You could even extend this to scientific publishing, to go beyond the arts and into financing science publishing.
Especially considering there are many continued lawsuits about abuse of copyright to train the models. Everyone knew where the books3 corpus came from, it was known from the beginning that it was all pirated books from Bibliotik, all these groups really shot themselves in the foot by using a well-known pirated document of thousands of copyrighted books for training. They could make all those lawsuits go away with agreements with writers, journalists, and publishers and come out looking like heroes for saving the entire publishing industry and helping clean up the internet and publishing by prioritizing quality, well-written content.
A new study released Thursday by research group Epoch AI projects that tech companies will exhaust the supply of publicly available training data for AI language models by roughly the turn of the decade -- sometime between 2026 and 2032.
But how much it’s worth worrying about the data bottleneck is debatable.
“I think it’s important to keep in mind that we don’t necessarily need to train larger and larger models,” said Nicolas Papernot, an assistant professor of computer engineering at the University of Toronto and researcher at the nonprofit Vector Institute for Artificial Intelligence.
Papernot, who was not involved in the Epoch study, said building more skilled AI systems can also come from training models that are more specialized for specific tasks.