AI race for chatbot training data could run out of human-written text

Artificial intelligence (AI) 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 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. 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 rely on less-reliable “synthetic data” spit out by the chatbots themselves. Tamay Besiroglu, an author of the study, said AI researchers realized more than a decade ago that aggressively expanding two key ingredients—computing power and vast stores of internet data—could significantly improve the performance of AI systems. “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. But he has concerns about training generative AI systems on the same outputs they’re producing, leading to degraded performance known as “model collapse.” Training on AI-generated data is “like what happens when you photocopy a piece of paper and then you photocopy the photocopy. You lose some of the information,” Papernot said. Not only that, but Papernot’s research has also found it can further encode the mistakes, bias, and unfairness that’s already baked into the information ecosystem. This article was provided by The Associated Press.