Fabricio F. Costa

ChatGPT has now a big problem. Researchers at Oxford exposed a massive threat to large language models.” It’s called "Model Collapse." And it explains why the internet is about to become a toxic waste dump for Large Language Models. Right now, AI companies are scraping the entire web to train their next-generation models. But there’s a massive problem. The internet is no longer just human data. It is rapidly filling up with AI-generated text, images, and code. What happens when an AI trains on data generated by another AI? A new paper in Nature just gave us the answer. And it is terrifying. When AI models are recursively trained on AI-generated data, they suffer from irreversible brain damage. The researchers found that the model starts forgetting the "tails" of the original data distribution. The rare, weird, unique, and complex human nuances completely disappear. It's like making a photocopy of a photocopy. Every generation loses resolution. The output gets flatter, blander, and more generic, until the model completely collapses in on itself. For years, we thought the biggest bottleneck for AI was compute, getting enough H100 GPUs from Nvidia. This paper proves the real bottleneck is us. If we keep flooding the internet with synthetic AI slop, we will poison the very data pool required to build smarter models. The ultimate irony? We thought AI was going to make human creators obsolete. Instead, this research proves that verified, genuine human data is about to become the most valuable commodity in the tech industry.

As scientists increasingly incorporate artificial intelligence into their work, it raises the question: What does it mean to be an author in the age of #AI? Learn more: scim.ag/3Ujf8xL

It’s happened again. Just 3 weeks after OpenAI dumped hundreds of mathematical results onto the internet—a move that came 1 month after OpenAI’s solution to the famed Navier-Stokes problem—the San Francisco–based company is back with more. The frontier artificial intelligence developer released solutions to three new problems spanning math, computer science, and physics. Widely regarded as most consequential among them was an apparent refutation of the exponential time hypothesis, a pillar of theoretical computer science that has stood for almost 30 years. Learn more: scim.ag/3U8QFey