Overtraining as the path to human-like AI

The anonymous blogger Gwern recently completed a thirteen thousand word post called Human-like Neural Nets by Catapulting, in which he offers a theory about why LLMs don’t possess truly flexible human-like intelligence, and how we might train LLMs that do. Theories like this are entirely unremarkable: every crank researcher on the internet has a theory about how to crack AI. But Gwern is remarkable. Outside of OpenAI itself, Gwern is the earliest person to anticipate the potential of large language models, and the scaling arms-race involved in making them larger and more powerful still. I often cite Leopold Aschenbrenner’s Situational Awareness as an example of someone correctly predicting the future of AI. Written in 2024, just after the release of GPT-4, Aschenbrenner gets a lot of things right: the rush to build billion or trillion-dollar GPU clusters, the importance of the code around the LLM (what he calls “unhobbling”)1, and the fact that scaling would continue through the…