A Path Towards Autonomous Machine Intelligence

LeCun's 2022 manifesto laying out a full research roadmap for achieving autonomous machine intelligence (AMI) — what he calls AGI without the science-fiction connotations. The paper is notable because it comes from inside Meta, a major AI lab, and acknowledges openly that current approaches (LLMs, RLHF, generative models) are insufficient. The core architecture LeCun proposes has six modules: (1) a world model that predicts consequences of actions; (2) an actor that proposes actions; (3) an intrinsic cost that encodes objectives; (4) a short-term memory module (similar to a key-value store); (5) a perception module; (6) a configurator that modulates all other modules. The key insight: the world model is the core unsolved problem. Everything else in modern ML (RLHF, next-token prediction) occupies the 'actor' and 'cost' modules, which are relatively tractable compared to learning an accurate world model. This paper is the intellectual foundation for Meta's JEPA research program and explains why LeCun has been publicly skeptical of LLM-based AGI timelines.