This episode explores Yann LeCun's vision for creating autonomous intelligent agents that learn and interact with the world like humans, as outlined in his paper, "A Path Towards Autonomous Machine Intelligence." LeCun emphasizes the importance of world models, which allow agents to predict the consequences of their actions, making AI more efficient and capable of generalization.
The proposed cognitive architecture includes key modules like Perception, World Model, Cost Module, Short-Term Memory, Actor, and Configurator. The system operates in two modes: Mode-1 (reactive behavior) and Mode-2 (reasoning and planning). Initially, the agent uses Mode-2 to carefully plan, then transitions to faster Mode-1 execution through training.LeCun highlights self-supervised learning (SSL) as essential for training world models, particularly using Joint Embedding Predictive Architecture (JEPA), which focuses on predicting abstract world representations. Hierarchical JEPAs allow for multi-level planning and handle uncertainty through latent variables.
The episode concludes by discussing the potential implications of this approach for achieving human-level AI, beyond scaling existing models or relying solely on rewards.
https://openreview.net/pdf?id=BZ5a1r-kVsf