Introduction to Latent Variable Energy-Based Models: A Path Towards Autonomous Machine Intelligence
This work proposes a foundational approach to enable AI systems to learn reliable world models and plan complex actions, potentially impacting broad areas of machine learning and AI.
The paper introduces latent variable energy-based models as a building block for autonomous machine intelligence, specifically within the hierarchical joint embedding predictive architecture (H-JEPA) proposed by Yann LeCun, aiming to address limitations in current AI systems like self-driving cars and domestic robots.
Current automated systems have crucial limitations that need to be addressed before artificial intelligence can reach human-like levels and bring new technological revolutions. Among others, our societies still lack Level 5 self-driving cars, domestic robots, and virtual assistants that learn reliable world models, reason, and plan complex action sequences. In these notes, we summarize the main ideas behind the architecture of autonomous intelligence of the future proposed by Yann LeCun. In particular, we introduce energy-based and latent variable models and combine their advantages in the building block of LeCun's proposal, that is, in the hierarchical joint embedding predictive architecture (H-JEPA).