Introduction to Latent Variable Energy-Based Models: A Path Towards Autonomous Machine Intelligence

TL;DR

This paper discusses the integration of energy-based and latent variable models within a hierarchical architecture to advance autonomous machine intelligence capable of reasoning and planning.

Contribution

It introduces the hierarchical joint embedding predictive architecture (H-JEPA) that combines energy-based and latent variable models for autonomous AI development.

Findings

Proposes a new architecture for autonomous intelligence

Combines energy-based and latent variable models effectively

Lays groundwork for future self-learning AI systems

Abstract

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).