#jepa #ai #machinelearning
Yann LeCun’s position paper on a path towards machine intelligence combines Self-Supervised Learning, Energy-Based Models, and hierarchical predictive embedding models to arrive at a system that can teach itself to learn useful abstractions at multiple levels and use that as a world model to plan ahead in time.
OUTLINE:
0:00 – Introduction
2:00 – Main Contributions
5:45 – Mode 1 and Mode 2 actors
15:40 – Self-Supervised Learning and Energy-Based Models
20:15 – Introducing latent variables
25:00 – The problem of collapse
29:50 – Contrastive vs regularized methods
36:00 – The JEPA architecture
47:00 – Hierarchical JEPA (H-JEPA)
53:00 – Broader relevance
56:00 – Summary & Comments
Paper:
Abstract: How could machines learn as efficiently as humans and animals? How could machines learn to reason and plan? How could machines learn representations of percepts and action plans at multiple levels of abstraction, enabling them to reason, predict, and plan at multiple time horizons? This position paper proposes an architecture and training paradigms with which to construct autonomous intelligent agents. It combines concepts such as configurable predictive world model, behavior driven through intrinsic motivation, and hierarchical joint embedding architectures trained with self-supervised learning.
Author: Yann LeCun
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