Introduction to JEPA — Why Do We Need a World Model?

Full transcript

Imagine a machine watching the world for the very first time — a blank slate, seeing everything around it with no prior knowledge, no context, and no understanding of what any of it means. In front of this machine lies a rich, complex world — a house, a tree, a bright sun overhead, and small everyday objects scattered across the scene, all waiting to be interpreted. The machine can see all of this clearly — every shape, every detail — but seeing is not the same as understanding. Perception alone does not give meaning to what is observed. So we place this raw visual information into an artificial intelligence system — a box that receives the world as input — and we immediately face the most important question in modern AI research. Can this machine truly understand what it sees? Not just recognize patterns, but grasp meaning, predict outcomes, and reason about a world it has never been explicitly taught to interpret? A large question mark looms. This is exactly the challenge that JEPA was designed to address — a bold architectural idea built around one central question: how can a machine learn to model the world without ever being given a single label?