by Amit Shekhar · 7 June 2026
Joint Embedding Predictive Architecture (JEPA)
Joint Embedding Predictive Architecture (JEPA). This is one of the most exciting ideas in modern AI, and it comes from Yann LeCun, one of the most respected researchers in the field. Do not worry, we will learn about each part of it slowly, in very simple words. By the end, a complete beginner will understand every single word.
Read on Outcome School ↗then come back to lock it in
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What this article covers
- How humans and animals learn by observing the world
- Yann LeCun's vision of autonomous machine intelligence
- A simple everyday analogy
- What does JEPA mean
- What is an embedding or representation space
- The problem with predicting raw pixels
- The problem with contrastive methods
- The core idea of JEPA
- The building blocks of JEPA
- The energy-based view in simple words
- How I-JEPA works (for images)
- V-JEPA and the world-model vision
- When and why JEPA matters
The article lives on outcomeschool.com. Read it there, then come back: the tutor in the margin has read it and will answer questions, and the questions below check what stayed.