​The Next Word: AI's Search for a Model of Reality

​What does it take for a machine to model the world?
A deep-dive into world model architectures, from auto-regressive models to JEPA.

​For the past decade, the story of AI progress has largely been the story of Transformers and text. World models pose a different question entirely: How do you build a system that represents not language, but reality itself- its physics, its geometry, and the way events unfold through time?
World models have become one of the most discussed ideas in the field, yet what these systems actually are, and where they actually stand, is poorly understood.

​In this lecture, we'll map the entire world-model landscape. We'll construct a working taxonomy of the field, drawing sharp lines between auto-regressive approaches and their non-predictive counterparts such as JEPA, Fei-Fei Li's recent spatial-intelligence work, and the emerging wave of game-based models, while highlighting what each architecture assumes and what it commits to.

​We'll examine what kinds of multi-modal signals are needed to give a machine an intuitive grasp of physics, space, and interaction; how the scaling picture changes when video replaces text; and the structural redundancy that auto-regressive generation carries with it.

​An open Q&A follows the lecture.

​🗣️ Please note: This description is in English, but the lecture will be delivered in Hebrew.

​📍 Time & Location
Date: Monday, June 22, 2026
Time: 12:00 PM – 1:00 PM
Location: WeWork Toha, 11th Floor (Yigal Alon St 114, Tel Aviv-Yafo)

​🎙️ About the Speaker Gilad Levy is an AI researcher, lecturer at the Technion, and CEO of Manifold.
His work spans frontier research around world model architectures and continual learning.