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Modern Non-LLM AI

World Models

AI systems that learn how the physical world works, the foundation for robotics and simulation

What it is

A world model is a system that learns to predict the consequences of actions in an environment, essentially a simulatable representation of how the world works. Humans have rich world models: drop a phone, it falls; push a glass to the edge of a table, it will fall.

AI world models like Google's Genie 2 and Meta's V-JEPA learn these dynamics from video data, building internal representations that can predict future states given an action. This enables: planning by simulating possible futures, generating training data for robotics by running simulated rollouts, and video generation that respects physical plausibility.

World models are considered a key missing piece for achieving robust robotic manipulation and autonomous driving.

Why it matters

World models represent a distinct paradigm from language modeling, moving from predicting tokens to predicting states of the world. They're why the robotics and autonomous driving industries are watching AI developments closely, and why synthetic data generation for physical tasks is becoming feasible.

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