World models are internal simulators that learn the structure and dynamics of an environment, and they have emerged as one of the most debated concepts in AI.
Researchers across subfields like model-based reinforcement learning, video generation, embodied robotics, and ultimately physical AI are building systems they refer to as 'world models.' However, there is no consensus on what a world model fundamentally is, what it should predict, or how it should be constructed.
This perspective article provides a scientific definition of world models, discusses their key technical aspects, and outlines a staged roadmap for developing effective world models.
Blogger's Review: The exploration of world models presents immense potential for AI systems to gain deeper understanding and adaptability, particularly in dynamic environments. Establishing a coherent definition and developmental framework will drive further innovation and exploration in this field.