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WTH are World Models

What if AI could understand the physical world the way humans do? In this video, we explore the idea of world models, a class of AI systems designed to understand physics, space, causality, and how objects interact in the real universe. Unlike large language models that primarily learn from text, world models are trained on multimodal data such as images, videos, motion, and interactions, allowing them to build internal representations of entire environments rather than isolated tokens. We discuss why realistic video generation, autonomous driving, and physical AI all require models that understand gravity, fluids, object dynamics, and cause-and-effect relationships. Using examples from self-driving simulation, video generation, and research from companies like DeepMind and NVIDIA, the video explains how world models can generate interactive environments instead of static simulations, enabling AI agents to learn, plan, and self-correct before acting in the real world. The discussion also covers the concept of World Foundation Models, how they differ from language models, and why they may play a critical role in the path toward Artificial General Intelligence. We explore how these models can be fine-tuned for domain-specific physical tasks such as robotics, autonomous systems, and embodied AI, while remaining general enough to scale across domains. This video is intended to build a strong mental model of why language alone is not enough for AGI, and why teaching AI to understand the structure of reality itself may be the next major leap in artificial intelligence.