Explore the shift from Large Language Models to World Models. Learn how World-Action Models (WAMs) bridge the grounding gap to understand the physical world.
We’re moving from the library to the playground, and that shift is backed by the idea that predicting the next pixel is more important for physical intelligence than predicting the next word.
What's next beyond LLMs and Agents, specifically focusing on the development of world models and physical AI.
Large Language Models are masters of text and language, often compared to someone who has read every book in a library but never stepped outside. While LLMs excel at explaining concepts, they often lack an understanding of physical reality. World Models and World-Action Models (WAMs) represent the next step in AI, moving beyond words to understand the physical consequences of actions and how the real world functions.
The grounding gap refers to the disconnect between an AI's digital intelligence and its understanding of the physical world. As Yann LeCun points out, the world is not made of words, yet current LLMs are primarily trained on text. This gap means that while an AI can debate philosophy or physics, it may not understand basic physical interactions, such as how a shadow stretches or how to catch a falling glass.
World-Action Models, or WAMs, are advanced AI systems that are essentially pretrained to imagine the physical outcomes of their actions before they take them. Unlike standard LLMs or simple digital agents, WAMs create a mental playground or simulation to predict consequences. This capability is essential for autonomous systems, such as robots cleaning a kitchen or self-driving cars navigating a storm, to function safely and effectively in real-life environments.
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