NVIDIA AI Podcast How World Foundation Models Will Advance Physical AI With NVIDIA’s Ming-Yu Liu - Ep. 240
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Jan 7, 2025 Ming-Yu Liu, Vice President of Research at NVIDIA and an IEEE Fellow, dives into world foundation models transforming industries like self-driving cars and robotics. He explains how these advanced neural networks simulate real-world environments to enhance AI workflows. Liu highlights the crucial differences between world models and generative AI, discusses the significance of open-weight models, and introduces the Cosmos platform. The conversation sheds light on the future of physical AI and the importance of accurate simulations for safe deployment.
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World Foundation Models
- World Foundation models are deep learning-based space-time visual simulators.
- They can simulate physics, intentions, and activities to help train AI agents.
World Models vs. LLMs/Generative Video
- World models differ from LLMs by focusing on generating simulations, primarily videos, rather than text.
- They predict future events based on current observations and actor intentions, unlike general video models.
Simulate Before Deploying
- Use world models to verify AI policies in simulations before real-world deployment, preventing potential damage.
- This allows for efficient testing in various environments, simplifying physical AI deployment.