In the fast-evolving landscape of AI, it’s becoming increasingly important to develop models that can accurately simulate and predict outcomes in physical, real-world environments to enable the next generation of physical AI systems.
Ming-Yu Liu, vice president of research at NVIDIA and an IEEE Fellow, joined the NVIDIA AI Podcast to discuss the significance of world foundation models (WFM) — powerful neural networks that can simulate physical environments. WFMs can generate detailed videos from text or image input data and predict how a scene evolves by combining its current state (image or video) with actions (such as prompts or control signals).
Why Are World Foundation Models Important?
Building world models often requires vast amounts of data, which can be difficult and expensive to collect. WFMs can generate synthetic data, providing a rich, varied dataset that enhances the training process.
In addition, training and testing physical AI systems in the real world can be resource-intensive. WFMs provide virtual, 3D environments where developers can simulate and test these systems in a controlled setting without the risks and costs associated with real-world trials.
Open Access to World Foundation Models
At the CES trade show, NVIDIA announced NVIDIA Cosmos, a platform of generative WFMs that accelerate the development of physical AI systems such as robots and self-driving cars.
The platform is designed to be open and accessible, and includes pretrained WFMs based on diffusion and auto-regressive architectures, along with tokenizers that can compress videos into tokens for transformer models.
Liu explained that with these open models, enterprises and developers have all the ingredients they need to build large-scale models. The open platform also provides teams with the flexibility to explore various options for training and fine-tuning models, or build their own based on specific needs.
Enhancing AI Workflows Across Industries
WFMs are expected to enhance AI workflows and development in various industries. Liu sees particularly significant impacts in two areas:
“The self-driving car industry and the humanoid [robot] industry will benefit a lot from world model development,” said Liu. “[WFMs] can simulate different environments that will be difficult to have in the real world, to make sure the agent behaves respectively.”
For self-driving cars, these models can simulate environments that allow for comprehensive testing and optimization. For example, a self-driving car can be tested in various simulated weather conditions and traffic scenarios to help ensure it performs safely and efficiently before deployment on roads.
In robotics, WFMs can simulate and verify the behavior of robotic systems in different environments to make sure they perform tasks safely and efficiently before deployment.
Conclusion
World foundation models have the potential to revolutionize the development of physical AI systems, enabling them to simulate and predict outcomes in complex, real-world environments. With NVIDIA Cosmos, developers and enterprises now have access to powerful, open-world models that can accelerate the development of these systems.
FAQs
What are world foundation models?
World foundation models are powerful neural networks that can simulate physical environments, generate detailed videos from text or image input data, and predict how a scene evolves by combining its current state with actions.
Why are world foundation models important?
Building world models often requires vast amounts of data, which can be difficult and expensive to collect. WFMs can generate synthetic data, providing a rich, varied dataset that enhances the training process. They also provide virtual, 3D environments where developers can simulate and test physical AI systems in a controlled setting without the risks and costs associated with real-world trials.
What is NVIDIA Cosmos?
NVIDIA Cosmos is a platform of generative world foundation models that accelerate the development of physical AI systems such as robots and self-driving cars. The platform is designed to be open and accessible, and includes pretrained WFMs based on diffusion and auto-regressive architectures, along with tokenizers that can compress videos into tokens for transformer models.
What industries will benefit from world foundation models?
WFMs are expected to enhance AI workflows and development in various industries, particularly in the self-driving car industry and the humanoid [robot] industry, where they can simulate different environments and ensure that agents behave correctly.
