This podcast will discuss the NVIDIA Cosmos™ platform, which utilises state-of-the-art generative world foundation models to accelerate the development of physical AI systems like autonomous vehicles and robots.
Physical AI models are expensive to develop, requiring large amounts of real-world data and testing. Cosmos world foundation models offer a way to produce large quantities of photorealistic, physics-based synthetic data to train and assess existing models.
Key features of the NVIDIA Cosmos platform:
Open models: Developers can customise the models with datasets tailored to their specific applications. For example, you can use video recordings of autonomous vehicles or robots operating in a warehouse.
Physics-based video generation: Cosmos world foundation models can generate physics-based videos from a range of inputs, including:
Focus on physical interactions: The models are designed to handle physically based interactions, object permanence, and the generation of high-quality simulated environments, such as warehouses, factories, and diverse driving conditions.
Video tokenizers: These tools accelerate model training by converting images and videos into compact representations.
Guardrail system: A two-stage guardrail system ensures safe and trustworthy AI.
Benefits of NVIDIA Cosmos for Physical AI development:
Accelerated development: Cosmos models can significantly speed up the development process for robots and autonomous vehicles.
Reduced costs: Using synthetic data generated by Cosmos can lower the expense of data collection and testing.
Improved safety: The guardrail system promotes responsible AI development by mitigating potential risks.
Democratization of physical AI: NVIDIA aims to make general robotics accessible to every developer.
More about LunarTech: https://lunartech.ai
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