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NVIDIA's Cosmos: A Platform for the Development of Physical AI

The development of Artificial Intelligence (AI) that understands and can simulate physical processes presents a particular challenge. This so-called "physical AI" requires digital twins: a policy model that represents the AI itself, and a world model that represents the environment. NVIDIA has created Cosmos, a platform designed to help developers with this.

Cosmos is more than just a collection of pre-trained models. It's a comprehensive platform that offers tools for the creation, training, and fine-tuning of world models. It includes:

- Pre-trained World Foundation Models (WFMs) - Video tokenizers - An accelerated data processing pipeline - Frameworks for model adaptation and optimization - Safety mechanisms (Guardrails)

The WFMs are neural networks trained on millions of hours of video footage from robotics and autonomous driving. They can predict and generate future states of a virtual environment. Developers can use these models directly to generate physics-based synthetic data, or fine-tune them with their own videos for specific applications using the NVIDIA NeMo framework.

The Importance of World Models

World models play a crucial role in the development of physical AI. They allow AI agents to be trained in a safe, virtual environment before being deployed in the real world. This saves time and costs and minimizes risks. Through simulations in the virtual world, different scenarios can be played out and the AI's reactions optimized.

Cosmos offers different types of WFMs tailored to different needs:

Nano: Optimized for real-time inference and edge deployment with low latency.

Super: Powerful base models for general applications.

Ultra: Models with maximum quality and accuracy, ideal for creating custom models.

The platform provides both diffusion and autoregressive transformer models. Diffusion models generate controllable, high-quality synthetic video data, while autoregressive models predict what should happen next in a video sequence. This allows physical AI models to act proactively.

Application Examples and Advantages

Cosmos finds application in various fields, including robotics and autonomous driving. In robotics, WFMs can create synthetic environments where robots can be trained without having to conduct expensive and time-consuming tests in the real world. In the field of autonomous driving, WFMs enable the simulation of various traffic situations to improve the safety and reliability of autonomous vehicles.

Cosmos's accelerated data processing pipeline, optimized on NVIDIA GPUs, allows developers to process vast amounts of data in a very short time. The Cosmos tokenizers efficiently convert videos into tokens, reducing training and inference costs.

Openness and Responsibility

NVIDIA has released Cosmos under an open model license to democratize the development of physical AI. The platform also includes safety mechanisms (Guardrails) to prevent misuse of the models. An integrated watermarking system enables the identification of AI-generated content.

Cosmos represents an important step in the development of physical AI. The platform provides developers with the tools and resources they need to develop innovative applications in robotics, autonomous driving, and many other areas.

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