Sponsored by Google Cloud Choosing Your First Generative AI Use Cases To get started with generative AI, first focus on areas that can improve human experiences with information. The systems, powered by Cosmos 3, are designed to accelerate development of autonomous vehicles, robots and vision AI systems. Nvidia has released a spate of new physical AI research tools, agent workflows and open source models to train more advanced AI systems for the real world. Unveiled this week at the Computer Vision and Pattern Recognition conference in Denver, the updates build on Nvidia's recently launched Cosmos 3 world foundation model and are designed to help researchers automate key stages of physical AI development, including simulation, synthetic data generation, policy training and evaluation. Physical AI refers to AI systems that interact with and operate in the physical world, including self-driving vehicles, industrial robots and embodied AI agents. The company said the new capabilities address a major challenge facing engineers in the industry: creating scalable workflows to train and test AI virtually before real-world deployment. “The core challenge in physical AI research isn’t simply developing stronger models. It’s building a full workflow around them,” Nvidia said in a blog post. “Today, these steps are fragmented across separate tools, slowing the pace of experimentation as researchers struggle to piece them together.” Among the announcements are new agent skills integrated across Nvidia Omniverse, Isaac Sim, Isaac Lab and Cosmos, enabling developers to automate tasks such as scene reconstruction, simulation setup, environment generation and reinforcement learning workflows. For autonomous vehicle development, Nvidia introduced tools to help researchers address the industry's “long-tail problem” --difficult-to-capture driving scenarios that are critical for training and validation. To bridge this gap, Nvidia said its AI agents can now automate the reconstruction of real-world driving environments from fleet data and generate synthetic