Scaling Physical AI Data Generation with NVIDIA Cosmos for Secure and Compliant Models
Introduction to Physical AI Data Challenges Developing physical AI models requires extensive data that accurately represents real-world phenomena. Such data must be diverse, controllable, and grounded in physical reality to enable reliable AI performance. However, collecting large-scale real-world datasets often involves high costs, long timeframes, and potential safety risks. These challenges can hinder progress in AI applications that depend on physical data. The Importance of Data Privacy and Control When gathering data for AI, protecting privacy and ensuring data security are critical. Real-world data may contain sensitive information or expose individuals to risks during collection. Maintaining control over data generation also allows for reproducibility and validation, which are essential for trustworthy AI systems. Hence, solutions that offer controllable and secure data generation are highly valuable. NVIDIA Cosmos: An Overview NVIDIA Cosmos introduces an open-world ...