Open models and the future of Physical AI with NVIDIA
Ming-Yu Liu, Vice President of Cosmos Lab at NVIDIA, joins Daniel Whitenack and Chris Benson to discuss the critical role of open foundation models and world models in advancing physical AI. He explains the differences between digital and physical AI, the architecture of NVIDIA Cosmos models for physical simulation and understanding, and how multi-agent robotics ecosystems will transform real-world automation.
- Physical AI refers strictly to models deployed on physical devices that perturb the physical state of the environment to produce tangible, material results. Read →
- Cloud language models hide hardware inefficiencies by batching hundreds of concurrent user requests; physical robots cannot aggregate requests and must run batch-size-one inference. Read →
- Ming-Yu Liu leads NVIDIA Cosmos Lab to build foundation models that simulate physical physics rather than parse web text. Read →
- Ming-Yu Liu, Vice President of Cosmos Lab at NVIDIA, defines an open model by three components: open weights hosted on Hugging Face, training frameworks on GitHub, and shared datasets. Read →
- Closed APIs block internal inspection: Ming-Yu Liu points out that hosted endpoints prevent engineers from modifying model architectures or running local, zero-latency inference. Read →