On April 8, 2025, Daniela Rus, Director of the MIT Computer Science and AI Laboratory, stopped by the lab for her talk, “Physical AI.”
In today’s robot revolution, a record 3.1 million robots are now working in factories, doing everything from assembling computers to packing goods and monitoring air quality and performance. A far greater number of smart machines impact our lives in countless other ways—improving the precision of surgeons, cleaning our homes, extending our reach to distant worlds—and we are on the cusp of even more exciting opportunities. Future machines, enabled by recent advances in AI, will come in diverse forms and materials, embodying a new level of physical intelligence. Physical AI is achieved when the power of AI to understand text, images, signals, and other information is used to make physical machines such as robots intelligent. However, a critical challenge remains: balancing the capabilities of AI with sustainable energy usage. To achieve effective Physical AI, we need energy-efficient AI systems that can run reliably on robots, sensors, and other edge devices. In this talk I will discuss the energy challenges of transformer-based foundational AI models, I will introduce several state space models for Physical AI, and explain how they achieve energy efficiency, and how state-space models enable physical intelligence.
Daniela Rus is the Andrew (1956) and Erna Viterbi Professor of Electrical Engineering and Computer Science and Director of the Computer Science and Artificial Intelligence Laboratory (CSAIL) at MIT. Rus’s research interests are in robotics, mobile computing, and data science. Rus is a Class of 2002 MacArthur Fellow, a fellow of ACM, AAAI and IEEE, and a member of the National Academy of Engineering, and the American Academy for Arts and Science. She earned her PhD in Computer Science from Cornell University.
