From Chips to Thoughts: Building Physical Intelligence into Robotic Systems - Daniela Rus (MIT)

Daniela Rus, Massachusetts Institute of Technology, Director, CSAIL & Andrew (1956) and Erna Viterbi Professor, Cambridge, MA 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 Intelligence 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 intelligence, we need energy-efficient AI systems that can run reliably on robots, sensors, and other edge devices. In this paper I will discuss the energy challenges of transformer-based foundational AI models, I will introduce several state space models, and explain how they achieve energy efficiency, and how state-space models enable physical intelligence.