Gokul N A

Technology gaps in Physical intelligence - the physics gaps in intelligence models and opportunities that lies within

Physical intelligence, the ability of machines to perceive, reason, and act in the physical world remains one of the key frontiers in artificial intelligence. While modern AI systems have made significant progress in language, vision, and pattern recognition, they still struggle to fully understand and interact with the physical laws that govern real-world environments. This session explores the technology gaps in physical intelligence, focusing on the disconnect between current intelligence models and the principles of physics that shape real-world interactions. It will examine limitations such as weak physical reasoning, challenges in predicting object dynamics, and the difficulty of learning from limited real-world data. The talk will also highlight the “physics gaps” in today’s AI models, where purely data-driven approaches fall short in capturing causal and dynamic properties of the physical world and discuss emerging opportunities in areas such as embodied AI, simulation-based learning, robotics, and hybrid physics-AI systems that aim to bridge these gaps.