Today's AI systems can beat the best human chess or Go players. However, they fail to perform tasks involving physical intelligence that humans don't even think about such as walking, climbing stairs or opening doors. Things that are easy for humans, are often very hard for machines and vice-versa, an observation often known as the Moravec's paradox. Human intuition of what is easy and what is hard gets in the way of building truly intelligent AI systems and has been a major lesson of AI research in the past seventy years. Using all the data on the internet, we have made impressive progress in reasoning and language understanding. However, there is no such data repository for learning physical intelligence and evolution spent 50,000x more time perfecting physical intelligence as opposed to language understanding. In this talk, learn why is physical intelligence so hard and how do we get there?

AI, Brain, Decision making, Evolution, Intelligence, Machine Learning, Robots, Technology Dr. Pulkit Agrawal is Steven and Renee Finn Chair Assistant Professor in the Department of Electrical Engineering and Computer Science at MIT. He earned his Ph.D. from UC Berkeley and co-founded SafelyYou Inc. His research interests span robotics, deep learning, computer vision, and reinforcement learning. Pulkit completed his bachelor's from IIT Kanpur and was awarded the Directors Gold Medal. His work received the Best Paper Award at Conference on Robot Learning 2021 and Best Student Paper Award at Conference on Computer Supported Collaborative Learning 2011. He is a recipient of Sony Faculty Research Award, Salesforce Research Award, Amazon Research Award, Signatures Fellow Award, Fulbright Science and Technology Award, Goldman Sachs Global Leadership Award, etc. His work has appeared multiple times in MIT Tech Review, Quanta, New Scientist, NYPost, etc. This talk was given at a TEDx event using the TED conference format but independently organized by a local community. Learn more at https://www.ted.com/tedx