TTIC Distinguished Lecture Series - Josh Tenenbaum

Title: Engineering and reverse-engineering common sense
Abstract: Many recent successes in computer vision, machine learning and other areas of artificial intelligence have been driven by methods for sophisticated pattern recognition, such as deep neural networks. But human intelligence is more than just pattern recognition. In particular, it depends on a suite of commonsense capacities for modeling the world: for explaining and understanding what we see, imagining things we could see but haven’t yet, solving problems and planning actions to make these things real, and building new models as we learn more about the world. I will talk about how we are beginning to capture these distinctively human capacities in computational models using the tools of probabilistic programs and program induction, embedded in a Bayesian framework for inference from data. These models help to explain how humans can perceive rich three-dimensional structure in visual scenes and objects, perceive and predict objects' motion based on their intrinsic physical characteristics, and learn new visual object concepts from just one or a few examples.