Programming: Past, Present, and Future
- Friday, April 28, 2017, 11:00 am - 12:00 pm PDTiCal
- 11th floor large conference room
This event is open to the public.
- AI Seminar
- Avi Pfeffer, Charles River Analytics
Probabilistic reasoning lets you predict the future, infer past causes of current observations, and learn from experience. It can be hard to implement a probabilistic application because you have to implement the representation, inference, and learning algorithms. Probabilistic programming makes this much easier by providing an expressive language to represent models as well as inference and learning algorithms that automatically apply to models written in the language. In this talk, I will present the past, present, and future of probabilistic programming and our Figaro probabilistic programming system. I will start with the motivation for probabilistic programming and Figaro. After presenting some basic Figaro concepts, I will introduce several applications we have been developing at Charles River Analytics using Figaro. Finally, I will describe our future vision of providing a probabilistic programming tool that domain experts with no machine learning knowledge can use. In particular, I will present a new inference method that is designed to work well on a wide variety of problems with no user configuration. Prior knowledge of machine learning is not required to follow the talk.
Dr. Avi Pfeffer is Chief Scientist at Charles River Analytics. Dr. Pfeffer is a leading researcher on a variety of computational intelligence techniques including probabilistic reasoning, machine learning, and computational game theory. Dr. Pfeffer has developed numerous innovative probabilistic representation and reasoning frameworks, such as probabilistic programming, which enables the development of probabilistic models using the full power of programming languages, and statistical relational learning, which provides the ability to combine probabilistic and relational reasoning. He is the lead developer of Charles River Analytics’ Figaro probabilistic programming language. As an Associate Professor at Harvard, he developed IBAL, the first general-purpose probabilistic programming language. While at Harvard, he also produced systems for representing, reasoning about, and learning the beliefs, preferences, and decision making strategies of people in strategic situations. Prior to joining Harvard, he invented object-oriented Bayesian networks and probabilistic relational models, which form the foundation of the field of statistical relational learning. Dr. Pfeffer serves as Action Editor of the Journal of Machine Learning Research and served as Associate Editor of Artificial Intelligence Journal and as Program Chair of the Conference on Uncertainty in Artificial Intelligence. He has published many journals and conference articles and is the author of a text on probabilistic programming. Dr. Pfeffer received his Ph.D. in computer science from Stanford University and his B.A. in computer science from the University of California, Berkeley.