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Center for Nonlinear Studies |
Probabilistic programming is an emerging field at the intersection of programming languages, probability theory, and artificial intelligence. This talk will show how to use recently developed probabilistic programming languages to build systems for robust 3D computer vision, without requiring any labeled training data; for automatic modeling of complex real-world time series; and for machine-assisted analysis of experimental data that is too small and/or messy for standard approaches from machine learning and statistics.This talk will use these applications to illustrate recent technical innovations in probabilistic programming that formalize and unify modeling approaches from multiple eras of AI, including generative models, neural networks, symbolic programs, causal Bayesian networks, and hierarchical Bayesian modeling. Specifically, it will present languages in which models are represented using executable code, and in which inference is programmable using novel constructs for Monte Carlo, optimization-based, and neural inference. It will also present techniques for Bayesian learning of probabilistic program structure and parameters from real-world data. Finally, this talk will review challenges and research opportunities in the development and use of general-purpose probabilistic programming languages that performant enough and flexible enough for real-world AI engineering. **This seminar is part of a series on Artificial Intelligence for Computational Science.
Host: Aric Hagberg