Probabilistic Programming and AI
IDS.190 – Topics in Bayesian Modeling and Computation
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.
Vikash Mansinghka is a Principal Research Scientist at MIT, where he leads the MIT Probabilistic Computing Project. Vikash holds S.B. degrees in Mathematics and in Computer Science from MIT, as well as an M.Eng. in Computer Science and a PhD in Computation. He also held graduate fellowships from the National Science Foundation and MIT’s Lincoln Laboratory. His PhD dissertation on natively probabilistic computation won the MIT George M. Sprowls dissertation award in computer science, and his research on the Picture probabilistic programming language won an award at CVPR. He co-founded two VC-backed startups — Prior Knowledge (acquired by Salesforce in 2012) and Empirical Systems (acquired by Tableau in 2018) — and has consulted on probabilistic programming for leading companies in the semiconductor, biopharma, IT services, and banking sectors. He served on DARPA’s Information Science and Technology advisory board from 2010-2012, currently serves on the editorial boards for the Journal of Machine Learning Research and the journal Statistics and Computation, and co-founded the International Conference on Probabilistic Programming.
For more information and an up-to-date schedule, please see https://stellar.mit.edu/S/course/IDS/fa19/IDS.190/
**Taking IDS.190 satisfies the seminar requirement for students in MIT’s Interdisciplinary Doctoral Program in Statistics (IDPS), but formal registration is open to any graduate student who can register for MIT classes. And the meetings are open to any interested researcher. Talks will be followed by 30 minutes of tea/snacks and informal discussion.**