SCS Faculty Candidate Talk

  • Gates&Hillman Centers
  • ASA Conference Room 6115
  • Research Scientist
  • Probabilistic Computing Project
  • Massachusetts Institute of Technology

Probabilistic Programming for Augmented Intelligence

If people could communicate with and interactively modify the behavior of AI systems, both people and machines could behave more intelligently. Unfortunately, most AI systems are black boxes designed to solve a single narrowly defined problem, such as chess or face recognition or click prediction, and adjusting their behavior requires deep technical expertise. In this talk, I will describe progress towards more transparent and flexible AI systems capable of augmenting rather than just replacing human intelligence, building on the emerging field of probabilistic programming. Probabilistic programming draws on probability theory, programming languages, and system software to provide concise, expressive languages for modeling and general-purpose inference engines that both humans and machines can use.

This talk focuses on BayesDB and Picture, domain-specific probabilistic programming platforms being developed by my research group, aimed at augmenting intelligence in the fields of data science and computer vision, respectively. BayesDB, which is open source and in use by organizations like the Bill & Melinda Gates Foundation and JPMorgan, lets users who lack statistics training understand the probable implications of data by writing queries in a simple, SQL-like language. Picture, a probabilistic language being developed in collaboration with Microsoft, lets users solve hard computer vision problems such as inferring 3D models of faces, human bodies and novel generic objects from single images by writing short (<50 line) computer graphics programs that generate and render random scenes. Unlike bottom-up vision algorithms, Picture programs build on prior knowledge about scene structure and produce complete 3D wireframes that people can manipulate using ordinary graphics software.

This talk will also briefly illustrate the fundamentals of probabilistic programming using Venture, an interactive platform suitable for teaching and applications in fields ranging from statistics to robotics, and concludes with a summary of current and future research directions.

Vikash Mansinghka is a research scientist at MIT, where he founded and leads the 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 2009 MIT George M. Sprowls dissertation award in computer science, and his research on the Picture probabilistic programming language won a research award at CVPR 2015. He previously co-founded Navia Systems, a San Francisco-based analytics startup that was acquired by in 2012; he was an advisor to Google DeepMind; and he is a co-founder of Empirical Systems, a new startup based in Cambridge, MA. He served on DARPA's Information Science and Technology advisory board from 2010-2012, and currently serves on the editorial boards for the Journal of Machine Learning Research and the journal Statistics and Computation.

Faculty Host: William Cohen

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