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Probabilistic Programming for Augmented Intelligence
Speaker: Vikash Mansingkha
Speaker Affiliation: MIT, CSAIL
Host: MIT Media Lab
Date: Tuesday, March 15th
Time: 2 PM
Location: E14-244, MIT Campus, Cambridge, MA
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.
Speaker biography: 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 Salesforce.com 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.
For more information about the Probabilistic Computing Project, please
see http://probcomp.csail.mit.edu/ ; for more information about the talk,
please contact Rax Dillon at firstname.lastname@example.org.