NEAI - September 16 2013 - Brian Ruttenberg: Probabilistic Programming and the Democratization of AI





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Published on Sep 18, 2013

Probabilistic models form the foundation of modern Machine Learning and Artificial Intelligence. However, building and reasoning on models that represent large and complex scenarios is a daunting task for even the most expert and experienced programmers. As a result, there has been significant effort lately on the development of probabilistic programming languages, which allow probabilistic processes, models and algorithms to be specified using familiar programming language constructs. These languages are democratizing the AI field, enabling users with little ML and AI experience to easily construct and perform inference on large and complex probabilistic models.

In this talk, Brian will discuss the motivation for the advent of probabilistic programming languages, detail some of the major problems they are trying to solve, and will throw in a little theory for good measure. He will present many examples of the power of these new languages using Figaro, a free, open-source (BSD license), probabilistic programming language.

Bio: Brian Ruttenberg is a scientist at Charles River Analytics. His primary area of expertise is machine learning, data mining and probabilistic modeling. His current area of research focuses on exploring new representations and inference mechanisms in probabilistic programming languages, and applications of probabilistic programming to new fields such as cyber security and malware analysis. Prior to joining Charles River Analytics, he designed graphics and memory systems for Intel and Qualcomm Corporations. Dr. Ruttenberg holds a B.S.E. in computer engineering from the University of Michigan, Ann Arbor and a Ph.D. in computer science from the University of California, Santa Barbara.

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