Probabilistic modeling and machine learning provide powerful tools for processing ambiguous and noisy information. They play crucial roles in commercial systems ranging from search to autonomous vehicles to user interfaces. Unfortunately, state-of-the-art probabilistic modeling and machine learning can be difficult, involving custom mathematics, custom approximation algorithms, and custom software that all require expert tuning.
I will describe new, stochastic software and hardware abstractions, along with implemented prototypes. These are all based on an emerging marriage of ideas from probability and statistics, specifically stochastic processes, random variables and Bayesian inference, with ideas from computer science, specifically circuits, processors and programs. Throughout, we will abandon the reliance on rock-solid determinism that is central to computer science and engineering.
I will give an overview of three probabilistic computing systems:
Dr. Vikash Mansinghka is an Intelligence Initiative Fellow at MIT's Computer Science and AI Laboratory and the Department of Brain & Cognitive Sciences, where he leads the Probabilistic Computing Project. Dr. Mansinghka received an SB in Mathematics, and SB in Computer Science, an MEng in Computer Science, and a PhD in Computation, all from MIT. He 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 award for best dissertation in computer science. He previously co-founded and led a venture-backed startup selling predictive database software that was ultimately acquired by Salesforce.com in 2012. He served on DARPA's Information Science and Technology advisory board from 2010-2012.