Kulkarni uses probabilistic programming to code less and do more


April 29, 2015

Most recent advances in artificial intelligence — such as mobile apps that convert speech to text — are the result of machine learning, in which computers are turned loose on huge data sets to look for patterns. To make machine-learning applications easier to build, computer scientists have begun developing so-called probabilistic programming languages, which let researchers mix and match machine-learning techniques that have worked well in other contexts. In 2013, the U.S. Defense Advanced Research Projects Agency, an incubator of cutting-edge technology, launched a four-year program to fund probabilistic-programming research.

At the Computer Vision and Pattern Recognition conference  in June, MIT researchers will demonstrate that on some standard computer-vision tasks, short programs — less than 50 lines long — written in a probabilistic programming language are competitive with conventional systems with thousands of lines of code. “This is the first time that we’re introducing probabilistic programming in the vision area,” says Tejas Kulkarni, an MIT graduate student in brain and cognitive sciences and first author on the new paper. Continue reading on MIT News.

Leave a Reply

Your email address will not be published. Required fields are marked *