Logic and Probabilistic Programming

Programming Trends to Watch: Logic and Probabilistic Programming by Dean Wampler.

From the post:

I believe there are two other emerging trends in programming worth watching that will impact the data world.

Logic Programming, like FP, is actually not new at all, but it is seeing a resurgence of interest, especially in the Clojure community. Rules engines, like Drools, are an example category of logic programming that has been in use for a long time.

We’re on the verge of moving to the next level, probabilistic programming languages and systems that make it easier to build probabilistic models, where the modeling concepts are promoted to first-class primitives in new languages, with underlying runtimes that do the hard work of inferring answers, similar to the way that logic programming languages work already. The ultimate goal is to enable end users with limited programming skills, like domain experts, to build effective probabilistic models, without requiring the assistance of Ph.D.-level machine learning experts, much the way that SQL is widely used today.

DARPA, the research arm of the U.S. Department of Defense, considers this trend important enough that they are starting an initiative to promote it, called Probabilistic Programming for Advanced Machine Learning, which is also described in this Wired article.

Registration for the DARPA event (April 10, 2013) is closed but a video recording will be posted at: http://www.darpa.mil/Opportunities/Solicitations/I2O_Solicitations.aspx after April 10, 2013.

I suspect semantics are going to be at issue in any number of ways.

The ability to handle semantics robustly may be of value.

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