20 April 2015

Graphics in Reverse

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.


By the standards of conventional computer programs, those models can seem absurdly vague. One of the tasks that the researchers investigate, for instance, is constructing a 3D model of a human face from 2D images. Their program describes the principal features of the face as being two symmetrically distributed objects (eyes) with two more centrally positioned objects beneath them (the nose and mouth). It requires a little work to translate that description into the syntax of the probabilistic programming language, but at that point, the model is complete. Feed the program enough examples of 2D images and their corresponding 3D models, and it will figure out the rest for itself.

More information: