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.
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