Probabilistic Programming Simplifies Coding

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Up-to-date and technically advanced systems especially in artificial
intelligence (AI) — such as mobile apps that convert speech to text — are the
result of machine learning, in which computers are twisted loose on huge data
sets to look for patterns.

To make machine-learning applications easier to shape, computer scientists
have begun developing so-called probabilistic programming languages, which let researchers
make a perfect combination 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.  You might be interested in ; Evidence of liquid water found on Mars

At the Computer Vision and Pattern Recognition conference in June, MIT profound
researchers will demonstrate that on some   standard and customary computer-vision responsibilities,
short programs — less than 50 lines long — written in a probabilistic
programming language are competitive with conservative and positively
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. “The whole hope is to
write very flexible models, both generative and discriminative models, as short
probabilistic code, and then not do anything else. General-purpose inference
schemes solve the problems.”

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 3-D model of a human face from 2-D 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 2-D
images and their corresponding 3-D models, and it will figure out the rest for
itself.

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“When you think about probabilistic programs, you think very intuitively
when you’re modeling,” Kulkarni says. “You don’t think mathematically. It’s a
very different style of modeling.”

Joining Kulkarni on the paper are his adviser, professor of brain and
cognitive sciences Josh Tenenbaum; Vikash Mansinghka, a research scientist in
MIT’s Department of Brain and Cognitive Sciences; and Pushmeet Kohli of
Microsoft Research Cambridge. For their experiments, they created a
probabilistic programming language they call Picture, which is an extension of Julia, another language developed at MIT.

What’s Old Is New?

The new work, Kulkarni says, revives an idea known as inverse graphics,
which dates from the infancy of artificial-intelligence research. Even though
their computers were painfully slow by today’s standards, the artificial
intelligence pioneers saw that graphics programs would soon be able to
synthesize realistic images by calculating the way in which light reflected off
of virtual objects. This is, essentially, how Pixar makes movies.

Some researchers, like the MIT graduate student Larry Roberts, argued that
deducing objects’ three-dimensional shapes from visual information was simply
the same problem in reverse. But a given color patch in a visual image can, in
principle, be produced by light of any color, coming from any direction,
reflecting off of a surface of the right color with the right orientation.
Calculating the color value of the pixels in a single frame of “Toy Story” is a
huge computation, but it’s deterministic: All the variables are known.
Inferring shape, on the other hand, is probabilistic: It means canvassing lots
of rival possibilities and selecting the one that seems most likely.

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That kind of inference is exactly what probabilistic programming languages
are designed to do. Kulkarni and his colleagues considered four different
problems in computer vision, each of which involves inferring the
three-dimensional shape of an object from 2-D information. On some tasks, their
simple programs actually outperformed prior systems. The error rate of the
program that estimated human poses, for example, was between 50 and 80 percent lower
than that of its predecessors.

Learning to Learn

In a probabilistic programming language, the heavy lifting is done by the
inference algorithm — the algorithm that continuously readjusts probabilities
on the basis of new pieces of training data. In that respect, Kulkarni and his
colleagues had the advantage of decades of machine-learning research. Built
into Picture are several different inference algorithms that have fared well on
computer-vision tasks. Time permitting; it can try all of them out on any given
problem, to see which works best.

Furthermore, Kulkarni says, Picture is designed so that its inference
algorithms can themselves benefit from machine learning, modifying them as they
go to emphasize strategies that seem to lead to good results. “Using learning
to improve inference will be task-specific, but probabilistic programming may
alleviate re-writing code across different problems,” he says. “The code can be
generic if the learning machinery is powerful enough to learn different strategies
for different tasks.”

“Picture provides a general framework that aims to solve nearly all tasks in
computer vision,” says Jianxiong Xiao, an assistant professor of computer
science at Princeton University, who was not involved in the work. “It goes
beyond image classification — the most popular task in computer vision — and
tries to answer one of the most fundamental questions in computer vision: 
What
is the right representation of visual scenes? It is the beginning of modern
revisit for inverse-graphics reasoning.”

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