Scientists See Promise in Deep-Learning Programs - The New York ...
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Nov 23, 2012 - Advances
in an artificial intelligence technology that can recognize patterns
offer the possibility of machines that perform human activities like ...Scientists See Promise in Deep-Learning Programs
Hao Zhang/The New York Times
A voice recognition program translated a speech given by Richard F. Rashid, Microsoft’s top scientist, into Mandarin Chinese.
By JOHN MARKOFF
Published: November 23, 2012
Using an artificial intelligence technique inspired by theories about
how the brain recognizes patterns, technology companies are reporting
startling gains in fields as diverse as computer vision, speech
recognition and the identification of promising new molecules for
designing drugs.
Keith Penner
A student team led by the computer scientist Geoffrey
E. Hinton used deep-learning technology to design software.
The advances have led to widespread enthusiasm among researchers who
design software to perform human activities like seeing, listening and
thinking. They offer the promise of machines that converse with humans
and perform tasks like driving cars and working in factories, raising the specter of automated robots that could replace human workers.
The technology, called deep learning, has already been put to use in
services like Apple’s Siri virtual personal assistant, which is based on
Nuance Communications’ speech recognition service, and in Google’s
Street View, which uses machine vision to identify specific addresses.
But what is new in recent months is the growing speed and accuracy of
deep-learning programs, often called artificial neural networks or just
“neural nets” for their resemblance to the neural connections in the
brain.
“There has been a number of stunning new results with deep-learning
methods,” said Yann LeCun, a computer scientist at New York University
who did pioneering research in handwriting recognition at Bell
Laboratories. “The kind of jump we are seeing in the accuracy of these
systems is very rare indeed.”
Artificial intelligence researchers are acutely aware of the dangers of
being overly optimistic. Their field has long been plagued by outbursts
of misplaced enthusiasm followed by equally striking declines.
In the 1960s, some computer scientists believed that a workable
artificial intelligence system was just 10 years away. In the 1980s, a
wave of commercial start-ups collapsed, leading to what some people
called the “A.I. winter.”
But recent achievements have impressed a wide spectrum of computer
experts. In October, for example, a team of graduate students studying
with the University of Toronto computer scientist Geoffrey E. Hinton
won the top prize in a contest sponsored by Merck to design software to
help find molecules that might lead to new drugs.
From a data set describing the chemical structure of thousands of
different molecules, they used deep-learning software to determine which
molecule was most likely to be an effective drug agent.
The achievement was particularly impressive because the team decided to
enter the contest at the last minute and designed its software with no
specific knowledge about how the molecules bind to their targets. The
students were also working with a relatively small set of data; neural
nets typically perform well only with very large ones.
“This is a really breathtaking result because it is the first time that
deep learning won, and more significantly it won on a data set that it
wouldn’t have been expected to win at,” said Anthony Goldbloom, chief
executive and founder of Kaggle, a company that organizes data science
competitions, including the Merck contest.
Advances in pattern recognition hold implications not just for drug
development but for an array of applications, including marketing and
law enforcement. With greater accuracy, for example, marketers can comb
large databases of consumer behavior to get more precise information on
buying habits. And improvements in facial recognition are likely to make
surveillance technology cheaper and more commonplace.
Artificial neural networks, an idea going back to the 1950s, seek to
mimic the way the brain absorbs information and learns from it. In
recent decades, Dr. Hinton, 64 (a great-great-grandson of the
19th-century mathematician George Boole,
whose work in logic is the foundation for modern digital computers),
has pioneered powerful new techniques for helping the artificial
networks recognize patterns.
Modern artificial neural networks are composed of an array of software
components, divided into inputs, hidden layers and outputs. The arrays
can be “trained” by repeated exposures to recognize patterns like images
or sounds.
These techniques, aided by the growing speed and power of modern
computers, have led to rapid improvements in speech recognition, drug
discovery and computer vision.
Deep-learning systems have recently outperformed humans in certain limited recognition tests.
Last year, for example, a program created by scientists at the Swiss A. I. Lab
at the University of Lugano won a pattern recognition contest by
outperforming both competing software systems and a human expert in
identifying images in a database of German traffic signs.
The winning program accurately identified 99.46 percent of the images in
a set of 50,000; the top score in a group of 32 human participants was
99.22 percent, and the average for the humans was 98.84 percent.
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This article has been revised to reflect the following correction:
Correction: November 29, 2012
An article on Saturday about rapid advances in the artificial intelligence technique called deep learning misstated the number of molecules analyzed in a contest sponsored by Merck and won by students using deep-learning software. Contestants analyzed thousands of molecules that might lead to new drugs, not 15. (There were 15 data files, each containing thousands of molecules.)
end quote from:
Correction: November 29, 2012
An article on Saturday about rapid advances in the artificial intelligence technique called deep learning misstated the number of molecules analyzed in a contest sponsored by Merck and won by students using deep-learning software. Contestants analyzed thousands of molecules that might lead to new drugs, not 15. (There were 15 data files, each containing thousands of molecules.)
end quote from:
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