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| Robots are common in automobile manufacturing |
By John Markoff, The New York Times, 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.
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 15 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.
This summer, Jeff
Dean, a Google technical fellow, and Andrew Y. Ng, a Stanford computer
scientist, programmed a cluster of 16,000 computers to train itself to
automatically recognize images in a library of 14 million pictures of 20,000
different objects. Although the accuracy rate was low — 15.8 percent — the
system did 70 percent better than the most advanced previous one.
Deep learning was given a particularly audacious display at a
conference last month in Tianjin, China, when Richard F. Rashid, Microsoft’s top scientist,
gave a lecture in a cavernous auditorium while a computer program recognized
his words and simultaneously displayed them in English on a large screen above
his head.
Then, in a demonstration that led to stunned applause, he paused after
each sentence and the words were translated into Mandarin Chinese characters,
accompanied by a simulation of his own voice in that language, which Dr. Rashid
has never spoken.
The feat was made possible, in part, by deep-learning techniques that
have spurred improvements in the accuracy of speech recognition.
Dr. Rashid, who oversees Microsoft’s worldwide research organization,
acknowledged that while his company’s new speech recognition software made 30
percent fewer errors than previous models, it was “still far from perfect.”
“Rather than having one word in four or five incorrect, now the error
rate is one word in seven or eight,” he wrote on Microsoft’s Web site. Still,
he added that this was “the most dramatic change in accuracy” since 1979, “and
as we add more data to the training we believe that we will get even better
results.”
One of the most striking aspects of the research led by Dr. Hinton is
that it has taken place largely without the patent restrictions and bitter
infighting over intellectual property that characterize high-technology fields.
“We decided early on not to make money out of this, but just to sort
of spread it to infect everybody,” he said. “These companies are terribly
pleased with this.”
Referring to the rapid deep-learning advances made possible by greater
computing power, and especially the rise of graphics processors, he added:
“The
point about this approach is that it scales beautifully. Basically you just
need to keep making it bigger and faster, and it will get better. There’s no
looking back now.”
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