By JOHN MARKOFF
Published: November 23, 2012
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.
