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 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 startups collapsed, leading to what some people called the “AI winter.”
A win for deep learning
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 Hinton won the top prize, $22,000, 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, 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 assembled by Stanford and Princeton researchers. Although the accuracy rate was low — 15.8 percent — the system did 70 percent better than the most advanced previous one.
‘No looking back now’
Deep learning was given a particularly audacious display at a conference last month in Tianjin, China, when Richard 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 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.
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 website. 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 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.”