j**********i
发帖数: 3758 | 1 有一场像 stem cell一样的大讨论，不过要去联合国吵。
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.
Connect With Us on Social Media
@nytimesscience on Twitter.
Science Reporters and Editors on Twitter
Like the science desk on Facebook.
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 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. | w***g
发帖数: 5958 | 2 可算是参数调对了accuracy上去了，要知道这些可是他们80年代promise to deliver的
。过了20年拿了那点仿生学的东西又出来蹦跶，我真是看不过去。我对Hinton还是非常
尊重的，搞NN搞了那么多年不死心还在搞，是个做学问的人，现在的风光也是他应得的
。我就是看不惯那批跟风的出来骗钱。
startling
【在 j**********i 的大作中提到】
: 有一场像 stem cell一样的大讨论，不过要去联合国吵。
: 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.
: Connect With Us on Social Media
: @nytimesscience on Twitter.
: Science Reporters and Editors on Twitter
: Like the science desk on Facebook.
: Keith Penner
| k**o
发帖数: 8 | 3 你说得对。
这精度上升就是一靠调参数，还有很隐蔽地增加函数类的复杂度,
并在这过程中对照结果来“人肉学习”。
【在 w***g 的大作中提到】
: 可算是参数调对了accuracy上去了，要知道这些可是他们80年代promise to deliver的
: 。过了20年拿了那点仿生学的东西又出来蹦跶，我真是看不过去。我对Hinton还是非常
: 尊重的，搞NN搞了那么多年不死心还在搞，是个做学问的人，现在的风光也是他应得的
: 。我就是看不惯那批跟风的出来骗钱。
:
: startling
| t********e
发帖数: 1169 | 4 第二次上nytimes了，deep learning OK!!! |
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