From: Eliezer S. Yudkowsky (sentience@pobox.com)
Date: Fri Oct 01 1999 - 07:34:57 MDT
http://www.eurekalert.org/releases/usc-nnn093099.html
I knew those discrete models were oversimplified! It's like I've been
saying all along: The little tokens in so-called "neural nets" are
*nothing* like actual neurons.
> Novel neural net recognizes spoken
> words better than human listeners
>
> Machine demonstrates superhuman speech recognition
> abilities. University of Southern California biomedical
> engineers have created the world's first machine system
> that can recognize spoken words better than humans can. A
> fundamental rethinking of a long-underperforming computer
> architecture led to their achievement.
>
> The system might soon facilitate voice control of computers
> and other machines, help the deaf, aid air traffic controllers
> and others who must understand speech in noisy
> environments, and instantly produce clean transcripts of
> conversations, identifying each of the speakers. The U.S.
> Navy, which listens for the sounds of submarines in the
> hubbub of the open seas, is another possible user.
> Potentially, the system's novel underlying principles could
> have applications in such medical areas as patient
> monitoring and the reading of electrocardiograms.
>
> In benchmark testing using just a few spoken words, USC's
> Berger-Liaw Neural Network Speaker Independent Speech
> Recognition System not only bested all existing computer
> speech recognition systems but outperformed the keenest
> human ears.
>
> Neural nets are computing devices that mimic the way brains
> process information. Speaker-independent systems can
> recognize a word no matter who or what pronounces it. No
> previous speaker-independent computer system has ever
> outperformed humans in recognizing spoken language, even in
> very small test bases, says system co-designer Theodore W.
> Berger, Ph.D., a professor of biomedical engineering in the
> USC School of Engineering.
>
> The system can distinguished words in vast amounts of
> random "white" noise, noise with amplitude 1,000 times the
> strength of the target auditory signal. Human listeners can
> deal with only a fraction as much. And the system can pluck
> words from the background clutter of other voices, the
> hubbub heard in bus stations, theater lobbies and cocktail
> parties, for example. Even the best existing systems fail
> completely when as little as 10 percent of hubbub masks a
> speaker's voice. At slightly higher noise levels, the
> likelihood that a human listener can identify spoken test
> words is mere chance. By contrast, Berger and Liaw's system
> functions at 60 percent recognition with a hubbub level 560
> times the strength of the target stimulus. With just a minor
> adjustment, the system can identify different speakers of
> the same word with superhuman acuity.
>
> Berger and system co-designer Jim-Shih Liaw, Ph.D.,
> achieved this improved performance by paying closer
> attention to the signal characteristics used by real
> flesh-and-blood brains in processing information.
>
> First proposed in the 1940s and the subject of intensive
> research in the '80s and early '90s, neural nets are
> computers configured to imitate the brain's system of
> information processing, wherein data are structured not by a
> central processing unit but by an interlinked network of
> simple units called neurons. Rather than being programmed,
> neural nets learn to do tasks through a training regimen in
> which desired responses to stimuli are reinforced and
> unwanted ones are not.
>
> "Though mathematical theorists demonstrated that nets
> should be highly effective for certain kinds of computation
> (particularly pattern recognition), it has been difficult for
> artificial neural networks even to approach the power of
> biological systems," said Liaw, director of the Laboratory
> for Neural Dynamics and a research assistant professor of
> biomedical engineering at the USC School of Engineering.
>
> "Even large nets with more than 1,000 neurons and 10,000
> interconnections have shown lackluster results compared
> with theoretical capabilities. Deficiencies were often laid to
> the fact that even 1,000-neuron networks are tiny, compared
> with the millions or billions of neurons in biological
> systems." Remarkably, USC's neural net system uses an
> architecture consisting of just 11 neurons connected by a
> mere 30 links.
>
> According to Berger, who has spent years studying biological
> data-processing systems, previous computer neural nets
> went wrong by oversimplifying their biological models,
> omitting a crucial dimension.
>
> "Neurons process information structured in time," he
> explained. "They communicate with one another in a
> 'language' whereby the 'meaning' imparted to the receiving
> neuron is coded into the signal's timing. A pair of pulses
> separated by a certain time interval excites a certain
> neuron, while a pair of pulses separated by a shorter or
> longer interval inhibits it. "So far," Berger continued,
> "efforts to create neural networks have had silicon neurons
> transmitting only discreet signals of varying intensity, all
> clocked the way a computer is clocked, in beats of unvarying
> duration. But in living cells, the temporal dimension, both in
> the exciting signal and in the response, is as important as
> the intensity."
>
> Berger and Liaw created computer chip neurons that closely
> mimic the signaling behavior of living cells, those of the
> hippocampus, the brain structure involved in associative
> learning. "You might say, we let our cells hear the music,"
> Berger said. Berger and Liaw's computer chip neurons were
> combined into a small neural network using standard
> architecture. While all the neurons shared the same
> hippocampus-mimicking general characteristics, each was
> randomly given slightly different individual characteristics,
> in much the same way that individual hippocampus neurons
> would have slightly different individual characteristics. The
> network created was then trained, using a procedure as
> unique as the neurons , again taken from the biological
> model, a learning rule that allows the temporal properties of
> the net connections to change.
>
> The USC research was funded by the Office of Naval
> Research; the Defense Department's Advanced Research
> Projects Agency; the National Centers for Research
> Resources, and the National Institute of Mental Health. The
> university has applied for a patent on the system and the
> architectural concepts on which it is based.
>
> ###
>
> RealVideo demonstration at:
> http://www.usc.edu/ext-relations/news_service/real/real_video.html
>
>
> Back to EurekAlert!
--
sentience@pobox.com Eliezer S. Yudkowsky
http://pobox.com/~sentience/tmol-faq/meaningoflife.html
Running on BeOS Typing in Dvorak Programming with Patterns
Voting for Libertarians Heading for Singularity There Is A Better Way
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