Brainlike Computers, Learning From Experience
Brainlike Computers, Learning From Experience
By JOHN MARKOFF
Published: December 28, 2013
PALO ALTO, Calif. — Computers have entered the age when
they are able to learn from their own mistakes, a development that is about to
turn the digital world on its head.
The first commercial version of the new kind of computer
chip is scheduled to be released in 2014. Not only can it automate tasks that
now require painstaking programming — for example, moving a robot’s arm
smoothly and efficiently — but it can also sidestep and even tolerate errors,
potentially making the term “computer crash” obsolete.
The new computing approach, already in use by some large
technology companies, is based on the biological nervous system, specifically
on how neurons react to stimuli and connect with other neurons to interpret
information. It allows computers to absorb new information while carrying out a
task, and adjust what they do based on the changing signals.
In coming years, the approach will make possible a new
generation of artificial intelligence systems that will perform some functions
that humans do with ease: see, speak, listen, navigate, manipulate and control.
That can hold enormous consequences for tasks like facial and speech
recognition, navigation and planning, which are still in elementary stages and
rely heavily on human programming.
Designers say the computing style can clear the way for
robots that can safely walk and drive in the physical world, though a thinking
or conscious computer, a staple of science fiction, is still far off on the
digital horizon.
“We’re moving from engineering computing systems to something
that has many of the characteristics of biological computing,” said Larry
Smarr, an astrophysicist who directs the California Institute for
Telecommunications and Information Technology, one of many research centers
devoted to developing these new kinds of computer circuits.
Conventional computers are limited by what they have been
programmed to do. Computer vision systems, for example, only “recognize”
objects that can be identified by the statistics-oriented algorithms programmed
into them. An algorithm is like a recipe, a set of step-by-step instructions to
perform a calculation.
But last year, Google researchers were able to get a
machine-learning algorithm, known as a neural network, to perform an
identification task without supervision. The network scanned a database of 10
million images, and in doing so trained itself to recognize cats.
In June, the company said it had used those neural
network techniques to develop a new search service to help customers find
specific photos more accurately.
The new approach, used in both hardware and software, is
being driven by the explosion of scientific knowledge about the brain. Kwabena
Boahen, a computer scientist who leads Stanford’s Brains in Silicon research
program, said that is also its limitation, as scientists are far from fully
understanding how brains function.
“We have no clue,” he said. “I’m an engineer, and I build
things. There are these highfalutin theories, but give me one that will let me
build something.”
Until now, the design of computers was dictated by ideas
originated by the mathematician John von Neumann about 65 years ago.
Microprocessors perform operations at lightning speed, following instructions
programmed using long strings of 1s and 0s. They generally store that
information separately in what is known, colloquially, as memory, either in the
processor itself, in adjacent storage chips or in higher capacity magnetic disk
drives.
The data — for instance, temperatures for a climate model
or letters for word processing — are shuttled in and out of the processor’s
short-term memory while the computer carries out the programmed action. The
result is then moved to its main memory.
The new processors consist of electronic components that
can be connected by wires that mimic biological synapses. Because they are
based on large groups of neuron-like elements, they are known as neuromorphic
processors, a term credited to the California Institute of Technology physicist
Carver Mead, who pioneered the concept in the late 1980s.
They are not “programmed.” Rather the connections between
the circuits are “weighted” according to correlations in data that the
processor has already “learned.” Those weights are then altered as data flows
in to the chip, causing them to change their values and to “spike.” That
generates a signal that travels to other components and, in reaction, changes
the neural network, in essence programming the next actions much the same way
that information alters human thoughts and actions.
“Instead of bringing data to computation as we do today,
we can now bring computation to data,” said Dharmendra Modha, an I.B.M.
computer scientist who leads the company’s cognitive computing research effort.
“Sensors become the computer, and it opens up a new way to use computer chips
that can be everywhere.”
The new computers, which are still based on silicon
chips, will not replace today’s computers, but will augment them, at least for
now. Many computer designers see them as coprocessors, meaning they can work in
tandem with other circuits that can be embedded in smartphones and in the giant
centralized computers that make up the cloud. Modern computers already consist
of a variety of coprocessors that perform specialized tasks, like producing
graphics on your cellphone and converting visual, audio and other data for your
laptop.
One great advantage of the new approach is its ability to
tolerate glitches. Traditional computers are precise, but they cannot work
around the failure of even a single transistor. With the biological designs,
the algorithms are ever changing, allowing the system to continuously adapt and
work around failures to complete tasks.
Traditional computers are also remarkably energy
inefficient, especially when compared to actual brains, which the new neurons
are built to mimic.
I.B.M. announced last year that it had built a
supercomputer simulation of the brain that encompassed roughly 10 billion
neurons — more than 10 percent of a human brain. It ran about 1,500 times more
slowly than an actual brain. Further, it required several megawatts of power,
compared with just 20 watts of power used by the biological brain.
Running the program, known as Compass, which attempts to
simulate a brain, at the speed of a human brain would require a flow of
electricity in a conventional computer that is equivalent to what is needed to
power both San Francisco and New York, Dr. Modha said.
I.B.M. and Qualcomm, as well as the Stanford research
team, have already designed neuromorphic processors, and Qualcomm has said that
it is coming out in 2014 with a commercial version, which is expected to be
used largely for further development. Moreover, many universities are now
focused on this new style of computing. This fall the National Science
Foundation financed the Center for Brains, Minds and Machines, a new research
center based at the Massachusetts Institute of Technology, with Harvard and
Cornell.
The largest class on campus this fall at Stanford was a
graduate level machine-learning course covering both statistical and biological
approaches, taught by the computer scientist Andrew Ng. More than 760 students
enrolled. “That reflects the zeitgeist,” said Terry Sejnowski, a computational neuroscientist
at the Salk Institute, who pioneered early biologically inspired algorithms.
“Everyone knows there is something big happening, and they’re trying find out
what it is.”
A version of this article appears in print on December
29, 2013, on page A1 of the New York edition with the headline: Brainlike
Computers, Learning From Experience.
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