Kwabena Boahen, right, holding a biologically inspired processor attached to a robotic arm, with student Samir Menon in a Stanford University laboratory in Palo Alto, Calif., Dec. 19, 2013. The new computing approach is based on the biological nervous system, specifically on how neurons react to stimuli and connect with other neurons to interpret information.
Erin Lubin, New York Times
Advanced computer systems can see, speak and listen
- Article by: John Markoff
- New York Times
- December 28, 2013 - 7:43 PM
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, although 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 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 techniques to develop a new search service to help customers find specific photos more accurately.
The new approach, used in 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 was also its limitation, as scientists are far from 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.”
Microprocessors now 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 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.
They are not “programmed.” The connections between the circuits are “weighted” according to correlations in data that the processor has “learned.” Those weights are then altered as data flows into the chip, causing them to change their values and to “spike.”
That generates a signal that travels to other components and 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 IBM 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.”
IBM and Qualcomm, as well as the Stanford research team, already have designed neuromorphic processors. The largest class at Stanford this fall was a machine-learning course covering statistical and biological approaches. 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 to find out what it is.”
© 2014 Star Tribune