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The beginning of the end of the 'computer crash'
Dr Kwabena Boahen of Stanford University holding a biologically inspired processor, which was built using a new computing system based on the biological nervous system.
PALO ALTO (California) - 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 next year. Not only can it automate tasks that now require painstaking programming - for example, moving a robot's 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 safety 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 Dr Larry Smarr, an astrophysicist who directs the California Institute for Telecommunications and Information Technology, one of many research centres 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 "recognise" 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 recognise 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 hardware and software, is being driven by the explosion of scientific knowledge about the brain.
Dr Kwabena Boahen, a computer scientist who leads Stanford University's Brains in Silicon research programme, said that was 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."
NEW YORK TIMES
Dr Kwabena Boahen of Stanford University holding a biologically inspired processor, which was built using a new computing system based on the biological nervous system.
PALO ALTO (California) - 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 next year. Not only can it automate tasks that now require painstaking programming - for example, moving a robot's 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 safety 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 Dr Larry Smarr, an astrophysicist who directs the California Institute for Telecommunications and Information Technology, one of many research centres 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 "recognise" 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 recognise 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 hardware and software, is being driven by the explosion of scientific knowledge about the brain.
Dr Kwabena Boahen, a computer scientist who leads Stanford University's Brains in Silicon research programme, said that was 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."
NEW YORK TIMES
