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TMCNet:  Brainlike computers to learn from experience [Virginian - Pilot]

[December 29, 2013]

Brainlike computers to learn from experience [Virginian - Pilot]

(Virginian - Pilot Via Acquire Media NewsEdge) By John Markoff The New York Times 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 also can 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 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 far off on the digital horizon.

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 neuronlike elements, they are known as neuromorphic processors.

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." (c) 2013 ProQuest Information and Learning Company; All Rights Reserved.

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