Panel ponders many-core ICs tripping 'the singularity'
3/28/2012 12:09 PM EDT
Computers already smarter than humans -- at specific things
Imagination's Oliver added that there are many examples in human brains of linkages to the body that help drive the behavior. "Can you have intelligence without a body?" he asked adding that if we wish to see the advent of the singularity perhaps we should look for it in robots.
The audience engaged with the panel arguing on the one hand that megaflops were not what is needed to approach human intelligence and that hardware is the easy part on the other; that the missing element is software.
Another member of the audience asked what is the application for such levels of performance, apart from creating an automaton. Jem Davies, ARM vice president of technology, came back with the response that in specific domains you want computers to do things that humans cannot. Laser eye surgery is now done by a machine, he said, because it is more precise and capable than any human.
This led the panel on to a discussion of the Turing test and whether supercomputers had yet been able to meet it. The test, proposed by Alan Turing, is that if a human judge, when devoid of visual and other cues, cannot tell the difference between talking to another human being and talking to a machine, then the machine is effectively intelligent.
Imagination's Oliver argued that the definition of Artificial Intelligence seemed to change so that it encompassed those things that computers are not yet capable of, something more akin to an Arthur C Clarke definition of magic. As soon as computers do become capable of a function, for example speech recognition, that task gets reclassified as not being part of intelligence.
From the floor it was asked if the known inefficiencies of multicore arrays for many tasks, were a limitation that would prevent the advent of the singularity. Oliver said there is no doubt that parallel processing is the best way to simulate or recreate brain-like thinking. Computers just happen to be good at only a few tasks such as high-speed numerical processing, that humans are not so good at.
ARM's Davies admitted that general purpose GPU type processing tends to favor particular classes of problem such as computational image processing "but we should not be limited by our imagination," he said. He took a build-it-and-they-will-come position. "I don't need to know what the killer application is going to be. Human ingenuity will find a way to use the technology." Intel's Dubey also argued in favor of the hardware approaches we have today. "It is not a system problem. It's a programming model problem." Dubey qualified that by saying in an email that the problem hinges on a better algorithmic understanding of the human brain.