The saying of philosopher René Descartes of what makes humans unique is beginning to sound hollow. 'I think—therefore soon I am obsolete' seems more appropriate. When a computer routinely beats us at chess and we can barel
Lawrence Livermore National Laboratory (LLNL) has purchased IBM Research’s supercomputing platform for deep-learning inference, based on 16 IBM TrueNorth neurosynaptic computer chips, to explore deep learning algorithms.
IBM says the scalable platform processing power is the equivalent of 16 million artificial “neurons” and 4 billion “synapses.” The brain-like neural-network design of the IBM Neuromorphic System can process complex cognitive tasks such as pattern recognition and integrated sensory processing far more efficiently than conventional chips, says IBM.
The technology represents a fundamental departure from computer design that has been prevalent for the past 70 years and could be incorporated in next-generation supercomputers able to perform at exascale speeds — 50 times faster than today’s most advanced petaflop (quadrillion floating point operations per second) systems.
Beyond that, the ultimate goal of MICrONS is to implement the algorithms and learning rules that scientists decipher from the brain to advance the field of artificial intelligence by improving artificial neural networks algorithms — for speech recognition, recognizing faces, and helping analyze big data for biomedical research, for example.
“In many ways, these artificial neural networks are still primitive compared to biological networks of neurons and do not learn the way real brains do,” says Andreas Tolias, Ph.D., Associate Professor in the Department of Neuroscience at Baylor College of Medicine. “Our goal is to fill this gap and apply the algorithms of the brain to engineer novel artificial network architectures.”
It was hailed as the most significant test of machine intelligence since Deep Blue defeated Garry Kasparov in chess nearly 20 years ago. Google’s AlphaGo has won two of the first three games against grandmaster Lee Sedol in a Go tournament, showing the dramatic extent to which AI has improved over the years. That fateful day when machines finally become smarter than humans has never appeared closer—yet we seem no closer in grasping the implications of this epochal event.
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