read more – copyright by singularityhub.com
From AlphaGo’s historic victory against world champion Lee Sedol to DeepStack’s sweeping win against professional poker players, artificial intelligence is clearly on a roll.
Part of the momentum comes from breakthroughs in artificial neural networks, which loosely mimic the multi-layer structure of the human brain. But that’s where the similarity ends. While the brain can hum along on energy only enough to power a light bulb , AlphaGo’s neural network runs on a whopping 1,920 CPUs and 280 GPUs , with a total power consumption of roughly one million watts—50,000 times more than its biological counterpart. Our super-efficient brains run on the energy needed power a single light bulb. Extrapolate those numbers, and it’s easy to see that artificial neural networks have a serious problem—even if scientists design powerfully intelligent machines, they may demand too much energy to be practical for everyday use.
Hardware is no brain yet
Hardware structure is partly to blame. Our computers, with their separate processor and memory units, are simply not wired appropriately to support the type of massively parallel, energy-efficient computing that the brain elegantly performs. Recently, a team from Stanford University and Sandia National Laboratories took a different approach to brain-like computing systems. Rather than simulating a neural network with software, they made a device that behaves like the brain’s synapses—the connection between neurons that processes and stores information—and completely overhauled our traditional idea of computing hardware.
Human-made synapses
The artificial synapse, dubbed the “electrochemical neuromorphic organic device (ENODe),” may one day be used to create chips that perform brain-like computations with minimal energy requirements. Made of flexible, organic material compatible with the brain, it may even lead to better brain-computer interfaces, paving the way for a cyborg future. The team published their findings in Nature Materials . “It’s an entirely new family of devices because this type of architecture has not been shown before. For many key metrics, it also performs better than anything that’s been done before with inorganics,” says study lead author Dr. Alberto Salleo, a material engineer at Stanford […]
read more – copyright by singularityhub.com
From AlphaGo’s historic victory against world champion Lee Sedol to DeepStack’s sweeping win against professional poker players, artificial intelligence is clearly on a roll.
Part of the momentum comes from breakthroughs in artificial neural networks, which loosely mimic the multi-layer structure of the human brain. But that’s where the similarity ends. While the brain can hum along on energy only enough to power a light bulb , AlphaGo’s neural network runs on a whopping 1,920 CPUs and 280 GPUs , with a total power consumption of roughly one million watts—50,000 times more than its biological counterpart. Our super-efficient brains run on the energy needed power a single light bulb. Extrapolate those numbers, and it’s easy to see that artificial neural networks have a serious problem—even if scientists design powerfully intelligent machines, they may demand too much energy to be practical for everyday use.
Hardware is no brain yet
Hardware structure is partly to blame. Our computers, with their separate processor and memory units, are simply not wired appropriately to support the type of massively parallel, energy-efficient computing that the brain elegantly performs. Recently, a team from Stanford University and Sandia National Laboratories took a different approach to brain-like computing systems. Rather than simulating a neural network with software, they made a device that behaves like the brain’s synapses—the connection between neurons that processes and stores information—and completely overhauled our traditional idea of computing hardware.
The artificial synapse, dubbed the “electrochemical neuromorphic organic device (ENODe),” may one day be used to create chips that perform brain-like computations with minimal energy requirements. Made of flexible, organic material compatible with the brain, it may even lead to better brain-computer interfaces, paving the way for a cyborg future. The team published their findings in Nature Materials . “It’s an entirely new family of devices because this type of architecture has not been shown before. For many key metrics, it also performs better than anything that’s been done before with inorganics,” says study lead author Dr. Alberto Salleo, a material engineer at Stanford […]
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