IBM supercomputer simulates 530 billion neurons and a whole lot of synapses

IBM supercomputer simulates 530 billion neurons and a whole lot of synapses

IBM Research, in collaboration with DARPA's Systems of Neuromorphic Adaptive Plastic Scalable Electronics (SyNAPSE) program, has reached another brain simulation milestone. Powered by its new TrueNorth system on the world's second fastest supercomputer, IBM was capable of crafting a 2.084 billion neurosynaptic cores and 100 trillion synapses -- all at a speed "only" 1,542 times slower than real life. The abstract explains that this isn't a biologically realistic simulation of the human brain, but rather mathematically abstracted -- and little more dour -- versions steered towards maximizing function and minimizing cost. DARPA's SyNAPSE project aims to tie together supercomputing, neuroscience and neurotech for a future cognitive computing architecture far beyond what's running behind your PC screen at the moment. Want to know more? We've included IBM's video explanation of cognitive computing after the break.

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Via: Kurzweil AI

Source: SC12

Google simulates the human brain with 1000 machines, 16000 cores and a love of cats

Google simulates the human brain with 1000 machines, 16000 cores and a love of cats

Don't tell Google, but its latest X lab project is something performed by the great internet public every day. For free. Mountain View's secret lab stitched together 1,000 computers totaling 16,000 cores to form a neural network with over 1 billion connections, and sent it to YouTube looking for cats. Unlike the popular human time-sink, this was all in the name of science: specifically, simulating the human brain. The neural machine was presented with 10 million images taken from random videos, and went about teaching itself what our feline friends look like. Unlike similar experiments, where some manual guidance and supervision is involved, Google's pseudo-brain was given no such assistance.

It wasn't just about cats, of course -- the broader aim was to see whether computers can learn face detection without labeled images. After studying the large set of image-data, the cluster revealed that indeed it could, in addition to being able to develop concepts for human body parts and -- of course -- cats. Overall, there was 15.8 percent accuracy in recognizing 20,000 object categories, which the researchers claim is a 70 percent jump over previous studies. Full details of the hows and whys will be presented at a forthcoming conference in Edinburgh.

Google simulates the human brain with 1000 machines, 16000 cores and a love of cats originally appeared on Engadget on Tue, 26 Jun 2012 07:22:00 EDT. Please see our terms for use of feeds.

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