Saturday, October 2, 2021

g-f(2)534 THE BIG PICTURE OF THE DIGITAL AGE (10/2/2021), AAAS, Learning curve

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"g-f" fishing of golden knowledge (GK) of the fabulous treasure of the digital ageArtificial Intelligence, Neuromorphic Computing (10/2/2021)  g-f(2)426 


New brain-inspired chips could provide the smarts for autonomous robots and self-driving cars, AAAS

    • Neuromorphic computing “is going to be a rock star,” says Thomas Cleland, a neurobiologist at Cornell University. “It won’t do everything better. But it will completely own a fraction of the field of computing.”
    • This week, Intel released the second generation of its neuromorphic chip, Loihi. It packs in 1 million artificial neurons, six times more than its predecessor, which connect to one another through 120 million synapses. Other companies, such as BrainChip and SynSense, have also recently rolled out new neuromorphic hardware, with chips that speed tasks such as computer vision and audio processing.

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                The hope for smarter computers in an upstart technology called neuromorphic computing, AAAS

                • Garrett Kenyon, a physicist at Los Alamos National Laboratory, calls artificial intelligence (AI) “overhyped.”
                • Kenyon and many others see hope for smarter computers in an upstart technology called neuromorphic computing. In place of standard computing architecture, which processes information linearly, neuromorphic chips emulate the way our brains process information, with myriad digital neurons working in parallel to send electrical impulses, or spikes, to networks of other neurons. Each silicon neuron fires when it receives enough spikes, passing along its excitation to other neurons, and the system learns by reinforcing connections that fire regularly while paring away those that don’t. The approach excels at spotting patterns in large amounts of noisy data, which can speed learning. Because information processing takes place throughout the network of neurons, neuromorphic chips also require far less shuttling of data between memory and processing circuits, boosting speed and energy efficiency.

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                Opportunity, Neuromorphic chips can match the capabilities of some of the most advanced AI programs on the market, AAAS

                • Two groups have already shown neuromorphic chips can match the capabilities of some of the most advanced AI programs on the market. Today’s workhorse AI software relies on a deep learning algorithm known as a backpropagation neural network (BPNN), which enables AI systems to learn from their mistakes as they are trained. In a preprint posted on arXiv in August, Andrew Sornborger, a physicist at Los Alamos, and colleagues reported programming the first-generation Loihi to carry out backpropagation. The chip learned to interpret a commonly used visual data set of handwritten numerals as quickly as conventional BPNNs, while drawing just 1/100 as much power.
                • Likewise, in unpublished work, Wolfgang Maass, a computer scientist at the Graz University of Technology, and his colleagues have developed a neuromorphic system that carries out BPNN learning with 1/1000 as much power as standard GPU-driven AI.
                • KENYON SAYS that having benefited from an understanding of biology, neuromorphic processors may soon return the favor, helping neuroscientists better understand the evolution and workings of the brain. Standard AI systems aren’t much help, because they tend to be black boxes that don’t reveal how their learning takes place. But Loihi and similar chips are a better model because they behave like biological networks of neurons. Researchers can track the firing patterns in the silicon-based systems to reveal how they learn to process visual, auditory, and olfactory information—and hopefully gain new insights into how biology does similar jobs.

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