Jan 28, 2018 08:47 PM
https://www.nature.com/articles/d41586-018-01290-0
EXCERPT: Superconducting computing chips modelled after neurons can process information faster and more efficiently than the human brain. [...] Artificial intelligence software has increasingly begun to imitate the brain. [...] But because conventional computer hardware was not designed to run brain-like algorithms, these machine-learning tasks require orders of magnitude more computing power than the human brain does. “There must be a better way to do this, because nature has figured out a better way to do this,” says Michael Schneider [...] a co-author of the study.
NIST is one of a handful of groups trying to develop ‘neuromorphic’ hardware that mimics the human brain in the hope that it will run brain-like software more efficiently. In conventional electronic systems, transistors process information at regular intervals and in precise amounts — either 1 or 0 bits. But neuromorphic devices can accumulate small amounts of information from multiple sources, alter it to produce a different type of signal and fire a burst of electricity only when needed — just as biological neurons do. As a result, neuromorphic devices require less energy to run...
MORE: https://www.nature.com/articles/d41586-018-01290-0
EXCERPT: Superconducting computing chips modelled after neurons can process information faster and more efficiently than the human brain. [...] Artificial intelligence software has increasingly begun to imitate the brain. [...] But because conventional computer hardware was not designed to run brain-like algorithms, these machine-learning tasks require orders of magnitude more computing power than the human brain does. “There must be a better way to do this, because nature has figured out a better way to do this,” says Michael Schneider [...] a co-author of the study.
NIST is one of a handful of groups trying to develop ‘neuromorphic’ hardware that mimics the human brain in the hope that it will run brain-like software more efficiently. In conventional electronic systems, transistors process information at regular intervals and in precise amounts — either 1 or 0 bits. But neuromorphic devices can accumulate small amounts of information from multiple sources, alter it to produce a different type of signal and fire a burst of electricity only when needed — just as biological neurons do. As a result, neuromorphic devices require less energy to run...
MORE: https://www.nature.com/articles/d41586-018-01290-0