Artificial neurons compute faster than the Human Brain

Posted by Peter Rudin on 2. February 2018 in News

Because conventional binary computer hardware was not designed to run brain-like algorithms, machine-learning tasks require orders of magnitude more computing power than the human brain does.

NIST, the US National Institute of Standards and Technology, 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.

Superconducting computing chips modelled after neurons can process information faster and more efficiently than the human brain.

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