Originally published January 26, 2018
Superconducting computing chips modelled after neurons can process information faster and more efficiently than the human brain. That achievement, described in Science Advances on 26 January, is a key benchmark in the development of advanced computing devices designed to mimic biological systems. And it could open the door to more natural machine-learning software, although many hurdles remain before it could be used commercially.
Artificial intelligence software has increasingly begun to imitate the brain. Algorithms such as Google’s automatic image-classification and language-learning programs use networks of artificial neurons to perform complex tasks. 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 physicist at the US National Institute of Standards and Technology (NIST) in Boulder, Colorado, and a co-author of the study.
The article is here.