AI breakthrough: effects of "edge of chaos" allows on nanowires discovered

Theories from some of the branches of neuroscience include a suggestion that the human brain performs at its optimal state when it reaches what they call "edge of chaos".
By: HyperCharge Technology
 
RAMAT GAN, Israel - Sept. 27, 2021 - PRLog -- With this theory, scientists from the University of Sydney along with Japan's National Institute of Material Science performed a research using nanowires of an artificial network to be stimulated electrically to respond the same way a brain responds when forced to a state of "edge of chaos".

This international team, which was led by Joel Hochstetter partnered with Professor Tomonobu Nakayama and Professor Zdenka Kuncuc, was successful in the research of inducing an optimal state for an artificial intelligence using the said method. This breakthrough is a big leap when taking into account the possibilities it would bring to the development of artificial intelligence in the future.

Their paper was released last June 29 in Nature Communications, and they've included all their findings on the research. Based on the theories, the human mind could be pushed it its best possible state only when induced to a critical state. Thought processing is multiplied and sped up. In order to learn more about this possibility, the team of researches even used 10 micrometers long and less than 500 nanometers in diameter, arranged in random order on a 2D plane.

The paper stated the finding the critical state was the arduous process, as using weak stimulation of electrical signal wold not improve the thought process if the AI and will continue to provide generic and orderly results that are not complex enough to bring any value, while the pushing it too hard would be too chaotic for the AI to perform any prediction or calculations.

The design they used for the revolves around the fact that the nanowires are able to keep an imprint, serving as the memory of the brain-like design. And as it reaches its critical state, the performance of the AI improves and comes up with what they call learning ability. The only downside is the large energy consumption needed to keep the AI in such state, which they focused to resolve on their future research.

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