Human learning can be duplicated in solid matter
Findings may help to advance artificial intelligence

Date:
September 22, 2021
Source:
Rutgers University
Summary:
Researchers have found that learning -- a universal feature of
intelligence in living beings -- can be mimicked in synthetic
matter, a discovery that in turn could inspire new algorithms for
artificial intelligence (AI).



FULL STORY ========================================================================== Rutgers researchers and their collaborators have found that learning --
a universal feature of intelligence in living beings -- can be mimicked
in synthetic matter, a discovery that in turn could inspire new algorithms
for artificial intelligence (AI).


==========================================================================
The study appears in the journal PNAS.

One of the fundamental characteristics of humans is the ability to
continuously learn from and adapt to changing environments. But until
recently, AI has been narrowly focused on emulating human logic. Now, researchers are looking to mimic human cognition in devices that can
learn, remember and make decisions the way a human brain does.

Emulating such features in the solid state could inspire new algorithms
in AI and neuromorphic computing that would have the flexibility to
address uncertainties, contradictions and other aspects of everyday
life. Neuromorphic computing mimics the neural structure and operation
of the human brain, in part, by building artificial nerve systems to
transfer electrical signals that mimic brain signals.

Researchers from Rutgers, Purdue and other institutions studied how the electrical conductivity of nickel oxide, a special type of insulating
material, responded when its environment was changed repeatedly over
various time intervals.

"The goal was to find a material whose electrical conductivity can be
tuned by modulating the concentration of atomic defects with external
stimuli such as oxygen, ozone and light," said Subhasish Mandal, a
postdoctoral associate in the Department of Physics and Astronomy at Rutgers-New Brunswick. "We studied how this material behaves when we
dope the system with oxygen or hydrogen, and most importantly, how the
external stimulation changes the material's electronic properties."
The researchers found that when the gas stimulus changed rapidly,
the material couldn't respond in full. It stayed in an unstable state
in either environment and its response began to decrease. When the
researchers introduced a noxious stimulus such as ozone, the material
began to respond more strongly only to decrease again.

"The most interesting part of our results is that it demonstrates
universal learning characteristics such as habituation and sensitization
that we generally find in living species," Mandal said. "These material characteristics in turn can inspire new algorithms for artificial
intelligence. Much as collective motion of birds or fish have inspired
AI, we believe collective behavior of electrons in a quantum solid can
do the same in the future.

"The growing field of AI requires hardware that can host adaptive memory properties beyond what is used in today's computers," he added. "We find
that nickel oxide insulators, which historically have been restricted
to academic pursuits, might be interesting candidates to be tested in
future for brain- inspired computers and robotics." The study included Distinguished Professor Karin Rabe from Rutgers and researchers from
Purdue University, the University of Georgia and Argonne National
Laboratory.

========================================================================== Story Source: Materials provided by Rutgers_University. Original written
by John Cramer.

Note: Content may be edited for style and length.


========================================================================== Journal Reference:
1. Zhen Zhang, Sandip Mondal, Subhasish Mandal, Jason M. Allred, Neda
Alsadat Aghamiri, Alireza Fali, Zhan Zhang, Hua Zhou, Hui Cao,
Fanny Rodolakis, Jessica L. McChesney, Qi Wang, Yifei Sun, Yohannes
Abate, Kaushik Roy, Karin M. Rabe, Shriram Ramanathan. Neuromorphic
learning with Mott insulator NiO. Proceedings of the National
Academy of Sciences, 2021; 118 (39): e2017239118 DOI:
10.1073/pnas.2017239118 ==========================================================================

Link to news story: https://www.sciencedaily.com/releases/2021/09/210922121828.htm

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