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). (Courtesy: Rutgers University-New Brunswick)
Topics: Artificial Intelligence, Computer Science, Materials Science, Quantum Mechanics
Quantum materials known as Mott insulators can “learn” to respond to external stimuli in a way that mimics animal behavior, say researchers at Rutgers University in the US. The discovery of behaviors such as habituation and sensitization in these non-living systems could lead to new algorithms for artificial intelligence (AI).
Neuromorphic, or brain-inspired, computers aim to mimic the neural systems of living species at the physical level of neurons (brain nerve cells) and synapses (the connections between neurons). Each of the 100 billion neurons in the human brain, for example, receives electrical inputs from some of its neighbors and then “fires” an electrical output to others when the sum of the inputs exceeds a certain threshold. This process, also known as “spiking”, can be reproduced in nanoscale devices such as spintronic oscillators. As well as being potentially much faster and energy-efficient than conventional computers, devices based on these neuromorphic principles might be able to learn how to perform new tasks without being directly programmed to accomplish them.
Quantum material ‘learns’ like a living creature, Isabelle Dumé, Physics World
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