It’s easy to think that machine learning is a completely digital phenomenon, made possible by computers and algorithms that can mimic brain-like behaviors.
But the first machines were analog and now, a small but growing body of research is showing that mechanical systems are capable of learning, too. Physicists at the University of Michigan have provided the latest entry into that field of work.
The U-M team of Shuaifeng Li and Xiaoming Mao devised an algorithm that provides a mathematical framework for how learning works in lattices called mechanical neural networks.
“We’re seeing that materials can learn tasks by themselves and do computation,” Li said.
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