Non-Living Intelligence

The Basics

Researchers at the University of Michigan's Department of Physics are exploring innovative materials that can "learn" from their environment, a concept they refer to as Non-Living Intelligence. This involves creating materials that, while not alive, can adapt and respond to stimuli in ways similar to learning processes in living organisms.

A key development in this field is the creation of mechanical neural networks. These are structures composed of interconnected components arranged in patterns, such as lattices of triangles. By applying specific forces or pressures, these networks can be trained to perform certain tasks or solve problems, akin to how artificial neural networks in computers are trained.

Not So Simple Machines: Cracking the Code for Materials That Can Learn

University of Michigan Physics Postdoctoral Researcher Shuaifeng Li and Professor Xiaoming Mao devised an algorithm that provides a mathematical framework for how learning works in lattices called mechanical neural networks.

The research team, including Postdoctoral Researcher Shuaifeng Li and Professor Xiaoming Mao, has developed an algorithm that provides a mathematical framework for how learning works in these mechanical systems. This framework allows the materials to be programmed to exhibit desired behaviors when subjected to external forces.

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 University of Michigan 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.

The researchers have shown how that algorithm can be used to “train” materials to solve problems, such as identifying different species of iris plants. One day, these materials could create structures capable of solving even more advanced problems—such as airplane wings that optimize their shape for different wind conditions—without humans or computers stepping in to help.

That future is a ways off, but insights from The University of Michigan’s new research could also provide more immediate inspiration for researchers outside the field, said Li, a postdoctoral researcher.

The algorithm is based on an approach called backpropagation, which has been used to enable learning in both digital and optical systems. Because of the algorithm’s apparent indifference to how information is carried, it could also help open new avenues of exploration into how living systems learn, the researchers said.

“We’re seeing the success of backpropagation theory in many physical systems,” Li said. “I think this might also help biologists understand how biological neural networks in humans and other species work.”

This work is part of a broader initiative at the University of Michigan to design materials that combine order and disorder at various scales, aiming to establish simple chemical pathways to resource-conscious, multifunctional, and intelligent materials for a sustainable and healthy future.

In summary, the University of Michigan's Department of Physics is pioneering the development of non-living materials that can learn and adapt, potentially leading to new technologies that mimic the adaptive behaviors of living systems without being alive themselves.

Cracking The Code For Materials That Can Learn

Keith Cowing. Astrobiology. December 23, 2024.

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

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Episode 4010 - Feb 3 - Tiếng Anh - Trí thông minh không sống - Vina Technology at AI time