This article explores groundbreaking research by physicists at the University of Michigan on mechanical neural networks (MNNs)—materials capable of autonomous learning and problem-solving. Using an algorithm inspired by backpropagation, a staple in machine learning, the team demonstrated how 3D-printed rubber lattices could be trained to perform tasks, such as classifying plant species based on leaf and petal features. These materials adapt by altering their internal stiffness, mimicking neural network processes. Potential applications include advanced systems like self-optimizing airplane wings. Additionally, the algorithm could bridge understanding between artificial and biological neural networks, offering insights into learning across diverse systems.
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