A team led by the BRAINS Center for Brain-Inspired Computing at the University of Twente has demonstrated a new way to make electronic materials adapt in a manner comparable to machine learning. Their study, published in Nature Communications, introduces a method for physical learning that does not require software algorithms such as backpropagation. Backpropagation—the optimization method popularized in the 1980s by Nobel Prize winner Geoffrey Hinton and colleagues—is at the heart of today’s AI revolution.
A new route to optimize AI hardware: Homodyne gradient extraction
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