A first physical system to learn nonlinear tasks without a traditional computer processor

Sam Dillavou, a postdoc in the Durian Research Group in the School of Arts & Sciences, built the components of this contrastive local learning network, an analog system that is fast, low-power, scalable, and able to learn nonlinear tasks. Credit: Erica Moser

Scientists run into a lot of tradeoffs trying to build and scale up brain-like systems that can perform machine learning. For instance, artificial neural networks are capable of learning complex language and vision tasks, but the process of training computers to perform these tasks is slow and requires a lot of power.

Training machines to learn digitally but …

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