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Researchers have created protonic programmable resistors — the building blocks of analog deep learning systems — that can process data 1 million times faster than the synapses in the human brain. These ultrafast, low-energy resistors could enable analog deep learning systems that can train new and more powerful neural networks rapidly, which could then be used for novel applications in areas like self-driving cars, fraud detection, and health care.