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Picture a person reading these words on a laptop in a coffee shop. The machine made of metal, plastic, and silicon consumes about 50 watts of power as it translates bits of information—a long string of 1s and 0s—into a pattern of dots on a screen. Meanwhile, inside that person’s skull, a gooey clump of proteins, salt, and water uses a fraction of that power not only to recognize those patterns as letters, words, and sentences but to recognize the song playing on the radio.

Computers are incredibly inefficient at lots of tasks that are easy for even the simplest brains, such as recognizing images and navigating in unfamiliar spaces. Machines found in research labs or vast data centers can perform such tasks, but they are huge and energy-hungry, and they need specialized programming. Google recently made headlines with software that can reliably recognize cats and human faces in video clips, but this achievement required no fewer than 16,000 powerful processors.

A new breed of computer chips that operate more like the brain may be about to narrow the gulf between artificial and natural computation—between circuits that crunch through logical operations at blistering speed and a mechanism honed by evolution to process and act on sensory input from the real world. Advances in neuroscience and chip technology have made it practical to build devices that, on a small scale at least, process data the way a mammalian brain does. These “neuromorphic” chips may be the missing piece of many promising but unfinished projects in artificial intelligence, such as cars that drive themselves reliably in all conditions, and smartphones that act as competent conversational assistants.

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