Inside a soundproofed crate sits one of the world’s worst neural networks. After being presented with an image of the number 6, it pauses for a moment before identifying the digit: zero. Peter McMahon, the physicist-engineer at Cornell University who led the development of the network, defends it with a sheepish smile, pointing out that the handwritten number looks sloppy. Logan Wright, a postdoc visiting McMahon’s lab from NTT Research, assures me that the device usually gets the answer right, but acknowledges that mistakes are common. “It’s just this bad,” he said.
Despite the underwhelming performance, this neural network is a groundbreaker. The researchers tip the crate over, revealing not a computer chip but a microphone angled toward a titanium plate that’s bolted to a speaker. Other neural networks operate in the digital world of 0s and 1s, but this device runs on sound. When Wright cues up a new image of a digit, its pixels get converted into audio and a faint chattering fills the lab as the speaker shakes the plate. Metallic reverberations do the “reading” rather than software running on silicon. That the device often succeeds beggars belief, even to its designers.
“Whatever the function of the shaking metal is, it shouldn’t have anything to do with classifying a handwritten digit,” said McMahon.
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