Unfortunately, while these chips can outpace digital computers in conserving energy, they’ve lacked the computational power needed to run a sizable deep neural network.As a result, the new chip can perform as well as digital computers on complex AI tasks like image and speech recognition, and the authors claim it is up to 1,000 times more energy efficient, opening up the possibility for tiny chips to run increasingly complicated algorithms within small devices previously unsuitable for AI like smart watches and phones.For the information coursing through the processor, “it’s kind of like you spend eight hours on the commute, but you do two hours of work,” said Wan, a computer scientist formerly at Stanford University who recently moved to the AI startup Aizip.Indeed, the first such device dates back to at least 1964, when electrical engineers at Stanford discovered they could manipulate certain materials, called metal oxides, to turn their ability to conduct electricity on and off.But unlike with lightning, whose path disappears, the path through the metal oxide remains, meaning it stays conductive indefinitely."