Modern information technology (IT) relies on division of labour. Photons carry data around the world and electrons process them. But, before optical fibres, electrons did both--and some people hope to complete the transition by having photons process data as well as carrying them. Your browser does not support the audio element. Unlike electrons, photons (which are electrically neutral) can cross each others' paths without interacting, so glass fibres can handle many simultaneous signals in a way that copper wires cannot.

Improved computing power and an exponential increase in data have helped fuel the rapid rise of artificial intelligence. But as AI systems become more sophisticated, they'll need even more computational power to address their needs, which traditional computing hardware most likely won't be able to keep up with. To solve the problem, MIT spinout Lightelligence is developing the next generation of computing hardware. The Lightelligence solution makes use of the silicon fabrication platform used for traditional semiconductor chips, but in a novel way. Rather than building chips that use electricity to carry out computations, Lightelligence develops components powered by light that are low energy and fast, and they might just be the hardware we need to power the AI revolution.

Inside a small laboratory in Boston's seaport district, buried within a jumble of lasers, lenses, mirrors, and a tangle of wiring, is a tiny chip that might be about to have a big impact on the world of artificial intelligence. The lab belongs to Lightelligence, a startup that's developing a radically new kind of AI accelerator chip. Instead of using electrons to carry out the core mathematical computations needed for machine learning, the company's prototype device uses light. In theory, transferring information at the speed of light means such a device could let AI algorithms run hundreds of times faster than today's best AI chips. Since raw computer power makes such a difference in machine learning, this could mean vastly more powerful and capable algorithms.

William Andregg ushers me into the cluttered workshop of his startup Fathom Computing and gently lifts the lid from a bulky black box. Inside, green light glows faintly from a collection of lenses, brackets, and cables that resemble an exploded telescope. It's a prototype computer that processes data using light, not electricity, and it's learning to recognize handwritten digits. In other experiments the device learned to generate sentences in text. Right now, this embryonic optical computer is good, not great: on its best run it read 90 percent of scrawled numbers correctly.