Yichen Shen

There are two basic types of computations involving neural networks. First, the networks must be trained, which usually involves showing them lots of data, causing them to adjust the strength of the connections between their numerous “neurons.” Next, those existing connections are used to make decisions. It’s the difference between learning to drive and driving.

The difference is crucial. If a neural network takes weeks to learn how to recognize images, that’s not necessarily a problem. But if it is driving an autonomous car, it needs to be able to make life-or-death inferences in fractions of a second.

That’s where optical computers come in. Despite decades of research, they’ve never worked that well. It’s harder to manipulate photons than electrons. But for certain types of computations—like those commonly needed when using an existing neural network to make inferences—photons are just the thing.

In 2017, Yichen Shen and Nicholas Harris published a widely cited paper on the use of optical circuits for machine-learning tasks including speech and image recognition. Their design, one review article notes, “represents a truly parallel implementation of one of the most crucial building blocks of neural networks using light, and modern foundries could easily mass-fabricate this type of photonic system.” This means that optical computers on a chip could become a huge business, with one in every device that uses a neural network to make decisions.

Shen and Harris now run competing startups. Shen’s firm, Lightelligence, released a prototype optical AI chip in 2019, and Shen says they have secured over $100 million in funding.