While there are lots of things that artificial intelligence can't do yet--science being one of them--neural networks are proving themselves increasingly adept at a huge variety of pattern recognition tasks. These tasks can range anywhere from recognizing specific faces in photos to identifying specific patterns of particle decays in physics. Right now, neural networks are typically run on regular computers. Unfortunately, those networks are a poor architectural match; neurons combine both memory and calculations into a single unit, while our computers keep those functions separate. For this reason, some companies are exploring dedicated neural network chips.

It's one thing to create computers that behave like brains, but it's something else to make them perform as well as brains. Conventional circuitry can only operate so quickly as part of a neural network, even if it's sometimes much more powerful than standard computers. However, Princeton researchers might have smashed that barrier: they've built what they say is the first photonic neural network. The system mimics the brain with "neurons" that are really light waveguides cut into silicon substrates. As each of those nodes operates in a specific wavelength, you can make calculations by summing up the total power of the light as it's fed into a laser -- and the laser completes the circuit by sending light back to the nodes.

Computers could not only act like brains, but respond as quickly.

It's one thing to create computers that behave like brains, but it's something else to make them perform as well as brains. Conventional circuitry can only operate so quickly as part of a neural network, even if it's sometimes much more powerful than standard computers. However, Princeton researchers might have smashed that barrier: they've built what they say is the first photonic neural network. The system mimics the brain with "neurons" that are really light waveguides cut into silicon substrates. As each of those nodes operates in a specific wavelength, you can make calculations by summing up the total power of the light as it's fed into a laser -- and the laser completes the circuit by sending light back to the nodes. The result is a machine that can calculate a differential math equation 1,960 times faster than a typical processor.