Researchers developed a fully integrated photonic processor that can perform all the key computations of a deep neural network on a photonic chip, using light. This advance could improve the speed and ...

The deep neural network models that power today’s most demanding machine-learning applications are pushing the limits of traditional electronic computing hardware, according to scientists working on a ...

A project at the University of California, San Diego has developed a transparent neural implant intended to sit on the surface of the brain and record neural activity deeper inside. Published in ...

Fiber-optic technology revolutionized the telecommunications industry and may soon do the same for brain research. A group of researchers from Washington University in St. Louis in both the McKelvey ...

(a) Deep-learning-pre-processing for phase recovery. (b) Deep-learning-in-processing for phase recovery. (c) Deep-learning-post-processing for phase recovery. (d) Deep learning for phase processing.

Stanford engineers debuted a new framework introducing computational tools and self-reflective AI assistants, potentially advancing fields like optical computing and astronomy.

Neural networks are one typical structure on which artificial intelligence can be based. The term neural describes their learning ability, which to some extent mimics the functioning of neurons in our ...

A technical paper titled “Training neural networks with end-to-end optical backpropagation” was published by researchers at University of Oxford and Lumai Ltd. “Optics is an exciting route for the ...

During my first semester as a computer science graduate student at Princeton, I took COS 402: Artificial Intelligence. Toward the end of the semester, there was a lecture about neural networks. This ...

The deep neural network models that power today’s most demanding machine-learning applications have grown so large and complex that they are pushing the limits of traditional electronic computing ...