Researchers are hard at work on systems for identifying people based on their physical characteristics. The aim is to do a better job of verifying who you are for situations like crossing national borders or conducting financial transactions, or to ferret out your identity if you refuse to identify yourself to police. There are already huge biometric databases full of fingerprints, face scans, and genetic material. As iris and palm-vein scanners become more widespread, so will the number of database records based on those features.

Last year, Anil Jain, a Michigan State University computer science professor and coauthor of ”A Touch of Money” in the July 2006 issue of IEEE Spectrum , expanded the field of biometrics when he announced he had begun work on an automated tattoo recognition system that will allow authorities to identify people more quickly and accurately based on the ink embedded under their skin. Jain points out that tattoos and other marks cannot uniquely identify a person. But he says that his system will still be valuable because of its ability to help authorities narrow down the list of identities to which they’ll compare a suspect they have in custody.

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The Future of Deep Learning Is Photonic

Computing with light could slash the energy needs of neural networks

10 min read

This computer rendering depicts the pattern on a photonic chip that the author and his colleagues have devised for performing neural-network calculations using light.

Alexander Sludds

Think of the many tasks to which computers are being applied that in the not-so-distant past required human intuition. Computers routinely identify objects in images, transcribe speech, translate between languages, diagnose medical conditions, play complex games, and drive cars.

The technique that has empowered these stunning developments is called deep learning, a term that refers to mathematical models known as artificial neural networks. Deep learning is a subfield of machine learning, a branch of computer science based on fitting complex models to data.

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