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Hawk-Eye in the Crosshairs at Wimbledon Again

Cardiff University researchers question how the technology is used; inventor pushes back

4 min read

23 June 2008--When Roger Federer and Rafael Nadal took to Centre Court for the Wimbledon men's singles final in July 2007, the last thing they expected was a controversial line call. The tournament organizers had introduced Hawk-Eye, an automated line-call system, which its makers claim can decide whether a ball is in or out of play with an average accuracy of 3.6 millimeters, or about the width of the fuzz on the ball.

In the fourth set, Nadal asked for Hawk-Eye's judgment on a shot that looked to all and sundry as if it had landed beyond the baseline and was out. But Hawk-Eye said it had hit the line and called it in by a single millimeter. That gave Nadal the point, which he went on to convert into a three games to nil lead in the set. It was an angry Federer, however, who went on to win the match and his fifth consecutive Wimbledon title.

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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
DarkBlue1

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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