From 2300 transistors on an Intel 4004 chip to the forest of 2 ­billion transistors residing on the ­latest generation of Intel microprocessors, Gordon E. Moore’s famous law has guided the steady shrinking of ­transistors and their consequent ­density on ­microchips. That doubling about every two years is how we went from Pong in 1972 to the astonishing real-time ­rendering of hair that moves according to the laws of real-world physics in the video game Heavenly Sword in 2007.

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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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