Back Story: What's Your Superpower?

Google engineers debate the best superhuman abilities

2 min read

On a typically cloudless day in Mountain View, Calif., Google engineers are brainstorming feverishly, making emphatic notations on a whiteboard. Arcane tech problem? New business plan? Latest twist in the plot to rule cyberspace? No, these engineers are arguing the relative merits of superhuman powers. The choices? Invisibility versus flight, invisibility versus teleportation, flight versus teleportation, and the power to kill from 200 meters versus the power to move people with your mind.

The engineering mind-set knows no boundaries. Therefore, a traditionally short conversation ("Would you rather have the power of invisibility or the power of flight?") rapidly spins off new and philosophical threads enumerating hidden caveats and analyzing cost-benefit scenarios. Under the black-markered "The power to move you," someone uses a blue marker to replace "you" with "anyone." "What do you mean by move?" asks a red marker. "You made this no fun anymore," complains a blue marker, underscoring that point with a frowning face.

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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
Image of a computer rendering.

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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