Remembrance of Everything Past

More information could enhance cognition and lead to better decision making—or drown us in a deluge of data points

3 min read
Remembrance of Everything Past
Illustration: Greg Mably

The pursuit of enhanced cognition—from sharper recall to more lucid reasoning—is now the greatest animating impulse behind innovative computer engineering. Across the world, clever designers are leveraging ever-expanding storage, processing power, and communications networks to build personalized encyclopedias that document all aspects of an individual’s inner and outer lives. If the engineers have their way, every idea, memory, and feeling—the recorded consciousness of a single lifetime—will be stored in the cloud.

“What’s in this for me?” you may ask. For starters, you might very well benefit from retracing your past steps at key junctures of your personal or professional life—perhaps experiencing the breakthrough insight that eluded you the first time around but that can help you the next time you arrive at a similar juncture. Even after you die, your digital files would confer a new kind of immortality that others could inherit, permitting them to traverse the associative trails that made up your life’s work.

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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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