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Now You Too Can Buy Cloud-Based Deep Learning

Cloud-computing services deliver AI to the rest of us

4 min read
Illustration: iStockphoto
Illustration: iStockphoto

Facebook’s deep-learningartificial intelligence systems have learned to recognize your friends in your photos, and Google’s AI has learned to anticipate what you’ll be searching for. But there’s no need to feel left out, even if your company’s computers haven’t learned much lately.

A growing number of tech giants and startups have begun offering machine learning as a cloud service. That means other companies and startups do not need to develop their own specialized hardware or software to apply deep ­learning—the high-powered version du jour of machine learning—to their specific business needs.

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Quantum Error Correction: Time to Make It Work

If technologists can’t perfect it, quantum computers will never be big

13 min read
Quantum Error Correction: Time to Make It Work
Chad Hagen
Blue

Dates chiseled into an ancient tombstone have more in common with the data in your phone or laptop than you may realize. They both involve conventional, classical information, carried by hardware that is relatively immune to errors. The situation inside a quantum computer is far different: The information itself has its own idiosyncratic properties, and compared with standard digital microelectronics, state-of-the-art quantum-computer hardware is more than a billion trillion times as likely to suffer a fault. This tremendous susceptibility to errors is the single biggest problem holding back quantum computing from realizing its great promise.

Fortunately, an approach known as quantum error correction (QEC) can remedy this problem, at least in principle. A mature body of theory built up over the past quarter century now provides a solid theoretical foundation, and experimentalists have demonstrated dozens of proof-of-principle examples of QEC. But these experiments still have not reached the level of quality and sophistication needed to reduce the overall error rate in a system.

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