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Deep learning is now being used to translate between languages, predict how proteins fold, analyze medical scans, and play games as complex as Go, to name just a few applications of a technique that is now becoming pervasive. Success in those and other realms has brought this machine-learning technique from obscurity in the early 2000s to dominance today. • Although deep learning's rise to fame is relatively recent, its origins are not. In 1958, back when mainframe computers filled rooms and ran on vacuum tubes, knowledge of the interconnections between neurons in the brain inspired Frank Rosenblatt at Cornell to design the first artificial neural network, which he presciently described as a “pattern-recognizing device.” But Rosenblatt's ambitions outpaced the capabilities of his era—and he knew it. Even his inaugural paper was forced to acknowledge the voracious appetite of neural networks for computational power, bemoaning that “as the number of connections in the network increases…the burden on a conventional digital computer soon becomes excessive.” • Fortunately for such artificial neural networks—later rechristened “deep learning” when they included extra layers of neurons—decades of Moore's Law and other improvements in computer hardware yielded a roughly 10-million-fold increase in the number of computations that a computer could do in a second. So when researchers returned to deep learning in the late 2000s, they wielded tools equal to the challenge. [ABSTRACT FROM AUTHOR]

Consumer technology is often a story of revolutionary leaps followed by a descent into familiarity. The first computers advanced so quickly that new models went obsolete while they were still on store shelves. Today, any US $500 laptop will be relevant for a decade. A similar story can be told of smartphones, TVs, even cars. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

For Yung-Hsiang Lu, improving the energy efficiency of computer technology to provide real-world benefits has been a lifelong focus. [ABSTRACT FROM AUTHOR]

On 12 August 1981, at the Waldorf Astoria Hotel in midtown Manhattan, IBM unveiled its entrant into the nascent personal computer market: the IBM PC. With that, the preeminent U.S. computer maker launched another revolution in computing. Soon the world began embracing little computers by the millions, with IBM dominating those sales. Other companies, including Apple and Tandy Corp., were already making personal computers, but no other machine carried the revered IBM name. IBM's essential contributions were to position the technology as suitable for wide use and to set a technical standard. Rivals were compelled to meet a demand that they had all grossly underestimated. As such, IBM had a greater impact on the personal computer's acceptance than did Apple, Compaq, Dell, and even Microsoft. And yet IBM's lead in this new market would last only a few years, and the eclipse of the IBM PC came to mirror the decline of Big Blue. [ABSTRACT FROM AUTHOR]