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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]

Painful exercises in basic arithmetic are a vivid part of our elementary school memories. A multiplication like 3,752 ? 6,901 carried out with just pencil and paper for assistance may well take up to a minute. Of course, today, with a cellphone always at hand, we can quickly check that the result of our little exercise is 25,892,552. Indeed, the processors in modern cellphones can together carry out more than 100 billion such operations per second. What's more, the chips consume just a few watts of power, making them vastly more efficient than our slow brains, which consume about 20 watts and need significantly more time to achieve the same result. Of course, the brain didn't evolve to perform arithmetic. So it does that rather badly. But it excels at processing a continuous stream of information from our surroundings. And it acts on that information-sometimes far more rapidly than we're aware of. No matter how much energy a conventional computer consumes, it will struggle with feats the brain finds easy, such as understanding language and running up a flight of stairs. [ABSTRACT FROM AUTHOR]

BEFORE SILICON GOT ITS OWN VALLEY, THIS mild-mannered element had to vanquish many other contenders to prove itself the premier semiconductor technology. It did so in the 1950s and 1960s. Today, carbon is poised at a similar crossroads, with carbon-based technologies on the verge of transforming computing and boosting batterystorage capacities. Already, researchers have used these technologies to demonstrate paper-thin batteries, unbreakable touch screens, and terabit-speed wireless communications And on the farther horizon they envision such carbon-enabled wonders as space elevators, filters that can make seawater drinkable, bionic organs, and transplantable neurons. Whatever miracles emerge from Carbon Valley, its carbon-techtitans will surely think fondly upon their field's founding mother, Mildred Dresselhaus. This MIT professor of physics and engineering has, since the early 1960s, been laying the groundwork for networks of nanometerscale carbon sheets, lattices, wires, and switches. Future engineers will turn these things, fabricated from carbon-based materials such as graphene, into the systems that will carry computing into its next era. [ABSTRACT FROM PUBLISHER]

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]

Paper punch cards, each representing a line of code or data, were how programmers got information into computers from the 1950s on. [ABSTRACT FROM PUBLISHER]