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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]

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]

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]

Immediately following the Second World War, electrical engineers grappled with a fundamental but open question: How should electronic digital computers be built? What kind of switch would serve best for logic circuits? And what should be used for main memory? [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]

Artificial intelligence today is much less than it could be, according to Andrew Feldman, CEO and cofounder of AI computer startup Cerebras Systems. [ABSTRACT FROM AUTHOR]

Quantum Computing IS ALL THE RAGE. It seems like hardly a day goes by without some news outlet describing the extraordinary things this technology promises. Most commentators forget, or just gloss over, the fact that people have been working on quantum computing for decades—and without any practical results to show for it. t IBM notes that quantum computers could "provide breakthroughs in many disciplines, including materials and drug discovery, the optimization of complex systems, and artificial intelligence." Microsoft assures us that quantum computers will "forever alter our economic, industrial, academic, and societal landscape." And journalists repeatedly warn that quantum computers may soon break the encryption that protects the Internet. It has gotten to the point where many researchers in various fields of physics feel obliged to justify whatever work they are doing by claiming that it has some relevance to quantum computing. [ABSTRACT FROM AUTHOR]

Last December, a glitch in the crew-scheduling system for American Airlines threatened to disrupt thousands of flights over the holiday season. The error allowed pilots to drop flights without requiring another pilot to cover for them, imperiling as many as 15,000 trips. And while the airline managed to spot the problem and staff the flights, the snafu was a reminder of how much we depend on computers to schedule the vast array of services and functions on which our societies have become completely dependent. All major airlines, for example, use sophisticated scheduling-optimization algorithms to assign crews to planes. And while the American Airlines incident was not caused directly by the failure of one of these algorithms, the end result was much the same. Such a failure would make it likely that hundreds of thousands of people would be stranded or seriously inconvenienced while the airline sought a solution. [ABSTRACT FROM AUTHOR]

On 17 July 2014, a group of New York City police officers approached 43-year-old Eric Garner on a Staten Island sidewalk and attempted to arrest him—for allegedly selling cigarettes illegally. When Garner pulled free, one officer wrapped an arm around Garner's neck, forced him to the ground, and pressed his face into the sidewalk. Garner, who had asthma and heart disease, repeatedly pleaded, "I can't breathe," before passing out. Unconscious, he was transported to a hospital, where he was pronounced dead an hour later. The medical examiner later ruled Garner's death a homicide. [ABSTRACT FROM AUTHOR]

In electronics, the past half century has been a steady march away from analog and toward digital. Telephony, music recording and playback, cameras, and radio and television broadcasting have all followed the lead of computing, which had largely gone digital by the middle of the 20th century. Yet many of the signals that computers- and our brains-process are analog. And analog has some inherent advantages: If an analog signal contains small errors, it typically won't really matter. Nobody cares, for example, if a musical note in a recorded symphony is a smidgen louder or softer than it should actually be. Nor is anyone bothered if a bright area in an image is ever so slightly lighter than reality. Human hearing and vision aren't sensitive enough to register those subtle differences anyway. [ABSTRACT FROM PUBLISHER]

NOVEMBER 1958, AT A SIX-DAY INTERNATIONAL CONFERence devoted to scientific information, the inventor Hans Peter Luhn demonstrated a series of his electromechanical machines. They looked rather ordinary. Much like other computing devices of the day, they were boxy and utilitarian, designed to scoop and sort tall stacks of punch cards into slots and bins. [ABSTRACT FROM AUTHOR]

Bitcoin was hatched as an act of defiance. Unleashed in the wake of the Great Recession, the cryptocurrency was touted by its early champions as an antidote to the inequities and corruption of the traditional financial system. They cherished the belief that as this parallel currency took off, it would compete with and ultimately dismantle the institutions that had brought about the crisis. Bitcoin's unofficial catchphrase, "In cryptography we trust," left no doubt about who was to blame: It was the middlemen, the bankers, the "trusted" third parties who actually couldn't be trusted. These humans simply got in the way of other humans, skimming profits and complicating transactions. [ABSTRACT FROM AUTHOR]

People in the tech world talk of a technology "crossing the chasm" by making the leap from early adopters to the mass market. A case study in chasm crossing is now unfolding in neuromorphic computing. The approach mimics the way neurons are connected and communicate in the human brain, and enthusiasts say neuromorphic chips can run on much less power than traditional CPUs. The problem, though, is proving that neuromorphics can move from research labs to commercial applications. The field's leading researchers spoke frankly about that challenge at the Neuro Inspired Computational Elements Workshop, held in March at the IBM research facility at Almaden, Calif. "There currently is a lot of hype about neuromorphic computing," said Steve Furber, the researcher at the University of Manchester, in England, who heads the SpiNN aker project, a major neuromorphics effort. "It's true that neuromorphic systems exist, and you can get one and use one. But all of them have fairly small user bases, in universities or industrial research groups. All require fairly specialized knowledge. And there is currently no compelling demonstration of a high-volume application where neuromorphic outperforms the alternative." [ABSTRACT FROM AUTHOR]

Computers have transformed work and play, transportation and medicine, entertainment and sports. Yet for all their power, these machines still cannot perform simple tasks that a child can do, such as navigating an unknown room or using a pencil. The solution is finally coming within reach. It will emerge from the intersection of two major pursuits: the reverse engineering of the brain and the burgeoning field of artificial intelligence. Over the next 20 years, these two pursuits will combine to usher in a new epoch of intelligent machines. Why do we need to know how the brain works to build intelligent machines? Although machine-learning techniques such as deep neural networks have recently made impressive gains, they are still a world away from being intelligent, from being able to understand and act in the world the way that we do. The only example of intelligence, of the ability to learn from the world, to plan and to execute, is the brain. Therefore, we must understand the principles underlying human intelligence and use them to guide us in the development of truly intelligent machines. [ABSTRACT FROM AUTHOR]

When Sean James, who works on data-center technology for Microsoft, suggested that the company put server farms entirely underwater, his colleagues were a bit dubious. But for James, who had earlier served on board a submarine for the U.S. Navy, submerging whole data centers beneath the waves made perfect sense. . This tactic, he argued, would not only limit the cost of cooling the machines-an enormous expense for many data-center operators-but it could also reduce construction costs, make it easier to power these facilities with renewable energy, and even improve their performance. [ABSTRACT FROM AUTHOR]

The article discusses the problem in the way the performance of supercomputers are measured and evaluated. Topics covered include researchers' opinion that high-performance computers should be rated based on "gigasteps" or billions of traversed edges per second and the concept of the supercomputer benchmark called Graph 500.

The silicon semiconductor industry has chugged along for more than 50 years. Like a steamroller, it has trundled over bumps and holes, while defying repeated warnings that it was running out of fuel or was about to be overtaken by flashier competitors. [ABSTRACT FROM PUBLISHER]

As the River Thames meanders eastward through London, it horseshoes around a lobe of land called the Isle of Dogs. In the 19th century, the area boasted one of the world?s busiest dock complexes, but by 1980 it had deteriorated into an industrial wasteland. More recently, thanks to massive redevelopment, the area has blossomed again to become Canary Wharf, an enclave of glittering glass skyscrapers that is now a global financial center. And here, in the murky waters of the North Dock, sits one of the largest and sleekest stations in London?s newest railway: Crossrail. [ABSTRACT FROM AUTHOR]

Besides the overpowering bouquet of raspberries in this guy's beer, this review is remarkable for another reason. It was produced by a computer program instructed to hallucinate a review for a "fruit/vegetable beer." Using a powerful artificial-intelligence tool called a recurrent neural network, the software that produced this passage isn't even programmed to know what words are, much less to obey the rules of English syntax. Yet, by mining the patterns in reviews from the barflies at BeerAdvocate. com, the program learns how to generate similarly coherent (or incoherent) reviews. [ABSTRACT FROM PUBLISHER]

Forty years ago, a New York University graduate student named Arieh Aviram opened his Ph.D. dissertation with a bold suggestion: "Taking a clue from nature, [which] utilizes molecules for the carrying out of many physical phenomena, it may be possible to miniaturize electronic components down to molecular size." What Aviram was proposing was revolutionary: leapfrogging the ongoing miniaturization trend of Moore's Law by substituting single organic molecules for silicon transistors and diodes. [ABSTRACT FROM PUBLISHER]

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

The imitation game, the recent biopic about Alan Turing's efforts to decipher Nazi naval codes, was showered with award nominations. It even won the 2015 Academy Award for Best Adapted Screenplay. One thing it won't win any awards for, though, is its portrayal of the "imitation game" itself- Turing's proposed test of machine thinking, which hinges on whether a computer can convincingly imitate a person. The Turing test, as it is now called, doesn't really feature in the file. (Given that the movie gets so much of the history wrong, perhaps that's a good thing.) [ABSTRACT FROM PUBLISHER]

In 2013, a team at Stanford reported that it had arranged arrays of tiny, atom-thick cylinders to create an entirely new machine: the world's very first carbon nanotube computer. With a clock speed of just 1 kilohertz and fewer than 200 transistors, it didn?t stand a chance of competing with today's silicon-based processors. [ABSTRACT FROM PUBLISHER]

Today, the leading contenders are all quite exotic: There are superconducting circuits printed from materials such as aluminum and cooled to one-hundredth of a degree above absolute zero, floating ions that are made to hover above chips and are interrogated with lasers, and atoms such as nitrogen trapped in diamond matrices. [ABSTRACT FROM AUTHOR]

In June 2000, a press conference was held in the White House to announce an extraordinary feat: the completion of a draft of the human genome. For the first time, researchers had read all 3 billion of the chemical "letters" that make up a human DNA molecule, which would allow geneticists to investigate how that chemical sequence codes for a human being. In his remarks, President Bill Clinton recalled the moment nearly 50 years prior when Francis Crick and James Watson first discovered the double-helix structure of DNA. "How far we have come since that day," Clinton said. [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]

Describes the microfluidic computer developed by Daniel T. Chiu and Elena Pezzoli for solving complex computations. Details on how the microfluidic computer was developed; Components of the microfluidic computer; Application of microfluidics to DNA computing.

If everything had gone according to plan, the Internet as we know it would never have sprung up. That plan, devised 35 years ago, instead would have created a comprehensive set of standards for computer networks called Open Systems Interconnection, or OSI. Its architects were a dedicated group of computer industry representatives in the United Kingdom, France, and the United States who envisioned a complete, open, and multilayered system that would allow users all over the world to exchange data easily and thereby unleash new possibilities for collaboration and commerce. [ABSTRACT FROM PUBLISHER]

It was the summer of 2001, and computer programmer Michael Phipps had a problem: His favorite operating system, BeOS, was about to go extinct. Having an emotional attachment to a piece of software may strike you as odd, but to Phipps and many others (including me), BeOS deserved it. It ran amazingly fast on the hardware of its day; it had a clean, intuitive user interface; and it offered a rich, fun, and modern programming environment. In short, we found it vastly superior to every other computer operating system available. But the company that had created BeOS couldn't cut it in the marketplace, and its assets, including BeOS, were being sold to a competitor. [ABSTRACT FROM PUBLISHER]

In some ways, collecting old computers isn't much different from collecting anything old: You have to take care of the stuff. "Is it decaying?" asks Dag Spicer, senior curator at the Computer History Museum, in Mountain View, Calif. He describes the remains of sound- dampening foam that once hushed the whir of cooling fans in 1960s and '70s mainframes. "It turns into a tarry mess- really gross, black sludge," he says. That's relatively easy to clean out, but some troubles require more technical expertise. Reading the information on a 1950s disk stack might be hard, says Spicer, a circuit designer turned historian, but harder still is making sense of it. "Do you recognize what these bits are?" he asks, explaining the need for both obsolete hardware and outdated operating systems. [ABSTRACT FROM PUBLISHER]