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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]

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]

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]