Dmitry Malioutov can't say much about what he built. As a research scientist at IBM, Malioutov spends part of his time building machine learning systems that solve difficult problems faced by IBM's corporate clients. One such program was meant for a large insurance corporation. It was a challenging assignment, requiring a sophisticated algorithm. When it came time to describe the results to his client, though, there was a wrinkle. "We couldn't explain the model to them because they didn't have the training in machine learning." In fact, it may not have helped even if they were machine learning experts. That's because the model was an artificial neural network, a program that takes in a given type of data--in this case, the insurance company's customer records--and finds patterns in them. These networks have been in practical use for over half a century, but lately they've seen a resurgence, powering breakthroughs in everything from speech recognition and language translation to Go-playing robots and self-driving cars.

The terms "machine learning" and "artificial intelligence" (AI) conjure up feelings that are equal parts fear and fascination. Until recently, the prospect of a piece of software making human-like decisions resided safely in the far-fetched expectations of 1960s-era computer scientists or the plot lines of science fiction novels. Today, however, after decades of unmet expectations, we finally have AI systems that are beginning to influence our lives in tangible ways. Voice recognition systems like Amazon's Echo and Apple's Siri, and once-unimaginable fantasies like self-driving cars, are on the market for consumers, with more exciting life-like systems to come. We have also seen a few early signs of robotic autonomy that makes us feel uneasy, like the Russian robot that learned how to escape the lab!

Responses to a White House request for information about the future of artificial intelligence show a continued divide between those who are ready to embrace intelligent machines and those who worry about a future in which robots run the world. The responses were made public this month after the White House Office of Science and Technology issued a call for input about how artificial intelligence technology is currently shaping the world, how AI is likely to develop in the future and what role the government should play in either encouraging or regulating development. The request for information drew responses from large corporations, such as IBM, Google and Microsoft, as well as from academia and private citizens. The responses show there still is little agreement about the future of AI. "The danger is not machines run amok, as suggested by some, like [Elon] Musk or [Stephen] Hawking (who know nothing about AI). The danger is, like nuclear weapons, what AI will allow us to do to ourselves. And it is not a remote possibility, but already happening: Uber, for example, is proposing a fleet of driverless cars. What happens when the profits associated with whole industries are not distributed across the whole world, but flow into the coffers of a single company or person?"

This article is part of an MIT SMR initiative exploring how technology is reshaping the practice of management. Smart machines need ethics, too: Remember that movie in which a computer asked an impossibly young Matthew Broderick, "Shall we play a game?" Four decades later, it turns out that global thermonuclear war may be the least likely of a slew of ethical dilemmas associated with smart machines -- dilemmas with which we are only just beginning to grapple. The worrisome lack of a code of ethics for smart machines has not been lost on Alphabet, Amazon, Facebook, IBM, and Microsoft, according to a report by John Markoff in The New York Times. The five tech giants (if you buy Mark Zuckerberg's contention that he isn't running a media company) have formed an industry partnership to develop and adopt ethical standards for artificial intelligence -- an effort that Markoff infers is motivated as much to head off government regulation as to safeguard the world from black-hearted machines. On the other hand, the first of a century's worth of quinquennial reports from Stanford's One Hundred Year Study on Artificial Intelligence (AI100) throws the ethical ball into the government's court.

The same technology that allows online advertisers to target consumers as we browse the web could eventually play a key role in communication network security – even at the national-defense level. An award-winning group of researchers published a study in IEEE Xplore saying that advertising algorithms--frequently used by search engines like Google and Yahoo--could help develop a machine-learning approach for navigating complex communication networks. In the past, it was relatively easy to both protect communication networks from attempted hacking and intercept (or jam) signals from other networks. These days, though, networks are anything but simple thanks to various factors such as mobile devices, sensing technologies, social networks, and others. As a result, understanding how these networks communicate is a challenge.

Cyberspace is an increasingly hostile environment. In 2015, a PwC study of U.S. organizations found that 79 percent of respondents had detected a security incident during the year. Today, malicious hackers continue to wage on business networks and systems. Their aim - to extract data to sell on the black market. Making use of the latest technology, these criminals exert huge pressure on businesses to defend its assets.

The ongoing struggle between good and evil that characterizes the ongoing battles between cyber warriors and cyber criminals only grows more technologically complex. Hear what leading cybersecurity leaders have to say about the application of artificial intelligence and cognitive computing to help cybersecurity best practices stay at least one step ahead of cyber criminals.

We derive generalization error bounds for traditional time-series forecasting models. Our results hold for many standard forecasting tools including autoregressive models, moving average models, and, more generally, linear state-space models. These non-asymptotic bounds need only weak assumptions on the data-generating process, yet allow forecasters to select among competing models and to guarantee, with high probability, that their chosen model will perform well. We motivate our techniques with and apply them to standard economic and financial forecasting tools---a GARCH model for predicting equity volatility and a dynamic stochastic general equilibrium model (DSGE), the standard tool in macroeconomic forecasting. We demonstrate in particular how our techniques can aid forecasters and policy makers in choosing models which behave well under uncertainty and mis-specification.

Android designed to test out NASA's pressurized space suits In the 1960s, NASA had a bunch of dummies working to bring humans to space. Well, it was just two dummies, really. Each'Power Driven Articulated Dummy' was a 230-pound robot that NASA engineers designed to test space suits. One of the dummies now resides at the Smithsonian National Air and Space Museum, and the other is being auctioned off later this month. Popular Science wrote about the dummies back in 1967.

Using airdrops to deliver relief to disaster zones may sound like a simple solution, but these missions have proved to be inaccurate, wasteful and expensive. With that in mind, ex-British Army veteran Nigel Gifford is developing a drone with edible wings that is capable of carrying 100-pounds of vacuum-packed food and medical supplies. Although in early stages, the'Pouncer' would be released from a plane or catapult and dropped within a 25 mile radius of the target. Nigel Gifford is developing a drone with edible wings that is capable of carrying 100-pounds of vacuum-packed food and medical supplies. Although in early stages, the'Pouncer' would be released from a plane or catapult and dropped within a 25 mile radius of the target Pouncer is the brain child of engineer and ex-British Army veteran Nigel Gifford.