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.

Artificial intelligence might be on its way Photo credit: David Molina Evan Roberts and David MolinaSeptember 10, 2016Filed under Opinion # Hang on for a minute...we're trying to find some more stories you might like. Close # Email This Story Send Email Cancel Siri was first introduced in 2011 on the iPhone 4S. When it was first introduced it was fun to play with and receive cute responses from, but it was not too useful. Since then, Siri and its competitors have become increasingly more use... U.S. – Google Inc. (NASDAQ: inGOOGL) is working with a third-party company called DeepMind to enhance their AI personal assistant.

A cybersecurity startup is applying the same "deep learning" techniques that are used in modern image and voice recognition to detect malware. Deep Instinct launched late last year with a system that it says can go beyond typical antivirus programs by not only detecting known malware but also flagging dangerous software it's never encountered before. The company doesn't need to have security experts create digital rules specifying what kind of characteristics should trigger alerts, says Maya Schirmann, Deep Instinct's chief marketing officer. Instead, the system essentially trains itself by studying enormous numbers of applications, documents, images, and other common types of files, labeled simply for whether they contain malware or not. "The training phase happens on our own premises, at Deep Instinct," Schirmann says.

Mitsubishi Electric, Zenrin and nine other automakers will start collecting high resolution 3D maps for self-driving cars to use, in preparation for autonomous car deployment in the country. The project is backed by the Cabinet Office's Cross-ministerial Strategic Innovation Promotion Program (quite a mouthful), which commissioned Dynamic Map Planning, a joint venture of the 11 companies, to build the 3D maps. Japan's government hopes that self-driving cars will be on the road before the start of the Tokyo Summer Olympics in 2020, according to Nikkei. Highly detailed 3D maps provide a ton of data for self-driving cars, which can be analyzed by machine learning systems and fed into an entire fleet of vehicles. Once an entire city is mapped, the self-driving car could, in theory, know every single traffic light, walkway, and intersection while driving less than a mile.

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.