A few years ago, Michael Osborne and a colleague at Oxford University caused a stir when they published research suggesting 47 percent of jobs in the U.S. are at risk of being replaced with robot labor. Subsequent studies suggest closer to 10 percent of jobs in developed countries could be automated, which is only marginally less worrying for workers. What isn't in doubt is that advances in algorithms and robotics will transform the workplace, with both rote manual labor and higher-level cognitive tasks soon to be performed by machines. Robotics companies, keen to avoid the insinuation their products take jobs from humans, talk a lot these days about "co-bots" (collaborative robots). Humans and robots will increasingly collaborate, they say, with humans freed to do more productive, fulfilling tasks thanks to machines taking on the grunt work.

Any day now, Uber will introduce a fleet of self-driving cars in Pittsburgh, making this former steel town the world's first city to let passengers hail autonomous vehicles. So with the world watching, what has the city of 306,000 done to prepare for Uber's unprecedented test? The answer is not much. There have been no public service announcements or demonstrations of the technology. Except for the mayor and one police official, no other top city leader has seen a self-driving Uber vehicle operate up close.

Where did the "cyber" in "cyberspace" come from? Most people, when asked, will probably credit William Gibson, who famously introduced the term in his celebrated 1984 novel, Neuromancer. It came to him while watching some kids play early video games. Searching for a name for the virtual space in which they seemed immersed, he wrote "cyberspace" in his notepad. "As I stared at it in red Sharpie on a yellow legal pad," he later recalled, "my whole delight was that it meant absolutely nothing."

Two of today's major business and intellectual trends offer complementary insights about the challenge of making forecasts in a complex and rapidly changing world. Forty years of behavioral science research into the psychology of probabilistic reasoning have revealed the surprising extent to which people routinely base judgments and forecasts on systematically biased mental heuristics rather than careful assessments of evidence. These findings have fundamental implications for decision making, ranging from the quotidian (scouting baseball players and underwriting insurance contracts) to the strategic (estimating the time, expense, and likely success of a project or business initiative) to the existential (estimating security and terrorism risks). The bottom line: Unaided judgment is an unreliable guide to action. Consider psychologist Philip Tetlock's celebrated multiyear study concluding that even top journalists, historians, and political experts do little better than random chance at forecasting such political events as revolutions and regime changes.1 The second trend is the increasing ubiquity of data-driven decision making and artificial intelligence applications. Once again, an important lesson comes from behavioral science: A body of research dating back to the 1950s has established that even simple predictive models outperform human experts' ability to make predictions and forecasts. This implies that judiciously constructed predictive models can augment human intelligence by helping humans avoid common cognitive traps.

The video analytics industry is typically split into two distinct camps: (1) systems designed around rules and user-specified rules or models and (2) autonomous systems designed around machine learning. Supervised learning systems require heavy training and feedback to achieve the desired output, where unsupervised learning systems train themselves from the input data and require minimal human input. The video analytic solutions we saw in the market a decade ago seem rudimentary compared to today's offerings; partly due to the technology catching up with early promises and partly due to the industry's understanding and level-setting of expectations from the initial splash of analytics hyped as a panacea and the future of security. However, some of the extreme claims such as its ability to replace trained human operators, eliminate the need for well-designed camera placement, completely eliminate false positives, and determine a person's intent ahead of an action have proven to be more hype than reality for many end users.

The industry has come a long way from analog CCTV video surveillance systems. The days of a few low-resolution cameras being monitored by a security guard at a desk are becoming rarer in mid-sized to enterprise organizations. Putting the cameras on an enterprise network and treating the video like any other data gives us endless possibilities of what we can do with this powerful and complex information. We witnessed the rise of video content analysis (VCA) technology, or video analytics, in the early 2000s in response to the growth of cameras and general surveillance, spurred on by the emergence of IP cameras, the falling costs of data storage and IT infrastructure, a reactive security posture to a changing threat landscape, and the quick realization that traditional monitoring approaches couldn't keep pace with the growth in video data. The video analytics industry is typically split into two distinct camps: (1) systems designed around rules and user-specified rules or models and (2) autonomous systems designed around machine learning.

PsiberLogic is a completely free, open-source fuzzy logic controller package for Python 3. Psibernetix proudly supports the amazing Python community, and is happy to contribute to Python's open-source movement. This package is for anyone seeking a high-performance, python3-callable package for creating fuzzy logic controllers. Details on ALPHA – a significant breakthrough in the application of what's called genetic-fuzzy systems are published in the most-recent issue of the Journal of Defense Management, as this application is specifically designed for use with Unmanned Combat Aerial Vehicles (UCAVs) in simulated air-combat missions for research purposes. The tools used to create ALPHA as well as the ALPHA project have been developed by Psibernetix, Inc., recently founded by UC College of Engineering and Applied Science 2015 doctoral graduate Nick Ernest, now president and CEO of the firm; as well as David Carroll, programming lead, Psibernetix, Inc.; with supporting technologies and research from Gene Lee; Kelly Cohen, UC aerospace professor; Tim Arnett, UC aerospace doctoral student; and Air Force Research Laboratory sponsors. ALPHA is currently viewed as a research tool for manned and unmanned teaming in a simulation environment.

Stanford has undertaken an important effort: envisioning the implications of artificial intelligence over a 100-year span, to "anticipate how the effects of artificial intelligence will ripple through every aspect of how people work, live, and play." But there is a problem, potentially fundamental enough that the team may want to revisit its first report or adjust its approach as it goes forward. This is the report's relatively weak coverage of the urban, human security implications of AI. According to the purpose statement, this first study focuses on the implications of AI in 2030 in the "typical North American city." I suppose the thin treatment of security may derive from the huge assumption that North American cities will remain peaceful and secure, and thus AI and intelligent machines won't carry significant human security implications.

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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.