We have seen how the internet of things and the growing phenomenon of'big data' will throw up major problems for consumers and citizens, problems that have as yet barely been grasped by most policy-makers. In this world of growing complexity, the potential for an unintended consequence becomes greater and greater from machines performing an action that was not anticipated. There are key issues, too, about our reliance on data at a time of massive data generation, data storing and data preservation which have the potential to both obscure results and generate injustices. Perhaps the greatest issue that we now face is caused by our blind faith in machines. We have invested them with certainty and – as we have pointed out – we trust them. Part of the reason for this is an odd confusion that has conflated the machines of the industrial age with the machines of the information age.

When you're a firefighter, your life is always on the brink of danger. Now, NASA has developed an artificial intelligence system called AUDREY that could help save the lives of firefighters by nagivating them in smoke-filled burning establishments. Meet AUDREY (Assistant for Understanding Data through Reasoning, Extraction, and sYnthesis), an artificial intelligence system being developed by NASA's Jet Propulsion Laboratory. According to a report from Smithsonian, AUDREY is a technology that tracks the firefighters movements and guides them to safety during rescue. The technology originated from the Jet Propulsion Laboratory's work on space rovers kused on Mars.

Advances in machine learning and mobile robotics mean that robots could do your job better than you. That's led to some radical predictions of mass unemployment, much more leisure or a work free future. Queen Elizabeth I denied a patent for a knitting machine over fears it would create unemployment, Ricardo thought technology would lower wages and Keynes famously predicted a 15 hour working week by 2030. Understanding why these beliefs proved to be wrong gives us important insights into why similar claims about robotisation might be incorrect. But automation could nevertheless have sizeable distributional implications and ramifications well beyond the industries in which it's deployed.

Last year, McKinsey launched a multi-year study to explore the potential impact of automation technologies on jobs, organizations and the future of work. "Can we look forward to vast improvements in productivity, freedom from boring work, and improved quality of life?," its initial article on the study asked, or "Should we fear threats to jobs, disruptions to organizations, and strains on the social fabric?" Most jobs involve a number of different tasks or activities. Some of these activities are more amenable to automation than others. But just because some of the activities have been automated, does not imply that the whole job has disappeared. To the contrary, automating parts of a job will often increase the productivity and quality of workers by complementing their skills with machines and computers, as well as by enabling them to focus on those aspects of the job that most need their attention.

CIA Director John Brennan participates in a session at the third annual Intelligence and National Security Summit in Washington, D.C., on Sept. 8, 2016. WASHINGTON -- CIA Director John Brennan warned on Sunday that Russia has "exceptionally capable and sophisticated" computer capabilities and that the U.S. must be on guard. "I think that we have to be very, very wary of what the Russians might be trying to do in terms of collecting information in a cyber realm, as well as what they might want to do with it," he told CBS' "Face the Nation" on the 15th anniversary of the Sept. 11 attacks. On the terrorism threat, Brennan said the U.S. government is much better now at sharing information. He praised Saudi Arabia as "a good example of how foreign intelligence services can work against these terrorist organizations."

Thus far, the dangers associated with self-driving cars have been largely relegated to their ability to safely navigate our roads and highways. But now the U.S. Department of Justice is interested in taking a closer look at a different kind of danger altogether -- and this has nothing to do with how well an autonomous vehicle can brake or steer. Rather, a new threat analysis team has been tasked with determining the potential security issues associated with not only self-driving cars, but other Internet of Things and connected devices as well. After all, if you can control everything from the palm of your hand, who's to say someone else can't do the same thing? Under Assistant Attorney General John Carlin, who heads the Justice Department's national security team, the new team seeks to secure the internet of things and protect it from potential terrorist threats.

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.