The US Navy is developing a pilotless solar-powered plane that can fly for 90 days at a time to help keep a watchful eye on naval ships below or act as a communications relay platform. The plane, dubbed'Skydweller' and developed by Skydweller Aero, builds on the manned Solar Impulse 2 aircraft that flew around the world in 2015 and 2016, but had to stop every five days. The upgraded version will eliminate the cockpit, allowing space for hardware that allows for autonomous abilities. Skydweller Aero CEO Robert Miller told New Scientist: 'When we remove the cockpit, we are enabling true persistence and providing the opportunity to install up to about 400 kilograms of payload capacity.' The pilotless craft will feature 236-foot long wings that are blanked in solar cells, but its makers may add hydrogen fuel cells for an additional boost.

These concerns have been present whenever we make important decisions. What's new is the much, much larger scale at which we now rely on algorithms to help us decide. Human errors that may have once been idiosyncratic may now become systematic. "Artificial intelligence is the pursuit of machines that are able to act purposefully to make decisions towards the pursuit of goals," wrote Harvard University Professor David Parkes in "A Responsibility to Judge Carefully in the Era of Decision Machines," an essay recently published as part of Harvard's Digital Initiative. "Machines need to be able to predict to decide, but decision making requires much more," he wrote.

"I say this to everyone in the media world who I talk to," says Darren Atkins, wrapping up our phone interview: "Please, absolutely do not portray this as a hidden agenda to get rid of staff." Atkins is the Chief Technology Office for AI automation at East Suffolk and North Essex NHS Foundation Trust – group of hospitals employing more than 10,000 staff, who serve a quarter of a million people in the South East of England. "If this technology is applied in the wrong way, it can be very threatening," Atkins says. "Our main priority is to free up time for staff to do the work that they should be doing, rather than the work that has no value." Just over a year ago, Atkins led the deployment of virtual workers across his group of NHS hospitals – and according to him, it's been an unqualified success. Patients are missing fewer appointments and staff are happier.

In 1969, artificial-intelligence pioneer and Nobel laureate Herbert Simon proposed a new science, one that approached the study of artificial objects just as one would study natural objects. "Natural science is knowledge about natural objects and phenomena," Simon wrote. "We ask whether there cannot also be'artificial' science -- knowledge about artificial objects and phenomena." Now, 50 years later, a team of researchers from Harvard, MIT, Stanford, the University of California, San Diego, Google, Facebook, Microsoft, and other institutions is renewing that call. In a recent paper published in the journal Nature, the researchers proposed a new, interdisciplinary field -- machine behavior -- that would study artificial intelligence through the lens of biology, economics, psychology, and other behavioral and social sciences.

Marketing firms in the Asia Pacific region lead the world in implementing artificial intelligence, a new briefing shows. Data from Adobe's 2018 edition of its annual'Digital Intelligence Briefing' shows that digital marketing firms in the Asia Pacific (APAC) region are leading the industry in employing artificial intelligence (AI). Adobe's surveys found that 28% of top performing companies – those with profits over £150m (AU$270m) – are implementing or planning to implement AI, relative to the rest of the market at 12%. Speaking about the APAC region particularly, Paula Parkes, senior director, APAC Enterprise Marketing said, "smart organisations are investing in disruptive technologies to drive productivity and deliver compelling experiences for customers". Adobe's survey data revealed that majority of marketers currently engaging with the technology are using AI to analyse data, however APAC firms also lead in adopting AI in more niche areas, such as content creation and creative and design work.

We tackle the problem of identifiability and efficient learning of mixtures of Random Utility Models (RUMs). We show that when the PDFs of utility distributions are symmetric, the mixture of k RUMs (denoted by k-RUM) is not identifiable when the number of alternatives m is no more than 2k-1. On the other hand, when m ≥ max{4k-2,6}, any k-RUM is generically identifiable. We then propose three algorithms for learning mixtures of RUMs: an EM-based algorithm, which we call E-GMM, a direct generalized-method-of-moments (GMM) algorithm, and a sandwich (GMM-E-GMM) algorithm that combines the other two. Experiments on synthetic data show that the sandwich algorithm achieves the highest statistical efficiency and GMM is the most computationally efficient. Experiments on real-world data at Preflib show that Gaussian k-RUMs provide better fitness than a single Gaussian RUM, the Plackett-Luce model, and mixtures of Plackett-Luce models w.r.t. commonly-used model fitness criteria. To the best of our knowledge, this is the first work on learning mixtures of general RUMs.

Imagine you're sitting in a self-driving car that's about to make a left turn into on-coming traffic. One small system in the car will be responsible for making the vehicle turn, one system might speed it up or hit the brakes, other systems will have sensors that detect obstacles, and yet another system may be in communication with other vehicles on the road. Each system has its own goals -- starting or stopping, turning or traveling straight, recognizing potential problems, etc. -- but they also have to all work together toward one common goal: turning into traffic without causing an accident. Harvard professor and FLI researcher, David Parkes, is trying to solve just this type of problem. Parkes told FLI, "The particular question I'm asking is: If we have a system of AIs, how can we construct rewards for individual AIs, such that the combined system is well behaved?"

Imagine you're sitting in a self-driving car that's about to make a left turn into on-coming traffic. One small AI system in the car will be responsible for making the vehicle turn, one system might speed it up or hit the brakes, other systems will have sensors that detect obstacles, and yet another system may be in communication with other vehicles on the road. Each system has its own goals -- starting or stopping, turning or traveling straight, recognizing potential problems, etc. -- but they also have to all work together toward one common goal: turning into traffic without causing an accident. Harvard professor and Future of Life researcher, David Parkes, is trying to solve just this type of problem. Parkes told FLI, "The particular question I'm asking is: If we have a system of AIs, how can we construct rewards for individual AIs, such that the combined system is well behaved?"

A paradigmatic problem in social choice theory deals with the aggregation of subjective preferences of individuals --- represented as rankings of alternatives --- into a social ranking. We are interested in settings where individuals are uncertain about their own preferences, and represent their uncertainty as distributions over rankings. Under the classic objective of minimizing the (expected) sum of Kendall tau distances between the input rankings and the output ranking, we establish that preference elicitation is surprisingly straightforward and near-optimal solutions can be obtained in polynomial time. We show, both in theory and using real data, that ignoring uncertainty altogether can lead to suboptimal outcomes.

Online mechanism design has been widely applied to various practical applications. However, designing a strategy-proof online mechanism is much more challenging than that in a static scenario due to short of knowledge of future information. In this paper, we investigate online auctions with time discounting values, in contrast to the flat values studied in most of existing work. We present a strategy-proof 2-competitive online auction mechanism despite of time discounting values. We also implement our design and compare it with off-line optimal solution. Our numerical results show that our design achieves good performance in terms of social welfare, revenue, average winning delay, and average valuation loss.