Separate from the two main camps in ethics, deontological ethics (D) and consequentialism (C), there is virtue ethics (V). While there has been extensive formal, computational, and mathematical work done on deontological ethics and consequentialism, there has been very little or almost no work done in formalizing and making rigorous virtue ethics. Proponents of V might claim that it is not feasible to do so given V's emphasis on character and traits, rather than individual actions or consequencens. From the perspective of machine and robot ethics, this is not satisfactory. If V is to be considered to be on equal footing with D and C for the purpose of building morally competent machines, we need to start with formalizing parts of virtue ethics.

This paper presents a novel approach to kernel tuning. The method presented borrows techniques from reproducing kernel Banach space (RKBS) theory and tensor kernels and leverages them to convert (re-weight in feature space) existing kernel functions into new, problem-specific kernels using auxiliary data. The proposed method is applied to accelerating Bayesian optimisation via covariance (kernel) function pre-tuning for short-polymer fibre manufacture and alloy design.

The goal of online prediction with expert advice is to find a decision strategy which will perform almost as well as the best expert in a given pool of experts, on any sequence of outcomes. This problem has been widely studied and $O(\sqrt{T})$ and $O(\log{T})$ regret bounds can be achieved for convex losses (\cite{zinkevich2003online}) and strictly convex losses with bounded first and second derivatives (\cite{hazan2007logarithmic}) respectively. In special cases like the Aggregating Algorithm (\cite{vovk1995game}) with mixable losses and the Weighted Average Algorithm (\cite{kivinen1999averaging}) with exp-concave losses, it is possible to achieve $O(1)$ regret bounds. \cite{van2012exp} has argued that mixability and exp-concavity are roughly equivalent under certain conditions. Thus by understanding the underlying relationship between these two notions we can gain the best of both algorithms (strong theoretical performance guarantees of the Aggregating Algorithm and the computational efficiency of the Weighted Average Algorithm). In this paper we provide a complete characterization of the exp-concavity of any proper composite loss. Using this characterization and the mixability condition of proper losses (\cite{van2012mixability}), we show that it is possible to transform (re-parameterize) any $\beta$-mixable binary proper loss into a $\beta$-exp-concave composite loss with the same $\beta$. In the multi-class case, we propose an approximation approach for this transformation.

The purpose of this paper is to indicate that the recently proposed Momentum fractional least mean squares (mFLMS) algorithm has some serious flaws in its design and analysis. Our apprehensions are based on the evidence we found in the derivation and analysis in the paper titled: \textquotedblleft \textit{Momentum fractional LMS for power signal parameter estimation}\textquotedblright. In addition to the theoretical bases our claims are also verified through extensive simulation results. The experiments clearly show that the new method does not have any advantage over the classical least mean square (LMS) method.

But current flying robo-insects are still tethered to the ground. The electronics they need to power and control their wings are too heavy for these miniature robots to carry. Now, engineers at the University of Washington have for the first time cut the cord and added a brain, allowing their RoboFly to take its first independent flaps. This might be one small flap for a robot, but it's one giant leap for robot-kind. The team will present its findings May 23 at the International Conference on Robotics and Automation in Brisbane, Australia.

UAVs are tackling everything from disease control to vacuuming up ocean waste to delivering pizza, and more. Drone technology has been used by defense organizations and tech-savvy consumers for quite some time. However, the benefits of this technology extends well beyond just these sectors. With the rising accessibility of drones, many of the most dangerous and high-paying jobs within the commercial sector are ripe for displacement by drone technology. The use cases for safe, cost-effective solutions range from data collection to delivery. And as autonomy and collision-avoidance technologies improve, so too will drones' ability to perform increasingly complex tasks. According to forecasts, the emerging global market for business services using drones is valued at over $127B. As more companies look to capitalize on these commercial opportunities, investment into the drone space continues to grow. A drone or a UAV (unmanned aerial vehicle) typically refers to a pilotless aircraft that operates through a combination of technologies, including computer vision, artificial intelligence, object avoidance tech, and others. But drones can also be ground or sea vehicles that operate autonomously.

Robofly, designed by engineers from the University of Washington, can flap on its own, isn't tethered to any devices and powered by a laser beam. Slightly more substantial than a wooden toothpick, engineers from the University of Washington have created a robot insect that can fly untethered. Dubbed the RoboFly, the engineers gave the robotic flying insect a brain (a microcontroller) and offset the need for heavy electronics traditionally used to power miniature robotics by powering it with a laser beam. Engineers said that the biggest challenge to creating the free-flying robotic insect was to understand how to generate enough power for it to flap its wings. "Wing flapping is a power-hungry process, and both the power source and the controller that directs the wings are too big and bulky to ride aboard a tiny robot," said Sawyer Fuller, assistant professor, UW Department of Mechanical Engineering.

Across consumer-facing industries including hospitality and quality ranking sites, customer service is one of the most critical metrics determining the value of a product against competitors. Planned standards are well and good, but if a process doesn't work or malfunctions, consumers want help, and they want that help to be easy to access, able to handle the problem and resolve it quickly. Moreover, they want anybody (including our often hated self-check-in machines), to be understanding, considerate, and happy to assist. For decades this "human touch" has been the one thing the digital age could not offer, even as the shopping path, and the products themselves have become more and more interwoven with computer and internet technology. The U.S. 3D design company Autodesk has teamed up with Soul Machines, a New Zealand developer of human-like avatars, to produce its first digital customer service agent. AVA (Autodesk virtual agent) is ready to interact with customers 24 hours a day to resolve their concerns.

"Music isn't just what we listen to, it's who we are." This is Google's hypothesis, as laid out in marketing materials for YouTube Music – a revamped streaming service it hopes will rival Spotify and Apple Music. And who knows who people really are better than Google? Through its blanket dominance in everything from email and search, to maps and calendars, Google knows its users' location, habits, tastes and future plans. By accessing the vast amounts of data swept up by these digital services, Google wants to offer a new type of personalised music streaming service. Combining this personal knowledge with the AI-powered Google Assistant, YouTube Music should in theory be able to offer listening suggestions suitable to whatever the situation – be it falling asleep or a morning commute.

This article was originally published on 5 May, 2016 and updated with the latest tools on May 16, 2018. Programming is an integral part of data science. Among other things, it is acknowledged that a person who understands programming logic, loops and functions has a higher chance of becoming a successful data scientist. But, what about those folks who never studied programming in their school or college days? Is there no way for them to become a data scientist then?