Engineering giant Rolls-Royce plans to make an autonomous navy ship, powered by artificial intelligence, sophisticated sensors and advanced propulsion, for sale to militaries throughout the world. The British company, known for its aircraft engines and luxury automotive heritage, revealed a concept version of the naval vessel in multiple photos released Tuesday. Amid increasing concern among some technologists about the prospect of self-aware artificial intelligence systems becoming a threat to humanity, Rolls-Royce said it was already conducting "significant analysis of potential cyber risks" to "ensure end-to-end security." With range of 3,500 nautical miles, the 60-meter-long Rolls-Royce vessel would be able to operate on its own without human intervention for more than 100 days. Its missions could include patrol and surveillance, fleet watch or sea mine detection.

PISA, ITALY – Italian tenor Andrea Bocelli's voice soars to the rafters of the Tuscan theater, but all eyes are on the orchestral conductor beside him -- a robot with an apparent penchant for Verdi. The concert in the heart of Pisa is a world first, with two mechanical "arms" conducting live music at the grand finale of the first International Festival of Robotics. The Swiss-designed YuMi sweeps its baton skyward with one hand, while the other curves around in a caress that spurs on the strings as the operatic "La Donna E' Mobile" ("Woman Is Fickle") reaches its climax. But music lovers beware: YuMi can conduct set pieces, but cannot improvise, react or interact with the musicians. "It was extremely difficult to train," says Andrea Colombini, the conductor of the Lucca Philharmonic Orchestra, which performed with Bocelli and soprano Maria Luigia Borsi on Tuesday.

Summary: With only slight tongue in cheek about the road ahead we report on the just passed House of Representative's new "Federal Automated Vehicle Policy" as well as similar policy just emerging in Germany. As a model of regulation on emerging AI technology we think they got this just about right. Just today (9/6/17) the US House of Representatives released its 116 page "Federal Automated Vehicles Policy". This still has to be reconciled and approved by the Senate but word is that shouldn't take long. Equally as interesting is that just two weeks ago the German federal government published its guidelines for Highly Automated Vehicles (HAV being the new name of choice for these vehicles).

These are just a few of the latest military and security innovations from around the world on offer at the Defence and Security Equipment International Show (DSEI) in the U.K. this week. DSEI runs from Sept. 10 through Sept 15 at the Excel Center in London. The gigantic scale of biennial DSEI is often described as unrivalled. If you are a country looking to upgrade your military might then this "one stop shop" is the place to be. Pretty much anything you would need to defend your country in war – or to launch a war for that matter - is here in London at largest show of its kind on Earth.

In the UK, the first proper machine that was tasked with playing a game was the Hollerith Electronic Computer (HEC), which is currently on display at The National Museum of Computing (TNMOC) at Bletchley Park. The machine was displayed to the public in 1953 at the Business Efficiency Exhibition in London. Raymond Bird, the electronics engineer who was tasked with developing the HEC, described the demonstration of the noughts and crosses game as a great success in showing the potential power of computers. Primary Key Associates co-founder Andrew Lea says there are three types of AI. The first is the so-called fake AI, where AI is used as a moniker for smart technology that exhibits pseudo-intelligence.

Answer Set Programming (ASP) is a well-known paradigm of declarative programming with roots in logic programming and non-monotonic reasoning. Similar to other closely related problem-solving technologies, such as SAT/SMT, QBF, Planning and Scheduling, advancements in ASP solving are assessed in competition events. In this paper, we report about the design and results of the Sixth ASP Competition, which was jointly organized by the University of Calabria (Italy), Aalto University (Finland), and the University of Genoa (Italy), in affiliation with the 13th International Conference on Logic Programming and Non-Monotonic Reasoning. This edition maintained some of the design decisions introduced in 2014, e.g., the conception of sub-tracks, the scoring scheme, and the adherence to a fixed modeling language in order to push the adoption of the ASP-Core-2 standard. On the other hand, it featured also some novelties, like a benchmark selection stage classifying instances according to their empirical hardness, and a "Marathon" track where the top-performing systems are given more time for solving hard benchmarks.

Argumentation is an active area of modern artificial intelligence (AI) research, with connections to a range of fields, from computational complexity theory and knowledge representation and reasoning to philosophy and social sciences, as well as application-oriented work in domains such as legal reasoning, multi-agent systems, and decision support. Argumentation frameworks (AFs) of abstract argumentation have become the graph-based formal model of choice for many approaches to argumentation in AI, with semantics defining sets of jointly acceptable arguments, i.e., extensions. Understanding the dynamics of AFs has been recently recognized as an important topic in the study of argumentation in AI. In this work, we focus on the so-called extension enforcement problem in abstract argumentation as a recently proposed form of argumentation dynamics. We provide a nearly complete computational complexity map of argument-fixed extension enforcement under various major AF semantics, with results ranging from polynomial-time algorithms to completeness for the second level of the polynomial hierarchy. Complementing the complexity results, we propose algorithms for NP-hard extension enforcement based on constraint optimization under the maximum satisfiability (MaxSAT) paradigm. Going beyond NP, we propose novel MaxSAT-based counterexample-guided abstraction refinement procedures for the second-level complete problems and present empirical results on a prototype system constituting the first approach to extension enforcement in its generality.

In conventional chemisorption model, the d-band center theory (augmented sometimes with the upper edge of d-band for imporved accuarcy) plays a central role in predicting adsorption energies and catalytic activity as a function of d-band center of the solid surfaces, but it requires density functional calculations that can be quite costly for large scale screening purposes of materials. In this work, we propose to use the d-band width of the muffin-tin orbital theory (to account for local coordination environment) plus electronegativity (to account for adsorbate renormalization) as a simple set of alternative descriptors for chemisorption, which do not demand the ab initio calculations. This pair of descriptors are then combined with machine learning methods, namely, artificial neural network (ANN) and kernel ridge regression (KRR), to allow large scale materials screenings. We show, for a toy set of 263 alloy systems, that the CO adsorption energy can be predicted with a remarkably small mean absolute deviation error of 0.05 eV, a significantly improved result as compared to 0.13 eV obtained with descriptors including costly d-band center calculations in literature. We achieved this high accuracy by utilizing an active learning algorithm, without which the accuracy was 0.18 eV otherwise. As a practical application of this machine, we identified Cu3Y@Cu as a highly active and cost-effective electrochemical CO2 reduction catalyst to produce CO with the overpotential 0.37 V lower than Au catalyst.

Multi-scanner Antivirus systems provide insightful information on the nature of a suspect application; however there is often a lack of consensus and consistency between different Anti-Virus engines. In this article, we analyze more than 250 thousand malware signatures generated by 61 different Anti-Virus engines after analyzing 82 thousand different Android malware applications. We identify 41 different malware classes grouped into three major categories, namely Adware, Harmful Threats and Unknown or Generic signatures. We further investigate the relationships between such 41 classes using community detection algorithms from graph theory to identify similarities between them; and we finally propose a Structure Equation Model to identify which Anti-Virus engines are more powerful at detecting each macro-category. As an application, we show how such models can help in identifying whether Unknown malware applications are more likely to be of Harmful or Adware type.

July 2016 marked a before and after in the annals of law. Under fire from a suspect who wanted to "kill white officers," and with a dozen badges down, the Dallas police force sent a bomb-bearing robot in to deal with lone gunman, Micah Johnson. The case is thought to have been the first time a U.S. police force had used a robot in a show of lethal force. More widely, though, police forces around the world are making more and more use of robots and artificial intelligence (AI) to combat crime. The results are often less striking than the Dallas shootout, but no less vital in helping to maintain law and order. In the United Kingdom, for instance, an AI system called VALCRI (which stands for Visual Analytics for Sense-Making in Criminal Intelligence Analysis) is helping police spot patterns that could help solve crimes, acting as a kind of robot Miss Marple.