Missing teen's remains found in Lerderderg State Park Is the Australian Defence Force the next big customer for unmanned aerial vehicles? Australia has warned the world that artificially intelligent killer robots "may be closer than many of us had imagined" and nations need to work harder to tackle the future threat they may pose. At a United Nations meeting on "lethal autonomous weapons systems" in Geneva, Switzerland, the Australian delegation on Monday night called on the world to come up with agreed rules about how to handle the rapid pace in technology in military artificial intelligence. The Terminator movies imagined a future where killer robots posed a threat to humanity: some warn that the threat is real. "The development of fully autonomous systems able to conduct military targeting operations which kill and injure combatants or civilians may be closer than many of us had imagined," the delegation's statement said.

Some of the world's most prominent scientists and technology entrepreneurs including physicist Stephen Hawking, Tesla CEO Elon Musk and Apple co-founder Steve Wozniak signed a letter warning about the dangers of autonomous weapons, which they said would be technologically feasible "within years, not decades". Australia has warned the world that artificially intelligent killer robots "may be closer than many of us had imagined" and nations need to work harder to tackle the future threat they may pose. At a United Nations meeting on "lethal autonomous weapons systems" in Geneva, Switzerland, the Australian delegation on Monday night called on the world to come up with agreed rules about how to handle the rapid pace in technology in military artificial intelligence. "The development of fully autonomous systems able to conduct military targeting operations which kill and injure combatants or civilians may be closer than many of us had imagined," the delegation's statement said. "It is an appropriate time to consider the risks of such weapons systems and to make sure we understand fully what might constitute misuse as well as legitimate use of emerging technologies."

"The robots are coming" is not something Paul Revere said during the American Revolution, but it is certainly something many people have uttered over the years. So have we finally reached the tipping point where artificial intelligence and robots will begin to take over human jobs en masse? Perhaps not, but we are closer to the time when they will be even more essential assets and presences in the workforce, explains Martin Ford, the author of the book "Rise of the Robots." I caught up with Ford at The Economist magazine's Innovation Forum event, which was held earlier this month. He pointed out that artificial intelligence is making its way into sectors that were once manned by only man, including the legal profession, where computer systems such as Watson could muscle in on human territory to provide legal counsel, and even journalism where stories are being written without direct human input about some articles.

Planning with hybrid domains modelled in PDDL+ has been gaining research interest in the Automated Planning community in recent years. Hybrid domain models capture a more accurate representation of real world problems that involve continuous processes than is possible using discrete systems. However, solving problems represented as PDDL+ domains is very challenging due to the construction of complex system dynamics, including non-linear processes and events. In this paper we introduce DiNo, a new planner capable of tackling complex problems with non-linear system dynamics governing the continuous evolution of states. DiNo is based on the discretise-and-validate approach and uses the novel Staged Relaxed Planning Graph+ (SRPG+) heuristic, which is introduced in this paper. Although several planners have been developed to work with subsets of PDDL+ features, or restricted forms of processes, DiNo is currently the only heuristic planner capable of handling non-linear system dynamics combined with the full PDDL+ feature set.

Humans may one day create superintelligence, artificially intelligent machines that surpass mankind's intellect. Would these artificial intelligences choose to play games with us, and if so, which games? We believe this question is relevant for the ethics of general AI, the current widespread integration of AI systems into daily life, and for game AI research. We present a catalog of scenarios, some good for humanity and some bad, in which various kinds of play might take place between humans and intelligent machines. We assume a superintelligence, because of its greater cognitive ability, would stand in a similar relation to us as an adult does to a child, an expert to a novice, or a human to an animal. We define friendly games, learning games, observational games, and domination games, and proceed to consider games adults play with children, experts play with novices, and humans play with animals. Reasoning by analogy, we imagine corresponding games that superintelligences might choose to play with us, finding that domination games would pose a significant risk to humanity.

In this paper we describe the Unified Cybersecurity Ontology (UCO) that is intended to support information integration and cyber situational awareness in cybersecurity systems. The ontology incorporates and integratesheterogeneous data and knowledge schemas from different cybersecurity systems and most commonly usedcybersecurity standards for information sharing and exchange. The UCO ontology has also been mapped to anumber of existing cybersecurity ontologies as well asconcepts in the Linked Open Data cloud (Berners-Lee,Bizer, and Heath 2009). Similar to DBpedia (Auer etal. 2007) which serves as the core for general knowledge in Linked Open Data cloud, we envision UCO toserve as the core for cybersecurity domain, which wouldevolve and grow with the passage of time with additional cybersecurity data sets as they become available.We also present a prototype system and concrete usecases supported by the UCO ontology. To the best of ourknowledge, this is the first cybersecurity ontology thathas been mapped to general world ontologies to support broader and diverse security use cases. We comparethe resulting ontology with previous efforts, discuss itsstrengths and limitations, and describe potential futurework directions.

We present JudgeD, a probabilistic datalog. A JudgeD program defines a distribution over a set of traditional datalog programs by attaching logical sentences to clauses to implicitly specify traditional data programs. Through the logical sentences, JudgeD provides a novel method for the expression of complex dependencies between both rules and facts. JudgeD is implemented as a proof-of-concept in the language Python. The implementation allows connection to external data sources, and features both a Monte Carlo probability approximation as well as an exact solver supported by BDDs. Several directions for future work are discussed and the implementation is released under the MIT license.

Emerging AI systems will be making more and more decisions that impact the lives of humans in a significant way. It is essential, then, that these AI systems make decisions that take into account the desires, goals, and preferences of other people, while simultaneously learning about what those preferences are. In this work, we argue that the reinforcement-learning framework achieves the appropriate generality required to theorize about an idealized ethical artificial agent, and offers the proper foundations for grounding specific questions about ethical learning and decision making that can promote further scientific investigation. We define an idealized formalism for an ethical learner, and conduct experiments on two toy ethical dilemmas, demonstrating the soundness and flexibility of our approach. Lastly, we identify several critical challenges for future advancement in the area that can leverage our proposed framework.

In this paper we approach a subset of semantic analysis tasks through a symbiotic cognitive computing approach -- the user and the system learn from each other and accomplish the tasks better than they would do on their own. Our approach starts with a domain expert building a simplified domain model (e.g. semantic lexicons) and annotating documents with that model. The system helps the user by allowing them to obtain quicker results, and by leading them to refine their understanding of the domain. Meanwhile, through the feedback from the user, the system adapts more quickly and produces more accurate results. We believe this virtuous cycle is key for building next generation high quality semantic analysis systems. We present some preliminary findings and discuss our results on four aspects of this virtuous cycle, namely: the intrinsic incompleteness of semantic models, the need for a human in the loop, the benefits of a computer in the loop and finally the overall improvements offered by the human-computer interaction in the process.

In this paper we explain our concept of Interaction and Task Patterns, and discuss how such patterns can be applied to support mixed-initiative in symbiotic human-robot interaction both with service and industrial robotic systems.