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. Is the Australian Defence Force the next big customer for unmanned aerial vehicles? 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."

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."

A key front for ethical questions in artificial intelligence, and computer science more generally, is teaching students how to engage with the questions they will face in their professional careers based on the tools and technologies we teach them.  In past work (and current teaching) we have advocated for the use of science fiction as an appropriate tool which enables AI researchers to engage students and the public on the current state and potential impacts of AI. We present teaching suggestions for E.M. Forster's 1909 story, "The Machine Stops," to teach topics in computer ethics.  In particular, we use the story to examine ethical issues related to being constantly available for remote contact, physically isolated, and dependent on a machine --- all without mentioning computer games or other media to which students have strong emotional associations. We give a high-level view of common ethical theories and indicate how they inform the questions raised by the story and afford a structure for thinking about how to address them.

The focus of this work is to investigate how children's perception of the robot changes with age and gender, and to enable the robot to adapt to these differences for improving human-robot interaction (HRI). We propose a neural network-based learning architecture to estimate children's age and gender based on the body motion performing a set of actions. To evaluate our system, we collected a fully annotated depth dataset of 28 children (aged between 7 and 16 years old) and applied it to a learning-based method for age and gender estimation by modeling children's 3D skeleton motion data. We discuss our results that show an average accuracy of 95.2% and 90.3% for age and gender respectively in the context of a real-world scenario.

Staying up to date with the latest discoveries is a challenge in any scientific field. In planetary science, new observation targets on the surface of Mars are identified and named every day, and new publications announcing new discoveries and conclusions provide frequent updates about these targets. We are constructing a system that uses information extraction and retrieval methods to mine the steadily growing body of planetary science publications about Mars surface targets and automatically construct a concise summary of what is known about each target. The Mars Target Encyclopedia will provide a central, continually updated resource for use by planetary scientists and the interested public. We describe our use of Tika, Sundance, and AutoSlog to extract and summarize information, some of the challenges associated with this domain, and our plans for maturing the system.

In this paper, we present an open-source emotion tracker and its innovative applications. Our tracker, EmoGram, tracks emotion changes for a sequence of textual units. It is versatile in terms of the textual unit (tweets, sentences in discourse, etc.) and also what constitutes the time sequence (timestamps of tweets, discourse nature of text, etc.). We demonstrate the utility of our system through our applications: a sequence of commentaries in cricket matches, a sequence of dialogues in a play, and a sequence of tweets related to the Maggi controversy in India in 2015. That one system can be used for these applications is the merit of EmoGram.

In the field of General Game Playing the imperfectinformationgames present a special challenge for researchers.In general the search space is larger, and thelack of information requires a different decision makingtechnique. A simple Monte Carlo sampling using a particlefilter may serve for the simple games, but this soonfails when more complex games are played. The HyperPlaytechnique was one such ”simple” player, soonenhanced to HyperPlay-II capable of handling the mostcomplex of games. However, this technique is very resourcehungry and may not scale well for larger games.We explore the scalability of HyperPlay-II for a varietyof imperfect-information games and test some perfectinformationpruning techniques to see if they will improveefficiency.

The role of decomposition-trees (also known as junction and clique trees) in probabilistic inference is widely known and has been the basis for many well known inference algorithms.Recent approaches have demonstrated that such trees have a "hidden structure," which enables the characterization of tractable problem instances as well as lead to insights that enable boosting the performance of inference algorithms. We consider the MPE problem on a Boolean formula in CNF where each literal in the formula is associated with a weight.We describe techniques for exploiting the junction-tree structure of these formulas in the context of a branch-and-bound algorithm for MPE.

The activities of daily living of a patient in a smart home environment can be detected to a large extent by the real-time analysis of characteristics of the habitat's electrical consumption. However, reasoning over the conduct of these activities occurs at a much higher level of abstraction than what the sensors generally produce. In this paper, we leverage the concept of Complex Event Processing (CEP), in which low-level data streams are progressively transformed into higher-level ones, to the task of activity recognition. We show how the use of an appropriate representation for each level of abstraction can greatly simplify the process. We also report on the use of an existing event stream processor to successfully implement the complete chain, from low-level sensor data up to a sequence of discrete and high-level actions.