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 development of new scientific concepts today is an outcome of the accumulated knowledge built over time. Every scientific domain requires understanding of the trends of the dependencies between its subdomains. Analyses of trends to capture such dependencies using conventional document modeling techniques is a challenging task due to two reasons: (1) conventional vector-space modeling based representation of documents does not realize the history of the content, and (2) neither feature-level nor document-level causality is provided with any digital library metadata or citation network. In this paper, we propose an intuitive temporal representation of a scientific article that encodes inherent historic characteristics of the content. This intuitive representation of each document is then leveraged to discover causal relationships between scientific articles. In addition, we provide a mechanism to explore the lineage of each document in terms of other previously published documents, which illustrates how the theme of the document under analysis evolved over time. Empirical studies reported in the paper show that the proposed technique identifies meaningful causal relationships and discovers meaningful lineage in the scientific literature that could not be discovered through the citation network of the articles.

An effective approach to solving problems involving mixed (continuous and discrete) variables and constraints, such as hybrid systems, is to decompose them into subproblems and integrate dedicated solvers geared toward those subproblems. Here, we introduce a new framework based on a tree search algorithm to solve hybrid discrete-continuous problems that incorporates: (1) a quantum annealer that samples from the configuration space for the discrete portion and provides information about the quality of the samples, and (2) a classical computer that makes use of information from the quantum annealer to prune and focus the search as well as check a continuous constraint. We consider four variants of our algorithm, each with progressively more guidance from the results provided by the quantum annealer. We empirically test our algorithm and compare the variants on a simplified Mars Lander task scheduling problem. Variants with more guidance from the quantum annealer have better performance.

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.

Participants in recent discussions of AI-related issues ranging from intelligence explosion to technological unemployment have made diverse claims about the nature, pace, and drivers of progress in AI. However, these theories are rarely specified in enough detail to enable systematic evaluation of their assumptions or to extrapolate progress quantitatively, as is often done with some success in other technological domains. After reviewing relevant literatures and justifying the need for more rigorous modeling of AI progress, this paper contributes to that research program by suggesting ways to account for the relationship between hardware speed increases and algorithmic improvements in AI, the role of human inputs in enabling AI capabilities, and the relationships between different subfields of AI. It then outlines ways of tailoring AI progress models to generate insights on the specific issue of technological unemployment, and outlines future directions for research on AI progress.

The hospital readmission rate of patients within 30 days after discharge is broadly accepted as a healthcare quality measure and cost driver in the United States. The ability to estimate hospitalization costs alongside 30 day risk-stratification for such readmissions provides additional benefit for accountable care, now a global issue and foundation for the U.S.~government mandate under the Affordable Care Act. Recent data mining efforts either predict healthcare costs or risk of hospital readmission, but not both. In this paper we present a dual predictive modeling effort that utilizes healthcare data to predict the risk and cost of any hospital readmission (``all-cause''). For this purpose, we explore machine learning algorithms to do accurate predictions of healthcare costs and risk of 30-day readmission.Results on risk prediction for ``all-cause'' readmission compared to the standardized readmission tool (LACE) are promising, and the proposed techniques for cost prediction consistently outperform baseline models and demonstrate substantially lower mean absolute error (MAE).

This paper describes Hy-CIRCA, an architecture for verified, correct-by-construction planning and execution for hy- brid systems, including non-linear continuous dynamics. Hy-CIRCA addresses the high computational complexity of such systems by first planning at an abstract level, and then progressively refining the original plan. Hy-CIRCA is an extension of our Playbook approach, which aims to make it easy for users to exert supervisory control over multiple autonomous systems by “calling a play.” The Playbook approach is implemented by combining (1) a human-machine interface for commanding and monitoring the autonomous systems; (2) a hierarchical planner for translating commands into executable plans; and (3) a smart executive to manage plan execution by coordinating the control systems of the individual autonomous agents, tracking plan execution, and triggering replanning when necessary. Hy-CIRCA integrates the dReal non-linear SMT solver, with enhanced versions of the SHOP2 HTN planner and the CIRCA Controller Synthesis Module (CSM). Hy-CIRCA’s planning process has 5 steps: (1) Using SHOP2, compute an approximate mission plan. While computing this plan, compute a hybrid automaton model of the plan, featuring more expressive continuous dynamics. (2) Using dReal, solve this hybrid model, establishing the correctness of the plan, and computing values for its continuous parameters. To execute the plan, (3) extract from the plan specifications for closed-loop, hard real-time supervisory controllers for the agents that must execute the plan. (4) Based upon these specifications, use the CIRCA CSM to plan the controllers. To ensure correct execution, (5) verify the CSM-generated controllers with dReal.

Hypothetical “value learning” AIs learn human values and then try to act according to those values. The design of such AIs, however, is hampered by the fact that there exists no satisfactory definition of what exactly human values are. After arguing that the standard concept of preference is insufficient as a definition, I draw on reinforcement learning theory, emotion research, and moral psychology to offer an alternative definition. In this definition, human values are conceptualized as mental representations that encode the brain’s value function (in the reinforcement learning sense) by being imbued with a context-sensitive affective gloss. I finish with a discussion of the implications that this hypothesis has on the design of value learners.

We address the control of a vertically profiling float us- ing ocean-model-based predictions of future currents. While these problems are in reality continuous control problems, we solve them by searching a discrete space of future actions. Additionally, while the environment is a continuous space, the ocean model we use is a discrete cell-based model. We show that even with an imperfect model of ocean currents, planning in the ocean current model can significantly improve results for a specific problem of controlling a vertically profiling float when trading off remaining at the same location as a virtual mooring and collecting more data with more profiles.