Icelandic video game developer and publisher CCP Games has reportedly put its Newcastle office up for sale in the wake of its decision to terminate its virtual reality development efforts. On the other hand, its Atlanta office has been shut down. Icelandic business publication mbl.is was the first to report about CCP Games' unfortunate situation. According to the publication, the company's Newcastle office, which housed the developers who worked on "Eve: Valkyrie," is now up for sale. Meanwhile, CCP's Atlanta office, which housed the people who worked on "Sparc," is already closed.

Most people do not interact with Semantic Web data directly. Unless they have the expertise to understand the underlying technology, they need textual or visual interfaces to help them make sense of it. We explore the problem of generating natural language summaries for Semantic Web data. This is non-trivial, especially in an open-domain context. To address this problem, we explore the use of neural networks. Our system encodes the information from a set of triples into a vector of fixed dimensionality and generates a textual summary by conditioning the output on the encoded vector. We train and evaluate our models on two corpora of loosely aligned Wikipedia snippets and DBpedia and Wikidata triples with promising results.

We present an updated version of the VESICLE-CNN algorithm presented by Roncal et al. (2014). The original implementation makes use of a patch-based approach. This methodology is known to be slow due to repeated computations. We update this implementation to be fully convolutional through the use of dilated convolutions, recovering the expanded field of view achieved through the use of strided maxpools, but without a degradation of spatial resolution. This updated implementation performs as well as the original implementation, but with a $600\times$ speedup at test time. We release source code and data into the public domain.

This paper establishes the existence of observable footprints that reveal the "causal dispositions" of the object categories appearing in collections of images. We achieve this goal in two steps. First, we take a learning approach to observational causal discovery, and build a classifier that achieves state-of-the-art performance on finding the causal direction between pairs of random variables, given samples from their joint distribution. Second, we use our causal direction classifier to effectively distinguish between features of objects and features of their contexts in collections of static images. Our experiments demonstrate the existence of a relation between the direction of causality and the difference between objects and their contexts, and by the same token, the existence of observable signals that reveal the causal dispositions of objects.

Process mining is a research field focused on the analysis of event data with the aim of extracting insights related to dynamic behavior. Applying process mining techniques on data from smart home environments has the potential to provide valuable insights into (un)healthy habits and to contribute to ambient assisted living solutions. Finding the right event labels to enable the application of process mining techniques is however far from trivial, as simply using the triggering sensor as the label for sensor events results in uninformative models that allow for too much behavior (i.e., the models are overgeneralizing). Refinements of sensor level event labels suggested by domain experts have been shown to enable discovery of more precise and insightful process models. However, there exists no automated approach to generate refinements of event labels in the context of process mining. In this paper we propose a framework for the automated generation of label refinements based on the time attribute of events, allowing us to distinguish behaviourally different instances of the same event type based on their time attribute. We show on a case study with real-life smart home event data that using automatically generated refined labels in process discovery, we can find more specific, and therefore more insightful, process models. We observe that one label refinement could have an effect on the usefulness of other label refinements when used together. Therefore, we explore four strategies to generate useful combinations of multiple label refinements and evaluate those on three real-life smart home event logs.

This paper introduces Scavenger, the first theorem prover for pure first-order logic without equality based on the new conflict resolution calculus. Conflict resolution has a restricted resolution inference rule that resembles (a first-order generalization of) unit propagation as well as a rule for assuming decision literals and a rule for deriving new clauses by (a first-order generalization of) conflict-driven clause learning.

Both the ethics of autonomous systems and the problems of their technical implementation have by now been studied in some detail. Less attention has been given to the areas in which these two separate concerns meet. This paper, written by both philosophers and engineers of autonomous systems, addresses a number of issues in machine ethics that are located at precisely the intersection between ethics and engineering. We first discuss the main challenges which, in our view, machine ethics posses to moral philosophy. We them consider different approaches towards the conceptual design of autonomous systems and their implications on the ethics implementation in such systems. Then we examine problematic areas regarding the specification and verification of ethical behavior in autonomous systems, particularly with a view towards the requirements of future legislation. We discuss transparency and accountability issues that will be crucial for any future wide deployment of autonomous systems in society. Finally we consider the, often overlooked, possibility of intentional misuse of AI systems and the possible dangers arising out of deliberately unethical design, implementation, and use of autonomous robots.

The ability to act upon a new stimulus, based on previous experience with similar, but distinct, stimuli, sometimes denoted as generalization, is used extensively in our daily life. As a simple example, consider a driver's response to traffic lights: The driver need not recognize the details of a particular traffic light in order to respond to it correctly, even though traffic lights may appear different from one another. The only property that matters is the color, whereas neither shape nor size should play any role in the driver's reaction. Learning how to react to traffic lights thus involves an aspect of generalization. A learning agent, capable of a meaningful and useful generalization is expected to have the following characteristics: (a) an ability for categorization (recognizing that all red signals have a common property, which we can refer to as redness); (b) an ability to classify (a new red object is to be related to the group of objects with the redness property); (c) ideally, only generalizations that are relevant for the success of the agent should be learned (red signals should be treated the same, whereas squareshaped signals should not, as they share no property that is of relevance in this context); (d) correct actions should be associated with relevant generalized properties (the driver should stop whenever a red signal is shown); and (e) the generalization mechanism should be flexible. To illustrate what we mean by "flexible generalization", let us go back to our driver. After learning how to handle traffic lights correctly, the driver tries to follow arrow signs to, say, a nearby airport. Clearly, it is now the shape category of the signal that should guide the driver, rather than the color category.

It has been dubbed'the flying bum' - and could soon be taking holidaymakers for an'air cruise' around the world's most picturesque areas. The 20-tonne Airlander 10 is set to be tested by luxury travel firm Henry Cookson Adventures next year. It says it hopes to take the craft wherever clients want to go, promising passengers will'experience landscapes that vary as diversely as the North Pole, Bolivian Salt Pans and Namib Desert'. The 20-tonne Airlander 10 is set to be tested by luxury travel firm Henry Cookson Adventures next year, and will be fitted with a luxury interior meaning it can stay aloft for weeks at a time. Airlander is the largest aircraft in the world, bigger even than the Airbus A380 - but would be dwarfed by the historic zeppelins developed in Germany during the 1930s.

This RetroPie really happened: Watch (above) as our own Adam Patrick Murray and Alaina Yee build a RetroPie system after they weren't able to buy an SNES Classic. Go ahead, laugh at (and learn from) our mistakes. For the past 20 years, retrogaming enthusiasts have dreamed of building a "universal game console" capable of playing games from dozens of different systems. Their ideal was inexpensive, easy to control with a gamepad, and capable of hooking into a TV set. Thanks to the Raspberry Pi 3 hobbyist platform and the RetroPie software distribution, that dream is finally possible. For under $110, you can build a very nice emulation system that can play tens of thousands of retro games for systems such as the NES, Atari 2600, Sega Genesis, Super NES, Game Boy, and even the PlayStation. All you need to do is buy a handful of components, put them together, and configure some software. You'll also have to provide the games, but we'll talk about that later.