Over the past few years, adversarial training has become an extremely active research topic and has been successfully applied to various Artificial Intelligence (AI) domains. As a potentially crucial technique for the development of the next generation of emotional AI systems, we herein provide a comprehensive overview of the application of adversarial training to affective computing and sentiment analysis. Various representative adversarial training algorithms are explained and discussed accordingly, aimed at tackling diverse challenges associated with emotional AI systems. Further, we highlight a range of potential future research directions. We expect that this overview will help facilitate the development of adversarial training for affective computing and sentiment analysis in both the academic and industrial communities.

Asking questions is a pervasive human activity, but little is understood about what makes them difficult to answer. An analysis of a pair of large databases, of New York Times crosswords and questions from the quiz-show Jeopardy, establishes two orthogonal dimensions of question difficulty: obscurity (the rarity of the answer) and opacity (the indirectness of question cues, operationalized with word2vec). The importance of opacity, and the role of synergistic information in resolving it, suggests that accounts of difficulty in terms of prior expectations captures only a part of the question-asking process. A further regression analysis shows the presence of additional dimensions to question-asking: question complexity, the answer's local network density, cue intersection, and the presence of signal words. Our work shows how question-askers can help their interlocutors by using contextual cues, or, conversely, how a particular kind of unfamiliarity with the domain in question can make it harder for individuals to learn from others. Taken together, these results suggest how Bayesian models of question difficulty can be supplemented by process models and accounts of the heuristics individuals use to navigate conceptual spaces.

Aging population ratios are rising significantly. Meanwhile, smart home based health monitoring services are evolving rapidly to become a viable alternative to traditional healthcare solutions. Such services can augment qualitative analyses done by gerontologists with quantitative data. Hence, the recognition of Activities of Daily Living (ADL) has become an active domain of research in recent times. For a system to perform human activity recognition in a real-world environment, multiple requirements exist, such as scalability, robustness, ability to deal with uncertainty (e.g., missing sensor data), to operate with multi-occupants and to take into account their privacy and security. This paper attempts to address the requirements of scalability and robustness, by describing a reasoning mechanism based on modular spatial and/or temporal context models as a network of ontologies. The reasoning mechanism has been implemented in a smart home system referred to as Arianna+. The paper presents and discusses a use case, and experiments are performed on a simulated dataset, to showcase Arianna+'s modularity feature, internal working, and computational performance. Results indicate scalability and robustness for human activity recognition processes.

Artificial Intelligence (AI) approaches to problem-solving and decision-making are becoming more and more complex, leading to a decrease in the understandability of solutions. The European Union's new General Data Protection Regulation tries to tackle this problem by stipulating a "right to explanation" for decisions made by AI systems. One of the AI paradigms that may be affected by this new regulation is Answer Set Programming (ASP). Thanks to the emergence of efficient solvers, ASP has recently been used for problem-solving in a variety of domains, including medicine, cryptography, and biology. To ensure the successful application of ASP as a problem-solving paradigm in the future, explanations of ASP solutions are crucial. In this survey, we give an overview of approaches that provide an answer to the question of why an answer set is a solution to a given problem, notably off-line justifications, causal graphs, argumentative explanations and why-not provenance, and highlight their similarities and differences. Moreover, we review methods explaining why a set of literals is not an answer set or why no solution exists at all.

Sequential pattern mining techniques extract patterns corresponding to frequent subsequences from a sequence database. A practical limitation of these techniques is that they overload the user with too many patterns. Local Process Model (LPM) mining is an alternative approach coming from the field of process mining. While in traditional sequential pattern mining, a pattern describes one subsequence, an LPM captures a set of subsequences. Also, while traditional sequential patterns only match subsequences that are observed in the sequence database, an LPM may capture subsequences that are not explicitly observed, but that are related to observed subsequences. In other words, LPMs generalize the behavior observed in the sequence database. These properties make it possible for a set of LPMs to cover the behavior of a much larger set of sequential patterns. Yet, existing LPM mining techniques still suffer from the pattern explosion problem because they produce sets of redundant LPMs. In this paper, we propose several heuristics to mine a set of non-redundant LPMs either from a set of redundant LPMs or from a set of sequential patterns. We empirically compare the proposed heuristics between them and against existing (local) process mining techniques in terms of coverage, redundancy, and complexity of the produced sets of LPMs.

According to the report, global GDP could be up to 14 percent higher in 2030 as a result of AI. This is the equivalent of an additional USD 15.7 trillion – making it the biggest commercial opportunity in today's fast changing economy. Of this, USD 6.6 trillion is likely to come from increased productivity and USD 9.1 trillion from consumption side effects, the report shows. Accordingly, growth will be driven by three factors: productivity gains from businesses automating processes (including use of robots and autonomous vehicles); productivity gains from businesses augmenting their existing labour force with AI technologies (assisted and augmented intelligence) and increased consumer demand resulting from the availability of personalised and/or higher-quality AI-enhanced products and services. Over the past decade, almost all aspects of how we work and how we live – from retail to manufacturing to healthcare – have become increasingly digitised, the report notes.

Amazon has revealed a whole host of new Echo devices, as well as products that you can control by talking to the Alexa assistant that lives inside them. In all, it announced more than 70 updates – which touched on almost every part of its Alexa line-up. The very short version is this: Amazon updated just about every Echo to give it a better-looking grey mesh on the outside and to make it louder and better sounding on the inside. If you want the slightly less short version of each of the updates, then read on. Here's what happened to each of those products in slightly more detail.

Neo4j, the market leader in connected data, announced today the upcoming release of Neo4j 3.5, the native graph platform designed to drive the success and adoption of real-time business applications, including artificial intelligence (AI) and machine learning (ML) systems. Neo4j customers – including eBay and Caterpillar – have demonstrated that connected graph datasets are a foundational element of enterprise AI applications. Graph-based data models provide the necessary context for AI applications by capturing facts related to and relationships among people, processes, applications, data and machines. Informed by successful AI customer deployments – including knowledge graphs, fraud detection, recommendation systems and conversation engines – Neo4j 3.5 delivers foundational features for AI-powered systems of connection to generate bottom-line business value. "The way we organize and represent knowledge in AI-powered systems has a profound effect on what and how they can learn," said Bowles.

Amazon has released a new microwave and wall clock that connect to the internet. The company's new Amazon Basics Microwave can be controlled just by talking to it. You can simply say "one potato" to the cooker, for instance, and it will choose the right settings to cook your dinner. It costs only $59.99 and will be shipping later this year. The I.F.O. is fuelled by eight electric engines, which is able to push the flying object to an estimated top speed of about 120mph. The giant human-like robot bears a striking resemblance to the military robots starring in the movie'Avatar' and is claimed as a world first by its creators from a South Korean robotic company Waseda University's saxophonist robot WAS-5, developed by professor Atsuo Takanishi and Kaptain Rock playing one string light saber guitar perform jam session A man looks at an exhibit entitled'Mimus' a giant industrial robot which has been reprogrammed to interact with humans during a photocall at the new Design Museum in South Kensington, London Electrification Guru Dr. Wolfgang Ziebart talks about the electric Jaguar I-PACE concept SUV before it was unveiled before the Los Angeles Auto Show in Los Angeles, California, U.S The Jaguar I-PACE Concept car is the start of a new era for Jaguar.

Amazon has launched a whole raft of Echo devices, with the aim of getting Alexa into and around everyone's houses. In its biggest event of the year, the company unveiled a huge raft of new products. They included everything from a subwoofer for your Alexa speakers to a smart plug that will allow everything in your house to connect to the internet – as well as completely new kinds of products, like a smart microwave and wall clock. The new updates are part of Amazon's plan to have its internet-connected voice assistant to everyone's houses, allowing them to listen to music and control their houses. The I.F.O. is fuelled by eight electric engines, which is able to push the flying object to an estimated top speed of about 120mph.