Artificial Intelligence or AI is defined by the Oxford English Dictionary as "the theory and development of computer systems able to perform tasks normally requiring human intelligence, such as visual perceptions, speech recognition, decision-making, and translation between languages". The term is defined in popular culture, and in the eyes of employees the world over, as an ever-approaching threat. The World Economic Forum has discussed AI as a major element of the fourth industrial revolution (4IR) and something which will rapidly change our world and workplaces. Regardless of the definition, AI is coming into our workplaces and coming quickly. As with any change to workplaces, employment law will follow.

Rolls-Royce will co-operate with IT research institutes in the UK and Germany to increase its digital capabilities, particularly in the field of artificial intelligence. The engine maker says it has signed a tentative agreement with the London-based Alan Turing Institute to conduct collaborative data science research and to develop "next-generation artificial intelligence". Projects will explore how data science can be applied on an industrial scale, how AI can be employed across supply chains, data-centric engineering and predictive maintenance, and the role of data analytics and AI in science. R-R chief digital officer Neil Crockett states the partnership is about "delivering real-world impact from AI technologies… on an industrial scale". He says AI is "central to unleashing huge value" for R-R's customers and for the manufacturer's internal processes.

The world must prepare for potential malicious use of artificial intelligence (AI) by rogue states, criminals and terrorists, according to a report by a group of 26 security experts. Forecasting rapid growth in cybercrime and the misuse of drones during the next decade – as well as an unprecedented rise in the use of bots to manipulate everything from elections to the news agenda and social media, the report calls for governments and corporations worldwide to address the danger inherent in the myriad applications of AI. The report – The Malicious Use of Artificial Intelligence: Forecasting, Prevention, and Mitigation – also recommends interventions to mitigate the threats posed by the malicious use of AI. The report says that AI has many positive applications, but it is a dual-use technology and AI researchers and engineers should be proactive about the potential for its misuse. Policymakers and technical researchers need to work together now to understand and prepare for the malicious use of AI, according to the authors.

Recently published by Cambridge University Press, Re-Engineering Humanity explores how artificial intelligence, automated decisionmaking, the increasing use of Big Data are shaping the future of humanity. This excellent interdisciplinary book is co-authored by Professors Evan Selinger and Brett Frischmann, and it critically examines three interrelated questions. Under what circumstances can using technology make us more like simple machines than actualized human beings? Why does the diminution of our human potential matter? What will it take to build a high-tech future that human beings can flourish in?

Organization concepts and models are increasingly being adopted for the design and specification of multi-agent systems. Agent organizations can be seen as mechanisms of social order, created to achieve global (or organizational) objectives by more or less autonomous agents. In order to develop a theory on the relation between organizational structures, organizational objectives and the actions of agents fulfilling roles in the organization a theoretical framework is needed to describe organizational structures and actions of (groups of) agents. Current logical formalisms focus on specific aspects of organizations (e.g. power, delegation, agent actions, or normative issues) but a framework that integrates and relates different aspects is missing. Given the amount of aspects involved and the subsequent complexity of a formalism encompassing them all, it is difficult to realize. In this paper, a first step is taken to solve this problem. We present a generic formal model that enables to specify and relate the main concepts of an organization (including, activity, structure, environment and others) so that organizations can be analyzed at a high level of abstraction. However, for some aspects we use a simplified model in order to avoid the complexity of combining many different types of (modal) operators.

In mulsemedia applications, traditional media content (text, image, audio, video, etc.) can be related to media objects that target other human senses (e.g., smell, haptics, taste). Such applications aim at bridging the virtual and real worlds through sensors and actuators. Actuators are responsible for the execution of sensory effects (e.g., wind, heat, light), which produce sensory stimulations on the users. In these applications sensory stimulation must happen in a timely manner regarding the other traditional media content being presented. For example, at the moment in which an explosion is presented in the audiovisual content, it may be adequate to activate actuators that produce heat and light. It is common to use some declarative multimedia authoring language to relate the timestamp in which each media object is to be presented to the execution of some sensory effect. One problem in this setting is that the synchronization of media objects and sensory effects is done manually by the author(s) of the application, a process which is time-consuming and error prone. In this paper, we present a bimodal neural network architecture to assist the synchronization task in mulsemedia applications. Our approach is based on the idea that audio and video signals can be used simultaneously to identify the timestamps in which some sensory effect should be executed. Our learning architecture combines audio and video signals for the prediction of scene components. For evaluation purposes, we construct a dataset based on Google's AudioSet. We provide experiments to validate our bimodal architecture. Our results show that the bimodal approach produces better results when compared to several variants of unimodal architectures.

Neuroimaging datasets keep growing in size to address increasingly complex medical questions. However, even the largest datasets today alone are too small for training complex models or for finding genome wide associations. A solution is to grow the sample size by merging data across several datasets. However, bias in datasets complicates this approach and includes additional sources of variation in the data instead. In this work, we combine 15 large neuroimaging datasets to study bias. First, we detect bias by demonstrating that scans can be correctly assigned to a dataset with 73.3% accuracy. Next, we introduce metrics to quantify the compatibility across datasets and to create embeddings of neuroimaging sites. Finally, we incorporate the presence of bias for the selection of a training set for predicting autism. For the quantification of the dataset bias, we introduce two metrics: the Bhattacharyya distance between datasets and the age prediction error. The presented embedding of neuroimaging sites provides an interesting new visualization about the similarity of different sites. This could be used to guide the merging of data sources, while limiting the introduction of unwanted variation. Finally, we demonstrate a clear performance increase when incorporating dataset bias for training set selection in autism prediction. Overall, we believe that the growing amount of neuroimaging data necessitates to incorporate data-driven methods for quantifying dataset bias in future analyses.

Cutting edge solutions filled six event halls, with common themes of automation, connectivity and flexibility linking competitors and partners. In one display in the Zeiss booth, attendees could view inspection processes designed to aid additive manufacturers from the raw powder through dimensional and surface inspection of the final part and then to data analysis and statistics. Data collection, and seamless integration with cloud software was evidenced throughout the expo, alongside automated in-line and near-line solutions for measuring with everything from CMMs to the newest laser scanners. Doug Adkins, executive vice president of Mitutoyo, noted the progression of robotics in metrology. Robotic arms have advanced from simply handing a part to an inspection cell, to actually doing the inspection itself.

The rise of artificial intelligence in general, and machine learning in particular, has been an emerging theme at DIS for several years now. In 2018 the topic hit the mainstream as it was analysed in no fewer than five sessions with eight experts giving their views. The topics the presenters ran through ranged from the general impact AI will have on society, through to advice on how to harness the technology to monetise content now. The hunger for knowledge about the topic seems to be fuelled by a number of elements. On many levels there is a wider discussion about how AI will change society, and this has sparked some publishers interest in working out how it can used in the media.

While it seems like everybody and their cyborg seems to be thinking about artificial intelligence, a report seems to show there is real business value in the AI market. According to a report released today by the research firm Gartner, the global business value derived from AI is expected to increase to $1.2 trillion this year, up 70 percent from last year. That's expected to continue to grow over the next few years; AI-derived business value in 2022 could be as high as $3.9 trillion. Sources for the business value came from three areas: customer experience capabilities, new revenue from current and future products and services, and cost reductions from producing and delivering products and services. According to Gartner, customer experience is a "necessary precondition" for widespread adoption of AI--both in terms of its potential capabilities and the value it enables--having either a positive or negative effect on indirect costs to businesses.