Following the Sector Protocol for Quality Assurance for Practice-Based.Contributors Academy for AI, Games and Media:Mata Haggis Burridge (prof. EG), Qiqi Zhou, Hillevi Boerboom, Maria Pafi (postdoc, WuR), Alexander van Buggenum, Ella Betts, Wilma Franchimon (dir. AGM), Nick van Apeldoorn (Coord.Digireal), Harald Warmelink (Coord. Cradle & MSP Challenge), Magali Patrocínio Gonçalves, Ard Bonewald (MT Games), Marin Hekman, Marie Lhuissier, Carlos Santos (CTO Cradle), Jeremiah van Oosten (MT, games), Kevin Hutchinson, Frank Peters (MT ADS&AI), Bram Heijligers, Joey Relouw, Marnix van Gisbergen (Prof. DMC), Shima Rezaei Rashnoodi (Coord. DMC), Phil de Groot, Igor Mayer (prof. SG), Niels Voskens, Fabio Ferreira da Costa Campos, Tuki Clavero, Jens Hagen, Wilco Boode, Natalia Harazhanka-Pietjouw (PPC), Jacopo Fabrini & Silke Hassreiter.
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This study provides a comprehensive analysis of the AI-related skills and roles needed to bridge the AI skills gap in Europe. Using a mixed-method research approach, this study investigated the most in-demand AI expertise areas and roles by surveying 409 organizations in Europe, analyzing 2,563 AI-related job advertisements, and conducting 24 focus group sessions with 145 industry and policy experts. The findings underscore the importance of both general technical skills in AI related to big data, machine learning and deep learning, cyber and data security, large language models as well as AI soft skills such as problemsolving and effective communication. This study sets the foundation for future research directions, emphasizing the importance of upskilling initiatives and the evolving nature of AI skills demand, contributing to an EU-wide strategy for future AI skills development.
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Whitepaper: The use of AI is on the rise in the financial sector. Utilizing machine learning algorithms to make decisions and predictions based on the available data can be highly valuable. AI offers benefits to both financial service providers and its customers by improving service and reducing costs. Examples of AI use cases in the financial sector are: identity verification in client onboarding, transaction data analysis, fraud detection in claims management, anti-money laundering monitoring, price differentiation in car insurance, automated analysis of legal documents, and the processing of loan applications.
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An extensive inventory of 137 Dutch SMEs regarding the most important considerations regarding the use of emerging digital technologies shows that the selection process is difficult. En trepreneurs wonder which AI application suits them best and what the added (innovative) value is and how they can implement it. This outcome is a clear signal from SMEs to researchers in knowledge institutions and to developers of AI services and applications: Help! Which AI should I choose? With a consortium of students, researchers, and SMEs, we are creating an approach that will help SMEs make the most suitable AI choice. The project develops a data-driven advisory tool that helps SMEs choose, develop, implement and use AI applications focusing on four highly ranked topics.
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From the article: The ethics guidelines put forward by the AI High Level Expert Group (AI-HLEG) present a list of seven key requirements that Human-centered, trustworthy AI systems should meet. These guidelines are useful for the evaluation of AI systems, but can be complemented by applied methods and tools for the development of trustworthy AI systems in practice. In this position paper we propose a framework for translating the AI-HLEG ethics guidelines into the specific context within which an AI system operates. This approach aligns well with a set of Agile principles commonly employed in software engineering. http://ceur-ws.org/Vol-2659/
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Aging diversity in organizations creates potential challenges, particularly for knowledge management, skills update and skills obsolescence. Intergenerational learning (IGL) involves knowledge building, innovation and knowledge transfer between generations within an organization (Ropes 2011). Serious games refer to the use of computer games in raising awareness about educational topics, acquiring new knowledge and skills by enabling learners to engage and participate in situations that would otherwise be impossible to experience (Corti 2006). Although learning with the use of serious games is similar to traditional learning in several cognitive respects, there are noted differences in the learning style and structure of learning using serious games. The success of learning using serious games lies in the actual involvement of a participant playing the game, which in turn, creates increased cognitive links with real-life situations allowing the individual to make relevant associations, to use mnemonic strategies with the facilitation of multi-dimensional educational aids (e.g., visual, auditory). Some of the beneficial aspects of learning with the use of serious games include the elevation of several cognitive skills, which are directly or indirectly implicated in the learning process. Among them are attention and visuo-spatial abilities, memory and motor skills. However, several barriers have been noted that fall into two general categories: a) health issues (e.g., cognitive strain, headaches) and b) psychological issues (e.g., social isolation, emotional disturbances). Since the training conditions are learner-centered and highly determined by the individual, there is increased need for evaluating the learning outcomes using specific success indicators. Examples of games that are designed to facilitate IGL are scarce, while there are no examples of IGL games in most EU countries. The purpose of this paper is to critically evaluate the current literature of theories on learning through serious games in adults and the elderly with reference to the cognitive mechanisms implicated, benefits and barriers in learning using new technologies in different generations. Secondly, this paper reviews the existence of serious games designed to facilitate IGL in Europe, as well as the characteristics of serious games in raising awareness that could be used to facilitate IGL. In doing so, specific focus is placed on the development of success indicators that determine the effectiveness of serious games on raising awareness on IGL.
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Artificial Intelligence (AI) offers organizations unprecedented opportunities. However, one of the risks of using AI is that its outcomes and inner workings are not intelligible. In industries where trust is critical, such as healthcare and finance, explainable AI (XAI) is a necessity. However, the implementation of XAI is not straightforward, as it requires addressing both technical and social aspects. Previous studies on XAI primarily focused on either technical or social aspects and lacked a practical perspective. This study aims to empirically examine the XAI related aspects faced by developers, users, and managers of AI systems during the development process of the AI system. To this end, a multiple case study was conducted in two Dutch financial services companies using four use cases. Our findings reveal a wide range of aspects that must be considered during XAI implementation, which we grouped and integrated into a conceptual model. This model helps practitioners to make informed decisions when developing XAI. We argue that the diversity of aspects to consider necessitates an XAI “by design” approach, especially in high-risk use cases in industries where the stakes are high such as finance, public services, and healthcare. As such, the conceptual model offers a taxonomy for method engineering of XAI related methods, techniques, and tools.
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Player behavioural modelling has grown from a means to improve the playing strength of computer programs that play classic games (e.g., chess), to a means for impacting the player experience and satisfaction in video games, as well as in cross-domain applications such as interactive storytelling. In this context, player behavioural modelling is concerned with two goals, namely (1) providing an interesting or effective game AI on the basis of player models and (2) creating a basis for game developers to personalise gameplay as a whole, and creating new user-driven game mechanics. In this article, we provide an overview of player behavioural modelling for video games by detailing four distinct approaches, namely (1) modelling player actions, (2) modelling player tactics, (3) modelling player strategies, and (4) player profiling. We conclude the article with an analysis on the applicability of the approaches for the domain of video games.
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Design schools in digital media and interaction design face the challenge of integrating recent artificial intelligence (AI) advancements into their curriculum. To address this, curricula must teach students to design both "with" and "for" AI. This paper addresses how designing for AI differs from designing for other novel technologies that have entered interaction design education. Future digital designers must develop new solution repertoires for intelligent systems. The paper discusses preparing students for these challenges, suggesting that design schools must choose between a lightweight and heavyweight approach toward the design of AI. The lightweight approach prioritises designing front-end AI applications, focusing on user interfaces, interactions, and immediate user experience impact. This requires adeptness in designing for evolving mental models and ethical considerations but is disconnected from a deep technological understanding of the inner workings of AI. The heavyweight approach emphasises conceptual AI application design, involving users, altering design processes, and fostering responsible practices. While it requires basic technological understanding, the specific knowledge needed for students remains uncertain. The paper compares these approaches, discussing their complementarity.
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One of the main aims of game AI research is the building of challenging and believable artificial opponents that act as if capable of strategic thinking. In this paper we describe a novel mechanism that successfully endows NPCs in real-time games with strategic planning capabilities. Our approach creates adaptive behaviours that take into account long-term and short term consequences. Our approach is unique in that: (i) it is sufficiently fast to be used for hundreds of agents in real time; (ii) it is flexible in that it requires no previous knowledge of the playing field; and (iii) it allows customization of the agents in order to generate differentiated behaviours that derive from virtual personalities.
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