365 research outputs found

    Evolution of artificial intelligence research in Technological Forecasting and Social Change: Research topics, trends, and future directions

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    Artificial intelligence (AI) is a set of rapidly expanding disruptive technologies that are radically transforming various aspects related to people, business, society, and the environment. With the proliferation of digital computing devices and the emergence of big data, AI is increasingly offering significant opportunities for society and business organizations. The growing interest of scholars and practitioners in AI has resulted in the diversity of research topics explored in bulks of scholarly literature published in leading research outlets. This study aims to map the intellectual structure and evolution of the conceptual structure of overall AI research published in Technological Forecasting and Social Change (TF&SC). This study uses machine learning-based structural topic modeling (STM) to extract, report, and visualize the latent topics from the AI research literature. Further, the disciplinary patterns in the intellectual structure of AI research are examined with the additional objective of assessing the disciplinary impact of AI. The results of the topic modeling reveal eight key topics, out of which the topics concerning healthcare, circular economy and sustainable supply chain, adoption of AI by consumers, and AI for decision-making are showing a rising trend over the years. AI research has a significant influence on disciplines such as business, management, and accounting, social science, engineering, computer science, and mathematics. The study provides an insightful agenda for the future based on evidence-based research directions that would benefit future AI scholars to identify contemporary research issues and develop impactful research to solve complex societal problems

    (b2023 to 2014) The UNBELIEVABLE similarities between the ideas of some people (2006-2016) and my ideas (2002-2008) in physics (quantum mechanics, cosmology), cognitive neuroscience, philosophy of mind, and philosophy (this manuscript would require a REVOLUTION in international academy environment!)

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    (b2023 to 2014) The UNBELIEVABLE similarities between the ideas of some people (2006-2016) and my ideas (2002-2008) in physics (quantum mechanics, cosmology), cognitive neuroscience, philosophy of mind, and philosophy (this manuscript would require a REVOLUTION in international academy environment!

    The Impact of Artificial Intelligence on Strategic and Operational Decision Making

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    openEffective decision making lies at the core of organizational success. In the era of digital transformation, businesses are increasingly adopting data-driven approaches to gain a competitive advantage. According to existing literature, Artificial Intelligence (AI) represents a significant advancement in this area, with the ability to analyze large volumes of data, identify patterns, make accurate predictions, and provide decision support to organizations. This study aims to explore the impact of AI technologies on different levels of organizational decision making. By separating these decisions into strategic and operational according to their properties, the study provides a more comprehensive understanding of the feasibility, current adoption rates, and barriers hindering AI implementation in organizational decision making

    Navigating expectations for sustainable product design: a discursive psychology analysis of designers’ accounts

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    Sustainable design is vital to achieving sustainable development. It is commonly argued that designers should ensure more sustainable design decisions are made, based on environmental values, and should take responsibility for the sustainability of product outcomes. In this thesis, I treat decision-making, personal values, and responsibility as psychological concepts, thus examining the setting of sustainable design through a psychological lens. I argue that the ways these concepts are talked about in design literature construct expectations regarding how designers should act. However, there is ambiguity in this literature regarding what the designer’s role is expected to be. There is a great deal of prescriptive literature providing tools to advise designers on how to make more sustainable design decisions. Yet there is debate regarding how decisions are or should be made, who makes design decisions related to sustainability, and who is responsible for how sustainable product outcomes are. How these concepts are theorised in design, and how practitioner guidance on decision-making in sustainable design is framed by campaign groups, is likely to influence how design is done in practice. There is therefore a need to find out how designers are navigating expectations that they should be doing more sustainable design. There is a key gap in empirical literature of gathering and analysing designers’ accounts of how decision-making, values, and responsibility come into their work from their own perspectives. To start to fill this gap, I collected instances of interactional talk involving product designers’ verbal accounts in two different contexts in 2020. I carried out sixteen semi-structured interviews with an international sample of sustainability-focused product designers, asking questions about decision-making, values, and responsibility in specific recent design projects. I selected seven recordings of panel discussions at design conferences with a focus on sustainability from YouTube, based on their relevance to the concepts of decision-making, values, and responsibility. These two types of data allow the identification of similarities in ways of talking to others about the same topics in both private and public settings. I analysed extracts of the verbal data using discursive psychology, a method that has been specifically developed to analyse interactions, treating talk as action, and commonly seeking to respecify how psychological concepts are understood. In the thesis, I present my analysis of how decision-making, values, and responsibility related to sustainability are constructed and managed in the designers’ accounts. This enables insights into how designers navigate the expectations that they should be making more sustainable design decisions. My analysis shows: 1) The designers manage the delicateness of decision-making, values, and responsibility in design in different ways. For example, participants either reject or orient to expectations regarding how design decision-making should be done, often contradicting themselves. Participants orient to the idea of values influencing their decisions but focus on explaining where values came from rather than how they influence. They negotiate expectations of responsibility by either deflecting or assuming it, depending on the framing of questions asked. 2) Participants take opportunities to portray their identities as sustainability-focused designers, depicting longstanding commitment. 3) When the designers portray a lack of agency to make sustainability-relevant design decisions, they then claim agency through focusing on their role in influencing and ‘pushing’ others. Thus, the complexity for designers of managing expectations, personal commitment, and limited agency related to making products more sustainable in professional settings is portrayed. The practical and theoretical contributions of these findings are provided, outlining how authors and practitioners who seek to make design more sustainable should carefully consider the expectations built into the way they frame their arguments and advice. Overall, this thesis demonstrates the usefulness of interdisciplinary research for providing novel insights, through examining sustainable design using a contemporary, qualitative method from psychology

    Proceedings of the 8th Workshop on Detection and Classification of Acoustic Scenes and Events (DCASE 2023)

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    This volume gathers the papers presented at the Detection and Classification of Acoustic Scenes and Events 2023 Workshop (DCASE2023), Tampere, Finland, during 21–22 September 2023

    Development of an Algorithm for Multicriteria Optimization of Deep Learning Neural Networks

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    Nowadays, machine learning methods are actively used to process big data. A promising direction is neural networks, in which structure optimization occurs on the principles of self-configuration. Genetic algorithms are applied to solve this nontrivial problem. Most multicriteria evolutionary algorithms use a procedure known as non-dominant sorting to rank decisions. However, the efficiency of procedures for adding points and updating rank values in non-dominated sorting (incremental non-dominated sorting) remains low. In this regard, this research improves the performance of these algorithms, including the condition of an asynchronous calculation of the fitness of individuals. The relevance of the research is determined by the fact that although many scholars and specialists have studied the self-tuning of neural networks, they have not yet proposed a comprehensive solution to this problem. In particular, algorithms for efficient non-dominated sorting under conditions of incremental and asynchronous updates when using evolutionary methods of multicriteria optimization have not been fully developed to date. To achieve this goal, a hybrid co-evolutionary algorithm was developed that significantly outperforms all algorithms included in it, including error-back propagation and genetic algorithms that operate separately. The novelty of the obtained results lies in the fact that the developed algorithms have minimal asymptotic complexity. The practical value of the developed algorithms is associated with the fact that they make it possible to solve applied problems of increased complexity in a practically acceptable time. Doi: 10.28991/HIJ-2023-04-01-011 Full Text: PD

    Sustainable Value Co-Creation in Welfare Service Ecosystems : Transforming temporary collaboration projects into permanent resource integration

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    The aim of this paper is to discuss the unexploited forces of user-orientation and shared responsibility to promote sustainable value co-creation during service innovation projects in welfare service ecosystems. The framework is based on the theoretical field of public service logic (PSL) and our thesis is that service innovation seriously requires a user-oriented approach, and that such an approach enables resource integration based on the service-user’s needs and lifeworld. In our findings, we identify prerequisites and opportunities of collaborative service innovation projects in order to transform these projects into sustainable resource integration once they have ended

    Internet and Biometric Web Based Business Management Decision Support

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    Internet and Biometric Web Based Business Management Decision Support MICROBE MOOC material prepared under IO1/A5 Development of the MICROBE personalized MOOCs content and teaching materials Prepared by: A. Kaklauskas, A. Banaitis, I. Ubarte Vilnius Gediminas Technical University, Lithuania Project No: 2020-1-LT01-KA203-07810
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