1. A ǂFramework for applying data-driven AI/ML models in reliabilityRok Hribar, Margarita Antoniou, Gregor Papa, 2024, independent scientific component part or a chapter in a monograph Abstract: In this chapter, we present a framework for applying artificial intelligence (AI)/machine learning (ML) in reliability, in the context of the iRel40 project. Data-driven models are becoming an increasingly fruitful tool for detecting patterns in complex data and identifying the circumstances in which they occur. Using only data, gathered along the value chain, data-driven methods are now being used to detect indications of potential early failures, signs of wear out or degradation, and other unwanted events within the development, fabrication, or service phases of the electronic components and systems. We present general considerations that were found to be important during the iRel40 project, when designing pipelines that combine data processing with the AI/ML models for predicting or detecting reliability issues. This chapter serves as an introduction to the definitions and concepts used within the specific use cases that rely on the AI/ML methodology within the iRel40 project.
Keywords: machine learning, artificial intelligence, data-driven models
Published in DiRROS: 23.07.2024; Views: 78; Downloads: 31
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2. Artificial intelligence (AI) and strategic planning process within VUCA environments : a ǂresearch agenda and guidelinesRoberto Biloslavo, David A. Edgar, Erhan Aydin, Çağri Bulut, 2024, original scientific article Abstract: Purpose – This study demonstrates how artificial intelligence (AI) shapes the strategic planning process in
volatile, uncertain, complex and ambiguous (VUCA) business environments.Having adopted various domains
of the Cynefin framework, the research explores AI’s transformative potential and provide insights regarding
how organisations can harness AI-driven solutions for strategic planning.
Design/methodology/approach –This conceptual papertheorises the role of AIin strategic planning process
in a VUCA world by integrating extant knowledge across multiple literature streams. The “model paper”
approach was adopted to provide a theoretical framework predicting relationships among considered concepts.
Findings – The paper highlights potential application of the Cynefin framework to manage complexities in
strategic decision-making process, the transformative impact of AI at different stages of strategic planning,
the required strategic planning characteristics within VUCA to be supported by AI and the attendant
challenges posed by AI integration in the uncertain business landscape.
Originality/value –This study pioneers a theoretical exploration of AI’s role in strategic planning within the
VUCA business landscape, guided by the Cynefin framework. Thus, it enriches scholarly discourse and
expands knowledge frontiers.
Keywords: artificial intelligence, strategic planning, VUCA
Published in DiRROS: 12.07.2024; Views: 91; Downloads: 66
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3. Dreaming with AISheldon Juncker, 2023, original scientific article Abstract: our goal is to highlight the capabilities of modern, generative aI systems using the widely used and accessible ChatGPT text completion models from openaI, focusing on how they can be used for the analysis of dreams and dream journals. We start with a brief overview of the nature of dreams, methods of dream inter-pretation, and the importance of the human-dream relationship. We explore the ways that technology, specifically aI, fits into this space and examine the ways in which aI can be used to help us understand our dreams. We progress from simple dream interpretations, to interpretations according to different schools of thought, to interpreting symbols within individual dreams, and finally to analyzing pat-terns in individual dream journals. We conclude with a discussion of the ethical concerns surrounding aI and dreams, providing insights from past technological revolutions and how they have both helped and hindered the human endeavor. We finally outline what we believe to be a practical, realistic, and hopeful vision of how we see this field progressing based on the experiments and methodologies that were explored in this paper.
Keywords: dreams, dream interpretation, artificial intelligence, gernerative AI, psychoanalysis, ethics
Published in DiRROS: 13.05.2024; Views: 258; Downloads: 173
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4. The last sanctum of archetypes : rethinking dreams in the light of ancient knowledge and artificial intelligenceMaja Gutman Mušič, 2023, original scientific article Abstract: Despite numerous attempts to integrate dream research into a vast array of sci-entific disciplines, there appears to be no consensus on why and how we dream. This millennia-old universal human phenomenon appears to be too elusive to be thoroughly understood by a single scientific discipline and too complex and data--rich to be studied only theoretically. However, another dimension to dreams and dreaming could promise an integrative approach: the culture-historical compo-nent that merges with recent advances in artificial Intelligence. This paper briefly examines conceptual understandings of dreams before the dawn of modern science – specifically, the Native american, Mesopotamian, ancient Greek, and Hippocra-tic principles of dream practices and knowledge – in an attempt to understand the contemporary dream research field better and to outline future avenues for a data-driven approach while remaining grounded in its epistemological foundation.
Keywords: ancient dreaming, archetypes, artificial intelligence, dream data, cross-cultural dream analysis
Published in DiRROS: 13.05.2024; Views: 200; Downloads: 141
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