artificial intelligence

Operational Art In The Digital Age

Beyond their socio-economic implications, disruptive technologies influence all instruments of state power, especially the military. Artificial intelligence (AI), Machine Learning (ML), and autonomous systems are transforming traditional ways of thinking, leadership concepts, and processes, necessitating new approaches to prepare military decision-makers at all levels of command for a complex, technology-driven environment. This article explores how these technologies are reshaping military decision-making and outlines why adapting education and processes is critical to maintaining operational effectiveness in future conflicts. In most Western armed forces, the operational level of command serves as an interface between the tactical and military-strategic levels. It enables the translation of strategic objectives into tactical actions. In addition to its vertically hierarchical role, the operational level also performs a horizontal interface function with other state and non-state actors. These environmental systems (System of Systems), involving a wide range of actors, increase the complexity of the operational level of command.

Deriving business insights from the brain:How neural activity illuminates economic decision-making

Modern businesses increasingly rely on big data and artificial intelligence. However, behind every data point lies a human decision. To be successful, managers must also understand the psychological processes that drive decision-making. Traditional research methods, such as surveys and focus groups, provide valuable insights, but may fail to capture the subconscious processes that influence decisions. This dissertation demonstrates how neuroscientific methods can generate novel business insights by measuring these processes in the brain. Using functional magnetic resonance imaging (fMRI), this dissertation studies brain activity in three distinct domains of business. In marketing, it shows that storytelling advertisements persuade consumers not because they share a character’s feelings, but because they engage brain networks involved in understanding a character’s intentions. In finance, it finds that reward-related brain activity of professional investors is associated with future stock performance, even when their explicit market forecasts are not. In organizational management, brain activity reveals how biases shape social judgments: while expectations of cooperation are based on perceived similarity and attractiveness, actual cooperation depends primarily on the explicit commitments that people make. Together, the findings of this dissertation leverage neuroscientific methods to reveal psychological processes underlying economic decision-making in business contexts. By illuminating the hidden mechanisms of narrative persuasion, financial intuition, and interpersonal cooperation, brain activity informs business practices in ways that self-report studies cannot. Even as artificial intelligence continues to transform businesses, these findings prove that the biological mind remains an indispensable source of insights.

The AI Moment Isn't About AI: Lessons in Learning, Leadership, and Trust

Artificial Intelligence may be the latest technology transforming education, but the challenges institutions face are not new. From learning management systems and online learning to mobile devices and collaboration tools, every major technology shift has followed a familiar pattern: experimentation, uncertainty, governance, and ultimately scale. The technology changes, but the leadership and learning challenges remain. This session explores what previous waves of educational technology can teach us about navigating the AI era. Participants will examine how AI is changing long-held assumptions about teaching, learning, and institutional decision-making, while exploring practical strategies for evaluating impact, increasing institutional visibility, and creating responsible governance frameworks. The discussion will focus on maintaining human judgment, discernment, and accountability as AI becomes increasingly integrated into learning environments. Attendees will leave with a framework for moving beyond the excitement and anxiety surrounding AI to focus on what matters most: fostering meaningful learning, supporting learner growth, and using technology in ways that align with educational purpose and values.

AI-POWERED PREDICTIVE ANALYTICS FOR INTELLIGENT DECISION SUPPORT SYSTEMS

The digital landscape is undergoing a seismic shift, moving beyond the era of simple data storage into an age where the true value of information lies in its ability to forecast the future. AI-Powered Predictive Analytics for Intelligent Decision Support Systems is designed as a comprehensive guide to navigating this transition, blending the technical rigor of machine learning with the pragmatic needs of modern industrial and academic decision-making. In an increasingly complex world, the scope of global systems—spanning healthcare, finance, and manufacturing—has begun to exceed the limits of unassisted human intuition. This book explores the vital synergy between Artificial Intelligence (AI) and Decision Support Systems (DSS), illustrating how predictive modeling transforms raw, historical data into a strategic asset that anticipates trends, mitigates risks, and optimizes outcomes in real-time. While many existing texts focus solely on the mathematical algorithms of machine learning or the administrative management of information systems, this work bridges the gap by covering the entire system lifecycle. It takes the reader on a structured journey from the foundational theories of predictive analytics to the cutting edge of autonomous decision systems. Throughout these chapters, we delve into the intricate nuances of feature engineering, data governance, and the deployment of models within modern MLOps frameworks. Furthermore, the book provides deep technical explorations of supervised learning, ensemble methods, and deep learning architectures like CNNs and LSTMs, while placing a heavy emphasis on Explainable AI (XAI) to ensure that automated decisions remain transparent and trustworthy. The theory presented in these pages is anchored by extensive case studies that reflect both global and regional perspectives, providing a balanced view of how these technologies are implemented across diverse economic and regulatory environments. As we move toward a future of fully autonomous systems, the book addresses the critical challenges of algorithmic bias, data privacy, and the indispensable role of human-AI collaboration. This text is intended for researchers, data scientists, and business leaders alike—anyone who seeks to understand the strategic implications and technical requirements of building systems that are not only intelligent and efficient but also ethical and transparent. It is our hope that this roadmap serves as a vital resource for those looking to harness the analytical power of machines to solve the most pressing challenges of our time.

AI-POWERED PREDICTIVE ANALYTICS FOR INTELLIGENT DECISION SUPPORT SYSTEMS

The digital landscape is undergoing a seismic shift, moving beyond the era of simple data storage into an age where the true value of information lies in its ability to forecast the future. AI-Powered Predictive Analytics for Intelligent Decision Support Systems is designed as a comprehensive guide to navigating this transition, blending the technical rigor of machine learning with the pragmatic needs of modern industrial and academic decision-making. In an increasingly complex world, the scope of global systems—spanning healthcare, finance, and manufacturing—has begun to exceed the limits of unassisted human intuition. This book explores the vital synergy between Artificial Intelligence (AI) and Decision Support Systems (DSS), illustrating how predictive modeling transforms raw, historical data into a strategic asset that anticipates trends, mitigates risks, and optimizes outcomes in real-time. While many existing texts focus solely on the mathematical algorithms of machine learning or the administrative management of information systems, this work bridges the gap by covering the entire system lifecycle. It takes the reader on a structured journey from the foundational theories of predictive analytics to the cutting edge of autonomous decision systems. Throughout these chapters, we delve into the intricate nuances of feature engineering, data governance, and the deployment of models within modern MLOps frameworks. Furthermore, the book provides deep technical explorations of supervised learning, ensemble methods, and deep learning architectures like CNNs and LSTMs, while placing a heavy emphasis on Explainable AI (XAI) to ensure that automated decisions remain transparent and trustworthy. The theory presented in these pages is anchored by extensive case studies that reflect both global and regional perspectives, providing a balanced view of how these technologies are implemented across diverse economic and regulatory environments. As we move toward a future of fully autonomous systems, the book addresses the critical challenges of algorithmic bias, data privacy, and the indispensable role of human-AI collaboration. This text is intended for researchers, data scientists, and business leaders alike—anyone who seeks to understand the strategic implications and technical requirements of building systems that are not only intelligent and efficient but also ethical and transparent. It is our hope that this roadmap serves as a vital resource for those looking to harness the analytical power of machines to solve the most pressing challenges of our time.

A Stability-Theoretic Foundation of Artificial General Intelligence

AbstractThis work proposes a formal foundation for Artificial General Intelligence (AGI) basedon stability theory and geometric control. We introduce a framework in whichintelligence is defined not as optimization of reward, imitation of cognition, or pursuitof stimulus variation, but as the capacity of a system to preserve viable trajectoriesunder conditions of dynamic instability and environmental uncertainty.The central object of the theory is an instability functional (\zeta(S)) defined over astate manifold (\mathcal{M}), together with a dimensionless stability invariant:[G(S) = \frac{\gamma}{\alpha C + D},]where (\gamma) denotes adaptive capacity, (C) structural complexity, (\alpha) systemload, and (D) accumulated structural deviation. The condition (G(S) \ge 1) defines theadmissible domain of operation, while (G(S) < 1) characterizes collapse-prone regimes.Within this framework, intelligence is formalized as a control process:[u(t) = -k \nabla \zeta(S(t)),]which enables the system to maintain trajectories inside the stability domain despiteperturbations. We demonstrate that behaviors commonly associated with intelligentsystems—adaptation, exploration, learning, and generation of new structuredstates—emerge as consequences of stability-constrained motion in the state space,rather than as primary objectives.In particular, we show that the drive toward state-space expansion and variation is notfundamental, but arises as a secondary effect of navigating instability gradients underbounded control resources. This result unifies previously disconnected paradigms,including reward-based learning, unsupervised adaptation, and stimulus-drivenbehavioral variation, as restricted regimes within a broader stability-theoretic structure. The proposed framework provides a rigorous definition of AGI as a system capable ofsustaining admissible trajectories across open-ended environments. It furtherestablishes explicit conditions for collapse, limits of controllability, and measurableindicators of system viability. These results position stability-preserving control as thefundamental principle underlying general intelligence across artificial, biological, andsocio-technical systems.

A Stability-Theoretic Foundation of Artificial General Intelligence

AbstractThis work proposes a formal foundation for Artificial General Intelligence (AGI) basedon stability theory and geometric control. We introduce a framework in whichintelligence is defined not as optimization of reward, imitation of cognition, or pursuitof stimulus variation, but as the capacity of a system to preserve viable trajectoriesunder conditions of dynamic instability and environmental uncertainty.The central object of the theory is an instability functional (\zeta(S)) defined over astate manifold (\mathcal{M}), together with a dimensionless stability invariant:[G(S) = \frac{\gamma}{\alpha C + D},]where (\gamma) denotes adaptive capacity, (C) structural complexity, (\alpha) systemload, and (D) accumulated structural deviation. The condition (G(S) \ge 1) defines theadmissible domain of operation, while (G(S) < 1) characterizes collapse-prone regimes.Within this framework, intelligence is formalized as a control process:[u(t) = -k \nabla \zeta(S(t)),]which enables the system to maintain trajectories inside the stability domain despiteperturbations. We demonstrate that behaviors commonly associated with intelligentsystems—adaptation, exploration, learning, and generation of new structuredstates—emerge as consequences of stability-constrained motion in the state space,rather than as primary objectives.In particular, we show that the drive toward state-space expansion and variation is notfundamental, but arises as a secondary effect of navigating instability gradients underbounded control resources. This result unifies previously disconnected paradigms,including reward-based learning, unsupervised adaptation, and stimulus-drivenbehavioral variation, as restricted regimes within a broader stability-theoretic structure. The proposed framework provides a rigorous definition of AGI as a system capable ofsustaining admissible trajectories across open-ended environments. It furtherestablishes explicit conditions for collapse, limits of controllability, and measurableindicators of system viability. These results position stability-preserving control as thefundamental principle underlying general intelligence across artificial, biological, andsocio-technical systems.

Explainable artificial intelligence for plant disease detection: a scoping review of methods, datasets, and evaluation practices

How generative artificial intelligence reshapes students’ interdisciplinary cognitive structures: Implications for management education

Training Without Gathering the Data: A Interdisciplinary Mapping Review of Federated Learning and Privacy-Preserving AI

This article presents a narrative review of Federated Learning and Privacy-Preserving AI in the context of Artificial Intelligence. The literature on this topic has expanded substantially over recent decades, yet it remains fragmented across subfields, methods, and national research traditions. Drawing on an interpretive synthesis of representative contributions, the review reconstructs the historical development of the area, examines the conceptual foundations and definitional disputes that organize its debates, and maps the contemporary landscape of research, including the methodological shift toward data-intensive approaches and the institutional pressures that shape publication practice. Particular attention is given to the role of federated learning and privacy as organizing themes, and to the conditions under which findings from different research traditions can be brought into productive comparison. The review identifies three synthetic conclusions: the literature is cumulatively strong but organizationally weak; methodological pluralism is better understood as a resource than as a defect; and the growing practical salience of the topic raises the stakes of its unresolved conceptual questions. An agenda for future work is proposed, emphasizing integrative research designs, transparent synthesis practices, and the protection of definitional and infrastructural work on which cumulative progress depends. The article is intended as both a reference map for newcomers and a provocation for specialists in Artificial Intelligence.

Training Without Gathering the Data: A Interdisciplinary Mapping Review of Federated Learning and Privacy-Preserving AI

This article presents a narrative review of Federated Learning and Privacy-Preserving AI in the context of Artificial Intelligence. The literature on this topic has expanded substantially over recent decades, yet it remains fragmented across subfields, methods, and national research traditions. Drawing on an interpretive synthesis of representative contributions, the review reconstructs the historical development of the area, examines the conceptual foundations and definitional disputes that organize its debates, and maps the contemporary landscape of research, including the methodological shift toward data-intensive approaches and the institutional pressures that shape publication practice. Particular attention is given to the role of federated learning and privacy as organizing themes, and to the conditions under which findings from different research traditions can be brought into productive comparison. The review identifies three synthetic conclusions: the literature is cumulatively strong but organizationally weak; methodological pluralism is better understood as a resource than as a defect; and the growing practical salience of the topic raises the stakes of its unresolved conceptual questions. An agenda for future work is proposed, emphasizing integrative research designs, transparent synthesis practices, and the protection of definitional and infrastructural work on which cumulative progress depends. The article is intended as both a reference map for newcomers and a provocation for specialists in Artificial Intelligence.

MATHEMATICAL FOUNDATIONS OF AI-ASSISTED AUTOMATED TEXTURING OF 3D OBJECTS

Abstract The article explores the mathematical foundations and architectural solutions for developing an automated texturing system for three-dimensional objects using artificial intelligence. It analyzes the limitations of classical UV mapping and justifies the use of a projection mapping method combined with the developed "Smart Stencil" algorithm to eliminate visual artifacts and texture stretching. Particular attention is given to the integration of latent diffusion models and the ControlNet architecture, which utilizes normalized depth maps to accurately control the spatial structure of textures. The proposed approach enables efficient generation of high-quality seamless textures on consumer-grade hardware in alignment with the actual geometric shape of 3D models.