Cognitive Architectures, AI, and Cognitive Science

Building a human-like integrative artificial cognitive system, that is, an artificial general intelligence (AGI), is the holy grail of the artificial intelligence (AI) field. Furthermore, a computational model that enables an artificial system to achieve cognitive development will be an excellent reference for brain and cognitive science. This paper describes an approach to develop a cognitive architecture by integrating elemental cognitive modules to enable the training of the modules as a whole. This approach is based on two ideas: (1) brain-inspired AI, learning human brain architecture to build human-level intelligence, and (2) a probabilistic generative model (PGM)-based cognitive architecture to develop a cognitive system for developmental robots by integrating PGMs. The proposed development framework is called a whole brain PGM (WB-PGM), which differs fundamentally from existing cognitive architectures in that it can learn continuously through a system based on sensory-motor information.In this paper, we describe the rationale for WB-PGM, the current status of PGM-based elemental cognitive modules, their relationship with the human brain, the approach to the integration of the cognitive modules, and future challenges. Our findings can serve as a reference for brain studies. As PGMs describe explicit informational relationships between variables, WB-PGM provides interpretable guidance from computational sciences to brain science. By providing such information, researchers in neuroscience can provide feedback to researchers in AI and robotics on what the current models lack with reference to the brain. Further, it can facilitate collaboration among researchers in neuro-cognitive sciences as well as AI and robotics.

The quest to create artificial consciousness stands as a formidable challenge at the intersection of artificial intelligence and cognitive science. This paper delves into the theoretical underpinnings, methodological approaches, and ethical considerations surrounding the concept of machine consciousness. By integrating insights from computational modeling, neuroscience, and philosophy, we propose a roadmap for comprehending and potentially realizing conscious behavior in artificial systems. Furthermore, we address the critical challenges of validating machine consciousness, ensuring its safe development, and navigating its integration into society.

Artificial intelligence (AI) is reshaping financial decision-making processes by enhancing analytical precision, reducing uncertainty, and enabling data-driven strategies. This research proposes an AI-driven strategic decision-support framework to optimize financial decision models in complex, uncertain environments. The framework integrates predictive analytics and optimization algorithms to help financial managers and policymakers improve investment decisions, risk assessment, and resource allocation. Building on decision-engineering principles, the study conceptualizes a hybrid model combining elements of machine learning, data analytics, and cognitive decision theory to strengthen the robustness and adaptability of strategic financial decisions. The proposed approach is structured to accommodate dynamic financial and market data, allowing for continuous improvement of forecasts and decision outcomes as new information becomes available. Preliminary results suggest that AI-enhanced decision frameworks can improve forecasting reliability and strategic responsiveness, particularly in contexts characterized by volatility and incomplete information. This research contributes to the growing body of work at the intersection of artificial intelligence and financial management, offering both theoretical and practical insights for the design of intelligent financial systems. This conceptual study also lays the groundwork for a forthcoming empirical validation using financial datasets to evaluate the framework’s robustness and adaptability.

The intersection of artificial intelligence (AI) and renewable energy systems is transforming how energy is generated, distributed, and consumed. This paper explores AI's applications in three key renewable energy sectors: solar, wind, and green hydrogen. AI technologies such as machine learning, neural networks, and predictive analytics address critical challenges such as the variability and intermittency of renewable resources. Additionally, AI-enhanced renewable systems have broader implications, such as democratizing energy access, fostering economic growth, and aligning with global climate goals like the Paris Agreement and the United Nations' Sustainable Development Goals. By leveraging the synergies between AI and renewable energy, society can achieve a cleaner, more resilient, and equitable energy future, critical for combating climate change and promoting global sustainability. This paper highlights the transformative potential of AI while offering insights into overcoming barriers to its widespread adoption in renewable energy systems.

The fundamental rights were included in the constitution because they were considered essential for the growth of the personality of every individual to preserve human dignity and liberty, such are freedom of speech, expression movement, residence, trade and occupation protected by the State because they were considered essential for the development of the personality of every individual. Right to life guaranteed under Fundamental Rights article 21 of the Constitution of India, is not merely a fundamental right, it includes right to life and personal liberty which are most precious, sacrosanct inalienable and fundamental of all the fundamental rights of citizens which also assure, right to privacy. Right to privacy which are the most essential and basic human rights in a democratic form of government. In our increasingly digitized world, the intersection of artificial intelligence (AI) and the right to privacy has become a paramount concern. The right to privacy, a fundamental human right, faces unprecedented challenges in the wake of AI advancements, like personal data, questions about consent, transparency, and individuals’ privacy. This research paper seeks to unravel the intricate relationship among all these realms, exploring the ethical dimensions that arise as AI technologies permeate various aspects of our lives. Furthermore, our discussion will extend to the ethical responsibilities of AI developers, policymakers, and users, will gain insights into designing AI systems that prioritize privacy, emphasizing the need for ethical considerations throughout the development life cycle. A responsible and privacy-centric approach to AI deployment. This seminar paper seeks to unravel the intricate relationship among all these realms, exploring the ethical dimensions that arise as AI technologies permeate various aspects of our lives. The right to privacy, a fundamental human right, faces unprecedented challenges in the wake of AI advancements, like personal data, questions about consent, transparency, and individuals’ privacy. The methodology of the study is the descriptive analysis of doctrinal research and based on secondary sources like book, journal. articles and case laws

Artificial Intelligence (AI) and the Special Theory of Relativity (STR) have united to form a new multidisciplinary area that combines the relativistic structure of space-time with computational intelligence. This study looks at how AI can be used to describe, simulate, and analyze relativistic phenomena. On the other side, we look at how the relativistic restrictions of time and space influence the design and operation of intelligent systems, particularly in space-based and high-velocity applications. Theory implications, future relativistic AI system frameworks, and practical applications including quantum communication protocols, relativistic sensor networks, and self-navigating spacecraft are all examined. While AI and Special Relativity appear to be diametrically opposed, their mathematical foundations, particularly those involving tensors and space-time, suggest potential synergies. This synthesis creates new opportunities for space exploration, computer science, and physics study. The traditional boundaries of AI and the Special Theory of Relativity are being challenged by their interaction.

In recent years, the intersection of artificial intelligence (AI) and talent management has revolutionized the way organizations identify, recruit, and retain top talent. This chapter explores the transformative impact of machine learning on talent management processes, shedding light on the innovative ways AI is reshaping recruitment and retention strategies. The discourse then shifts to AI-powered recruitment, exploring the utilization of predictive analytics to forecast hiring needs, the automation of resume screening for efficiency and bias reduction, and the application of video and behavioral analysis to refine candidate assessment processes. These AI-driven methodologies not only enhance the precision of talent acquisition but also ensure a more profound alignment between job requirements and candidate capabilities.Further, the chapter addresses the role of AI in bolstering employee retention, with a focus on predictive modeling to identify turnover risks and personalized development programs.

The intersection of Artificial Intelligence (AI) and educational leadership has garnered significant attention in recent years due to advancements in knowledge technology and digital literacy. Despite growing interest from scholars and policymakers over the past decade, a comprehensive review of AI in the context of educational leadership and policy is still lacking. This study addresses this gap by systematically analyzing 168 peer-reviewed papers published between 2000 and 2024 through bibliometric analysis. It then conducts a thematic analysis of the top 20 highly cited papers to focus on key challenges and opportunities identified in applying AI from the perspectives of educational leadership and management. The results reveal major trends, geographic distributions, topical focuses, core concepts, themes, and their interconnections, as well as their implications from the viewpoints of educational leadership and management. The study highlights strategies and potential for integrating AI into educational leadership roles in the current era. Additionally, this study discusses the implications, limitations, and future research directions based on the reviewed studies.

The intersection of artificial intelligence (AI) and bilingualism represents a rapidly evolving field with far-reaching implications for language education and global communication. Adaptive learning systems powered by AI algorithms transform the way individuals acquire new languages by tailoring instructions to each learner's unique needs, pace, and learning style. These systems analyze user performance in real time by adjusting the difficulty and content of lessons to optimize the learning outcomes. Concurrently, natural language processing (NLP) and speech recognition technologies are enhancing the accuracy and efficiency of language translation tools, breaking down the communication barriers between speakers of different languages. As AI continues to advance, its impact on bilingualism extends beyond traditional language learning environments. AI-powered chatbots and virtual language partners provide learners with opportunities for immersive real-time conversational practices that simulate authentic language interactions.

The intersection of Artificial Intelligence (AI) and Agricultural Extension holds immense potential for enhancing sustainable development in the agricultural sector. This paper explores the transformative role of AI in agricultural extension services and its implications for sustainable development. Through a comprehensive literature review, data analysis, and case studies, we assess the current state of AI applications in agriculture and highlight the gaps and challenges in this field. Our study reveals that AI technologies offer innovative solutions for knowledge dissemination, decision support, and resource management in agriculture, contributing to increased yields, reduced environmental impact, and improved livelihoods for farmers. However, challenges related to data quality, accessibility, and ethical considerations need to be addressed. The paper also discusses the policy implications of integrating AI into agricultural extension programs and suggests future research directions. In conclusion, this research underscores the critical role of AI in agricultural extension for sustainable development, emphasizing its potential to revolutionize the agricultural landscape.

In the contemporary era of advanced gaming platforms and digital distribution, artificial intelligence (AI) could play a transformative role in revitalizing older games. This paper explores how AI-driven techniques and artificial methods are reshaping remasters, remakes, ports, and collections, making classic titles relevant for modern audiences while leveraging new monetization models. For instance, advanced character AI and algorithmic improvements can provide richer, more realistic interactions in remakes compared to their original iterations. The study also delves into the intersection of AI and nostalgia marketing, examining older games. We analyze how remasters improve graphics and sound, while remakes often completely rework AI, gameplay and design. These forms also allow old titles to be updated with enhanced features that were not present in the titles at the time of release, e.g. improved character AI. An important part of the research is also exploring how these approaches are perceived by gamers themselves and how they are rated in reviews. The aim of this exploratory study is to define and describe the current ways of monetising old titles.

The intersection of artificial intelligence (AI) and agricultural finance has emerged as a transformative force in enhancing the efficiency and sustainability of farming practices. This review article explores the multifaceted applications of AI technologies in agricultural finance, focusing on credit scoring, risk assessment, and financial decision-making for farmers and agricultural businesses. We examine the integration of machine learning algorithms and big data analytics in providing tailored financial products, enabling precision agriculture, and improving access to credit in under banked communities. By synthesizing current literature and case studies, we identify key challenges and opportunities, such as the need for data security, transparency, and the democratization of AI tools in rural finance. Furthermore, we discuss the potential of AI to address climate change impacts on agriculture and its implications for financing sustainable practices. This article aims to provide stakeholders in the agricultural finance sector—policymakers, financial institutions, and technology developers—with a comprehensive understanding of how AI can drive innovation and improvement in financing agricultural activities, ultimately fostering a resilient and sustainable food system.

This comprehensive chapter delves into the intersection of artificial intelligence (AI) technology with cross-platform design principles to enhance usability and user experience (UX) across diverse digital environments. With the proliferation of platforms and services, designers face the challenge of creating seamless and satisfying user experiences across web, mobile, wearable, IoT, virtual reality (VR), augmented reality (AR), and other digital platforms. AI offers innovative solutions to address these challenges by automating tasks, personalizing interactions, and optimizing user interfaces. This chapter discusses the role of AI in enhancing cross-platform design, covering topics such as AI-driven user research, multi-modal interaction design, personalized content delivery, and AI-based performance optimization. Case studies and practical insights illustrate the application of AI technology in improving usability and UX across various digital contexts.

In chess, zugzwang describes a scenario where any move worsens the player’s position. Organisations face a similar dilemma right now at the intersection of artificial intelligence (AI) and cyber security. AI adoption creates an inevitable paradox: delaying it poses strategic risks, rushing it introduces poorly understood vulnerabilities, and even incremental adoption leads to cascading complexities. In this paper we formalise this challenge as the AI security zugzwang — a phenomenon whereby security leaders must make decisions under conditions of inevitable risk. Grounded in game theory, security economics and organisational decision theory, we characterise AI security zugzwang through three key properties: forced movement, predictable vulnerability creation and temporal pressure. Additionally, we develop a taxonomy to categorise forced-move scenarios across AI adoption, implementation, operational and governance contexts and provide corresponding strategic mitigations. Our framework is supported by a practical decision flowchart, demonstrated through a real-world example of Copilot adoption, thereby showing how security leaders can manage zugzwang positions balancing risk and innovation. This article is also included in The Business & Management Collection which can be accessed at https://hstalks.com/business/.

The intersection of artificial intelligence (AI) with cancer research is increasing, and many of the advances have focused on the analysis of cancer images. To describe and synthesize the literature on the diagnostic accuracy of AI in early imaging diagnosis of cervical cancer following Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA-ScR). Arksey and O'Malley methodology was used and PubMed, Scopus, and Google Scholar databases were searched using a combination of English and Spanish keywords. Identified titles and abstracts were screened to select original reports and cross-checked for overlap of cases. A descriptive summary was organized by the AI algorithm used, total of images analyzed, data source, clinical comparison criteria, and diagnosis performance. We identified 32 studies published between 2009 and 2022. The primary sources of images were digital colposcopy, cervicography, and mobile devices. The machine learning/deep learning (DL) algorithms applied in the articles included support vector machine (SVM), random forest classifier, k-nearest neighbors, multilayer perceptron, C4.5, Naïve Bayes, AdaBoost, XGboots, conditional random fields, Bayes classifier, convolutional neural network (CNN; and variations), ResNet (several versions), YOLO+EfficientNetB0, and visual geometry group (VGG; several versions). SVM and DL methods (CNN, ResNet, VGG) showed the best diagnostic performances, with an accuracy of over 97%. We concluded that the use of AI for cervical cancer screening has increased over the years, and some results (mainly from DL) are very promising. However, further research is necessary to validate these findings.