Intelligence Beyond Human Limits: The Evolution and Implications of Machine Cognition

Chapter 7 - Artificial general intelligence

Artificial Intelligence (AI) is a branch of computer science that seeks to simulate certain aspects of human intelligence [1]. Specifically, it aims to enable computers and software to impersonate human cognitive functions such as thinking, planning, learning, communicating, perceiving the environment, and moving and manipulating objects [2-3]. Such activities are generally considered to require intelligence when performed by humans or other animals. Although AI has achieved significant success in many areas, it still has some limitations. The development of AI can be broadly categorized into three groups: Narrow AI, Artificial General Intelligence, and Artificial Superintelligence. Narrow AI can perform certain specific tasks at a narrow level of intelligence. General AI can perform any intellectual task in various domains that humans are capable of. Superintelligent AI can perform intellectual tasks surpassing human intelligence [2,4]. As the definition of intelligence is subjective and no clear consensus exists, these categorizations are based on possible distinctions rather than standards. Regardless of these limitations and classifications, current advances in AI have led to widespread usage in various sectors, including e-commerce, education, research, and service industries.

This paper explains the ethical implications of artificial intelligence (AI) development towards achieving the level of artificial general intelligence (AGI) and analyzes the need for its social control. With the acceleration and intensification of AI growth and development, especially with the ongoing AI race, the transition from narrow AI to AGI becomes certain. The achievement of generative AI, which climaxes with chatbots (such as ChatGPT and others), transforms AI into a machine capable of creation. Although this AI application still appears relatively limited by algorithms, its learning ability is remarkable, and continuous advancements and the launch of increasingly sophisticated versions bring it ever closer to the AGI model. Each day brings us closer to that moment, which will signify AI’s transition from narrow AI to AGI. Unlike narrow AI, AGI deeply delves into the realm of ethics, and interpersonal and social relationships. Regulating AI-related policy and legally controlling AI represents one of the most serious and complex issues. In recent years, the community, led by corporate executives developing AI, prominent experts, researchers, scientists, writers, and other stakeholders, has made significant steps towards raising public awareness of the risks posed by advanced AI and making decisions, initiatives, and measures for monitoring, analyzing, and socially controlling the use of AI.

The research relevance is determined by the importance of a thorough study of methods, schemes and models used by artificial intelligence to mechanise creativity in modern conditions of active technological development. The study aims to analyse the main processes taking place in modern art in connection with active technologization of work processes, to identify the leading concepts regarding the possibility of creating machine art in the future, etc. The employed methods are theoretical, such as analysis, systematisation, generalisation, etc., for studying key problems and further development of creativity based on artificial intelligence. The study examines in detail the main developments of Artificial General Intelligence and Artificial Narrow Intelligence, in particular the achievements of Generative adversarial networks and Creative adversarial networks. Artificial intelligence-generated art demonstrates the remarkable capabilities of technologies. The evolving artificial intelligence in the arts introduces “digital art”. Generative Adversarial Networks are used as a foundational tool for artists who use digital methods and texture generation to create unique compositions. Furthermore, sculptors collaborate with artificial intelligence tools to convert drawings into 3D models or transform historical art databases into sculptures. Creative thinking, a hallmark of human intelligence, is determined as artificial intelligence’s ability to generate new and original ideas. The development of emotional intelligence in artificial intelligence enables empathetic responses and the identification of human emotions through voice and facial expressions. The issues of authorised internationality, awareness of the creative process, psychological foundations of artificial empathy and emotional intelligence define the prospects for the development of neuroscience. Challenges persist in defining creativity, authorship, and legal aspects of artificial intelligence-generated art. The study materials may be useful for artists, art educators, technologists, and researchers interested in the intersection of technology and art, legal professionals (especially intellectual property law), and individuals involved in artificial intelligence development may find these findings valuable

The landscape of embodied Artificial Intelligence (AI) with liquid crystals (LC) as a hardware solution for sensory and communication capabilities simultaneously is first proposed in this work, targeting an expanded portfolio of devices for city-scale public sectors beyond small-scale indoor applications. The productivity and necessity of using LC are due to its versatility of continuous phase programmability (phase shifters), impedance variability (impedance-tuning adapters), resonance tunability (variable bandpass filters), and polarization changeability (variable polarizers). Furthermore, the stepless tuning (analogue functionality) for high-fidelity, high-resolution spatial/temporal control is highly suitable for explainable and scalable AI. All these functionalities are achievable in low insertion loss and with low-cost, low-power (up to 10 V) electronic biasing, exhibiting the potential of upgrading into Artificial General Intelligence (AGI) and gravitating towards Artificial Superintelligence (ASI) products, solutions and services. This work identifies three cross-domain research activities that integrate AI with LC, drawing on insights gained from our previous research endeavors specific to LC. Additionally, it explores the challenges and strategic roadmaps that support LC-assisted tunability within the contexts of Artificial General Intelligence (AGI) and Artificial Superintelligence (ASI).

This paper explores the critical role of internal control (IC) in the management of enterprises and organizations, emphasizing its importance for sustainable growth and operational efficiency. It further investigates the potential of advanced artificial intelligence (AI) technologies, such as Artificial General Intelligence (AGI), Artificial Super Intelligence (ASI), and Universal Basic Income (UBI) related systems, in enhancing internal control mechanisms. The paper provides a comprehensive analysis of how AI can be integrated into internal control to improve efficiency, execution, and governance effectiveness, supported by practical case studies and theoretical frameworks from recent academic research. Keywords: Internal Control, Business Management, Artificial General Intelligence (AGI), Artificial Super Intelligence (ASI), Unive

The present study investigates the potential impact of artificial intelligence (AI) on the future trajectory of human civilization. It focuses on topics such as super-exponential growth, the potential emergence of a galactic civilization, and the associated "doom" hazards. A significant advancement in machine intelligence with human-like consciousness, strong artificial intelligence (AI), also known as artificial general intelligence (AGI), creates new opportunities and capacities. There's growing anxiety about the risk that weak AI will eventually become strong AI. Every year, new transformer models that are more like human interactions are being created, and we have already witnessed some indications of AGI. It is anticipated that AI will reach a "singularity" and advance on its own without assistance from humans. This thesis explores the theoretical and practical foundations, model building blocks, development processes, challenges, and ethical issues surrounding the creation of Consciousness AI (AGI). This paper examines the meaning of the term "technological singularity," the various types of singularities that have no point of return idea, the philosophical risks associated with the development of AI, and the implications of AI singularity for monetary theory and the new economic order. As a new perspective on the deployment of ethical AI in the face of tremendous technological advancements, the study not only contributes to the theoretical discourse but also explores the possible practical implications of AI on our shared future. Several obstacles to AI advancement are covered in the paper, along with prospective directions for future research.

This study investigates how generative artificial intelligence (AI) could influence the media environment of that country. Given their prevalence in the industry, it seeks to uncover how general process of national advancement is supported by AI-powered technologies in media content generation, distribution, and personalisation. The research gathers data from media professionals, techies, and communication academics in major Chinese cities all around using properly crafted questionnaires in a quantitative manner. One examined the interactions of AI-driven media innovation, modernising pace, and consequences of national development using statistical methods like structural equation modelling and regression analysis. Linking modernism with national development projects, the indices of generative AI used in media show a definite positive link with their influence. Generative AI is becoming more and more important for China's attempts to modernise its communication infrastructure and undergo digital transformation, thereby enhancing the nation's cultural impact, technical capacity, and media creation. Modernism approaches enhance the possibilities presented by AI in media; this research reveals more dynamic storylines, audience interaction feasible, and global outreach. Every one of these components helps to forward objectives for national growth. Regarding developing plans to mix AI with goals for sustainable development, the findings give legislators, media outlets, and AI developers’ useful insights. This study offers a structure for further studies in new media technologies and national transformation by means of an experimental interaction between generative AI and national development seen from the perspective of modernism. It opens the conversation on the social opportunities AI offers.

Artificial General Intelligence is the idea that someday an hypothetical agent will arise from artificial intelligence (AI) progresses, and will surpass by far the brightest and most gifted human minds. This idea has been around since the early development of AI. Since then, scenarios on how such AI may behave towards humans have been the subject of many fictional and research works. This paper analyzes the current state of artificial intelligence progresses, and how the current AI race with the ever faster release of impressive new AI methods (that can deceive humans, outperform them at tasks we thought impossible to tackle by AI a mere decade ago, and that disrupt the job market) have raised concerns that Artificial General Intelligence (AGI) might be coming faster that we thought. In particular, we focus on 3 specific families of modern AIs to develop the idea that deep neural networks, which are the current backbone of nearly all artificial intelligence methods, are poor candidates for any AGI to arise due to their many limitations, and therefore that any threat coming from the recent AI race does not lie in AGI but in the limitations, uses, and lack of regulations of our current models and algorithms. This article appears in the AI & Society track.

The history of artificial intelligence (AI), from its conception to the creation of general AI (Gen-AI), is a fascinating story of human inventiveness, technical growth, and philosophical research. This article examines the historical milestones, major inventions, and transformational concepts that have influenced AI's trajectory. Beginning with early symbolic AI and rule-based systems, it investigates the shift to machine learning, highlighting discoveries in neural networks and deep learning that transformed disciplines such as computer vision and natural language processing. The introduction of generative models, such as GANs and VAEs, resulted in a considerable increase in AI capabilities, paving the path for Gen-AI. Unlike narrow AI, Gen-AI strives to imitate human-like intelligence and adaptability across a wide range of jobs, bringing serious ethical and philosophical concerns. This essay also looks at the current state of Gen-AI, its problems, and possible applications in healthcare, education, finance, and other areas. It finishes by picturing a future in which human-machine collaboration and ethical AI development are prioritized, highlighting the need of continual learning and responsible innovation.

Increasingly artificial intelligence (AI) has been cast in "god-like" roles (to name a few: film industry - Matrix, The Creator, Mission Impossible, Foundation, Dune etc.; literature - Children of Time, Permutation City, Neuromancer, I Have no Mouth and I Must Scream, Alphaville etc.). This trend has accelerated with the advent of sophisticated Large Language Models such as ChatGPT. For this phenomenon, where AI is perceived as divine, we use the term GPTheology, where ChatGPT and other AI models are treated as potential oracles of a semi-divine nature. This paper explores the emergence of GPTheology as a form of techno-religion, examining how narratives around AI echo traditional religious constructs. We draw on community narratives from online forums - Reddit - and recent projects - AI-powered Mazu Statue in Malaysia (Lu, 2025); "ShamAIn" Project in Korea (He-rim, 2025); AI Jesus in a Swiss Church (Kennedy, 2024). These examples show striking similarities to technological notions of the Singularity and the development of Artificial General Intelligence (AGI). Additionally, we analyse how daily interactions with AI are acquiring ritualistic associations and how AI-centric ideologies clash with or are integrated into established religions. This study uses a dataset of Reddit posts discussing AI to identify recurring themes of salvation, prophecy, and demonization surrounding AI. Our findings suggest that new belief systems are developing around AI, and this carries both philosophical and sociotechnical implications. Our paper critically analyses the benefits and dangers, as well as the social, political and ethical challenges of this development. This transdisciplinary inquiry highlights how AI and religion are increasingly intertwined, prompting necessary questions about humanity's relationship with its creations and the future of belief.

In this final set of explorations/meditations (of three), we examine the requirements for developing artificial general intelligence (AGI) through the lens of human cognitive architecture, with particular emphasis on the role of narrative selfhood and social cognition. Drawing on perspectives from cognitive science, philosophy of mind, and artificial intelligence research, we critically evaluate current claims about the capabilities of large language models, particularly regarding their purported achievements of theory of mind and self-awareness. We argue that genuinely human-like artificial intelligence may require more than sophisticated pattern recognition and language modeling, potentially necessitating the development of coherent narrative self-models and rich causal understanding. Special attention is given to the relationship between consciousness, conscience, and trustworthy AI systems, suggesting that meaningful artificial intelligence may require forms of richly-embodied and socially-embedded development to achieve robust and reliable functionality. We conclude by proposing that the path to artificial general intelligence may require recapitulating aspects of human cognitive development, particularly regarding the construction of narrative identity and social-moral reasoning capabilities. This analysis has implications for both the technical development of AI systems and the ethical frameworks through which we evaluate artificial minds.

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 purpose—explain identify the features of General Artificial Intelligence (AGI), from the standpoint of scientific rationality stages (classic, non-classical, post-non-classical), and show its difference from traditional Artificial Intelligence (AI). The latter is currently implemented mainly with digital computers and implements the functions of recognition, forecasting and preparation of answers to simple questions. New conditions force us to consider AGI from the standpoint of functionalism, as a man–machine system, purposefully functioning in a self-developing poly-subject (reflexive-active) environment.Design/Methodology/Approach—AI development paradigm should consider aspects of its immersion in the social and humanitarian environment and the innovative atmosphere. The new paradigm of AI development should reflect the unformalized cognitive dynamics of AI models and support the self-development of AI systems under pressure from the external environment. The methodology of creating AGI is based on the ideas of the subject-oriented and ontological approach, functionalism, the phenomenology of subjective reality, the convergent cognitive architectures, as well as the methods of creating a self-developing poly-subject (reflexive-active) environment. AGI becomes a hybrid, purposefully integrating the capabilities of a machine and a person.Findings—General properties, value-semantic and intentional-volitional operational structures of the phenomenon of subjective reality do not lean itself on direct formalized and algorithmic representation in discrete computer systems of von Neumann architecture. The study of consciousness in the context of subjective reality made it possible to formulate the main systemic, structural, functional, and operational characteristics of human cognitive activity, which allows a new approach to the modelling of cognitive architectures that meet the tasks of building AGI. The characteristics of subjective reality cannot be fully represented in the paradigm of physicalism; that is, it cannot be represented only with the help of physical devices. The chapter proposes a non-reductionist way of taking this characteristic into account by considering the problem of consciousness in an ontological and epistemological context, which allows representing the processes of consciousness and cognitive activity of a person and a group of people indirectly and inversely.Originality/Value—State-of-the-art cognitive architectures and traditional AI approaches practically ignore solving the problems of AGI. They are more focused on the formalized construction of a thinking model, identifying physical blocks and processes of mental activity. At the same time, for AGI, ontological, subjective and hybrid reality issues are of the most importance, especially in explanations of the activity of consciousness, unconsciousness, and causeless processes, which can act purposefully in conditions of goal uncertainty. AGI must help to describe the phenomena of subjective reality, which causes physical changes, explain the ability of goal setting, free will, the ability of self-management by the physical actions of an individual in a team, etc.Research/Practical/Social/Environment implications—the chapter give rise to a new type of control, which differs from the traditional control in digital reality. The chapter’s results made it possible to uniquely find the optimal measure of centralization and autonomy of control loops that can ensure the preservation and strengthening of the integrity of a complex poly-subject system functioning in a reflexive-active environment, the interpretation of which does not fit into the narrow framework of digital and algorithmic reality, and traditional AI.Research limitations—the AGI approach based on exceptional methods of constructing subjective reality also has its limitations. For example, the approach we propose to explain the connection between the human brain, consciousness, thought processes and environment does not yet allow us to explain the information and cognitive processes generated by the effect of subjective reality nonlocality, which arises, e.g., at the atomic level of the human brain and should be considered when studying cognitive processes.KeywordsArtificial general intelligenceScientific rationalityCognitive semanticsSelf-developing poly-subject (reflexive-active) environmentSubjective reality

Recent technological advances are challenging established conventions for creating, managing and communicating expertise. Technology is central to many occupations—social communities comprised of knowledge workers who act as gatekeepers to expert domains. The creative industries are full of such communities experiencing transformation in their work due to the increasing accessibility and computing power of artificial intelligence (AI). Nonetheless, the specificity of traditional AI technology has, hitherto, not heavily impacted creative workers. That was until the latest development in AI. Generative AI is different. Unlike traditional AI—designed to perform specific tasks—generative AI has a general intelligence with broad capabilities that can imitate inherent human knowledge and expertise. This research examines the dynamic pace of change and capability brought by generative AI by drawing on first-hand accounts from 100 in-depth interviews with design experts. An affordances lens is employed to examine and explain the space between designers and generative AI to understand the acceptance and use of the technology and its impact on their expertise and daily work. The proximity of expertise is introduced to theorise when, where, and how designers use generative AI in their daily work and to help predict its acceptance and use. It also provides a fertile foundation for future enquiry into creating, managing, and communicating expertise.