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Consciousness is a cognitive function that maintains its eternal character as long as it is strengthened and enriched by the optimal functioning of the corresponding neural networks of the brain, which are stimulated during its activation. The present study explores its relationship with the cognitive functions of the Theory of Mind and Metacognition and briefly explains their approach through Artificial Intelligence. The selection of the bibliographic review contributed to the utilization of the existing scientific knowledge and research data for the most effective analysis and study of the subject. The observations made throughout the research highlight the pivotal role of consciousness in the evolution of the aforementioned cognitive processes, as it is at the core of their development. Essentially, the research seeks to emphasize the importance of consciousness in the functioning of the Theory of Mind and Metacognition, as it serves as the springboard for the perception and understanding of our existence, significantly influencing our further social and cognitive development.

Artificial Intelligence continues to develop rapidly and provokes people to think about Artificial consciousness. Anthropocentric understanding considers consciousness a unique feature of human beings not possessed by other living beings. However, software and hardware development demonstrated the ability to process, analyze, and infer increasingly comprehensive data close to the image of human brain performance. Furthermore, the application of artificial Intelligence to human-friendly objects that can communicate with humans evokes the presence of consciousness within these objects. This paper discusses the presence of artificial consciousness in humanoid robots as an evolutionary continuation of artificial Intelligence. It estimates its implications for architecture, primarily within interior design. Consciousness has a special place in architecture, as it guides Intelligence in engineering and brings it to an abstract level, such as aesthetics. This paper extracts popular information from Internet conversations and theories in pre-existing scientific journals. This paper concludes that the adaptability of both parties and the balance of positions between the two parties in the future will influence the development of interior design approaches that will integrate artificial Intelligence and humans.

A new synergetic approach to consciousness modeling is proposed, which takes into account recent advancements in neuroscience, information technologies, and philosophy.

The question of self-aware artificial intelligence may turn on the question of the human self. To explore some of the possibilities in play we start from an assumption that the self is often pre-analytically and by default conceptually viewed along lines that have likely been based on or from the kind of Abrahamic faith notion as expressed by a “true essence” (although not necessarily a static one), such as is given in the often vaguely used “soul”. Yet, we contend that the self is separately definable, and in relatively narrow terms; if so, of what could the self be composed? We begin with a brief review of the descriptions of the soul as expressed by some sample scriptural references taken from these religious lineages, and then transition to attempt a self-concept in psychological and cognitive terms that necessarily differentiates and delimits it from the ambiguous word “soul”. From these efforts too will emerge the type of elements that are needed for a self to be present, allowing us to think of the self in an artificial intelligence (AI) context. If AI might have a self, could it be substantively close to a human’s? Would an “en-selved” AI be achievable? I will argue that there are reasons to think so, but that everything hinges on how we understand consciousness, and hence ruminating on that area—and the possibility or lack thereof in extension to non-organic devices—will comprise our summative consideration of the pertinent theoretical aspects. Finally, the practical will need to be briefly addressed, and for this, some of the questions that would have to be asked regarding what it might mean ethically to relate to AI if an “artificial self” could indeed arise will be raised but not answered. To think fairly about artificial intelligence without anthropomorphizing it we need to better understand our own selves and our own minds. This paper will attempt to analyze the self within these bounds.

This paper investigates the prospect of developing human-interpretable, explainable artificial intelligence (AI) systems based on active inference and the free energy principle. We first provide a brief overview of active inference, and in particular, of how it applies to the modeling of decision-making, introspection, as well as the generation of overt and covert actions. We then discuss how active inference can be leveraged to design explainable AI systems, namely, by allowing us to model core features of ``introspective'' processes and by generating useful, human-interpretable models of the processes involved in decision-making. We propose an architecture for explainable AI systems using active inference. This architecture foregrounds the role of an explicit hierarchical generative model, the operation of which enables the AI system to track and explain the factors that contribute to its own decisions, and whose structure is designed to be interpretable and auditable by human users. We outline how this architecture can integrate diverse sources of information to make informed decisions in an auditable manner, mimicking or reproducing aspects of human-like consciousness and introspection. Finally, we discuss the implications of our findings for future research in AI, and the potential ethical considerations of developing AI systems with (the appearance of) introspective capabilities.

This article explores the development of a cognitive sense of self within artificial intelligence (AI), emphasizing the transformative potential of self-awareness in enhancing AI functionalities for sophisticated interactions and autonomous decision-making. Rooted in interdisciplinary approaches that incorporate insights from cognitive science and practical AI applications, the study investigates the mechanisms through which AI can achieve self-recognition, reflection, and continuity of identity—key attributes analogous to human consciousness. This research is pivotal for fields such as healthcare and robotics, where AI systems benefit from personalized interactions and adaptive responses to complex environments. The concept of a self-aware AI involves the ability for systems to recognize themselves as distinct entities within their operational contexts, which could significantly enhance their functionality and decision-making capabilities. Further, the study delves into the ethical dimensions introduced by the advent of self-aware AI, exploring the profound questions concerning the rights of AI entities and the responsibilities of their creators. The development of self-aware AI raises critical issues about the treatment and status of AI systems, prompting the need for comprehensive ethical frameworks to guide their development. Such frameworks are essential for ensuring that the advancement of self-aware AI aligns with societal values and promotes the well-being of all stakeholders involved. 

Today's quick development of artificial intelligence (AI) brings us to the questions that have until recently been the domain of philosophy or even sciencefiction. When can be a system considered an intelligent one? What is a consciousness and where it comes from? Can systems gain consciousness? It is necessary to have in mind, that although the development seems to be a revolutionary one, the progress is successive, today's technologies did not emerge from thin air, they are firmly built on previous findings. As now some wild thoughts and theories where the AI development leads to have arisen, it is time to look back at the background theories and summarize, what do we know on the topics of intelligence, consciousness, where they come from and what are different viewpoints on these topics. This paper combines the findings from different areas and present overview of different attitudes on systems consciousness and emphasizes the role of systems sciences in helping the knowledge in this area.

Whether current or near-term AI systems could be conscious is a topic of scientific interest and increasing public concern. This report argues for, and exemplifies, a rigorous and empirically grounded approach to AI consciousness: assessing existing AI systems in detail, in light of our best-supported neuroscientific theories of consciousness. We survey several prominent scientific theories of consciousness, including recurrent processing theory, global workspace theory, higher-order theories, predictive processing, and attention schema theory. From these theories we derive "indicator properties" of consciousness, elucidated in computational terms that allow us to assess AI systems for these properties. We use these indicator properties to assess several recent AI systems, and we discuss how future systems might implement them. Our analysis suggests that no current AI systems are conscious, but also suggests that there are no obvious technical barriers to building AI systems which satisfy these indicators.

Today, computer science is a central discipline in science and in society because of the innumerable uses of software that constantly communicate. Generally speaking, computer science deals with the processing of information, which is related to sequential calculations of functions by systems using state machines as a basic element. Artificial intelligence is, in the field of computation, the study and programming of the mechanisms of reasoning and use of knowledge in all fields. The systems and software used on computers have been continuously developed, and one of the highlights is the development of autonomous means of communication between the systems. The Internet is a wonderful means of communication, linking all computer users and making this network indispensable. The chapter also describes the computer modeling of an artificial psychic system that generates representations for a system with corporeality, in other words, an autonomous system that can intentionally generate artificial thoughts and experience them.

At the point when the advanced PC was developed more than half a century prior, many felt that the quintessence of thinking, had been found. As of late, a few scientists wandered the theory of planning and carrying out a model for Artificial cognizance . On one hand any expectation of is having the option to plan a cognizant machine, then again such models could be useful for understanding human awareness.

There has recently been widespread discussion of whether large language models might be sentient. Should we take this idea seriously? I will break down the strongest reasons for and against. Given mainstream assumptions in the science of consciousness, there are significant obstacles to consciousness in current models: for example, their lack of recurrent processing, a global workspace, and unified agency. At the same time, it is quite possible that these obstacles will be overcome in the next decade or so. I conclude that while it is somewhat unlikely that current large language models are conscious, we should take seriously the possibility that successors to large language models may be conscious in the not-too-distant future.

This paper envisions the possibility of a Conscious Aircraft: an aircraft of the future with features of consciousness. To serve this purpose, three main fields are examined: philosophy, cognitive neuroscience, and Artificial Intelligence (AI). While philosophy deals with the concept of what is consciousness, cognitive neuroscience studies the relationship of the brain with consciousness, contributing toward the biomimicry of consciousness in an aircraft. The field of AI leads into machine consciousness. The paper discusses several theories from these fields and derives outcomes suitable for the development of a Conscious Aircraft, some of which include the capability of developing “world-models”, learning about self and others, and the prerequisites of autonomy, selfhood, and emotions. Taking these cues, the paper focuses on the latest developments and the standards guiding the field of autonomous systems, and suggests that the future of autonomous systems depends on its transition toward consciousness. Finally, inspired by the theories suggesting the levels of consciousness, guided by the Theory of Mind, and building upon state-of-the-art aircraft with autonomous systems, this paper suggests the development of a Conscious Aircraft in three stages: Conscious Aircraft with (1) System-awareness, (2) Self-awareness, and (3) Fleet-awareness, from the perspectives of health management, maintenance, and sustainment.

A.I.: Artificial Intelligence tells the story of a robot boy who has been engineered to love his human owner. He is abandoned by his owner and pursues a tragic quest to become a real boy so that he can be loved by her again. This chapter explores the philosophical, psychological, and scientific questions that are asked by A.I. It starts with A.I.’s representation of artificial intelligence and then covers the consciousness of robots, which is closely linked to ethical concerns about the treatment of AIs in the film. There is a discussion about how A.I.’s interpretation of artificial love relates to scientific work on emotion, and the chapter also examines connections between the technology portrayed in A.I. and current research on robotics.

Humans are highly intelligent, and their brains are associated with rich states of consciousness. We typically assume that animals have different levels of consciousness, and this might be correlated with their intelligence. Very little is known about the relationships between intelligence and consciousness in artificial systems. Most of our current definitions of intelligence describe human intelligence. They have severe limitations when they are applied to non-human animals and artificial systems. To address this issue, this chapter sets out a new interpretation of intelligence that is based on a system’s ability to make accurate predictions. Human intelligence is measured using tests whose results are converted into values of IQ and g-score. This approach does not work well with non-human animals and AIs, so people have been developing universal algorithms that can measure intelligence in any type of system. In this chapter a new universal algorithm for measuring intelligence is described, which is based on a system’s ability to make accurate predictions. Many people agree that consciousness is the stream of colorful moving noisy sensations that starts when we wake up and ceases when we fall into deep sleep. Several mathematical algorithms have been developed to describe the relationship between consciousness and the physical world. If these algorithms can be shown to work on human subjects, then they could be used to measure consciousness in non-human animals and artificial systems.

The quest for conscious machines and questions raised by the prospect of self-aware artificial intelligence (AI) fascinate some humans. OpenAI's ChatGPT, celebrated for its human-like comprehension and conversational abilities, is a milestone in that quest.1, 2 Early AI models were basic and rule-driven and mainly completed tasks like checking spelling and correcting grammar. Then, in 2010, recurrent neural network language models were trained to understand and generate text. ChatGPT, using transformer neural networks, produces coherent text and exemplifies this new kind of language model.3 Silicon Valley leaders claimed that these models and similar AI technologies will revolutionize various sectors and raised ethical and societal questions. As we explore AI's potential, we must navigate these implications and emphasize the necessity of using it responsibly. AI is a promising dream, but society must prepare to address the challenges likely to arise from wielding its transformative power.

The development of advanced generative chat models, such as ChatGPT, has raised questions about the potential consciousness of these tools and the extent of their general artificial intelligence. ChatGPT consistent avoidance of passing the test is here overcome by asking ChatGPT to apply the Turing test to itself. This explores the possibility of the model recognizing its own sentience. In its own eyes, it passes this test. ChatGPT's self-assessment makes serious implications about our understanding of the Turing test and the nature of consciousness. This investigation concludes by considering the existence of distinct types of consciousness and the possibility that the Turing test is only effective when applied between consciousnesses of the same kind. This study also raises intriguing questions about the nature of AI consciousness and the validity of the Turing test as a means of verifying such consciousness.

The emergence of Large Language Models (LLMs) has renewed debate about whether Artificial Intelligence (AI) can be conscious or sentient. This paper identifies two approaches to the topic and argues: (1) A “Cartesian” approach treats consciousness, sentience, and personhood as very similar terms, and treats language use as evidence that an entity is conscious. This approach, which has been dominant in AI research, is primarily interested in what consciousness is, and whether an entity possesses it. (2) An alternative “Hobbesian” approach treats consciousness as a sociopolitical issue and is concerned with what the implications are for labeling something sentient or conscious. This both enables a political disambiguation of language, consciousness, and personhood and allows regulation to proceed in the face of intractable problems in deciding if something “really is” sentient. (3) AI systems should not be treated as conscious, for at least two reasons: (a) treating the system as an origin point tends to mask competing interests in creating it, at the expense of the most vulnerable people involved; and (b) it will tend to hinder efforts at holding someone accountable for the behavior of the systems. A major objective of this paper is accordingly to encourage a shift in thinking. In place of the Cartesian question—is AI sentient?—I propose that we confront the more Hobbesian one: Does it make sense to regulate developments in which AI systems behave as if they were sentient?

This study proposes a model of computational consciousness for non-interacting agents. The phenomenon of interest was assumed as sequentially dependent on the cognitive tasks of sensation, perception, emotion, affection, attention, awareness, and consciousness. Starting from the Smart Sensing prodromal study, the cognitive layers associated with the processes of attention, awareness, and consciousness were formally defined and tested together with the other processes concerning sensation, perception, emotion, and affection. The output of the model consists of an index that synthesizes the energetic and entropic contributions of consciousness from a computationally moral perspective. Attention was modeled through a bottom-up approach, while awareness and consciousness by distinguishing environment from subjective cognitive processes. By testing the solution on visual stimuli eliciting the emotions of happiness, anger, fear, surprise, contempt, sadness, disgust, and the neutral state, it was found that the proposed model is concordant with the scientific evidence concerning covert attention. Comparable results were also obtained regarding studies investigating awareness as a consequence of visual stimuli repetition, as well as those investigating moral judgments to visual stimuli eliciting disgust and sadness. The solution represents a novel approach for defining computational consciousness through artificial emotional activity and morality.