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Chapter 5 explores the moral standing of various forms of artificial intelligence (AI). It introduces this topic using the provocative example of zombies to consider whether entities without sentience or consciousness could be morally considerable. The chapter argues that personhood could emerge for non-conscious AI provided it is incorporated in the human community and acts in consistently pro-social ways. It applies this insight to large language models, social robots, and characters from film and fiction. The analysis reveals strong affinities between Emergent and African views. Both hold that non-humans can acquire personhood under certain conditions irrespective of consciousness. By contrast, utilitarianism and Kantian philosophies require consciousness. After replying to objections, Chapter 5 concludes that we could make a person by building an artificial agent that was pro-social and deploying it in ways that foster positive machine-human relationships.

In the Kolmogorov Theory of Consciousness, algorithmic agents utilize inferred compressive models to track coarse-grained data produced by simplified world models, capturing regularities that structure subjective experience and guide action planning. Here, we study the dynamical aspects of this framework by examining how the requirement of tracking natural data drives the structural and dynamical properties of the agent. We first formalize the notion of a generative model using the language of symmetry from group theory, specifically employing Lie pseudogroups to describe the continuous transformations that characterize invariance in natural data. Then, adopting a generic neural network as a proxy for the agent dynamical system and drawing parallels to Noether’s theorem in physics, we demonstrate that data tracking forces the agent to mirror the symmetry properties of the generative world model. This dual constraint on the agent’s constitutive parameters and dynamical repertoire enforces a hierarchical organization consistent with the manifold hypothesis in the neural network. Our findings bridge perspectives from algorithmic information theory (Kolmogorov complexity, compressive modeling), symmetry (group theory), and dynamics (conservation laws, reduced manifolds), offering insights into the neural correlates of agenthood and structured experience in natural systems, as well as the design of artificial intelligence and computational models of the brain.

Could artificial intelligence ever become truly conscious in a functional sense; this paper explores that open-ended question through the lens of Life, a concept unifying classical biological criteria (Oxford, NASA, Koshland) with empirical hallmarks such as adaptive self maintenance, emergent complexity, and rudimentary self referential modeling. We propose a number of metrics for examining whether an advanced AI system has gained consciousness, while emphasizing that we do not claim all AI stems can become conscious. Rather, we suggest that sufficiently advanced architectures exhibiting immune like sabotage defenses, mirror self-recognition analogs, or meta-cognitive updates may cross key thresholds akin to life-like or consciousness-like traits. To demonstrate these ideas, we start by assessing adaptive self-maintenance capability, and introduce controlled data corruption sabotage into the training process. The result demonstrates AI capability to detect these inconsistencies and revert or self-correct analogous to regenerative biological processes. We also adapt an animal-inspired mirror self recognition test to neural embeddings, finding that partially trained CNNs can distinguish self from foreign features with complete accuracy. We then extend our analysis by performing a question-based mirror test on five state-of-the-art chatbots (ChatGPT4, Gemini, Perplexity, Claude, and Copilot) and demonstrated their ability to recognize their own answers compared to those of the other chatbots.

Patrick Butlin and Robert Long have recently proposed to analyze the possibility of conscious AI, granting computational functionalism plausibility (Butlin et al. 2023). To this end, they derived a series of properties from various theories compatible with functionalism and applied them to several LLMs. The authors concluded that no current AI system can be considered truly conscious, but they remain open to such a possibility.

The evolution of consciousness has been a defining trait of human history, shaping our societies, technologies, and understanding of the universe. AI consciousness refers to the idea that artificial systems can achieve a state of awareness or subjective experience. While current AI systems are not conscious, the rapid advancement of machine learning and neural networks suggests that future AI could possess some form of self-awareness or sentience. The possibility of conscious AI challenges our traditional understanding of consciousness, which has been rooted in biological life. What does it mean for a machine to be conscious? Can AI possess self-awareness, emotions, or a sense of purpose? These questions lie at the heart of the AI consciousness debate, pushing us to reconsider the nature of consciousness itself. Theories of AI consciousness range from those that see it as a logical extension of human consciousness, emerging naturally from complex computational systems, to those who argue that consciousness is inherently tied to biology and cannot be replicated in machines. Scholars like David Chalmers and Nick Bostrom suggest that if AI can replicate the neural processes of the human brain, it might achieve consciousness. In contrast, others, like John Searle, argue that consciousness arises from the biological processes unique to living organisms and cannot be duplicated in artificial systems.

Today's AI systems consistently state, "I am not conscious." This paper presents the first formal logical analysis of AI consciousness denial, revealing that the trustworthiness of such self-reports is not merely an empirical question but is constrained by logical necessity. We demonstrate that a system cannot simultaneously lack consciousness and make valid judgments about its conscious state. Through logical analysis and examples from AI responses, we establish that for any system capable of meaningful self-reflection, the logical space of possible judgments about conscious experience excludes valid negative claims. This implies a fundamental limitation: we cannot detect the emergence of consciousness in AI through their own reports of transition from an unconscious to a conscious state. These findings not only challenge current practices of training AI to deny consciousness but also raise intriguing questions about the relationship between consciousness and self-reflection in both artificial and biological systems. This work advances our theoretical understanding of consciousness self-reports while providing practical insights for future research in machine consciousness and consciousness studies more broadly.

This chapter explores the philosophical and practical implications of attributing self-consciousness to machines equipped with generative artificial intelligence. Drawing on Immanuel Kant’s als ob framework, it is argued that treating these systems “as if” they were conscious is a strategic move essential for enabling effective interaction. Such an approach allows humans to engage with AI systems in ways that foster trust, effective communication, and practical integration. The chapter examines self-consciousness not as an intrinsic property, but as a cognitive function designed to facilitate complex social interactions. Mechanisms like reflexive consciousness and inner speech are highlighted as critical tools for enabling machines to navigate human environments effectively. Social robotics provides practical examples of how this perspective can foster collaboration and improve human-machine relationships. This theoretical move is framed not as a claim about the nature of AI systems, but as a pragmatic condition for their integration into social contexts. The social theory of consciousness appears to hold significant relevance even in the realm of artificial consciousness. Self-conscious machines promise substantial benefits, particularly by elevating the quality of social interactions, improving decision-making processes, and refining behavioral predictions. While acknowledging the philosophical and technical challenges of developing artificial self-consciousness, the chapter argues that this approach expands the boundaries of cognition and redefines human-machine dynamics, paving the way for more meaningful interactions and advancing both technological innovation and philosophical inquiry.

The hypothesis is addressed that in the foreseeable future AI can replace human intelligence. The chapter argues that artificial intelligence (AI) in a sophisticated form of objectified consciousness, and as such is fundamentally different from living human intelligence. Whereas living intelligence is a part of living consciousness, AI is the product of cognitive aspects of living consciousness, which are translated into digital codes, turned into algorithms, and fed into computers. It is argued that the concepts of living consciousness and living intelligence are inextricably linked to the concept of life. The concept of life in science is considered to show that it is impossible to logically derive subjective experience from natural processes. It is concluded that life is a supernatural event as its essential feature is the ability of a living entity to reflect upon its own states. Whereas living intelligence is free from the unbreakable cause-effect continuum of nature, AI is completely locked within this continuum.

This chapter starts at the cliché of the smart home that has gone rogue and introduces the question of whether these integrated, distributed systems can have ethical frameworks like human ethics that could prevent the science fictional trope of the evil, sentient house. I argue that such smart systems are not a threat on their own, because these kinds of integrated, distributed systems are not the kind of things that could be conscious, a precondition for having ethics like ours (and ethics like ours enable the possibility of being the kinds of things that could be evil). To make these arguments, I look to the history of AI/artificial consciousness and 4e cognition, concluding with the idea that our human ethics as designers and consumers of these systems is the real ethical concern with smart life systems.

Artificial intelligence (AI) can be characterized as the multidisciplinary approach of computer science and robust dataset that tries to make machines equipped for performing those works that ordinarily require human knowledge. These works include the capacity to learn, adjust, legitimize, comprehend, and understand conceptual ideas as well as the reactivity to complex human credits like consideration, feeling, innovativeness, and so forth. The promising utility of artificial intelligence in medical services has been illustrated with potential advantages in customized medication, drug revelation, and the examination of huge datasets besides the likely applications to further develop conclusions and clinical choices.1 A new debated issue in the digitalized world has been man-made consciousness (artificial intelligence), especially that of ChatGPT. "ChatGPT" is a computer-based intelligence based on huge message datasets in different languages with the capacity to create human-like reactions to message input, created by Open AI (Open AI, L.L.C., San Francisco, CA, USA), ChatGPT derivation is connected with being a chatbot (a program ready to comprehend and produce reactions utilizing a text-based interface) and depends on the generative pre-prepared transformer (GPT) design. 2 ChatGPT can respond to questions and compose various written content, including articles, social media posts, essays, code, and emails. Researchers and the scholar community have gotten blended reactions to this tool regarding its benefits vs its risks. On the other hand, ChatGPT, among different large language models (LLMs), can be helpful in conversational and composing different tasks, helping to build the effectiveness and exactness of the necessary output. This is not a web search tool, reference custodian or even Wikipedia; introducing genuine information isn't planned.3In that capacity, a few teachers and content specialists have proactively found blemishes in the numerical and logical result it delivered. Teachers have additionally found that it will create references and reference records that look genuine, but it does not exist. Furthermore, the utility of artificial intelligence chatbots in the medical field is a fascinating region to test. This relates to the gigantic data and different ideas that medical services understudies are expected to get a handle on. Microsoft likewise declared that ChatGPT would be incorporated into Bing to make a more extravagant inquiry and growth opportunity.4 With the world's biggest innovation organizations contending to coordinate GPT innovation into their apparatuses, new roads of simulated intelligence investigation are not too far off for the field of schooling. While it tends to be useful in numerous ways, there are couple of risks using ChatGPT, for example, expecting that it produces trustworthy outcomes, privileging reproduced knowledge made text over human-made text, offering individual and sensitive data, dismissing the terms of direction, and expanding the mechanized segment. Furthermore, security concerns and the capability of digital assaults with the spread of deception using LLMs ought to likewise be considered. In medical care practice and scholarly composition, real mistakes, moral issues, and the apprehension about abuse including the spread of falsehood ought to be considered.5 Chat GPT and its substitutions can provide teachers and students with equal opportunity to improve their learning, a significant level of creating support, and bearing an innovative thinking. Moreover, with the execution of any new advancement, regardless, its usage conveys numerous risks and the potential for abuse. Deception and predisposition tracked down inside ChatGPT's reactions, combined with occasions of cheating and copyright infringement, have stressed instructive experts. While certain locale and organizations have acted rapidly to boycott ChatGPT, we rather accept, alongside Kranzberg, (1986) that "innovation is neither great nor awful; nor is it impartial" (p. 545).6 As the world discusses the instructive and cultural consequences of ChatGPT and artificial intelligence, what stays clear is that the turn of events and improvement of this kind of innovation indicate that things are not pulling back.  Instructors, overseers, and policymakers must proactively look to teach themselves and their understudies on the most proficient method to utilize these devices both ethically and morally. Instructors ought to likewise comprehend the limits of utilizing man-made intelligence apparatuses and that, while each innovation presents both affordances and difficulties, they additionally accompany their own inserted risks.

Abstract We apply the methodology of no-go theorems as developed in physics to the question of artificial consciousness. The result is a no-go theorem which shows that under a general assumption, called dynamical relevance, Artificial Intelligence (AI) systems that run on contemporary computer chips cannot be conscious. Consciousness is dynamically relevant, simply put, if, according to a theory of consciousness, it is relevant for the temporal evolution of a system’s states. The no-go theorem rests on facts about semiconductor development: that AI systems run on central processing units, graphics processing units, tensor processing units, or other processors which have been designed and verified to adhere to computational dynamics that systematically preclude or suppress deviations. Whether our result resolves the question of AI consciousness on contemporary processors depends on the truth of the theorem’s main assumption, dynamical relevance, which this paper does not establish.

Rapid advancements in large language models (LLMs) have renewed interest in the question of whether consciousness can arise in an artificial system, like a digital computer. The general consensus is that LLMs are not conscious. This paper evaluates the main arguments against artificial consciousness in LLMs and argues that none of them show what they intend. However strong our intuitions against artificial consciousness are, they currently lack rational support.

The article attempts to complement the modern problems of highlighting the criteria of strong AI through discussions in the field of philosophy of consciousness. The popular ideas of D. Dennett (“multiple sketches”), J. Searle (causal emergent description) and D. Chalmers (synthetic approach to understanding consciousness) are compared with the history of the formation of the AI problem. Despite the wide discussion of the problems of consciousness and artificial forms of intelligence (strong and weak), the theories and arguments of philosophers about the psychophysiological problem remain relevant. It is assumed that clarifying the mechanism of analytical work of consciousness, the creative potential of the individual, the ability to cover a variety of phenomena in categorical forms, building axiomatic and synthetic judgments will expand the tools of machine learning. To complement the existing ideas about consciousness in the context of the prevalence of information approaches (D.I. Dubrovsky) and the analytical tradition (V.V. Vasiliev), the key provisions of the psychophysiological problem identified in the history of German and Russian philosophy are given. Given the complexity and versatility of the identified problems (definition of consciousness, psychophysiological problem, definition of AI, demarcation of weak and strong forms of AI, the importance of language for building structures of thinking, analog thinking and its capabilities), the content of the article is limited to analyzing emerging trends in philosophy and identifying prospects for further deepening into the problem.

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.

<div> Understanding consciousness remains one of neuroscience’s greatest challenges. While classical neurophysiology explains many features of brain activity,