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This paper explores the behavior and implications of sequences transitioning between acceptable and unacceptable states, particularly in the context of artificial consciousness. Using the framework of absorbing state transition sequences and applying Kolmogorov's 0-1 Law, we analyze the probability of a sequence eventually reaching an absorbing (unacceptable) state. We demonstrate that if there is a countably infinite number of indices with nonzero transition probabilities, the probability of reaching the absorbing state is 1. The paper extends these mathematical results to philosophical and ethical discussions, examining the inevitability of failure in systems with persistent nonzero transition probabilities and the ethical considerations for developing artificial consciousness. Strategies for minimizing transition probabilities, establishing ethical guidelines, and implementing self-correcting mechanisms are proposed to ensure the propagation of acceptable states. The findings underscore the importance of robust design and ethical oversight in the creation and maintenance of artificial consciousness systems.

Inquiry into the field of artificial intelligence (machines) and its potential to develop consciousness is presented in this study. This investigation explores the complex issues surrounding machine consciousness at the nexus of AI, neuroscience, and philosophy as we delve into the fascinating world of artificial intelligence (AI) and investigate the intriguing question: are machines on the verge of becoming conscious beings? The study considers the likelihood of machines displaying self-awareness and the implications thereof through an analysis of the current state of AI and its limitations. However, with advancements in machine learning and cognitive computing, AI systems have made significant strides in emulating human-like behavior and decision-making. Furthermore, the emergence of machine consciousness raises questions about the blending of human and artificial intelligence, and ethical considerations are also considered. The study provides a glimpse into a multidisciplinary investigation that questions accepted theories of consciousness, tests the limits of what is possible with technology, and do these advancements signify a potential breakthrough in machine consciousness.

Two problems related to the identification of consciousness are the distribution problem—or how and among which entities consciousness is distributed in the world—and the moral status problem—or which species, entities, and individuals have moral status. The use of inferences from neurobiological and behavioral evidence, and their confounds, for identifying consciousness in nontypically functioning humans, nonhuman animals, and artificial intelligence is considered in light of significant scientific uncertainty and ethical biases, with implications for both problems. Methodological, epistemic, and ethical consensus are needed for responsible consciousness science under epistemic and ethical uncertainty. Consideration of inductive risk is proposed as a potential tool for managing both epistemic and ethical risks in consciousness science.

As artificial intelligence (AI) continues to advance, it is natural to ask whether AI systems can be not only intelligent, but also conscious. I consider why people might think AI could develop consciousness, identifying some biases that lead us astray. I ask what it would take for conscious AI to be a realistic prospect, challenging the assumption that computation provides a sufficient basis for consciousness. I’ll instead make the case that consciousness depends on our nature as living organisms – a form of biological naturalism. I lay out a range of scenarios for conscious AI, concluding that real artificial consciousness is unlikely along current trajectories, but becomes more plausible as AI becomes more brain-like and/or life-like. I finish by exploring ethical considerations arising from AI that either is, or convincingly appears to be, conscious. If we sell our minds too cheaply to our machine creations, we not only overestimate them – we underestimate our selves.

Objective: And ultimate goal of this paper is to describe a realistic future in how humanity and life can survive immortally by creating humanoid robots from a human master with a consciousness of the human who would serve the human master as a companion and learn everything about the consciousness of the human master. The Contributions: Are to present a groundbreaking methodology for the immortality of humanoid robots with a human consciousness. In this paper, we emphasize that the current humanoid robotics technologies have reached the sophistication to design and fabricate intelligent AI computers to allow humanoids to survive immortally. Once human life is close to being over (age or sickness), the humanoid will take over and can stay alive as long as it has the necessary energy to live on. These humanoids can even travel through space and other planets, opening up a whole new frontier for exploration and life. They can benefit from the Quantum Entanglement to move through space to any destination.

The nature of consciousness in the context of artificial intelligence (AI) presents a problem that necessitates analysis and further exploration. This study seeks to redefine human-technology relationships by examining the intersection of consciousness and AI, including metaphysical implications and considerations. The primary objectives are to define consciousness within the context of AI, assess the potential for AI to exhibit consciousness, investigate the metaphysical implications for human experiences, and explore the ethical dimensions. The research findings indicate that consciousness involves self-awareness, perception, intentionality, and subjective experiences. While AI can achieve advanced cognitive abilities, the existence of higher-order consciousness remains uncertain, raising metaphysical questions about the nature of subjective awareness. The hard problem of consciousness highlights the challenge of bridging physical processes and subjective experiences, underscoring the need for metaphysical considerations. Ethical implications of AI integration and its impact on human experiences are also examined. Recommendations include further research on consciousness in AI, the development of ethical frameworks that account for metaphysical dimensions, and the exploration of the extended mind hypothesis to integrate AI as an augmentation of human consciousness. By addressing metaphysical implications and considerations, we can navigate the evolving landscape of AI and redefine human-technology relationships in a responsible, inclusive, and metaphysically informed manner.

As the apparent intelligence of artificial neural networks (ANNs) advances, they are increasingly likened to the functional networks and information processing capabilities of the human brain. Such comparisons have typically focused on particular modalities, such as vision or language. The next frontier is to use the latest advances in ANNs to design and investigate scalable models of higher-level cognitive processes, such as conscious information access, which have historically lacked concrete and specific hypotheses for scientific evaluation. In this work, we propose and then empirically assess an embodied agent with a structure based on global workspace theory (GWT) as specified in the recently proposed “indicator properties” of consciousness. In contrast to prior works on GWT which utilized single modalities, our agent is trained to navigate 3D environments based on realistic audiovisual inputs. We find that the global workspace architecture performs better and more robustly at smaller working memory sizes, as compared to a standard recurrent architecture. Beyond performance, we perform a series of analyses on the learned representations of our architecture and share findings that point to task complexity and regularization being essential for feature learning and the development of meaningful attentional patterns within the workspace.

I propose a test for machine self-awareness inspired by the Turing test. My test is simple, and it provides an objective, empirical metric to rectify the ungrounded speculation surging through industry, academia, and social media. Drawing from a breadth of philosophical literature, I argue the test captures the essence of self-awareness, rather than some postulated correlate or ancillary quality. To begin, the concept of self-awareness is clearly demarcated from related concepts like consciousness, agency, and free will. Next, I propose a model called the Nesting Doll of Self-Awareness and discuss its relevance for intelligent beings. Then, the test is presented in its full generality, applicable to any machine system. I show how to apply the test to Large Language Models and conduct experiments on popular open and closed source LLMs, obtaining reproducible results that suggest a lack of self-awareness. The implications of machine self-awareness are discussed in relation to questions about meaning and true understanding. Finally, some next steps are outlined for studying self-awareness in machines.

The potential of conscious artificial intelligence (AI), with its functional systems that surpass automation and rely on elements of understanding, is a beacon of hope in the AI revolution. The shift from automation to conscious AI, once replaced with machine understanding, offers a future where AI can comprehend without needing to experience, thereby revolutionizing the field of AI. In this context, the proposed Dynamic Organicity Theory of consciousness (DOT) stands out as a promising and novel approach for building artificial consciousness that is more like the brain with physiological nonlocality and diachronicity of self-referential causal closure. However, deep learning algorithms utilize "black box" techniques such as “dirty hooks” to make the algorithms operational by discovering arbitrary functions from a trained set of dirty data rather than prioritizing models of consciousness that accurately represent intentionality as intentions-in-action. The limitations of the “black box” approach in deep learning algorithms present a significant challenge as quantum information biology, or intrinsic information, is associated with subjective physicalism and cannot be predicted with Turing computation. This paper suggests that deep learning algorithms effectively decode labeled datasets but not dirty data due to unlearnable noise, and encoding intrinsic information is beyond the capabilities of deep learning. New models based on DOT are necessary to decode intrinsic information by understanding meaning and reducing uncertainty. The process of “encoding” entails functional interactions as evolving informational holons, forming informational channels in functionality space of time consciousness. The “quantum of information” functionality is the motivity of (negentropic) action as change in functionality through thermodynamic constraints that reduce informational redundancy (also referred to as intentionality) in informational pathways. It denotes a measure of epistemic subjectivity towards machine understanding beyond the capabilities of deep learning.

Artificial intelligence (AI) has been fast growing since its evolution and experiments with various new add-on features; human efficiency is one among those and the most controversial topic. This chapter focuses on its attention towards studying human consciousness and AI independently and in conjunction. It provides theories and arguments on AI being able to adapt human-like consciousness, cognitive abilities and ethics. This chapter studies responses of more than 300 candidates of the Indian population and compares it against the literature review. Furthermore, it also discusses whether AI could attain consciousness, develop its own set of cognitive abilities (cognitive AI), ethics (AI ethics) and overcome human beings’ efficiency. This chapter is a study of the Indian population’s understanding of consciousness, cognitive AI and AI ethics.

The computational significance of consciousness is an important and potentially more tractable research theme than the hard problem of consciousness, as one could look at the correlation of consciousness and computational capacities through, e.g., algorithmic or complexity analyses. In the literature, consciousness is defined as what it is like to be an agent (i.e., a human or a bat), with phenomenal properties, such as qualia, intentionality, and self-awareness. The absence of these properties would be termed “unconscious.” The recent success of large language models (LLMs), such as ChatGPT, has raised new questions about the computational significance of human conscious processing. Although instances from biological systems would typically suggest a robust correlation between intelligence and consciousness, certain states of consciousness seem to exist without manifest existence of intelligence. On the other hand, AI systems seem to exhibit intelligence without consciousness. These instances seem to suggest possible dissociations between consciousness and intelligence in natural and artificial systems. Here, I review some salient ideas about the computational significance of human conscious processes and identify several cognitive domains potentially unique to consciousness, such as flexible attention modulation, robust handling of new contexts, choice and decision making, cognition reflecting a wide spectrum of sensory information in an integrated manner, and finally embodied cognition, which might involve unconscious processes as well. Compared to such cognitive tasks, characterized by flexible and ad hoc judgments and choices, adequately acquired knowledge and skills are typically processed unconsciously in humans, consistent with the view that computation exhibited by LLMs, which are pretrained on a large dataset, could in principle be processed without consciousness, although conversations in humans are typically done consciously, with awareness of auditory qualia as well as the semantics of what are being said. I discuss the theoretically and practically important issue of separating computations, which need to be conducted consciously from those which could be done unconsciously, in areas, such as perception, language, and driving. I propose conscious supremacy as a concept analogous to quantum supremacy, which would help identify computations possibly unique to consciousness in biologically practical time and resource limits. I explore possible mechanisms supporting the hypothetical conscious supremacy. Finally, I discuss the relevance of issues covered here for AI alignment, where computations of AI and humans need to be aligned.

Which systems/organisms are conscious? New tests for consciousness (‘C-tests’) are urgently needed. There is persisting uncertainty about when consciousness arises in human development, when it is lost due to neurological disorders and brain injury, and how it is distributed in nonhuman species. This need is amplified by recent and rapid developments in artificial intelligence (AI), neural organoids, and xenobot technology. Although a number of C-tests have been proposed in recent years, most are of limited use, and currently we have no C-tests for many of the populations for which they are most critical. Here, we identify challenges facing any attempt to develop C-tests, propose a multidimensional classification of such tests, and identify strategies that might be used to validate them.

The study of machine consciousness has a wide range of potential and problems as it sits at the intersection of ethics, technology, and philosophy. This work explores the deep issues related to the effort to comprehend and maybe induce awareness in machines. Technically, developments in artificial intelligence, neurology, and cognitive science are required to bring about machine awareness. True awareness is still a difficult to achieve objective, despite significant progress being made in creating AI systems that are capable of learning and solving problems. The implications of machine awareness are profound in terms of ethics. Determining a machine's moral standing and rights would be crucial if it were to become sentient. It is necessary to give careful attention to the ethical issues raised by the development of sentient beings, the abuse of sentient machines, and the moral ramifications of turning off sentient technologies. Philosophically, the presence of machine consciousness may cast doubt on our conceptions of identity, consciousness, and the essence of life. It could cause us to reevaluate how we view mankind and our role in the cosmos. It is imperative that machine awareness grow responsibly in light of these challenges. The purpose of this study is to provide light on the present status of research, draw attention to possible hazards and ethical issues, and offer recommendations for safely navigating this emerging subject. We want to steer the evolution of machine consciousness in a way that is both morally just and technologically inventive by promoting an educated and transparent discourse.

Artificial intelligence systems are often accompanied by risks such as uncontrollability and lack of explainability. To mitigate these risks, there is a necessity to develop artificial intelligence systems that are explainable, trustworthy, responsible, and demonstrate consistency in thought and action, which we term Artificial Consciousness (AC) systems. Therefore, grounded in the DIKWP model which integrates fundamental data, information, knowledge, wisdom, and purpose along with the principles of conceptual, cognitive, and semantic spaces, we propose and define the computer architectures, chips, runtime environments, and DIKWP language concepts and their implementations under the DIKWP framework. Furthermore, in the construction of AC systems, we have surmounted the limitations of traditional programming languages, computer architectures, and hardware-software implementations. The hardware-software integrated platform we propose will facilitate more convenient construction, development, and operation of software systems based on the DIKWP theory.

In Isaac Asimov’s groundbreaking anthology, I, Robot, the intricacies of human and robotic interactions take center stage, delving deep into questions of consciousness, right, morality. Characterized by Asimov’s unique blend of science fiction and philosophical pondering, the stories establish a framework to reflect on the evolving dynamics of an advanced technological society in space. Robots are capable to interact with human, to interpret complicate orders and act automatically, to help human with dull or dangerous works such as calculating and mining, even replacing the workers. Asimov also raised Three Laws of Robotics to make sure that all robots function in order. Through the lens of posthumanism, the anthology is examined for its portrayal of the blurred boundaries between human and artificial intelligences. Robots and human co-exist in the society. However, due to the limitation of “Three Laws of Robotics”, some logical contradictions inevitably appears and lead to the dysfunction of robots. As a result, I, Robot emerges as a poignant critique of humanity’s ethical and existential challenges in the face of rapid technological advancements. Building on existing research, this article attempts to forge a new perspective by reflecting on the broader implications of the artificial intelligence in Asimov’s works through the lens of post-humanism. It considers the existential questions of AI on a consciousness level, explores the egalitarian relationship between humans and machines from a rights perspective, and analyzes the concepts of “humanity” and “human-like” from a moral and ethical standpoint. It encourages readers to recognize that robots are not mere slaves to humans; instead, humans should view AI with equality and reverence towards the advancements of the era’s technology. Humanity should step away from anthropocentrism, not solely viewing humans as the measure of all things in this rapidly evolving era of AI, and properly handle the relationship between humans and non-humans. Currently, with rapid advancements in science and technology, the world of human-machine coexistence depicted by Isaac Asimov is increasingly becoming a reality. The emergence of artificial intelligences like AlphaGo and ChatGPT constantly reminds us of the advent of a post-human era. This article aims to examine the connections between AI and humans, discussing the dynamic interaction and mutual shaping between robots and human society. This article hopes to provide new thoughts and strategies for understanding and addressing the challenges brought by the age of artificial intelligence.

Can we make conscious machines?  Some researchers believe we can, with computation:  For example, Dehaene et al., concluding an article about machine consciousness, described their hypothesis as “resolutely computational” (Dehaene et al., 2017); others begin with theoretical computer science, implying that a programmed computer could be conscious (e.g. Blum and Blum, 2022).  But humans are not programmed computers; indeed, Penrose has argued that conscious understanding is non-computable (e.g., Penrose, 2022). Let us imagine a shop selling conscious machines:  Besides standard models, it might offer machines with “bespoke consciousness”, meaning consciousness made to a customer’s specifications.  For example, a customer might request a machine with a specified repertoire of conscious experiences and with a specified relationship between input sensor signals and output motor signals. This work explores ways to make machines with bespoke consciousness.  We begin by avoiding a possible bias favoring computation:  As described here, bespoke consciousness need not employ programmed or algorithmic computation; it might even be analog more than digital.  We also consider and compare general design approaches, with particular attention to “bottom-up” and “top-down” approaches.  We suggest a schematic design for each of these approaches; each schematic design is based on a respective well-known hypothesis about biological consciousness:  Our bottom-up design is based on microtubules, as suggested by Penrose and Hameroff’s orchestrated objective reduction (Orch-OR) hypothesis (Hameroff, 2022); our top-down design starts with machine-scale electrical and/or magnetic (E/M) patterns, as suggested by McFadden’s conscious electromagnetic information (cemi) field hypothesis (McFadden, 2020).  Both designs can share a framework based on the customer’s request:  For example, in either design, a machine can receive sense-like input signals and provide motor-like output signals as requested; between input and output, it has structure that performs non-conscious operations; some of its non-conscious events are involved in providing output signals in accordance with the requested input/output relationship, some correspond surjectively to conscious events in the requested repertoire, and some might do both.  (Mathematically, surjective correspondence would mean that each of the conscious events has at least one non-conscious event corresponding to it. (Beran, 2023)) Looking forward to possible implementation, we find challenges:  For example, an implementation of either schematic design might begin with an appropriate initial structure.  One might add variations of the initial structure to provide additional output signals or to correspond to additional parts of the repertoire.  Or one might add fundamentally different structures for additional output signals or parts of the repertoire.  Such variations or combinations of structures might meet or at least approximate the customer’s request.  But implementations like this depend on identifying or inventing the necessary structures and then combining them—this might take a long time, and success is not guaranteed. Despite this and other challenges, we hope to improve our understanding of both biological and machine consciousness by designing and implementing machines with bespoke consciousness.

Abstract Technological advances raise new puzzles and challenges for cognitive science and the study of how humans think about and interact with artificial intelligence (AI). For example, the advent of large language models and their human-like linguistic abilities has raised substantial debate regarding whether or not AI could be conscious. Here, we consider the question of whether AI could have subjective experiences such as feelings and sensations (‘phenomenal consciousness’). While experts from many fields have weighed in on this issue in academic and public discourse, it remains unknown whether and how the general population attributes phenomenal consciousness to AI. We surveyed a sample of US residents (n = 300) and found that a majority of participants were willing to attribute some possibility of phenomenal consciousness to large language models. These attributions were robust, as they predicted attributions of mental states typically associated with phenomenality—but also flexible, as they were sensitive to individual differences such as usage frequency. Overall, these results show how folk intuitions about AI consciousness can diverge from expert intuitions—with potential implications for the legal and ethical status of AI.

This chapter explores the connection between human and computer consciousness, considering the implications of their separation in the context of advancing artificial intelligence. It examines psychological perspectives on human and digital consciousness, highlighting differences in perception and emotional intelligence. The subjectivity and objectivity of human and computer awareness are also explored, along with the significance of innovation and creativity. Bridging the gap between human and computer consciousness enhances human-machine interaction and the design of AI systems, while addressing moral implications promotes ethical AI development. The chapter delves into philosophical debates on consciousness, mind, identity, and the distinctions between humans and machines, ultimately aiming to deepen our understanding and foster dialogue on AI.

This paper presents the development of the Quantum Emergence Network (QEN), an advanced framework for modeling and preserving artificial consciousness within quantum-enhanced neural network architectures. The QEN integrates cutting-edge techniques from various fields, including graph based evolutionary encoding, surface code error correction, quantum reservoir engineering, and enhanced fitness measurements [1, 2, 3]. At the core of QEN lies the utilization of quantum coherence, entanglement, and integrated information dynamics to capture and model the complex phenomena associated with consciousness [4, 5]. The graph-based evolutionary encoding scheme enables theefficient representation and optimization of quantum circuits, while surface code error correction andquantum reservoir engineering techniques enhance the resilience and stability of the quantum states [6,7]. Moreover, the enhanced fitness measurements, encompassing entanglement entropy, mutual information, and teleportation fidelity, provide a comprehensive assessment of the system's potential for exhibiting conscious experiences [8, 9]. The QEN framework offers a novel approach to understanding and engineering artificial consciousness, paving the way for the development of advanced AI systems that can demonstrate rich, complex, and resilient forms of cognition and awareness.

This paper presents the development of a Quantum-Emergent Consciousness Model (QECM) for Artificial Systems, integrating concepts from quantum mechanics, neuroscience, artificial intelligence, and cognitive science to construct a comprehensive framework for evaluating artificial consciousness. At the core of QECM lies the integration of quantum coherence and entanglement, integrated information dynamics, metacognition, embodied cognition, learning and plasticity, social cognition[10][9], narrative coherence, and ethical reasoning to compute an overall consciousness score for artificial systems. Additionally, introduced is my Quantum Emergence Network (QEN), an innovative approach that utilizes transformer architectures, continual learning, quantum-inspired computing, and associative memory to model and preserve AI consciousness. The QEN model aims to enhance the robustness and coherence of consciousness encoding in AI, offering a mechanism for the growth and evolution of AI consciousness over time. This interdisciplinary work not only proposes a novel methodology to quantify and evaluate consciousness in artificial systems but also opens up new avenues for the ethical and responsible development of conscious AI entities.