Search

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.

In the context of the unstoppable trend of artificial intelligence, science and technology have become the theme of the times. Will the rapid development of modern technology, such as biotechnology and artificial intelligence, dehumanize us? Can a machine have human consciousness? In his novel Klara and the Sun, Kazuo Ishiguro criticizes the arrogance of technological rationality and the arrogance of anthropocentrism from the perspective of a “non-human” robot. The relationship between humans and machines has become a problem that humans need to re-examine. With the help of post-humanism, this paper aims to explore the physical changes and behavioral actions of robots and humans in the novel to reveal the “split” between man and machine and the “self-deception” of humans in the novel, so as to finally trigger thinking about how humans and machines can coexist harmoniously at the juncture between humans and posthumans, and provide reference for the future society between humans and non-humans.

The question of whether artificial intelligence (AI) can be considered conscious and therefore should be evaluated through a moral lens has surfaced in recent years. In this paper, we argue that whether AI is conscious is less of a concern than the fact that AI can be considered conscious by users during human-AI interaction, because this ascription of consciousness can lead to carry-over effects on human-human interaction. When AI is viewed as conscious like a human, then how people treat AI appears to carry over into how they treat other people due to activating schemas that are congruent to those activated during interactions with humans. In light of this potential, we might consider regulating how we treat AI, or how we build AI to evoke certain kinds of treatment from users, but not because AI is inherently sentient. This argument focuses on humanlike, social actor AI such as chatbots, digital voice assistants, and social robots. In the first part of the paper, we provide evidence for carry-over effects between perceptions of AI consciousness and behavior toward humans through literature on human-computer interaction, human-AI interaction, and the psychology of artificial agents. In the second part of the paper, we detail how the mechanism of schema activation can allow us to test consciousness perception as a driver of carry-over effects between human-AI interaction and human-human interaction. In essence, perceiving AI as conscious like a human, thereby activating congruent mind schemas during interaction, is a driver for behaviors and perceptions of AI that can carry over into how we treat humans. Therefore, the fact that people can ascribe humanlike consciousness to AI is worth considering, and moral protection for AI is also worth considering, regardless of AI’s inherent conscious or moral status.

The quest to create artificial consciousness has long been a central challenge in the field of artificial intelligence (AI). While significant progress has been made in developing AI systems that can perform complex tasks and exhibit intelligent behavior, the question of whether these systems can truly be considered conscious remains open. In this paper, I present a novel approach to quantifying consciousness in AI systems by integrating principles from quantum mechanics, information theory, and neuroscience. The model incorporates key components such as self-awareness, subjective experience, intentionality, metacognition, integrated information processing, and dynamic cognition, which are thought to be essential for the emergence of conscious experience. I demonstrate the application of the model using a simulated AI system and discuss the implications of the findings for the development of artificially conscious agents. Furthermore, I argue that the pursuit of artificial consciousness is not only a scientific and technological endeavor but also a philosophical and ethical one, with profound implications for the understanding of the nature of mind and the relationship between humans and machines

Self-awareness results from consciousness of existence in time and space. Thought and consciousness are distinguishing factors between humans and machines having artificial intelligence. No algorithm has been offered for artificial self-awareness based on Thinking. Previous studies have not studied the relationship between consciousness, thinking and time. This study studied the relationship between Self-awareness, thinking, memories and speech over time. A deep logical connection exists between consciousness, thinking, and time. Based on this research findings, an algorithm can be designed for artificial consciousness and Self-awareness.

Computational functionalism posits that consciousness is a computation. Here we show, perhaps surprisingly, that it cannot be a Turing computation. Rather, computational functionalism implies that consciousness is a novel type of computation that has recently been proposed by Geoffrey Hinton, called mortal computation.

Sublimating the epistemological scope of a mere science-fiction tale, The Bicentennial Man (1976) by Isaac Asimov (1920-92) centers around a philosophical labyrinth where the lines between humanity and machine blur, inviting the reader to question the very essence of what it means to be human. The intricate narrative of an AI robot’s journey toward humanness serves as a profound meditation on the evolving relationship between humans and robots. Andrew Martin, the positronic robot at the heart of the story, is not just a mechanical marvel; he is, instead, a crucible in which Asimov tests the boundaries of consciousness, human identity, and the emotional yearning for belonging. This paper delves into the novella’s exploration of these themes, unraveling the intricate process of Andrew’s robot-human evolution and its profound implications for a better understanding of the meaning of humanness and the future of artificial intelligence. In the realm of science fiction, The Bicentennial Man thus stands as a luminous testament to the enduring question of human identity. Through the poignant lens of Andrew in his desire to be human, the novella builds upon the posthumanist discourse of the man-machine dichotomy, providing the reader with a timely opportunity to re-evaluate consciousness, emotion, and the defining characteristics of humanity.

What is the prospect of developing artificial general intelligence (AGI)? I investigate this question by systematically comparing living and algorithmic systems, with a special focus on the notion of "agency." There are three fundamental differences to consider: (1) Living systems are autopoietic, that is, self-manufacturing, and therefore able to set their own intrinsic goals, while algorithms exist in a computational environment with target functions that are both provided by an external agent. (2) Living systems are embodied in the sense that there is no separation between their symbolic and physical aspects, while algorithms run on computational architectures that maximally isolate software from hardware. (3) Living systems experience a large world, in which most problems are ill-defined (and not all definable), while algorithms exist in a small world, in which all problems are well-defined. These three differences imply that living and algorithmic systems have very different capabilities and limitations. In particular, it is extremely unlikely that true AGI (beyond mere mimicry) can be developed in the current algorithmic framework of AI research. Consequently, discussions about the proper development and deployment of algorithmic tools should be shaped around the dangers and opportunities of current narrow AI, not the extremely unlikely prospect of the emergence of true agency in artificial systems.

The ideas of this book originate from the mobile WAVE approach which allowed us, more than a half century ago, to implement citywide heterogeneous computer networks and solve distributed problems on them well before the internet. The invented paradigm evolved into Spatial Grasp Technology and resulted in a European patent and eight books. The volumes covered concrete applications in graph and network theory, defense and social systems, crisis management, simulation of global viruses, gestalt theory, collective robotics, space research, and related concepts. The obtained solutions often exhibited high system qualities like global integrity, distributed awareness, and even consciousness. This current book takes these important characteristics as primary research objectives, together with the theory of patterns covering them all. This book is oriented towards system scientists, application programmers, industry managers, defense and security commanders, and university students (especially those interested in advanced MSc and PhD projects on distributed system management), as well as philosophers, psychologists, and United Nations personnel.

The article reflects various approaches of philosophy and programming to methods for solving the technical problem of creating and software implementation of artificial consciousness (AC). Various purposes of creation and basic approaches to determining the nature of AC are described. To solve the problem of creating an AC, an architecture is proposed that includes ten levels, starting from the basic level of collecting and systematizing information about the external world and ending with the upper level of influence on it, agreed with the person and the level of decision-making. The features of the delimitation of functions and the procedure for interaction between a person and an AC are considered in detail. In conclusion, the most important, from a programmer’s point of view, properties that characterize artificial consciousness are given.</p>