The Relationships Between Intelligence and Consciousness in Natural and Artificial Systems

Consciousness is a natural phenomenon, familiar to every person. At the same time, it cannot be described in singular terms. The rise of Artificial Intelligence in recent years has made the topic of Artificial Consciousness highly debated. The paper discusses the main general theories of consciousness and their relationship with proposed Artificial Consciousness solutions. There are a number of well-established models accepted in the area of research: Higher Order Thoughts/Higher Order Perception, Global Network Workspace, Integrated Information Theory, reflexive, representative, functional, connective, Multiple Draft Model, Neural Correlate of Consciousness, quantum consciousness, to name just a few. Some theories overlap, which allows for speaking about more advanced, complex models. The disagreement in theories leads to different views on animal consciousness and human conscious states. As a result, there are also variations in opinions about Artificial Consciousness based on the discrepancy between qualia and the nature of AI. The hard problem of consciousness, an epitome of qualia, is often seen as an insurmountable barrier or, at least, an “explanatory gap”. Nevertheless, AI constructs allow imitations of some models in silico, which are presented by several authors as full-fledged Artificial Consciousness or as strong AI. This itself does not make the translation of consciousness into the AI space easier but allows decent progress in the domain. As argued in this paper, there will be no universal solution to the Artificial Consciousness problem, and the answer depends on the type of consciousness model. A more pragmatic view suggests the instrumental interaction between humans and AI in the environment of the Fifth Industrial Revolution, limiting expectations of strong AI outcomes to cognition but not consciousness in wide terms.

Global gamma band synchronisation is perhaps the most extensively studied candidate for a Neural Correlate of Consciousness (NCC) . Yet despite numerous studies confirming its association with consciousness, it seems to be neither sufficient nor necessary for the presence of all subjective experiences. Analysis of gamma synchronisation studies suggests that it is a correlate of the initial binding of expected, attended, task-dependent contents of consciousness, whereas task-irrelevant contents do not seem to require gamma synchronisation. While discovery of such a content-related NCC is a remarkable achievement for the neurophysiological research of consciousness, it does not fully explain some of the fundamental structural properties of consciousness, namely the temporal and spatial integration of all available experiences into a coherent stream of consciousness. As an alternative, instead of focusing solely on the selected contents of consciousness, the neural mechanisms of the fundamental properties of consciousness could be studied by contrasting states of (un)consciousness. Recent research into the states of consciousness suggests that, for instance, informational complexity is a highly sensitive predictor of the presence of consciousness, possibly reflecting background structural properties of the unity of subjective experiences. As a limiting factor, though, such a state-related NCC does not seem to reflect the phenomenal diversity of the contents of consciousness. Arguably, these limitations could be overcome by devising experimental setups that would simultaneously probe the neural correlates of the contents and the state of consciousness.

The rapid advances in the capabilities of Large Language Models (LLMs) have galvanised public and scientific debates over whether artificial systems might one day be conscious. Prevailing optimism is often grounded in computational functionalism: the assumption that consciousness is determined solely by the right pattern of information processing, independent of the physical substrate. Opposing this, biological naturalism insists that conscious experience is fundamentally dependent on the concrete physical processes of living systems. Despite the centrality of these positions to the artificial consciousness debate, there is currently no coherent framework that explains how biological computation differs from digital computation, and why this difference might matter for consciousness. Here, we argue that the absence of consciousness in artificial systems is not merely due to missing functional organisation but reflects a deeper divide between digital and biological modes of computation and the dynamico-structural dependencies of living organisms. Specifically, we propose that biological systems support conscious processing because they (i) instantiate scale-inseparable, substrate-dependent multiscale processing as a metabolic optimisation strategy, and (ii) alongside discrete computations, they perform continuous-valued computations due to the very nature of the fluidic substrate from which they are composed. These features - scale inseparability and hybrid computations - are not peripheral, but essential to the brain's mode of computation. In light of these differences, we outline the foundational principles of a biological theory of computation and explain why current artificial intelligence systems are unlikely to replicate conscious processing as it arises in biology.

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.

The rise of Artificial Intelligence (AI) has produced prophets and prophecies announcing that the age of artificial consciousness is near. Not only does the mere idea that any machine could ever possess the full potential of human consciousness suggest that AI could replace the role of God in the future, it also puts into question the fundamental human right to freedom and dignity. This position paper takes the stand that, in the light of all we currently know about brain evolution and the never-stopping formation of adaptive neural circuitry for learning, memory, decision making and, ultimately, fully conscious reasoning and creativity in the human species, the idea of an artificial consciousness appears misconceived. The paper highlights some of the major reasons why. While awareness to external stimuli for processes such as perception, recognition, and operational problem solving is under the direct control of functionally specific brain networks associated with sensory and cognitive functions across animal species, consciousness is a unique property of the human mind. Potentiated by brain evolution, consciousness has come to be when humans became able to represent, and reflect on, the Self in relation to past, present and future, and to project these representations into possible worlds by drawing and other forms of conceptual and creative expression. Epigenetically determined, shaped by experience, capable of representing real and non-real world states, consciousness is enabled by context-dependent adaptive brain circuits that have evolved on the grounds of self-organizing functional interactions at different levels of integration in a from-local-to global functional brain design. The evolution of the latter being continuous, the limits of consciousness are unpredictable. If cracking the computational code to human consciousness were possible, the resulting algorithms would have to be able to generate temporal activity patterns simulating long-distance signal reverberation across the brain and the de-correlation of spatial signal contents from their temporal signatures. In the light of scientific evidence for complex interactions between implicit (non-conscious) and explicit (conscious) representations in learning, memory, and the construction of conscious representation, the code would have to be capable of making all implicit processing explicit. Algorithms would have to be capable of a progressive, less and less arbitrary selection of temporal activity patterns in a continuously developing neural network structure akin to that of the human brain, from synapses to higher cognitive functional integration. The code would have to possess the self-organizing capacities that generate the brain signatures of phenomenal consciousness. In the biological brain, consolidation or extinction of these temporal brain signatures is driven by external event probabilities according to the principles of Hebbian learning. Consciousness is constantly fed by such learning, capable of generating stable representations despite an incommensurable amount of variability in input data, across time and across individuals, for life-long integration of experience data. Artificial consciousness would require probabilistic adaptive computations capable of emulating all the dynamics of human learning and memory that enable human intelligence and creativity. No AI is likely to ever have such potential.

This is a companion review article to a narrative medicine story in this issue of the journal. “In The Ring” is a true story of a patient’s father’s near-death experience of Hell and the history of its follow up impact on the patient and her doctor. This article explores the medical perspective on NDE’s. How are NDE’s different from other altered mental states? What about deep anesthesia recollections? How do the NDE’s compare to psychedelic experiences and the new psylocibin treatments for psychiatric conditions? How are they different from our usual dreams and nightmares? What is the line between our imagination, dreams, and our conscious state? What determines that line? What does science now understand about consciousness itself? What is the difference between computer artificial intelligence (AI) and sentience? Can there be “machine consciousness”? A deep dive into these existential and highly relevant questions is beyond the scope of this manuscript, but this article is an attempt to succinctly address some of these questions in relation to this journal’s narrative medicine story of Hell, “In The Ring” and presents a similar additional distressing NDE case.

The logic of development of artificial and natural intelligence systems, confirmed by practice, suggests that they need to be considered as a single vitasystem conglomerate, in which AI remains problem-oriented and requires the implementation of certain rules for using a computer product, 'borrowed' from natural intelligence with its purely individual history of the transition from objective thinking to abstract. The paper reveals the potential of mechanisms for supporting the structural-parametric method of storing and converting information in smoothing conflicts that inevitably arise in vitasystems between artificial and natural intelligence, which is fraught with disastrous consequences for aerospace applications.

LAW IN DIGITAL REALITY

The problem of consciousness is mostly regarded as identical to the mind-body problem. According to Chalmers’ philosophical arguments, the hard problem of consciousness lies in establishing and explaining the link between physical processes and conscious experiences, via psychological processes. A brief history of various theories of consciousness is given and a selection of theories are tested against Zeman’s three fundamental intuitions and Chalmers’ controversial zombie argument. The hard problem of consciousness is further described using Levine’s notion of an explanatory gap between physical matter and conscious experience, through the first and third persons. Various states, contents, levels and processes of consciousness are summarised, including Damasio and Meyer’s dual perspective for defining consciousness. Tart’s three definitions do not entirely describe altered states of consciousness. While the challenge of finding the core function of human and animal sleep remains unknown when tested under the null hypothesis, studies on the neural correlates of consciousness during meditation have revealed neuroplasticity effects. The synchrony of gamma brain oscillations reflecting various styles of meditation or attention, also known as the binding problem, may be related to conscious experiences. This binding problem with gamma brain oscillatory synchronization also arises in relation to sensory awareness or perception, affecting the perception of time and hallucinatory experiences in various disorders of consciousness such as severe schizophrenic and deja vu (in healthy or epileptic) patients. In conjunction with medication treatments, music therapy is often useful in accelerating the healing process in most such disorders of consciousness. It is still unknown how this sensory awareness to music is perceived in medicated patients suffering from disorders of consciousness. More clinically elusive are near death experiences, in which consciousness persists independently of brain function, where there is no scientific basis for such consciousness to exist and no physiological or psychological model that can explain it. Near death experiences can be regarded as a special state of consciousness, which provides further evidence that the consciousness problem may be very close to the mind-body problem that originates in Descartes’ classic theory of dualism and is transformed into Chalmers’ contemporary theory of natural dualism. The final section of this chapter offers an overview of all invited chapters.

Artificial intelligence of large ensembles of intelligent agents in terms of computing power surpasses human intelligence. He is capable of artificial thinking and understanding. Giant ensembles of intellectual agents with artificial consciousness and intelligence are able, for the results set by the person necessary for him, to find solutions for their obtaining on the basis of professional competence and experience accumulation. The professional competence of artificial intelligence is the ability to use technologies, including computer vision, natural language processing, speech recognition and synthesis, intelligent decision support, as well as the use of synergistic methods, functional self-organizing methods and utility and preference criteria. For artificial intelligence, the functional organization of the system is important. The functionalism of artificial intelligence does not depend on its carrier, allows many methods of its functional implementation, as well as to form the completeness of its functions. Giant ensembles of intellectual agents with a full set of functions gradually and flexibly form events into solutions or rational behavior to obtain a given necessary result. Intelligent artificial intelligence has psychological, analytical, research, language, professional and behavioral hybrid competencies. Each competence is exercised by an intelligent agent with a competent functional professional manner. Intelligent agents form an ensemble with intelligent ethical artificial intelligence. Functional harmonious self-organization of the interaction of intellectual agents in different environments is carried out on the basis of data of a specific environment obtained by analytical competent intellectual agents. For each set of functions and smart hybrid competencies of an intelligent ensemble, there is a critical importance for the number of its intellectual agents capable of synergistic self-organization of interaction.

The article examines the issues related to the creation of semantic models of knowledge that can be used to endow artificial intelligence systems with the ability to understand the meaning of text in natural or any other language. Possible means for constructing semantic models of knowledge include the mechanism of multi-system integration of knowledge developed by the authors earlier, formal ontologies, and techniques of understanding meaning that have emerged within the framework of philological hermeneutics. Significant components of the presented study include an examination of the currently used language models of artificial intelligence, a new approach to the conceptualization of knowledge through its generalization in the form of open models, an assessment of the genesis and prospects of teleological and axiological interpretations of meaning for natural and artificial cognitive systems. The methodological basis of the presented study consists of the authors’ developments in the field of systems analysis, well-known analytical methods adopted within hermeneutics, structuralism, classical epistemology, formal ontology theory, and linguistic and language modeling. The scientific novelty of this research lies in the determination of the necessary tools for creating semantic models that generalize knowledge. The mentioned tools include: multi-system integration of knowledge based on the integration of the subject of cognition into multiple systems with subsequent generalization of the patterns identified in these systems and their translation for solving tasks of understanding and creativity; formal ontologies that implement the description of knowledge from a specific domain in the form of conceptual schemes, taking into account existing rules and relationships between elements, allowing automatic extraction of knowledge; and a wide variety of hermeneutic techniques for understanding meanings. Objective limitations of use for artificial cognitive systems that lack subjectivity and value prioritization in understanding meanings are noted. Some limitations in the use for artificial cognitive systems are also found in hermeneutic techniques for understanding the meaning of text. This is related to the impossibility of full reflection without feelings, emotions, and desires generated by needs that also initiate subjectivity.

This thesis presents a Phenomenological investigation of the meditative and mystical experiences, lived by 12 Western meditators. Their voices are presented throughout this text. They tell the reader about their understanding of the ultimate reality, which underlies our human existence. The 12 interviewees were called co-researchers as the data gathered over two years was analysed in a collaborative way. While the open-ended and exploratory interviews were being conducted, the researcher completed an heuristic diary, which informed the topic of the final chapter of this thesis. The co-researcher's themes informed the other four discussion chapters of the thesis. The data was analysed using Moustakas' (1990, 1994) Heuristic Phenomenology, which was underpinned by Husserl's (1931/1958) Transcendental Phenomenology. The co-researchers were from various religious traditions: namely Christianity, Judaism, Hinduism, Buddhism, Sufism, and Islam. There were two Atheist meditators. The co¬researchers were aged between 26 and 64. They had meditated between 18 months and 30 years. The Phenomenological method was adjusted to accommodate the spiritual experiences described by the co-researchers. Essentially, Husserl assumed that there was an internal or external object of consciousness, experienced by an Egoic subject. In everyday experiencing the subject and object of consciousness may seem separate. However, in meditation one transcends everyday consciousness to a mystical state whereby the subject and object of consciousness are one. In this study, the Phenomenological method was expanded to accommodate the unified structures of meditative and mystical experience. This extension to the Phenomenological method is one of the several original contributions advanced in this thesis. One finding of this study was that meditation was not a technique. One can sit cross-legged and simply think. Meditation is a state of consciousness, which is distinct from ordinary states such as waking, sleeping and dreaming. What was common to each meditator in this study was that their meditation involved reaching a state of present moment awareness. The Christians were in the present moment while sitting cross-legged or in a chair. The Buddhists walked while baring attention to the present moment. The Sufi meditated and whirled with the dervishes, immersed in divine energy.

Artificial Intelligence–Aided Colonoscopy for Characterizing and Detecting Colorectal Polyps: Required, Nice to Have, or Overhyped?

Objective: to formulate proposals to form a system of subsidiary liability for harm resulting from the use of artificial intelligence systems. Methods: the research is based on a comprehensive methodological basis, including the abstract logical method for theoretical understanding of the legal nature of artificial intelligence as an object of legal regulation; the method of comparison to analyze the Russian and European legislations on tort liability; generalization to systematize the existing concepts of responsibility distribution between subjects of law; and correlation analysis to identify the relationships between the typology of artificial intelligence systems and the mechanisms of legal responsibility for their functioning. Results: the study summarizes and systematizes modern theoretical and legal concepts and regulations of the European Union and the Russian Federation on the distribution of subsidiary responsibility for the adverse effects of artificial intelligence. Potential subjects of responsibility were identified, as well as the key factors influencing the distribution of responsibility between them. A multidimensional matrix was developed for responsibility distribution between the subjects, taking into account their impact on the specific artificial intelligence system functioning and the systems typologization under the risk-based approach. Scientific novelty: for the first time, an original concept is proposed, which combines the differentiation of the subjects’ roles in terms of their real impact on the artificial intelligence results; the differentiation of artificial intelligence systems under the risk-based approach; and the system of legal presumptions of responsibility distribution corresponding to the above two classifications. The novelty lies in the creation of a multidimensional matrix of subsidiary liability, which allows taking into account many factors when determining the subject of responsibility in each specific case of harm caused by artificial intelligence systems, which differs significantly from existing unilateral approaches to this issue. Practical significance: the research conclusions and suggestions can be used to develop the doctrine of subsidiary responsibility in the field of artificial intelligence use, to develop and modify the legal norms regulating artificial intelligence. The proposed multidimensional matrix of responsibility distribution can serve as a theoretical basis for improving judicial practice in cases of compensation for damage caused by artificial intelligence systems, as well as for creating an effective balance between stimulating the development of AI technologies and ensuring the protection of the rights and legitimate interests of individuals and legal entities.

The article discusses the main trends in the development of artificial intelligence systems and robotics (AI&R). The main question that is considered in this context is whether artificial systems are going to become more and more anthropomorphic, both intellectually and physically. In the article, the author analyzes the current state and prospects of technological development of artificial intelligence and robotics and also determines the main aspects of the impact of these technologies on society and economy, indicating the geopolitical strategic nature of this influence. The author considers various approaches to the definition of artificial intelligence and robotics, focusing on the subject-oriented and functional ones. In the article, AI&R abilities and human abilities are compared in such areas as categorization, pattern recognition, planning and decision making, etc. Based on this comparison, it is concluded in which areas AI&R’s performance is inferior to a human and in which cases it is superior to one. The modern achievements in the field of robotics and artificial intelligence create the necessary basis for further discussion of the applicability of goal setting in engineering, in the form of a Turing test. It is shown that development of AI&R is associated with certain contradictions that impede the application of Turing’s methodology in its usual format. The basic contradictions in the development of AI&R technologies imply that there is to be a transition to a post-Turing methodology for assessing engineering implementations of AI&R. In such implementations, on the one hand, the “Turing wall” is removed, and on the other hand, artificial intelligence gets its physical implementation.