Artificial Consciousness Research Articles

Indian philosophy as a precursor of the string theory of consciousness. Part 2

Introduction. The article examines the formation of the Ancient Indian philosophy and its connection to consciousness. The Indian philosophy has come a long way from its onset, in parallel with the Chinese and Ancient Greek philosophy. At the same time, the ideas of some of its branches and schools differ in a number of features. Purpose setting. By the example of the Sankhya school of philosophy, we have explored the evolution of the views of Ancient Indian philosophers on the evolution of the Universe, nature and man. We have put particular emphasis on the concept of consciousness.Methodology and methods of the study. In Sankhya system, the fundamental Prakriti and Purusha principles are identified. Other elements are supplementary. Ancient philosophers laid special emphasis on the three vibrating gunas. These are sattva, rajas, and tamas. We have revealed the functional connections between the gunas. The way they interact, in balance or dominance, determines the entire variety of material and spiritual processes. The evolution of the Universe in the Sankhya interpretation is close to the scientific theory of the Big Bang. The development of nature can be traced through the complication of atoms starting from the influence of a stimulus on unconscious chaos. The result of human evolution is the emergence of consciousness derivatives: intellect, mind and I (Ego). This allows better understanding of the behavioral characteristics of an existing personality. Yoga complements Sankhya system and makes it possible to transform consciousness in a number of states.Results. The analysis was carried out using scientific articles and monographs relatively unknown in the Russian press. Cognitive development occurs in a spiral. The scientific theory of strings appeared two and a half millennia after the emergence of the gunas concept. We have made a number of analogies between Ancient Indian gunas and contemporary string theory. And have substantiated the theory of consciousness not with quantum theory, but with a deeper string theory. The concept of consciousness is supported in line with the scientific paradigm of T. Kuhn. The leading ideas are the ideas about information of D. Chalmers and the physicist J. Wheeler.Conclusion. Thus, the phenomenon of consciousness has a silicon and biological basis. We insist on using the semantic concept of artificial consciousness instead of artificial intelligence. Therefore, between human consciousness and artificial consciousness there may be dangerous risks of dominance of the latter.

Bilateral Turing Test: Assessing machine consciousness simulations

Recent advancements in Artificial Intelligence (AI) have brought discussions on machine consciousness back to the forefront. However, methodologies for assessing machine consciousness simulations remain highly constrained and lack consensus. This study introduces the Bilateral Turing Test, a thought experiment to assess machine consciousness simulations based on a functionalist perspective, focusing on external behavior and cognitive characteristics associated with consciousness. The test involves both machines and humans participating as judges in an enhanced Turing Test, and compares the external behavior of machine-simulated consciousness with the behavior of human consciousness. We derive the experimental principles via three theoretical propositions reflecting the human–machine cognitive relationship and elucidate the basic conditions and procedures of the thought experiment. A systematic measure is employed to evaluate emulation against human benchmarks, and we establish a statistical threshold for successful consciousness simulations. Our study provides a comprehensive framework for redefining machine consciousness simulations and enhancing interdisciplinary conversations across computer science, neuroscience, and philosophy. This framework also advances bottom-up approaches for assessing machine consciousness simulations and provides pivotal insights into human–machine interaction dynamics.

The conductor model of consciousness, our neuromorphic twins, and the human-AI deal

AbstractCritics of Artificial Intelligence (AI) posit that artificial agents cannot achieve consciousness even in principle, because they lack certain necessary pre-conditions present in biological agents. Here we highlight arguments from a neuroscientific and neuromorphic engineering perspective as to why such a strict denial of consciousness in artificial agents is not compelling. Based on the construction of a co-evolving neuromorphic twin, we argue that the differences between a developing biological and artificial brain are not fundamental and are vanishing with progress in neuromorphic architecture designs mimicking the human blueprint. To characterise this blueprint, we propose the Conductor Model of Consciousness (CMoC) that builds on neuronal implementations of an external and internal world model, while gating and labelling information flows. An extended turing test lists functional and neuronal correlates of biological consciousness that are captured by the CMoC. These correlates provide the grounding for how biological or artificial agents learn to distinguish between sensory activity generated from outside or inside of the brain, how the perception of these activities can itself be learned, and how the information flow for learning an internal world model is orchestrated by a cortical meta-instance, which we call the conductor. Perception comes with the distinction of sensory and affective components, with the affective component linking to ethical questions that are inherent in our multidimensional model of consciousness. Recognizing the existence of a blueprint for a possible artificial consciousness encompasses functional, neuronal and ethical dimensions, begging the question: How should we behave towards agents that are akin to us in the inner workings of their brains? We sketch a human-AI deal, balancing the growing cognitive abilities of artificial agents, and the possibility to relieve them from suffering of negative affects, with a protection for the rights of humans.

Clues and caveats concerning artificial consciousness from a phenomenological perspective

Clues and caveats concerning artificial consciousness from a phenomenological perspective

Is artificial consciousness achievable? Lessons from the human brain

We here analyse the question of developing artificial consciousness from an evolutionary perspective, taking the evolution of the human brain and its relation with consciousness as a reference model or as a benchmark. This kind of analysis reveals several structural and functional features of the human brain that appear to be key for reaching human-like complex conscious experience and that current research on Artificial Intelligence (AI) should take into account in its attempt to develop systems capable of human-like conscious processing. We argue that, even if AI is limited in its ability to emulate human consciousness for both intrinsic (i.e., structural and architectural) and extrinsic (i.e., related to the current stage of scientific and technological knowledge) reasons, taking inspiration from those characteristics of the brain that make human-like conscious processing possible and/or modulate it, is a potentially promising strategy towards developing conscious AI.Also, it cannot be theoretically excluded that AI research can develop partial or potentially alternative forms of consciousness that are qualitatively different from the human form, and that may be either more or less sophisticated depending on the perspectives. Therefore, we recommend neuroscience-inspired caution in talking about artificial consciousness: since the use of the same word “consciousness” for humans and AI becomes ambiguous and potentially misleading, we propose to clearly specify which level and/or type of consciousness AI research aims to develop, as well as what would be common versus differ in AI conscious processing compared to human conscious experience.

In‐Sensor Touch Analysis for Intent Recognition

AbstractTactile intent recognition systems, which are highly desired to satisfy human's needs and humanized services, shall be accurately understanding and identifying human's intent. They generally utilize time‐driven sensor arrays to achieve high spatiotemporal resolution, however, which encounter inevitable challenges of low scalability, huge data volumes, and complex processing. Here, an event‐driven intent recognition touch sensor (IR touch sensor) with in‐sensor computing capability is presented. The merit of event‐driven and in‐sensor computing enables the IR touch sensor to achieve ultrahigh resolution and obtain complete intent information with intrinsic concise data. It achieves critical signal extraction of action trajectories with a rapid response time of 0.4 ms and excellent durability of >10 000 cycles, bringing an important breakthrough of tactile intent recognition. Versatile applications prove the integrated functions of the IR touch sensor for great interactive potential in all‐weather environments regardless of shading, dynamics, darkness, and noise. Unconscious and even hidden action features can be perfectly extracted with the ultrahigh recognition accuracy of 98.4% for intent recognition. The further auxiliary diagnostic test demonstrates the practicability of the IR touch sensor in telemedicine palpation and therapy. This groundbreaking integration of sensing, data reduction, and ultrahigh‐accuracy recognition will propel the leapfrog development for conscious machine intelligence.

A universal knowledge model and cognitive architectures for prototyping AGI

The article identified 56 cognitive architectures for creating general artificial intelligence (AGI) and proposed a set of interrelated functional blocks that an agent approaching AGI in its capabilities should possess. Since the required set of blocks is not found in any of the existing architectures, the article proposes a reference cognitive architecture for intelligent systems approaching AGI in their capabilities. As one of the key solutions within the framework of the architecture, a universal method of knowledge representation is proposed, which allows combining various non-formalized, partially and fully formalized methods of knowledge representation in a single knowledge base, such as texts in natural languages, images, audio and video recordings, graphs, algorithms, databases, neural networks, knowledge graphs, ontologies, frames, essence-property-relation models, production systems, predicate calculus models, conceptual models, and others. To combine and structure various fragments of knowledge, archigraph model are used, constructed as a development of annotated metagraphs. As other components, the reference cognitive architecture being developed includes following modules: machine consciousness, machine subconsciousness, interaction with the external environment, a goal management, an emotional control, social interaction, reflection, ethics, worldview, learning, monitoring, statement problems, solving problems, self-organization and meta learning. Based on the composition of the proposed reference architecture modules, existing cognitive architectures containing the following modules were analyzed: machine consciousness, machine subconsciousness, reflection, worldview.

On the Way to Machine Consciousness: Identification of Hidden System Properties of Material Objects

On the Way to Machine Consciousness: Identification of Hidden System Properties of Material Objects

Can We Think Machines are Conscious? A Survey of Philosophical Problems Facing the Attribution of Consciousness to Machines

In this paper, I’ll examine whether we could be justified in attributing consciousness to artificial intelligent systems. First, I’ll give a brief history of the concept of artificial intelligence (AI) and get clear on the terms I’ll be using. Second, I’ll briefly review the kinds of AI programs on offer today, identifying which research program I think provides the best candidate for machine consciousness. Lastly, I’ll consider the three most plausible ways of knowing whether a machine is conscious: (1) an AI demonstrates a sufficient level of organizational similarity to that of a human thinker, (2) an inference to the best explanation, and (3) what I call “punting to panpsychism”, i.e., the idea that if everything is conscious, then we get machine consciousness in AI for free. However, I argue that all three of these methods for attributing machine consciousness are inadequate since they each face serious philosophical problems which I will survey and specifically tailor to each method.

Shared Awareness Across Domain‐Specific Artificial Intelligence: An Alternative to Domain‐General Intelligence and Artificial Consciousness

Creating artificial general intelligence is the solution most often in the spotlight. It is also linked with the possibility—or fear—of machines gaining consciousness. Alternatively, developing domain‐specific artificial intelligence is more reliable, energy‐efficient, and ethically tractable, and raises mostly a problem of effective coordination between different systems and humans. Herein, it is argued that it will not require machines to be conscious and that simpler ways of sharing awareness are sufficient.

Possibilities [Un] locked: Questioning the Paradigm of ‘Artificial Consciousness’ and the Promise of ‘Transcendence’ in Jack Paglen’s ‘Transcendence’

Possibilities [Un] locked: Questioning the Paradigm of ‘Artificial Consciousness’ and the Promise of ‘Transcendence’ in Jack Paglen’s ‘Transcendence’

Possibility of Machine Consciousness

Possibility of Machine Consciousness

Rise of the Machines: Exploring the Emergence of Machine Consciousness

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.

Artificial consciousness: a perspective from the free energy principle

Does the assumption of a weak form of computational functionalism, according to which the right form of neural computation is sufficient for consciousness, entail that a digital computational simulation of such neural computations is conscious? Or must this computational simulation be implemented in the right way, in order to replicate consciousness?From the perspective of Karl Friston’s free energy principle, self-organising systems (such as living organisms) share a set of properties that could be realised in artificial systems, but are not instantiated by computers with a classical (von Neumann) architecture. I argue that at least one of these properties, viz. a certain kind of causal flow, can be used to draw a distinction between systems that merely simulate, and those that actually replicate consciousness.

Do artificial intelligence systems understand?

Are intelligent machines really intelligent? Is the underlying philosoph- ical concept of intelligence satisfactory for describing how the present systems work? Is understanding a necessary and sufficient condition for intelligence? If a machine could understand, should we attribute subjectivity to it? This paper addresses the problem of deciding whether the so-called ”intelligent machines” are capable of understanding, instead of merely processing signs. It deals with the relationship between syntax and semantics. The main thesis concerns the inevitability of semantics for any discussion about the possibility of building conscious machines, condensed into the following two tenets: ”If a machine is capable of understanding (in the strong sense), then it must be capable of combining rules and intuitions”; “If semantics cannot be reduced to syntax, then a machine cannot understand.” Our conclusion states that it is not necessary to attribute understanding to a machine in order to explain its exhibited “intelligent” behavior; a merely syntactic and mechanistic approach to intelligence as a task-solving tool suffices to justify the range of operations that it can display in the current state of technological development.

Mathematical aspects of digital technologies: The problem of the absent-minded passenger

<p>The example of the absent-minded passenger problem illustrates the fundamental difficulty of estimating practical values of probabilistic parameters on the basis of general theorizing and allows us to give more accurate and subtle estimates of various parameters, as well as their practical computable approximation. This article is important for building systems of artificial intelligence and artificial consciousness, as well as for modern didactics of higher education, especially for the disciplines of “applied mathematics” and “theoretical computer science”. It is shown that a detailed study of a random process provides more significant applied materials than formally correct theoretical calculations.</p>

‘Quantum of information’ functionality as a measure of subjectivity beyond the capabilities of deep learning

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.

Exploring the Ethical and Technical Data of Machine Consciousness: Hazards, Implications, and Future Directions

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.

From McCulloch to GPT - 4: stages of development of artificial intelligence.

The article examines the history of the development of artificial intelligence (AI), starting from its first theoretical and practical steps and tracing the evolution to modern achievements. The article provides an overview of the key milestones, scientific discoveries and technological breakthroughs made in the field of AI. The most important figures, ideas and principles that influenced its development are also discussed. In the context of this development, various definitions of artificial intelligence are given. There are several key stages in the history of AI: the early stages, the quiet period, the AI renaissance, and the era of AI in the new millennium. Each of these stages made its own unique contribution to the progress of AI. The modern period is characterized by rapid development, especially in the field of machine learning and deep learning. These methods allow artificial intelligence to learn from data and identify complex patterns. Advances in natural language processing, such as models GPT and its modifications, have shown outstanding results. However, despite linguistic advances, GPT remains limited in aspects important to creating strong AI. The article discusses the limitations of modern language models, as well as the prerequisites and prospects for the development of strong artificial intelligence. Special attention is paid to the project of Elon Musk, who, having launched the company X.AI, is engaged in research in the field of creating strong AI with the goal of “knowledge of reality.” The article also proposes an alternative approach to creating strong artificial intelligence - the development of an artificial brain based on a multidimensional multi-connected receptor-effector neuron-like growing network. Some aspects of the emergence of artificial consciousness are also considered.

Artificial Self- Awareness In Over Time

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.