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There are several lessons that can already be drawn from the current research programs on strong AI and building conscious machines, even if they arguably have not produced fruits yet. The first one is that functionalist approaches to consciousness do not account for the key importance of subjective experience and can be easily confounded by the way in which algorithms work and succeed. Authenticity and emergence are key concepts that can be useful in discerning valid approaches versus invalid ones and can clarify instances where algorithms are considered conscious, such as Sophia or LaMDA. Subjectivity and embeddedness become key notions that should also lead us to re‐examine the ethics of decision delegation. In addition, the focus on subjective experience shifts what is relevant in our understanding of ourselves as human beings and as an image of God, namely, in de‐emphasizing intellectuality in favor of experience and contemplation over action.

Consciousness and intelligence are properties commonly understood as dependent by folk psychology and society in general. The term artificial intelligence and the kind of problems that it managed to solve in the recent years has been shown as an argument to establish that machines experience some sort of consciousness. Following the analogy of Russell, if a machine is able to do what a conscious human being does, the likelihood that the machine is conscious increases. However, the social implications of this analogy are catastrophic. Concretely, if rights are given to entities that can solve the kind of problems that a neurotypical person can, does the machine have potentially more rights that a person that has a disability? For example, the autistic syndrome disorder spectrum can make a person unable to solve the kind of problems that a machine solves. We believe that the obvious answer is no, as problem solving does not imply consciousness. Consequently, we will argue in this paper how phenomenal consciousness and, at least, computational intelligence are independent and why machines do not possess phenomenal consciousness, although they can potentially develop a higher computational intelligence that human beings. In order to do so, we try to formulate an objective measure of computational intelligence and study how it presents in human beings, animals and machines. Analogously, we study phenomenal consciousness as a dichotomous variable and how it is distributed in humans, animals and machines. As phenomenal consciousness and computational intelligence are independent, this fact has critical implications for society that we also analyze in this work.

The Transformer artificial intelligence model is one of the most accurate models to extract the meaning/semantics from sets of symbolic sequences of various lengths, including long sequences. These models transform the language spaces as per long and short-distance relationships among units of the language. These models thus minimize some aspects of human comprehension of the world. To frame a generalized theory of identification and generation of meaning in human thought, the transformer model needs to be understood in the context of generalized systems theory, such that other equivalent models can be discovered, compared and selected to converge on the base model of meaning identification and discovery aspect of the philosophy of knowledge or epistemology. This paper explores the relationships of the transformer model and its various component parts, processes and the phenomena to some critical aspects of generalized systems theory such as cognition, symmetry & equivalence, holons, emergence, identifiability, system spaces and system universe, reconstructability, equilibriums & oscillations, scaling, polystability, ontogeny, algedonic loops, heterarchy, holarchy, homeorhesis, isomorphism, homeostasis, attractors, equifinality, nesting, parallelization, loops, causal structure, transformations, feedbacks, encodings, and information complexity.

The problem of the origin of consciousness, on the one hand, and the problem of global challenges to humanity due to the intellectualization of the technical re-equipment of the means of production are among the most pressing problems of transdisciplinary science. Modern physicalism, denying consciousness, qualia, and equating artificial intelligence with the mentality of man wrongfully integrates the Subject with high technology. The agnosticism and physicalism of analytic philosophy considers thought to be material and is able to recognize (in perspective) the human virtues of honor, freedom, and conscience for the artificial intellect. In this way, society transfers power over the crises resulting from the development of science, towards the development of social technology. The main task of this article is to make evident again to everyone the idea of the existence of personality as a form of sociality in two opposing worlds at once - the material and the ideal. Including using the achievements of neurobiology in the knowledge of the functional asymmetry of the human brain, which is absent in the higher animals and is associated with the emergence of consciousness.

This article provides an analytical framework for how to simulate human-like thought processes within a computer. It describes how attention and memory should be structured, updated, and utilized to search for associative additions to the stream of thought. The focus is on replicating the dynamics of the mammalian working memory system, which features two forms of persistent activity: sustained firing (preserving information on the order of seconds) and synaptic potentiation (preserving information from minutes to hours). The article uses a series of figures to systematically demonstrate how the iterative updating of these working memory stores provides functional organization to behavior, cognition, and awareness. In a machine learning implementation, these two memory stores should be updated continuously and in an iterative fashion. This means each state should preserve a proportion of the coactive representations from the state before it (where each representation is an ensemble of neural network nodes). This makes each state a revised iteration of the preceding state and causes successive configurations to overlap and blend with respect to the information they contain. Thus, the set of concepts in working memory will evolve gradually and incrementally over time. Transitions between states happen as persistent activity spreads activation energy throughout the hierarchical network, searching long-term memory for the most appropriate representation to be added to the global workspace. The result is a chain of associatively linked intermediate states capable of advancing toward a solution or goal. Iterative updating is conceptualized here as an information processing strategy, a model of working memory, a theory of consciousness, and an algorithm for designing and programming artificial intelligence (AI, AGI, and ASI).

In this article, it is taken for granted that fully-human artificial intelligence—a term used to denote artificial life that is, in principle, more human than strong AI would be—must possess operational faculties for consciousness and selfhood. After clarifying relevant questions surrounding the interested socio-psychological phenomena, progress in animal and humanoid robotics is summarized. The aforementioned topics within philosophy and the social sciences are reviewed, noting their relevant overlaps with recent developments in cognitive and computer sciences. My working assumption is that the avowed conclusion of human AI cannot currently be written off as impossible and should therefore be critically engaged (the intent is to engage with humanoid robotics’ capabilities and features in relation to the present state of knowledge regarding the psychological phenomena discussed). It is argued that for human AI to fully succeed as a discipline, the discussed psychological notions as we understand and experience them must be further elucidated and adequately accounted for by AI research programs.

People have different opinions about which conditions robots would need to fulfil—and for what reasons—to be moral agents. Standardists hold that specific internal states (like rationality, free will or phenomenal consciousness) are necessary in artificial agents, and robots are thus not moral agents since they lack these internal states. Functionalists hold that what matters are certain behaviours and reactions—independent of what the internal states may be—implying that robots can be moral agents as long as the behaviour is adequate. This article defends a standardist view in the sense that the internal states are what matters for determining the moral agency of the robot, but it will be unique in being an internalist theory defending a large degree of robot responsibility, even though humans, but not robots, are taken to have phenomenal consciousness. This view is based on an event-causal libertarian theory of free will and a revisionist theory of responsibility, which combined explain how free will and responsibility can come in degrees. This is meant to be a middle position between typical compatibilist and libertarian views, securing the strengths of both sides. The theories are then applied to robots, making it possible to be quite precise about what it means that robots can have a certain degree of moral responsibility, and why. Defending this libertarian form of free will and responsibility then implies that non-conscious robots can have a stronger form of free will and responsibility than what is commonly defended in the literature on robot responsibility.

The established theories and frameworks on consciousness in the academic literature as related to artificial intelligence (AI), are rooted in anthropocentricism. Even those theories created intentionally for AI are based on the levels of consciousness as it is understood in humans primarily, and in other animals secondarily. This paper will discuss why such anthropocentric frameworks are built on unsecure foundations. We will do this by comparing the capacities and functions of human and AI cognitive architectures, discussing the ramifications and consequences of the behaviors that stem from these, and looking at the neurological conditions in humans that can give the most promising hints as to what a potential conscious AI entity would look like. The paper ends with a proposed solution for building a nonanthropocentric foundation of cognition that could lead toward a truly AI-focused framework of consciousness.

How does consciousness emerge from a brain that consists only of physical matter and electrical / chemical reactions? The deep mysteries of consciousness have plagued philosophers and scientists for thousands of years. This book approaches the problem through scientific studies that shed light on the neural mechanism of consciousness, and furthermore, delves into the possibility of artificial consciousness, a phenomenon that may ultimately solve the mystery. Finally, two key suggestions made in the book, namely, a method to test machine consciousness and a theory hypothesizing that consciousness emerges from a neural algorithm, reveal a novel and credible pathway to mind-uploading.The original Japanese version of this book has become a best-seller in popular neuroscience and has even led to a neurotech startup for mind-uploading.

Thus far, we have experienced three artificial intelligence (AI) booms. In the third one, we succeeded in developing AI that partially surpassed human capabilities. However, we are yet to develop AI that, like humans, can perform a series of cognitive processes. Consciousness built into devices is called machine consciousness. Related research has been conducted from two perspectives: studying machine consciousness as a tool to elucidate human consciousness and achieving the technological goal of furthering AI research with conscious AI. Herein, we survey the research conducted on machine consciousness from the second perspective. For AI to attain machine consciousness, its implementation must be evaluated. Therefore, we only surveyed attempts to implement consciousness as systems on devices. We collected research results in chronological order and found no breakthroughs that could deliver machine consciousness soon. Moreover, there is no method to evaluate whether an implemented machine consciousness system possesses consciousness, thus making it difficult to confirm the certainty of the implementation. This field of research is a new frontier. It is an exciting field with many discoveries expected in the future.

The article analyzes the concepts of artificial personality and artificial consciousness, and shows the key difficulties of implementing projects to create such artificial intelligence systems. These difficulties are related to the following characteristics of artificial personality and artificial consciousness: 1) creativity and free will; 2) intentionality; 3) qualia; 4) first person perspective; 5) the passage of time in consciousness. The basic needs for an artificial personality (in the context of the development of natural and artificial intelligence) are indicated. Two directions of artificial personality formation are highlighted: 1) transformation of an artificial system into an artificial personality; 2) transformation of a person into an artificial personality.

Consciousness is now what distinguishes humans from machines. This paper discusses artificial consciousness and how artificial general intelligence is progressing from current artificial intelligence. It also discusses human cognitive capacities, ethics, and how artificial intelligence may be used to supplement each of these. Several scientists discussed approaches for generating cognition in machines. This study presents scenarios that demonstrate how consciousness and ethics will play a significant role in future artificial intelligence. The impact of the consciousness and correlation with the AI cognitive abilities are discussed. The paper will also address the necessity of ethical norms in AI, particularly in modern self-driving cars. An overview of current Narrow AI capabilities will be provided, as well as discussion of present and future directions for Strong AI research. Can Strong AI become conscious? A few discussion points are provided.

Consciousness nearly in all its elusive history is a convoluted notion, often misconstrued or not understood enough to cause a reproducible representation. With these odious assertions, this publication is opening the box of consciousness with deviation from commonly understood notion of consciousness. The proposed paradigm of consciousness approaches this issue with speculative and intuitive perspectives, essentially it is a precursor activity in hope to materialize the elusive artificial general intelligence, the true carrier of exceptional human intelligence and consciousness. This paper posits a counterbalance approach to the current paradigm of consciousness and as an alternate a radical theory of consciousness is presented. This attempt on the behavioral, structural and functional working of consciousness is kept pragmatic in the intractable universe of consciousness.

This article is an attempt at the “hard problem” of Consciousness. As the era of Artificial Intelligence is looming ahead, we are concerned about our future environment.  We define human Consciousness and medium Consciousness. We clarify the difference between the brain and the mind. We demonstrate how the brain creates the mind but must do so only in the presence of Consciousness. We define a person and delve into the frequency of personhood to finally answer whether machines could become conscious one day or not.

An expert on the mind considers how animals and smart machines measure up to human intelligence. Octopuses can open jars to get food, and chimpanzees can plan for the future. An IBM computer named Watson won on Jeopardy! and Alexa knows our favorite songs. But do animals and smart machines really have intelligence comparable to that of humans? In Bots and Beasts, Paul Thagard looks at how computers (“bots”) and animals measure up to the minds of people, offering the first systematic comparison of intelligence across machines, animals, and humans. Thagard explains that human intelligence is more than IQ and encompasses such features as problem solving, decision making, and creativity. He uses a checklist of twenty characteristics of human intelligence to evaluate the smartest machines—including Watson, AlphaZero, virtual assistants, and self-driving cars—and the most intelligent animals—including octopuses, dogs, dolphins, bees, and chimpanzees. Neither a romantic enthusiast for nonhuman intelligence nor a skeptical killjoy, Thagard offers a clear assessment. He discusses hotly debated issues about animal intelligence concerning bacterial consciousness, fish pain, and dog jealousy. He evaluates the plausibility of achieving human-level artificial intelligence and considers ethical and policy issues. A full appreciation of human minds reveals that current bots and beasts fall far short of human capabilities.

This study was focused on reviewing several research articles related to various perspectives and significant recent developments of Artificial Intelligence (AI). The main goal of this review was to gain insight into the implications of causal reasoning models in artificial intelligence. This review analyses state-of-the-art research articles and evaluations of applications, techniques algorithms, and trends in the field of Artificial Intelligence. By presenting recent results, this study has a strong emphasis on fundamental aspects of causal reasoning, logic, and computational structures of Strong AI agents. Findings of this study outline the importance of implementing causal reasoning methods in AI systems in order to achieve in the future, a truly intelligent machine. We can conclude that causal reasoning provides an important approach to advancing our understanding of artificial consciousness. Extensive research is needed in the future to validate and evaluate different causal tools for supporting causal reasoning in AI.

Talking about consciousness and Artificial Intelligence, like two sides of a coin, are seen together but are not together. When combined it becomes a mastery that can take over the world. Consciousness is still been not defined by any researcher or scientist. Heuristically, we know that Artificial Intelligence is booming and technology being ever fastest-growing field, but with this, there are many factors which are been neglected and can cause some drastic changes and severe problems to mankind. Thinking that Artificial Intelligence is not beyond humans, there are times where things are neglected but the fact that AI is showing prominent signs that it has become far more superior than what it has been trained and tested on. Often there are some series or patterns of outputs observed, which were not been trained to the machine but were formed by the algorithm matches might be difficult to understand for humans as well. This paper discusses the thoughts which are alive in everyone’s brain but are unanswered and are finding a path to reach out a standard solution. Aspects of society being the oldest of one, which was formed by humans. How will it be if the “SOCIETY” is seen in the world lead by the robots becoming dominant and ruling over humans? Can the consciousness which is still abstract or fugitively subjective in humans work similarly in robots as it is felt within us? If it ever will, Would humans live their lives with freedom? Or will it be minimal and not according to themselves but according to the robots? Is it right to expound it in one word as Singularity? As it is still an unknown entity but also a toss of ambiguity. Researchers are quite near to develop self-learning robots, but hidden patterns of them communicating amongst themselves speculate more perplexed theories which are making them more complex for scientists and researchers. Deep-down significantly knowing that things are moving in a direction which are casting the risk factors but also if you flip and see the other side, it shows the positive results and the growth of Intelligence which is helping each individual to grow in their way.

How can the free energy principle contribute to research on neural correlates of consciousness, and to the scientific study of consciousness more generally? Under the free energy principle, neural correlates should be defined in terms of neural dynamics, not neural states, and should be complemented by research on computational correlates of consciousness – defined in terms of probabilities encoded by neural states. We argue that these restrictions brighten the prospects of a computational explanation of consciousness, by addressing two central problems. The first is to account for consciousness in the absence of sensory stimulation and behaviour. The second is to allow for the possibility of systems that implement computations associated with consciousness, without being conscious, which requires differentiating between computational systems that merely simulate conscious beings and computational systems that are conscious in and of themselves. Given the notion of computation entailed by the free energy principle, we derive constraints on the ascription of consciousness in controversial cases (e.g., in the absence of sensory stimulation and behaviour). We show that this also has implications for what it means to be, as opposed to merely simulate a conscious system.