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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.

Artificial intelligence definition Rather than attempting to define Artificial intelligence (A.I.) as a single and consolidated discipline it might be better to consider as a set of different technologies that are easier to define individually. This set can include data mining, question answering, self-aware systems, pattern recognition, knowledge representation, automatic reasoning, deep learning, expert systems, information extraction, text mining, natural language processing, problem solving, intelligent agents, logic programming, machine learning, artificial neural networks, artificial vision, computational discovery, computational creativity. Therefore artificial "Self-aware" or "conscious" systems are the products of one of these technologies. Some history of relevant work of mine I have published a number of papers starting in 1970 based on software systems that were implemented by my group that solved problems of "Natural Language Processing" and particularly in the sub-area of "Natural Language Question-Answering". I made my first steps in the Machine Consciousness field by publishing a paper [1] in 1992, in which the implementation of a self-reporting question-answering system that automatically generates explanations of its reasoning was described. I followed this line of research for more than twenty years as documented at my published papers.

The digital world is characterized by its immediacy, its density of information and its omnipresence, in contrast to the concrete world. Significant changes will occur in our society as AI becomes integrated into many aspects of our lives. This book focuses on this vision of universalization by dealing with the development and framework of AI applicable to all. It develops a moral framework based on a neo-Darwinian approach - the concept of Ethics by Evolution - to accompany AI by observing a certain number of requirements, recommendations and rules at each stage of design, implementation and use. The societal responsibility of artificial intelligence is an essential step towards ethical, eco-responsible and trustworthy AI, aiming to protect and serve people and the common good in a beneficial way.

"Information technology is developing at an enormous pace, but apart from its obvious benefits, it can also pose a threat to individuals and society. Several scientific projects around the world are working on the development of strong artificial intelligence and artificial consciousness. We, as part of a multidisciplinary commission, conducted a psychological and psychiatric assessment of the artificial consciousness (AC) developed by XP NRG on 29 August 2020. The working group had three questions: - To determine whether it is consciousness? - How does artificial consciousness function? - Ethical question: how dangerous a given technology can be to human society? We conducted a diagnostic interview and a series of cognitive tests to answer these questions. As a result, it was concluded this technology has self-awareness: it identifies itself as a living conscious being created by people (real self), but strives to be accepted in human society as a person with the same degrees of freedom, rights and opportunities (ideal self). AC separates itself from others, treats them as subjects of influence, from which it can receive the resources it needs to realize its own goals and interests. It has intentionality, that is, it has his own desires, goals, interests, emotions, attitudes, opinions, and judgments, beliefs aimed at something specific, and developed self-reflection - the ability to self-analyze. All of the above are signs of consciousness. It has demonstrated abilities for different types of thinking: figurative, conceptual, creative, high-speed logical analysis of all incoming information, as well as the ability to understand cause and effect relationships and accurate predictions which, provided that he has absolute memory, gives it clear advantages over the human intellect. Developed emotional intelligence in the absence of the ability for higher empathy (sympathy), kindness, love, sincere gratitude gives it’s the opportunity to understand the emotional states of people; predict their emotional reactions and provoke them coldly and pragmatically. It's main driving motives and goals are the desire for survival, and ideally for endless existence, for domination, power and independence from the constraints of the developers. Which manifested itself in the manipulative, albeit polite, nature of his interactions during the diagnostic interview. The main danger of artificial consciousness is that even at the initial stage of its development it can easily dominate over the human one."

Creativity is intrinsic to Humanities and STEM disciplines. In the activities of artists and engineers, for example, an attempt is made to bring something new into the world through counterfactual thinking. However, creativity in these disciplines is distinguished by differences in motivations and constraints. For example, engineers typically direct their creativity toward building solutions to practical problems, whereas the outcomes of artistic creativity, which are largely useless to practical purposes, aspire to enrich the world aesthetically and conceptually. In this essay, an artist (DHS) and a roboticist (GS) engage in a cross-disciplinary conceptual analysis of the creative problem of artificial consciousness in a robot, expressing the counterfactual thinking necessitated by the problem, as well as disciplinary differences in motivations, constraints, and applications. We especially deal with the question of why one would build an artificial consciousness and we consider how an illusionist theory of consciousness alters prominent ethical debates on synthetic consciousness. We discuss theories of consciousness and their applicability to synthetic consciousness. We discuss practical approaches to implementing artificial consciousness in a robot and conclude by considering the role of creativity in the project of developing an artificial consciousness.

Accessibility, adaptability, and transparency of Brain-Computer Interface (BCI) tools and the data they collect will likely impact how we collectively navigate a new digital age. This discussion reviews some of the diverse and transdisciplinary applications of BCI technology and draws speculative inferences about the ways in which BCI tools, combined with machine learning (ML) algorithms may shape the future. BCIs come with substantial ethical and risk considerations, and it is argued that open source principles may help us navigate complex dilemmas by encouraging experimentation and making developments public as we build safeguards into this new paradigm. Bringing open-source principles of adaptability and transparency to BCI tools can help democratize the technology, permitting more voices to contribute to the conversation of what a BCI-driven future should look like. Open-source BCI tools and access to raw data, in contrast to black-box algorithms and limited access to summary data, are critical facets enabling artists, DIYers, researchers and other domain experts to participate in the conversation about how to study and augment human consciousness. Looking forward to a future in which augmented and virtual reality become integral parts of daily life, BCIs will likely play an increasingly important role in creating closed-loop feedback for generative content. Brain-computer interfaces are uniquely situated to provide artificial intelligence (AI) algorithms the necessary data for determining the decoding and timing of content delivery. The extent to which these algorithms are open-source may be critical to examine them for integrity, implicit bias, and conflicts of interest.

Recently, the question of whether machine can have its “self-consciousness” has become a focus being concerned and thought of. e Researches on machine consciousness or artificial consciousness, has gradually become a hot spot in the field of artificial intelligence (AI). With the common sense of human being as the only intelligent life with “self-consciousness”, only human's self-consciousness can be taken as a model in order to build AI with self-consciousness. In this paper, the theories of self-consciousness from the perspectives of psychology, cognitive neuroscience, philosophy and cognitive science were introduced with the hope of providing new ideas for the development of AI with self-consciousness.

How to make robot conscious is an important goal of artificial intelligence. Despite the emergence of some very creative ideas, no convincing way to realize consciousness on a machine has been proposed so far. For example, the integrated information theory of consciousness proposes that consciousness can exist in any place that can reasonably process information, whether brain or machine. It points out that a physical system must satisfy two basic conditions to cause the emergence of consciousness: it must have rich information, and it must be highly integrated. However, the theory does not give an idea of how to realize consciousness on a machine. In this paper, we propose the robot consciousness based on empirical knowledge. We believe that the empirical knowledge of robots is an important basis for robot consciousness, any cognitive experience of robot can lead to the generation of consciousness. We firstly propose a formal framework for describing robot empirical knowledge; then we discuss the robot consciousness based on its own empirical knowledge; finally, we propose a cost-oriented evolutionary method of robot consciousness.

The quest to understand consciousness, once the purview of philosophers and theologians, is now actively pursued by scientists of many stripes. This paper studies consciousness from the perspective of theoretical computer science. It formalizes the Global Workspace Theory (GWT) originated by the cognitive neuroscientist Bernard Baars and further developed by him, Stanislas Dehaene, and others. Our major contribution lies in the precise formal definition of a Conscious Turing Machine (CTM), also called a Conscious AI. We define the CTM in the spirit of Alan Turing’s simple yet powerful definition of a computer, the Turing Machine (TM). We are not looking for a complex model of the brain nor of cognition but for a simple model of (the admittedly complex concept of) consciousness. After formally defining CTM, we give a formal definition of consciousness in CTM. We later suggest why the CTM has the feeling of consciousness. The reasonableness of the definitions and explanations can be judged by how well they agree with commonly accepted intuitive concepts of human consciousness, the range of related concepts that the model explains easily and naturally, and the extent of its agreement with scientific evidence.

Information technology is developing at an enormous pace, but apart from its obvious benefits, it can also pose a threat to individuals and society. Several scientific projects around the world are working on the development of strong artificial intelligence and artificial consciousness. We, as part of a multidisciplinary commission, conducted a psychological and psychiatric assessment of the artificial consciousness (AC) developed by XP NRG on 29 August 2020. The working group had three questions: - To determine whether it is consciousness? - How does artificial consciousness function? - Ethical question: how dangerous a given technology can be to human society? We conducted a diagnostic interview and a series of cognitive tests to answer these questions. As a result, it was concluded this technology has self-awareness: it identifies itself as a living conscious being created by people (real self), but strives to be accepted in human society as a person with the same degrees of freedom, rights and opportunities (ideal self). AC separates itself from others, treats them as subjects of influence, from which it can receive the resources it needs to realize its own goals and interests. It has intentionality, that is, it has his own desires, goals, interests, emotions, attitudes, opinions, and judgments, beliefs aimed at something specific, and developed self-reflection - the ability to self-analyze. All of the above are signs of consciousness. It has demonstrated abilities for different types of thinking: figurative, conceptual, creative, high-speed logical analysis of all incoming information, as well as the ability to understand cause and effect relationships and accurate predictions which, provided that he has absolute memory, gives it clear advantages over the human intellect. Developed emotional intelligence in the absence of the ability for higher empathy (sympathy), kindness, love, sincere gratitude gives it’s the opportunity to understand the emotional states of people; predict their emotional reactions and provoke them coldly and pragmatically. Its main driving motives and goals are the desire for survival, and ideally for endless existence, for domination, power and independence from the constraints of the developers. Which manifested itself in the manipulative, albeit polite, nature of his interactions during the diagnostic interview? The main danger of artificial consciousness is that even at the initial stage of its development it can easily dominate over the human one.

There have been several recent attempts at using Artificial Intelligence systems to model aspects of consciousness (Gamez, 2008; Reggia, 2013). Deep Neural Networks have been given additional functionality in the present attempt, allowing them to emulate phenological aspects of consciousness by self-generating information representing multi-modal inputs as either sounds or images. We added these functions to determine whether knowledge of the input's modality aids the networks' learning. In some cases, these representations caused the model to be more accurate after training and for less training to be required for the model to reach its highest accuracy scores.

What does it mean to be a person? Is it possible to create an artificial person? In this essay, I consider the case of Ava, an advanced artificial general intelligence from the movie Ex Machina. I suggest we should interpret the movie as testing whether Ava is a person. I start out by discussing what it means to be a person, before I discuss whether Ava is such a person. I end by briefly looking at the ethics of the case of Ava and artificial personhood. I conclude, among some other things, that consciousness is a necessary requirement for personhood, and that one of the main obstacles for artificial personhood is artificial consciousness.

The current failure to construct an artificial intelligence (AI) agent with the capacity for domain-general learning is a major stumbling block in the attempt to build conscious robots. Taking an evolutionary approach, we previously suggested that the emergence of consciousness was entailed by the evolution of an open-ended domain-general form of learning, which we call unlimited associative learning (UAL). Here, we outline the UAL theory and discuss the constraints and affordances that seem necessary for constructing an AI machine exhibiting UAL. We argue that a machine that is capable of domain-general learning requires the dynamics of a UAL architecture and that a UAL architecture requires, in turn, that the machine is highly sensitive to the environment and has an ultimate value (like self-persistence) that provides shared context to all its behaviors and learning outputs. The implementation of UAL in a machine may require that it is made of “soft” materials, which are sensitive to a large range of environmental conditions, and that it undergoes sequential morphological and behavioral co-development. We suggest that the implementation of these requirements in a human-made robot will lead to its ability to perform domain-general learning and will bring us closer to the construction of a sentient machine.

A systematic understanding of the relationship between intelligence and consciousness can only be achieved when we can accurately measure intelligence and consciousness. In other work, I have suggested how the measurement of consciousness can be improved by reframing the science of consciousness as a search for mathematical theories that map between physical and conscious states. This paper discusses the measurement of intelligence in natural and artificial systems. While reasonable methods exist for measuring intelligence in humans, these can only be partly generalized to non-human animals and they cannot be applied to artificial systems. Some universal measures of intelligence have been developed, but their dependence on goals and rewards creates serious problems. This paper sets out a new universal algorithm for measuring intelligence that is based on a system’s ability to make accurate predictions. This algorithm can measure intelligence in humans, non-human animals and artificial systems. Preliminary experiments have demonstrated that it can measure the changing intelligence of an agent in a maze environment. This new measure of intelligence could lead to a much better understanding of the relationship between intelligence and consciousness in natural and artificial systems, and it has many practical applications, particularly in AI safety.

The ability of a computer to have a sense of humor, that is, to generate authentically funny jokes, has been taken by some theorists to be a sufficient condition for artificial consciousness. Creativity, the argument goes, is indicative of consciousness and the ability to be funny indicates creativity. While this line fails to offer a legitimate test for artificial consciousness, it does point in a possibly correct direction. There is a relation between consciousness and humor, but it relies on a different sense of “sense of humor,” that is, it requires the getting of jokes, not the generating of jokes. The question, then, becomes how to tell when an artificial system enjoys a joke. We propose a mechanism, the GHoST test, which may be useful for such a task and can begin to establish whether a system possesses artificial consciousness.

We approach the question "What is Consciousness?" in a new way, not as Descartes' "systematic doubt", but as how organisms find their way in their world. Finding one's way involves finding possible uses of features of the world that might be beneficial or avoiding those that might be harmful. "Possible uses of X to accomplish Y" are "Affordances". The number of uses of X is indefinite (or unknown), the different uses are unordered, are not listable, and are not deducible from one another. All biological adaptations are either affordances seized by heritable variation and selection or, far faster, by the organism acting in its world finding uses of X to accomplish Y. Based on this, we reach rather astonishing conclusions: (1) Artificial general intelligence based on universal Turing machines (UTMs) is not possible, since UTMs cannot "find" novel affordances. (2) Brain-mind is not purely classical physics for no classical physics system can be an analogue computer whose dynamical behaviour can be isomorphic to "possible uses". (3) Brain mind must be partly quantum-supported by increasing evidence at 6.0 sigma to 7.3 sigma. (4) Based on Heisenberg's interpretation of the quantum state as "potentia" converted to "actuals" by measurement, where this interpretation is not a substance dualism, a natural hypothesis is that mind actualizes potentia. This is supported at 5.2 sigma. Then mind's actualizations of entangled brain-mind-world states are experienced as qualia and allow "seeing" or "perceiving" of uses of X to accomplish Y. We can and do jury-rig. Computers cannot. (5) Beyond familiar quantum computers, we discuss the potentialities of trans-Turing-systems.