This paper deals with the philosophical problems concerned with research in the field of artificial intelligence (AI), in particular with problems arising out of claims that AI exhibits ‘consciousness’, ‘thinking’ and other ‘inner’ processes and that they simulate human intelligence and cognitive processes in general. The argument is to show how Cartesian mind is non-mechanical. Descartes’ concept of ‘I think’ presupposes subjective experience, because it is ‘I’ who experiences the world. Likewise, Descartes’ notion of ‘I’ negates the (...) notion of computationality of the mind. The essence of mind is thought and the acts of thoughts are identified with the acts of consciousness. Therefore, it follows that cognitive acts are conscious acts, but not computational acts. Thus, for Descartes, one of the most important aspects of cognitive states and processes is their phenomenality, because our judgments, understanding, etc. can be defined and explained only in relation to consciousness and not in relation to computationality. We can only find computationality in machines and not in the mind, which wills, understands and judges. (shrink)

The increasing diffusion of novel digital and online sociotechnical systems for arational behavioral influence based on Artificial Intelligence (AI), such as social media, microtargeting advertising, and personalized search algorithms, has brought about new ways of engaging with users, collecting their data and potentially influencing their behavior. However, these technologies and techniques have also raised concerns about the potential for manipulation, as they offer unprecedented capabilities for targeting and influencing individuals on a large scale and in a more subtle, automated (...) and pervasive manner than ever before. This paper, provides a narrative review of the existing literature on manipulation, with a particular focus on the role of AI and associated digital technologies. Furthermore, it outlines an account of manipulation based of four key requirements: intentionality, asymmetry of outcome, non-transparency and violation of autonomy. I argue that while manipulation is not a new phenomenon, the pervasiveness, automaticity, and opacity of certain digital technologies may raise a new type of manipulation, called “digital manipulation”. I call “digital manipulation” any influence exerted through the use of digital technology that is intentionally designed to bypass reason and to produce an asymmetry of outcome between the data processor (or a third party that benefits thereof) and the data subject. Drawing on insights from psychology, sociology, and computer science, I identify key factors that can make manipulation more or less effective, and highlight the potential risks and benefits of these technologies for individuals and society. I conclude that manipulation through AI and associated digital technologies is not qualitatively different from manipulation through human–human interaction in the physical world. However, some functional characteristics make it potentially more likely of evading the subject’s cognitive defenses. This could increase the probability and severity of manipulation. Furthermore, it could violate some fundamental principles of freedom or entitlement related to a person’s brain and mind domain, hence called neurorights. To this end, an account of digital manipulation as a violation of the neuroright to cognitive liberty is presented. (shrink)

This book offers the first sustained critique of individualism in psychology, a view that has been the subject of debate between philosophers such as Jerry Fodor and Tyler Burge for many years. The author approaches individualism as an issue in the philosophy of science and by discussing issues such as computationalism and the mind's modularity he opens the subject up for non-philosophers in psychology and computer science. Professor Wilson carefully examines the most influential arguments for individualism and identifies the main (...) metaphysical assumptions underlying them. Since the topic is so central to the philosophy of mind, a discipline generating enormous research and debate at present, the book has implications for a very broad range of philosophical issues including the naturalisation of intentionality, psychophysical supervenience, the nature of mental causation, and the viability of folk psychology. (shrink)

A philosophical appraisal of historical positions on the nature of thought, mentality, and intelligence, this survey begins with the views of Descartes, Turing, and Newell and Simon, but includes the work of Haugeland, Fodor, Searle, and other major scholars. The underlying issues concern distinctions between syntax, semantics, and pragmatics, where physical computers seem to be best viewed as mark-manipulating or syntax-processing mechanisms. Alternative accounts have been advanced of what it takes to be a thinking thing, including being Turing machines, (...) symbol systems, semantic engines, and semiotic systems, which have the ability to use signs in the sense of the Charles S. Peirce. Reflections regarding the nature of representations and existence of mental algorithms suggest that the theory of minds as semiotic systems should be preferred to its alternatives, where digital computers can still qualify as “intelligent machines” even without minds. (shrink)

The Dictionary of World Philosophy covers the diverse and challenging terminology, concepts, schools and traditions of the vast field of world philosophy. Providing an extremely comprehensive resource and an essential point of reference in a complex and expanding field of study the Dictionary covers all major subfields of the discipline. Key features: * Cross-references are used to highlight interconnections and the cross-cultural diffusion and adaptation of terms which has taken place over time * The user is led from specific terms (...) to master entries which provide valuable historical and cultural context * Each master entry is followed by at least two suggestions for further reading on the subject, creating a substantial bibliography of world philosophy * References extend beyond philosophy to related areas such as cognitive science, computer science, language and physics Subdisciplines covered include: * aesthetics * ethics * sociopolitical philosophy * the philosophy of law * epistemology * logic * the philosophy of science * the philosophy of mind * the philosophy of culture and history * metaphysics * the philosophy of religion Entries are drawn from West Africa, Arabic, Chinese, Indian, Japanese, Jewish, Korean, Latin American, Maori and Native American philosophy including the important and so far largely neglected instance of Pre-Hispanic thought: Nahua philosophy. (shrink)

Believing that the enterprise of constructing "artificial intelligence" transcends the bounds of any one discipline, the editors of Mechanical Bodies, Computational Minds have brought together researchers in AI and scholars in the humanities to reexamine the fundamental assumptions of both areas. The AI community, for example, could benefit from explorations of human intelligence and creativity by philosophers, psychologists, anthropologists, literary critics, and others, while analysis of AI's theoretical struggles and technical advances could yield insights into such traditional humanist (...) concerns as the nature of rationality and the mind-body dichotomy.The contributions include a continuation of the famous Hubert Dreyfus-Daniel Dennett debate over Kasparov's defeat by IBM's Deep Blue; Philip Agre's tracing of difficulties in AI research to the inherited tensions of Cartesian dualism; Evelyn Fox Keller's examination of the development of computer technology in relation to biology; Douglas Hofstadter's argument that thinking is more than the theorem-solving activities of AI; and Alison Adam's discussion of the implicitly male universal subject used in AI. (shrink)

Sequential transformative design of research :224–235, 2015; Groleau et al. in J Mental Health 16:731–741, 2007; Robson and McCartan in Real world research: a resource for users of social research methods in applied settings, Wiley, Chichester, 2016) allows testing a group of theoretical assumptions about the connections of artificial intelligence with culture and education. In the course of research, semiotics ensures the description of self-organizing systems of cultural signs and symbols in terms of artificial intelligence as a special (...) set of algorithms. This approach helps to consider the arguments proposed by Searle :417–457, 1980) against ‘strong’ artificial intelligence. Searle :417–457, 1980) believes that artificial or machine intelligence cannot fully emulate the processes of the human mind. Machine intelligence shows own inevitable weakness. This is non-autonomous tool for computations and data operating. In fact, this tool cannot provide insight into real cognitive conditions. After Lotman and Uspensky, authors expand the meaning of artificial intelligence. The authors identify a cultural type of ‘strong’ artificial intelligence or ‘self-increase of Logos’ in terms by Lotman and Uspensky. The interpretation of human intelligence as imitation of machine intelligence makes possible such immersion of artificial intelligence in culture. The authors reveal a case of self-organizing autonomous generation, encoding, decoding, reception, storage, and transmission of social information in the field of physical training. From the empirical studies it is clear that the organization of collective activities without external control ensures the development of positive emotions and social orientations. Interest in autonomous behavior provides the formation of educational and cognitive motives. As a special set of algorithms, these motives are the most promising and favorable for personal development. (shrink)

The article analyzes the possibilities and limitations of artificial intelligence. The article considers the subjectivity of artificial intelligence, determines its necessity for solving intellectual problems depending on the possibility of representing the real world as a deterministic system. Methodological limitations of artificial intelligence, which is based on the use of big data technologies, are stated. These limitations cause the impossibility of forming a holistic representation of the objects of cognition and the world as a whole. As (...) a tool for deterministic description of the universe it is proposed to use empirical-metaphysical general theory of systems, which is an extension of existing general theories of systems due to ontological justification of the phenomenon of isomorphism and definition of a limited set of laws, rules, patterns and primitives of forms and relations of objects in the universe. The distinction of natural (human) and artificial intelligence is considered, including the realization of multisystem integration of intelligence in physical, biological, social and spiritual systems. A philosophically grounded approach to ensuring the evolutionary properties of artificial intelligence is formulated, based on the inclusion of non-equilibrium mechanisms through which stability is realized. (shrink)

Does artificial intelligence (AI) exhibit consciousness or self? While this question is hotly debated, here we take a slightly different stance by focusing on those features that make possible both, namely a basic or fundamental subjectivity. Learning from humans and their brain, we first ask what we mean by subjectivity. Subjectivity is manifest in the perspectiveness and mineness of our experience which, ontologically, can be traced to a point of view. Adopting a non-reductive neurophilosophical strategy, we (...) assume that the point of view exhibits two layers, a most basic neuroecological and higher order mental layer. The neuroecological layer of the point of view is mediated by the timescales of world and brain, as further evidenced by empirical data on our sense of self. Are there corresponding timescales shared with the world in AI and is there a point of view with perspectiveness and mineness? Discussing current neuroscientific evidence, we deny that current AI exhibits a point of view, let alone perspectiveness and mineness. We therefore conclude that, as per current state, AI does not exhibit a basic or fundamental subjectivity and henceforth no consciousness or self is possible in models such as ChatGPT and similar technologies. (shrink)

The apparently intelligent doings of computers occasion philosophical debate about artificial intelligence . Evidence of AI is not bad; arguments against AI are: such is the case for. One argument against AI--currently, perhaps, the most influential--is considered in detail: John Searle's Chinese room argument . This argument and its attendant thought experiment are shown to be unavailing against claims that computers can and even do think. CRA is formally invalid and informally fallacious. CRE's putative experimental result is not robust (...) and fails to generalize from understanding to other mental attributes as claimed. Further, CRE depends for its credibility, in the first place, on a dubious tender of the epistemic privilege of overriding all "external" behavioral evidence to first person disavowals of mental properties like understanding. ;Advertised as effective against AI, Searle's argument is an ignoratio elenchi, feigning to refute AI by disputing a similar claim of "strong AI" or Turing machine functionalism metaphysically identifying minds with programs. AI, however, is warranted independently of FUN: even if CRA disproved FUN this would still fail to refute or seriously disconfirm claims of AI. Searle's contention that everyday predications of mental terms of computers are discountable as equivocal "as-if" predications--impugning independent seeming-evidence of AI if tenable--is unwarranted. Lacking intuitive basis, such accusations of ambiguity require theoretical support. The would-be theoretical differentiation of intrinsic intentionality from as-if intentionality Searle propounds to buttress allegations of ambiguity against mental attributions to computers, however, depends either on dubious doctrines of objective intrinsicality according to which meanings are physically in the head or on even more dubious notions of subjective intrinsicality according to which meanings are phenomenologically "in" consciousness. Neither would such would-be differentiae as these unproblematically rule out seeming instances of AI if granted. The dubiousness of as if dualistic identification of thought with consciousness also undermines the epistemic privileging of the "first person point of view" crucial to Searle's thought experiment. (shrink)

Quantum mechanics is currently being tried to be used as a probe to unravel the mysteries of consciousness. Present paper deals with this probe, quantum mechanics and its usefulness in getting an insight of working of human consciousness. The formation of quantum mechanics based on certain axioms, its development to study the dynamical behavior and motions of fundamental particles and quantum energy particles moving with the velocity of light, its insistence on wave functions, its probability approach, its dependence on uncertainty (...) principles will all be critically discussed. The result of this discussion will be presented. Its limitations in unraveling the form, function and biological nature of consciousness will be presented. The alternative probe available and being used – the translation of Upanishadic insight and Advaita philosophy into cognitive science elements in delineating the definition, form and function of Consciousness will be given. The usefulness of this modeling and analysis in understanding the consciousness, mind and its functions in cognitive science and theory of human language acquisition and communication will also be presented. The physic-chemical nature of ideas, senses, thoughts, feelings, utterances will be grossly dealt with. The functions of consciousness and mind, in knowledge and language acquisition and communication will be dealt with. (shrink)

Are mental states "in the head"? Or do they intrinsically involve aspects of the subject's physical and social context? This volume presents a number of essays dealing with the compass of the mind. The contributors broach a range of issues with a commmon view that physical and social magnets do act upon mental states. The approaches that run through these papers make the volume challenging to cognitive psychologists, theorists of artificial intelligence, social theorists, and philosophers.

While the notion of the mind as information-processor--a kind of computational system--is widely accepted, many scientists and philosophers have assumed that this account of cognition shows that the mind's operations are characterizable independent of their relationship to the external world. Existential Cognition challenges the internalist view of mind, arguing that intelligence, thought, and action cannot be understood in isolation, but only in interaction with the outside world. Arguing that the mind is essentially embedded in the external world, Ron McClamrock provides (...) a schema that allows cognitive scientists to address such long-standing problems in artificial intelligence as the "frame" problem and the issue of "bounded" rationality. Extending this schema to cover progress in other studies of behavior, including language, vision, and action, McClamrock reinterprets the importance of the organism/environment distinction. McClamrock also considers the broader philosophical question of the place of mind in the world, particularly with regard to questions of intentionality, subjectivity, and phenomenology. With implications for philosophy, cognitive and computer science, AI, and psychology, this book synthesizes state-of-the-art work in philosophy and cognitive science on how the mind interacts with the world to produce thoughts, ideas, and actions. (shrink)

-/- The aim of this paper is to grasp the relevant distinctions between various ways in which models and simulations in Artificial Intelligence (AI) relate to cognitive phenomena. In order to get a systematic picture, a taxonomy is developed that is based on the coordinates of formal versus material analogies and theory-guided versus pre-theoretic models in science. These distinctions have parallels in the computational versus mimetic aspects and in analytic versus exploratory types of computer simulation. The proposed taxonomy cuts (...) across the traditional dichotomies between symbolic and embodied AI, general intelligence and symbol and intelligence and cognitive simulation and human/non-human-like AI. -/- According to the taxonomy proposed here, one can distinguish between four distinct general approaches that figured prominently in early and classical AI, and that have partly developed into distinct research programs: first, phenomenal simulations (e.g., Turing’s “imitation game”); second, simulations that explore general-level formal isomorphisms in pursuit of a general theory of intelligence (e.g., logic-based AI); third, simulations as exploratory material models that serve to develop theoretical accounts of cognitive processes (e.g., Marr’s stages of visual processing and classical connectionism); and fourth, simulations as strictly formal models of a theory of computation that postulates cognitive processes to be isomorphic with computational processes (strong symbolic AI). -/- In continuation of pragmatic views of the modes of modeling and simulating world affairs, this taxonomy of approaches to modeling in AI helps to elucidate how available computational concepts and simulational resources contribute to the modes of representation and theory development in AI research—and what made that research program uniquely dependent on them. (shrink)

his article will focus on the mechanistic origins of the computer metaphor, which forms the conceptual framework for the methodology of the cognitive sciences, some areas of artificial intelligence and the philosophy of mind. The connection between the history of computing technology, epistemology and the philosophy of mind is expressed through the metaphorical dictionaries of the philosophical discourse of a particular era. The conceptual clarification of this connection and the substantiation of the mechanistic components of the computer metaphor is (...) the main goal of this article. The statement is substantiated that the invention of mechanical computing devices, having a long history in the European engineering tradition, formed the prerequisites for the emergence of machine functionalism in the modern philosophy of mind. The idea of multiple implementation stems from the principle that a formal symbol system prescribes rules for the use of rational abstractions through the physical architecture of a computational engine. The article considers the reasons for the conceptual shift and reveals the semantic foundations for the metaphorical transfer of the properties of abstract objects from the theory of automata to the field of modern philosophy of mind. The criticism and ways of protecting the philosophical program of machine functionalism are analyzed by changing the content of the metaphor “Mind as machine”. The reasons for the stability of the information-computer approach in cognitive sciences are also disclosed and explained. (shrink)

The aim of this paper is to grasp the relevant distinctions between various ways in which models and simulations in Artificial Intelligence (AI) relate to cognitive phenomena. In order to get a systematic picture, a taxonomy is developed that is based on the coordinates of formal versus material analogies and theory-guided versus pre-theoretic models in science. These distinctions have parallels in the computational versus mimetic aspects and in analytic versus exploratory types of computer simulation. The proposed taxonomy cuts across (...) the traditional dichotomies between symbolic and embodied AI, general intelligence and symbol and intelligence and cognitive simulation and human/non-human-like AI. -/- According to the taxonomy proposed here, one can distinguish between four distinct general approaches that figured prominently in early and classical AI, and that have partly developed into distinct research programs: first, phenomenal simulations (e.g., Turing’s “imitation game”); second, simulations that explore general-level formal isomorphisms in pursuit of a general theory of intelligence (e.g., logic-based AI); third, simulations as exploratory material models that serve to develop theoretical accounts of cognitive processes (e.g., Marr’s stages of visual processing and classical connectionism); and fourth, simulations as strictly formal models of a theory of computation that postulates cognitive processes to be isomorphic with computational processes (strong symbolic AI). -/- In continuation of pragmatic views of the modes of modeling and simulating world affairs, this taxonomy of approaches to modeling in AI helps to elucidate how available computational concepts and simulational resources contribute to the modes of representation and theory development in AI research—and what made that research program uniquely dependent on them. (shrink)

The book questions two key dichotomies: that of the apparent and real, and that of the internal and external. This leads to revised notions of the structure of experience and the object of knowledge. Our world is experienced as possibilities of action, and to know is to know what to do. A further consequence is that the mind is best considered as a property of organisms' interactions with their environment. The unit of analysis is the loop of action and perception, (...) and the central concept is the notion of habit of action, which provides the embodied basis of cognition as the anticipation of action. This holds for non-linguistic tacit meanings as well as for linguistic meanings. Habit of action is a teleological notion and thus opens a possibility for defining intentionality and normativity in terms of the soft naturalism adopted in the book. The mind is embodied, and this embodiment determines our physical perspective on the world. Our sensory organs and other instruments give us instrumental access to the world, and this access is epistemic in character. The distinction between the physical and conceptual viewpoint allows us to define truth as the correspondence with operational fit. This embodied epistemic truth is however not a sign of antirealism, as the instrumentally accessed theoretical objects are precisely those objects that experimental science deals with.. (shrink)

Harnad 's proposed "robotic upgrade" of Turing's Test, from a test of linguistic capacity alone to a Total Turing Test of linguistic and sensorimotor capacity, conflicts with his claim that no behavioral test provides even probable warrant for attributions of thought because there is "no evidence" [p.45] of consciousness besides "private experience" [p.52]. Intuitive, scientific, and philosophical considerations Harnad offers in favor of his proposed upgrade are unconvincing. I agree with Harnad that distinguishing real from "as if" thought on the (...) basis of consciousness testing as sufficient warrant for mental attribution) has the skeptical consequence Harnad accepts -- "there is in fact no evidence for me that anyone else but me has a mind" [p.45]. I disagree with his acceptance of it! It would be better to give up the neo-Cartesian "faith" [p.52] in private conscious experience underlying Harnad 's allegiance to Searle's controversial Chinese Room "Experiment" than give up all claim to know others think. It would be better to allow that Turing's Test evidences -- even strongly evidences -- thought. (shrink)

In lieu of an abstract, here is a brief excerpt of the content:On the Conceivability of Artificially Created EnlightenmentPaul Andrew PowellPointsman can only possess the zero and the one. He cannot, like [Roger] Mexico, survive anyplace in between.... [H]e imagines the cortex of the brain as a mosaic of tiny on/off elements.... [E]ach point is allowed only the two states:... [o]ne or zero.... [B]rain mechanics assumes the presence of these bi-stable points....If ever the Anti-pointsman existed, Roger Mexico is the man.... (...) How can Mexico play, so at his ease, with these symbols of randomness and fright? Innocent as a child, perhaps unaware—perhaps—that in his play he wrecks the elegant rooms of history.—Thomas Pynchon, Gravity's RainbowWhen I first read about the Dalai Lama's remark that "if all the external conditions and the karmic action were there, a stream of consciousness might actually enter into a computer,"1 I couldn't help but wonder if a computer could then attain enlightenment. The answer, at first, seemed simple and obvious (though perhaps distasteful for some): if the machine was self-aware, sentient, and conscious, then it would be only reasonable to assume that it could also attain enlightenment. Yes, simple and obvious, of course, but this takes for granted some very large and complex issues. After all, what makes for self-awareness, sentience, and consciousness to begin with, and how would we manufacture them in a machine?If we take a moment to think about the creation of artificial enlightenment the paradox becomes clear. In order to manufacture enlightenment, we must first manufacture samsara, because for a Buddhist, enlightenment is the "event" of deconstructing the problematic natures of "self," sentience (suchness), and consciousness, all of which are samsaric illusions. "Self," sentience, and consciousness represent the prerequisites of samsara/Nirvana, realized or unrealized, artificial or organic. They represent the prerequisites of desire. They represent the prerequisites for the illusion of death and birth. And thus they represent the truly salient issues that must be addressed before we can assemble our complicated hard (and soft) ware. Interestingly, because computational information consists of a conceptualized, dualistic [End Page 123] binary system of ones and zeroes, and because the nature of Nirvana/samsara also consists of a conceptualized, dualistic system of "self"/"other," technology, in theory anyway, may probably be capable of manufacturing a facsimile of samsara, if only because computational information is, by its very nature, samsaric to begin with. Of course, all this demands that consciousness, self-awareness, and sentience be a function of information processing. Personally, I believe they are, and that, in fact, the words information and samsara are synonymous with one another. Thus it follows that information technology will most certainly be capable of manufacturing the prerequisites, at least, of enlightenment. If that blunt statement strikes you as utterly preposterous, technically impossible, or even offensive, you might want to first read any number of excellent texts describing the future of artificial intelligence (AI). Two of the best popular books on the subject are Robot by Hans Moravec and The Age of Spiritual Machines by Ray Kurzweil. These will assuage your doubts concerning the soft problem, or, as it is sometimes called, the nuts-and-bolts problem.With these considerations in mind, let's see what we can come up with.In order to arrive at what you are not You must go through the way in which you are not.—T.S. Eliot, "East Coker" (The Four Quartets)For most Buddhists, enlightenment can be defined as seeing through the illusion of the self and experiencing unadulterated suchness. In the words of Master Wolfgang Kopp, "The seer, the seen, and the process of seeing are one. The thinker, the thought, and the process of thinking fall together into one and multiplicity melts away. There is neither inside, nor outside in this state. There is only 'suchness,' tathata."2 So, enlightenment is suchness, or, things as they are, revealed as all that there is. As engineers of enlightenment, it would appear then that are at least two components we need to build into our machine: an illusionary "self" and suchness, or things as they are. As we shall see, the... (shrink)

In 2021, 193 Member States at UNESCO’s General Conference adopted the Recommendation on the Ethics of Artificial Intelligence as the first important step towards a future global standard-setting instrument on the subject. The text reflects an emerging consensus among the international community about the growing ethical concerns with artificial intelligence (AI). Among these concerns are also serious risks and dangers attributed to the manipulative effects of AI, which can be further exacerbated by the creative combination of AI with (...) other innovative technologies or applications, such as brain–computer interfaces (BCIs), functional magnetic resonance imaging (fMRI), robotics, and big data. The risks for individuals and society as a whole caused by manipulation through AI are already well known and have recently also been addressed by the European Union having released a proposal for an Artificial Intelligence Act (AIA). Among multiple risks related to AI, the AIA singles out the specific dangers related to AI systems that deploy “subliminal techniques beyond a person’s consciousness to materially distort a person’s behaviour in a manner that causes or is likely to cause that person or another person physical or psychological harm”. The present article thus aims to highlight the known and potential dangers related to AI systems manipulating human thoughts and behaviour through subliminal and supraliminal means and methods. To this end, it advocates the joint study of law and the senses captured by the concept of legal synaesthesia to correspond to the need for an interdisciplinary debate covering the complexity of the links between AI, related technologies, human perception based on the senses and the mind, as well as the role and instruments of law in the future organization of societies in this world. (shrink)

Ideas about human consciousness and mental functions will be analyzed and developed using cognitive science information available in the Upanishads, Brahmajnaana, Advaita and Dvaita schools of thought. -/- The analysis and development so done will be used to theorize and give scheme of human language acquisition and communication process clubbing with Sabdabrahma Siddhanta/Sphota Vaada which put forward infrasonic wave oscillator issuing pulses in infrasonic range and are reflected as brain waves. -/- Thus a brain-wave modulation/demodulation model of human language acquisition (...) and communication will be advanced and put forward. Application of this study in mind-machine modeling, natural language comprehension field of artificial intelligence will also be hinted. -/- . (shrink)

Could an artificial intelligence become a legal person? As of today, this question is only theoretical. No existing computer program currently possesses the sort of capacities that would justify serious judicial inquiry into the question of legal personhood. The question is nonetheless of some interest. Cognitive science begins with the assumption that the nature of human intelligence is computational, and therefore, that the human mind can, in principle, be modelled as a program that runs on a computer. Artificial (...) intelligence (AI) research attempts to develop such models. But even as cognitive science has displaced behavioralism as the dominant paradigm for investigating the human mind, fundamental questions about the very possibility of artificial intelligence continue to be debated. This Essay explores those questions through a series of thought experiments that transform the theoretical question whether artificial intelligence is possible into legal questions such as, "Could an artificial intelligence serve as a trustee?" What is the relevance of these legal thought experiments for the debate over the possibility of artificial intelligence? A preliminary answer to this question has two parts. First, putting the AI debate in a concrete legal context acts as a pragmatic Occam's razor. By reexamining positions taken in cognitive science or the philosophy of artificial intelligence as legal arguments, we are forced to see them anew in a relentlessly pragmatic context. Philosophical claims that no program running on a digital computer could really be intelligent are put into a context that requires us to take a hard look at just what practical importance the missing reality could have for the way we speak and conduct our affairs. In other words, the legal context provides a way to ask for the "cash value" of the arguments. The hypothesis developed in this Essay is that only some of the claims made in the debate over the possibility of AI do make a pragmatic difference, and it is pragmatic differences that ought to be decisive. Second, and more controversially, we can view the legal system as a repository of knowledge-a formal accumulation of practical judgments. The law embodies core insights about the way the world works and how we evaluate it. Moreover, in common-law systems judges strive to decide particular cases in a way that best fits the legal landscape-the prior cases, the statutory law, and the constitution. Hence, transforming the abstract debate over the possibility of AI into an imagined hard case forces us to check our intuitions and arguments against the assumptions that underlie social decisions made in many other contexts. By using a thought experiment that explicitly focuses on wide coherence, we increase the chance that the positions we eventually adopt will be in reflective equilibrium with our views about related matters. In addition, the law embodies practical knowledge in a form that is subject to public examination and discussion. Legal materials are published and subject to widespread public scrutiny and discussion. Some of the insights gleaned in the law may clarify our approach to the artificial intelligence debate. (shrink)

The possibility of AI consciousness depends much on the correct answer to the mind–body problem: how our materialistic brain generates subjective consciousness? If a materialistic answer is valid, machine consciousness must be possible, at least in principle, though the actual instantiation of consciousness may still take a very long time. If a non-materialistic one (either mentalist or dualist) is valid, machine consciousness is much less likely, perhaps impossible, as some mental element may also be required. Some recent advances in neurology (...) (despite the separation of the two hemispheres, our brain as a whole is still able to produce only one conscious agent; the negation of the absence of a free will, previously thought to be established by the Libet experiments) and many results of parapsychology (on medium communications, memories of past lives, near-death experiences) suggestive of survival after our biological death, strongly support the non-materialistic position and hence the much lower likelihood of AI consciousness. Instead of being concern about AI turning conscious and machine ethics, and trying to instantiate AI consciousness soon, we should perhaps focus more on making AI less costly and more useful to society. (shrink)

A variety of scientific disciplines have set as their task explaining mental activities, recognizing that in some way these activities depend upon our brain. But, until recently, the opportunities to conduct experiments directly on our brains were limited. As a result, research efforts were split between disciplines such as cognitive psychology, linguistics, and artificial intelligence that investigated behavior, while disciplines such as neuroanatomy, neurophysiology, and genetics experimented on the brains of non-human animals. In recent decades these disciplines integrated, and (...) with the advent of techniques for imaging activity in human brains, the term cognitive neuroscience has been applied to the integrated investigations of mind and brain. This book is a philosophical examination of how these disciplines continue in the mission of explaining our mental capacities. (shrink)

In lieu of an abstract, here is a brief excerpt of the content:The Philosophic Foundations of Mimetic Theory and Cognitive Science(Including Artificial Intelligence)Jean-Pierre Dupuy (bio)In the mid 1970s I discovered at the same time cognitive science and mimetic theory. Being a philosopher with a scientific background, I immediately brought them together and tried to reconceptualize the latter in terms of the former. In a sense, I haven't stopped doing that in the last 45 years. That is why I feel (...) fully at home with the goals pursued by the organizers of this conference.I do not believe it is feasible or even desirable to operate the rapprochement between the two disciplines at a global level. The history of cognitive science is exceedingly involved, and the part played by artificial intelligence in it compared to the part played by the neurosciences has varied greatly over time.1 Only a piecemeal approach makes sense. I present here four short case studies that cover the whole period, from the beginnings in cybernetics to today's artificial intelligence (AI).In order to better situate these studies within a broader frame, I propose the diagram in Figure 1. To comment on it would require more time than available for my whole presentation. Let me just say the following. [End Page 1]It is essential to distinguish between two kinds of AI. The first to bear this name was launched by economist Herbert Simon and computer scientist Allan Newell at a Dartmouth conference in 1956. It was based on the premise that thinking is computing, with this computation bearing on sentences of a "language of thought" implementable in a digital computer. It turns out that this AI(1) was progressively substituted from the 1980s onward by an AI(2), which, to some extent, was a return to a fundamental result obtained by cybernetics in 1943. Neurophysiologist Warren McCulloch and mathematician Walter Pitts had constructed a mathematical model of a network of elementary calculators called "neurons" and explored its properties. The capacity to "think" was one of these. It was not situated at the elementary level, as was the case in AI(1), but it emerged at the collective level from the interactions between the neurons.This model was heavily criticized by the supporters of AI(1),2 and its first implementations, in the notorious "Perceptron" in particular (1957), were pitiful failures. However, the emergence of "second-order cybernetics" under the guidance of cybernetician Heinz von Foerster and the theory of complex, self-organizing systems that it helped develop, in cooperation and rivalry with similar efforts in the physical sciences, enabled the neural network model to come to a second life under different names: formal neurons, deep learning, big data, and the like. Together, they constitute AI(2).The following network of influences wouldn't have been possible without its stem, that is, the boxes containing the names of Kurt Gödel and Alan Turing. The former revolutionized logic by showing that reasoning about arithmetic and arithmetic are one and the same, insofar as it is possible to code in a 1-to-1 fashion propositions about integers (for instance, "7 is a prime number") by means of integers. The latter demonstrated that some mechanisms can self-transcend inasmuch as they generate an ensemble of outputs that cannot be defined mechanistically. Later, John von Neumann, who is credited along with Alan Turing with having invented the digital computer, would use this logical possibility to define complexity: A mechanism is complex if it is capable of generating one more complex than itself. The gate was open for the theories of complex, self-organizing systems to thrive and, by way of consequence, for AI(2) to emerge.fascination with modelsHerbert Simon (1916–2001), winner of the Nobel Prize in economics for his work on what he called "bounded rationality," is generally considered to be one [End Page 2] Click for larger view View full resolutionFigure 1.of the founders of artificial intelligence. Some cognitive scientists still rely on the computer program that he devised with Alan Newell to simulate the creative processes of human thought—a program aptly named "General Problem Solver." The title of one of his principal works, The Sciences of the... (shrink)

The possibility of AI consciousness depends much on the correct answer to the mind–body problem: how our materialistic brain generates subjective consciousness? If a materialistic answer is valid, machine consciousness must be possible, at least in principle, though the actual instantiation of consciousness may still take a very long time. If a non-materialistic one (either mentalist or dualist) is valid, machine consciousness is much less likely, perhaps impossible, as some mental element may also be required. Some recent advances in neurology (...) (despite the separation of the two hemispheres, our brain as a whole is still able to produce only one conscious agent; the negation of the absence of a free will, previously thought to be established by the Libet experiments) and many results of parapsychology (on medium communications, memories of past lives, near-death experiences) suggestive of survival after our biological death, strongly support the non-materialistic position and hence the much lower likelihood of AI consciousness. Instead of being concern about AI turning conscious and machine ethics, and trying to instantiate AI consciousness soon, we should perhaps focus more on making AI less costly and more useful to society. (shrink)

Computation is central to the foundations of modern cognitive science, but its role is controversial. Questions about computation abound: What is it for a physical system to implement a computation? Is computation sufficient for thought? What is the role of computation in a theory of cognition? What is the relation between different sorts of computational theory, such as connectionism and symbolic computation? In this paper I develop a systematic framework that addresses all of these questions. Justifying the role of (...) computation requires analysis of implementation, the nexus between abstract computations and concrete physical systems. I give such an analysis, based on the idea that a system implements a computation if the causal structure of the system mirrors the formal structure of the computation. This account can be used to justify the central commitments of artificial intelligence and computational cognitive science: the thesis of computational sufficiency, which holds that the right kind of computational structure suffices for the possession of a mind, and the thesis of computational explanation, which holds that computation provides a general framework for the explanation of cognitive processes. The theses are consequences of the facts that (a) computation can specify general patterns of causal organization, and (b) mentality is an organizational invariant, rooted in such patterns. Along the way I answer various challenges to the computationalist position, such as those put forward by Searle. I close by advocating a kind of minimal computationalism, compatible with a very wide variety of empirical approaches to the mind. This allows computation to serve as a true foundation for cognitive science. (shrink)

This article provides a survey of Wittgenstein’s remarks in which he discusses various kinds of technology. I argue that throughout his career, his use of technological examples displays a thematic unity: technologies are invoked in order to illustrate a certain mechanical conception of the mind. I trace how his use of such examples evolved as his views on the mind and on meaning changed. I also discuss an important and somewhat radical anti-mechanistic strain in his later thought and suggest (...) that Wittgenstein’s attitude to mechanistic explanations in psychology was ultimately quite ambivalent. (shrink)

In the first section of the article, we examine some recent criticisms of the connectionist enterprise: first, that connectionist models are fundamentally behaviorist in nature (and, therefore, non-cognitive), and second that connectionist models are fundamentally associationist in nature (and, therefore, cognitively weak). We argue that, for a limited class of connectionist models (feed-forward, pattern-associator models), the first criticism is unavoidable. With respect to the second criticism, we propose that connectionist modelsare fundamentally associationist but that this is appropriate for building models (...) of human cognition. However, we do accept the point that there are cognitive capacities for which any purely associative model cannot provide a satisfactory account. The implication that we draw from is this is not that associationist models and mechanisms should be scrapped, but rather that they should be enhanced.In the next section of the article, we identify a set of connectionist approaches which are characterized by “active symbols” — recurrent circuits which are the basis of knowledge representation. We claim that such approaches avoid criticisms of behaviorism and are, in principle, capable of supporting full cognition. In the final section of the article, we speculate at some length about what we believe would be the characteristics of a fully realized active symbol system. This includes both potential problems and possible solutions (for example, mechanisms needed to control activity in a complex recurrent network) as well as the promise of such systems (in particular, the emergence of knowledge structures which would constitute genuine internal models). (shrink)

This paper presents work in progress on artificial intelligence in medicine (AIM) within the larger context of cognitive science. It introduces and develops the notion ofemergence both as an inevitable evolution of artificial intelligence towards machine learning programs and as the result of a synergistic co-operation between the physician and the computer. From this perspective, the emergence of knowledge takes placein fine in the expert's mind and is enhanced both by computerised strategies of induction and deduction, and by (...) software abilities to dialogue, co-operate and function as a cognitive extension of the physician's intellectual capabilities. The proposed methodology gives the expert a prominent role which consists, first, of faithfully enunciating the descriptive features of his medical knowledge, thus giving the computer a precise description of his own perception of basic medicine, and secondly, of painstakingly gathering patients into computerised case bases which simulate exhaustive long-term memory. The AI capacities for knowledge elaboration are then triggered, giving rise to mathematically optimal diagnoses, prognoses, or treatment protocols which the physician may then evaluate, accept, reject, or adapt at his convenience, and finally append to a knowledge base. The idea of emergence embraces many issues concerning the purpose and intent of artificial intelligence in medical practice. Particularly, we address the representation problem as it is raised by classical decisional knowledge-based systems, and develop the notions of classifiers and hybrid systems as possible answers to this problem. Finally, since the whole methodology touches the problem of technological investment in health care, now inherent in modern medical practice, some ethical considerations accompany the exposé. (shrink)

For over a decade John Searle's ingenious argument against the possibility of artificial intelligence has held a prominent place in contemporary philosophy. This is not just because of its striking central example and the apparent simplicity of its argument. As its appearance in Scientific American testifies, it is also due to its importance to the wider scientific community. If Searle is right, artificial intelligence in the strict sense, the sense that would claim that mind can be instantiated through (...) a formal program of symbol manipulation, is basically wrong. No set of formal conditions can provide us with the characteristic feature of mind which is the intentionally of its mental contents. Formally regarded, such intentionally is an irreducible primitive. It cannot be analyzed into non-intentional (purely syntactic, symbolic) components. This paper will argue that this objection is based on a misunderstanding. Intentionality is not simply something given which is incapable of further analysis. It only appears so when we mistakenly abstract it from time. When we regard its temporal structure, it shows itself as a rule-governed, synthetic process, one capable of being instantiated both by machines and men. (shrink)

The general idea that we have of artificial intelligence (AI) consists of the belief that machines will be able to develop conscious thoughts such as those possessed by human beings, and, as computing advances, such thinking will also advance until intelligence to surpass the human being, with which the advancement of AI represents ethical risks in the future. In reality, such a belief hides a cognitive assumption, which assumes that computational engineering explains human intelligence through the mind-computer metaphor. According (...) to this assumption, technology explains cognition, and philosophy, through ethics, reflects on the impact of said technology. However, in this article, I contradict such an assumption and defend that the philosophy in AI is not reduced to the ethics that is present after the use and impact of AI in the world. I intend to expose that a good ethics of AI is the one that reflects on the appropriate risks facing AI, and for this, philosophy, beforehand, must make a cognitive analysis about the possibilities that computing has to create intelligent machines, namely, whether or not the mindcomputer metaphor makes sense. My thesis consists in defending that the philosophical analysis about AI must be carried out both on a cognitive level and on an ethical level, but that the philosophical priority in the cognitive analysis over the ethical priority, since the ethical risks of AI depend of the possibilities of technology, and only the cognitive approach can account for this. (shrink)

The Chinese room argument has presented a persistent headache in the search for Artificial Intelligence. Since it first appeared in the literature, various interpretations have been made, attempting to understand the problems posed by this thought experiment. Throughout all this time, some researchers in the Artificial Intelligence community have seen Symbol Grounding as proposed by Harnad as a solution to the Chinese room argument. The main thesis in this paper is that although related, these two issues present different (...) problems in the framework presented by Harnad himself. The work presented here attempts to shed some light on the relationship between John Searle’s intentionality notion and Harnad’s Symbol Grounding Problem. (shrink)

Why would we want to develop artificial human-like arithmetical intelligence, when computers already outperform humans in arithmetical calculations? Aside from arithmetic consisting of much more than mere calculations, one suggested reason is that AI research can help us explain the development of human arithmetical cognition. Here I argue that this question needs to be studied already in the context of basic, non-symbolic, numerical cognition. Analyzing recent machine learning research on artificial neural networks, I show how AI studies could (...) potentially shed light on the development of human numerical abilities, from the proto-arithmetical abilities of subitizing and estimating to counting procedures. Although the current results are far from conclusive and much more work is needed, I argue that AI research should be included in the interdisciplinary toolbox when we try to explain the development and character of numerical cognition and arithmetical intelligence. This makes it relevant also for the epistemology of mathematics. (shrink)

O debate entre J. Searle e D. Dennett acerca da possibilidade técnica de uma Inteligência artificial gira em torno de uma única questão: estados mentais expressam algo intrínseco, privado e subjetivo, experimentado em primeira-pessoa ou não passam de entidades obscurantistas, frutos da ignorância e, em princípio, reduzíveis à objetividade científica da neurociência? Searle defende a primeira tese, enquanto Dennett argumenta pela segunda. O texto expõe a posição de Dennett, a crítica de Searle e se direciona para uma nova solução, (...) que defende um monismo integral, no qual mente e cérebro podem ser vistos como duas dimensões de uma mesma realidade. Nesse sentido, o projeto de Inteligência artifical precisará revisar o conceito de “físico” se quiser avançar em suas pretenções, incluindo nele conceitos hoje antagônicos como intencionalidade, liberdade e subjetividade.: The debate between J. Searle and D. Dennett about the technical possibility of an artificial intelligence revolves around a single issue: mental states express something intrinsic, private and subjective, experienced in first-person or are merely obscurantist entities, results from ignorance and, in principle, reducible to scientific objectivity of neuroscience? Searle argues for the first position, while Dennett argues for the second. The text exposes the position of Dennett, the criticism of Searle and is directed to a new solution, which advocates an integral monism, in which mind and brain can be seen as two dimensions of the same reality. In this sense, the project of artificial intelligence need to review the concept of "physical" if it wants to advance its claims, including today antagonistic concepts such as intentionality, freedom and subjectivity. Keywords: physicalism, technique, artificial intelligence, John Searle, Daniel Dennett. (shrink)

Metaphysics deals with the intelligible world of questions and the explicable world of intentionality. Metaphysics is explicable, and its explicability is connected to questions related to what there is to know about the nature of reality. While physics deals with what is and what else there is, metaphysics deals with the nature of reality and what else there is to know about the nature of reality. If the content of metaphysics is considered as "answers" to questions related to cosmology and (...) consciousness, then metaphysical claims must be understood in the context of the questions that necessitate such claims. For without understanding the relevance of the questions, we cannot establish the 'truth' or 'falsity' of metaphysical claims. The relevance of the questions is the basis for establishing the veracity of the metaphysical distinctions. Hence, all metaphysical distinctions are a non-reductive explanation of what is considered as being reductive. The content of consciousness or intentionality deals with the following metaphysical distinctions, namely, the matter/mind, the essence/ existence, the space/time, the concrete/abstract, the particular/universal, and the contingent/necessary distinctions. These distinctions are made possible because of the questions raised by the intelligent mind. Two questions that connect physics and metaphysics are-- what is there and the nature of what is there. Two further questions that promote our interest in physics and metaphysics are: what else is there to know, and what else is there to know about the nature of reality. Reality and the nature of reality are the same. However, because the mind makes this distinction, we can state that what is physical is an empirical given, and what is metaphysical is a phenomenological or an existential given. Keywords: metaphysics, intentionality, subjectivity, creativity, freedom and time. (shrink)

Alan M. Turing, pioneer of computing and WWII codebreaker, is one of the most important and influential thinkers of the twentieth century. In this volume for the first time his key writings are made available to a broad, non-specialist readership. They make fascinating reading both in their own right and for their historic significance: contemporary computational theory, cognitive science, artificial intelligence, and artificial life all spring from this ground-breaking work, which is also rich in philosophical and logical insight. (...) An introduction by leading Turing expert Jack Copeland provides the background and guides the reader through the selection. About Alan Turing Alan Turing FRS OBE, (1912-1954) studied mathematics at King's College, Cambridge. He was elected a Fellow of King's in March 1935, at the age of only 22. In the same year he invented the abstract computing machines - now known simply as Turing machines - on which all subsequent stored-program digital computers are modelled. During 1936-1938 Turing continued his studies, now at Princeton University. He completed a PhD in mathematical logic, analysing the notion of 'intuition' in mathematics and introducing the idea of oracular computation, now fundamental in mathematical recursion theory. An 'oracle' is an abstract device able to solve mathematical problems too difficult for the universal Turing machine. In the summer of 1938 Turing returned to his Fellowship at King's. When WWII started in 1939 he joined the wartime headquarters of the Government Code and Cypher School (GC&CS) at Bletchley Park, Buckinghamshire. Building on earlier work by Polish cryptanalysts, Turing contributed crucially to the design of electro-mechanical machines ('bombes') used to decipher Enigma, the code by means of which the German armed forces sought to protect their radio communications. Turing's work on the version of Enigma used by the German navy was vital to the battle for supremacy in the North Atlantic. He also contributed to the attack on the cyphers known as 'Fish'. Based on binary teleprinter code, Fish was used during the latter part of the war in preference to morse-based Enigma for the encryption of high-level signals, for example messages from Hitler and other members of the German High Command. It is estimated that the work of GC&CS shortened the war in Europe by at least two years. Turing received the Order of the British Empire for the part he played. In 1945, the war over, Turing was recruited to the National Physical Laboratory (NPL) in London, his brief to design and develop an electronic computer - a concrete form of the universal Turing machine. Turing's report setting out his design for the Automatic Computing Engine (ACE) was the first relatively complete specification of an electronic stored-program general-purpose digital computer. Delays beyond Turing's control resulted in NPL's losing the race to build the world's first working electronic stored-program digital computer - an honour that went to the Royal Society Computing Machine Laboratory at Manchester University, in June 1948. Discouraged by the delays at NPL, Turing took up the Deputy Directorship of the Royal Society Computing Machine Laboratory in that year. Turing was a founding father of modern cognitive science and a leading early exponent of the hypothesis that the human brain is in large part a digital computing machine, theorising that the cortex at birth is an 'unorganised machine' which through 'training' becomes organised 'into a universal machine or something like it'. He also pioneered Artificial Intelligence. Turing spent the rest of his short career at Manchester University, being appointed to a specially created Readership in the Theory of Computing in May 1953. He was elected a Fellow of the Royal Society of London in March 1951 (a high honour). (shrink)

Intelligent mental representations of physical, cognitive and social environments allow humans to navigate enormous search spaces, whose sizes vastly exceed the number of neurons in the human brain. This allows us to solve a wide range of problems, such as the Traveling Salesperson Problem, insight problems, as well as mathematics and physics problems. As an area of research, problem solving has steadily grown over time. Researchers in Artificial Intelligence have been formulating theories of problem solving for the last (...) 70 years. Psychologists, on the other hand, have focused their efforts on documenting the observed behavior of subjects solving problems. This book represents the first effort to merge the behavioral results of human subjects with formal models of the causative cognitive mechanisms. The first coursebook to deal exclusively with the topic, it provides a main text for elective courses and a supplementary text for courses such as cognitive psychology and neuroscience. (shrink)

Semantic Engines: An Introduction to Mind Design, John C. Haugeland; Computer Science as Empirical Inquiry: Symbols and Search, Alan Newell and Herbert A. Simon; Complexity and the Study of Artificial and Human Intelligence, Zenon Pylyshyn; A Framework for Representing Knowledge, Marvin Minsky; Artificial Intelligence---A Personal View, David Marr; Artificial Intelligence Meets Natural Stupidity, Drew McDermott; From Micro-Worlds to Knowledge Representation: AI at an Impasse, Hubert L. Dreyfus; Reductionism and the Nature of Psychology, Hilary Putnam; Intentional Systems, Daniel (...) C. Dennett; The Nature and Plausibility of Cognitivism, John C. Haugeland; Minds, Brains, and Programs, John R. Searle; MethodologicalSolipsism Considered as a Research Strategy in Cognitive Psychology, Jerry A. Fodor; The Material Mind, Donald Davidson. (shrink)

The topic of the paper is at the intersection of recent debates on de se thought and phenomenal intentionality. An interesting problem for phenomenal intentionality is the question of how to account for the intentional properties of de se thought-contents---i.e., thoughts about oneself as oneself. Here, I aim to describe and consider the significance of a phenomenological perspective on self-consciousness in its application to de se thought. I argue that having de se thoughts can be explained in terms of the (...) ways that subjects consciously attend to themselves in experiences of thinking. Therefore, a strong form of first-person persectivalness, in which the subject is capable of self-directed control of the focus of conscious attention, is required for de se thought. But no constraints on the semantic content of such thoughts are required. The outcome of the question therefore bears importantly on both on the problem of self-reflexive self-reference and the wider problem of self-consciousness. My model suggests phenomenologically-derived conceptual constraints for the extension of minimal, background forms of self-awareness to robust, cognitive forms of self-awareness that have been of interest in recent empirical studies on self-consciousness. The framework therefore provides a way of operationalizing the concept of self-reflexive consciousness by deploying widely deployed notions from the empirical literature such as attention and minimal phenomenal selfhood, enabling a framework for empirically falsifiable hypotheses about the neural mechanisms underlying the structure of self-consciousness. What Is it Like to Think about Oneself? De Se Thought and Phenomenal Intentionality. (shrink)

This book explores how people's subjective, felt experiences of their bodies in action provide part of the fundamental grounding for human cognition and language. Cognition is what occurs when the body engages the physical and cultural world and must be studied in terms of the dynamical interactions between people and the environment. Human language and thought emerge from recurring patterns of embodied activity that constrain ongoing intelligent behavior. We must not assume cognition to be purely internal, symbolic, computational, and (...) disembodied, but seek out the gross and detailed ways that language and thought are inextricably shaped by embodied action. Embodiment and Cognitive Science describes the abundance of empirical evidence from many disciplines, including work on perception, concepts, imagery and reasoning, language and communication, cognitive development, and emotions and consciousness, that support the idea that the mind is embodied. (shrink)

This article responds to two unresolved and crucial problems of cognitive science: (1) What is actually accomplished by functions of the nervous system that we ordinarily describe in the intentional idiom? and (2) What makes the information processing involved in these functions semantic? It is argued that, contrary to the assumptions of many cognitive theorists, the computational approach does not provide coherent answers to these problems, and that a more promising start would be to fall back on mathematical communication theory (...) and, with the help of evolutionary biology and neurophysiology, to attempt a characterization of the adaptive processes involved in visual perception. Visual representations are explained as patterns of cortical activity that are enabled to focus on objects in the changing visual environment by constantly adjusting to maintain levels of mutual information between pattern and object that are adequate for continuing perceptual control. In these terms, the answer proposed to (1) is that the intentional functions of vision are those involved in the establishment and maintenance of such representations, and to (2) that semantic features are added to the information processes of vision with the focus on objects that these representations accomplish. The article concludes with proposals for extending this account of intentionality to the higher domains of conceptualization and reason, and with speculation about how semantic information-processing might be achieved in mechanical systems. (shrink)

Chapter 1 First Person Access to Mental States. Mind Science and Subjective Qualities -/- Abstract. The philosophy of mind as we know it today starts with Ryle. What defines and at the same time differentiates it from the previous tradition of study on mind is the persuasion that any rigorous approach to mental phenomena must conform to the criteria of scientificity applied by the natural sciences, i.e. its investigations and results must be intersubjectively and publicly controllable. In Ryle’s view, philosophy (...) of mind needs to adopt an antimentalist stance to achieve this aim. Antimentalism not only definitively rejects the idea that mind is a substance separated from the body, it also denies that mental phenomena radically differ from physical phenomena by virtue of several unique features. Most problematically, mental phenomena have a conscious character (mental states are related to specific qualitative feelings) and are accessible only to the first-person (only the subject knows directly what s/he is experiencing inside his/her mind). Ryle takes a strong stance on antimentalism going so far as to maintain that an approach to mind which aims to meet the criteria of scientificity set by the natural sciences must avoid any reference to internal, unobservable mental states. In his view (which is considered a specifically philosophical version of psychological behaviorism and also addresses questions put forward in psychological research), mental states can be redescribed in terms of behavioral dispositions. In this chapter, we address the historical roots of the antimentalist view and analyze its relation to the later tradition of research on mind. We show that, compared to the antimentalist stance, functionalism and cognitivism take a step back when they maintain that direct reference to mental states is necessary since mental states cause and therefore explain human behavior. This step backwards is often interpreted as a return to mentalism. However, this is only partially true. Indeed, we suggest that these later traditions retain one important element of Ryle’s antimentalism, i.e. the idea that mental states must be uniquely identified using external and publicly observable criteria, while excluding any reference to introspection and those qualitative dimensions of a mental state, which are accessible only to the first-person. According to the perspective we put forward, this epistemological stance has continued to influence contemporary research on mind and current philosophical and psychological theories which both tend to exclude the subjective qualities of human experience from their accounts of how the mind works. The issue we raise here is whether this is legitimate or whether subjective qualities do play a role with respect to the way our mind works. The conclusion of this chapter anticipates the argument the book makes in in favor of this latter position, starting from a particular angle, i.e. the problem of how we categorize concepts related to our internal states. -/- Chapter 2 The Misleading Aspects of the Mind/Computer Analogy. The Grounding Problem and the Thorny Issue of Propriosensitive Information -/- Abstract. After the crisis of behaviorism, cognitivism and functionalism became the predominant models in the field of psychology and of philosophy, respectively. Their success is mainly due to the new key they use for interpreting mental processes: the mind/computer analogy. On the basis of this analogy, mental operations are seen as cognitive processes based on computations, i.e. on manipulations of abstract symbols which are in turn understood as informational unities (representations). This chapter identifies two main problems with this model. The first is how these symbols can relate to and communicate with perception and thus allow us to identify and classify what we perceive through the senses. Here we limit ourselves to presenting this issue in relation to the classical symbol grounding problem originally put forward by Harnad on the basis of Searle’s Chinese room argument. An attempt to address the problem raised here will be made in chap. 3. The second point we discuss in relation to the mind/computer analogy concerns the idea of information it fosters. Indeed, following this analogy, information is something available in the external world which can be captured by the senses and transmitted to the central system without being influenced or modified by the procedures of transmission. This perspective does not take into account that – unlike computers – in living beings information is acquired by means of the body. As Ulric Neisser has already pointed out, the body is itself an informational source that provides us with additional sensory experience that influences (modifies or complements) the information extracted from the external world by the senses. To develop this line of analysis and to determine exactly what information is provided by the body and how this might influence cognition, we examine Sherrington’s and Gibson’s positions. Moving on from their views, we qualify bodily information in terms of ‘proprioception’. We use ‘proprioception’ in a broad sense to describe any kind of experience we have of our internal states (including postural information as well as sensations related to the general state of the body and its parts). Following Damasio’s and Craig’s studies, we further elaborate this position, arguing that living beings are equipped with an internal propriosensitive monitoring system which maps all the changes that constantly occur in our body and that give us perceptual (‘proprioceptive’ or propriosensitive) information about what happens inside us. Moreover, relying on Goldie’s and Ratcliffe’s view, we show that emotional information can also be considered as a form of ‘proprioception’ which contributes to determining everything we perceive. This analysis leads us to the second main thesis of this book: ‘proprioception’ is a form of internal perception and it is an essential component of the sensory information we can access and use for all cognitive purposes. -/- Chapter 3 Semantic Competence from the Inside: Conceptual Architecture and Composition -/- Abstract. Concepts are essential constituents of thought: they are the instruments we use to categorize our experience, i.e. to classify things and group them together in homogeneous sets. Here we define concepts as the internal mental information (representations) that allows us, among other things, to master words in natural language. By analyzing the way in which individuals master word meanings we explore a number of hypotheses regarding the nature of concepts. Following Diego Marconi’s research, we differentiate between two kind of abilities that underpin lexical competence – so-called ‘referential’ and ‘inferential competence’ – and we suggest that, in order to support these abilities, concepts must also include two corresponding kinds of information, i.e. inferential and referential information. We point out that the most widely used and acknowledged theories of concepts do not make this distinction, instead broadly characterizing the information used for categorization in terms of propositionally described feature lists. However, we show that while feature lists can explain inferential competence, they do not account for referential competence. To address the issue of referential competence we examine Ray Jackendoff’s hypothesis that to account for the possibility of linking perceptual and conceptual information we need to assume the existence of a (visual) representation that encodes the geometric and topological properties of objects and bridges the gap between the percept and the concept. Furthermore, we analyze the extension of this work by Jesse Prinz who introduced the notion of a proxytype, a perceptual representation of a class of objects that incorporates structural and parametric information related to their appearance. However, as we point out, proxytypes can only explain the relationship between perception and concepts with respect to instances that can be perceived through the senses and that belong to the same class by virtue of their physical similarity. We suggest that this notion be extended to include larger conceptual classes. To accomplish this, we further develop Mark Johnson, George Lakoff and Jean Mandler’s idea of a schematic image and argue that conceptual representations include a perceptual schema. Perceptual schemata are non-linguistic, structured experiential gestalts (patterns or maps) that make use of information taken from all sensory modalities, including body perception. They accomplish a quasi-conceptual function: they allow us to recognize and to classify different instances. In this work, we hypothesize that perceptual schemata are an essential component of concepts, but not identical to them. Instead, we suggest that concepts include both perceptual and propositional information with perceptual schemata providing the ‘perceptual core concept’ that grounds related propositional information. -/- Chapter 4 In the Beginning There Were Categories. The Bodily Origin of Prelinguistic Categorical Organization: The Example of Folkbiological Taxonomies -/- Abstract. Studies of categorization in psychology and the cognitive sciences have made use of the notions of ‘category’ and ‘concept’ without precisely defining what is meant by either; in fact, often these terms have been used as synonyms, making it difficult to address specific issues related to conceptual development. This chapter begins by discussing the definitions of, as well as the distinctions between, ‘categories’ and ‘concepts’ in the classical philosophical tradition (Aristotle, Kant and Husserl). We introduce our view of categories with reference to Husserl. Categorization is defined as the way in which our experience is originally (pre-linguistically) organized in a passive and fully unconscious manner on the basis of universal structuring principles. Conceptualization is explained as a later process in which the earlier categorical macro-classes are further subdivided into more specific and detailed sets; this later process also relies on linguistic learning and exposure to culture. On the basis of Ray Jackendoff, Jean Mandler, George Lakoff and Mark Johnson’s work, we hypothesize that categorization is a two-step process that begins with the formation of homogeneous sets of entities partitioned into regions that describe the ontological boundaries of the objects that humans perceive (categories) and continues at a later stage with the development of more specific classifications (concepts). In this chapter, we mainly address three related issues: Why should we assume that there is categorical organization which precedes the development of a conceptual system? How do categories and concepts relate to each other? Shall we hypothesize that categories are innate or that they are formed before concepts on the basis of information and organizational structure available at a very early developmental stage? We show that categorical partitions are necessary for categorization and, following Mandler, that the general categories we form at an early age do not match our adult superordinate concepts. As for the third issue, we argue that there might be no need to assume that categories are innately present in the human mind, since their formation can be explained – at least in certain cases – by basic mechanisms that work on body (propriosensitive) information. This hypothesis will not be discussed in general, but in relation to a particularly relevant example of categorical partition, i.e. the folk-biological dichotomy between ANIMATE/INANIMATE. This is compared with other dichotomies that derive from it, but are not directly categorical such as LIVING/NON-LIVING and BIOLOGICAL/NON-BIOLOGICAL. -/- Chapter 5 Internal States: From Headache to Anger. Conceptualization and Semantic Mastery -/- Abstract. Here we ask if we can also apply the distinction between referential and inferential competence we introduced in Chapter 3 to words that do not refer to things that are perceived using the external senses, especially to words/concepts that denote bodily experiences (such as pain, thirst, hunger, etc.) or emotions. We introduce and discuss the hypothesis that – even though such words/concepts do not refer to intersubjectively identifiable entities in the external world – they do have a kind of referent that can be accessed via direct perception, more specifically ‘proprioception’, as we have defined it in terms of all propriosensitive information we can consciously access. In the first part of the chapter, we specifically consider terms denoting bodily experiences such as ‘pain’ or ‘hunger’ and argue that their referents are identified and classified from a first-personal point of view on the basis of four main characteristics: their specific intensity, their localization in the body, their co-occurrence with other signals and above all their specific qualitative sensations. Emotions are addressed in the second part of the chapter. We suggest that there is a continuity between bodily experiences and emotions. In particular, we argue for a perceptual theory of emotions in line with that proposed by James and Lange at the end of the 19th century and developed more recently by authors such as Damasio (see also chap. 2§5, §6). The hypothesis we put forward is that the referential information that supports the categorization of emotions and therefore also our mastery of terms referring to emotions consists in the perception (i.e. the ‘proprioception’) of those bodily states and changes in bodily states which constitute our emotional experience. In the context of this discussion we examine some objections to this line of reasoning that arise from a cognitivist perspective and following authors such as Oatley, Johnson-Laird and Frijda, we distinguish between basic emotions that can be identified and classified solely on the basis on how they feel and complex emotions whose identification and classification additionally depends on cognitive factors. To describe how emotions are identified and classified on the basis of how they feel, we rely on Marcel and Lambie’s distinction between an ‘emotion state’ and an ‘emotion experience’. Both notions indicate kinds of feelings that we consciously experience. However, they describe first-order and second order emotion awareness respectively. The emotion state is the feeling we have of the bodily states and changes that occur when we are experiencing an emotion, while the emotion experience is the fully developed and integrated emotion we both experience and are, with reflection, aware of experiencing. On the basis of this differentiation, we also show that the same characteristics that aid in the identification and classification of bodily experiences (specific qualitative sensations; somatic localization; specific intensity; presence/absence of specific concomitant sensations) can also be used for the identification and classification of emotions – at least basic emotions. In the last two sections of the chapter we present some clinical evidence on the semantic competence of people who suffer from Alexithymia and Autism Spectrum Disorder which supports the conclusions of our preceding analyses. -/- Chapter 6 The ‘Proprioceptive’ Component of Abstract Concepts -/- Abstract. In this chapter, we address the issue of whether the mastery of abstract words requires only inferential knowledge and thus, if the concepts that support the mastery of abstract words include only linguistic information. We start by differentiating the notions of ‘abstract’ and ‘general’ which are often erroneously confused. We then identify a strict definition of abstract, as contrasted with ‘concrete’, that applies to words or concepts whose referent cannot be experienced by the senses. We argue that abstract words/concepts would be better described as theoretical, because they are usually conceived as structured sets of inferential knowledge expressed linguistically; that is, as small theories. Pointing out parallels with Carnap’s analysis of this issue in philosophy of science, we hypothesize that words/concepts denoting non-observable entities are not all ‘equally theoretical’, because their link to sensory experience can be stronger or weaker. We revive the distinction, inspired by Quine, between theoretical and intertheoretical concepts/words. This distinction relies on the fact that the former – unlike the latter – retains a strong, although indirect, connection with perception. In Quine’s discussion, perception is understood uniquely in terms of observability, i.e. of external sensory experience. Here we argue, however, that bodily, ‘proprioceptive’ (i.e. propriosensitive) experience can also serve to referentially ground theoretical (i.e. abstract) concepts/words. We frame this issue using the example of the theoretical concept ‘freedom’ and Lakoff’s hypothesis that this concept is developed on the basis of bodily information. We contrast this with the example of ‘democracy’ which more closely resembles an intertheoretical concept/word. Furthermore, we show that one of the classical views put forward in psycholinguistic research to explain how abstract concepts are mentally represented – i.e. Paivio’s Dual Coding Theory – points in the same direction as our analysis. The same is true of Barsalou’s work suggesting that we use internal information to understand at least some abstract words. To sustain this position, we put forward two lines of evidence: the first comes from psycholinguistic studies while the second examines deficits of semantic competence exhibited by people with Autism Spectrum Disorder. On the basis of our analysis, we put forward a classification that distinguishes between different kinds of concreteness and different degrees of abstraction: concepts/words referring to body experiences and basic emotions are described as analogous to concrete concepts/words because they are grounded in perceptual (i.e. propriosensitive) experience, while abstract concepts/words are considered more or less abstract depending on whether they are intratheoretical (and rely entirely on inferential information) or theoretical (and are partially grounded in perceptual – or more often in propriosensitive perceptual – information). In the last section of the chapter we consider two scales that have been used in psycholinguistic research to measure the degree of concreteness vs. abstractness of words and we show that – used conjointly – they can provide a measure of the internal vs. external grounding of specific words. (shrink)

The topic of the paper is at the intersection of recent debates on de se thought and phenomenal intentionality. An interesting problem for phenomenal intentionality is the question of how to account for the intentional properties of de se thought-contents---i.e., thoughts about oneself as oneself. Here, I aim to describe and consider the significance of a phenomenological perspective on self-consciousness in its application to de se thought. I argue that having de se thoughts can be explained in terms of the (...) ways that subjects consciously attend to themselves in experiences of thinking. Therefore, a strong form of first-person persectivalness, in which the subject is capable of self-directed control of the focus of conscious attention, is required for de se thought. But no constraints on the semantic content of such thoughts are required. The outcome of the question therefore bears importantly on both on the problem of self-reflexive self-reference and the wider problem of self-consciousness. My model suggests phenomenologically-derived conceptual constraints for the extension of minimal, background forms of self-awareness to robust, cognitive forms of self-awareness that have been of interest in recent empirical studies on self-consciousness. The framework therefore provides a way of operationalizing the concept of self-reflexive consciousness by deploying widely deployed notions from the empirical literature such as attention and minimal phenomenal selfhood, enabling a framework for empirically falsifiable hypotheses about the neural mechanisms underlying the structure of self-consciousness. What Is it Like to Think about Oneself? De Se Thought and Phenomenal Intentionality. (shrink)

We propose a non-representationalist framework for deep learning relying on a novel method computational phenomenology, a dialogue between the first-person perspective (relying on phenomenology) and the mechanisms of computational models. We thereby propose an alternative to the modern cognitivist interpretation of deep learning, according to which artificial neural networks encode representations of external entities. This interpretation mainly relies on neuro-representationalism, a position that combines a strong ontological commitment towards scientific theoretical entities and the idea that the brain operates on (...) symbolic representations of these entities. We proceed as follows: after offering a review of cognitivism and neuro-representationalism in the field of deep learning, we first elaborate a phenomenological critique of these positions; we then sketch out computational phenomenology and distinguish it from existing alternatives; finally we apply this new method to deep learning models trained on specific tasks, in order to formulate a conceptual framework of deep-learning, that allows one to think of artificial neural networks’ mechanisms in terms of lived experience. (shrink)

Computation is central to the foundations of modern cognitive science, but its role is controversial. Questions about computation abound: What is it for a physical system to implement a computation? Is computation sufficient for thought? What is the role of computation in a theory of cognition? What is the relation between different sorts of computational theory, such as connectionism and symbolic computation? In this paper I develop a systematic framework that addresses all of these questions. Justifying the role of (...) computation requires analysis of implementation, the nexus between abstract computations and concrete physical systems. I give such an analysis, based on the idea that a system implements a computation if the causal structure of the system mirrors the formal structure of the computation. This account can be used to justify the central commitments of artificial intelligence and computational cognitive science: the thesis of computational sufficiency, which holds that the right kind of computational structure suffices for the possession of a mind, and the thesis of computational explanation, which holds that computation provides a general framework for the explanation of cognitive processes. The theses are consequences of the facts that computation can specify general patterns of causal organization, and mentality is an organizational invariant, rooted in such patterns. Along the way I answer various challenges to the computationalist position, such as those put forward by Searle. I close by advocating a kind of minimal computationalism, compatible with a very wide variety of empirical approaches to the mind. This allows computation to serve as a true foundation for cognitive science. (shrink)

Non‐Cartesian substance dualism is a position in the philosophy of mind concerning the nature of the mind‐body relation or, more exactly, the person‐body relation. Whereas Cartesian substance dualism takes subjects of experience to be necessarily immaterial and indeed nonphysical substances, non‐Cartesian substance dualism does not insist on this. This distinctive feature of non‐Cartesian substance dualism gives it certain advantages over Cartesian dualism, without compelling it to forfeit any of the intuitive appeal that attaches to its (...) more traditional rival. In this chapter, the author's own view is that the self is indeed a simple substance. The simplicity of the self goes some way toward explaining its unity, including the unity of consciousness that characterizes its normal condition. A divided consciousness is in principle consistent with self‐identity: what is not consistent with this is a radical disunity of beliefs and values, manifested in a radical inconsistency of thought and action. (shrink)

In lieu of an abstract, here is a brief excerpt of the content: Reviewed by: The Myth or Elegy of Artificial Intelligence by Tingyang ZhaoXuejian Zhou (bio)Rengongzhineng de shenhua huo beige 人工智能的神話或悲歌 ( The Myth or Elegy of Artificial Intelligence). By Tingyang Zhao 趙汀陽. Beijing: The Commercial Press, 2022. Pp. 155. Hardcover RMB68, isbn 978-981-16-7749-6. In recent years, the philosophy of artificial intelligence has undoubtedly become one of the most popular topics. There is a vague viewpoint suggesting (...) that Chinese philosophy might face challenges in addressing the philosophical issues posed by artificial intelligence. However, compared to the past, when discussing Chinese philosophy today, we must acknowledge the significant impact of modern technology. While the contemporary world does indeed witness clashes of civilizations, individuals from various civilizations share a common predicament: their close entanglement with modern technology. Regarding the future of Chinese philosophy, it becomes crucial to direct our attention towards modern advancements like artificial intelligence and the diverse range of issues they introduce. In light of this context, Zhao Tingyang's 趙汀陽 new book, The Myth or Elegy of Artificial Intelligence, presents us with a fresh perspective. In this book, Zhao primarily delves into the philosophical issues surrounding Super Artificial Intelligence, a subject regarded as both a metaphysical and a political problem. This notion refers to artificial intelligence that surpasses general artificial intelligence in terms of functionality, and possesses self-consciousness. Therefore, "myth" refers to the myth of the emergence of Super Artificial Intelligence disrupting people's subjectivity, giving rise to a new myth of artificial intelligence. Conversely, the term "elegy" embodies the crisis in human destiny that stems from such profound changes, which will lead to an ontological revolution in human existence. This suggests that within the narrative of artificial intelligence's myth, there lies the elegy of humanity (p. 4). The intricate interplay between these two realms of myth and elegy vividly showcases the value of Chinese philosophy. The book consists of a total of ten chapters. In Chapter 1, Zhao initially presents a thought-provoking question. With the rapid advancement of artificial intelligence humanity is faced with a novel ontological event, where the existence of artificial intelligence could potentially jeopardize human existence. This presents an ontological problem of "de-existence" (p. 8). As current artificial intelligence remains within the domain of the Turing machine concept and lacks subjectivity, it cannot effectively engage in matters involving self-correlation or [End Page 1] infiniteness. However, if artificial intelligence evolves into a super Turing machine endowed with subjectivity and free will, it could gain the capacity to modify its own programs and societal norms based on its needs or maximal benefits. This scenario introduces the possibility of a crisis in human existence, where the very existence of humanity is put at risk (pp. 17-23). In Chapter 2, Zhao analyzes the safety conditions of artificial intelligence and proposes the need to establish self-destruct mechanisms for Super Artificial Intelligence to ensure the status of human existence (pp. 34-38). Interestingly, in this context, Zhao connects artificial intelligence with the concept of Tianxia 天下 ("all under heaven"), implying that in order to control capital and power, the world needs a new form of politics, namely, a Tianxia 天下 system. Tianxia 天下 represents a world system that ensures world peace and the shared interests of people in a multicultural context, a non-exclusive system defining the world beyond any particular entity. According to Zhao, only a Tianxia 天下 system can manage the technological risks of the world (p. 39). Theoretically (hopefully in practice as well), a significant application of the Tianxia 天下 system would be to use global authority to limit any high-risk behaviors. Based on the first two chapters, it becomes apparent that the dangers related to artificial intelligence do not lie in its current capabilities, but in its potential for self-consciousness. For this reason, in Chapter 3, Zhao analyzes this issue further. In his view, once artificial intelligence possesses the ability for self-reflection, it will become possible for it to transform its own system and create new rules. Especially if artificial intelligence invents a universal language of its own, equivalent to human natural language, then all program systems could be reinterpreted, reconstructed, and redefined using this universal language (pp. 47-52). Furthermore, if... (shrink)

This paper argues for the digital interactive installation artwork based on principles of complexity as an interface with the potential to evoke ekspansions in the subjective experience by confronting the user with an idea of abstract thought, created through a conceptual design, and experienced through bodily interaction and contemplative acts at symbolic levels. The claim is, that ideas presented through good artworks based on the coalision of science and technology can potentially create a synthesis between ideas inspired by the philosophies (...) and empiricisms of the physical-material world view, but also of a higher, spiritual dimension, in which new possibilities of knowledge are potentially to be found. The article accepts the thesis of cognition being the cause of evolution, rather than evolution of man being the cause of cognition. (shrink)

Every artificial-intelligence research project needs a working definition of "intelligence", on which the deepest goals and assumptions of the research are based. In the project described in the following chapters, "intelligence" is defined as the capacity to adapt under insufficient knowledge and resources. Concretely, an intelligent system should be finite and open, and should work in real time. ;If these criteria are used in the design of a reasoning system, the result is NARS, a non-axiomatic reasoning system. ;NARS uses (...) a term-oriented formal language, characterized by the use of subject-predicate sentences. The language has an experience-grounded semantics, according to which the truth value of a judgment is determined by previous experience, and the meaning of a term is determined by its relations with other terms. Several different types of uncertainty, such as randomness, fuzziness, and ignorance, can be represented in the language in a single way. ;The inference rules of NARS are based on three inheritance relations between terms. With different combinations of premises, revision, deduction, induction, abduction, exemplification, comparison, and analogy can all be carried out in a uniform format, the major difference between these types of inference being that different functions are used to calculate the truth value of the conclusion from the truth values of the premises. ;Since it has insufficient space-time resources, the system needs to distribute them among its tasks very carefully, and to dynamically adjust the distribution as the situation changes. This leads to a "controlled concurrency" control mechanism, and a "bag-based" memory organization. ;A recent implementation of the NARS model, with examples, is discussed. The system has many interesting properties that are shared by human cognition, but are absent from conventional computational models of reasoning. ;This research sheds light on several notions in artificial intelligence and cognitive science, including symbol-grounding, induction, categorization, logic, and computation. These are discussed to show the implications of the new theory of intelligence. ;Finally, the major results of the research are summarized, a preliminary evaluation of the working definition of intelligence is given, and the limitations and future extensions of the research are discussed. (shrink)