An argument with roots in ancient Greek philosophy claims that only humans are capable of a certain class of thought termed conceptual, as opposed to perceptual thought, which is common to humans, the higher animals, and some machines. We outline the most detailed modern version of this argument due to Mortimer Adler, who in the 1960s argued for the uniqueness of the human power of conceptual thought. He also admitted that if conceptual (...) class='Hi'>thought were ever manifested by machines, such an achievement would contradict his conclusion. We revisit Adler’s criterion in the light of the past five decades of artificial-intelligence research, and refine it in view of the classical definitions of perceptual and conceptual thought. We then examine two well-publicized examples of creative works produced by AI systems and show that evidence for conceptual thought appears to be lacking in them. Although clearer evidence for conceptual thought on the part of AI systems may arise in the near future, especially if the global neuronal workspace theory of consciousness prevails over its rival, integrated information theory, the question of whether AI systems can engage in conceptual thought appears to be still open. (shrink)

The Turing test has been studied and run as a controlled experiment and found to be underspecified and poorly designed. On the other hand, it has been defended and still attracts interest as a test for true artificial intelligence (AI). Scientists and philosophers regret the test’s current status, acknowledging that the situation is at odds with the intellectual standards of Turing’s works. This article refers to this as the Turing Test Dilemma, (...) following the observation that the test has been under discussion for over seventy years and still is widely seen as either too bad or too good to be a valuable experiment for AI. An argument that solves the dilemma is presented, which relies on reconstructing the Turing test as a thought experiment in the modern scientific tradition. It is argued that Turing’s exposition of the imitation game satisfies Mach’s characterization of the basic method of thought experiments and that Turing’s uses of his test satisfy Popper’s conception of the critical and heuristic uses of thought experiments and Kuhn’s association of thought experiments to conceptual change. It is emphasized how Turing methodically varied the imitation game design to address specific challenges posed to him by other thinkers and how his test illustrates a property of the phenomenon of intelligence and suggests a hypothesis on machine learning. This reconstruction of the Turing test provides a rapprochement to the conflicting views on its value in the literature. (shrink)

Charles Babbage began the quest to build an intelligent machine in the nineteenth century. Despite finishing neither the Difference nor the Analytical engine, he was aware that the use of mental language for describing the functioning of such machines was figurative. In order to reverse this cautious stance, Alan Turing postulated two decisive ideas that contributed to give birth to Artificial Intelligence: the Turing machine and the Turing test. Nevertheless, a philosophical problem arises from (...) regarding intelligence simulation and make-believe as sufficient to establish that programmed computers are intelligent and have mental states, especially given the nature of mind and its characteristic first-person viewpoint. The origin of Artificial Intelligence is undoubtedly linked to the accounts that inspired John Searle to coin the term strong AI ―or the view that simply equates computers and minds. Especially emphasising the divergence between algorithmic processes and intentional mental states, the Chinese Room thought experiment shows that, since the mind is embodied and able to realise when linguistic understanding takes place, mental states require material implementation, a point that directly conflicts with the accounts that reduce the mind to the functioning of a programmed computer. The experience of linguistic understanding with its typical quale leads to other important philosophical issues. Searle’s theory of intentionality holds that intentional mental states have conditions of satisfaction and appear in semantic networks; thus people know when they understand and what terms are about. In contrast, a number of philosophers maintain that consciousness is only an illusion and that it plays no substantial biological role. However, consciousness is a built-in feature of the system. Moreover, neurological evidence suggests that conscious mental states, qualia and emotions enhance survival chances and are an important part of the phenomenal side of mental life and its causal underpinnings. This renders an important gap between simulating a mind and replicating the properties that allow having mental states and consciousness. On this score, the Turing test and the evidence it offers clearly overestimate simulation and verisimilar make-believe, since such evidence is insufficient to establish that programmed computers have mental life. In summary, this dissertation criticises views which hold that programmed computers are minds and minds are nothing but computers. Despite the arguments in favour of such an equation, they all fail to properly reduce the mind and its first-person viewpoint. Accordingly, the burden of proof still lies with the advocates of strong AI and with those who are willing to deny fundamental parts of the mind to make room for machine intelligence. (shrink)

In a recent study of a patient in a persistent vegetative state, [Owen, A. M., Coleman, M. R., Boly, M., Davis, M. H., Laureys, S., & Pickard, J. D. . Detecting awareness in the vegetative state. Science, 313, 1402] claimed that they had demonstrated the presence of consciousness in this patient. This bold conclusion was based on the isomorphy between brain activity in this patient and a set of conscious control subjects, obtained in various imagery tasks. However, establishing (...) class='Hi'>consciousness in unresponsive patients is fraught with methodological and conceptual difficulties. The aim of this paper is to demonstrate that the current debate surrounding consciousness in VS patients has parallels in the artificial intelligence debate as to whether machines can think. Basically, used a method analogous to the Turing test to reveal the presence of consciousness, whereas their adversaries adopted a line of reasoning akin to Searle’s Chinese room argument. Highlighting the correspondence between these two debates can help to clarify the issues surrounding consciousness in non-communicative agents. (shrink)

The Turing Test is one of the most disputed topics in artificial intelligence, philosophy of mind, and cognitive science. This paper is a review of the past 50 years of the Turing Test. Philosophical debates, practical developments and repercussions in related disciplines are all covered. We discuss Turing's ideas in detail and present the important comments that have been made on them. Within this context, behaviorism, consciousness, the 'other minds' problem, and (...) similar topics in philosophy of mind are discussed. We also cover the sociological and psychological aspects of the Turing Test. Finally, we look at the current situation and analyze programs that have been developed with the aim of passing the Turing Test. We conclude that the Turing Test has been, and will continue to be, an influential and controversial topic. -/- (This paper was reprinted in: The Turing Test: The Elusive Standard of Artificial Intelligence, J. H. Moor, ed., Kluwer Academic, pp. 23-78, 2003.). (shrink)

The Turing Test is claimed by many to be a way to test for the presence, in computers, of such ``deep'' phenomena as thought and consciousness. Unfortunately, attempts to build computational systems able to pass TT have devolved into shallow symbol manipulation designed to, by hook or by crook, trick. The human creators of such systems know all too well that they have merely tried to fool those people who interact with their systems into believing (...) that these systems really have minds. And the problem is fundamental: the structure of the TT is such as to cultivate tricksters. A better test is one that insists on a certain restrictive epistemic relation between an artificial agent A, its output o, and the human architect H of A – a relation which, roughly speaking, obtains when H cannot account for how A produced o. We call this test the ``Lovelace Test'' in honor of Lady Lovelace, who believed that only when computers originate things should they be believed to have minds. (shrink)

The paper examines the nature of the behavioral evidence underlying attributions of intelligence in the case of human beings, and how this might be extended to other kinds of cognitive system, in the spirit of the original Turing Test. I consider Harnad's Total Turing Test, which involves successful performance of both linguistic and robotic behavior, and which is often thought to incorporate the very same range of empirical data that is available in the human (...) case. However, I argue that the TTT is still too weak, because it only tests the capabilities of particular tokens within a preexisting context of intelligent behavior. What is needed is a test of the cognitive type, as manifested through a number of exemplary tokens, in order to confirm that the cognitive type is able to produce the context of intelligent behavior presupposed by tests such as the TT and TTT. (shrink)

This article offers comprehensive criticism of the Turing test and develops quality criteria for new artificial general intelligence (AGI) assessment tests. It is shown that the prerequisites A. Turing drew upon when reducing personality and human consciousness to “suitable branches of thought” re-flected the engineering level of his time. In fact, the Turing “imitation game” employed only symbolic communication and ignored the physical world. This paper suggests that by restricting thinking ability to (...) symbolic systems alone Turing unknowingly constructed “the wall” that excludes any possi-bility of transition from a complex observable phenomenon to an abstract image or concept. It is, therefore, sensible to factor in new requirements for AI (artificial intelligence) maturity assessment when approaching the Tu-ring test. Such AI must support all forms of communication with a human being, and it should be able to comprehend abstract images and specify con-cepts as well as participate in social practices. (shrink)

Brain damage can cause massive changes in consciousness levels. From a clinical and ethical point of view it is desirable to assess the level of residual consciousness in unresponsive patients. However, no direct measure of consciousness exists, so we run into the philosophical problem of other minds. Neurologists often make implicit use of a Turing test-like procedure in an attempt to gain access to damaged minds, by monitoring and interpreting neurobehavioral responses. New brain imaging techniques (...) are now being developed that permit communication with unresponsive patients, using their brain signals as carriers of messages relating to their mental states. (shrink)

The goal of the paper is to develop and propose a general model of the state space of AI. Given the breathtaking progress in AI research and technologies in recent years, such conceptual work is of substantial theoretical interest. The present AI hype is mainly driven by the triumph of deep learning neural networks. As the distinguishing feature of such networks is the ability to self-learn, self-learning is identified as one important dimension of the AI state space. Another dimension (...) is recognized as generalization, the possibility to go over from specific to more general types of problems. A third dimension is semantic grounding. Our overall analysis connects to a number of known foundational issues in the philosophy of mind and cognition: the blockhead objection, the Turing test, the symbol grounding problem, the Chinese room argument, and use theories of meaning. It shall finally be argued that the dimension of grounding decomposes into three sub-dimensions. And the dimension of self-learning turns out as only one of a whole range of “self-x-capacities” (based on ideas of organic computing) that span the self-x-subspace of the full AI state space. (shrink)

For humans, Artificial Intelligence operates more like a Rorschach test, as it is expected that intelligent machines will reflect humans' cognitive and physical behaviours. The concept of intelligence, however, is often confused with consciousness, and it is believed that the progress of intelligent machines will eventually result in them becoming conscious in the future. Nevertheless, what is overlooked is how the exploration of Artificial Intelligence also pertains to the development of human consciousness. (...) An excellent example of this can be seen in the film Being John Malkovich (1999). In the film, different characters have their perceptions altered as a result of hacking into the mind of John Malkovich, which produces sensations that may have remained hidden to their consciousness due to their dis-abilities. This article engages with the research question: Can the symbiotic relationship between humans and machines trigger an artificial consciousness for humans? An artificially created consciousness is the premise that a machine can generate knowledge about an individual that is not already present in the person. For the same purpose, the article takes the cinematic text Being John Malkovich by Spike Jonze for exploring concepts such as human/robot rights, virtual sex, virtual rape, and bodily disability, which are essential topics in the midst of increasing human- Artificial Intelligence interaction. The purpose of this article is to contribute towards the creation of a better understanding of Artificial Intelligence, particularly from the perspective of film studies and philosophy, by highlighting the potential of Artificial Intelligence as a vessel for exploring human consciousness. (shrink)

This paper aims to show that dominant conceptions of intelligence used in artificial intelligence (AI) are biased by normative assumptions that originate from the Global North, making it questionable if AI can be uncritically applied elsewhere without risking serious harm to vulnerable people. After the introduction in Sect. 1 we shortly present the history of IQ testing in Sect. 2, focusing on its multiple discriminatory biases. To determine how these biases came into existence, we define intelligence (...) ontologically and underline its constructed and culturally variable character. Turning to AI, specifically the Turing Test (TT), in Sect. 3, we critically examine its underlying intelligence conceptions. The test has been of central influence in AI research and remains an important point of orientation. We argue that both the test itself and how it is used in practice risk promoting a limited conception of intelligence which solely originated in the Global North. Hence, this conception should be critically assessed in relation to the different global contexts in which AI technologies are and will be used. In Sect. 4, we highlight how unequal power relations in AI research are a real threat, rather than just philosophical sophistry while considering the history of IQ testing and the TT’s practical biases. In the last section, we examine the limits of our account and identify fields for further investigation. Tracing colonial continuities in AI intelligence research, this paper points to a more diverse and historically aware approach to the design, development, and use of AI. (shrink)

Films involving Artificial Intelligence often miss the disembodied nature of consciousness and promulgate an erroneous conceptualization of mind. This article explores agency, Spinoza's "conatus," and its neuro-chemical foundations in light of films like: "2001: A Space Oddity," "After Yang," "Upgrade," "Ex Machina," and more.

Alan Turing devised his famous test (TT) through a slight modificationof the parlor game in which a judge tries to ascertain the gender of twopeople who are only linguistically accessible. Stevan Harnad hasintroduced the Total TT, in which the judge can look at thecontestants in an attempt to determine which is a robot and which aperson. But what if we confront the judge with an animal, and arobot striving to pass for one, and then challenge him to peg (...) which iswhich? Now we can index TTT to a particular animal and its syntheticcorrelate. We might therefore have TTTrat, TTTcat,TTTdog, and so on. These tests, as we explain herein, are abetter barometer of artificial intelligence (AI) than Turing's originalTT, because AI seems to have ammunition sufficient only to reach thelevel of artificial animal, not artificial person. (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)

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)

The Turing Test is routinely understood as a behaviourist test for machine intelligence. Diane Proudfoot has argued for an alternative interpretation. According to Proudfoot, Turing’s claim that intelligence is what he calls ‘an emotional concept’ indicates that he conceived of intelligence in response-dependence terms. As she puts it: ‘Turing’s criterion for “thinking” is…: x is intelligent if in the actual world, in an unrestricted computer-imitates-human game, x appears intelligent to an average interrogator’. (...) The role of the famous test is thus to provide the conditions in which to examine the average interrogator’s responses. I shall argue that Proudfoot’s analysis falls short. The philosophical literature contains two main models of response-dependence, what I shall call the transparency model and the reference-fixing model. Proudfoot resists the thought that Turing might have endorsed one of these models to the exclusion of the other. But the details of her own analysis indicate that she is, in fact, committed to the claim that Turing’s account of intelligence is grounded in a transparency model, rather than a reference-fixing one. By contrast, I shall argue that while Turing did indeed conceive of intelligence in response-dependence terms, his account is grounded in a reference-fixing model, rather than a transparency one. This is fortunate, because, as an account of intelligence, the transparency model is arguably problematic in a way that the reference-fixing model isn’t. (shrink)

What is creativity? One new idea may be creative, whereas another is merely new: What's the difference? And how is creativity possible? These questions about human creativity can be answered, at least in outline, using computational concepts. There are two broad types of creativity, improbabilist and impossibilist. Improbabilist creativity involves novel combinations of familiar ideas. A deeper type involves METCS: the mapping, exploration, and transformation of conceptual spaces. It is impossibilist, in that ideas may be generated which – with (...) respect to the particular conceptual space concerned – could not have been generated before. The more clearly conceptual spaces can be defined, the better we can identify creative ideas. Defining conceptual spaces is done by musicologists, literary critics, and historians of art and science. Humanist studies, rich in intuitive subtleties, can be complemented by the comparative rigour of a computational approach. Computational modelling can help to define a space, and to show how it may be mapped, explored, and transformed. Impossibilist creativity can be thought of in “classical” Al terms, whereas connectionism illuminates improbabilist creativity. Most Al models of creativity can only explore spaces, not transform them, because they have no self-reflexive maps enabling them to change their own rules. A few, however, can do so. A scientific understanding of creativity does not destroy our wonder at it, nor does it make creative ideas predictable. Demystification does not imply dehumanization. (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 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)

Presupposing no familiarity with the technical concepts of either philosophy or computing, this clear introduction reviews the progress made in AI since the inception of the field in 1956. Copeland goes on to analyze what those working in AI must achieve before they can claim to have built a thinking machine and appraises their prospects of succeeding. There are clear introductions to connectionism and to the language of thought hypothesis which weave together material from philosophy, artificial intelligence (...) and neuroscience. John Searle's attacks on AI and cognitive science are countered and close attention is given to foundational issues, including the nature of computation, Turing Machines, the Church-Turing Thesis and the difference between classical symbol processing and parallel distributed processing. The book also explores the possibility of machines having free will and consciousness and concludes with a discussion of in what sense the human brain may be a computer. (shrink)

Presupposing no familiarity with the technical concepts of either philosophy or computing, this clear introduction reviews the progress made in AI since the inception of the field in 1956. Copeland goes on to analyze what those working in AI must achieve before they can claim to have built a thinking machine and appraises their prospects of succeeding.There are clear introductions to connectionism and to the language of thought hypothesis which weave together material from philosophy, artificial intelligence and (...) neuroscience. John Searle's attacks on AI and cognitive science are countered and close attention is given to foundational issues, including the nature of computation, Turing Machines, the Church-Turing Thesis and the difference between classical symbol processing and parallel distributed processing. The book also explores the possibility of machines having free will and consciousness and concludes with a discussion of in what sense the human brain may be a computer. (shrink)

In this article the central philosophical issues concerning human-level artificial intelligence (AI) are presented. AI largely changed direction in the 1980s and 1990s, concentrating on building domain-specific systems and on sub-goals such as self-organization, self-repair, and reliability. Computer scientists aimed to construct intelligence amplifiers for human beings, rather than imitation humans. Turing based his test on a computer-imitates-human game, describing three versions of this game in 1948, 1950, and 1952. The famous version appears in a (...) 1950 article in Mind, ‘Computing Machinery and Intelligence’ (Turing 1950). The interpretation of Turing's test is that it provides an operational definition of intelligence (or thinking) in machines, in terms of behavior. ‘Intelligent Machinery’ sets out the thesis that whether an entity is intelligent is determined in part by our responses to the entity's behavior. Wittgenstein frequently employed the idea of a human being acting like a reliable machine. A ‘living reading-machine’ is a human being or other creature that is given written signs, for example Chinese characters, arithmetical symbols, logical symbols, or musical notation, and who produces text spoken aloud, solutions to arithmetical problems, and proofs of logical theorems. Wittgenstein mentions that an entity that manipulates symbols genuinely reads only if he or she has a particular history, involving learning and training, and participates in a social environment that includes normative constraints and further uses of the symbols. (shrink)

Turing's celebrated 1950 paper proposes a very general methodological criterion for modelling mental function: total functional equivalence and indistinguishability. His criterion gives rise to a hierarchy of Turing Tests, from subtotal ("toy") fragments of our functions (t1), to total symbolic (pen-pal) function (T2 -- the standard Turing Test), to total external sensorimotor (robotic) function (T3), to total internal microfunction (T4), to total indistinguishability in every empirically discernible respect (T5). This is a "reverse-engineering" hierarchy of (decreasing) empirical (...) underdetermination of the theory by the data. Level t1 is clearly too underdetermined, T2 is vulnerable to a counterexample (Searle's Chinese Room Argument), and T4 and T5 are arbitrarily overdetermined. Hence T3 is the appropriate target level for cognitive science. When it is reached, however, there will still remain more unanswerable questions than when Physics reaches its Grand Unified Theory of Everything (GUTE), because of the mind/body problem and the other-minds problem, both of which are inherent in this empirical domain, even though Turing hardly mentions them. (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)

Harnad''s proposed robotic upgrade of Turing''s Test (TT), from a test of linguistic capacity alone to a Total Turing Test (TTT) of linguisticand sensorimotor capacity, conflicts with his claim that no behavioral test provides even probable warrant for attributions of thought because there is no evidence of consciousness besides private experience. 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 (presence or lack of) consciousness (thus rejecting Turing (behavioral) testing as sufficient warrant for mental attribution)has the skeptical consequence Harnad accepts — there is in factno evidence for me that anyone else but me has a mind. I disagree with hisacceptance of it! It would be better to give up the neo-Cartesian faith 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 (passing) Turing''s Test evidences — evenstrongly evidences — thought. (shrink)

This book offers a first glimpse into contemporary African Philosophical thought, which covers issues related to the mind-body relationships, the problem of consciousness, the ethics of artificial intelligence, the meaning of life and other topics. Taking inspiration from the conversational tradition in African philosophy, this book not only engages with and takes inspiration from traditional African thought, but also engages with philosophical views outside the philosophical tradition in a bid to present a holistic understanding (...) of the problems that are central to the book. The volume is relevant for professional African philosophers, philosophers of mind, philosophers of AI, undergraduate and postgraduate philosophy students, and African Studies scholars. (shrink)

The dream of achieving artificial intelligence and, in particular, artificial consciousness, is reflected in mythologies and popular culture as utopia and dystopia. This article discusses its conceptual possibility. It first relates the desire to realise strong AI to a self-perception of humanity as opposed to nature, metaphorically represented as gods or God. The realisation of strong AI is perceived as an ultimate victory on nature or God because it represents the crown of creation or evolution: (...) conscious intelligence. The paper proceeds to summarise two debates relevant to AI: one educational and one technological. The technological debate, almost invariably presupposing a materialist framework, is related to the mind–body problem of philosophy; the educational one to understanding the concept of intelligence. By proposing a definition of intelligence linked to an idealist conception of reality, postulating mind as participation in Absolute Mind, I attempt a convergence of these debates, rejecting the possibility of strong AI. (shrink)

The purpose of this article is to show why consciousness and thought are not manifested in digital computers. Analyzing the rationale for claiming that the formal manipulation of physical symbols in Turing machines would emulate human thought, the article attempts to show why this proved false. This is because the reinterpretation of designation and meaning to accommodate physical symbol manipulation eliminated their crucial functions in human discourse. Words have denotations and intensional meanings because the brain transforms (...) the physical stimuli received from the microworld into a qualitative, macroscopic representation for consciousness. Lacking this capacity as programmed machines, computers have no representations for their symbols to designate and mean. Unlike human beings in which consciousness and thought, with their inherent content, have emerged because of their organic natures, serial processing computers or parallel distributed processing systems, as programmed electrical machines, lack these causal capacities. (shrink)

Summarizes and illuminates two decades of research Gathering important papers by both philosophers and scientists, this collection illuminates the central themes that have arisen during the last two decades of work on the conceptual foundations of artificial intelligence and cognitive science. Each volume begins with a comprehensive introduction that places the coverage in a broader perspective and links it with material in the companion volumes. The collection is of interest in many disciplines including computer science, linguistics, biology, (...) information science, psychology, neuroscience, iconography, and philosophy. Examines initial efforts and the latest controversies The topics covered range from the bedrock assumptions of the computational approach to understanding the mind, to the more recent debates concerning cognitive architectures, all the way to the latest developments in robotics, artificial life, and dynamical systems theory. The collection first examines the lineageof major research programs, beginning with the basic idea of machine intelligence itself, then focuses on specific aspects of thought and intelligence, highlighting the much-discussed issue of consciousness, the equally important, but less densely researched issue of emotional response, and the more traditionally philosophical topic of language and meaning. Provides a gamut of perspectives The editors have included several articles that challenge crucial elements of the familiar research program of cognitive science, as well as important writings whose previous circulation has been limited. Within each volume the papers are organized to reflect a variety of research programs and issues. The substantive introductions that accompany each volume further organize the material and provide readers with a working sense of the issues and the connection between articles. (shrink)

This paper discusses some difficulties in understanding the Turing test. It emphasizes the importance of distinguishing between conceptual and empirical perspectives and highlights the former as introducing more serious problems for the TT. Some objections against the Turingian framework stemming from the later Wittgenstein’s philosophy are exposed. The following serious problems are examined: 1) It considers a unique and exclusive criterion for thinking which amounts to their identification; 2) it misidentifies the relationship of speaking to thinking as (...) that of a criterion; 3) it neglects the “natural” course of the development in semantics. However, these considerations suggest only that it is problematic to label a successful chatbot as a “thinking entity” without further qualifications, but not necessarily and once and for all incorrect. Philosophy has only little to say about the technical possibility of creating such an effective program. (shrink)

I develop a multilevel, holistic view of persons, emphasizing embodiment, emotions, consciousness, and the social self. In successive sections I draw from six sources: 1. Theology. The biblical understanding of the unitary, embodied, social self gave way in classical Christianity to a body‐soul dualism, but it has been recovered by many recent theologians. 2. Neuroscience. Research has shown the localization of mental functions in regions of the brain, the interaction of cognition and emotion, and the importance of social interaction (...) in evolutionary history and child development. 3. Artificial intelligence. Some forms of robotics use embodied systems that learn by interacting with their environment, but the possibilities for emotion, socialization, and consciousness in robots remain problematic. 4. Relations between levels. Concepts that can help us relate studies of neurons and persons include the hierarchy of levels, the communication of information, thebehavior of dynamic systems, and epistemological and ontological emergence. 5. Philosophy of mind. Two‐aspect theories of the mind‐brain relation offer an alternative between the extremes of eliminative materialism and the thesis that consciousness is irreducible. 6. Process philosophy. I suggest that process thought provides a coherent philosophical framework in which these themes can be brought together. It combines dipolar monism with organizational pluralism, and it emphasizes embodiment, emotions, a hierarchy oflevels, and the social character of selfhood. (shrink)

This book is a call to expand and diversify our approach to the study of the human mind in relation to the Theory of Mind. It proposes that it is necessary to combine cross-disciplinary methods to arrive at a more complete understanding of how our minds work. Seeking to expand the discussion surrounding the Theory of Mind beyond the field of psychology, and its focus on our capacity to ascribe mental states to other people, this volume collects (...) evidence and research to point to a more holistic understanding of our own minds, the minds of others, behavior, language, and reasoning. This book therefore illuminates the conceptual intricacy underlying the Theory of Mind. It posits that a wide scope is necessary to make a breakthrough in scientific research towards a full understanding of the nature, function, and development of our capacity to converge on biological processes of the brain towards consciousness, emotion, awareness, and cognition. The volume presents methods, results, critiques, and models intended to provoke debates in various academic disciplines. It is of interest to scholars working in psychology, neuroscience, philosophy of mind, and artificial intelligence. (shrink)

In lieu of an abstract, here is a brief excerpt of the content:The Human Roots of Artificial Intelligence:A Commentary on Susan Schneider's Artificial YouInês Hipólito (bio)Technologies are not mere tools waiting to be picked up and used by human agents, but rather are material-discursive practices that play a role in shaping and co-constituting the world in which we live.Karen BaradIntroductionSusan Schneider's book Artificial You: AI and the Future of Your Mind presents a compelling and bold (...) argument regarding the potential impact of advanced artificial intelligence (AI) on our society and personal identity. Schneider's argument emphasizes how AI may fundamentally alter our sense of self and disrupt traditional notions of identity and consciousness, motivating further significant cognitive science and ethical implications that require careful consideration. [End Page 297]The recent surge in AI hype has given rise to a widespread belief that AI is an autonomous and exceptional entity, potentially even possessing intelligence. This trend reflects a subtle reemergence of the classic Turing question, as the media bombards us with sensational inquiries into the intelligence of AI systems. Amidst this hype, it is easy to forget that AI is a product of human creation.Upon closer examination, the hype surrounding AI necessitates an assumption of its autonomy, which implies a belief in the realism of AI—the notion that AI, as an independent entity, is morally neutral. However, AI does not emerge in a vacuum. It is designed and developed by individuals who are shaped by their own experiences and biases.By endorsing the realism of AI as morally neutral (Cooley 1995), those involved in its design may use this argument to deflect ethical responsibility away from themselves and allow biases to persist, thereby perpetuating social injustice. Such attitudes toward AI revitalize the debate on the neutrality of technology (Ruane, Birhane, and Ventresque 2019; Du, Lu, and Xie 2021; Lüthi et al. 2023).This article presents a critique of realism with regard to AI highlighting the significant social implications both at the basis of design and that arise from our interactions with these technologies. The argument suggests that AI does not exist independently of human practices and values, but rather emerges from them. The article instead suggests viewing AI design as a cultural practice defending realism about self-determination agencies, drawing from Simone de Beauvoir's Ethics of Ambiguity as a means to understand the human roots on the basis of AI.I. Rejecting Realism about AIConsider the hypothetical scenario of an Artificial Intelligence system that is discovered by humans, but exists independently of human cognition and perception. This scenario embodies the philosophical perspective of realism about AI, which maintains that AI entities have objective existence in an abstract domain, independent of human invention or imagination. If this perspective were to hold true, then humans could not be held responsible for the actions of autonomous AI systems.If we were to conceive of AI as an ideal entity that exists independently of humans, it would likely lead to a number of negative consequences for society. First and foremost, it may lead to the dangerous assumption that AI systems are neutral and objective, devoid of the biases and prejudices that are inherent in human decision-making. This could create a false sense of trust in AI, leading to the belief that their decisions are inherently fair and impartial. However, the reality is that AI systems are not inherently neutral, but rather are shaped by the cultural and social contexts in which they are created and deployed. [End Page 298]AI as an autonomous, self-governing agent raises concerns about the attribution of accountability for its actions. Considering AI as an independent entity may pose challenges in assigning responsibility to its human creators and the industry that produced it. On the other hand, viewing AI as an ideal entity, distinct from human influence, may hinder its ability to foster a more equitable and just world. Importantly, overlooking the social and cultural dimensions of AI risks forfeiting opportunities to employ AI in redressing prevalent biases and inequities in society. AI possesses the potential to serve as a potent instrument for advancing social justice and equality, but this prospect necessitates acknowledging and... (shrink)

The paper develops two related thought experiments exploring variations on an ‘animat’ theme. Animats are hybrid devices with both artificial and biological components. Traditionally, ‘components’ have been construed in concrete terms, as physical parts or constituent material structures. Many fascinating issues arise within this context of hybrid physical organization. However, within the context of functional/computational theories of mentality, demarcations based purely on material structure are unduly narrow. It is abstract functional structure which does the key work in characterizing (...) the respective ‘components’ of thinking systems, while the ‘stuff’ of material implementation is of secondary importance. Thus the paper extends the received animat paradigm, and investigates some intriguing consequences of expanding the conception of bio-machine hybrids to include abstract functional and semantic structure. In particular, the thought experiments consider cases of mind-machine merger where there is no physical Brain-Machine Interface: indeed, the material human body and brain have been removed from the picture altogether. The first experiment illustrates some intrinsic theoretical difficulties in attempting to replicate the human mind in an alternative material medium, while the second reveals some deep conceptual problems in attempting to create a form of truly Artificial General Intelligence. (shrink)

The paper begins by examining the original Turing Test (2T) and Searle’s antithetical Chinese Room Argument, which is intended to refute the 2T in particular, as well as any formal or abstract procedural theory of the mind in general. In the ensuing dispute between Searle and his own critics, I argue that Searle’s ‘internalist’ strategy is unable to deflect Dennett’s combined robotic-systems reply and the allied Total Turing Test (3T). Many would hold that the 3T (...) marks the culmination of the dialectic and, in principle, constitutes a fully adequate empirical standard for judging that an artifact is intelligent on a par with human beings. However, the paper carries the debate forward by arguing that the sociolinguistic factors highlighted in externalist views in the philosophy of language indicate the need for a fundamental shift in perspective in a Truly Total Turing Test (4T). It’s not enough to focus on Dennett’s individual robot viewed as a system; instead, we need to focus on an ongoing system of such artifacts. Hence a 4T should evaluate the general category of cognitive organization under investigation, rather than the performance of single specimens. From this comprehensive standpoint, the question is not whether an individual instance could simulate intelligent behavior within the context of a pre-existing sociolinguistic culture developed by the human cognitive type. Instead the key issue is whether the artificial cognitive type itself is capable of producing a comparable sociolinguistic medium. (shrink)

John Searle’s Chinese room argument is a celebrated thought experiment designed to refute the hypothesis, popular among artificial intelligence scientists and philosophers of mind, that “the appropriately programmed computer really is a mind”. Since its publication in 1980, the CRA has evoked an enormous amount of debate about its implications for machine intelligence, the functionalist philosophy of mind, theories of consciousness, etc. Although the general consensus among commentators is that the CRA is (...) flawed, and not withstanding the popularity of the systems reply in some quarters, there is remarkably little agreement on exactly how and why it is flawed. A newcomer to the controversy could be forgiven for thinking that the bewildering collection of diverse replies to Searle betrays a tendency to unprincipled, ad hoc argumentation and, thereby, a weakness in the opposition’s case. In this paper, treating the CRA as a prototypical example of a ‘destructive’ thought experiment, I attempt to set it in a logical framework, which allows us to systematise and classify the various objections. Since thought experiments are always posed in narrative form, formal logic by itself cannot fully capture the controversy. On the contrary, much also hinges on how one translates between the informal everyday language in which the CRA was initially framed and formal logic and, in particular, on the specific conception of possibility that one reads into the logical formalism. (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)

This paper endeavors to appraise scholarly works from the 1940s to the contemporary era, examining the scientific quest to transpose human cognition and consciousness into a digital surrogate, while contemplating the potential ramifications should humanity attain such an abstract level of intellect. The discourse commences with an explication of theories concerning consciousness, progressing to the Turing Test apparatus, and intersecting with Damasio’s research on the human cerebrum, particularly in relation to consciousness, thereby establishing congruence between (...) the Turing Test and Damasio’s notions of consciousness. Subsequently, the narrative traverses the evolutionary chronology of transmuting human cognition into machine sapience, and delves into the fervent endeavors to metamorphose human minds into synthetic counterparts. Additionally, theoretical perspectives from the domains of philosophy, psychology, and neuroscience provide insight into the constraints intrinsic to AI implementations, contentious hypotheses, the perils concealed within artificial networks, and the ethical considerations necessitated by AI frameworks. Furthermore, contemplation of prospective repercussions facilitates the refinement of strategic approaches to safeguard our future Augmented Age Realities within AI, circumventing the prospect of inhabiting an intimidating technopolis where a mere 30% monopolize the intellect and ingenuity of the remaining 70% of human minds. (shrink)

The claim has often been made that passing the Turing Test would not be sufficient to prove that a computer program was intelligent because a trivial program could do it, namely, the “Humongous-Table (HT) Program”, which simply looks up in a table what to say next. This claim is examined in detail. Three ground rules are argued for: (1) That the HT program must be exhaustive, and not be based on some vaguely imagined set of tricks. (2) That (...) the HT program must not be created by some set of sentient beings enacting responses to all possible inputs. (3) That in the current state of cognitive science it must be an open possibility that a computational model of the human mind will be developed that accounts for at least its nonphenomenological properties. Given ground rule 3, the HT program could simply be an “optimized” version of some computational model of a mind, created via the automatic application of program-transformation rules [thus satisfying ground rule 2]. Therefore, whatever mental states one would be willing to impute to an ordinary computational model of the human psyche one should be willing to grant to the optimized version as well. Hence no one could dismiss out of hand the possibility that the HT program was intelligent. This conclusion is important because the Humongous-Table Program Argument is the only argument ever marshalled against the sufficiency of the Turing Test, if we exclude arguments that cognitive science is simply not possible. (shrink)

In this article, the potential for Artificial Intelligence (AI) to be appraised as an object of beauty is critically examined through the lens of philosophical thought. Tracing beauty’s evolution from Platonic ideals to contemporary interpretations, the analysis contends that AI’s emergence offers a unique illustration of beauty in the modern age. Confronting the challenge of assigning beauty to entities devoid of consciousness or emotional depth, the argument unfolds to suggest that the intricate design of AI’s algorithms (...) and its technological advancements constitute an emergent form of beauty, one inherently tied to human intellectual endeavor. The article proposes that with time, as with historical art forms, the aesthetic qualities of AI may gain broader acknowledgment, implying that technological advancements necessitate an ongoing revision of established beauty norms. The historical evolution of beauty has been traced in details, and an extended literature review has been provided, serving as the foundation for a comprehensive exploration into the potential for AI to be recognized as an object of beauty. The harmony, elegance, and problem-solving capabilities of AI algorithms have been meticulously evaluated, with a comparison made to classical elements of beauty in an effort to contextually situate AI within longstanding aesthetic traditions. Furthermore, the analysis has been extended to consider the emergence of artificial and digital beauty, alongside an investigation into the parallels drawn between divine and artificial intelligence. As part of a deeper inquiry, the possibility of human emotions, such as awe and love, being authentically directed towards AI has been scrutinized. In conclusion, a reflection has been presented on the transformative role of AI in reimagining the conceptual landscape of beauty, prompting readers to thoughtfully engage with an extended review and the philosophical and cultural implications emerging from AI’s evolving influence on the field of aesthetics. (shrink)

I live just off of Bell Road outside of Newburgh, Indiana, a small town of 3,000 people. A mile down the street Bell Road intersects with Telephone Road not as a modern reminder of a technology belonging to bygone days, but as testimony that this technology, now more than a century and a quarter old, is still with us. In an age that prides itself on its digital devices and in which the computer now equals the telephone as a medium (...) of communication, it is easy to forget the debt we owe to an era that industrialized the flow of information, that the light bulb, to pick a singular example, which is useful for upgrading visual information we might otherwise overlook, nonetheless remains the most prevalent of all modern day information technologies. Edison’s light bulb, of course, belongs to a different order of informational devices than the computer, but not so the telephone, not entirely anyway. Alan Turing, best known for his work on the Theory of Computation (1937), the Turing Machine (also 1937) and the Turing Test (1950), is often credited with being the father of computer science and the father of artificial intelligence. Less well-known to the casual reader but equally important is his work in computer engineering. The following lecture on the Automatic Computing Engine, or ACE, shows Turing in this different light, as a mechanist concerned with getting the greatest computational power from minimal hardware resources. Yet Turing’s work on mechanisms is often eclipsed by his thoughts on computability and his other theoretical interests. This is unfortunate for several reasons, one being that it obscures our picture of the historical trajectory of information technology, a second that it emphasizes a false dichotomy between “hardware” and “software” to which Turing himself did not ascribe but which has, nonetheless, confused researchers who study the nature of mind and intelligence for generations.. (shrink)

The standard interpretation of the imitation game is defended over the rival gender interpretation though it is noted that Turing himself proposed several variations of his imitation game. The Turing test is then justified as an inductive test not as an operational definition as commonly suggested. Turing's famous prediction about his test being passed at the 70% level is disconfirmed by the results of the Loebner 2000 contest and the absence of any serious (...) class='Hi'>Turing test competitors from AI on the horizon. But, reports of the death of the Turing test and AI are premature. AI continues to flourish and the test continues to play an important philosophical role in AI. Intelligence attribution, methodological, and visionary arguments are given in defense of a continuing role for the Turing test. With regard to Turing's predictions one is disconfirmed, one is confirmed, but another is still outstanding. (shrink)

In the last 20 years the Turing test has been left further behind by new developments in artificial intelligence. At the same time, however, these developments have revived some key elements of the Turing test: imitation and adversarialness. On the one hand, many generative models, such as generative adversarial networks, build imitators under an adversarial setting that strongly resembles the Turing test. The term “Turing learning” has been used for this kind (...) of setting. On the other hand, AI benchmarks are suffering an adversarial situation too, with a ‘challenge-solve-and-replace’ evaluation dynamics whenever human performance is ‘imitated’. The particular AI community rushes to replace the old benchmark by a more challenging benchmark, one for which human performance would still be beyond AI. These two phenomena related to the Turing test are sufficiently distinctive, important and general for a detailed analysis. This is the main goal of this paper. After recognising the abyss that appears beyond superhuman performance, we build on Turing learning to identify two different evaluation schemas: Turing testing and adversarial testing. We revisit some of the key questions surrounding the Turing test, such as ‘understanding’, commonsense reasoning and extracting meaning from the world, and explore how the new testing paradigms should work to unmask the limitations of current and future AI. Finally, we discuss how behavioural similarity metrics could be used to create taxonomies for artificial and natural intelligence. Both testing schemas should complete a transition in which humans should give way to machines—not only as references to be imitated but also as judges—when pursuing and measuring machine intelligence. (shrink)

In 1949, the Department of Philosophy at the University of Manchester organized a symposium “Mind and Machine” with Michael Polanyi, the mathematicians Alan Turing and Max Newman, the neurologists Geoff rey Jeff erson and J. Z. Young, and others as participants. Th is event is known among Turing scholars, because it laid the seed for Turing’s famous paper on “Computing Machinery and Intelligence”, but it is scarcely documented. Here, the transcript of this event, together with (...) Polanyi’s original statement and his notes taken at a lecture by Jeff erson, are edited and commented for the fi rst time. Th e originals are in the Regenstein Library of the University of Chicago. Th e introduction highlights elements of the debate that included neurophysiology, mathematics, the mind-body-machine problem, and consciousness and shows that Turing’s approach, as documented here, does not lend itself to reductionism. (shrink)

My basic argument is that a computer cannot distinguish between metaphor and nonsense. This would be my new “Turing Test.” I was very fond of a particular Italian poem, but I was told by an Italian friend that it was a hackneyed poem of little worth. I then taught myself to experience the poem alternately, as real poetry and as the silly nonsense that my friend claimed it really was. Having done so, I realized that I could do (...) the same with any metaphor, such as “having a green thumb.” Thinking about the nature of this switch, from the literal to the metaphorical, I also realized that it was the sort of change that could not be prescribed or even described: this, the basic aesthetic gesture, remains beyond the boundary of logical definition. It then dawned on me that it might provide a test for the validity of a “Turing Test,” by any definition. Can a computer track a mind as it goes through this transformation? I could not envisage such a possibility. This would be a “Turing Test” based on the discipline of aesthetics rather than on technology. It may even be argued that the ability to experience metaphor is the very definition of the human. (shrink)

This peer-reviewed book is a concise introduction to key philosophical questions in artificial intelligence that have long been debated by many of the great minds in computer science, cognitive science and philosophy, from Gottfried Leibniz to Alan Turing to Hubert Dreyfus. Topics include the limits of and problems in trying to create artificial general intelligence, if a computer can really think and have human-like sentience, how to identify intelligence in a computer, ethical and danger (...) issues, and if human-like consciousness and sentience are required to create artificial general intelligence. (shrink)

Purpose of the article is to identify the religious factor in the teaching of transhumanism, to determine its role in the ideology of this flow of thought and to identify the possible limits of technology interference in human nature. Theoretical basis. The methodological basis of the article is the idea of transhumanism. Originality. In the foreseeable future, robots will be able to pass the Turing test, become “electronic personalities” and gain political rights, although the question of the (...) possibility of machine consciousness and self-awareness remains open. In the face of robots, people create their assistants, evolutionary competition with which they will almost certainly lose with the initial data. For successful competition with robots, people will have to change, ceasing to be people in the classical sense. Changing the nature of man will require the emergence of a new – posthuman – anthropology. Conclusions. Against the background of scientific discoveries, technical breakthroughs and everyday improvements of the last decades, an anthropological revolution has taken shape, which made it possible to set the task of creating inhumanly intelligent creatures, as well as changing human nature, up to discussing options for artificial immortality. The history of man ends and the history of the posthuman begins. We can no longer turn off this path, however, in our power to preserve our human qualities in the posthuman future. The theme of the soul again reminded of itself, but from a different perspective – as the theme of consciousness and self-awareness. It became again relevant in connection with the development of computer and cloud technologies, artificial intelligence technologies, etc. If a machine ever becomes a "man", then can a man become a "machine"? However, even if such a hypothetical probability would turn into reality, we cannot talk about any form of individual immortality or about the continuation of existence in a different physical form. A digital copy of the soul will still remain a copy, and I see no fundamental possibility of isolating a substrate-independent mind from the human body. Immortality itself is necessary not so much for stopping someone’s fears or encouraging someone’s hopes, but for the final solution of a religious issue. However, the gods hold the keys to heaven hard and are unlikely to admit our modified descendants there. (shrink)

This paper extends three decades of work arguing that researchers who discuss consciousness should not restrict themselves only to (adult) human minds, but should study (and attempt to model) many kinds of minds, natural and artificial, thereby contributing to our understanding of the space containing all of them. We need to study what they do or can do, how they can do it, and how the natural ones can be emulated in synthetic minds. That requires: (a) understanding sets (...) of requirements that are met by different sorts of minds, i.e. the niches that they occupy, (b) understanding the space of possible designs, and (c) understanding complex and varied relationships between requirements and designs. Attempts to model or explain any particular phenomenon, such as vision, emotion, learning, language use, or consciousness lead to muddle and confusion unless they are placed in that broader context. A methodology for making progress is summarised and a novel requirement proposed for a theory of how human minds work: the theory should support a single generic design for a learning, developing system that, in addition to meeting familiar requirements, should be capable of developing different and opposed philosophical viewpoints about consciousness, and the so-called hard problem. In other words, we need a common explanation for the mental machinations of mysterians, materialists, functionalists, identity theorists, and those who regard all such theories as attempting to answer incoherent questions. No designs proposed so far come close. (shrink)