Abstract: As autonomous mobile robots (AMRs) begin living in the home, performing service tasks and assisting with daily activities, their actions will have profound ethical implications. Consequently, AMRs need to be outfitted with the ability to act morally with regard to human life and safety. Yet, in the area of robotics where morality is a relevant field of endeavor (i.e. human-robot interaction) the sub-discipline of morality does not exist. In response, the Utilibot project seeks to provide a point of initiation for the implementation of ethics in an AMR. The Utilibot is a decision-theoretic AMR guided by the utilitarian notion of the maximization of human well-being. The core ethical decision-making capacity of the Utilibot consists of two dynamic Bayesian networks that model human and environmental health, a dynamic decision network that accounts for decisions and utilities, and a Markov decision process (MDP) that decomposes the planning problem to solve for the optimal course of action to maximize human safety and well-being.

Abstract: he question of the possibility of artificial consciousness is both very new and very old. It is new in the context of contemporary cognitive science and its concern with whether a machine can be conscious; it is old in the form of the mind/body problem and the "other minds" problem of philosophy. Contemporary enthusiasts proceed at their peril if they ignore or are ignorant of the false starts and blind alleys that the older thinkers have painfully worked through

Abstract: Current research on artificial consciousness is focused on phenomenal consciousness and on functional consciousness. We propose to shift the focus to self-consciousness in order to open new areas of investigation. We use an existing scenario where self-consciousness is considered as the result of an evolution of representations. Application of the scenario to the possible build up of a conscious robot also introduces questions relative to emotions in robots. Areas of investigation are proposed as a continuation of this approach

Abstract: Syntactical treatments of propositional attitudes are attractive to artificial intelligence researchers. But results of Montague (1974) and Thomason (1980) seem to show that syntactical treatments are not viable. They show that if representation languages are sufficiently expressive, then axiom schemes characterizing knowledge and belief give rise to paradox. Des Rivières and Levesque (1988) characterize a class of sentences within which these schemes can safely be instantiated. These sentences do not quantify over the propositional objects of knowledge and belief. We argue that their solution is incomplete, and extend it by characterizing a more inclusive class of sentences over which the axiom schemes can safely range. Our sentences do quantify over propositional objects.

Abstract: In this paper I critically examine the notion of explanation used in artificial intelligence in general, and in the theory of belief revision in particular. I focus on two of the best known accounts in the literature: Pagnucco’s abductive expansion functions and Gärdenfors’ counterfactual analysis. I argue that both accounts are at odds with the way in which this notion has historically been understood in philosophy. They are also at odds with the explanatory strategies used in actual scientific practice. At the end of the paper I outline a set of desiderata for an epistemologically motivated, scientifically informed belief revision model for explanation

Abstract: Department of Computer Engineering, Bilkent University, 06533 Ankara, Turkey E-mail: akman@cs.bilkent.edu.tr; http://www.cs.bilkent.edu.tr/?akman..

Abstract: Ned Block ((1981). Psychologism and behaviorism. Philosophical Review, 90, 5-43.) argued that a behaviorist conception of intelligence is mistaken, and that the nature of an agent's internal processes is relevant for determining whether the agent has intelligence. He did that by describing a machine which lacks intelligence, yet can answer questions put to it as an intelligent person would. The nature of his machine's internal processes, he concluded, is relevant for determining that it lacks intelligence. I argue against Block that it is not the nature of its processes but of its linguistic behavior which is responsible for his machine's lack of intelligence. As I show, not only has Block failed to establish that the nature of internal processes is conceptually relevant for psychology, in fact his machine example actually supports some version of behaviorism. As Wittgenstein has maintained, as far as psychology is concerned, there may be chaos inside

Abstract: Let psychologism be the doctrine that whether behavior is intelligent behavior depends on the character of the internal information processing that produces it. More specifically, I mean psychologism to involve the doctrine that two systems could have actual and potential behavior _typical_ of familiar intelligent beings, that the two systems could be exactly alike in their actual and potential behavior, and in their behavioral dispositions and capacities and counterfactual behavioral properties (i.e., what behaviors, behavioral dispositions, and behavioral capacities they would have exhibited had their stimuli differed)--the two systems could be alike in all these ways, yet there could be a difference in the information processing that mediates their stimuli and responses that determines that one is not at all intelligent while the other is fully intelligent

Abstract: 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

Abstract:   The Turing Test (TT) 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 (or at least restricted versions of this test) 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 (or system) 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

Abstract: It seems plausible that under the conditions of the Turing test, congenitally blind people could nevertheless, with sufficient preparation, successfully represent themselves to remotely located interrogators as sighted. Having never experienced normal visual sensations, the successful blind player can prevail in this test only by playing a ‘lying game’—imitating the phenomenological claims of sighted people, in the absence of the qualitative visual experiences to which such statements purportedly refer. This suggests that a computer or robot might pass the Turing test in the same way, in the absence not only of visual experience, but qualitative consciousness in general. Hence, the standard Turing test does not provide a valid criterion for the presence of consciousness. A ‘sensorimetric’ version of the Turing test fares no better, for the apparent correlations we observe between cognitive functions and qualitative conscious experiences seems to be contingent, not necessary. We must therefore define consciousness not in terms of its causes and effects, but rather, in terms of the distinctive properties of its content, such as its possession of qualitative character and apparent intrinsic value—the property which confers upon consciousness its moral significance. As a means of determining whether or nor a machine is conscious, in this sense, an alternative to the standard Turing test is proposed

Abstract:   Turing''s test has been much misunderstood. Recently unpublished material by Turing casts fresh light on his thinking and dispels a number of philosophical myths concerning the Turing test. Properly understood, the Turing test withstands objections that are popularly believed to be fatal

Abstract: The so-called Turing test, as it is usually interpreted, sets a benchmark standard for determining when we might call a machine intelligent. We can call a machine intelligent if the following is satisfied: if a group of wise observers were conversing with a machine through an exchange of typed messages, those observers could not tell whether they were talking to a human being or to a machine. To pass the test, the machine has to be intelligent but it also should be responsive in a manner which cannot be distinguished from a human being. This standard interpretation presents the Turing test as a criterion for demarcating intelligent from non-intelligent entities. For a long time proponents of artificial intelligence have taken the Turing test as a goalpost for measuring progress

Abstract: I have discussed the frame problem and the Turing test at length, but I have not attempted to spell out what I think the implications of the frame problem ...

Abstract: Searle’s discussions over the years 1980-2004 of the implications of his “Chinese Room” Gedanken experiment are frustrating because they proceed from a correct assertion: (1) “Instantiating a computer program is never by itself a sufficient condition of intentionality;” and an incorrect assertion: (2) “The explanation of how the brain produces intentionality cannot be that it does it by instantiating a computer program.” In this article, I describe how to construct a Gedanken zombie Chinese Room program that will pass the Turing test and at the same time unambiguously demonstrates the correctness of (1). I then describe how to construct a Gedanken Chinese brain program that will pass the Turing test, has a mind, and understands Chinese, thus demonstrating that (2) is incorrect. Searle’s instantiation of this program can and does produce intentionality. Searle’s longstanding ignorance of Chinese is simply irrelevant and always has been. I propose a truce and a plan for further exploration

Abstract: The Turing Test (TT), as originally specified, centres on theability to perform a social role. The TT can be seen as a test of anability to enter into normal human social dynamics. In this light itseems unlikely that such an entity can be wholly designed in anoff-line mode; rather a considerable period of training insitu would be required. The argument that since we can pass the TT,and our cognitive processes might be implemented as a Turing Machine(TM), that consequently a TM that could pass the TT could be built, isattacked on the grounds that not all TMs are constructible in a plannedway. This observation points towards the importance of developmentalprocesses that use random elements (e.g., evolution), but in these casesit becomes problematic to call the result artificial. This hasimplications for the means by which intelligent agents could bedeveloped

Abstract: The Turing Test, as originally specified, centres on the ability to perform a social role. The TT can seen as a test of an ability to enter into normal human social dynamics. In this light it seems unlikely that such an entity can be wholly designed in an `off-line' mode, but rather a considerable period of training in situ would be required. The argument that since we can pass the TT and our cognitive processes might be implemented as a TM that, in theory, an TM that could pass the TT could be built is attacked on the grounds that not all TMs are constructable in a planned way. This observation points towards the importance of developmental processes that include random elements (e.g. evolution), but in these cases it becomes problematic to call the result artificial

Abstract:   In Part V of his Discourse on the Method, Descartes introduces a test for distinguishing people from machines that is similar to the one proposed much later by Alan Turing. The Cartesian test combines two distinct elements that Keith Gunderson has labeled the language test and the action test. Though traditional interpretation holds that the action test attempts to determine whether an agent is acting upon principles, I argue that the action test is best understood as a test of common sense. I also maintain that this interpretation yields a stronger test than Turing's, and that contemporary artificial intelligence should consider using it as a guide for future research

Abstract: This paper has three goals. The first is to introduce the “knowledge game”, a new, simple and yet powerful tool for analysing some intriguing philosophical questions. The second is to apply the knowledge game as an informative test to discriminate between conscious (human) and conscious-less agents (zombies and robots), depending on which version of the game they can win. And the third is to use a version of the knowledge game to provide an answer to Dretske’s question “how do you know you are not a zombie?”

Abstract: An evaluation of the 2008 Loebner contest

Abstract: An Evaluation of the 2008 Loebner Contest.

Abstract: No computer that had not experienced the world as we humans had could pass a rigorously administered standard Turing Test. We show that the use of “subcognitive” questions allows the standard Turing Test to indirectly probe the human subcognitive associative concept network built up over a lifetime of experience with the world. Not only can this probing reveal differences in cognitive abilities, but crucially, even differences in _physical aspects_ of the candidates can be detected. Consequently, it is unnecessary to propose even harder versions of the Test in which all physical and behavioral aspects of the two candidates had to be indistinguishable before allowing the machine to pass the Test. Any machine that passed the “simpler” symbols- in/symbols-out test as originally proposed by Turing would be intelligent. The problem is that, even in its original form, the Turing Test is already too hard and too anthropocentric for any machine that was not a physical, social, and behavioral carbon copy of ourselves to actually pass it. Consequently, the Turing Test, even in its standard version, is not a reasonable test for general machine intelligence. There is no need for an even stronger version of the Test

Abstract: This commentary attempts to show that the inverted Turing Test (Watt 1996) could be simulated by a standard Turing test and, most importantly, claims that a very simple program with no intelligence whatsoever could be written that would pass the inverted Turing test. For this reason, the inverted Turing test in its present form must be rejected

Abstract: The Turing Test, originally proposed as a simple operational definition of intelligence, has now been with us for exactly half a century. It is safe to say that no other single article in computer science, and few other articles in science in general, have generated so much discussion. The present article chronicles the comments and controversy surrounding Turing's classic article from its publication to the present. The changing perception of the Turing Test over the last fifty years has paralleled the changing attitudes in the scientific community towards artificial intelligence: from the unbridled optimism of 1960's to the current realization of the immense difficulties that still lie ahead. I conclude with the prediction that the Turing Test will remain important, not only as a landmark in the history of the development of intelligent machines, but also with real relevance to future generations of people living in a world in which the cognitive capacities of machines will be vastly greater than they are now

Abstract: Some of the papers in this special issue distribute cognition between what is going on inside individual cognizers' heads and their outside worlds; others distribute cognition among different individual cognizers. Turing's criterion for cognition was individual, autonomous input/output capacity. It is not clear that distributed cognition could pass the Turing Test

Abstract: Cognitive science is a form of "reverse engineering" (as Dennett has dubbed it). We are trying to explain the mind by building (or explaining the functional principles of) systems that have minds. A "Turing" hierarchy of empirical constraints can be applied to this task, from t1, toy models that capture only an arbitrary fragment of our performance capacity, to T2, the standard "pen-pal" Turing Test (total symbolic capacity), to T3, the Total Turing Test (total symbolic plus robotic capacity), to T4 (T3 plus internal [neuromolecular] indistinguishability). All scientific theories are underdetermined by data. What is the right level of empirical constraint for cognitive theory? I will argue that T2 is underconstrained (because of the Symbol Grounding Problem and Searle's Chinese Room Argument) and that T4 is overconstrained (because we don't know what neural data, if any, are relevant). T3 is the level at which we solve the "other minds" problem in everyday life, the one at which evolution operates (the Blind Watchmaker is no mind-reader either) and the one at which symbol systems can be grounded in the robotic capacity to name and manipulate the objects their symbols are about. I will illustrate this with a toy model for an important component of T3 -- categorization -- using neural nets that learn category invariance by "warping" similarity space the way it is warped in human categorical perception: within-category similarities are amplified and between-category similarities are attenuated. This analog "shape" constraint is the grounding inherited by the arbitrarily shaped symbol that names the category and by all the symbol combinations it enters into. No matter how tightly one constrains any such model, however, it will always be more underdetermined than normal scientific and engineering theory. This will remain the ineliminable legacy of the mind/body problem

Abstract: Both Artificial Life and Artificial Mind are branches of what Dennett has called "reverse engineering": Ordinary engineering attempts to build systems to meet certain functional specifications, reverse bioengineering attempts to understand how systems that have already been built by the Blind Watchmaker work. Computational modelling (virtual life) can capture the formal principles of life, perhaps predict and explain it completely, but it can no more be alive than a virtual forest fire can be hot. In itself, a computational model is just an ungrounded symbol system; no matter how closely it matches the properties of what is being modelled, it matches them only formally, with the mediation of an interpretation. Synthetic life is not open to this objection, but it is still an open question how close a functional equivalence is needed in order to capture life. Close enough to fool the Blind Watchmaker is probably close enough, but would that require molecular indistinguishability, and if so, do we really need to go that far?

Abstract:   Any attempt to explain the mind by building machines with minds must confront the other-minds problem: How can we tell whether any body other than our own has a mind when the only way to know is by being the other body? In practice we all use some form of Turing Test: If it can do everything a body with a mind can do such that we can't tell them apart, we have no basis for doubting it has a mind. But what is everything a body with a mind can do? Turing's original pen-pal version of the Turing Test (the TT) only tested linguistic capacity, but Searle has shown that a mindless symbol-manipulator could pass the TT undetected. The Total Turing Test (TTT) calls instead for all of our linguistic and robotic capacities; immune to Searle's argument, it suggests how to ground a symbol manipulating system in the capacity to pick out the objects its symbols refer to. No Turing Test, however, can guarantee that a body has a mind. Worse, nothing in the explanation of its successful performance requires a model to have a mind at all. Minds are hence very different from the unobservables of physics (e.g., superstrings); and Turing Testing, though essential for machine-modeling the mind, can really only yield an explanation of the body

Abstract: This quote/commented critique of Turing's classical paper suggests that Turing meant -- or should have meant -- the robotic version of the Turing Test (and not just the email version). Moreover, any dynamic system (that we design and understand) can be a candidate, not just a computational one. Turing also dismisses the other-minds problem and the mind/body problem too quickly. They are at the heart of both the problem he is addressing and the solution he is proposing

Abstract: This quote/commented critique of Turing's classical paper suggests that Turing meant -- or should have meant -- the robotic version of the Turing Test (and not just the email version). Moreover, any dynamic system (that we design and understand) can be a candidate, not just a computational one. Turing also dismisses the other-minds problem and the mind/body problem too quickly. They are at the heart of both the problem he is addressing and the solution he is proposing

Abstract: It is important to understand that the Turing Test (TT) is not, nor was it intended to be, a trick; how well one can fool someone is not a measure of scientific progress. The TT is an empirical criterion: It sets AI's empirical goal to be to generate human-scale performance capacity. This goal will be met when the candidate's performance is totally indistinguishable from a human's. Until then, the TT simply represents what it is that AI must endeavor eventually to accomplish scientifically

Abstract:   The abject failure of Turing's first prediction (of computer success in playing the Imitation Game) confirms the aptness of the Imitation Game test as a test of human level intelligence. It especially belies fears that the test is too easy. At the same time, this failure disconfirms expectations that human level artificial intelligence will be forthcoming any time soon. On the other hand, the success of Turing's second prediction (that acknowledgment of computer thought processes would become commonplace) in practice amply confirms the thought that computers think in some manner and are possessed of some level of intelligence already. This lends ever-growing support to the hypothesis that computers will think at a human level eventually, despite the abject failure of Turing's first prediction

Abstract:   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

Abstract: The main factor of intelligence is defined as the ability tocomprehend, formalising this ability with the help of new constructsbased on descriptional complexity. The result is a comprehension test,or C-test, which is exclusively defined in computational terms. Due toits absolute and non-anthropomorphic character, it is equally applicableto both humans and non-humans. Moreover, it correlates with classicalpsychometric tests, thus establishing the first firm connection betweeninformation theoretical notions and traditional IQ tests. The TuringTest is compared with the C-test and the combination of the two isquestioned. In consequence, the idea of using the Turing Test as apractical test of intelligence should be surpassed, and substituted bycomputational and factorial tests of different cognitive abilities, amuch more useful approach for artificial intelligence progress and formany other intriguing questions that present themselves beyond theTuring Test

Abstract: As is well known, Alan Turing drew a line, embodied in the "Turing test," between intellectual and physical abilities, and hence between cognitive and natural sciences. Less familiarly, he proposed that one way to produce a "passer" would be to educate a "child machine," equating the experimenter's improvements in the initial structure of the child machine with genetic mutations, while supposing that the experimenter might achieve improvements more expeditiously than natural selection. On the other hand, in his foundational "On the chemical basis of morphogenesis," Turing insisted that biological explanation clearly confine itself to purely physical and chemical means, eschewing vitalist and teleological talk entirely and hewing to D'Arcy Thompson's line that "evolutionary 'explanations,'" are historical and narrative in character, employing the same intentional and teleological vocabulary we use in doing human history, and hence, while perhaps on occasion of heuristic value, are not part of biology as a natural science. To apply Turing's program to recent issues, the attempt to give foundations to the social and cognitive sciences in the "real science" of evolutionary biology (as opposed to Turing's biology) is neither to give foundations, nor to achieve the unification of the social/cognitive sciences and the natural sciences

Abstract: A. M. Turing has bequeathed us a conceptulary including 'Turing, or Turing-Church, thesis', 'Turing machine', 'universal Turing machine', 'Turing test' and 'Turing structures', plus other unnamed achievements. These include a proof that any formal language adequate to express arithmetic contains undecidable formulas, as well as achievements in computer science, artificial intelligence, mathematics, biology, and cognitive science. Here it is argued that these achievements hang together and have prospered well in the 50 years since Turing's death

Abstract: The Turing Test blurs the distinction between a model and irrelevant) instantiation details. Modeling only functional modules is problematic if these are interconnected and cognitively penetrable

Abstract: Heyes's literature review of deception, imitation, and self-recognition is inadequate, misleading, and erroneous. The anaesthetic artifact hypothesis of self-recognition is unsupported by the data she herself examines. Her proposed experiment is tantalizing, indicating that theory of mind is simply a Turing test

Abstract:   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 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

Abstract: For the last 25 years discussions and debates about commonsense psychology (or “folk psychology,” as it is often called) have been center stage in the philosophy of mind. There have been heated disagreements both about what folk psychology is and about how it is related to the scientific understanding of the mind/brain that is emerging in psychology and the neurosciences. In this chapter we will begin by explaining why folk psychology plays such an important role in the philosophy of mind. Doing that will require a quick look at a bit of the history of philosophical discussions about the mind. We’ll then turn our attention to the lively contemporary discussions aimed at clarifying the philosophical role that folk psychology is expected to play and at using findings in the cognitive sciences to get a clearer understanding of the exact nature of folk psychology

Abstract:   In the 1950s, Alan Turing proposed his influential test for machine intelligence, which involved a teletyped dialogue between a human player, a machine, and an interrogator. Two readings of Turing''s rules for the test have been given. According to the standard reading of Turing''s words, the goal of the interrogator was to discover which was the human being and which was the machine, while the goal of the machine was to be indistinguishable from a human being. According to the literal reading, the goal of the machine was to simulate a man imitating a woman, while the interrogator – unaware of the real purpose of the test – was attempting to determine which of the two contestants was the woman and which was the man. The present work offers a study of Turing''s rules for the test in the context of his advocated purpose and his other texts. The conclusion is that there are several independent and mutually reinforcing lines of evidence that support the standard reading, while fitting the literal reading in Turing''s work faces severe interpretative difficulties. So, the controversy over Turing''s rules should be settled in favor of the standard reading

Abstract: I advocate a theory of syntactic semantics as a way of understanding how computers can think (and how the Chinese-Room-Argument objection to the Turing Test can be overcome): (1) Semantics, considered as the study of relations between symbols and meanings, can be turned into syntax – a study of relations among symbols (including meanings) – and hence syntax (i.e., symbol manipulation) can suffice for the semantical enterprise (contra Searle). (2) Semantics, considered as the process of understanding one domain (by modeling it) in terms of another, can be viewed recursively: The base case of semantic understanding –understanding a domain in terms of itself – is syntactic understanding. (3) An internal (or narrow), first-person point of view makes an external (or wide), third-person point of view otiose for purposes of understanding cognition

Abstract: Stuart M. Shieber’s name is well known to computational linguists for his research and to computer scientists more generally for his debate on the Loebner Turing Test competition, which appeared a decade earlier in Communications of the ACM (Shieber 1994a, 1994b; Loebner 1994).¹ With this collection, I expect it to become equally well known to philosophers

Abstract: Many philosophers and cognitive scientists claim that our everyday or "folk" understanding of mental states constitutes a theory of mind. That theory is widely called "folk psychology" (sometimes "commonsense" psychology). The terms in which folk psychology is couched are the familiar ones of "belief" and "desire", "hunger", "pain" and so forth. According to many theorists, folk psychology plays a central role in our capacity to predict and explain the behavior of ourselves and others. However, the nature and status of folk psychology remains controversial

Abstract: In this paper, I argue that certain Machines can have rights independently of whether they are sentient, or conscious, or whatever you might call it.

Abstract:   This paper considers undecidability in the imitation game, the so-called Turing Test. In the Turing Test, a human, a machine, and an interrogator are the players of the game. In our model of the Turing Test, the machine and the interrogator are formalized as Turing machines, allowing us to derive several impossibility results concerning the capabilities of the interrogator. The key issue is that the validity of the Turing test is not attributed to the capability of human or machine, but rather to the capability of the interrogator. In particular, it is shown that no Turing machine can be a perfect interrogator. We also discuss meta-imitation game and imitation game with analog interfaces where both the imitator and the interrogator are mimicked by continuous dynamical systems

Abstract:   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

Abstract: 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. Philo- sophical 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

Abstract:   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 (TT). I consider Harnad's Total Turing Test (TTT), 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

Abstract: This paper presents an analysis of three major contests for machine intelligence. We conclude that a new era for Turing’s test requires a fillip in the guise of a committed sponsor, not unlike DARPA, funders of the successful 2007 Urban Challenge.

Abstract: Stuart M. Shieber’s name is well known to computational linguists for his research and to computer scientists more generally for his debate on the Loebner Turing Test competition, which appeared a decade earlier in Communications of the ACM (Shieber 1994a, 1994b; Loebner 1994).¹ With this collection, I expect it to become equally well known to philosophers

Abstract:   On a literal reading of `Computing Machinery and Intelligence'', Alan Turing presented not one, but two, practical tests to replace the question `Can machines think?'' He presented them as equivalent. I show here that the first test described in that much-discussed paper is in fact not equivalent to the second one, which has since become known as `the Turing Test''. The two tests can yield different results; it is the first, neglected test that provides the more appropriate indication of intelligence. This is because the features of intelligence upon which it relies are resourcefulness and a critical attitude to one''s habitual responses; thus the test''s applicablity is not restricted to any particular species, nor does it presume any particular capacities. This is more appropriate because the question under consideration is what would count as machine intelligence. The first test realizes a possibility that philosophers have overlooked: a test that uses a human''s linguistic performance in setting an empirical test of intelligence, but does not make behavioral similarity to that performance the criterion of intelligence. Consequently, the first test is immune to many of the philosophical criticisms on the basis of which the (so-called) `Turing Test'' has been dismissed

Abstract: Under general assumptions, the Turing test can be easily passed by an appropriate algorithm. I show that for any test satisfying several general conditions, we can construct an algorithm that can pass that test, hence, any operational definition is easy to fulfill. I suggest a test complementary to Turing's test, which will measure our understanding of the human mind. The Turing test is required to fix the operational specifications of the algorithm under test; under this constrain, the additional test simply consists in measuring the length of the algorithm

Abstract:   The test Turing proposed for machine intelligence is usually understood to be a test of whether a computer can fool a human into thinking that the computer is a human. This standard interpretation is rejected in favor of a test based on the Imitation Game introduced by Turing at the beginning of "Computing Machinery and Intelligence."

Abstract: Robert French has argued that a disembodied computer is incapable of passing a Turing Test that includes subcognitive questions. Subcognitive questions are designed to probe the network of cultural and perceptual associations that humans naturally develop as we live, embodied and embedded in the world. In this paper, I show how it is possible for a disembodied computer to answer subcognitive questions appropriately, contrary to French’s claim. My approach to answering subcognitive questions is to use statistical information extracted from a very large collection of text. In particular, I show how it is possible to answer a sample of subcognitive questions taken from French, by issuing queries to a search engine that indexes about 350 million Web pages. This simple algorithm may shed light on the nature of human (sub-) cognition, but the scope of this paper is limited to demonstrating that French is mistaken: a disembodied computer can answer subcognitive questions

Abstract: I propose to consider the question, "Can machines think?" This should begin with definitions of the meaning of the terms "machine" and "think." The definitions might be framed so as to reflect so far as possible the normal use of the words, but this attitude is dangerous, If the meaning of the words "machine" and "think" are to be found by examining how they are commonly used it is difficult to escape the conclusion that the meaning and the answer to the question, "Can machines think?" is to be sought in a statistical survey such as a Gallup poll. But this is absurd. Instead of attempting such a definition I shall replace the question by another, which is closely related to it and is expressed in relatively unambiguous words. The new form of the problem can be described in terms of a game which we call the 'imitation game." It is played with three people, a man (A), a woman (B), and an interrogator (C) who may be of either sex. The interrogator stays in a room apart front the other two. The object of the game for the interrogator is to determine which of the other two is the man and which is the woman. He knows them by labels X and Y, and at the end of the game he says either "X is A and Y is B" or "X is B and Y is A." The interrogator is allowed to put questions to A and B. We now ask the question, "What will happen when a machine takes the part of A in this game?" Will the interrogator decide wrongly as often when the game is played like this as he does when the game is played between a man and a woman? These questions replace our original, "Can machines think?"

Abstract: This target article argues that the Turing test implicitly rests on a "naive psychology," a naturally evolved psychological faculty which is used to predict and understand the behaviour of others in complex societies. This natural faculty is an important and implicit bias in the observer's tendency to ascribe mentality to the system in the test. The paper analyses the effects of this naive psychology on the Turing test, both from the side of the system and the side of the observer, and then proposes and justifies an inverted version of the test which allows the processes of ascription to be analysed more directly than in the standard version

Abstract:   This paper argues that the Turing test is based on a fixed and de-contextualized view of communicative competence. According to this view, a machine that passes the test will be able to communicate effectively in a variety of other situations. But the de-contextualized view ignores the relationship between language and social context, or, to put it another way, the extent to which speakers respond dynamically to variations in discourse function, formality level, social distance/solidarity among participants, and participants' relative degrees of power and status (Holmes, 1992). In the case of the Loebner Contest, a present day version of the Turing test, the social context of interaction can be interpreted in conflicting ways. For example, Loebner discourse is defined 1) as a friendly, casual conversation between two strangers of equal power, and 2) as a one-way transaction in which judges control the conversational floor in an attempt to expose contestants that are not human. This conflict in discourse function is irrelevant so long as the goal of the contest is to ensure that only thinking, human entities pass the test. But if the function of Loebner discourse is to encourage the production of software that can pass for human on the level of conversational ability, then the contest designers need to resolve this ambiguity in discourse function, and thus also come to terms with the kind of competence they are trying to measure

Abstract: In his stimulating book SHADOWS OF THE MIND, Roger Penrose presents arguments, based on Gödel's theorem, for the conclusion that human thought is uncomputable. There are actually two separate arguments in Penrose's book. The second has been widely ignored, but seems to me to be much more interesting and novel than the first. I will address both forms of the argument in some detail. Toward the end, I will also comment on Penrose's proposals for a "new science of consciousness"

Abstract: In his PhD thesis (1938) Turing introduced what he described as 'a new kind of machine'. He called these 'O-machines'. The present paper employs Turing's concept against a number of currently fashionable positions in the philosophy of mind

Abstract: The idea that a computer could be conscious--or equivalently, that human consciousness is the effect of some complex computation mechanically performed by our brains--strikes some scientists and philosophers as a beautiful idea. They find it initially surprising and unsettling, as all beautiful ideas are, but the inevitable culmination of the scientific advances that have gradually demystified and unified the material world. The ideologues of Artificial Intelligence (AI) have been its most articulate supporters. To others, this idea is deeply repellent: philistine, reductionistic (in some bad sense), as incredible as it is offensive. John Searle's attack on "strong AI" is the best known expression of this view, but others in the same camp, liking Searle's destination better than his route, would dearly love to see a principled, scientific argument showing that strong AI is impossible. Roger Penrose has set out to provide just such an argument

Abstract:   Gödel's Theorem is often used in arguments against machine intelligence, suggesting humans are not bound by the rules of any formal system. However, Gödelian arguments can be used to support AI, provided we extend our notion of computation to include devices incorporating random number generators. A complete description scheme can be given for integer functions, by which nonalgorithmic functions are shown to be partly random. Not being restricted to algorithms can be accounted for by the availability of an arbitrary random function. Humans, then, might not be rule-bound, but Gödelian arguments also suggest how the relevant sort of nonalgorithmicity may be trivially made available to machines

Abstract: In his book Shadows of the Mind: A search for the missing science of con- sciousness [SM below], Roger Penrose has turned in another bravura perfor- mance, the kind we have come to expect ever since The Emperor’s New Mind [ENM ] appeared. In the service of advancing his deep convictions and daring conjectures about the nature of human thought and consciousness, Penrose has once more drawn a wide swath through such topics as logic, computa- tion, artificial intelligence, quantum physics and the neuro-physiology of the brain, and has produced along the way many gems of exposition of difficult mathematical and scientific ideas, without condescension, yet which should be broadly appealing.¹ While the aims and a number of the topics in SM are the same as in ENM , the focus now is much more on the two axes that Pen- rose grinds in earnest. Namely, in the first part of SM he argues anew and at great length against computational models of the mind and more specifi- cally against any account of mathematical thought in computational terms. Then in the second part, he argues that there must be a scientific account of consciousness but that will require a (still to be found) non-computational extension or modification of present-day quantum physics

Abstract: In a recent paper S. McCall adds another link to a chain of attempts to enlist Gödel’s incompleteness result as an argument for the thesis that human reasoning cannot be construed as being carried out by a computer.1 McCall’s paper is undermined by a technical oversight. My concern however is not with the technical point. The argument from Gödel’s result to the no-computer thesis can be made without following McCall’s route; it is then straighter and more forceful. Yet the argument fails in an interesting and revealing way. And it leaves a remainder: if some computer does in fact simulate all our mathematical reasoning, then, in principle, we cannot fully grasp how it works. Gödel’s result also points out a certain essential limitation of self-reflection. The resulting picture parallels, not accidentally, Davidson’s view of psychology, as a science that in principle must remain “imprecise”, not fully spelt out. What is intended here by “fully grasp”, and how all this is related to self-reflection, will become clear at the end of this comment

Abstract: I will present a conceptual argument for a simulationist answer to (2). Given that our conception of mental states is employed in attributing mental states to others, a simulationist answer to (2) supports a simulationist answer to (1). I will not address question (3). Answers to (1) and (2) do not yield an answer to (3), since (1) and (2) concern only our actual practices and concepts. For instance, an error theory about (1) and (2) would say that our practices and concepts manifest a mistaken view about the real nature of the mental. Finally, I will not address question (2a), which is an empirical question and so is not immediately relevant to the conceptual argument that is of concern here

Abstract: by, or is otherwise relevant to the seminar "Folk Psychology vs. Mental Simulation: How Minds Understand Minds," a National

Abstract: Using the Gödel Incompleteness Result for leverage, Roger Penrose has argued that the mechanism for consciousness involves quantum gravitational phenomena, acting through microtubules in neurons. We show that this hypothesis is implausible. First, the Gödel Result does not imply that human thought is in fact non algorithmic. Second, whether or not non algorithmic quantum gravitational phenomena actually exist, and if they did how that could conceivably implicate microtubules, and if microtubules were involved, how that could conceivably implicate consciousness, is entirely speculative. Third, cytoplasmic ions such as calcium and sodium are almost certainly present in the microtubule pore, barring the quantum mechanical effects Penrose envisages. Finally, physiological evidence indicates that consciousness does not directly depend on microtubule properties in any case, rendering doubtful any theory according to which consciousness is generated in the microtubules

Abstract:   In this paper I discuss the topics of mechanism and algorithmicity. I emphasise that a characterisation of algorithmicity such as the Turing machine is iterative; and I argue that if the human mind can solve problems that no Turing machine can, the mind must depend on some non-iterative principle — in fact, Cantor's second principle of generation, a principle of the actual infinite rather than the potential infinite of Turing machines. But as there has been theorisation that all physical systems can be represented by Turing machines, I investigate claims that seem to contradict this: specifically, claims that there are noncomputable phenomena. One conclusion I reach is that if it is believed that the human mind is more than a Turing machine, a belief in a kind of Cartesian dualist gulf between the mental and the physical is concomitant

Abstract: In the last few years, off-line simulation has become an increasingly important alternative to standard explanations in cognitive science. The contemporary debate began with Gordon (1986) and Goldman's (1989) off-line simulation account of our capacity to predict behavior. On their view, in predicting people's behavior we take our own decision making system `off line' and supply it with the `pretend' beliefs and desires of the person whose behavior we are trying to predict; we then let the decision maker reach a decision on the basis of these pretend inputs. Figure 1 offers a `boxological' version of the off-line simulation theory of behavior prediction.(1)

Abstract: It is commonly agreed that the well-known Lucas–Penrose arguments and even Penrose’s ‘new argument’ in [Penrose, R. (1994): Shadows of the Mind, Oxford University Press] are inconclusive. It is, perhaps, less clear exactly why at least the latter is inconclusive. This note continues the discussion in [Lindström, P. (2001): Penrose’s new argument, J. Philos. Logic 30, 241–250; Shapiro, S.(2003): Mechanism, truth, and Penrose’s new argument, J. Philos. Logic 32, 19–42] and elsewhere of this question

Abstract: We can imagine a human operator playing a game of one-upmanship against a programmed computer. If the program is Fn, the human operator can print the theorem Gn, which the programmed computer, or, if you prefer, the program, would never print, if it is consistent. This is true for each whole number n, but the victory is a hollow one since a second computer, loaded with program C, could put the human operator out of a job.... It is useless for the `mentalist' to argue that any given program can always be improves since the process for improving programs can presumably be programmed also; certainly this can be done if the mentalist describes how the improvement is to be made. If he does give such a description, then he has not made a case

Abstract: Goedel's theorem states that in any consistent system which is strong enough to produce simple arithmetic there are formulae which cannot be proved-in-the-system, but which we can see to be true. Essentially, we consider the formula which says, in effect, "This formula is unprovable-in-the-system". If this formula were provable-in-the-system, we should have a contradiction: for if it were provablein-the-system, then it would not be unprovable-in-the-system, so that "This formula is unprovable-in-the-system" would be false: equally, if it were provable-in-the-system, then it would not be false, but would be true, since in any consistent system nothing false can be provedin-the-system, but only truths. So the formula "This formula is unprovable-in-the-system" is not provable-in-the-system, but unprovablein-the-system. Further, if the formula "This formula is unprovablein- the-system" is unprovable-in-the-system, then it is true that that formula is unprovable-in-the-system, that is, "This formula is unprovable-in-the-system" is true. Goedel's theorem must apply to cybernetical machines, because it is of the essence of being a machine, that it should be a concrete instantiation of a formal system. It follows that given any machine which is consistent and capable of doing simple arithmetic, there is a formula which it is incapable of producing as being true---i.e., the formula is unprovable-in-the-system-but which we can see to be true. It follows that no machine can be a complete or adequate model of the mind, that minds are essentially different from machines

Abstract: The argument is a dialectical one. It is not a direct proof that the mind is something more than a machine, but a schema of disproof for any particular version of mechanism that may be put forward. If the mechanist maintains any specific thesis, I show that [146] a contradiction ensues. But only if. It depends on the mechanist making the first move and putting forward his claim for inspection. I do not think Benacerraf has quite taken the point. He criticizes me both for "failing to notice" that my ability to show that the Gödel sentence of a formal system is true "depends very much on how he is given

Abstract: The views of Redhead ([2004]) are defended against the argument by Panu Raatikainen ([2005]). The importance of informal rigour is canvassed, and the argument for the a priori nature of induction is explained. The significance of Gödel's theorem is again rehearsed

Abstract: It might be the case that absence of constraint is the relevant sense of ' freedom' when we are discussing the freedom of the will, but it needs arguing for. ...

Abstract: I have no quarrel with the first two sentences: but the third, though charitable and courteous, is quite untrue. Although there are criticisms which can be levelled against the Gödelian argument, most of the critics have not read either of my, or either of Penrose's, expositions carefully, and seek to refute arguments we never put forward, or else propose as a fatal objection one that had already been considered and countered in our expositions of the argument. Hence my title. The Gödelian Argument uses Gödel's theorem to show that minds cannot be explained in purely mechanist terms. It has been put forward, in different forms, by Gödel himself, by Penrose, and by me

Abstract: In 1931 Kurt Gödel proved two theorems about the completeness and consistency of first-order arithmetic. Their implications for philosophy are profound. Many fashionable tenets are shown to be untenable: many traditional intuitions are vindicated by incontrovertible arguments

Abstract: In this paper we interpret a characterization of the Gödel speed-up phenomenon as providing support for the ‘Nagel-Newman thesis’ that human theorem recognizers differ from mechanical theorem recognizers in that the former do not seem to be limited by Gödel's incompleteness theorems whereas the latter do seem to be thus limited. However, we also maintain that (currently non-existent) programs which are open systems in that they continuously interact with, and are thus inseparable from, their environment, are not covered by the above (or probably any other recursion-theoretic) argument

Abstract: This paper concerns Alan Turing’s ideas about machines, mathematical methods of proof, and intelligence. By the late 1930s, Kurt Gödel and other logicians, including Turing himself, had shown that no finite set of rules could be used to generate all true mathematical statements. Yet according to Turing, there was no upper bound to the number of mathematical truths provable by intelligent human beings, for they could invent new rules and methods of proof. So, the output of a human mathematician, for Turing, was not a computable sequence (i.e., one that could be generated by a Turing machine). Since computers only contained a finite number of instructions (or programs), one might argue, they could not reproduce human intelligence. Turing called this the “mathematical
objection” to his view that machines can think. Logico-mathematical reasons, stemming from his own work, helped to convince Turing that it should be possible to reproduce human intelligence, and eventually compete with it, by developing the appropriate kind of digital computer. He felt it
should be possible to program a computer so that it could learn or discover new rules, overcoming the limitations imposed by the incompleteness and undecidability results in the same way that human
mathematicians presumably do.

Abstract: Storrs McCall continues the tradition of Lucas and Penrose in an attempt to refute mechanism by appealing to Gödel’s incompleteness theorem (McCall 2001). That is, McCall argues that Gödel’s theorem “reveals a sharp dividing line between human and machine thinking”. According to McCall, “[h]uman beings are familiar with the distinction between truth and theoremhood, but Turing machines cannot look beyond their own output”. However, although McCall’s argumentation is slightly more sophisticated than the earlier Gödelian anti-mechanist arguments, in the end it fails badly, as it is at odds with the logical facts

Abstract: Gödel began his 1951 Gibbs Lecture by stating: “Research in the foundations of mathematics during the past few decades has produced some results which seem to me of interest, not only in themselves, but also with regard to their implications for the traditional philosophical problems about the nature of mathematics.” (Gödel 1951) Gödel is referring here especially to his own incompleteness theorems (Gödel 1931). Gödel’s first incompleteness theorem (as improved by Rosser (1936)) says that for any consistent formalized system F, which contains elementary arithmetic, there exists a sentence GF of the language of the system which is true but unprovable in that system. Gödel’s second incompleteness theorem states that no consistent formal system can prove its own consistency

Abstract: Michael Redhead's recent argument aiming to show that humanly certifiable truth outruns provability is critically evaluated. It is argued that the argument is at odds with logical facts and fails

Abstract: Lucas and Redhead ([2007]) announce that they will defend the views of Redhead ([2004]) against the argument by Panu Raatikainen ([2005]). They certainly re-state the main claims of Redhead ([2004]), but they do not give any real arguments in their favour, and do not provide anything that would save Redhead’s argument from the serious problems pointed out in (Raatikainen [2005]). Instead, Lucas and Redhead make a number of seemingly irrelevant points, perhaps indicating a failure to understand the logico-mathematical points at issue

Abstract: Granted that truth is valuable we must recognize that certifiable truth is hard to come by, for example in the natural and social sciences. This paper examines the case of mathematics. As a result of the work of Gödel and Tarski we know that truth does not equate with proof. This has been used by Lucas and Penrose to argue that human minds can do things which digital computers can't, viz to know the truth of unprovable arithmetical statements. The argument is given a simple formulation in the context of sorites (Robinson) arithmetic, avoiding the complexities of formulating the Gödel sentence. The pros and cons of the argument are considered in relation to the conception of mathematical truth. * Paper contributed to the Conference entitled The Place of Value in a World of Facts, held at the LSE in October 2003

Abstract: Roger Penrose is justly famous for his work in physics and mathematics but he is _notorious_ for his endorsement of the Gödel argument (see his 1989, 1994, 1997). This argument, first advanced by J. R. Lucas (in 1961), attempts to show that Gödel’s (first) incompleteness theorem can be seen to reveal that the human mind transcends all algorithmic models of it¹. Penrose's version of the argument has been seen to fall victim to the original objections raised against Lucas (see Boolos (1990) and for a particularly intemperate review, Putnam (1994)). Yet I believe that more can and should be said about the argument. Only a brief review is necessary here although I wish to present the argument in a somewhat peculiar form

Abstract: This paper is about the contemporary debate concerning folk psychology – the debate between the proponents of the theory theory of folk psychology and the friends of the simulation alternative.¹ At the outset, we need to ask: What should we mean by this term ‘folk psychology’?

Abstract:   G. Priest's anti-consistency argument (Priest 1979, 1984, 1987) and J. R. Lucas's anti-mechanist argument (Lucas 1961, 1968, 1970, 1984) both appeal to Gödel incompleteness. By way of refuting them, this paper defends the thesis of quartet compatibility, viz., that the logic of the mind can simultaneously be Gödel incomplete, consistent, mechanical, and recursion complete (capable of all means of recursion). A representational approach is pursued, which owes its origin to works by, among others, J. Myhill (1964), P. Benacerraf (1967), J. Webb (1980, 1983) and M. Arbib (1987). It is shown that the fallacy shared by the two arguments under discussion lies in misidentifying two systems, the one for which the Gödel sentence is constructable and to be proved, and the other in which the Gödel sentence in question is indeed provable. It follows that the logic of the mind can surpass its own Gödelian limitation not by being inconsistent or non-mechanistic, but by being capable of representing stronger systems in itself; and so can a proper machine. The concepts of representational provability, representational maximality, formal system capacity, etc., are discussed

Abstract: I argue that the behavior of other agents is insufficiently described in current debates as a dichotomy between tacit theory (attributing beliefs and desires to predict behavior) and simulation theory (imagining what one would do in similar circumstances in order to predict behavior). I introduce two questions about the foundation and development of our ability both to attribute belief and to simulate it. I then propose that there is one additional method used to predict behavior, namely, an inductive strategy

Abstract: When philosophers think about mental phenomena, they focus on several features of human experience: (1) the existence of consciousness, (2) the intentionality of mental states, that property by which beliefs, desires, anger, etc. are directed at, are about, or refer to objects and states of affairs, (3) subjectivity, characterized by my feeling my pains but not yours, by my experiencing the world and myself from my point of view and not yours, (4) mental causation, that thoughts and feelings have physical effects on the world: I decide to raise my arm and my arm rises. In a world described by theories of physics and chemistry, what place in that physical description do descriptions of the mental have?

Abstract:   Searle's Chinese Room was supposed to prove that computers can't understand: the man in the room, following, like a computer, syntactical rules alone, though indistinguishable from a genuine Chinese speaker, doesn't understand a word. But such a room is impossible: the man won't be able to respond correctly to questions like What is the time?, even though such an ability is indispensable for a genuine Chinese speaker. Several ways to provide the room with the required ability are considered, and it is concluded that for each of these the room will have understanding. Hence, Searle's argument is invalid

Abstract: Whether human thinking can be formalized and whether machines can think in a human sense are questions that have been addressed by both Peirce and Searle. Peirce came to roughly the same conclusion as Searle, that the digital computer would not be able to perform human thinking or possess human understanding. However, his rationale and Searle's differ on several important points. Searle approaches the problem from the standpoint of traditional analytic philosophy, where the strict separation of syntax and semantics renders understanding impossible for a purely syntactical device. Peirce disagreed with that analysis, but argued that the computer would only be able to achieve algorithmic thinking, which he considered the simplest type. Although their approaches were radically dissimilar, their conclusions were not. I will compare and analyze the arguments of both Peirce and Searle on this issue, and outline some implications of their conclusions for the field of Artificial Intelligence

Abstract: More than a decade ago, philosopher John Searle started a long-running controversy with his paper “Minds, Brains, and Programs” (Searle, 1980a), an attack on the ambitious claims of artificial intelligence (AI). With his now famous _Chinese Room_ argument, Searle claimed to show that despite the best efforts of AI researchers, a computer could never recreate such vital properties of human mentality as intentionality, subjectivity, and understanding. The AI research program is based on the underlying assumption that all important aspects of human cognition may in principle be captured in a computational model. This assumption stems from the belief that beyond a certain level, implementational details are irrelevant to cognition. According to this belief, neurons, and biological wetware in general, have no preferred status as the substrate for a mind. As it happens, the best examples of minds we have at present have arisen from a carbon-based substrate, but this is due to constraints of evolution and possibly historical accidents, rather than to an absolute metaphysical necessity. As a result of this belief, many cognitive scientists have chosen to focus not on the biological substrate of the mind, but instead on the abstract causal structure_ _that the mind embodies (at an appropriate level of abstraction). The view that it is abstract causal structure that is essential to mentality has been an implicit assumption of the AI research program since Turing (1950), but was first articulated explicitly, in various forms, by Putnam (1960), Armstrong (1970) and Lewis (1970), and has become known as _functionalism_. From here, it is a very short step to _computationalism_, the view that computational structure is what is important in capturing the essence of mentality. This step follows from a belief that any abstract causal structure can be captured computationally: a belief made plausible by the Church–Turing Thesis, which articulates the power

Abstract:   Considerations of personal identity bear on John Searle's Chinese Room argument, and on the opposed position that a computer itself could really understand a natural language. In this paper I develop the notion of a virtual person, modelled on the concept of virtual machines familiar in computer science. I show how Searle's argument, and J. Maloney's attempt to defend it, fail. I conclude that Searle is correct in holding that no digital machine could understand language, but wrong in holding that artificial minds are impossible: minds and persons are not the same as the machines, biological or electronic, that realize them

Abstract:   Searle has recently used two adaptations of his Chinese room argument in an attack on connectionism. I show that these new forms of the argument are fallacious. First I give an exposition of and rebuttal to the original Chinese room argument, and then a brief introduction to the essentials of connectionism

Abstract: Searle’s discussions over the years 1980-2004 of the implications of his “Chinese Room” Gedanken experiment are frustrating because they proceed from a correct assertion: (1) “Instantiating a computer program is never by itself a sufficient condition of intentionality;” and an incorrect assertion: (2) “The explanation of how the brain produces intentionality cannot be that it does it by instantiating a computer program.” In this article, I describe how to construct a Gedanken zombie Chinese Room program that will pass the Turing test and at the same time unambiguously demonstrates the correctness of (1). I then describe how to construct a Gedanken Chinese brain program that will pass the Turing test, has a mind, and understands Chinese, thus demonstrating that (2) is incorrect. Searle’s instantiation of this program can and does produce intentionality. Searle’s longstanding ignorance of Chinese is simply irrelevant and always has been. I propose a truce and a plan for further exploration

Abstract: John Searle’s Chinese room argument (CRA) is a celebrated thought experiment designed to refute the hypothesis, popular among artificial intelligence (AI) 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 (due to Sorensen), 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(s) of possibility that one reads into the logical formalism

Abstract: In this paper I submit that the “Chinese room” argument rests on the assumption that understanding a sentence necessarily implies being conscious of its content. However, this assumption can be challenged by showing that two notions of consciousness come into play, one to be found in AI, the other in Searle’s argument, and that the former is an essential condition for the notion used by Searle. If Searle discards the first, he not only has trouble explaining how we can learn a language but finds the validity of his own argument in jeopardy

Abstract: Critique of Computationalism as merely projecting hermeneutics (i.e., meaning originating from the mind of an external interpreter) onto otherwise intrinsically meaningless symbols. Projecting an interpretation onto a symbol system results in its being reflected back, in a spuriously self-confirming way

Abstract: Searle's celebrated Chinese Room Argument has shaken the foundations of Artificial Intelligence. Many refutations have been attempted, but none seem convincing. This paper is an attempt to sort out explicitly the assumptions and the logical, methodological and empirical points of disagreement. Searle is shown to have underestimated some features of computer modeling, but the heart of the issue turns out to be an empirical question about the scope and limits of the purely symbolic (computational) model of the mind. Nonsymbolic modeling turns out to be immune to the Chinese Room Argument. The issues discussed include the Total Turing Test, modularity, neural modeling, robotics, causality and the symbol-grounding problem

Abstract: When in 1979 Zenon Pylyshyn, associate editor of Behavioral and Brain Sciences (BBS, a peer commentary journal which I edit) informed me that he had secured a paper by John Searle with the unprepossessing title of [XXXX], I cannot say that I was especially impressed; nor did a quick reading of the brief manuscript -- which seemed to be yet another tedious "Granny Objection"[1] about why/how we are not computers -- do anything to upgrade that impression

Abstract: "in an academic generation a little overaddicted to "politesse," it may be worth saying that violent destruction is not necessarily worthless and futile. Even though it leaves doubt about the right road for London, it helps if someone rips up, however violently, a

Abstract: Computationalism. According to computationalism, to explain how the mind works, cognitive science needs to find out what the right computations are -- the same ones that the brain performs in order to generate the mind and its capacities. Once we know that, then every system that performs those computations will have those mental states: Every computer that runs the mind's program will have a mind, because computation is hardware independent : Any hardware that is running the right program has the right computational states

Abstract: Searle's Chinese Room Argument showed a fatal flaw in computationalism (the idea that mental states are just computational states) and helped usher in the era of situated robotics and symbol grounding (although Searle himself thought neuroscience was the only correct way to understand the mind)

Abstract: The intelligent-seeming deeds of computers are what occasion philosophical debate about artificial intelligence (AI) in the first place. Since evidence of AI is not bad, arguments against seem called for. John Searle's Chinese Room Argument (1980a, 1984, 1990, 1994) is among the most famous and long-running would-be answers to the call. Surprisingly, both the original thought experiment (1980a) and Searle's later would-be formalizations of the embedding argument (1984, 1990) are quite unavailing against AI proper (claims that computers do or someday will think ). Searle lately even styles it a "misunderstanding" (1994, p. 547) to think the argument was ever so directed! The Chinese room is now advertised to target Computationalism (claims that computation is what thought essentially is ) exclusively. Despite its renown, the Chinese Room Argument is totally ineffective even against this target

Abstract:   John Searle's Chinese room argument is perhaps the most influential andwidely cited argument against artificial intelligence (AI). Understood astargeting AI proper – claims that computers can think or do think– Searle's argument, despite its rhetorical flash, is logically andscientifically a dud. Advertised as effective against AI proper, theargument, in its main outlines, is an ignoratio elenchi. It musterspersuasive force fallaciously by indirection fostered by equivocaldeployment of the phrase "strong AI" and reinforced by equivocation on thephrase "causal powers" (at least) equal to those of brains." On a morecarefully crafted understanding – understood just to targetmetaphysical identification of thought with computation ("Functionalism"or "Computationalism") and not AI proper the argument is still unsound,though more interestingly so. It's unsound in ways difficult for high church– "someday my prince of an AI program will come" – believersin AI to acknowledge without undermining their high church beliefs. The adhominem bite of Searle's argument against the high church persuasions of somany cognitive scientists, I suggest, largely explains the undeserved reputethis really quite disreputable argument enjoys among them

Abstract: John Searle's 1980a) thought experiment and associated 1984a) argument is one of the best known and widely credited counters to claims of artificial intelligence (AI), i.e., to claims that computers _do_ or at least _can_ (roughly, someday will) think. According to Searle's original presentation, the argument is based on two truths: _brains cause minds_ , and _syntax doesn't suffice_ _for semantics_ . Its target, Searle dubs "strong AI": "according to strong AI," according to Searle, "the computer is not merely a tool in the study of the mind, rather the appropriately programmed computer really _is_ a mind in the sense that computers given the right programs can be literally said to _understand_ and have other cognitive states" 1980a, p. 417). Searle contrasts "strong AI" to "weak AI". According to weak AI, according to Searle, computers just

Abstract: _The Chinese room argument_ - John Searle's (1980a) thought experiment and associated (1984) derivation - is one of the best known and widely credited counters to claims of artificial intelligence (AI), i.e., to claims that computers _do_ or at least _can_ (someday might) think. According to Searle's original presentation, the argument is based on two truths: _brains cause minds_ , and _syntax doesn't_ _suffice for semantics_ . Its target, Searle dubs "strong AI": "according to strong AI," according to Searle, "the computer is not merely a tool in the study of the mind, rather the appropriately programmed computer really _is_ a mind in the sense that computers given the right programs can be literally said to _understand_ and have other cognitive states" (1980a, p. 417). Searle contrasts "strong AI" to "weak AI". According to weak AI, according to Searle, computers just

Abstract:   When certain formal symbol systems (e.g., computer programs) are implemented as dynamic physical symbol systems (e.g., when they are run on a computer) their activity can be interpreted at higher levels (e.g., binary code can be interpreted as LISP, LISP code can be interpreted as English, and English can be interpreted as a meaninguful conversation). These higher levels of interpretability are called ‘virtual’ systems. If such a virtual system is interpretable as if it had a mind, is such a ‘virtual mind’ real? This is the question addressed in this ‘virtual’ symposium, originally conducted electronically among four cognitive scientists. Donald Perlis, a computer scientist, argues that according to the computationalist thesis, virtual minds are real and hence Searle's Chinese Room Argument fails, because if Searle memorized and executed a program that could pass the Turing Test in Chinese he would have a second, virtual, Chinese-understanding mind of which he was unaware (as in multiple personality). Stevan Harnad, a psychologist, argues that Searle's Argument is valid, virtual minds are just hermeneutic overinterpretations, and symbols must be grounded in the real world of objects, not just the virtual world of interpretations. Computer scientist Patrick Hayes argues that Searle's Argument fails, but because Searle does not really implement the program: a real implementation must not be homuncular but mindless and mechanical, like a computer. Only then can it give rise to a mind at the virtual level. Philosopher Ned Block suggests that there is no reason a mindful implementation would not be a real one

Abstract: John R. Searle's problem of the Chinese Room poses an important philosophical challenge to the foundations of strong artificial intelligence, and functionalist, cognitivist, and computationalist theories of mind. Searle has recently responded to three categories of criticisms of the Chinese Room and the consequences he attempts to conclude from it, redescribing the essential features of the problem, and offering new arguments about the syntax-semantics gap it is intended to demonstrate. Despite Searle's defense, the Chinese Room remains ineffective as a counterexample, and poses no real threat to artificial intelligence or mechanist philosophy of mind. The thesis that intentionality is a primitive irreducible relation exemplified by biological phenomena is preferred in opposition to Searle's contrary claim that intentionality is a biological phenomenon exhibiting abstract properties

Abstract:   The author defends John R. Searle's Chinese Room argument against a particular objection made by William J. Rapaport called the Korean Room. Foundational issues such as the relationship of strong AI to human mentality and the adequacy of the Turing Test are discussed. Through undertaking a Gedankenexperiment similar to Searle's but which meets new specifications given by Rapaport for an AI system, the author argues that Rapaport's objection to Searle does not stand and that Rapaport's arguments seem convincing only because they assume the foundations of strong AI at the outset

Abstract: The discussion between Searle and the Churchlands over whether or not symbolmanipulating computers generate semantics will be confronted both with the rulesceptical considerations of Kripke/Wittgenstein and with Wittgenstein's privatelanguage argument in order to show that the discussion focuses on the wrong place: meaning does not emerge in the brain. That a symbol means something should rather be conceived as a social fact, depending on a mutual imputation of linguistic competence of the participants of a linguistic practice to one another. The alternative picture will finally be applied to small children, animals, and computers as well

Abstract: To convince us that computers cannot have mental states, Searle (1980) imagines a “Chinese room” that simulates a computer that “speaks” Chinese and asks us to find the understanding in the room. It's a trick. There is no understanding in the room, not because computers can't have it, but because the room's computer-simulation is defective. Fix it and understanding appears. Abracadabra!

Abstract: Psychologists and philosophers have recently been exploring whether the mechanisms which underlie the acquisition of ‘theory of mind’ (ToM) are best charac- terized as cognitive modules or as developing theories. In this paper, we attempt to clarify what a modular account of ToM entails, and why it is an attractive type of explanation. Intuitions and arguments in this debate often turn on the role of develop- ment: traditional research on ToM focuses on various developmental sequences, whereas cognitive modules are thought to be static and ‘anti-developmental’. We suggest that this mistaken view relies on an overly limited notion of modularity, and we explore how ToM might be grounded in a cognitive module and yet still afford development. Modules must ‘come on-line’, and even fully developed modules may still develop internally, based on their constrained input. We make these points con- crete by focusing on a recent proposal to capture the development of ToM in a module via parameterization

Abstract: John Searle begins his (1990) ``Consciousness, Explanatory Inversion and Cognitive Science'' with

``Ten years ago in this journal I published an article (Searle, 1980a and 1980b) criticising what I call Strong
AI, the view that for a system to have mental states it is sufficient for the system to implement the right sort of
program with right inputs and outputs. Strong AI is rather easy to refute and the basic argument can be
summarized in one sentence: {it a system, me for example, could implement a program for understanding
Chinese, for example, without understanding any Chinese at all.} This idea, when developed, became
known as the Chinese Room Argument.''

The Chinese Room Argument can be refuted in one sentence

Abstract:   Discussion of Searle's case against strong AI has usually focused upon his Chinese Room thought-experiment. In this paper, however, I expound and then try to refute what I call his abstract argument against strong AI, an argument which turns upon quite general considerations concerning programs, syntax, and semantics, and which seems not to depend on intuitions about the Chinese Room. I claim that this argument fails, since it assumes one particular account of what a program is. I suggest an alternative account which, however, cannot play a role in a Searle-type argument, and argue that Searle gives no good reason for favoring his account, which allows the abstract argument to work, over the alternative, which doesn't. This response to Searle's abstract argument also, incidentally, enables the Robot Reply to the Chinese Room to defend itself against objections Searle makes to it

Abstract: John Searle has argued that one can imagine embodying a machine running any computer program without understanding the symbols, and hence that purely computational processes do not yield understanding. The disagreement this argument has generated stems, I hold, from ambiguity in talk of 'understanding'. The concept is analysed as a relation between subjects and symbols having two components: a formal and an intentional. The central question, then becomes whether a machine could possess the intentional component with or without the formal component. I argue that the intentional state of a symbol's being meaningful to a subject is a functionally definable relation between the symbol and certain past and present states of the subject, and that a machine could bear this relation to a symbol. I sketch a machine which could be said to possess, in primitive form, the intentional component of understanding. Even if the machine, in lacking consciousness, lacks full understanding, it contributes to a theory of understanding and constitutes a counterexample to the Chinese Room argument

Abstract: The most famous challenge to computational cognitive science and artificial intelligence is the philosopher John Searle's "Chinese Room" argument.

Abstract:   A computer can come to understand natural language the same way Helen Keller did: by using “syntactic semantics”—a theory of how syntax can suffice for semantics, i.e., how semantics for natural language can be provided by means of computational symbol manipulation. This essay considers real-life approximations of Chinese Rooms, focusing on Helen Keller’s experiences growing up deaf and blind, locked in a sort of Chinese Room yet learning how to communicate with the outside world. Using the SNePS computational knowledge-representation system, the essay analyzes Keller’s belief that learning that “everything has a name” was the key to her success, enabling her to “partition” her mental concepts into mental representations of: words, objects, and the naming relations between them. It next looks at Herbert Terrace’s theory of naming, which is akin to Keller’s, and which only humans are supposed to be capable of. The essay suggests that computers at least, and perhaps non-human primates, are also capable of this kind of naming

Abstract: Strong Al presupposes (1) that Super-Searle (henceforth ‘Searle’) comes to know that the symbols he manipulates are meaningful , and (2) that there cannot be two or more semantical interpretations for the system of symbols that Searle manipulates such that the set of rules constitutes a language comprehension program for each interpretation. In this paper, I show that Strong Al is false and that presupposition #1 is false, on the assumption that presupposition #2 is true. The main argument of the paper constructs a second program, isomorphic to Searle’s, to show that if someone, say Dan, runs this isomorphic program, he cannot possibly come to know what its mentioned symbols mean because they do not mean anything to anybody. Since Dan and Searle do exactly the same thing, except that the symbols they manipulate are different, neither Dan nor Searle can possibly know whether the symbols they manipulate are meaningful (let alone what they mean, if they are meaningful). The remainder of the paper responds to an anticipated Strong Al rejoinder, which, I believe, is a necessary extension of Strong Al

Abstract: What psychological and philosophical significance should we attach to recent efforts at computer simulations of human cognitive capacities? In answering this question, I find it useful to distinguish what I will call "strong" AI from "weak" or "cautious" AI (artificial intelligence). According to weak AI, the principal value of the computer in the study of the mind is that it gives us a very powerful tool. For example, it enables us to formulate and test hypotheses in a more rigorous and precise fashion. But according to strong AI, the computer is not merely a tool in the study of the mind; rather, the appropriately programmed computer really is a mind, in the sense that computers given the right programs can be literally said to..

Abstract: Searle’s Chinese Room Argument (CRA) has been the object of great interest in the philosophy of mind, artificial intelligence and cognitive science since its initial presentation in ‘Minds, Brains and Programs’ in 1980. It is by no means an overstatement to assert that it has been a main focus of attention for philosophers and computer scientists of many stripes. It is then especially interesting to note that relatively little has been said about the detailed logic of the argument, whatever significance Searle intended CRA to have. The problem with the CRA is that it involves a very strong modal claim, the truth of which is both unproved and highly questionable. So it will be argued here that the CRA does not prove what it was intended to prove

Abstract: The paper is concerned with John Searle’s famous Chinese room argument. Despite being objected to by some, Searle’s Chinese room argument appears very appealing. This is because Searle’s argument is based on an intuition about the mind that ‘we’ all seem to share. Ironically, however, Chinese philosophers don’t seem to share this same intuition. The paper begins by first analysing Searle’s Chinee room argument. It then introduces what can be seen as the (implicit) Chinese view of the mind. Lastly, it demonstrates a conceptual difference between Chinese and Western philosophy with respect to the notion of mind. Thus, it is shown that one must carefully attend to the presuppositions underlying Chinese philosophising in interpreting Chinese philosophers

Abstract: Searle's Chinese room argument is analyzed from a cognitive point of view. The analysis is based on a newly developed model of conceptual integration, the many space model proposed by Fauconnier and Turner. The main point of the analysis is that the central inference constructed in the Chinese room scenario is a result of a dynamic, cognitive activity of conceptual blending, with metaphor defining the basic features of the blending. Two important consequences follow: (1) Searle's recent contention that syntax is not intrinsic to physics turns out to be a slightly modified version of the old Chinese room argument; and (2) the argument itself is still open to debate. It is persuasive but not conclusive, and at bottom it is a topological mismatch in the metaphoric conceptual integration that is responsible for the non-conclusive character of the Chinese room argument

Abstract:   I argue that John Searle's (1980) influential Chinese room argument (CRA) against computationalism and strong AI survives existing objections, including Block's (1998) internalized systems reply, Fodor's (1991b) deviant causal chain reply, and Hauser's (1997) unconscious content reply. However, a new ``essentialist'' reply I construct shows that the CRA as presented by Searle is an unsound argument that relies on a question-begging appeal to intuition. My diagnosis of the CRA relies on an interpretation of computationalism as a scientific theory about the essential nature of intentional content; such theories often yield non-intuitive results in non-standard cases, and so cannot be judged by such intuitions. However, I further argue that the CRA can be transformed into a potentially valid argument against computationalism simply by reinterpreting it as an indeterminacy argument that shows that computationalism cannot explain the ordinary distinction between semantic content and sheer syntactic manipulation, and thus cannot be an adequate account of content. This conclusion admittedly rests on the arguable but plausible assumption that thought content is interestingly determinate. I conclude that the viability of computationalism and strong AI depends on their addressing the indeterminacy objection, but that it is currently unclear how this objection can be successfully addressed

Abstract: This short paper (4 pages) demonstrates how subjective experience, language, and consciousness can be explained in terms of abilities we share with the simplest of creatures, specifically the ability to detect, react to, and associate various aspects of the world.

Abstract: Why should one believe that conscious awareness is solely the result of organizational complexity? What is the connection between consciousness and combinatorics: transformation of quantity into quality? The claim that the former is reducible to the other seems unconvincing—as unlike as chalk and cheese! In his book1 Penrose is at least attempting to compare like with like: the enigma of consciousness with the progress of physics

Abstract: We consider the problem of executing conscious behavior i.e., of driving an agent’s actions and of allowing it, at the same time, to run concurrent processes reflecting on these actions. Toward this end, we express a single agent’s plans as reflexive dialogs in a multi-agent system defined by a virtual machine. We extend this machine’s planning language by introducing two specific operators for reflexive dialogs i.e., conscious and caught for monitoring beliefs and actions, respectively. The possibility to use the same language both to drive a machine and to establish a reflexive communication within the machine itself stands as a key feature of our model.

Abstract:   Zlatev offers surprisingly weak reasoning in support of his view that robots with the right kind of developmental histories can have meaning. We ought nonetheless to praise Zlatev for an impressionistic account of how attending to the psychology of human development can help us build robots that appear to have intentionality

Abstract: You are offered one billion dollars to 'simply' produce a proof-of-concept robot that has phenomenal consciousness -- in fact, you can receive a deliciously large portion of the money up front, by simply starting a three-year work plan in good faith. Should you take the money and commence? No. I explain why this refusal is in order, now and into the foreseeable future

Abstract: The best reason for believing that robots might some day become conscious is that we human beings are conscious, and we are a sort of robot ourselves. That is, we are extraordinarily complex self-controlling, self-sustaining physical mechanisms, designed over the eons by natural selection, and operating according to the same well-understood principles that govern all the other physical processes in living things: digestive and metabolic processes, self-repair and reproductive processes, for instance. It may be wildly over-ambitious to suppose that human artificers can repeat Nature's triumph, with variations in material, form, and design process, but this is not a deep objection. It is not as if a conscious machine contradicted any fundamental laws of nature, the way a perpetual motion machine does. Still, many skeptics believe--or in any event want to believe--that it will never be done. I wouldn't wager against them, but my reasons for skepticism are mundane, economic reasons, not theoretical reasons

Abstract: Arguments about whether a robot could ever be conscious have been conducted up to now in the factually impoverished arena of what is possible "in principle." A team at MIT of which I am a part is now embarking on a longterm project to design and build a humanoid robot, Cog, whose cognitive talents will include speech, eye-coordinated manipulation of objects, and a host of self-protective, self-regulatory and self-exploring activities. The aim of the project is not to make a conscious robot, but to make a robot that can interact with human beings in a robust and versatile manner in real time, take care of itself, and tell its designers things about itself that would otherwise be extremely difficult if not impossible to determine by examination. Many of the details of Cog's "neural" organization will parallel what is known (or presumed known) about their counterparts in the human brain, but the intended realism of Cog as a model is relatively coarse-grained, varying opportunistically as a function of what we think we know, what we think we can build, and what we think doesn't matter. Much of what we think will of course prove to be mistaken; that is one advantage of real experiments over thought experiments

Abstract: What type of artificial systems will claim to be conscious and will claim to experience qualia? The ability to comment upon physical states of a brain-like dynamical system coupled with its environment seems to be sufficient to make claims. The flow of internal states in such system, guided and limited by associative memory, is similar to the stream of consciousness. Minimal requirements for an artificial system that will claim to be conscious were given in form of specific architecture named articon. Nonverbal discrimination of the working memory states of the articon gives it the ability to experience different qualities of internal states. Analysis of the inner state flows of such a system during typical behavioral process shows that qualia are inseparable from perception and action. The role of consciousness in learning of skills, when conscious information processing is replaced by subconscious, is elucidated. Arguments confirming that phenomenal experience is a result of cognitive processes are presented. Possible philosophical objections based on the Chinese room and other arguments are discussed, but they are insufficient to refute claims articon’s claims. Conditions for genuine understanding that go beyond the Turing test are presented. Articons may fulfill such conditions and in principle the structure of their experiences may be arbitrarily close to human

Abstract: The development of conscious machines faces a number of difficult issues such as the apparent immateriality of mind, qualia and self-awareness. Also consciousness-related cognitive processes such as perception, imagination, motivation and inner speech are a technical challenge. It is foreseen that the development of machine consciousness would call for a system approach; the developer of conscious machines should consider complete systems that integrate the cognitive processes seamlessly and process information in a transparent way with representational and non-representational information-processing modes. An overview of the main issues is given and some possible solutions are outlined

Abstract: A "machine" is any causal physical system, hence we are machines, hence machines can be conscious. The question is: which kinds of machines can be conscious? Chances are that robots that can pass the Turing Test -- completely indistinguishable from us in their behavioral capacities -- can be conscious (i.e. feel), but we can never be sure (because of the "other-minds" problem). And we can never know HOW they have minds, because of the "mind/body" problem. We can only know how they pass the Turing Test, but not how, why or whether that makes them feel

Abstract: Over sixty years ago, Kenneth Craik noted that, if an organism (or an artificial agent) carried 'a small-scale model of external reality and of its own possible actions within its head', it could use the model to behave intelligently. This paper argues that the possible actions might best be represented by interactions between a model of reality and a model of the agent, and that, in such an arrangement, the internal model of the agent might be a transparent model of the sort recently discussed by Metzinger, and so might offer a useful analogue of a conscious entity. The CRONOS project has built a robot functionally similar to a human that has been provided with an internal model of itself and of the world to be used in the way suggested by Craik; when the system is completed, it will be possible to study its operation from the perspective not only of artificial intelligence, but also of machine consciousness

Abstract: In this collection of essays we hear from an international array of computer and brain scientists who are actively working from both the machine and human ends...

Abstract: Scepticism about the possibility of machine consciousness comes in at least two forms. Some argue that our neurobiology is special, and only something sharing our neurobiology could be a subject of experience. Others argue that a machine couldn't be anything else but a zombie: there could never be something it is like to be a machine. I advance a dynamic sensorimotor account of consciousness which argues against both these varieties of scepticism

Abstract:   According to a common philosophical distinction, the `original' intentionality, or `aboutness' possessed by our thoughts, beliefs and desires, is categorically different from the `derived' intentionality manifested in some of our artifacts –- our words, books and pictures, for example. Those making the distinction claim that the intentionality of our artifacts is `parasitic' on the `genuine' intentionality to be found in members of the former class of things. In Kinds of Minds: Toward an Understanding of Consciousness, Daniel Dennett criticizes that claim and the distinction it rests on, and seeks to show that ``metaphysically original intentionality'' is illusory by working out the implications he sees in the practical possibility of a certain type of robot, i.e., one that generates `utterances' which are `inscrutable to the robot's designers' so that we, and they, must consult the robot to discover the meaning of its utterances. I argue that the implications Dennett finds are erroneous, regardless of whether such a robot is possible, and therefore that the real existence of metaphysically original intentionality has not been undermined by the possibility of the robot Dennett describes

Abstract: Recent empirical work calls into question the so-called Simple View that an agent who A’s intentionally intends to A. In experimental studies, ordinary speakers frequently assent to claims that, in certain cases, agents who knowingly behave wrongly intentionally bring about the harm they do; yet the speakers tend to deny that it was the intention of those agents to cause the harm. This paper reports two additional studies that at first appear to support the original ones, but argues that in fact, the evidence of all the studies considered is best understood in terms of the Simple View.

Abstract: In AI, consciousness of self consists in a program having certain kinds of facts about its own mental processes and state of mind. We discuss what consciousness of its own mental structures a robot will need in order to operate in the common sense world and accomplish the tasks humans will give it. It's quite a lot. Many features of human consciousness will be wanted, some will not, and some abilities not possessed by humans have already been found feasible and useful in limited contexts. We give preliminary fragments of a logical language a robot can use to represent information about its own state of mind. A robot will often have to conclude that it cannot decide a question on the basis of the information in memory and therefore must seek information externally. Gödel's idea of relative consistency is used to formalize non-knowledge. Programs with the kind of consciousness discussed in this article do not yet exist, although programs with some components of it exist. Thinking about consciousness with a view to designing it provides a new approach to some of the problems of consciousness studied by philosophers. One advantage is that it focusses on the aspects of consciousness important for intelligent behavior

Abstract: In this commentary, I discuss the three main articles in this volume that present survey data relevant to a search for something that might merit the label “the folk concept of intentional action” – the articles by Joshua Knobe and Arudra Burra, Bertram Malle, and Thomas Nadelhoffer. My guiding question is this: What shape might we find in an analysis of intentional action that takes at face value the results of all of the relevant surveys about vignettes discussed in these three articles?1 To simplify exposition, I assume that there is something that merits the label I mentioned

Abstract: Philosophical discussion of the nature of know-how has focused on the relation between know-how and ability. Broadly speaking, neo-Ryleans attempt to identify know-how with a certain type of ability,1 while, traditionally, intellectualists attempt to reduce it to some form of propositional knowledge. For our purposes, however, this characterization of the debate is too crude. Instead, we prefer the following more explicit taxonomy. Anti-intellectualists, as we will use the term, maintain that knowing how to ? entails the ability to ?. Dispositionalists maintain that the ability to ? is sufficient (modulo some fairly innocuous constraints) for knowing how to ?. Intellectualists, as we will use the term, deny the anti-intellectualist claim. Finally, radical intellectualists deny both the anti-intellectualist and dispositionalist claims. Pace neo-Ryleans (who in our taxonomy are those who accept both dispositionalism and anti-intellectualism), radical intellectualists maintain that the ability to ? is neither necessary nor sufficient for knowing how to ?

Abstract: Analytic philosophers have long used a priori methods to characterize folk concepts like knowledge, belief, and wrongness. Recently, researchers have begun to exploit social scientific methodologies to characterize such folk concepts. One line of work has explored folk intuitions on cases that are disputed within philosophy. A second approach, with potentially more radical implications, applies the methods of cross-cultural psychology to philosophical intuitions. Recent work suggests that people in different cultures have systematically different intuitions surrounding folk concepts like wrong, knows, and refers. A third strand of research explores the emergence and character of folk concepts in children. These approaches to characterizing folk concepts provide important resources that will supplement, and perhaps sometimes displace, a priori approaches

Abstract: In this paper, I argue that certain Machines can have rights independently of whether they are sentient, or conscious, or whatever you might call it.

Abstract:   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

Abstract: One of the most important ongoing debates in the philosophy of mind is the debate over the reality of the first-person character of consciousness.[1] Philosophers on one side of this debate hold that some features of experience are accessible only from a first-person standpoint. Some members of this camp, notably Frank Jackson, have maintained that epiphenomenal properties play roles in consciousness [2]; others, notably John R. Searle, have rejected dualism and regarded mental phenomena as entirely biological.[3] In the opposite camp are philosophers who hold that all mental capacities are in some sense computational - or, more broadly, explainable in terms of features of information processing systems.[4] Consistent with this explanatory agenda, members of this camp normally deny that any aspect of mind is accessible solely from a first-person standpoint. This denial sometimes goes very far - even as far as Dennett's claim that the phenomenology of conscious experience does not really exist

Abstract: Machine consciousness exists already in organic systems and it is only a matter of time -- and some agreement -- before it will be realised in reverse-engineered organic systems and forward- engineered inorganic systems. The agreement must be over the preconditions that must first be met if the enterprise is to be successful, and it is these preconditions, for instance, being a socially-embedded, structurally-coupled and dynamic, goal-directed entity that organises its perceptual input and enacts its world through the application of both a cognitive and kinaesthetic imagination, that I shall concentrate on presenting in this paper. It will become clear that these preconditions will present engineers with a tall order, but not, I will argue, an impossible one. After all, we might agree with Freeman and Núñez's claim that the machine metaphor has restricted the expectations of the cognitive sciences (Freeman & Núñez, 1999); but it is a double-edged sword, since our limited expectations about machines also narrow the potential of our cognitive science

Abstract:   Oscar is going to be the first artificial person — at any rate, he is going to be the first artificial person to be built in Tucson's Philosophy Department. Oscar's creator, John Pollock, maintains that once Oscar is complete he will experience qualia, will be self-conscious, will have desires, fears, intentions, and a full range of mental states (Pollock 1989, pp. ix–x). In this paper I focus on what seems to me to be the most problematical of these claims, viz., that Oscar will experience qualia. I argue that we have not been given sufficient reasons to believe this bold claim. I doubt that Oscar will enjoy qualitative conscious phenomena and I maintain that it will be like nothing to be Oscar

Abstract: Current approaches to machine consciousness (MC) tend to offer a range of characteristic responses to critics of the enterprise. Many of these responses seem to marginalize phenomenal consciousness, by presupposing a 'thin' conception of phenomenality. This conception is, we will argue, largely shared by anti- computationalist critics of MC. On the thin conception, physiological or neural or functional or organizational features are secondary accompaniments to consciousness rather than primary components of consciousness itself. We outline an alternative, 'thick' conception of phenomenality. This shows some signposts in the direction of a more adequate approach to MC

Abstract: Beginning with physical reactions as simple and mechanical as rust, From Dust to Descartes goes step by evolutionary step to explore how the most remarkable and personal aspects of consciousness have arisen, how our awareness of the world of ourselves differs from that of other species, and whether machines could ever become self-aware. Part I addresses a newborn’s innate abilities. Part II shows how with these and experience, we can form expectations about the world. Parts III concentrates on the essential role that others play in the formation of self-awareness. Part IV then explores what follows from this explanation of human consciousness, touching on topics such as free will, personality, intelligence, and color perception which are often associated with self-awareness and the philosophy of mind.

Abstract: The central paradigm of arti?cial intelligence is rapidly shifting toward biological models for both robotic devices and systems performing such critical tasks as network management, vehicle navigation, and process control. Here we use a recent mathematical analysis of the necessary conditions for consciousness in humans to explore likely failure modes inherent to a broad class of biologically inspired computing machines. Analogs to developmental psychopathology, in which regulatory mechanisms for consciousness fail progressively and subtly understress, and toinattentional blindness, where a narrow 'syntactic band pass' de?ned by the rate distortion manifold of conscious attention results in pathological ?xation, seem inevitable. Similar problems are likely to confront other possible architectures, although their mathematical description may be far less straightforward. Computing devices constructed on biological paradigms will inevitably lack the elaborate, but poorly understood, system of control mechanisms which has evolved over the last few hundred million years to stabilize consciousness in higher animals. This will make such machines prone to insidious degradation, and, ultimately, catastrophic failure

Abstract: No philosopher has worked harder than Dan Dennett to set the possibility of machine mentality on firm philosophical footing. Dennett’s defense of this possibility has both a positive and a negative thrust. On the positive side, he has developed an account of mental activity that is tailor-made for the attribution of intentional states to purely mechanical contrivances, while on the negative side, he pillories as mystery mongering and skyhook grasping any attempts to erect barriers to the conception of machine mentality by excavating gulfs to keep us “bona fide” thinkers apart from the rest of creation. While I think he’s “won” the rhetorical tilts with his philosophical adversaries, I worry that Dennett’s negative side sometimes gets the better of him, and that this obscures advances that can be made on the positive side of his program. In this paper, I show that Dennett is much too dismissive of original intentionality in particular, and that this notion can be put to good theoretical use after all. Though deployed to distinguish different grades of mentality, it can (and should) be incorporated into a philosophical account of the mind that is recognizably Dennettian in spirit

Abstract: In an earlier paper in this journal[1], I sought to defend the claims that (1) substantial probability should be assigned to the hypothesis that machines will outsmart humans within 50 years, (2) such an event would have immense ramifications for many important areas of human concern, and that consequently (3) serious attention should be given to this scenario. Here, I will address a number of points made by several commentators

Abstract:   The dominant scientific and philosophical view of the mind – according to which, put starkly, cognition is computation – is refuted herein, via specification and defense of the following new argument: Computation is reversible; cognition isn't; ergo, cognition isn't computation. After presenting a sustained dialectic arising from this defense, we conclude with a brief preview of the view we would put in place of the cognition-is-computation doctrine

Abstract: The emergence of electronic computers in the last thirty years has given rise to many interesting questions. Many of these questions are technical, relating to a machine’s ability to perform complex operations in a variety of circumstances. While some of these questions are not without philosophical interest, the one question which above all others has stimulated philosophical interest is explicitly non-technical and it can be expressed crudely as follows: Can a machine be said to think and, if so, in what sense? The issue has received much attention in the scholarly journals with articles and arguments appearing in great profusion, some resolutely answering this question in the affirmative, some, equally resolutely, answering this question in the negative, and others manifesting modified rapture. While the ramifications of the question are enormous I believe that the issue at the heart of the matter has gradually emerged from the forest of complications

Abstract: My claim is clear and unambiguous: no machine will pass a well-designed Turing Test unless we find some means of embedding it in lived social life. We have no idea how to do this but my argument, and all our evidence, suggests that it will not be a necessary condition that the machine have more than a minimal body. Exactly how minimal is still being worked out

Abstract: For two hundred years materialist philosophers have argued that man is some sort of machine. The claim began with French materialists of the Enlightenment such as Pierre Cabanis, Julien La Mettrie, and Baron d’Holbach (La Mettrie even wrote a book titled Man the Machine). Likewise contemporary materialists like Marvin Minsky, Daniel Dennett, and Patricia Churchland claim that the motions and modifications of matter are sufficient to account for all human experiences, even our interior and cognitive ones. Whereas the Enlightenment philosophes might have thought of humans in terms of gear mechanisms and fluid flows, contemporary materialists think of humans in terms of neurological systems and computational devices. The idiom has been updated, but the underlying impulse to reduce mind to matter remains unchanged

Abstract: The first robot homicide was committed in 1981, according to my files. I have a yellowed clipping dated 12/9/81 from the Philadelphia Inquirer--not the National Enquirer--with the headline: Robot killed repairman, Japan reports The story was an anti-climax: at the Kawasaki Heavy Industries plant in Akashi, a malfunctioning robotic arm pushed a repairman against a gearwheel-milling machine, crushing him to death. The repairman had failed to follow proper instructions for shutting down the arm before entering the workspace. Why, indeed, had this industrial accident in Japan been reported in a Philadelphia newspaper? Every day somewhere in the world a human worker is killed by one machine or another. The difference, of course, was that in the public imagination at least, this was no ordinary machine; this was a robot, a machine that might have a mind, might have evil intentions, might be capable not just of homicide but of murder

Abstract:   Do computers have beliefs? I argue that anyone who answers in the affirmative holds a view that is incompatible with what I shall call the commonsense approach to the propositional attitudes. My claims shall be two. First,the commonsense view places important constraints on what can be acknowledged as a case of having a belief. Second, computers – at least those for which having a belief would be conceived as having a sentence in a belief box – fail to satisfy some of these constraints. This second claim can best be brought out in the context of an examination of the idea of computer self-knowledge and self-deception, but the conclusion is perfectly general: the idea that computers are believers, like the idea that computers could have self-knowledge or be self-deceived, is incompatible with the commonsense view. The significance of the argument lies in the choice it forces on us: whether to revise our notion of belief so as to accommodate the claim that computers are believers, or to give up on that claim so as to preserve our pretheoretic notion of the attitudes. We cannot have it both ways

Abstract:   It will be found that the great majority, given the premiss that thought is not distinct from corporeal motion, take a much more rational line and maintain that thought is the same in the brutes as in us, since they observe all sorts of corporeal motions in them, just as in us. And they will add that the difference, which is merely one of degree, does not imply any essential difference; from this they will be quite justified in concluding that, although there may be a smaller degree of reason in the beasts than there is in us, the beasts possess minds which are of exactly the same type as ours. (Descartes 1642: 288–289.)

Abstract: My pocket calculator (Cal) has certain arithmetical abilities: it seems Cal calculates. That calculating is thinking seems equally untendentious. Yet these two claims together provide premises for a seemingly valid syllogism whose conclusion -- Cal thinks -- most would deny. I consider several ways to avoid this conclusion, and find them mostly wanting. Either we ourselves can't be said to think or calculate if our calculation-like performances are judged by the standards proposed to rule out Cal; or the standards -- e.g., autonomy and self-consciousness -- make it impossible to verify whether anything or anyone (save myself) meets them. While appeals to the intentionality of thought or the unity of minds provide more credible lines of resistance, available accounts of intentionality and mental unity are insufficiently clear and warranted to provide very substantial arguments against Cal's title to be called a thinking thing. Indeed, considerations favoring granting that title are more formidable than generally appreciated

Abstract:   We confront the following popular views: that mind or life are algorithms; that thinking, or more generally any process other than computation, is computation; that anything other than a working brain can have thoughts; that anything other than a biological organism can be alive; that form and function are independent of matter; that sufficiently accurate simulations are just as genuine as the real things they imitate; and that the Turing test is either a necessary or sufficient or scientific procedure for evaluating whether or not an entity is intelligent. Drawing on the distinction between activities and tasks, and the fundamental scientific principles of ontological lawfulness, epistemological realism, and methodological skepticism, we argue for traditional scientific materialism of the emergentist kind in opposition to the functionalism, behaviourism, tacit idealism, and merely decorative materialism of the artificial intelligence and artificial life communities

Abstract:   This paper explores Church's Thesis and related claims madeby Turing. Church's Thesis concerns computable numerical functions, whileTuring's claims concern both procedures for manipulating uninterpreted marksand machines that generate the results that these procedures would yield. Itis argued that Turing's claims are true, and that they support (the truth of)Church's Thesis. It is further argued that the truth of Turing's and Church'sTheses has no interesting consequences for human cognition or cognitiveabilities. The Theses don't even mean that computers can do as much as peoplecan when it comes to carrying out effective procedures. For carrying out aprocedure is a purposive, intentional activity. No actual machine does, orcan do, as much

Abstract:   According to the conventional wisdom, Turing (1950) said that computing machines can be intelligent. I don''t believe it. I think that what Turing really said was that computing machines –- computers limited to computing –- can only fake intelligence. If we want computers to become genuinelyintelligent, we will have to give them enough initiative (Turing, 1948, p. 21) to do more than compute. In this paper, I want to try to develop this idea. I want to explain how giving computers more ``initiative'''' can allow them to do more than compute. And I want to say why I believe (and believe that Turing believed) that they will have to go beyond computation before they can become genuinely intelligent

Abstract: Since there isn't a computer that seems conscious at this time, the idea of machine consciousness is supported by thought experiments. Here's one old chestnut: "What if you replaced your neurons one by one with neuron sized and shaped substitutes made of silicon chips that perfectly mimicked the chemical and electric functions of the originals? If you just replaced one single neuron, surely you'd feel the same. As you proceed, as more and more neurons are replaced, you'd stay conscious. Why wouldn't you still be conscious at the end of the process, when you'd reside in a brain shaped glob of silicon? And why couldn't the resulting replacement brain have been manufactured by some other means?"

Abstract: Over three decades ago, in a brief but provocative essay, Paul Ziff argued for the thesis that robots cannot have feelings because they are "mechanisms, not organisms, not living creatures. There could be a broken-down robot but not a dead one. Only living creatures can literally have feelings."[i] Since machines are not living things they cannot have feelings

Abstract: Ascribing mental qualities like beliefs, intentions and wants to a machine is sometimes correct if done conservatively and is sometimes necessary to express what is known about its state. We propose some new definitional tools for this: definitions relative to an approximate theory and second order structural definitions

Abstract: Most people think computers will never be able to think. That is, really think. Not now or ever. To be sure, most people also agree that computers can do many things that a person would have to be thinking to do. Then how could a machine seem to think but not actually think? Well, setting aside the question of what thinking actually is, I think that most of us would answer that by saying that in these cases, what the computer is doing is merely a superficial imitation of human intelligence. It has been designed to obey certain simple commands, and then it has been provided with programs composed of those commands. Because of this, the computer has to obey those commands, but without any idea of what's happening

Abstract: One of the most influential arguments against the claim that computers can think is that while our intentionality is intrinsic, that of computers is derived: it is parasitic on the intentionality of the programmer who designed the computer-program. Daniel Dennett chose a surprising strategy for arguing against this asymmetry: instead of denying that the intentionality of computers is derived, he endeavours to argue that human intentionality is derived too. I intend to examine that biological plausibility of Dennett’s suggestion and show that Dennett’s argument for the claim that human intentionality is derived because it was designed by natural selection is based on the misunderstanding of how natural selection works

Abstract:   Given (1) Wittgensteins externalist analysis of the distinction between following a rule and behaving in accordance with a rule, (2) prima facie connections between rule-following and psychological capacities, and (3) pragmatic issues about training, it follows that most, even all, future artificially intelligent computers and robots will not use language, possess concepts, or reason. This argument suggests that AIs traditional aim of building machines with minds, exemplified in current work on cognitive robotics, is in need of substantial revision

Abstract: This is a draft of the written version of comments on a paper by David Cole, presented orally at the American Philosophical Association Central Division meeting in New Orleans, 27 April 1990. Following the written comments are 2 appendices: One contains a letter to Cole updating these comments. The other is the handout from the oral presentation

Abstract: Over recent decades there has been a growing interest in the question of whether computer programs are capable of genuinely creative activity. Although this notion can be explored as a purely philosophical debate, an alternative perspective is to consider what aspects of the behaviour of a program might be noted or measured in order to arrive at an empirically supported judgement that creativity has occurred. We sketch out, in general abstract terms, what goes on when a potentially creative program is constructed and run, and list some of the relationships (for example, between input and output) which might contribute to a decision about creativity. Specifically, we list a number of criteria which might indicate interesting properties of a program’s behaviour, from the perspective of possible creativity. We go on to review some ways in which these criteria have been applied to actual implementations, and some possible improvements to this way of assessing creativity

Abstract:   Since the introduction of the imitation game by Turing in 1950 there has been much debate as to its validity in ascertaining machine intelligence. We wish herein to consider a different issue altogether: granted that a computing machine passes the Turing Test, thereby earning the label of ``Turing Chatterbox'', would it then be of any use (to us humans)? From the examination of scenarios, we conclude that when machines begin to participate in social transactions, unresolved issues of trust and responsibility may well overshadow any raw reasoning ability they possess

Abstract: Discussion about the application of scientific knowledge in robotics in order to build people helpers is widespread. The issue herein addressed is philosophically poignant, that of robots that are “people”. It is currently popular to speak about robots and the image of Man. Behind this lurks the dialogical mind and the questions about the significance of an artificial version of it. Without intending to defend or refute the discourse in favour of ‘recreating’ Man, a lesser familiar question is brought forth: “and what if we were capable of creating a very convincible replica of man (constructing a robot-person), what would the consequences of this be and would we be satisfied with such technology?” Thorny topic; it questions the entire knowledge foundation upon which strong AI/Robotics is positioned. The author argues for improved monitoring of technological progress and thus favours implementing weaker techniques

Abstract: Harry Collins interprets Hubert Dreyfus’s philosophy of embodiment as a criticism of all possible forms of artificial intelligence. I argue that this characterization is inaccurate and predicated upon a misunderstanding of the relevance of phenomenology for empirical scientific research

Abstract: There has been much debate whether computers can be responsible. This question is usually discussed in terms of personhood and personal characteristics, which a computer may or may not possess. If a computer fulfils the conditions required for agency or personhood, then it can be responsible; otherwise not. This paper suggests a different approach. An analysis of the concept of responsibility shows that it is a social construct of ascription which is only viable in certain social contexts and which serves particular social aims. If this is the main aspect of responsibility then the question whether computers can be responsible no longer hinges on the difficult problem of agency but on the possibly simpler question whether responsibility ascriptions to computers can fulfil social goals. The suggested solution to the question whether computers can be subjects of responsibility is the introduction of a new concept, called “quasi-responsibility” which will emphasise the social aim of responsibility ascription and which can be applied to computers

Abstract: Taking a series of key words such as calculation, language, information and memory, Professor Tallis shows how their misuse has lured a whole generation into...

Abstract: I argue that participants in a virtual world such as "Second Life" exercise genuine agency via their avatars. Indeed, their avatars are fictional bodies with which they act in the virtual world, just as they act in the real world with their physical bodies. Hence their physical bodies can be regarded as their default avatars. I also discuss recent research into "believable" software agents, which are designed on principles borrowed from the character-based arts, especially cinematic animation as practiced by the artists at Disney and Warner Brothers Studios. I claim that these agents exemplify a kind of autonomy that should be of greater interest to philosophers than that exemplified by the generic agent modeled in current philosophical theory. The latter agent is autonomous by virtue of being governed by itself; but a believable agent appears to be governed by a self, which is the anima by which it appears to be animated. Putting these two discussions together, I suggest that philosophers of action should focus their attention on how we animate our bodies

Abstract: Rodney Brooks' call for 'new mathematics' to revitalize the disciplines of artificial intelligence and artificial life can be answered by adaptation of what Adams has called 'the informational turn in philosophy', aided by the novel perspectives that program gives regarding empirical studies of animal cognition and consciousness. Going backward from the necessary conditions communication theory imposes on animal cognition and consciousness to sufficient conditions for machine design is, however, an extraordinarily difficult engineering task. The most likely use of the first generations of conscious machines will be to model the various forms of psychopathology, since we have little or no understanding of how consciousness is stabilized in humans or other animals

Abstract:   John Searle distinguished between weak and strong artificial intelligence (AI). This essay discusses a third alternative, mild AI, according to which a machine may be capable of possessing a species of mentality. Using James Fetzer's conception of minds as semiotic systems, the possibility of what might be called ``mild AI'' receives consideration. Fetzer argues against strong AI by contending that digital machines lack the ground relationship required of semiotic systems. In this essay, the implementational nature of semiotic processes posited by Charles S. Peirce's triadic sign relation is re-examined in terms of the underlying dispositional processes and the ontological levels they would span in an inanimate machine. This suggests that, if non-human mentality can be replicated rather than merely simulated in a digital machine, the direction to pursue appears to be that of mild AI