The ethical concerns regarding the successful development of an Artificial Intelligence have received a lot of attention lately. The idea is that even if we have good reason to believe that it is very unlikely, the mere possibility of an AI causing extreme human suffering is important enough to warrant serious consideration. Others look at this problem from the opposite perspective, namely that of the AI itself. Here the idea is that even if we have good reason to believe that (...) it is very unlikely, the mere possibility of humanity causing extreme suffering to an AI is important enough to warrant serious consideration. This paper starts from the observation that both concerns rely on problematic philosophical assumptions. Rather than tackling these assumptions directly, it proceeds to present an argument that if one takes these assumptions seriously, then one has a moral obligation to advocate for a ban on the development of a conscious AI. (shrink)

In this paper I evaluate the soundness of the prototype paradigm, in particular its basic assumption that there are pan-human psychological essences or core meanings that refer to basic-level natural kinds, explaining why, on the whole, human communication and learning are successful. Instead I argue that there are no particular pan-human basic elements for thought, meaning and cognition, neither prototypes, nor otherwise. To illuminate my view I draw on examples from anthropology. More generally I argue that the prototype paradigm exemplifies (...) two assumptions that dominate cognitive science: (1) If human beings use words they mean something particular and what they mean can be discovered by scientific methods. (2) There is a fixed number of domains of categorization, each made up of a fixed number of basic categories. I suggest that these two assumptions lead to Brave New World. (shrink)

My aim in this paper is to use a formal approach to the Turing test. This approach is based on a tool developed within Inferential Erotetic Logic, so called erotetic search scenarios. First, I reconstruct the setting of the Turing test proposed by A.M. Turing. On this basis, I build a model of the test using erotetic search scenarios framework. I use the model to investigate one of the most interesting issues of the TT setting – the interrogator’s perspective and (...) role in the test. (shrink)

The aim of this paper is to present and discuss the issue of the adequacy of the Minimum Intelligent Signal Test (MIST) as an alternative to the Turing Test. MIST has been proposed by Chris McKinstry as a better alternative to Turing’s original idea. Two of the main claims about MIST are that (1) MIST questions exploit commonsense knowledge and as a result are expected to be easy to answer for human beings and difficult for computer programs; and that (2) (...) the MIST design aims at eliminating the problem of the role of judges in the test. To discuss these design assumptions we will present Peter D. Turney’s PMI-IR algorithm which allows for MIST-type questions to be answered. We will also present and discuss the results of our own study aimed at the judge problem for MIST. (shrink)

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. (shrink)

In 1950, Alan Turing proposed his eponymous test based on indistinguishability of verbal behavior as a replacement for the question "Can machines think?" Since then, two mutually contradictory but well-founded attitudes towards the Turing Test have arisen in the philosophical literature. On the one hand is the attitude that has become philosophical conventional wisdom, viz., that the Turing Test is hopelessly flawed as a sufficient condition for intelligence, while on the other hand is the overwhelming sense that were a machine (...) to pass a real live full-fledged Turing Test, it would be a sign of nothing but our orneriness to deny it the attribution of intelligence. The arguments against the sufficiency of the Turing Test for determining intelligence rely on showing that some extra conditions are logically necessary for intelligence beyond the behavioral properties exhibited by an agent under a Turing Test. Therefore, it cannot follow logically from passing a Turing Test that the agent is intelligent. I argue that these extra conditions can be revealed by the Turing Test, so long as we allow a very slight weakening of the criterion from one of logical proof to one of statistical proof under weak realizability assumptions. The argument depends on the notion of interactive proof developed in theoretical computer science, along with some simple physical facts that constrain the information capacity of agents. Crucially, the weakening is so slight as to make no conceivable difference from a practical standpoint. Thus, the Gordian knot between the two opposing views of the sufficiency of the Turing Test can be cut. (shrink)

Embodied Cognition is an approach to cognition that departs from traditional cognitive science in its reluctance to conceive of cognition as computational and in its emphasis on the significance of an organism's body in how and what the organism thinks. Three lines of embodied cognition research are described and some thoughts on the future of embodied cognition offered.

Block’s, 5–43 1981) anti-behaviourist attack of the Turing Test not only illustrates that the test is a non-sufficient criterion for attributing thought; I suggest that it also exemplifies the limiting case of the more general concern that a machine which has access to enormous amounts of data can pass the Turing Test by simple symbol-manipulation techniques. If the answers to a human interrogator are entailed by the machines’ data, the Turing Test offers no clear criterion to distinguish between a thinking (...) machine and a machine that merely manipulates representations of words and sentences as it is found in contemporary Natural Language Processing models. This paper argues that properties about vagueness are accessible to any human-like thinker but do not normally display themselves in ordinary language use. Therefore, a machine that merely performs simple symbol manipulation from large amounts of previously acquired data – where this body of data does not contain facts about vagueness – will not be able to report on these properties. Conversely, a machine that has the capacity to think would be able to report on these properties. I argue that we can exploit this fact to establish a sufficient criterion of thought. The criterion is a specification of some of the questions that, as I explain, should be asked by the interrogator in a Turing Test situation. (shrink)

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. (shrink)

In this prcis we focus on phenomena central to the reaction against similarity-based theories that arose in the 1980s and that subsequently motivated the approach to semantic knowledge. Specifically, we consider (1) how concepts differentiate in early development, (2) why some groupings of items seem to form or coherent categories while others do not, (3) why different properties seem central or important to different concepts, (4) why children and adults sometimes attest to beliefs that seem to contradict their direct experience, (...) (5) how concepts reorganize between the ages of 4 and 10, and (6) the relationship between causal knowledge and semantic knowledge. The explanations our theory offers for these phenomena are illustrated with reference to a simple feed-forward connectionist model. The relationships between this simple model, the broader theory, and more general issues in cognitive science are discussed. (shrink)

In proposing that their model can operate in the concrete, perceptual world, Rogers & McClelland (R&M) have not done justice to the complexities of the ecological sphere and its invariance laws. The structure of concrete events forces a different framework, both for retrieval of events and concepts defined across events, than that upon which the proposed model, rooted in essence in the verbal learning tradition, implicitly rests.

In their exhaustive study of the cognitive operation of analogy, Hofstadter and Sander arrive at a paradox: the creative and inexhaustible production of analogies in our thought must derive from a “reminding” operation based upon the availability of the detailed totality of our experience. Yet the authors see no way that our experience can be stored in the brain in such detail nor do they see how such detail could be accessed or retrieved such that the innumerable analogical remindings we (...) experience can occur. Analogy creation, then, should not be possible. The intent here is to sharpen and deepen our understanding of the paradox, emphasizing its criticality. It will be shown that the retrieval problem has its origins in the failure of memory theory to recognize the actual dynamic structure of events. This structure is comprised of invariance laws as per J. J. Gibson, and this event “invariance structure” is exactly what supports Hofstadter and Sander’s missing mechanism for analogical reminding. Yet these structures of invariants, existing only over optical flows, auditory flows, haptic flows, etc., are equally difficult to imagine being stored in a static memory, and thus only exacerbate the problem of the storage of experience in the brain. A possible route to the solution of this dilemma, based in the radical model of Bergson, is also sketched. (shrink)

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

: Artificial intelligences and robots increasingly mimic human mental powers and intelligent behaviour. However, many authors claim that ascribing human mental powers to them is both conceptually mistaken and morally dangerous. This article defends the view that artificial intelligences can have human-like mental powers, by claiming that both human and artificial minds can be seen as extended minds – along the lines of Chalmers and Clark’s view of mind and cognition. The main idea of this article is that the Extended (...) Mind Model is independently plausible and can easily be extended to artificial intelligences, providing a solid base for concluding that artificial intelligences possess minds. This may warrant viewing them as morally responsible agents. Keywords: Artificial Intelligence; Mind; Moral Responsibility; Extended Cognition Intelligenze artificiali come menti estese. Perché no? Riassunto: Intelligenze artificiali e robot simulano in misura sempre crescente le capacità mentali e i comportamenti intelligenti umani. Molti autori, tuttavia, sostengono che attribuire loro capacità mentali umane sia concettualmente errato e moralmente pericoloso. In questo lavoro si difende l’idea per cui le intelligenze artificiali possano avere capacità mentali simili a quelle umane, sostenendo che menti umane e artificiali possano essere considerate come menti estese – sulla scorta della prospettiva di Chalmers e Clark circa la mente e la cognizione. L’idea principale alla base di questo lavoro è che il Modello della Mente Estesa abbia plausibilità a prescindere e che possa essere facilmente esteso alle intelligenze artificiali, fornendo una base solida per concludere che le intelligenze artificiali possiedano delle menti e si possano considerare come agenti moralmente responsabili. Parole chiave: Intelligenza artificiale; Mente; Responsabilità morale; Conoscenza estesa. (shrink)

Showing remarkable insight into the relationship between language and thought, Alan Turing in 1950 proposed the Imitation Game as a proxy for the question “Can machines think?” and its meaning and practicality have been debated hotly ever since. The Imitation Game has come under criticism within the Computer Science and Artificial Intelligence communities with leading scientists proposing alternatives, revisions, or even that the Game be abandoned entirely. Yet Turing’s imagined conversational fragments between human and machine are rich with complex instances (...) of inference of implied information, reasoning from generalizations, and meta-reasoning, challenges AI practitioners have wrestled with since at least 1980 and continue to study. We argue that the very fact the Imitation Game is so difficult may be the very reason it shouldn’t be changed or abandoned. The semi-decidability of the game at this point hints at the possibility of a hard limit to the powers of technology. (shrink)

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

The development of robots that closely resemble human beings can contribute to cognitive research. An android provides an experimental apparatus that has the potential to be controlled more precisely than any human actor. However, preliminary results indicate that only very humanlike devices can elicit the broad range of responses that people typically direct toward each other. Conversely, to build androids capable of emulating human behavior, it is necessary to investigate social activity in detail and to develop models of the cognitive (...) mechanisms that support this activity. Because of the reciprocal relationship between android development and the exploration of social mechanisms, it is necessary to establish the field of android science. Androids could be a key testing ground for social, cognitive, and neuroscientific theories as well as platform for their eventual unification. Nevertheless, subtle flaws in appearance and movement can be more apparent and eerie in very humanlike robots. This uncanny phenomenon may be symptomatic of entities that elicit our model of human other but do not measure up to it. If so, very humanlike robots may provide the best means of pinpointing what kinds of behavior are perceived as human, since deviations from human norms are more obvious in them than in more mechanical-looking robots. In pursuing this line of inquiry, it is essential to identify the mechanisms involved in evaluations of human likeness. One hypothesis is that, by playing on an innate fear of death, an uncanny robot elicits culturally-supported defense responses for coping with death’s inevitability. An experiment, which borrows from methods used in terror management research, was performed to test this hypothesis. [Thomson Reuters Essential Science Indicators: Fast Breaking Paper in Social Sciences, May 2008]. (shrink)

The Human Brain Project (HBP) is a massive interdisciplinary project involving hundreds of researchers across more than eighty institutions that seeks to leverage cutting edge information and communication technologies to create a multi-level brain simulation platform (BSP). My worry is that some brain models running on the BSP will be persons. If this is right then not only will the in silico experiments the HBP envisions being carried on the BSP be unethical the mere termination of certain brain models running (...) on the BSP will be unethical. To assess the possible personhood of certain brain simulations I consider John Searle’s critique of strong AI. In arguing that Searle’s critique fails I conclude that the HBP must tread carefully and devise strict rules on how research using the BSP ought to proceed. (shrink)

Imagine advanced computers that could, by virtue merely of being programmed in the right ways, act, react, communicate, and otherwise behave like humans. Might such computers be capable of understanding, thinking, believing, and the like? The framework developed in this paper for tackling challenging questions of concept application (in any realm of discourse) answers in the affirmative, contrary to Searle’s famous ‘Chinese Room’ thought experiment, which purports to prove that ascribing such mental processes to computers like these would be necessarily (...) incorrect. The paper begins by arguing that the core issue concerns language, specifically the discourse-community-guided mapping of phenomena onto linguistic categories. It then offers a model of how people adapt language to deal with novel states of affairs and thereby lend generality to their words, employing processes of assimilation, lexemic creation, and accommodation (in intersense and intrasense varieties). Attributions of understanding to some computers lie in the middle range on a spectrum of acceptability and are thus reasonable. Possible objections deriving from Searle’s writings require supplementing the model with distinctions between present and future acceptability, and between contemplated and uncontemplated word uses, as well as a literal-figurative distinction that is more sensitive than Searle’s to actual linguistic practice and the multiplicity of subsenses possible within a single literal sense. The paper then critiques two misleading rhetorical features of Searle’s Chinese Room presentation, and addresses a contemporary defense of Searle that seems to confront the sociolinguistic issue, but fails to allow for intrasense accommodation. It concludes with a brief consideration of the proper course for productive future discussion. (shrink)

A fundamental problem in artificial intelligence is that nobody really knows what intelligence is. The problem is especially acute when we need to consider artificial systems which are significantly different to humans. In this paper we approach this problem in the following way: we take a number of well known informal definitions of human intelligence that have been given by experts, and extract their essential features. These are then mathematically formalised to produce a general measure of intelligence for arbitrary machines. (...) We believe that this equation formally captures the concept of machine intelligence in the broadest reasonable sense. We then show how this formal definition is related to the theory of universal optimal learning agents. Finally, we survey the many other tests and definitions of intelligence that have been proposed for machines. (shrink)

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

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

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. (shrink)

The Turing Test, 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, 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, but in these casesit becomes problematic to call the result artificial. This hasimplications for the means by which intelligent agents could bedeveloped. (shrink)

Alan Turing’s 1950 imitation game has been widely understood as a means for testing if an entity is intelligent. Following a series of papers by Diane Proudfoot, I offer a socio-technological interpretation of Turing’s paper and present an alternative way of understanding both the imitation game and Turing’s concept of intelligence. Turing, I claim, saw intelligence as a social concept, meaning that possession of intelligence is a property determined by society’s attitude toward the entity. He realized that as long as (...) human society held a prejudiced attitude toward machinery—seeing machines a priori as mindless objects—machines could not be said to be intelligent, by definition. He also realized, though, that if humans’ a priori, chauvinistic attitude toward machinery changed, the existence of intelligent machines would become logically possible. Turing thought that such a change would eventually occur: He believed that when scientists overcome the technological challenge of constructing sophisticated machines that could imitate human verbal behavior—i.e., do well in the imitation game—humans’ prejudiced attitude toward machinery will have altered in such a way that machines could be said to be intelligent. The imitation game, for Turing, was not an intelligence test, but a technological aspiration whose realization would likely involve a change in society’s attitude toward machines. (shrink)

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

Turing’s Imitation Game is often viewed as a test for theorised machines that could ‘think’ and/or demonstrate ‘intelligence’. However, contrary to Turing’s apparent intent, it can be shown that Turing’s Test is essentially a test for humans only. Such a test does not provide for theorised artificial intellects with human-like, but not human-exact, intellectual capabilities. As an attempt to bypass this limitation, I explore the notion of shifting the goal posts of the Turing Test, and related tests such as the (...) Total Turing Test, away from the exact imitation of human capabilities, and towards communication with humans instead. While the continued philosophical relevance of such tests is open to debate, the outcome is a different class of tests which are, unlike the Turing Test, immune to failure by means of sub-cognitive questioning techniques. I suggest that attempting to instantiate such tests could potentially be more scientifically and pragmatically relevant to some Artificial Intelligence researchers, than instantiating a Turing Test, due to the focus on producing a variety of goal directed outcomes through communicative methods, as opposed to the Turing Test’s emphasis on ‘fooling’ an Examiner. (shrink)

In their enthusiasm for programming, computational linguists have tended to lose sight of what humansdo. They have conceived of conversations as independent of sound and the bodies that produce it. Thus, implicit in their simulations is the assumption that the text is the essence of talk. In fact, unlike electronic mail, conversations are acoustic events. During everyday talk, human understanding depends both on the words spoken and on fine interpersonal vocal coordination. When utterances are analysed into sequences of word-based forms, (...) however, these prosodic aspects of language disappear. Therefore, to investigate the possibility that machines might talk, we propose acommunion game that includes this interpersonal patterning. Humans and machines would talk together and, based on recordings of them, a panel would appraise the relevant merit of each machine's simulation by how true to life it sounded. Unlike Turing's imitation game, the communion game overtly focuses attention, not on intelligence, but on language. It is designed to facilitate the development of social groups of adaptive robots that exploit complex acoustic signals in real time. We consider how the development of such machines might be approached. (shrink)

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.

In this paper I evaluate the soundness of the prototype paradigm, in particular its basic assumption that there are pan-human psychological essences or core meanings that refer to basic-level natural kinds, explaining why, on the whole, human communication and learning are successful. Instead I argue that there are no particular pan-human basic elements for thought, meaning and cognition, neither prototypes, nor otherwise. To illuminate my view I draw on examples from anthropology. More generally I argue that the prototype paradigm exemplifies (...) two assumptions that dominate cognitive science: (1) If human beings use words they mean something particular and what they mean can be discovered by scientific methods. (2) There is a fixed number of domains of categorization, each made up of a fixed number of basic categories. I suggest that these two assumptions lead to Brave New World. (shrink)

For many, the very idea of an artificialintelligence has always been ethicallytroublesome. The putative ability of machinesto mimic human intelligence appears to callinto question the stability of taken forgranted boundaries between subject/object,identity/similarity, free will/determinism,reality/simulation, etc. The artificiallyintelligent object thus appears to threaten thehuman subject with displacement and redundancy.This article takes as its starting point AlanTuring''s famous ''imitation game,'' (the socalled ''Turing Test''), here treated as aparable of the encounter between human originaland machine copy – the born and the made. Thecultural (...) resonances of the recent on-lineperformance of a ''Turing Test'' for computergenerated art are then explored. Arttraditionally taken to stand for all that isconsidered quintessentially human – andtherefore resistant to mechanisation –represents in this sense a kind of ''criticalcase'' in the advance of machine intelligence.The article focuses on the moral status of thebody, human agency, and social knowledge in theongoing (re-)constructions of copy, original,and of the difference between them. (shrink)

In this paper I argue that whether or not a computer can be built that passes the Turing test is a central question in the philosophy of mind. Then I show that the possibility of building such a computer depends on open questions in the philosophy of computer science: the physical Church-Turing thesis and the extended Church-Turing thesis. I use the link between the issues identified in philosophy of mind and philosophy of computer science to respond to a prominent argument (...) against the possibility of building a machine that passes the Turing test. Finally, I respond to objections against the proposed link between questions in the philosophy of mind and philosophy of computer science. (shrink)

Proceedings of the papers presented at the Symposium on "Revisiting Turing and his Test: Comprehensiveness, Qualia, and the Real World" at the 2012 AISB and IACAP Symposium that was held in the Turing year 2012, 2–6 July at the University of Birmingham, UK. Ten papers. - http://www.pt-ai.org/turing-test --- Daniel Devatman Hromada: From Taxonomy of Turing Test-Consistent Scenarios Towards Attribution of Legal Status to Meta-modular Artificial Autonomous Agents - Michael Zillich: My Robot is Smarter than Your Robot: On the Need for (...) a Total Turing Test for Robots - Adam Linson, Chris Dobbyn and Robin Laney: Interactive Intelligence: Behaviour-based AI, Musical HCI and the Turing Test - Javier Insa, Jose Hernandez-Orallo, Sergio España - David Dowe and M.Victoria Hernandez-Lloreda: The anYnt Project Intelligence Test (Demo) - Jose Hernandez-Orallo, Javier Insa, David Dowe and Bill Hibbard: Turing Machines and Recursive Turing Tests — Francesco Bianchini and Domenica Bruni: What Language for Turing Test in the Age of Qualia? - Paul Schweizer: Could there be a Turing Test for Qualia? - Antonio Chella and Riccardo Manzotti: Jazz and Machine Consciousness: Towards a New Turing Test - William York and Jerry Swan: Taking Turing Seriously (But Not Literally) - Hajo Greif: Laws of Form and the Force of Function: Variations on the Turing Test. (shrink)

This paper provides a survey of philosophical discussion of the "the Turing Test". In particular, it provides a very careful and thorough discussion of the famous 1950 paper that was published in Mind.

This is the guest editor's introduction to a JETAI special issue on philosophical foundations of AI.

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 cognition, but the scope of this paper is limited to demonstrating that French is mistaken: a disembodied computer can answer subcognitive questions. (shrink)

Disertační práce se zabývá problematikou připisování myšlení jiným entitám, a to pomocí imitační hry navržené v roce 1950 britským filosofem Alanem Turingem. Jeho kritérium, známé v dějinách filosofie jako Turingův test, je podrobeno detailní analýze. Práce popisuje nejen původní námitky samotného Turinga, ale především pozdější diskuse v druhé polovině 20. století. Největší pozornost je věnována těmto kritikám: Lucasova matematická námitka využívající Gödelovu větu o neúplnosti, Searlův argument čínského pokoje konstatující nedostatečnost syntaxe pro sémantiku, Blockův návrh na použití brutální síly pro (...) řešení imitační hry, Frenchova teorie subkognitivních informací a Michieho skepticismus ohledně možnosti umělého vědomí. Závěr práce zachycuje současný stav recepce Turingova testu a představuje pokusy o jeho praktickou realizaci, například v každoroční soutěži o Loebnerovu cenu. Autor práce zastává názor, že ani po více než šedesáti letech od uveřejnění Turingova paradigmatického eseje stále neexistují žádné vážné důvody pro zamítnutí jeho tvrzení. Tradiční komputační funkcionalismus možná není ideální teorií vysvětlující činnost myslí a jako slibnější se může jevit vývoj v neurálních vědách, ale Turingův test je přesto užitečným a snad i jediným nástrojem pro detekci inteligence u lidmi vytvořených strojů. (shrink)

I claim that in order to pass the Turing Test over any period of extended time, it will necessary to embed the entity into society. This chapter discusses why this is, and how it might be brought about. I start by arguing that intelligence is better characterised by tests of social interaction, especially in open-ended and extended situations. I then argue that learning is an essential component of intelligence and hence that a universal intelligence is impossible. These two arguments support (...) the relevance of Turing Test as a particular but appropriate test of interactive intelligence. I look to the human case to argue that individual intelligence utilises society to a considerable extent for its development. Taking a lead from the human case I outline how a socially embedded artificial intelligence might be brought about in terms of four aspects: free-will, emotion, empathy and self-modelling. In each case I try to specify what social ‘hooks’ might be required in order for the full ability to develop during a considerable period of in situ acculturation. The chapter ends by speculating what it might be like to live with the result. (shrink)

The main trends underlying advancements in AI technology are increased autonomy, increased functionality, higher level social interaction and integration, and, many suggest, hard-wired ethical codes of conduct. These trends, along with the promises of engineers and scientists, give us ample reason to evaluate the agency of future AI machines.