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Turing's sexual guessing game
Abstract
Bien qu'importante, la question: L'ordinateur, pense-t-il? n'est pas la seule question sur la nature de la pensee posee, consciemment ou inconsciemment, par Turing dans son texte, extremement complexe et riche « Computing machinery and intelligence » (1950). Moins apparentes, mais non pas moins importantes d'apres l'A., sont les questions sur la nature du sexe, le naturel et l'artificiel, l'analogue et le discret, le biologique et le culturel
... (a) literally what he says -that the computer must pretend to be a woman, and the other participant in the game actually is a woman (see Genova [6] and Traiger [9]); (b) that the computer must pretend to be a woman, and the other participant in the game is a man who must also pretend to be a woman 6 . ...
... (a) literally what he says -that the computer must pretend to be a woman, and the other participant in the game actually is a woman (see Genova [6] and Traiger [9]); (b) that the computer must pretend to be a woman, and the other participant in the game is a man who must also pretend to be a woman 6 . ...
... Copeland, in commenting on the revised 1952 test [5], argues that the 1950 version is the better, as the single interview mode is open to a "biasing effect which disfavours the machine". 6 Towards the end of section (5) of the 1950 paper [11] Turing, perhaps rather confusingly suggests, [the computer] "can be made to play satisfactorily the part of (A) in the imitation game, the other part being taken by a man". 7 See Copeland [4], Piccinini [8], and Moor [7]. ...
... Judith Genova draws attention to the gender issue in the IG (Genova, 1994b). She, as we have done in Section 2.1, remarks that Turing's description of the game involves, not a question of species, but one of gender. ...
... Genova regards the IG as part of Turing's general philosophy of 'transgressing boundaries' (Genova, 1994b). Under the assumption that Turing admired such transformations that do not conform to the given discrete categories, Genova suggests that Turing might be marking the woman as an inferior thinker because he believes her to be unable to deceive. ...
... We discussed this question at length in Section 2.1. Some comments have been made on the issue (for instance Genova, 1994b;Lassegue, 1996;Abelson, 1968) but we think the best explanation is the one we provided: In the IG, the machine is supposed to be as good as a man who is imitating a woman. This gender-based design might be a methodological choice. ...
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 the 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 the 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.
... Turing quite rightly raised that question realising after WWII that man does not think like every other man; man does think like woman; an Occidental woman may not think like a woman from the Orient. Gender is regarded as an important feature in Turing's game by some (Copeland & Proudfoot 2008;Sterrett, 2000;Lassègue, 1996;Hayes & Ford, 1995;Genova, 1994). The contention is that both man and machine impersonating a woman provides a stronger test for intelligence. ...
... Turing's 1952 BBC radio discussion shows that he did not exclude women from acting as the interrogator of the machine 'witness' in his one-to-one test (Shah, 2013). Genova (1994) states "computing accomplishes the miracle of creation" (p. 320), viewing the computer "as the ultimate kind of dynamic technology" (p. ...
Should intelligent agents and robots possess gender? If so, which gender and why? The authors explore one root of the gender-in-AI question from Turing’s introductory male-female imitation game, which matured to his famous Turing test examining machine thinking and measuring its intelligence against humans. What we find is gender is not clear cut and is a social construct. Nonetheless there are useful applications for gender-cued intelligent agents, for example robots caring for elderly patients in their own home.
... It has been said to be accessible to a general readership. 19 And yet, on close reading, it has been said to be a complex, multilayered text (Genova, 1994), or one that is too ambiguous for interpretation (Hayes and Ford, 1995;McDermott, 2014). For example, an intriguing puzzle about Turing's 1950 text is why he considered the original question, whether machines can think, to be 'too meaningless to deserve discussion' and proposed to replace it with a test, an experiment, and yet spent most of his paper (Sections 6 and 7, which make up almost 70% of the paper) discussing that very question. ...
... There is significant disagreement on how the two passages should be read. Some authors acknowledged the presence of a 'gender test' in the first passage (Genova, 1994;Hayes & Ford, 1995). Others considered it to serve as a scoring protocol for a nongendered test read from the second passage (Copeland, 2004, p. 436;Proudfoot, 2013, p. 395). ...
The Turing test has been studied and run as a controlled experiment and found to be underspecified and poorly designed. On the other hand, it has been defended and still attracts interest as a test for true artificial intelligence (AI). Scientists and philosophers regret the test’s current status, acknowledging that the situation is at odds with the intellectual standards of Turing’s works. This article refers to this as the Turing Test Dilemma, following the observation that the test has been under discussion for over seventy years and still is widely seen as either too bad or too good to be a valuable experiment for AI. An argument that solves the dilemma is presented, which relies on reconstructing the Turing test as a thought experiment in the modern scientific tradition. It is argued that Turing’s exposition of the imitation game satisfies Mach’s characterization of the basic method of thought experiments and that Turing’s uses of his test satisfy Popper’s conception of the critical and heuristic uses of thought experiments and Kuhn’s association of thought experiments to conceptual change. It is emphasized how Turing methodically varied the imitation game design to address specific challenges posed to him by other thinkers and how his test illustrates a property of the phenomenon of intelligence and suggests a hypothesis on machine learning. This reconstruction of the Turing test provides a rapprochement to the conflicting views on its value in the literature.
... This is the kind of machine that Turing thought was in the right ballpark to exhibit intelligence, in virtue of the fact that digital computers were explicitly designed to carry out operations standardly performed by human computers (Turing 1950, p. 444). The role of B (the truth-teller) is to be taken by a human being, with the gender of that participant presumably no longer intended to be salient (although this is a debated issue; for discussion, see Genova 1994;Traiger 2000;Copeland 2000;Piccinini 2000;Moor 2001). Thus, when Turing returns to it later in 'Computing Machinery and Intelligence', stage two of the imitation game looks like this: 'Let us fix our attention on one particular digital computer C. Is it true that by modifying this computer to have an adequate storage, suitably increasing its speed of action, and providing it with an appropriate programme, C can be made to play satisfactorily the part of A in the imitation game, the part of B being taken by [a human being]?' (Turing 1950, p. 448). ...
The Turing Test is routinely understood as a behaviourist test for machine intelligence. Diane Proudfoot (Rethinking Turing’s Test, Journal of Philosophy, 2013) has argued for an alternative interpretation. According to Proudfoot, Turing’s claim that intelligence is what he calls ‘an emotional concept’ indicates that he conceived of intelligence in response-dependence terms. As she puts it: ‘Turing’s criterion for “thinking” is…: x is intelligent (or thinks) if in the actual world, in an unrestricted computer-imitates-human game, x appears intelligent to an average interrogator’. The role of the famous test is thus to provide the conditions in which to examine the average interrogator’s responses. I shall argue that Proudfoot’s analysis falls short. The philosophical literature contains two main models of response-dependence, what I shall call the transparency model and the reference-fixing model. Proudfoot resists the thought that Turing might have endorsed one of these models to the exclusion of the other. But the details of her own analysis indicate that she is, in fact, committed to the claim that Turing’s account of intelligence is grounded in a transparency model, rather than a reference-fixing one. By contrast, I shall argue that while Turing did indeed conceive of intelligence in response-dependence terms, his account is grounded in a reference-fixing model, rather than a transparency one. This is fortunate (for Turing), because, as an account of intelligence, the transparency model is arguably problematic in a way that the reference-fixing model isn’t.
... In this respect Haraway had history on her side, since the original problem posed by Alan Turing's famed test was not how to decide whether the interlocutor is human or machine but whether it is male or female. No more technology was involved than, say, the curtain separating the front and back stage of a theatrethough Turing may have been inspired by the early predominance of women as computer programmers (Genova 1994). And all of this may explain why Haraway flagged that her appeal to the cyborg was 'ironic'. ...
P> Nietzschean Meditations takes its inspiration from the version of Nietzsche that was popular before the Second World War, which stressed the ‘Zarathustrian’ elements of his thought as the harbinger of a new sort of being – the Übermensch . The book updates the image of this creature to present a version of ‘transhumanism’ that breaks with the more precautionary and pessimistic approaches of humanity’s future in contemporary ‘posthumanist’ thought. Fuller follows Nietzsche in discussing deeply and frankly the challenging issues that aspiring transhumanists face. They include their philosophical and especially theological roots, the implications of transhumanism for matters of life and death, and whether any traces of classical humanity will remain in the ‘transhuman’ being.</P
... Issues [1] surrounding the rules of TT have also affected this changing attitude. For example, because the machine is judged by how well it imitates something it is not, perhaps it would be fair that the machine is compared against a person who is also imitating something he/she is not [5,6]. Nonetheless, although it is debatable whether machines can "think," the fact that machines can learn is definitely not. ...
The emulation of human behavior for autonomous problem solving has been an interdisciplinary field of research. Generally, classical control systems are used for static environments, where external disturbances and changes in internal parameters can be fully modulated before or neglected during operation. However, classical control systems are inadequate at addressing environmental uncertainty. By contrast, autonomous systems, which were first studied in the field of control systems, can be applied in an unknown environment. This paper summarizes the state of the art autonomous systems by first discussing the definition, modeling, and system structure of autonomous systems and then providing a perspective on how autonomous systems can be integrated with advanced resources (e.g., the Internet of Things, big data, Over-the-Air, and federated learning). Finally, what comes after reaching full autonomy is briefly discussed.
... This shows that, despite some interpretations, Turing did not favor a gender revolutionary project. This is worth mentioning, as some interpretations, which have focused upon gender (Genova 1994;Lassègue 1996), claim that Turing had a political agenda in "Computing Machinery and Intelligence." However, it is unlikely that Turing had had such a political project in mind. ...
This paper examines an insoluble Cartesian problem for classical AI, namely, how linguistic understanding involves knowledge and awareness of u’s meaning, a cognitive process that is irreducible to algorithms. As analyzed, Descartes’ view about reason and intelligence has paradoxically encouraged certain classical AI researchers to suppose that linguistic understanding suffices for machine intelligence. Several advocates of the Turing Test, for example, assume that linguistic understanding only comprises computational processes which can be recursively decomposed into algorithmic mechanisms. Against this background, in the first section, I explain Descartes’ view about language and mind. To show that Turing bites the bullet with his imitation game and in the second section I analyze this method to assess intelligence. Then, in the third section, I elaborate on Schank and Abelsons’ Script Applier Mechanism (SAM, hereby), which supposedly casts doubt on Descartes’ denial that machines can think. Finally, in the fourth section, I explore a challenge that any algorithmic decomposition of linguistic understanding faces. This challenge, I argue, is the core of the Cartesian problem: knowledge and awareness of meaning require a first-person viewpoint which is irreducible to the decomposition of algorithmic mechanisms.
... It is 'rear-guard' because Dreyfus and his followers-an impressive recent example of which is Frischmann and Selinger (2018) scholars, who argue that the embodiment differences between, say, men and women or Whites and Blacks renders illusory any sense of 'universal humanity' at the phenomenological level. Indeed, I believe that the unapologetically constructivist cast of the Turing Test-originally genderblind but equally substrate-blind-remains the benchmark of demarcating the human and nonhuman (Genova 1994). In other words, if a candidate entity passes as 'human' on the basis of its sustained performance, then it counts as a human, regardless of its class, race, gender or substrate. ...
The classical political economists generally believed that free markets are the ideal setting to release previously untapped human creative potential, ultimately resulting in prosperity for all. However, David Ricardo added an ironic twist to this article of faith by observing that much—if not most—of our creative potential has been dedicated to the replacement of human by non-human labour, first by animals and then by machines. Marx was among the first to realize that one long-term consequence of this tendency is that under capitalism humans are always under the threat of redundancy. More to the point, humans will continue to need to justify their existence—the ‘added value’ of their labour. The past 50 years have arguably witnessed an acceleration in this tendency, especially with the roughly simultaneous rise of artificial intelligence and decline of socialism. This chapter explores the serious prospect that humanity might become an ‘ontologically endangered species’, unless it is continually prepared to redefine its ‘nature’. In particular, doubts are cast on claims nowadays associated with followers of Hubert Dreyfus that humanity has a fixed essence associated with powers that a ‘superintelligent’ machine could never match. Indeed, much of this anti-AI thinking overlooks the increasing significance that cyborgs are likely to play in defining what it means to be human.
... We acknowledge as examples of this, that some see it as being something to do with artistic and emotional intelligence [24], whereas others deem it to be concerned with modelling the human mind by generating its verbal performance capacity [8]. Others meanwhile regard it in terms of considering the gender aspect, the sex of the human foil being important in the test [7,9,12,26]. None of these views, however, do we see as indicating the test to be detrimental to the human race. ...
In this paper we look at the phenomenon that is the Turing test. We consider how Turing originally introduced his imitation game and discuss what this means in a practical scenario. Due to its popular appeal we also look into different representations of the test as indicated by numerous reviewers. The main emphasis here, however, is to consider what it actually means for a machine to pass the Turing test and what importance this has, if any. In particular does it mean that, as Turing put it, a machine can “think”. Specifically we consider claims that passing the Turing test means that machines will have achieved human-like intelligence and as a consequence the singularity will be upon us in the blink of an eye.
... Jeżeli zaś chodzi o zwolenników literalnego odczytywania "Computing Machinery..." (por. [Genova 1994], [Lassègue 1996], [Lassègue 2009], [Naur 1986, [Gelernter 1994], [Hayes, Ford 1995]), to: ...
... Reflect, though, on what undoubtedly is the most striking feature of the Turing Test: Turing's positioning of the computer in the place of a man imitating a woman. Conversational agents do more in their fabrication of character than persuade us they are humanlike; they persuade us of a particular vision of what it means to be gendered (Genova, 1994). ...
Two fundamental (and oftentimes opposing) metaphors have directed much of HCI design: HCI is communication and HCI is direct manipulation. Beneath these HCI metaphors, however, is the unspoken metaphor of computer is woman. In this paper we expose this foundational metaphor. We begin by identifying the origin of computer is woman in the early history of computing. Drawing upon postmodern feminist theory, we then explore how this metaphor has resulted
in the feminization of HCI is communication and second person interfaces. We show how images of femininity proliferate, becoming the projected images of male fantasies
and ideals of womanhood. In becoming these idealized images, the interface is revealed as man in female drag. Finally, not only do we undress the interface to uncover how HCI is communication wraps the computer’s difference from human being within the more basic metaphor of computer is woman, but we also disclose dangers that can arise when this metaphor goes unacknowledged and unexamined.
In 1950, Alan Turing proposed his iconic imitation game, calling it a 'test', an 'experiment', and the 'the only really satisfactory support' for his view that machines can think. Following Turing's rhetoric, the 'Turing test' has been widely received as a kind of crucial experiment to determine machine intelligence. In later sources, however, Turing showed a milder attitude towards what he called his 'imitation tests'. In 1948, Turing referred to the persuasive power of 'the actual production of machines' rather than that of a controlled experiment. Observing this, I propose to distinguish the logical structure from the rhetoric of Turing's argument. I argue that Turing's proposal of a crucial experiment may have been a concession to meet the standards of his interlocutors more than his own, while his construction of machine intelligence rather reveals a method of successive idealizations and exploratory experiments. I will draw a parallel with Galileo's construction of idealized fall in a void and the historiographical controversies over the role of experiment in Galilean science. I suggest that Turing, like Galileo, relied on certain kinds of experiment, but also on rhetoric and propaganda to inspire further research that could lead to convincing scientific and technological progress.
A re-reading of the Turing Test is developed and applied to contemporary challenges facing “nudging” and AI. Turing’s 1936 analysis of computation is reviewed in light of its resonance with Wittgenstein’s Blue and Brown Books; current objections to the Turing Test (including Searle’s Chinese Room) are answered; and Turing’s Test for “intelligent machinery” is construed as a social experiment between humans in the face of emerging technology. The significance of what Wolfram has called “computational irreducibility” and what Wittgenstein called the need for “surveyability” of algorithms are stressed and then placed beside contemporary concerns about nudging, algorithms and Artificial Intelligence as these are applied with increasing precision and ubiquity in everyday life. Concerns about privacy, surveillance, informatics domination, lack of explicability of decision-making, biases, ethics, and unequal negative impacts on populations are discussed in light of these concepts. Turing’s prescient idea, that it is human beings who bear responsibility for meaningful public discussion of the sorting, typing and design of algorithms, is defended as something more than what Kahnemann, Sibony and Sunstein have recently denigrated as mere “noise”.
Turing proposed in 1950 his famous imitation game or test: a machine is supposed to imitate, sometimes a woman, sometimes a man. In 1995 scientists in artificial intelligence complained that, according to Turing, the goal of the field should be to build a ``mechanical transvestite.'' Supporters of Turing's test as a decisive experiment for machine intelligence then suggested to read ``man'' in Turing's text as masculine generics. Drawing also from primary sources other than Turing's 1950 text, they argued that Turing must have proposed not a gender, but a species test. My contention is that Turing did propose gender learning and imitation as one of his various tests for machine intelligence. I shall reconstruct the context of Turing's 1950 proposal and point out that it came out of a 1949 controversy, notably with neurosurgeon Geoffrey Jefferson. I will then try to show that Turing designed his imitation game as a thought experiment to refute, among other things, an \emph{a priori} view of Jefferson that intelligence was an exclusive feature of the animal nervous system, and that interesting behavior in the male and the female was largely determined by sex hormones.
C. Wright Mills views sociological imagination as the ability to relate the most intimate to the most impersonal. There are essential linkages between personal troubles and social issues. The sociologist should be able to trace the linkages between biographies and histories. Sociological imagination necessitates sensibility, commitment and responsibility since sociology is a practice of life as well as a practice of work. Sociology is, then, a practice that potentially everyone can perform. The crucial condition is the existence of sociological imagination and sensibility. This sensibility indicates the capacity to picture a social imaginary, however broad or limited. This paper traces the sociological imagination of Alan Turing, who is often considered as the founder of modern computing technology. The history of Turing's scientific endeavors follows (and is followed by) his biography, revealing the strong linkages between his life and work. Turing's sensible, committed and responsible attitude is clear in several cases. This paper focuses on the case of artificial intelligence in order to assess Turing's sociological imagination. The paper claims that Alan Turing has the sensibility and imagination.to picture a social imaginary. In order to analyze Turing's imagination in the example of artificial intelligence, the paper refers to three faces of sociological imagination of Mills: (1) emphasis on the relation between the most intimate and the most impersonal; (2) developing new sensibilities and new spaces of sensibility; (3) imagining a social picture.
C. Wright Mills, toplumbilimsel düşünü, en yakın ile kişisellikten en uzak olanı ilişkilendirebilme yeteneği olarak görür. Mills'e göre, kişisel sorunlar ile toplumsal meseleler arasında zorunlu bağlantılar vardır. Bir sosyolog kişisel yaşam öyküleri ile tarihin hikâyesi arasındaki bağların izini sürebilmelidir. Toplumbilim bir iş olduğu kadar bir yaşam pratiği olduğu için, toplumbilimsel düşün duyarlılık, sadakat ve sorumluluk gerektirir. Şu halde toplumbilim aslında potansiyel olarak herkesin gerçekleştirebileceği bir pratiktir. Burada önemli olan toplumbilimsel düşünün ve duyarlılığın varlığıdır. Bu duyarlılık, geniş veya sınırlı, bir toplumsal düşü resmedebilme yeterliliğini işaret eder. Bu yazı, kimilerince modern bilgisayar teknolojisinin kurucusu olarak kabul edilen Alan Turing'in toplumbilimsel düşününün izini sürmeyi amaçlamaktadır. Turing'in bilimsel çabalarının tarihi kendi yaşam öyküsü ile yaşamı ve işi arasındaki güçlü bağlantıları açık edecek şekilde iç içe geçmiş durumdadır. Onun duyarlı, adanmış ve sorumlu tavrı farklı durumlarda karşımıza çıkar. Bu yazı, Turing'in toplumbilimsel düşününü değerlendirebilmek amacıyla yapay zekâ örneğine odaklanmaktadır. Yazı, Alan Turing'in bir toplumsal hayal resmedilme duyarlılığı ve düşününe sahip olduğu iddiasından hareket etmektedir. Turing'in bu düşününü yapay zekâ örneğinde kalarak analiz etmek üzere, metin C. W. Mills'in toplumbilimsel düşün analizinin, diğer özelliklerinin yanında, üç çehresine başvurur: (1) en yakın ve kişisel olan ile en uzak ve en az kişisel olan arasındaki ilişkiyi vurgulamak; (2) yeni duyular geliştirmek ve duyarlılık alanları açmak; (3) toplumsal bir resim hayal edebilmek. C. W. Mills'in işaret ettiği üç çehreyi kullanarak Turing'in biyografisini ve ardından Turing'in matematiksel ama aynı zamanda toplumbilimsel imgelemini ve bu imgelemin bir ürünü olarak yapay zekâyı analiz eden metin, eş zamanlı olarak C. W. Mllls'in toplumbilimsel imgelem kavramının farklı açılımlarını yine yapay zekâ üzerinden göstermeyi amaçlamaktadır.
C. Wright Mills views sociological imagination as the ability to relate the most intimate to the most impersonal. There are essential linkages between personal troubles and social issues. The sociologist should be able to trace the linkages between biographies and histories. Sociological imagination necessitates sensibility, commitment and responsibility since sociology is a practice of life as well as a practice of work. Sociology is, then, a practice that potentially everyone can perform. The crucial condition is the existence of sociological imagination and sensibility. This sensibility indicates the capacity to picture a social imaginary, however broad or limited. This paper traces the sociological imagination of Alan Turing, who is often considered as the founder of modern computing technology. The history of Turing's scientific endeavors follows (and is followed by) his biography, revealing the strong linkages between his life and work. Turing's sensible, committed and responsible attitude is clear in several cases. This paper focuses on the case of artificial intelligence in order to assess Turing's sociological imagination. The paper claims that Alan Turing has the sensibility and imagination.to picture a social imaginary. In order to analyze Turing's imagination in the example of artificial intelligence, the paper refers to three faces of sociological imagination of Mills: (1) emphasis on the relation between the most intimate and the most impersonal; (2) developing new sensibilities and new spaces of sensibility; (3) imagining a social picture.
Turing's Imitation Game was a brilliant early proposed test of machine intelligence - one that is still compelling today, despite the fact that in the hindsight of all that we've learned in the intervening 65 years we can see the flaws in his original test. And our field needs a good "Is it AI yet?" test more than ever today, with so many of us spending our research time looking under the "shallow processing of big data" lamppost. If Turing were alive today, what sort of test might he propose? Copyright © 2016, Association for the Advancement of Artificial Intelligence. All rights reserved.
In this paper, we look at a specific issue with practical Turing tests, namely the right of the machine to remain silent during interrogation. In particular, we consider the possibility of a machine passing the Turing test simply by not saying anything. We include a number of transcripts from practical Turing tests in which silence has actually occurred on the part of a hidden entity. Each of the transcripts considered here resulted in a judge being unable to make the ‘right identification’, i.e., they could not say for certain which hidden entity was the machine.
In this article the central philosophical issues concerning human-level artificial intelligence (AI) are presented. AI largely changed direction in the 1980s and 1990s, concentrating on building domain-specific systems and on subgoals such as self-organization, self-repair, and reliability. Computer scientists aimed to construct intelligence amplifiers for human beings, rather than imitation humans. Turing based his test on a computer-imitates-human game, describing three versions of this game in 1948, 1950, and 1952. The famous version appears in a 1950 article in Mind, 'Computing Machinery and Intelligence' (Turing 1950). The interpretation of Turing's test is that it provides an operational definition of intelligence (or thinking) in machines, in terms of behavior. 'Intelligent Machinery' sets out the thesis that whether an entity is intelligent is determined in part by our responses to the entity's behavior. Wittgenstein frequently employed the idea of a human being acting like a reliable machine. A 'living reading-machine' is a human being or other creature that is given written signs, for example Chinese characters, arithmetical symbols, logical symbols, or musical notation, and who produces text spoken aloud, solutions to arithmetical problems, and proofs of logical theorems. Wittgenstein mentions that an entity that manipulates symbols genuinely reads only if he or she has a particular history, involving learning and training, and participates in a social environment that includes normative constraints and further uses of the symbols.
Según la versión estándar del juego de la imitación, la determinación del sexo de los participantes no desempeña ningún papel en el testeo de la inteligencia de máquina. Desafortunadamente, tal simplificación soslaya la teoría de la mente que fundamenta dicho juego. Teniendo en consideración este problema, en este ensayo argumento en contra de la simplificación del Test de Turing. En efecto, tal como sostengo, la determinación del sexo de los participantes no debe obviarse: la mente de una mujer y su inteligencia son imitables y no dependen de realización física específica. Esto ocurre porque el funcionalismo de Turing supone un vínculo entre imitación, engaño y aprendizaje, tres claves para que el proyecto de la Inteligencia Artificial tenga éxito.
:In popular culture and in artificial intelligence, the Turing test has been understood as a means to distinguish between human and machine. Through a discussion of Richard Powers's Galatea 2.2: A Novel, Joseph Weizenbaum's computer program therapist ELIZA, and Emily Short's interactive fiction Galatea, this essay argues that our continued fascination with the Turing test can also be understood through Turing's introduction of the very possibility of misidentifying human for machine, and machine for human. This spectre of misidentification can open up potential recalibrations of human-machine interactivities, as well as the very categories of human and machine. Reading these literary and computational works alongside theoretical discussions of the Turing test, the essay attends to anthropomorphization as a productive metaphor in the Turing test. Anthropomorphization is a significant cultural force that shapes and undergirds multiple discursive spaces, operating varyingly therein to articulate conceptions of the human that are not reified and inviolable, but that continuously re-emerge through dynamic human-machine relations.
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.
Passing the Turing Test is not a sensible goal for Artificial Intelligence. Adherence to Turing's vision from 1950 is now actively harmful to our field. We review problems with Turing's idea, and suggest that, ironically, the very cognitive science that he tried to create must reject his research goal.
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.
Passing the Turing Test is not a sensible goal for Artificial Intelligence. Adherence to Turing's vision from 1950 is now actively harmful to our field. We review problems with Turing's idea, and suggest that, ironically, the very cognitive science that he tried to create must reject his research goal. 1
On March 27, 2006, I gave a light-hearted and occasionally bittersweet presentation on "Whatever Happened to AI?" at the Stanford Spring Symposium presentation - to a lively audience of active AI researchers and formerly active ones (whose current inaction could be variously ascribed to their having aged, reformed, given up, redefined the problem, and so on). This article is a brief chronicling of that talk, and I entreat the reader to take it in that spirit: a textual snapshot of a discussion with friends and colleagues, rather than a scholarly article. I begin by whining about the Turing test, but only for a thankfully brief bit, and then get down to my top-10 list of factors that have retarded progress in our field, that have delayed the emergence of a true strong AI. Copyright © 2008, Association for the Advancement of Artificial Intelligence. All rights reserved.
The Turing Test (TT) works by assuming that humans have minds and that natural language is sufficient to represent mind, a machine has a mind if a machine and a human are indistinguishable in discourse. An important consequence of the TT is that the machine's internal mechanism, as well as its outward visual or aural presence, is irrelevant. To test the output of generative music systems, the TT might be altered by making aesthetic artifacts, music or other creative forms, the medium of the test leads to the development of two tests musical directive toy test (MDtT) and musical output toy test (MOtT). In MDtT test the interrogator who uses a computer interface, sends a musical directive to two composer-agents that includes machine and the other is a human. This test would permit the interrogator to submit as many musical directives as desired. The other test MOtT two composer-agents provide are synthetic digital audio, or digital audio of a recorded performance to the interrogator.
In ‘Computing Machinery and Intelligence’, Alan Turing actually proposed not one, but two, practical tests for deciding the question ‘Can a machine 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’. Although the first, neglected, test uses a human’s linguistic performance in setting an empirical test of intelligence, it does not make behavioral similarity to that performance the criterion of intelligence. The two tests yield different results, and the first provides a more appropriate measure of intelligence.
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Thesis (Ph. D.)--Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, 2000. Includes bibliographical references (leaves 184-200).
For thousands of years communication has functioned principally by means of linguistic and iconic messages. In the first case linguistic symbols serve as intermediaries; in the second, images or, more broadly, representations. In order to be transmitted, linguistic and/or iconic symbols need to be re -produced, re -presented, vocally, through writing, painting, sculpture or any other means of re -production. But re-production requires a space that, through use of an appropriate material, serves as its medium; forms to occupy it; rules to control it, and a certain stability. In other words representation is impossible without a certain fixity of the message that alone can ensure its duration, whether short or long being unimportant, but which it needs precisely in order to function as a message.
173Boston: Little, Brown, & Co. draws a parallel between sex and consciousness in his new book
- Dan Dennett