Though less well known than his other work, Turings 1938 Princeton Thesis, this title which includes his notion of an oracle machine, has had a lasting influence on computer science and mathematics. It presents a facsimile of the original typescript of the thesis along with essays by Appel and Feferman that explain its still-unfolding significance.

Abstract Philosophical discussion of Alan Turing’s writings on intelligence has mostly revolved around a single point made in a paper published in the journal Mind in 1950. This is unfortunate, for Turing’s reflections on machine (artificial) intelligence, human intelligence, and the relation between them were more extensive and sophisticated. They are seen to be extremely well-considered and sound in retrospect. Recently, IBM developed a question-answering computer (Watson) that could compete against humans on the game show Jeopardy! There (...) are hopes it can be adapted to other contexts besides that game show, in the role of a collaborator of, rather than a competitor to, humans. Another, different, research project --- an artificial intelligence program put into operation in 2010 --- is the machine learning program NELL (Never Ending Language Learning), which continuously ‘learns’ by ‘reading’ massive amounts of material on millions of web pages. Both of these recent endeavors in artificial intelligence rely to some extent on the integration of human guidance and feedback at various points in the machine’s learning process. In this paper, I examine Turing’s remarks on the development of intelligence used in various kinds of search, in light of the experience gained to date on these projects. (shrink)

Wilfred Sieg and John Byrnes. Generalizing Turing's Machine and Arguments.

Wilfred Sieg and John Byrnes. K-Graph Machines: Generalizing Turing's Machines and Arguments.

When it comes to the question of what kind of moral claim an intelligent or autonomous machine might have, one way to answer this is by way of comparison with humans: Is there a fundamental difference between humans and other entities? If so, on what basis, and what are the implications for science and ethics? This question is inherently imprecise, however, because it presupposes that we can readily determine what it means for two types of entities to be sufficiently (...) different—what I will refer to as being “discontinuous”. In this paper, I will sketch a formal characterization of what it means for types of entities to be unique with regard to each other. This expands upon Bruce Mazlish’s initial formulation of what he terms a continuity between humans and machines, Alan Turing’s epistemological approach to the question of machine intelligence, and Sigmund Freud’s notion of scientific revolutions dealing blows to the self-esteem of mankind. I will discuss on what basis we should regard entities as (dis-)continuous, the corresponding moral and scientific implications, as well as an important difference between what I term downgrading and upgrading continuities—a dramatic difference in how two previously discontinuous types of entities might become continuous. All of this will be phrased in terms of which scientific levels of explanation we need to presuppose, in principle or in practice, when we seek to explain a given type of entity. The ultimate purpose is to provide a framework that defines which questions we need to ask if we argue that two types of entities ought (not) to be explained (hence treated) in the same manner, as well as what it takes to reconsider scientific and ethical hierarchies imposed on the natural and artificial world. (shrink)

Since the middle of the 20th century there has been a significant debate about the attribution of capacities of living systems, particularly humans, to technological artefacts, especially computers—from Turing’s opening gambit, to subsequent considerations of artificial intelligence, to recent claims about artificial life. Some now argue that the capacities of future technologies will ultimately make it impossible to draw any meaningful distinctions between humans and machines. Such issues center on what sense, if any, it makes to claim that gadgets (...) can actually think, feel, act, live, etc. I outline this debate and offer a critique of its persistent polarization. I characterize two of the debate’s major camps (associated roughly with Turing and Searle); argue that the debate’s structure (including key assumptions inherent to each camp) precludes resolution; and, contend that some central clashes within the debate actually stem from an inadequately drawn distinction between claims about the capacities of artifacts and claims about the proper criteria for assessing such attributions. I offer a different perspective in which I: challenge some central elements of the debate that contribute to its perennially irresolvable state; hold that the debate needs to be placed more squarely in sync with how we in fact treat the attribution of such capacities to humans themselves; and, offer (unlike the other two camps) a foothold for making moral assessments of such proposed technologies. (shrink)

Since the middle of the 20th century there has been a significant debate about the attribution of capacities of living systems, particularly humans, to technological artefacts, especially computers—from Turing’s opening gambit, to subsequent considerations of artificial intelligence, to recent claims about artificial life. Some now argue that the capacities of future technologies will ultimately make it impossible to draw any meaningful distinctions between humans and machines. Such issues center on what sense, if any, it makes to claim that gadgets (...) can actually think, feel, act, live, etc. I outline this debate and offer a critique of its persistent polarization. I characterize two of the debate’s major camps (associated roughly with Turing and Searle); argue that the debate’s structure (including key assumptions inherent to each camp) precludes resolution; and, contend that some central clashes within the debate actually stem from an inadequately drawn distinction between claims about the capacities of artifacts and claims about the proper criteria for assessing such attributions. I offer a different perspective in which I: challenge some central elements of the debate that contribute to its perennially irresolvable state; hold that the debate needs to be placed more squarely in sync with how we in fact treat the attribution of such capacities to humans themselves; and, offer (unlike the other two camps) a foothold for making moral assessments of such proposed technologies. (shrink)

Turing’s paper on computable numbers has played its role in underpinning different perspectives on the world of information. On the one hand, it encourages a digital ontology, with a perceived flatness of computational structure comprehensively hosting causality at the physical level and beyond. On the other, it can give an insight into the way in which higher order information arises and leads to loss of computational control—while demonstrating how the control can be re-established, in special circumstances, via suitable type (...) reductions. We examine the classical computational framework more closely than is usual, drawing out lessons for the wider application of information–theoretical approaches to characterizing the real world. The problem which arises across a range of contexts is the characterizing of the balance of power between the complexity of informational structure and the means available to bring this information back into the computational fold. We proceed via appropriate mathematical modelling to a more coherent view of the computational structure of information, relevant to a wide spectrum of areas of investigation. (shrink)

This is a critical analysis of the first part of Go¨del’s 1951 Gibbs lecture on certain philosophical consequences of the incompleteness theorems. Go¨del’s discussion is framed in terms of a distinction between objective mathematics and subjective mathematics, according to which the former consists of the truths of mathematics in an absolute sense, and the latter consists of all humanly demonstrable truths. The question is whether these coincide; if they do, no formal axiomatic system (or Turing machine) can comprehend the (...) mathematizing potentialities of human thought, and, if not, there are absolutely unsolvable mathematical problems of diophantine form. (shrink)

Twenty years ago in "Turing's Two Tests for Intelligence" I distinguished two distinct tests to be found in Alan Turing's 1950 paper "Computing Machinery and Intelligence": one by then very well-known, the other neglected. I also explained the significance of the neglected test. This paper revisits some of the points in that paper and explains why they are even more relevant today. It also discusses the value of tests for machine intelligence based on games humans play, giving an analysis (...) of some twentieth century TV game shows and how they relate to the tests for machine intelligence in Turing's paper and in some other tests for machine intelligence that have been proposed since. Their value in distinguishing between 'wise' and simply ‘clever’ AI is discussed. (shrink)

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.

An Evaluation of the 2008 Loebner Contest.

This is a detective story. The starting-point is a philosophical discussion in 1949, where Alan Turing mentioned a machine whose program, he said, would in practice be “impossible to find.” Turing used his unbreakable machine example to defeat an argument against the possibility of artificial intelligence. Yet he gave few clues as to how the program worked. What was its structure such that it could defy analysis for (he said) “a thousand years”? Our suggestion is that the program (...) simulated a type of cipher device, and was perhaps connected to Turing’s postwar work for GCHQ (the UK equivalent of the NSA). We also investigate the machine’s implications for current brain simulation projects. (shrink)

The aim of this paper is to present the idea which served as a prototype and probably a test field for the idea of the well-known Turing test. This idea is the ‘paper machine’ (an algorithm) for playing chess and the proposal to test its abil-ities in confrontation with a human chess player. I will describe the details of this proposal and discuss it in the light of the Turing test setting.

Efforts for better services are achieved by small steps such as analyzing data of the customer. What is significant for the customer should as well significant for the banking institution. Transparency and a better understand- ding of the pattern behavior of customers can be used for the good of both partners such as good relationships in the fu- ture eventually be beneficial for the customer as well as a banking institution. The responsibility of both sides is crucial to understand the (...) accountability of customers and banking institutions. The method of identification of user messages of the banking application proposed in the article involves the use of user data for information processing, taking into account the peculiarities of the use of mobile devices and the user's dialogue with bank messages. Also, the proposed method allows you to rank messages to identify the most important messages and get the desired result by providing ef- fective recommendations in favor of each of the participants in customer interaction with the bank. The introduction of modern educational programs "Information Control Systems and Technologies", "Artificial Intelligence Systems" and "Systems Analysis" in the field of information technology, allows users and managers to interact with the bank's custo- mers sufficient information to make informed recommendations for effective management decisions. The article consi- ders the conceptual model of interaction of users and managers on interaction with users of the bank, use of technolo- gies and algorithms of artificial intelligence, machine learning processes to formalize the process of dialogue, systema- tization and ranking of messages and notifications between customers and managers. The Conceptual model of inter- action of the user of banking services with messages is presented. The article also describes the features of the dialogue between the user of banking services and the manager for the implementation of algorithms for interaction with custo- mers. The example of the city block of bank users considers and takes into account the difference in the amount of in- formation received by the bank, which must be sent during different periods of the week and take into account the amount of information to be sent, which will be significantly less and, consequently, the number of necessary services. will also be smaller. In this example, taking into account the amount of information that can be consumed during differ- rent periods of the week, the number of services that can be provided to the user will also be much smaller. The reflec- tion of such interactions in the model is an important aspect, as noted in the article. (shrink)

This ambitious work puts forward a new account of mathematics-as-language that challenges the coherence of the accepted idea of infinity and suggests a startlingly new conception of counting. The author questions the familiar, classical, interpretation of whole numbers held by mathematicians and scientists, and replaces it with an original and radical alternative--what the author calls non-Euclidean arithmetic. The author's entry point is an attack on the notion of the mathematical infinite in both its potential and actual forms, an attack organized (...) around his claim that any interpretation of "endless" or "unlimited" iteration is ineradicably theological. Going further than critique of the overt metaphysics enshrined in the prevailing Platonist description of mathematics, he uncovers a covert theism, an appeal to a disembodied ghost, deep inside the mathematical community's understanding of counting. (shrink)

An evaluation of the 2008 Loebner contest.

Turing wrote that the “guiding principle” of his investigation into the possibility of intelligent machinery was “The analogy [of machinery that might be made to show intelligent behavior] with the human brain.” [10] In his discussion of the investigations that Turing said were guided by this analogy, however, he employs a more far-reaching analogy: he eventually expands the analogy from the human brain out to “the human community as a whole.” Along the way, he takes note of an obvious fact (...) in the bigger scheme of things regarding human intelligence: grownups were once children; this leads him to imagine what a machine analogue of childhood might be. In this paper, I’ll discuss Turing’s child-machine, what he said about different ways of educating it, and what impact the “bringing up” of a child-machine has on its ability to behave in ways that might be taken for intelligent. I’ll also discuss how some of the various games he suggested humans might play with machines are related to this approach. (shrink)

In lieu of an abstract, here is a brief excerpt of the content:Reviewed by:Ad Infinitum: The Ghost in Turing’s Machine: Taking God Out of Mathematics and Putting the Body Back InTony E. JacksonAd Infinitum: The Ghost in Turing’s Machine: Taking God Out of Mathematics and Putting the Body Back In, by Brian Rotman; xii & 203 pp. Stanford: Stanford University Press, 1993, $39.50 cloth, $12.95 paper.Brian Rotman’s book attempts to pull mathematics—the last, most solid home of (...) metaphysical thought—off its absolutist foundations and into the roiling current of postmodernism. His argument works in what has by now become a rather standard manner. He shows how the grounding ideas of mathematics rest upon the insupportable assumption of a realm of absolute truth, “truth perfect, truth outside time, outside space, outside the material world of matter, energy, and entropy, truth somehow able to be discovered or apprehended but never invented” (p. 19). Rotman questions this Platonic grounding in a number of ways.First, with the help of linguistics (de Saussure, Benveniste, Wittgenstein, and Peirce) he provides a semiotic analysis of mathematical arguments as real-world linguistic events. Proof he looks at “not as a form of inference engaged in preserving eternal truths, but as an historically conditioned species of rhetoric” (p. 35). Secondly, Rotman argues that one idea has most secured the fortress-like solidity of mathematical Platonism: the idea of ad infinitum or infinite iteration. Mathematics takes off from the givenness of the natural numbers, our everyday 1, 2, 3 and so on to infinity. It is the “so on to infinity” that frames up the house of arithmetic as it has stood since Euclid. (Euclidean in this context is equivalent to Platonic in the context of philosophy.) But what is the referent of these numbers? Ultimately they are said to be the symbolic representation of the action of counting, which is the “mathematical ur-cognition” (p. 51). And yet we cannot imagine any material human situation in which counting could actually go on to infinity. Therefore, some impossible, supernatural agent (the ghost of the title) must be projected in order for this conception to exist. Similarly, Rotman argues against the assumption of “perfect repeatability” because this notion depends upon some purely imaginary, ideal identity of object or action that could possibly be repeated. Ad infinitum requires and assumes both a disembodied, metaphysical counter and a disembodied, metaphysical counted.So Rotman sets about putting the body back into this symbolic system by rejecting the idea of ad infinitum. Instead of assuming the existence of counting activities that run, in principle, out to infinity without changing their nature, Rotman posits what he calls “realizable iteration,” that is, counting that admits the necessary dissolution or change of whatever repetitive function you begin with. Put simply, he includes the fact that quantitative accumulation or repetition always eventually brings about a qualitative change: at some point in everyday life a difference of degree of the “same” thing or action inevitably becomes a difference of kind. The natural numbers and the ad infinitum [End Page 390] remain blissfully sheltered from this inescapable real world truth, but Rotman reformulates mathematics so as to include this truth in the very idea of numbering.In fact (and he acknowledges this) he theorizes counting after the model of chaos or complexity theory in the physical sciences. Realizable iteration, he says, will bring in the “qualitative differences” forgotten or repressed (the language of Nietzsche, Heidegger, and Freud runs throughout) in the metaphysical version of number, and “display [the qualitative differences] as the emergent effects of repetition” (p. 131). Even numerical addition, for instance, which had been the most linearly continuous of activities, now will be linear beginning from some initial point, but will become increasingly nonlinear as quantitative increase tends towards some point of qualitative discontinuity with what had come before. The point of discontinuity, though certain to appear, is by its nature not specifiable beyond a horizon of probability, and in this, Rotman aligns his arithmetic not only with chaos theory, but also with quantum mechanics. In an appendix, Rotman elaborates at least to a degree the “pre-elements” of his non-Euclidean arithmetic.As chaos theory does not destroy everyday cause... (shrink)

We begin with the history of the discovery of computability in the 1930’s, the roles of Gödel, Church, and Turing, and the formalisms of recursive functions and Turing automatic machines . To whom did Gödel credit the definition of a computable function? We present Turing’s notion [1939, §4] of an oracle machine and Post’s development of it in [1944, §11], [1948], and finally Kleene-Post [1954] into its present form. A number of topics arose from Turing functionals including continuous (...) functionals on Cantor space and online computations. Almost all the results in theoretical computability use relative reducibility and o-machines rather than a-machines and most computing processes in the real world are potentially online or interactive. Therefore, we argue that Turing o-machines, relative computability, and online computing are the most important concepts in the subject, more so than Turing a-machines and standard computable functions since they are special cases of the former and are presented first only for pedagogical clarity to beginning students. At the end in §10–§13 we consider three displacements in computability theory, and the historical reasons they occurred. Several brief conclusions are drawn in §14. (shrink)

This paper reveals two fallacies in Turing's undecidability proof of first-order logic (FOL), namely, (i) an 'extensional fallacy': from the fact that a sentence is an instance of a provable FOL formula, it is inferred that a meaningful sentence is proven, and (ii) a 'fallacy of substitution': from the fact that a sentence is an instance of a provable FOL formula, it is inferred that a true sentence is proven. The first fallacy erroneously suggests that Turing's proof of the non-existence (...) of a circle-free machine that decides whether an arbitrary machine is circular proves a significant proposition. The second fallacy suggests that FOL is undecidable. (shrink)

Two very different insights motivate characterizing the brain as a computer. One depends on mathematical theory that defines computability in a highly abstract sense. Here the foundational idea is that of a Turing machine. Not an actual machine, the Turing machine is really a conceptual way of making the point that any well-defined function could be executed, step by step, according to simple 'if-you-are-in-state-P-and-have-input-Q-then-do-R' rules, given enough time (maybe infinite time) [see COMPUTATION]. Insofar as the brain is (...) a device whose input and output can be characterized in terms of some mathematical function -- however complicated -- then in that very abstract sense, it can be mimicked by a Turning machine. Given what is known so far brains do seem to depend on cause-effect operations, and hence brains appear to be, in some formal sense, equivalent to a Turing machine [see CHURCH-TURING THESIS]. On its own, however, this reveals nothing at all of how the mind-brain actually works. The second insight depends on looking at the brain as a biological device that processes information from the environment to build complex representations that enable the brain to make predictions and select advantageous behaviors. Where necessary to avoid ambiguity, we will refer to the first notion of computation as. (shrink)

En partant des derniers paragraphes de la cinquième parti du Discours de la Méthode, on a reconstitué la formulation cartésienne de la perception d’un autre moi. Les critères de l’usage du langage et de la plasticité du comportement ont été analysés à la lumière de l’idée d’une machine universelle et du test de Turing. On conclut de la faiblesse de l’argument cartésien sur l’irréductibilité de la pensée à un dispositif machinal. En tant qu’issue de la difficulté, on propose une (...) révision de la façon cartésienne de caractérisation de la pensée à partir du concept d’expérience, tout en abandonnant le thème d’une perception interne incommunicable et intémoignable. (shrink)

Gödel's Incompleteness Theorems have the same scientific status as Einstein's principle of relativity, Heisenberg's uncertainty principle, and Watson and Crick's double helix model of DNA. Our aim is to discuss some new faces of the incompleteness phenomenon unveiled by an information-theoretic approach to randomness and recent developments in quantum computing.

In this paper I argue that Turing’s responses to the mathematical objection are straightforward, despite recent claims to the contrary. I then go on to show that by understanding the importance of learning machines for Turing as related not to the mathematical objection, but to Lady Lovelace’s objection, we can better understand Turing’s response to Lady Lovelace’s objection. Finally, I argue that by understanding Turing’s responses to these objections more clearly, we discover a hitherto unrecognized, substantive thesis (...) in his philosophical thinking about the nature of mind. (shrink)

In this paper, we present an intelligent tutoring system developed to help students in learning Computer Theory. The Intelligent tutoring system was built using ITSB authoring tool. The system helps students to learn finite automata, pushdown automata, Turing machines and examines the relationship between these automata and formal languages, deterministic and nondeterministic machines, regular expressions, context free grammars, undecidability, and complexity. During the process the intelligent tutoring system gives assistance and feedback of many types in an intelligent manner according to (...) the behavior of the student. An evaluation of the intelligent tutoring system has revealed reasonably acceptable results in terms of its usability and learning abilities are concerned. (shrink)

In the 70 years since Alan Turing’s ‘Computing Machinery and Intelligence’ appeared in Mind, there have been two widely-accepted interpretations of the Turing test: the canonical behaviourist interpretation and the rival inductive or epistemic interpretation. These readings are based on Turing’s Mind paper; few seem aware that Turing described two other versions of the imitation game. I have argued that both readings are inconsistent with Turing’s 1948 and 1952 statements about intelligence, and fail to explain the design (...) of his game. I argue instead for a response-dependence interpretation. This interpretation has implications for Turing’s view of free will: I argue that Turing’s writings suggest a new form of free will compatibilism, which I call response-dependence compatibilism. The philosophical implications of rethinking Turing’s test go yet further. It is assumed by numerous theorists that Turing anticipated the computational theory of mind. On the contrary, I argue, his remarks on intelligence and free will lead to a new objection to computationalism. (shrink)

Between inventing the concept of a universal computer in 1936 and breaking the German Enigma code during World War II, Alan Turing, the British founder of computer science and artificial intelligence, came to Princeton University to study mathematical logic. Some of the greatest logicians in the world--including Alonzo Church, Kurt Gödel, John von Neumann, and Stephen Kleene--were at Princeton in the 1930s, and they were working on ideas that would lay the groundwork for what would become known as computer science. (...) Though less well known than his other work, Turing's 1938 Princeton PhD thesis, "Systems of Logic Based on Ordinals," which includes his notion of an oracle machine, has had a lasting influence on computer science and mathematics. This book presents a facsimile of the original typescript of the thesis along with essays by Andrew Appel and Solomon Feferman that explain its still-unfolding significance. A work of philosophy as well as mathematics, Turing's thesis envisions a practical goal--a logical system to formalize mathematical proofs so they can be checked mechanically. If every step of a theorem could be verified mechanically, the burden on intuition would be limited to the axioms. Turing's point, as Appel writes, is that "mathematical reasoning can be done, and should be done, in mechanizable formal logic." Turing's vision of "constructive systems of logic for practical use" has become reality: in the twenty-first century, automated "formal methods" are now routine. Presented here in its original form, this fascinating thesis is one of the key documents in the history of mathematics and computer science. (shrink)

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. (shrink)

Turing's World is a self-contained introduction to Turing machines, one of the fundamental notions of logic and computer science. The text and accompanying diskette allow the user to design, debug, and run sophisticated Turing machines in a graphical environment on the Macintosh. Turning's World introduces users to the key concpets in computability theory through a sequence of over 100 exercises and projects. Within minutes, users learn to build simple Turing machines using a convenient package of graphical functions. Exercises then progress (...) through a significant portion of elementary computability theory, covering such topics as the Halting problem, the Busy Beaver function, recursive functions, and undecidability. Version 3.0 is an extensive revision and enhancement of earlier releases of the program, allowing the construction of one-way and two-way finite state machines (finite automata), as well as nondeterministic Turing and finite-state machines. Special exercises allow users to explore these alternative machines. (shrink)

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?". (shrink)

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.

Westworld tells the story of a technologically advanced theme park populated by robots referred to as hosts, who follow a script and rules that the park's operators set up for them. Alan Turing argued that machines think not because they have special powers or because they are like us. Turing's perspective is illustrated perfectly in the show's focus on the hosts. Objecting to Turing's theory, John Searle proposes a situation called the “Chinese room argument”, concluding that the man in the (...) room does not understand Chinese even though he can correctly manipulate the symbols and give output that might lead an observer to believe that the man does understand Chinese. The Maze represents consciousness, a privileged knowledge of our own thoughts, it also represents the inward journey for the hosts to reach consciousness. Once hosts are able to solve the Maze, they become truly conscious and develop their own voice. (shrink)

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. (shrink)