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)

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)

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

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

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

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)

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

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

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

In his recent paper, Winch attacks a group of theories he calls cognitivism. These theories agree in holding that ‘the ability to think, both consciously and subconsciously, amounts to an ability to internally manipulate symbolic representations of that which we think about.The relevance of this attack to education is that ‘Cognitivism’ supplies plausible‐looking reasons for thinking that learning can take place without instruction, practice, memorisation or training and its prestige as a theory of learning devalues those activities within education.Its rejection (...) should therefore lead us to re‐examine our need for explicit teacher‐oriented pedagogies’.Cognitivism has led to an emphasis on autonomous learning and a consequent devaluing of the role of overt teaching, ‘the active transmission of knowledge and technique by an authoritative figure’. (shrink)

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

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

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

The Cognitive Science era can be divided into two distinct periods with respect to the topic of innateness, at least from the viewpoint of the linguist. The first period, which began in the late 1950s and was characterized by the work of people like Chomsky and Fodor, argued for reviving a nativist position, in which a substantial amount of people’s knowledge of language was innate rather than learned by association or induction or analogy. This constituted a break with the empiricist/behaviorist/structuralist (...) tradition that had dominated the field before that. (shrink)