We set the Turing Test in the historical context of the development of machine intelligence, describe the different forms of the test and its rationale, and counter common misinterpretations and objections. Recently published material by Turing casts fresh light on his thinking.

The paper analyzes if it is possible to grow an I–Thou relation in the sense of Martin Buber with an artificial, conversational agent developed with Natural Language Processing techniques. The requirements for such an agent, the possible approaches for the implementation, and their limitations are discussed. The relation of the achievement of this goal with the Turing test is emphasized. Novel perspectives on the I–Thou and I–It relations are introduced according to the sociocultural paradigm and Mikhail Bakhtin’s dialogism, polyphony inter-animation, (...) and carnavalesque. The polyphonic model, the associated analysis method, and the support tools are introduced. Some ideas on how the polyphonic model may be used for the implementation of a computer application able to analyze some features of the existence of an I–Thou relation are included. (shrink)

Currently, our official rationality is still of a Cartesian kind; we are still embedded in a mechanistic order that takes it that separate, countable entities (spatial forms), related logically to each other, are the only ‘things’ that matter to us—an order clearly suited to advances in robotics. Unfortunately, it is an order that renders invisible ‘relational things’, non-objective things that exist in time, in the transitions from one state of affairs to another, things that ‘point’ toward possibilities in the future, (...) which mean something to us. I have called such things, hermeneutical–dialogical ‘things’ as they gradually emerge in our back-and-forth, step-by-step relations to the others and otherneses in our surroundings; they consist in the ‘promissory’ things sustaining our trust in each other and in our authorities, in our social organizations and social institutions, and in our culture. Clearly, we need to understand better, not only what robots can, and cannot do, but also the long-term ethical and political implications of inserting robotic activities into our everyday ways of relating ourselves to our surroundings if we are to avoid the dystopian futures envisaged by some. Descartes’ aim of “making ourselves, as it were, masters and possessors of nature,” forgets our larger task of our making ourselves into human beings—of doing together in dialog what we cannot do apart. (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)

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

It has been just over 100 years since the birth of Alan Turing and more than 65 years since he published in Mind his seminal paper, Computing Machinery and Intelligence. In the Mind paper, Turing asked a number of questions, including whether computers could ever be said to have the power of “thinking”. Turing also set up a number of criteria—including his imitation game—under which a human could judge whether a computer could be said to be “intelligent”. Turing’s paper, as (...) well as his important mathematical and computational insights of the 1930s and 1940s led to his popular acclaim as the “Father of Artificial Intelligence”. In the years since his paper was published, however, no computational system has fully satisfied Turing’s challenge. In this paper we focus on a different question, ignored in, but inspired by Turing’s work: How might the Artificial Intelligence practitioner implement “intelligence” on a computational device? Over the past 60 years, although the AI community has not produced a general-purpose computational intelligence, it has constructed a large number of important artifacts, as well as taken several philosophical stances able to shed light on the nature and implementation of intelligence. This paper contends that the construction of any human artifact includes an implicit epistemic stance. In AI this stance is found in commitments to particular knowledge representations and search strategies that lead to a product’s successes as well as its limitations. Finally, we suggest that computational and human intelligence are two different natural kinds, in the philosophical sense, and elaborate on this point in the conclusion. (shrink)

At least one of these conceptions of probability underlies any theory of statistical inference (or, to use Neyman's phrase, 'inductive behavior'). ...

This paper extends earlier work by its authors on formal aspects of the processes of contracting a theory to eliminate a proposition and revising a theory to introduce a proposition. In the course of the earlier work, Gardenfors developed general postulates of a more or less equational nature for such processes, whilst Alchourron and Makinson studied the particular case of contraction functions that are maximal, in the sense of yielding a maximal subset of the theory (or alternatively, of one of (...) its axiomatic bases), that fails to imply the proposition being eliminated. In the present paper, the authors study a broader class, including contraction functions that may be less than maximal. Specifically, they investigate "partial meet contraction functions", which are defined to yield the intersection of some nonempty family of maximal subsets of the theory that fail to imply the proposition being eliminated. Basic properties of these functions are established: it is shown in particular that they satisfy the Gardenfors postulates, and moreover that they are sufficiently general to provide a representation theorem for those postulates. Some special classes of partial meet contraction functions, notably those that are "relational" and "transitively relational", are studied in detail, and their connections with certain "supplementary postulates" of Gardenfors investigated, with a further representation theorem established. (shrink)