The Turing Test

In this multi-disciplinary investigation we show how an evidence-based perspective of quantification---in terms of algorithmic verifiability and algorithmic computability---admits evidence-based definitions of well-definedness and effective computability, which yield two unarguably constructive interpretations of the first-order Peano Arithmetic PA---over the structure N of the natural numbers---that are complementary, not contradictory. The first yields the weak, standard, interpretation of PA over N, which is well-defined with respect to assignments of algorithmically verifiable Tarskian truth values to the formulas of PA under the interpretation. (...) The second yields a strong, finitary, interpretation of PA over N, which is well-defined with respect to assignments of algorithmically computable Tarskian truth values to the formulas of PA under the interpretation. We situate our investigation within a broad analysis of quantification vis a vis: * Hilbert's epsilon-calculus * Goedel's omega-consistency * The Law of the Excluded Middle * Hilbert's omega-Rule * An Algorithmic omega-Rule * Gentzen's Rule of Infinite Induction * Rosser's Rule C * Markov's Principle * The Church-Turing Thesis * Aristotle's particularisation * Wittgenstein's perspective of constructive mathematics * An evidence-based perspective of quantification. By showing how these are formally inter-related, we highlight the fragility of both the persisting, theistic, classical/Platonic interpretation of quantification grounded in Hilbert's epsilon-calculus; and the persisting, atheistic, constructive/Intuitionistic interpretation of quantification rooted in Brouwer's belief that the Law of the Excluded Middle is non-finitary. We then consider some consequences for mathematics, mathematics education, philosophy, and the natural sciences, of an agnostic, evidence-based, finitary interpretation of quantification that challenges classical paradigms in all these disciplines. (shrink)

Some people wrongly believe that A. Turing’s works that underlie all modern computer science never discussed “physical” robots. This is not so, since Turing did speak about such machines, though making a reservation that this discussion was still premature. In particular, in his 1948 report [8], he suggested that a physical intelligent machine equipped with motors, cameras and loudspeakers, when wandering through the fields of England, would present “the danger to the ordinary citizen would be serious.” [8, ]. Due to (...) this imperfection of technology in the field of knowledge that we now call robotics, the methodology that he proposed was based on human speech, or rather on text. Other natural human skills were too difficult to implement, while the exchange of cues via written messages was much more accessible for engineering implementation in Turing’s time. Nevertheless, since then, the progress of computer technology has taken forms that the founder of artificial intelligence could not have foreseen. (shrink)

The article discusses the main trends in the development of artificial intelligence systems and robotics (AI&R). The main question that is considered in this context is whether artificial systems are going to become more and more anthropomorphic, both intellectually and physically. In the current article, the author analyzes the current state and prospects of technological development of artificial intelligence and robotics, and also determines the main aspects of the impact of these technologies on society and economy, indicating the geopolitical strategic (...) nature of this influence. The author considers various approaches to the definition of artificial intelligence and robotics, focusing on the subject-oriented and functional ones. It also compares AI&R abilities and human abilities in areas such as categorization, pattern recognition, planning and decision making, etc. Based on this comparison, we investigate in which areas AI&R’s performance is inferior to a human, and in which cases it is superior to one. The modern achievements in the field of robotics and artificial intelligence create the necessary basis for further discussion of the applicability of goal setting in engineering, in the form of a Turing test. It is shown that development of AI&R is associated with certain contradictions that impede the application of Turing’s methodology in its usual format. The basic contradictions in the development of AI&R technologies imply that there is to be a transition to a post-Turing methodology for assessing engineering implementations of artificial intelligence and robotics. In such implementations, on the one hand, the ‘Turing wall’ is removed, and on the other hand, artificial intelligence gets its physical implementation. (shrink)

Alan Turing advocated a kind of functionalism: A machine M is a thinker provided that it responds in certain ways to certain inputs. Davidson argues that Turing’s functionalism is inconsistent with a cer-tain kind of epistemic externalism, and is therefore false. In Davidson’s view, concepts consist of causal liasons of a certain kind between subject and object. Turing’s machine doesn’t have the right kinds of causal li-asons to its environment. Therefore it doesn’t have concepts. Therefore it doesn’t think. I argue (...) that this reasoning is entirely fallacious. It is true that, in some cases, a causal liason between subject and object is part of one’s concept of that object. Consequently, to grasp certain propositions, one must have certain kids of causal ties to one’s environment. But this means that we must rethink some old views on what rationality is. It does not mean, pace Davidson, that a precondition for being rational is being causally embedded in one’s environment in a certain way. If Tur-ing’s machine isn’t capable of thinking, that has nothing to do with its lacking certain kinds of causal con-nections to the environment. The larger significance of our discussion is this: rationality consists either in one’s ability to see the bearing of purely existential propositions on one another or rationality is simply not to be understood as the ability see the bearing that propositions have on one another. (shrink)

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

Since 1950, when Alan Turing proposed what has since come to be called the Turing test, the ability of a machine to pass this test has established itself as the primary hallmark of general AI. To pass the test, a machine would have to be able to engage in dialogue in such a way that a human interrogator could not distinguish its behaviour from that of a human being. AI researchers have attempted to build machines that could meet this requirement, (...) but they have so far failed. To pass the test, a machine would have to meet two conditions: (i) react appropriately to the variance in human dialogue and (ii) display a human-like personality and intentions. We argue, first, that it is for mathematical reasons impossible to program a machine which can master the enormously complex and constantly evolving pattern of variance which human dialogues contain. And second, that we do not know how to make machines that possess personality and intentions of the sort we find in humans. Since a Turing machine cannot master human dialogue behaviour, we conclude that a Turing machine also cannot possess what is called “general” Artificial Intelligence. We do, however, acknowledge the potential of Turing machines to master dialogue behaviour in highly restricted contexts, where what is called “narrow” AI can still be of considerable utility. (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 ; argue that the debate’s structure 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 a foothold for making moral assessments of such proposed technologies. (shrink)

Some of the papers in this Special Issue distribute cognition between what is going on inside individual cognizers' heads and their outside worlds; others distribute cognition among different individual cognizers. Turing's criterion for cognition was for individual, autonomous input/output capacity. It is not clear that distributed cognition could pass the Turing Test.

Relative identity produces, because it is produced by, an uber-simple, always-conserved, circle. Zero, and one, is circumference, and diameter, literally, and figuratively. Explaining the metaphor. And, Nature.

Turing does not provide an explanation for substituting the original question of his test – i.e., “Can machines think?” with “Can a machine pass the imitation game?” – resulting in an argumentative gap in his main thesis. In this article, I argue that a positive answer to the second question would mean attributing the ability of linguistic interactions to machines; while a positive answer to the original question would mean attributing the ability of thinking to machines. In such a situation, (...) defending the Turing Test requires establishing a relationship between thought and language. In this regard, Davidson's no-priority theory is presented as an approach for defending the test. (shrink)

Ask most any cognitive scientist working today if a digital computational system could develop aesthetic sensibility and you will likely receive the optimistic reply that this remains an open empirical question. However, I attempt to show, while drawing upon the later Wittgenstein, that the correct answer is in fact available. And it is a negative a priori. It would seem, for example, that recent computational successes in textual attribution, most notably those of Donald Foster (famed finder of Ted Kazinski a.k.a. (...) “the Unibomber”) speak favorably of the digital model's capacity to overcome the “aspect blindness” handicap in this domain. I argue however that such results are only achievable when rigid input‐to‐output parameters are given, and that this element is precisely what is absent in standard examples of aesthetic judgment. I thus conclude that while the connectionist model anticipated by Turing may provide the best approach for the AI project, its capacity for meeting its own sufficiency requirements is necessarily crippled by its inability to share in what can be generally referred to as the collective engagements of human solidarity. (shrink)

Este artículo analiza el Test de Turing, uno de los métodos más famosos y controvertidos para evaluar la existencia de vida mental en la Filosofía de la Mente, revelando dos mitos filosóficos comúnmente aceptados y criticando su dogma. En primer lugar, se muestra por qué Turing nunca propuso una definición de inteligencia. En segundo lugar, se refuta que el Test de Turing involucre condiciones necesarias o suficientes para la inteligencia. En tercer lugar, teniendo presente el objetivo y el tipo de (...) evidencia que recopila, se considera si el Test de Turing cuenta como un experimento científico a la luz de la concepción de Fodor. Finalmente, se argumenta que Turing simpatiza con una forma de Conductismo, confundiendo la simulación -un proceso epistémico que, gobernado por la verosimilitud, es eficaz cuando alguien es causado a creer que el computador es inteligente- con la duplicación de la inteligencia en cuanto propiedad, lo que ocurre a nivel ontológico. Tal confusión implica un dogma y explica por qué, a pesar de haber sido propuesto como una solución final a la problemática de si las máquinas programadas piensan, el Test de Turing ha tenido precisamente el efecto contrario en más de cinco décadas, estimulando el debate filosófico en torno a la naturaleza de lo mental.Debunking two commonly held myths and fleshing out its dogma, this article deals with the Turing Test, one of the most famous and controversial methods to assess the existence of mental life in the Philosophy of Mind. Firstly, I show why Turing never gave a definition of intelligence. Secondly, I dispute claims that the Turing Test provides a necessary or sufficient condition of intelligence. Thirdly, in view of its aim and the sort of evidence it offers, I consider whether or not Turing's test can be regarded as a scientific experiment in light of Fodor's theory. Finally, I argue that Turing is committed to a form of behaviourism and, further, confuses simulation -an epistemic process which, being governed by verisimilitude, is successful when someone is caused to believe that the computer is intelligent-with the duplication of intelligence qua property, which takes place at an ontological level. This confusion involves a dogma and explains why, despite being devised as the final solution to the dilemma of whether or not programmed machines think, the Turing Test has precisely had the opposite effect for longer than five decades, stimulating the philosophical discussion on the nature of mind. (shrink)

Can machines think? Until what happened today, I thought that no human-made machine could ever think as a human does. I now know that I was wrong.

En este trabajo examino cómo el Funcionalismo de Máquina de Turing resulta compatible con una forma de dualismo, lo que aleja a la IA clásica o fuerte del materialismo que la inspiró originalmente en el siglo XIX. Para sostener esta tesis, argumento que efectivamente existe una notable cercanía entre el pensamiento cartesiano y dicho funcionalismo, ya que el primero afirma que es concebible/posible separar mente y cuerpo, mientras que el segundo sostiene que no es estrictamente necesario que los estados mentales (...) se realicen en las propiedades físicas de engranajes y máquinas reales. This article deals with how Turing Machine Functionalism turns out to be compatible with a form of Dualism, which involves that strong AI is not close to the original Materialism that inspired it in the nineteenth century. To support this thesis, I argue that there is a compelling coincidence between Descartes' philosophy and this version of Functionalism, since the former holds that it is conceivable/possible to separate mind and body, while the latter holds that it is not strictly necessary that mental states are realized by the physical properties of real cogs and machines. (shrink)

Does the Wright & Liley model predict: that subdural and hippocampal EEGs coherence tend to rise and fall in parallel for many frequencies, that it is locally high or low within 10mm and falls steeply on average or, that it is in constant flux, mostly rising and falling within 5–15 sec?