In this paper I argue that Turing proposed a new approach to the concept of thinking, based on his claim that intelligence is an ‘emotional concept’; and that the response-dependence interpretation of Turing’s ‘criterion for “thinking”’ is a better fit with his writings than orthodox interpretations. The aim of this paper is to clarify the response-dependence interpretation, by addressing such questions as: What did Turing mean by the expression ‘emotional’? Is Turing’s criterion subjective? Are ‘emotional’ judgements decided by social consensus? (...) Turing’s take on these issues impacts current philosophical debates on response-dependent concepts and on the nature of artificial intelligence. (shrink)

Alan Turing’s 1950 imitation game has been widely understood as a means for testing if an entity is intelligent. Following a series of papers by Diane Proudfoot, I offer a socio-technological interpretation of Turing’s paper and present an alternative way of understanding both the imitation game and Turing’s concept of intelligence. Turing, I claim, saw intelligence as a social concept, meaning that possession of intelligence is a property determined by society’s attitude toward the entity. He realized that as long as (...) human society held a prejudiced attitude toward machinery—seeing machines a priori as mindless objects—machines could not be said to be intelligent, by definition. He also realized, though, that if humans’ a priori, chauvinistic attitude toward machinery changed, the existence of intelligent machines would become logically possible. Turing thought that such a change would eventually occur: He believed that when scientists overcome the technological challenge of constructing sophisticated machines that could imitate human verbal behavior—i.e., do well in the imitation game—humans’ prejudiced attitude toward machinery will have altered in such a way that machines could be said to be intelligent. The imitation game, for Turing, was not an intelligence test, but a technological aspiration whose realization would likely involve a change in society’s attitude toward machines. (shrink)

Disertační práce se zabývá problematikou připisování myšlení jiným entitám, a to pomocí imitační hry navržené v roce 1950 britským filosofem Alanem Turingem. Jeho kritérium, známé v dějinách filosofie jako Turingův test, je podrobeno detailní analýze. Práce popisuje nejen původní námitky samotného Turinga, ale především pozdější diskuse v druhé polovině 20. století. Největší pozornost je věnována těmto kritikám: Lucasova matematická námitka využívající Gödelovu větu o neúplnosti, Searlův argument čínského pokoje konstatující nedostatečnost syntaxe pro sémantiku, Blockův návrh na použití brutální síly pro (...) řešení imitační hry, Frenchova teorie subkognitivních informací a Michieho skepticismus ohledně možnosti umělého vědomí. Závěr práce zachycuje současný stav recepce Turingova testu a představuje pokusy o jeho praktickou realizaci, například v každoroční soutěži o Loebnerovu cenu. Autor práce zastává názor, že ani po více než šedesáti letech od uveřejnění Turingova paradigmatického eseje stále neexistují žádné vážné důvody pro zamítnutí jeho tvrzení. Tradiční komputační funkcionalismus možná není ideální teorií vysvětlující činnost myslí a jako slibnější se může jevit vývoj v neurálních vědách, ale Turingův test je přesto užitečným a snad i jediným nástrojem pro detekci inteligence u lidmi vytvořených strojů. (shrink)

The Turing Test is routinely understood as a behaviourist test for machine intelligence. Diane Proudfoot 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 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, because, as an account of intelligence, the transparency model is arguably problematic in a way that the reference-fixing model isn’t. (shrink)

The ethical concerns regarding the successful development of an Artificial Intelligence have received a lot of attention lately. The idea is that even if we have good reason to believe that it is very unlikely, the mere possibility of an AI causing extreme human suffering is important enough to warrant serious consideration. Others look at this problem from the opposite perspective, namely that of the AI itself. Here the idea is that even if we have good reason to believe that (...) it is very unlikely, the mere possibility of humanity causing extreme suffering to an AI is important enough to warrant serious consideration. This paper starts from the observation that both concerns rely on problematic philosophical assumptions. Rather than tackling these assumptions directly, it proceeds to present an argument that if one takes these assumptions seriously, then one has a moral obligation to advocate for a ban on the development of a conscious AI. (shrink)

This issue of the Kybernetes journal is concerned with the philosophical question- Can a Machine Think? Famously, in his 1950 paper `Computing Machinery andIntelligence' [9], the British mathematician Alan Turing suggested replacing this question - which he found \too meaningless to deserve discussion" - with a simple -behavioural - test based on an imagined `Victorianesque' pastime he entitled the`imitation game'. In this special issue of Kybernetes a selection of authors with a special interest in Turing's work (including those who participated (...) in the 2008 AISB Turing Symposium) have been invited to explore and clarify issues arising from Turing's 1950 paper on the Imitation Game; now more widely known as the Turing test. (shrink)

Despite its significance in neuroscience and computation, McCulloch and Pitts's celebrated 1943 paper has received little historical and philosophical attention. In 1943 there already existed a lively community of biophysicists doing mathematical work on neural networks. What was novel in McCulloch and Pitts's paper was their use of logic and computation to understand neural, and thus mental, activity. McCulloch and Pitts's contributions included (i) a formalism whose refinement and generalization led to the notion of finite automata (an important formalism in (...) computability theory), (ii) a technique that inspired the notion of logic design (a fundamental part of modern computer design), (iii) the first use of computation to address the mind–body problem, and (iv) the first modern computational theory of mind and brain. (shrink)

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.".

In 2014, widespread reports in the popular media that a chatbot named Eugene Goostman had passed the Turing test became further grist for those who argue that the diversionary tactics of chatbots like Goostman and others, such as those who participate in the Loebner competition, are enabled by the open-ended dialog of the Turing test. Some claim a new kind of test of machine intelligence is needed, and one community has advanced the Winograd schema competition to address this gap. We (...) argue to the contrary that implicit in the Turing test is the cooperative challenge of using language to build a practical working understanding, necessitating a human interrogator to monitor and direct the conversation. We give examples which show that, because ambiguity in language is ubiquitous, open-ended conversation is not a flaw but rather the core challenge of the Turing test. We outline a statistical notion of practical working understanding that permits a reasonable amount of ambiguity, but nevertheless requires that ambiguity be resolved sufficiently for the agents to make progress. (shrink)

Some philosophers have conflated functionalism and computationalism. I reconstruct how this came about and uncover two assumptions that made the conflation possible. They are the assumptions that (i) psychological functional analyses are computational descriptions and (ii) everything may be described as performing computations. I argue that, if we want to improve our understanding of both the metaphysics of mental states and the functional relations between them, we should reject these assumptions. # 2004 Elsevier Ltd. All rights reserved.

Given the difficulties of defining “machine” and “think”, Turing proposed to replace the question “Can machines think?” with a proxy: how well can an agent engage in sustained conversation with a human? Though Turing neither described himself as a philosopher nor published much on philosophical matters, his Imitation Game has stood the test of time. Most understood at that time that success would not come easy, but few would have guessed just how difficult engaging in ordinary conversation would turn out (...) to be. Despite the proliferation of language processing tools, we have seen little progress towards doing well at the Imitation Game. Had Turing instead suggested ability at games or even translation as a proxy for intelligence, his paper might have been forgotten. We argue that these and related problems are amenable to mechanical, though sophisticated, formal techniques. Turing appears to have taken care to select sustained, productive conversation and that alone as his proxy. Even simple conversation challenges a machine to engage in the rich practice of human discourse in all its generality and variety. (shrink)