What if computers could think?

The question whether or not computers can think was first asked in print by Alan Turing in his seminal 1950 article. In order to avoid defining what a computer is or what thinking is, Turing resorts to the imitation game which is a test that allows us to determine whether or not a machine can think. That is, if an interrogator is unable to tell whether responses to his questions come from a human being or from a machine, the machine is imitating a human being so well that it has to be acknowledged that these responses result from its thinking. However, then as now, it is not an indisputable claim that machines could think, and an unceasing stream of papers discussing the validity of the test proves this point. There are many arguments in favour of, as well as against, the claims borne by the test, and Turing himself discusses some of them. In his view, there are mice possible objections to the concept of a thinking machine, which he eventually dismisses as weak, irrelevant, or plain false. However, as he admits, he can present no very convincing arguments of a positive nature to support my views. If I had I should not have taken such pains to point out the fallacies in contrary views.     

Turing's Rules for the Imitation Game

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.     

Intelligence is not Enough: On the Socialization of Talking Machines

Minds and Machines - Since the introduction of the imitation game by Turing in 1950 there has been much debate as to its validity in ascertaining machine intelligence. We wish herein to consider a...     

The Status and Future of the Turing Test

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.     

Peeking behind the screen: the unsuspected power of the standard Turing Test

No computer that had not experienced the world as we humans had could pass a rigorously administered standard Turing Test. This paper will show that the use of ‘subcognitive’ questions allows the standard Turing Test to indirectly probe the human subcognitive associative concept network built up over a lifetime of experience with the world. Not only can this probing reveal differences in cognitive abilities, but crucially, even differences in physical aspects of the candidates can be detected. Consequently, it is unnecessary to propose even harder versions of the Test in which all physical and behavioural aspects of the two candidates had to be indistinguishable before allowing the machine to pass the Test. Any machine that passed the ‘simpler’ symbols-in symbols-out test as originally proposed by Turing would be intelligent. The problem is that, even in its original form, the Turing Test is already too hard and too anthropocentric for any machine that was not a physical, social and behavioural carbon copy of ourselves to actually pass it. Consequently, the Turing Test, even in its standard version, is not a reasonable test for general machine intelligence. There is no need for an even stronger version of the Test.     

The Turing Test and the legal process

This paper proposes a novel thought-experiment, the ‘Turing litigation game’ – or ‘Turing game’ for short. Specifically, we propose replacing the existing arcane and archaic systems of civil and criminal procedure with a simple and probabilistic litigation game resembling the Turing Test from the world of computer science. The paper is divided into six sections. Section 1 provides a brief introduction. Section 2 provides some background by describing the original Turing Test and explaining how the Turing Test resembles the process of adjudication. Section 3 then describes our proposed Turing litigation game and identifies the conditions for implementing this alternative approach to litigation, while Section 4 introduces the possibility of probabilistic verdicts (as opposed to the traditional system of binary verdicts). Section 5 reviews (and refutes) several philosophical objections against our Turing-game concept. Section 6 concludes.     

Classical AI linguistic understanding and the insoluble Cartesian problem

This paper examines an insoluble Cartesian problem for classical AI, namely, how linguistic understanding involves knowledge and awareness of u’s meaning, a cognitive process that is irreducible to algorithms. As analyzed, Descartes’ view about reason and intelligence has paradoxically encouraged certain classical AI researchers to suppose that linguistic understanding suffices for machine intelligence. Several advocates of the Turing Test, for example, assume that linguistic understanding only comprises computational processes which can be recursively decomposed into algorithmic mechanisms. Against this background, in the first section, I explain Descartes’ view about language and mind. To show that Turing bites the bullet with his imitation game and in the second section I analyze this method to assess intelligence. Then, in the third section, I elaborate on Schank and Abelsons’ Script Applier Mechanism (SAM, hereby), which supposedly casts doubt on Descartes’ denial that machines can think. Finally, in the fourth section, I explore a challenge that any algorithmic decomposition of linguistic understanding faces. This challenge, I argue, is the core of the Cartesian problem: knowledge and awareness of meaning require a first-person viewpoint which is irreducible to the decomposition of algorithmic mechanisms.

     

Events and sightings

Scientists of the late 18th century did not have an obvious way to test a machine to see if it was actually mimicking human thought. In some ways, the Chess-Playing Turk was accepted as a thinking machine because it acted like a machine. The division of labor was the first step in designing a machine to do a complex task. The machine designer would have to analyze the task, identify individual actions, and build mechanisms that could perform each action in order. The game of chess had an important role in early computers. By 1951, Alan Turing had created hits Turing test for comparing human intelligence with machines actions and new computer scientists were beginning to devise ways of programming machines to play chess.     

Artificial Intelligence approaches for the generation and assessment of believable human-like behaviour in virtual characters

Having artificial agents to autonomously produce human-like behaviour is one of the most ambitious original goals of Artificial Intelligence (AI) and remains an open problem nowadays. The imitation game originally proposed by Turing constitute a very effective method to prove the indistinguishability of an artificial agent. The behaviour of an agent is said to be indistinguishable from that of a human when observers (the so-called judges in the Turing test) cannot tell apart humans and non-human agents. Different environments, testing protocols, scopes and problem domains can be established to develop limited versions or variants of the original Turing test. In this paper we use a specific version of the Turing test, based on the international BotPrize competition, built in a First-Person Shooter video game, where both human players and non-player characters interact in complex virtual environments. Based on our past experience both in the BotPrize competition and other robotics and computer game AI applications we have developed three new more advanced controllers for believable agents: two based on a combination of the CERA–CRANIUM and SOAR cognitive architectures and other based on ADANN, a system for the automatic evolution and adaptation of artificial neural networks. These two new agents have been put to the test jointly with CCBot3, the winner of BotPrize 2010 competition (Arrabales et al., 2012), and have showed a significant improvement in the humanness ratio. Additionally, we have confronted all these bots to both First-person believability assessment (BotPrize original judging protocol) and Third-person believability assessment, demonstrating that the active involvement of the judge has a great impact in the recognition of human-like behaviour.     

Are human beings humean robots?

David Hume, the Scottish philosopher, conceives reason as the slave of the passions, which implies that human reason has predetermined objectives it cannot question. An essential element of an algorithm running on a computational machine (or Logical Computing Machine, as Alan Turing calls it) is its having a predetermined purpose: an algorithm cannot question its purpose, because it would cease to be an algorithm. Therefore, if self-determination is essential to human intelligence, then human beings are neither Humean beings, nor computational machines. We examine also some objections to the Turing Test as a model to understand human intelligence.     

图灵机和图灵测试

阿兰?图灵为人工智能学的诞生做出了重大的贡献,本文介绍了图灵机和图灵测试,图灵机对计算机的结构、可实现性和局限性都产生了深远的影响,而图灵测试为机器能否思考的争论双方找到了一种公认的判决准则。     

Deceptive Appearances: the Turing Test,Response-Dependence,and Intelligence as an Emotional Concept

Minds and Machines - The Turing Test is routinely understood as a behaviourist test for machine intelligence. Diane Proudfoot (Rethinking Turing’s Test, Journal of Philosophy, 2013) has...     

Some Implications of a Sample of Practical Turing Tests

A series of imitation games involving 3-participant (simultaneous comparison of two hidden entities) and 2-participant (direct interrogation of a hidden entity) were conducted at Bletchley Park on the 100th anniversary of Alan Turing’s birth: 23 June 2012. From the ongoing analysis of over 150 games involving (expert and non-expert, males and females, adults and child) judges, machines and hidden humans (foils for the machines), we present six particular conversations that took place between human judges and a hidden entity that produced unexpected results. From this sample we focus on features of Turing’s machine intelligence test that the mathematician/code breaker did not consider in his examination for machine thinking: the subjective nature of attributing intelligence to another mind.     

Anthropomorphism and AI: Turingʼs much misunderstood imitation game

The widespread tendency, even within AI, to anthropomorphize machines makes it easier to convince us of their intelligence. How can any putative demonstration of intelligence in machines be trusted if the AI researcher readily succumbs to make-believe? This is (what I shall call) the forensic problem of anthropomorphism. I argue that the Turing test provides a solution. This paper illustrates the phenomenon of misplaced anthropomorphism and presents a new perspective on Turing?s imitation game. It also examines the role of the Turing test in relation to the current dispute between human-level AI and ‘mindless intelligence’.     

Turing Interrogative Games

The issue of adequacy of the Turing Test (TT) is addressed. The concept of Turing Interrogative Game (TIG) is introduced. We show that if some conditions hold, then each machine, even a thinking one, loses a certain TIG and thus an instance of TT. If, however, the conditions do not hold, the success of a machine need not constitute a convincing argument for the claim that the machine thinks.     

基于图灵机的递归技术的实现陈晓亮[1] 卢朝辉[2] 宋文[1]

图灵机是通用的计算机模型,一般程序设计和以图灵机为机器模型的计算也是支持递归的。本文首先分析了递归的特征,利用多带图灵机作为计算模型,定义了递归技术转移 函数形式,提出了图灵机递归过程信息传递与保存的方法,给出了图灵机调用的实现,继而给出了图灵机递归技术的实现,同时证明了图灵机的调用与图灵机的递归调用是图灵可识别的。     

Have we witnessed a real-life Turing Test?

Did Deep Blue ace the Turing Test? Did it do much more? It seems that the IBM creation not only beat the reigning chess World Champion Gary Kasparov, but also took a large step, in some people's eyes, toward true artificial intelligence. For AI professionals, a computer defeating a human in chess is probably neither surprising nor really significant. After all, they contend, chess can be described in terms of a nondeterministic alternating Turing machine. Despite the enormous number of possible positions and available moves, the task does not present a challenging theoretical AI problem of NP completeness. There are many well-developed AI strategies that limit the search for the best move to an analysis of the most promising positions. Therefore, the progress in logical and numerical methods of AI and a computer's computational speed and available memory made the computer's victory inevitable. Deep Blue's victory, then, was attributable to its ability to analyze 200 million positions per second and a refined algorithm that accounted for positional-in addition to material-advantage. In summary, most AI professionals conclude that the computer won by brute force, rather than a sophisticated or original strategy. What most AI experts have overlooked, though, is another aspect of the match, which may signify a milestone in the history of computer science: for the first time, a computer seems to have passed the Turing Test     

PessimalPrint: a reverse Turing test

Abstract. We exploit the gap in ability between human and machine vision systems to craft a family of automatic challenges that tell human and machine users apart via graphical interfaces including Internet browsers. Turing proposed [Tur50] a method whereby human judges might validate “artificial intelligence” by failing to distinguish between human and machine interlocutors. Stimulated by the “chat room problem” posed by Udi Manber of Yahoo!, and influenced by the CAPTCHA project [BAL00] of Manuel Blum et al. of Carnegie-Mellon Univ., we propose a variant of the Turing test using pessimal print: that is, low-quality images of machine-printed text synthesized pseudo-randomly over certain ranges of words, typefaces, and image degradations. We show experimentally that judicious choice of these ranges can ensure that the images are legible to human readers but illegible to several of the best present-day optical character recognition (OCR) machines. Our approach is motivated by a decade of research on performance evaluation of OCR machines [RJN96,RNN99] and on quantitative stochastic models of document image quality [Bai92,Kan96]. The slow pace of evolution of OCR and other species of machine vision over many decades [NS96,Pav00] suggests that pessimal print will defy automated attack for many years. Applications include `bot' barriers and database rationing. Received: February 14, 2002 / Accepted: March 28, 2002 An expanded version of: A.L. Coates, H.S. Baird, R.J. Fateman (2001) Pessimal Print: a reverse Turing Test. In: {\it Proc. 6th Int. Conf. on Document Analysis and Recognition}, Seattle, Wash., USA, September 10–13, pp. 1154–1158 Correspondence to: H. S. Baird