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1. The Turing test: verbal behaviour as the hallmark of intelligence - The Turing Test is part of the vocabulary of popular culture - it has appeared in works ranging from the Broadway play breaking the Code to the comic strip "Robotman". 2. Turing Test - In honour of Alan Turing, mathematician, cryptanalyst, and progenitor of computer science, we wanted to provide you with a demonstration of one of the areas in which his work has had an influence on the English language. The Turing test, ‘a test for intelligence in a computer, requiring that a human being should be unable to distinguish the machine from another human being by using the replies to questions put to both’, is commonly regarded as the barrier which a computer program must break to be considered an artificial intelligence. Though he didn’t use the word himself to describe it, the test was set out by Turing in his 1950 paper Computing machinery and intelligence, published in the journal Mind. 3. Beyond the Turing Test - Intelligence is, after all, a multidimensional variable, and no one test could possibly ever be definitive truly to measure it. 4. Visual Turing test for computer vision systems - Today, computer vision systems are tested by their accuracy in detecting and localizing instances of objects. As an alternative, and motivated by the ability of humans to provide far richer descriptions and even tell a story about an image, we construct a "visual Turing test": an operator-assisted device that produces a stochastic sequence of binary questions from a given test image. The query engine proposes a question; the operator either provides the correct answer or rejects the question as ambiguous; the engine proposes the next question ("just-in-time truthing"). The test is then administered to the computer-vision system, one question at a time. After the system's answer is recorded, the system is provided the correct answer and the next question. Parsing is trivial and deterministic; the system being tested requires no natural language processing. The query engine employs statistical constraints, learned from a training set, to produce questions with essentially unpredictable answers--the answer to a question, given the history of questions and their correct answers, is nearly equally likely to be positive or negative. In this sense, the test is only about vision. The system is designed to produce streams of questions that follow natural story lines, from the instantiation of a unique object, through an exploration of its properties, and on to its relationships with other uniquely instantiated objects. 5. Turing Test Considered Mostly Harmless - Turing’s landmark paper on computing machinery and intelligence is multifaceted and has an underemphasized ethical dimension. Turing’s notion of “intelligence” and “thinking” was far more encompassing than the common anthropocentric view may suggest. We discuss a number of open and underrated problems that the common interpretation of the Turing test as a test of machine intelligence entails. We suggest that a more meaningful question than “Can machines think?” is whether modern computing machinery can amplify human intelligence. We cite examples ranging from traditional silicon-based environments to carbon-based, living organisms in order to illustrate that this kind of intelligence amplification is indeed happening today. We conclude that in its interpretation as a test of machine intelligence, the Turing test may indeed be harmful for artificial intelligence (AI); in its wider interpretation, however, it remains an inspiring source for philosophy and AI alike. 6. Twitter turing test: Identifying social machines - Many machine-controlled Twitter accounts (also called “Sybils”) are created each day to provide services, flood out messages for astro turf political campaigns, write fake product reviews, or produce an underground marketplace for purchasing Twitter followers, retweets, or URL advertisements. In addition, fake identities and user accounts in online communities are resources used by adversaries to spread malware, spam, and harmful links over social networks. In social networks, Sybil detectors rely on the assumption that Sybils will find it harder to befriend real users; thus, Sybils that are connected to each other form strongly connected subgraphs, which can be detected using the graph theory. However, a majority of Sybils have actually successfully integrated themselves into real social media user communities (such as Twitter and Facebook). In this study, we compared the current methods used for detecting Sybil accounts. We also explored the detection features of various types of Twitter Sybil accounts in order to build an effective and practical classifier. To evaluate our classifier. 7. I-athlon: Toward a Multidimensional Turing Test - While the Turing test is a wellknown method for evaluating machine intelligence, it has a number of drawbacks that make it problematic as a rigorous and practical test for assessing progress in general-purpose AI. For example, the Turing test is deception based, subjectively evaluated, and narrowly focused on language use. We suggest that a test would benefit from including the following requirements: focus on rational behavior, test several dimensions of intelligence, automate as much as possible, score as objectively as possible, and allow incremental progress to be measured. In this article we propose a methodology for designing a test that consists of a series of events, analogous to the Olympic Decathlon, which complies with these requirements. The approach, which we call the I-athlon, is intended ultimately to enable the community to evaluate progress toward machine intelligence in a practical and repeatable way. 8. Machine humour: examples from Turing test experiments - In this paper, we look at the possibility of a machine having a sense of humour. In particular, we focus on actual machine utterances in Turing test discourses. In doing so, we do not consider the Turing test in depth and what this might mean for humanity, rather we merely look at cases in conversations when the output from a machine can be considered to be humorous. 9. A Turing test for collective motion – A widespread problem in biological research is assessing whether a model adequately describes some real-world data. However, even if a model captures the large-scale statistical properties of the data, should we be satisfied with it? We developed a method, inspired by Alan Turing, to assess the effectiveness of model fitting. We first built a self-propelled particle model whose properties (order and cohesion) statistically matched those of real fish schools. We then asked members of the public to play an online game (a modified Turing test) in which they attempted to distinguish between the movements of real fish schools or those generated by the model. Even though the statistical properties of the real data and the model were consistent with each other, the public could still distinguish between the two, highlighting the need for model refinement. Our results demonstrate that we can use ‘citizen science’ to cross-validate and improve model fitting not only in the field of collective behaviour, but also across a broad range of biological systems. J.E.H.-R., M.R. and D.J.T.S. conceived/designed the study and wrote the paper. J.E.H.-R. performed the experiments. M.R. designed the online game. M.R. and J.E.H.-R. analysed the data and prepared figures. All authors gave final approval for publication and agree to be held accountable for the work performed. 10. A Turing test for free will – Before Alan Turing made his crucial contributions to the theory of computation, he studied the question of whether quantum mechanics could throw light on the nature of free will. This paper investigates the roles of quantum mechanics and computation in free will. Although quantum mechanics implies that events are intrinsically unpredictable, the ‘pure stochasticity’ of quantum mechanics adds randomness only to decision-making processes, not freedom. By contrast, the theory of computation implies that, even when our decisions arise from a completely deterministic decision-making process, the outcomes of that process can be intrinsically unpredictable, even to—especially to— ourselves. I argue that this intrinsic computational unpredictability of the decisionmaking process is what gives rise to our impression that we possess free will. Finally, I propose a ‘Turing test’ for free will: a decision-maker who passes this test will tend to believe that he, she, or it possesses free will, whether the world is deterministic or not. 11. Dehumanising the Turing test – In the test devised by Alan Turing bearing his name in his seminal 1950 paper “Computing machinery and intelligence”, to determine whether a machine is able to demonstrate intelligence – in Turing’s words “Can machines act like they are thinking?” – a human examiner is asked to determine whether one or other of two subjects with whom he is communicating via a teletype is human. The examiner does this by establishing a question-and-answer session made up of a series of questions, possible identical, to the two subjects and the responses from both. He then assesses these responses against each other and his expectations of an intelligent human response. It is the contention of this letter that the direct involvement of a human questioner and a human “standard of intelligence” subject in determining the outcome of the Turing test creates a flawed and questionable set of results, because of the level of subjectivity involved. 12. An Artistic Turing Test – Alan Turing, the centenary of whose birth we are celebrating this year, had an important influence on artists. He has often been called one of the greatest minds Britain has ever produced - his theory of computation and formalisation of the concept of the algorithm laid down the scientific basis for the digital age. 13. A Turing Test for Computer Game Bots – In this paper, a version of the Turing Test is proposed, to test the ability of computer game playing agents (“bots”) to imitate human game players. The proposed test has been implemented as a bot design and programming competition, the 2K BotPrize Contest. The results of the 2008 competition are presented and analyzed. We find that the Test is challenging, but that current techniques show promise. We also suggest probable future directions for developing improved bots. 14. Beyond the Turing Test – Despite the technological marvel of the Internet and the rapidly proliferating mobile technologies that are fundamentally changing the way we interact, AI's original "grand dream" remains elusive as we approach the twilight of the first decade in the 21st century. By designing a roadmap to AGI (artificial general intelligence) and creating important benchmarks, we may yet achieve that dream. However, this will only happen if the nascent AGI community coalesces and works toward a common vision. 15. Teaching to the Turing Test with Cleverbot – The author describes how the program Cleverbot was subjected to the Turing Test of machine intelligence to see how technology is socially constructed. He states that Cleverbot learns conversation through imitation of human utterances and is not a neutral system apart from human culture, and that its Turing Testing can shed light on how language shapes perception. The author notes that the study allowed students to better deal with the politics of being human in a time of ubiquitous computing. 16. The Turing Test and the legal process – In this paper, the author 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. This paper is divided into five parts. Part 1 provides some background by describing the original Turing Test and explaining how the Turing Test resembles the process of adjudication. Part 2 then describes our proposed Turing litigation game and identifies the conditions for implementing this alternative approach to litigation, while Part 3 introduces the possibility of probabilistic verdicts (as opposed to the traditional system of binary verdicts). Part 4 reviews (and refutes) several philosophical objections against our Turing-game concept. Part 5 concludes. 17. Passing a Hide-and-Seek Third-Person Turing Test – The authors try to demonstrate the difference between the cognitive abilities can be replicate by the AI. 18. Software passes Turing test – The article 19. Computing machinery and creativity: lessons learned from the Turing test – The authors open a new avenue for viable and more meaningful testing procedures. 20. Computers, Conversation, and Controversy: Passing the Turing Test? - The author talking on the Turing test and some method for do it. Alarifi, Abdulrahman; Alsaleh, Mansour; Al-Salman, AbdulMalik - Information Sciences, 12/2016, Volume 372 Arel, I; Livingston, S - Computer, 2009, Volume 42, Issue 3 Berrar, Daniel; Konagaya, Akihiko; Schuster, Alfons - New Generation Computing, 10/2013, Volume 31, Issue 4 Catherine Mason is the author of A Computer in the Art Room: the origins of British computer arts 1950-80, published in 2008. Cenkner, Andrew; Bulitko, Vadim; Spetch, Marcia; Legge, Eric; Anderson, Craig G; Brown, Matthew - IEEE Transactions on Computational Intelligence and AI in Games, 2014, Volume 6, Issue 1 Donald Geman; Stuart Geman; Neil Hallonquist; Laurent Younes - Proceedings of the National Academy of Sciences, 03/2015, Volume 112, Issue 12 Gary Marcus; Francesca Rossi; Manuela Veloso - AI Magazine, 04/2016 Gehl, Robert W - Transformations: The Journal of Inclusive Scholarship and Pedagogy, 2013, Volume 24, Issue 1-2 Guerra-Pujol, F.E - Information & Communications Technology Law, 06/2012, Volume 21, Issue 2 Herbert-Read, J E; Romenskyy, M; Sumpter, D J T - Biology letters, 12/2015, Volume 11, Issue 12 Herzfeld, Noreen - Theology and Science, 10/2014, Volume 12, Issue 4 Hingston, P - IEEE Transactions on Computational Intelligence and AI in Games, 2009, Volume 1, Issue 3 ITNOW, 06/2012, Volume 54, Issue 2 ITNOW, 12/2012, Volume 54, Issue 4 Peter Berrar, Daniel; Schuster, Alfons - Kybernetes, 01/2014, Volume 43, Issue 1 Physics Today, 2011 Pleming, Robert - Kybernetes, 05/2010, Volume 39, Issue 3 Sam S Adams; Guruduth Banavar; Murray Campbell - AI Magazine, 04/2016, Volume 37, Issue 1 Seth Lloyd - Philosophical Transactions: Mathematical, Physical and Engineering Sciences, 07/2012, Volume 370, Issue 1971 Shah, Huma; Warwick, Kevin - AI & SOCIETY, 06/2016 Shieber, Stuart M – 2004