Search results for 'Artificial Intelligence*' (try it on Scholar)

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  1. Reactive Distributed Artificial (1996). Jacques Ferber. In N. Jennings & G. O'Hare (eds.), Foundations of Distributed Artificial Intelligence. Wiley. 287.score: 360.0
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  2. Modeling Distributed Artificial (1996). Michael Wooldridge. In N. Jennings & G. O'Hare (eds.), Foundations of Distributed Artificial Intelligence. Wiley. 269.score: 360.0
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  3. Hans F. M. Crombag (1993). On the Artificiality of Artificial Intelligence. Artificial Intelligence and Law 2 (1):39-49.score: 270.0
    In this article the question is raised whether artificial intelligence has any psychological relevance, i.e. contributes to our knowledge of how the mind/brain works. It is argued that the psychological relevance of artificial intelligence of the symbolic kind is questionable as yet, since there is no indication that the brain structurally resembles or operates like a digital computer. However, artificial intelligence of the connectionist kind may have psychological relevance, not because the brain is a neural network, but (...)
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  4. Murat Aydede & Guven Guzeldere (2000). Consciousness, Intentionality, and Intelligence: Some Foundational Issues for Artificial Intelligence. Journal of Experimental and Theoretical Artificial Intelligence 12 (3):263-277.score: 240.0
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  5. Selmer Bringsjord (2000). Animals, Zombanimals, and the Total Turing Test: The Essence of Artificial Intelligence. Journal of Logic Language and Information 9 (4):397-418.score: 240.0
    Alan Turing devised his famous test (TT) through a slight modificationof the parlor game in which a judge tries to ascertain the gender of twopeople who are only linguistically accessible. Stevan Harnad hasintroduced the Total TT, in which the judge can look at thecontestants in an attempt to determine which is a robot and which aperson. But what if we confront the judge with an animal, and arobot striving to pass for one, and then challenge him to peg which iswhich? (...)
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  6. John M. Preston & John Mark Bishop (eds.) (2002). Views Into the Chinese Room: New Essays on Searle and Artificial Intelligence. Oxford University Press.score: 240.0
    The most famous challenge to computational cognitive science and artificial intelligence is the philosopher John Searle's "Chinese Room" argument.
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  7. David Marr (1977). Artificial Intelligence: A Personal View. Artificial Intelligence 9 (September):37-48.score: 240.0
  8. James H. Fetzer (1990). Artificial Intelligence: Its Scope and Limits. Kluwer.score: 240.0
    1. WHAT IS ARTIFICIAL INTELLIGENCE? One of the fascinating aspects of the field of artificial intelligence (AI) is that the precise nature of its subject ..
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  9. Viola Schiaffonati (2003). A Framework for the Foundation of the Philosophy of Artificial Intelligence. Minds and Machines 13 (4):537-552.score: 240.0
    The peculiarity of the relationship between philosophy and Artificial Intelligence (AI) has been evidenced since the advent of AI. This paper aims to put the basis of an extended and well founded philosophy of AI: it delineates a multi-layered general framework to which different contributions in the field may be traced back. The core point is to underline how in the same scenario both the role of philosophy on AI and role of AI on philosophy must be considered. Moreover, (...)
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  10. Rajakishore Nath (2009). Philosophy of Artificial Intelligence: A Critique of the Mechanistic Theory of Mind. Universal Publishers.score: 240.0
    This book deals with the major philosophical issues in the theoretical framework of Artificial Intelligence (AI) in particular and cognitive science in general.
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  11. Lawrence B. Solum (1992). Legal Personhood for Artificial Intelligences. North Carolina Law Review 70:1231.score: 240.0
    Could an artificial intelligence become a legal person? As of today, this question is only theoretical. No existing computer program currently possesses the sort of capacities that would justify serious judicial inquiry into the question of legal personhood. The question is nonetheless of some interest. Cognitive science begins with the assumption that the nature of human intelligence is computational, and therefore, that the human mind can, in principle, be modelled as a program that runs on a computer. Artificial (...)
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  12. Selmer Bringsjord (2010). Meeting Floridi's Challenge to Artificial Intelligence From the Knowledge-Game Test for Self-Consciousness. Metaphilosophy 41 (3):292-312.score: 240.0
    Abstract: In the course of seeking an answer to the question "How do you know you are not a zombie?" Floridi (2005) issues an ingenious, philosophically rich challenge to artificial intelligence (AI) in the form of an extremely demanding version of the so-called knowledge game (or "wise-man puzzle," or "muddy-children puzzle")—one that purportedly ensures that those who pass it are self-conscious. In this article, on behalf of (at least the logic-based variety of) AI, I take up the challenge—which is (...)
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  13. Craig DeLancey (2001). Passionate Engines: What Emotions Reveal About the Mind and Artificial Intelligence. Oxford University Press.score: 240.0
    The emotions have been one of the most fertile areas of study in psychology, neuroscience, and other cognitive disciplines. Yet as influential as the work in those fields is, it has not yet made its way to the desks of philosophers who study the nature of mind. Passionate Engines unites the two for the first time, providing both a survey of what emotions can tell us about the mind, and an argument for how work in the cognitive disciplines can help (...)
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  14. Gary L. Drescher (1991). Made-Up Minds: A Constructivist Approach to Artificial Intelligence. Cambridge: MIT Press.score: 240.0
    Made-Up Minds addresses fundamental questions of learning and concept invention by means of an innovative computer program that is based on the cognitive ...
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  15. Marco Ernandes (2005). Artificial Intelligence & Games: Should Computational Psychology Be Revalued? Topoi 24 (2):229-242.score: 240.0
    The aims of this paper are threefold: To show that game-playing (GP), the discipline of Artificial Intelligence (AI) concerned with the development of automated game players, has a strong epistemological relevance within both AI and the vast area of cognitive sciences. In this context games can be seen as a way of securely reducing (segmenting) real-world complexity, thus creating the laboratory environment necessary for testing the diverse types and facets of intelligence produced by computer models. This paper aims to (...)
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  16. Richard E. Korf (1995). Heuristic Evaluation Functions in Artificial Intelligence Search Algorithms. Minds and Machines 5 (4):489-498.score: 240.0
    We consider a special case of heuristics, namely numeric heuristic evaluation functions, and their use in artificial intelligence search algorithms. The problems they are applied to fall into three general classes: single-agent path-finding problems, two-player games, and constraint-satisfaction problems. In a single-agent path-finding problem, such as the Fifteen Puzzle or the travelling salesman problem, a single agent searches for a shortest path from an initial state to a goal state. Two-player games, such as chess and checkers, involve an adversarial (...)
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  17. Evan Selinger (2008). Collins's Incorrect Depiction of Dreyfus's Critique of Artificial Intelligence. Phenomenology and the Cognitive Sciences 7 (2):301-308.score: 240.0
    Harry Collins interprets Hubert Dreyfus’s philosophy of embodiment as a criticism of all possible forms of artificial intelligence. I argue that this characterization is inaccurate and predicated upon a misunderstanding of the relevance of phenomenology for empirical scientific research.
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  18. Barbara Warnick (2004). Rehabilitating AI: Argument Loci and the Case for Artificial Intelligence. [REVIEW] Argumentation 18 (2):149-170.score: 240.0
    This article examines argument structures and strategies in pro and con argumentation about the possibility of human-level artificial intelligence (AI) in the near term future. It examines renewed controversy about strong AI that originated in a prominent 1999 book and continued at major conferences and in periodicals, media commentary, and Web-based discussions through 2002. It will be argued that the book made use of implicit, anticipatory refutation to reverse prevailing value hierarchies related to AI. Drawing on Perelman and Olbrechts-Tyteca's (...)
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  19. Douglas N. Walton (2008). Witness Testimony Evidence: Argumentation, Artificial Intelligence, and Law. Cambridge University Press.score: 240.0
    Recent work in artificial intelligence has increasingly turned to argumentation as a rich, interdisciplinary area of research that can provide new methods related to evidence and reasoning in the area of law. Douglas Walton provides an introduction to basic concepts, tools and methods in argumentation theory and artificial intelligence as applied to the analysis and evaluation of witness testimony. He shows how witness testimony is by its nature inherently fallible and sometimes subject to disastrous failures. At the same (...)
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  20. Tom Ziemke (2001). The Construction of 'Reality' in the Robot: Constructivist Perspectives on Situated Artificial Intelligence and Adaptive Robotics. [REVIEW] Foundations of Science 6 (1-3):163-233.score: 240.0
    This paper discusses different approaches incognitive science and artificial intelligenceresearch from the perspective of radicalconstructivism, addressing especially theirrelation to the biologically based theories ofvon Uexküll, Piaget as well as Maturana andVarela. In particular recent work in New AI and adaptive robotics on situated and embodiedintelligence is examined, and we discuss indetail the role of constructive processes asthe basis of situatedness in both robots andliving organisms.
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  21. Mariusz Flasiński (1997). "Every Man in His Notions" or Alchemists' Discussion on Artificial Intelligence. Foundations of Science 2 (1):107-121.score: 240.0
    A survey of the main approaches in a mind study -oriented part of Artificial Intelligence is made focusing on controversial issues and extreme hypotheses. Various meanings of terms: "intelligence" and "artificial intelligence" are discussed. Limitations for constructing intelligent systems resulting from the lack of formalized models of cognitive activity are shown. The approaches surveyed are then recapitulated in the light of these limitations.
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  22. Tracy B. Henley (1990). Natural Problems and Artificial Intelligence. Behavior and Philosophy 18 (2):43-55.score: 240.0
    Artificial Intelligence has become big business in the military and in many industries. In spite of this growth there still remains no consensus about what AI really is. The major factor which seems to be responsible for this is the lack of agreement about the relationship between behavior and intelligence. In part certain ethical concerns generated from saying who, what and how intelligence is determined may be facilitating this lack of agreement.
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  23. Frederick Kile (2013). Artificial Intelligence and Society: A Furtive Transformation. [REVIEW] AI and Society 28 (1):107-115.score: 240.0
    During the 1950s, there was a burst of enthusiasm about whether artificial intelligence might surpass human intelligence. Since then, technology has changed society so dramatically that the focus of study has shifted toward society’s ability to adapt to technological change. Technology and rapid communications weaken the capacity of society to integrate into the broader social structure those people who have had little or no access to education. (Most of the recent use of communications by the excluded has been disruptive, (...)
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  24. Amir S. Tabandeh (1994). Characterising Artificial Intelligence Technology for International Transfer. AI and Society 8 (4):315-325.score: 240.0
    One of the central factors influencing the process and the outcome of technology transfer is the nature of the technology being transferred. This paper identifies and discusses the main characteristics of Artificial Intelligence (AI) technology from the point of view of international technology transfer. It attempts to indicate the peculiarities of AI in this context and move towards a framework to assist recipient decision makers in optimising the formulation of their policies on AI technology transfer.
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  25. Morton Wagman (ed.) (2000). Historical Dictionary of Quotations in Cognitive Science: A Treasury of Quotations in Psychology, Philosophy, and Artificial Intelligence. Greenwood Press.score: 240.0
    Focuses on distinguished quotations representing the best thinking in philosophy, psychology, and artificial intelligence from classical civilization to ...
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  26. Hutan Ashrafian (forthcoming). Artificial Intelligence and Robot Responsibilities: Innovating Beyond Rights. Science and Engineering Ethics:1-10.score: 240.0
    The enduring innovations in artificial intelligence and robotics offer the promised capacity of computer consciousness, sentience and rationality. The development of these advanced technologies have been considered to merit rights, however these can only be ascribed in the context of commensurate responsibilities and duties. This represents the discernable next-step for evolution in this field. Addressing these needs requires attention to the philosophical perspectives of moral responsibility for artificial intelligence and robotics. A contrast to the moral status of animals (...)
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  27. Hutan Ashrafian (forthcoming). AIonAI: A Humanitarian Law of Artificial Intelligence and Robotics. Science and Engineering Ethics:1-12.score: 240.0
    The enduring progression of artificial intelligence and cybernetics offers an ever-closer possibility of rational and sentient robots. The ethics and morals deriving from this technological prospect have been considered in the philosophy of artificial intelligence, the design of automatons with roboethics and the contemplation of machine ethics through the concept of artificial moral agents. Across these categories, the robotics laws first proposed by Isaac Asimov in the twentieth century remain well-recognised and esteemed due to their specification of (...)
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  28. Carlotta Piscopo & Mauro Birattari (2008). The Metaphysical Character of the Criticisms Raised Against the Use of Probability for Dealing with Uncertainty in Artificial Intelligence. Minds and Machines 18 (2):273-288.score: 240.0
    In artificial intelligence (AI), a number of criticisms were raised against the use of probability for dealing with uncertainty. All these criticisms, except what in this article we call the non-adequacy claim, have been eventually confuted. The non-adequacy claim is an exception because, unlike the other criticisms, it is exquisitely philosophical and, possibly for this reason, it was not discussed in the technical literature. A lack of clarity and understanding of this claim had a major impact on AI. Indeed, (...)
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  29. Vincent Rialle (1995). Cognition and Decision in Biomedical Artificial Intelligence: From Symbolic Representation to Emergence. [REVIEW] AI and Society 9 (2-3):138-160.score: 240.0
    This paper presents work in progress on artificial intelligence in medicine (AIM) within the larger context of cognitive science. It introduces and develops the notion ofemergence both as an inevitable evolution of artificial intelligence towards machine learning programs and as the result of a synergistic co-operation between the physician and the computer. From this perspective, the emergence of knowledge takes placein fine in the expert's mind and is enhanced both by computerised strategies of induction and deduction, and by (...)
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  30. John Mark Bishop (2003). Dancing with Pixies: Strong Artificial Intelligence and Panpsychism. In John M. Preston & Michael A. Bishop (eds.), Views Into the Chinese Room: New Essays on Searle and Artificial Intelligence. Oxford University Press.score: 240.0
  31. Richard Ennals (1987). Socially Useful Artificial Intelligence. AI and Society 1 (1):5-15.score: 240.0
    Artificial intelligence is presented as a set of tools with which we can try to come to terms with human problems, and with the assistance of which, some human problems can be solved. Artificial intelligence is located in its social context, in terms of the environment within which it is developed, and the applications to which it is put. Drawing on social theory, there is consideration of the collaborative and social problem-solving processes which are involved in artificial (...)
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  32. Jay Liebowitz (1989). Artificial Intelligence: New Jobs From Old. AI and Society 3 (1):61-70.score: 240.0
    The age of artificial intelligence (AI) is upon us, and its effect upon society in the coming years will be noteworthy. Artificial intelligence is a field that encompasses such applications as robotics, expert systems, natural language understanding, speech recognition, and computer vision. The effect of these AI systems upon existing and future job occupations will be important. This paper takes a look at artificial intelligence in terms of the creation of new job categories. Also, the introduction of (...)
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  33. Daniel Memmi (1990). Connectionism and Artificial Intelligence as Cognitive Models. AI and Society 4 (2):115-136.score: 240.0
    The current renewal of connectionist techniques using networks of neuron-like units has started to have an influence on cognitive modelling. However, compared with classical artificial intelligence methods, the position of connectionism is still not clear. In this article artificial intelligence and connectionism are systematically compared as cognitive models so as to bring out the advantages and shortcomings of each. The problem of structured representations appears to be particularly important, suggesting likely research directions.
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  34. Tim Smithers (1988). Product Creation: An Appropriate Coupling of Human and Artificial Intelligence. [REVIEW] AI and Society 2 (4):341-353.score: 240.0
    Small batch manufacture dominates the manufacturing sector of a growing number of industrialised countries. The organisational structures and management methods currently adopted in such enterprises are firmly based upon historical developments which started with individual craftsmen. These structures and methods are primarily concerned with the co-ordination of human activities, rather than with the management of theknowledge process underlying the creation of products.This paper argues that it is the failure to understand this knowledge process and its effective integration at aKnowledge Level (...)
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  35. Dr Richard Susskind (1989). Pragmatism and Purism in Artificial Intelligence and Legal Reasoning. AI and Society 3 (1):28-38.score: 240.0
    The paper identifies and assesses the implications of two approaches to the field of artificial intelligence and legal reasoning. The first — pragmatism — concentrates on the development of working systems to the exclusion of theoretical problems. The second — purism — focuses on the nature of the law and of intelligence with no regard for the delivery of commercially viable systems. Past work in AI and law is classified in terms of this division. By reference to The Latent (...)
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  36. Achille Ardigo (1988). Artificial Intelligence: A Contribution to Systems Theories of Sociology. [REVIEW] AI and Society 2 (2):113-120.score: 240.0
    The aim of my contribution is to try to analyse some points of similarity and difference between post-Parsonian social systems theory models for sociology — with special reference to those of W. Buckley, F.E. Emery and N. Luhmann — and expert systems models1 from Artificial Intelligence. I keep specifically to post-Parsonian systems theories within sociology because they assume some postulates and criteria derived from cybernetics and which are at the roots of AI. I refer in particular to the fundamental (...)
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  37. Margaret A. Boden (ed.) (1990). The Philosophy of Artificial Intelligence. Oxford University Press.score: 240.0
    This interdisciplinary collection of classical and contemporary readings provides a clear and comprehensive guide to the many hotly-debated philosophical issues at the heart of artificial intelligence.
     
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  38. Chris Moss (1989). Artificial Intelligence and Symbols. AI and Society 3 (4):345-356.score: 240.0
    The introduction of massive parallelism and the renewed interest in neural networks gives a new need to evaluate the relationship of symbolic processing and artificial intelligence. The physical symbol hypothesis has encountered many difficulties coping with human concepts and common sense. Expert systems are showing more promise for the early stages of learning than for real expertise. There is a need to evaluate more fully the inherent limitations of symbol systems and the potential for programming compared with training. This (...)
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  39. Alberto Oliverio (1988). Biological and Artificial Intelligence. AI and Society 2 (2):152-161.score: 240.0
    The paper discusses the characteristics of Biological Intelligence (BI) and its differences with artificial intelligence. In particular the plasticity of the nervous system is considered in the different forms with special attention to deterministic and localizationist views of the brain vs holistic approaches. When memory and learning are considered the localizationist views do not offer a possible solution to a number of problems while memory may be better conceptualized in terms of categorization procedures and generalizing strategies. Finally, the problem (...)
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  40. Maria Miceli, Amedo Cesta & Paola Rizzo (1995). Distributed Artificial Intelligence From a Socio-Cognitive Standpoint: Looking at Reasons for Interaction. [REVIEW] AI and Society 9 (4):287-320.score: 240.0
    Distributed Artificial Intelligence (DAI) deals with computational systems where several intelligent components interact in a common environment. This paper is aimed at pointing out and fostering the exchange between DAI and cognitive and social science in order to deal with the issues of interaction, and in particular with the reasons and possible strategies for social behaviour in multi-agent interaction is also described which is motivated by requirements of cognitive plausibility and grounded the notions of power, dependence and help. Connections (...)
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  41. Otto Neumaier (1987). A Wittgensteinian View of Artificial Intelligence. In Rainer P. Born (ed.), Artificial Intelligence. St Martin's Press. 132--174.score: 240.0
  42. Kaj Sotala (2012). Advantages of Artificial Intelligences, Uploads, and Digital Minds. International Journal of Machine Consciousness 4 (01):275-291.score: 234.0
    I survey four categories of factors that might give a digital mind, such as an upload or an artificial general intelligence, an advantage over humans. Hardware advantages include greater serial speeds and greater parallel speeds. Self-improvement advantages include improvement of algorithms, design of new mental modules, and modification of motivational system. Co-operative advantages include copyability, perfect co-operation, improved communication, and transfer of skills. Human handicaps include computational limitations and faulty heuristics, human-centric biases, and socially motivated cognition. The shape of (...)
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  43. Massimo Negrotti (1987). The Piping of Thought and the Need for a Permanent Monitoring of the Cultural Effects of Artificial Intelligence. AI and Society 1 (2):85-91.score: 234.0
    Over the years, AI has undergone a transformation from its original aim of producing an ‘intelligent’ machine to that of producing pragmatic solutions of problems of the market place. In doing so, AI has made a significant contribution to the debate on whether the computer is an instrument or an interlocutor. This paper discusses issues of problem solving and creativity underlying this transformation, and attempts to clarify the distinction between theresolutive intelligence andproblematic intelligence. It points out that the advance of (...)
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  44. David J. Cole (1991). Artificial Intelligence and Personal Identity. Synthese 88 (September):399-417.score: 216.0
    Considerations of personal identity bear on John Searle's Chinese Room argument, and on the opposed position that a computer itself could really understand a natural language. In this paper I develop the notion of a virtual person, modelled on the concept of virtual machines familiar in computer science. I show how Searle's argument, and J. Maloney's attempt to defend it, fail. I conclude that Searle is correct in holding that no digital machine could understand language, but wrong in holding that (...)
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  45. Bruce Edmonds (2000). The Constructability of Artificial Intelligence (as Defined by the Turing Test). Journal of Logic Language and Information 9 (4):419-424.score: 216.0
    The Turing Test (TT), as originally specified, centres on theability to perform a social role. The TT can be seen as a test of anability to enter into normal human social dynamics. In this light itseems unlikely that such an entity can be wholly designed in anoff-line mode; rather a considerable period of training insitu would be required. The argument that since we can pass the TT,and our cognitive processes might be implemented as a Turing Machine(TM), that consequently (...)
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  46. B. Edmonds (2000). The Constructibility of Artificial Intelligence (as Defined by the Turing Test). Journal of Logic, Language and Information 9 (4):419-424.score: 216.0
    The Turing Test (TT), as originally specified, centres on theability to perform a social role. The TT can be seen as a test of anability to enter into normal human social dynamics. In this light itseems unlikely that such an entity can be wholly designed in an off-line mode; rather a considerable period of training insitu would be required. The argument that since we can pass the TT,and our cognitive processes might be implemented as a Turing Machine(TM), that consequently a (...)
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  47. [deleted]Thierry Chaminade, Delphine Rosset, David Da Fonseca, Bruno Nazarian, Ewald Lutcher, Gordon Cheng & Christine Deruelle (2012). How Do We Think Machines Think? An fMRI Study of Alleged Competition with an Artificial Intelligence. Frontiers in Human Neuroscience 6.score: 216.0
    Humans are particularly skilled in mentalizing, the inference of other agents’ hidden mental states. Here we question whether activity in brain areas involved in mentalizing is specific to the processing of mental states or can be generalized to the inference of non-mental states by investigating brain responses during the interaction with an artificial agent. Participants were scanned using fMRI during interactive rock-paper-scissors games while believing the opponent was a fellow human (Intentional agent), a humanoid robot endowed with an algorithm (...)
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  48. [deleted]Christine Deruelle Thierry Chaminade, Delphine Rosset, David Da Fonseca, Bruno Nazarian, Ewald Lutcher, Gordon Cheng (2012). How Do We Think Machines Think? An fMRI Study of Alleged Competition with an Artificial Intelligence. Frontiers in Human Neuroscience 6.score: 216.0
    Humans are particularly skilled in mentalizing, the inference of other agents’ hidden mental states. Here we question whether activity in brain areas involved in mentalizing is specific to the processing of mental states or can be generalized to the inference of non-mental states by investigating brain responses during the interaction with an artificial agent. Participants were scanned using fMRI during interactive rock-paper-scissors games while believing the opponent was a fellow human (Intentional agent), a humanoid robot endowed with an algorithm (...)
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  49. Bruce J. Berman (1992). Artificial Intelligence and the Ideology of Capitalist Reconstruction. AI and Society 6 (2):103-114.score: 216.0
    The growing interest in AI in advance capitalist societies can be understood not just in relation to its practial achievements, which remain modest, but also in its ideological role as a technological paradign for the reconstruction of capitalism. This is similar to the role played by scientific management during the second industrial revolution, circa 1880–1930, and involves the extension of the rationalization and routinization of labour to mental work. The conception of human intelligence and the emphasis on command and control (...)
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  50. Riccardo Manzotti (2007). From Artificial Intelligence to Artificial Consciousness. In Antonio Chella & Riccardo Manzotti (eds.), Artificial Consciousness. Imprint Academic. 174-190.score: 216.0
     
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