Search results for 'machine' (try it on Scholar)

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  1. Susan A. J. Stuart (2011). Enkinaesthesia: The Fundamental Challenge for Machine Consciousness. International Journal of Machine Consciousness 3 (01):145-162.
    In this short paper I will introduce an idea which, I will argue, presents a fundamental additional challenge to the machine consciousness community. The idea takes the questions surrounding phenomenology, qualia and phenomenality one step further into the realm of intersubjectivity but with a twist, and the twist is this: that an agent’s intersubjective experience is deeply felt and necessarily co-affective; it is enkinaesthetic, and only through enkinaesthetic awareness can we establish the affective enfolding which enables first the perturbation, (...)
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  2.  61
    Gary Hatfield (2007). The Passions of the Soul and Descartes's Machine Psychology. Studies in History and Philosophy of Science Part A 38 (1):1-35.
    Descartes developed an elaborate theory of animal physiology that he used to explain functionally organized, situationally adapted behavior in both human and nonhuman animals. Although he restricted true mentality to the human soul, I argue that he developed a purely mechanistic (or material) ‘psychology’ of sensory, motor, and low-level cognitive functions. In effect, he sought to mechanize the offices of the Aristotelian sensitive soul. He described the basic mechanisms in the Treatise on man, which he summarized in the Discourse. However, (...)
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  3. Jason Kawall (1999). The Experience Machine and Mental State Theories of Well-Being. Journal of Value Inquiry 33 (3):381-387.
    It is argued that Nozick's experience machine thought experiment does not pose a particular difficulty for mental state theories of well-being. While the example shows that we value many things beyond our mental states, this simply reflects the fact that we value more than our own well-being. Nor is a mental state theorist forced to make the dubious claim that we maintain these other values simply as a means to desirable mental states. Valuing more than our mental (...)
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  4. Ave Mets (2013). Measurement Theory, Nomological Machine And Measurement Uncertainties (In Classical Physics). Studia Philosophica Estonica 5 (2):167-186.
    Measurement is said to be the basis of exact sciences as the process of assigning numbers to matter (things or their attributes), thus making it possible to apply the mathematically formulated laws of nature to the empirical world. Mathematics and empiria are best accorded to each other in laboratory experiments which function as what Nancy Cartwright calls nomological machine: an arrangement generating (mathematical) regularities. On the basis of accounts of measurement errors and uncertainties, I will argue for two claims: (...)
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  5.  81
    Ryan Tonkens (2009). A Challenge for Machine Ethics. Minds and Machines 19 (3):421-438.
    That the successful development of fully autonomous artificial moral agents (AMAs) is imminent is becoming the received view within artificial intelligence research and robotics. The discipline of Machines Ethics, whose mandate is to create such ethical robots, is consequently gaining momentum. Although it is often asked whether a given moral framework can be implemented into machines, it is never asked whether it should be. This paper articulates a pressing challenge for Machine Ethics: To identify an ethical framework that is (...)
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  6.  43
    Karim Jebari (2013). Brain Machine Interface and Human Enhancement – An Ethical Review. Neuroethics 6 (3):617-625.
    Brain machine interface (BMI) technology makes direct communication between the brain and a machine possible by means of electrodes. This paper reviews the existing and emerging technologies in this field and offers a systematic inquiry into the relevant ethical problems that are likely to emerge in the following decades.
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  7. Aaron Sloman, Virtual Machine Functionalism: The Only Form of Functionalism Worth Taking Seriously in Philosophy of Mind.
    Most philosophers appear to have ignored the distinction between the broad concept of Virtual Machine Functionalism (VMF) described in Sloman&Chrisley (2003) and the better known version of functionalism referred to there as Atomic State Functionalism (ASF), which is often given as an explanation of what Functionalism is, e.g. in Block (1995). -/- One of the main differences is that ASF encourages talk of supervenience of states and properties, whereas VMF requires supervenience of machines that are arbitrarily complex networks of (...)
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  8.  12
    Karim Jebari & Sven-Ove Hansson (2013). European Public Deliberation on Brain Machine Interface Technology: Five Convergence Seminars. [REVIEW] Science and Engineering Ethics 19 (3):1071-1086.
    We present a novel procedure to engage the public in ethical deliberations on the potential impacts of brain machine interface technology. We call this procedure a convergence seminar, a form of scenario-based group discussion that is founded on the idea of hypothetical retrospection. The theoretical background of this procedure and the results of five seminars are presented.
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  9. Sharon Hewitt (2010). What Do Our Intuitions About the Experience Machine Really Tell Us About Hedonism? Philosophical Studies 151 (3):331 - 349.
    Robert Nozick's experience machine thought experiment is often considered a decisive refutation of hedonism. I argue that the conclusions we draw from Nozick's thought experiment ought to be informed by considerations concerning the operation of our intuitions about value. First, I argue that, in order to show that practical hedonistic reasons are not causing our negative reaction to the experience machine, we must not merely stipulate their irrelevance (since our intuitions are not always responsive to stipulation) but fill (...)
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  10.  60
    Maartje Schermer (2009). The Mind and the Machine. On the Conceptual and Moral Implications of Brain-Machine Interaction. NanoEthics 3 (3):217-230.
    Brain-machine interfaces are a growing field of research and application. The increasing possibilities to connect the human brain to electronic devices and computer software can be put to use in medicine, the military, and entertainment. Concrete technologies include cochlear implants, Deep Brain Stimulation, neurofeedback and neuroprosthesis. The expectations for the near and further future are high, though it is difficult to separate hope from hype. The focus in this paper is on the effects that these new technologies may (...)
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  11.  20
    Arnon Levy (2014). Machine-Likeness and Explanation by Decomposition. Philosophers' Imprint 14 (6).
    Analogies to machines are commonplace in the life sciences, especially in cellular and molecular biology — they shape conceptions of phenomena and expectations about how they are to be explained. This paper offers a framework for thinking about such analogies. The guiding idea is that machine-like systems are especially amenable to decompositional explanation, i.e., to analyses that tease apart underlying components and attend to their structural features and interrelations. I argue that for decomposition to succeed a system must exhibit (...)
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  12.  78
    Jack Copeland (1999). Beyond the Universal Turing Machine. Australasian Journal of Philosophy 77 (1):46-67.
    We describe an emerging field, that of nonclassical computability and nonclassical computing machinery. According to the nonclassicist, the set of well-defined computations is not exhausted by the computations that can be carried out by a Turing machine. We provide an overview of the field and a philosophical defence of its foundations.
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  13.  31
    David Leech Anderson (2012). Machine Intentionality, the Moral Status of Machines, and the Composition Problem. In Vincent C. Müller (ed.), The Philosophy & Theory of Artificial Intelligence. Springer 312-333.
    According to the most popular theories of intentionality, a family of theories we will refer to as “functional intentionality,” a machine can have genuine intentional states so long as it has functionally characterizable mental states that are causally hooked up to the world in the right way. This paper considers a detailed description of a robot that seems to meet the conditions of functional intentionality, but which falls victim to what I call “the composition problem.” One obvious way to (...)
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  14. Paul Richard Blum, Michael Polanyi: Can the Mind Be Represented by a Machine? Existence and Anthropology.
    On the 27th of October, 1949, the Department of Philosophy at the University of Manchester organized a symposium "Mind and Machine", as Michael Polanyi noted in his Personal Knowledge (1974, p. 261). This event is known, especially among scholars of Alan Turing, but it is scarcely documented. Wolfe Mays (2000) reported about the debate, which he personally had attended, and paraphrased a mimeographed document that is preserved at the Manchester University archive. He forwarded a copy to Andrew Hodges (...)
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  15.  57
    S. G. Sterrett, Turing on the Integration of Human and Machine Intelligence.
    Abstract Philosophical discussion of Alan Turing’s writings on intelligence has mostly revolved around a single point made in a paper published in the journal Mind in 1950. This is unfortunate, for Turing’s reflections on machine (artificial) intelligence, human intelligence, and the relation between them were more extensive and sophisticated. They are seen to be extremely well-considered and sound in retrospect. Recently, IBM developed a question-answering computer (Watson) that could compete against humans on the game show Jeopardy! There are hopes (...)
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  16.  9
    Guglielmo Tamburrini & Edoardo Datteri (2005). Machine Experiments and Theoretical Modelling: From Cybernetic Methodology to Neuro-Robotics. [REVIEW] Minds and Machines 15 (3-4):335-358.
    Cybernetics promoted machine-supported investigations of adaptive sensorimotor behaviours observed in biological systems. This methodological approach receives renewed attention in contemporary robotics, cognitive ethology, and the cognitive neurosciences. Its distinctive features concern machine experiments, and their role in testing behavioural models and explanations flowing from them. Cybernetic explanations of behavioural events, regularities, and capacities rely on multiply realizable mechanism schemata, and strike a sensible balance between causal and unifying constraints. The multiple realizability of cybernetic mechanism schemata paves the way (...)
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  17.  62
    Michael Anderson & Susan Leigh Anderson (2007). The Status of Machine Ethics: A Report From the AAAI Symposium. [REVIEW] Minds and Machines 17 (1):1-10.
    This paper is a summary and evaluation of work presented at the AAAI 2005 Fall Symposium on Machine Ethics that brought together participants from the fields of Computer Science and Philosophy to the end of clarifying the nature of this newly emerging field and discussing different approaches one could take towards realizing the ultimate goal of creating an ethical machine.
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  18.  53
    Basil Smith (2012). Affect, Rationality, and the Experience Machine. Ethical Perspectives 19 (2):268-276.
    Can we test philosophical thought experiments, such as whether people would enter an experience machine or would leave one once they are inside? Dan Weijers argues that since 'rational' subjects (e.g. students taking surveys in college classes) are believable, we can do so. By contrast, I argue that because such subjects will probably have the wrong affect (i.e. emotional states) when they are tested, such tests are almost worthless. Moreover, understood as a general policy, such pretend testing would ruin (...)
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  19.  24
    H. E. Baber (2008). The Experience Machine Deconstructed. Philosophy in the Contemporary World 15 (1):133-138.
    Nozick’s Experience Machine thought experiment is generally taken to make a compelling, if not conclusive, case against philosophical hedonism. I argue that it does not and, indeed, that regardless of the results, it cannot provide any reason to accept or reject either hedonism or any other philosophical account of wellbeing since it presupposes preferentism, the desire-satisfaction account ofwellbeing. Preferentists cannot take any comfort from the results of such thought experiments because they assume preferentism and therefore cannot establish it. Neither (...)
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  20.  20
    Herman T. Tavani (2015). Levels of Trust in the Context of Machine Ethics. Philosophy and Technology 28 (1):75-90.
    Are trust relationships involving humans and artificial agents possible? This controversial question has become a hotly debated topic in the emerging field of machine ethics. Employing a model of trust advanced by Buechner and Tavani :39–51, 2011), I argue that the “short answer” to this question is yes. However, I also argue that a more complete and nuanced answer will require us to articulate the various levels of trust that are also possible in environments comprising both human (...)
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  21.  3
    Todd S. Mei (forthcoming). Heidegger in the Machine: The Difference Between Techne and Mechane. Continental Philosophy Review:1-26.
    Machines are often employed in Heidegger’s philosophy as instances to illustrate specific features of modern technology. But what is it about machines that allows them to fulfill this role? This essay argues there is a unique ontological force to the machine that can be understood when looking at distinctions between techne and mechane in ancient Greek sources and applying these distinctions to a reading of Heidegger’s early thought on equipment and later thought on poiesis. Especially with respect to Heidegger’s (...)
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  22.  57
    Taner Edis (1998). How Godel's Theorem Supports the Possibility of Machine Intelligence. Minds and Machines 8 (2):251-262.
    Gödel's Theorem is often used in arguments against machine intelligence, suggesting humans are not bound by the rules of any formal system. However, Gödelian arguments can be used to support AI, provided we extend our notion of computation to include devices incorporating random number generators. A complete description scheme can be given for integer functions, by which nonalgorithmic functions are shown to be partly random. Not being restricted to algorithms can be accounted for by the availability of an arbitrary (...)
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  23.  47
    Andrew A. Fingelkurts, Alexander A. Fingelkurts & Carlos F. H. Neves (2012). Machine” Consciousness and “Artificial” Thought: An Operational Architectonics Model Guided Approach. Brain Research 1428:80-92.
    Instead of using low-level neurophysiology mimicking and exploratory programming methods commonly used in the machine consciousness field, the hierarchical Operational Architectonics (OA) framework of brain and mind functioning proposes an alternative conceptual-theoretical framework as a new direction in the area of model-driven machine (robot) consciousness engineering. The unified brain-mind theoretical OA model explicitly captures (though in an informal way) the basic essence of brain functional architecture, which indeed constitutes a theory of consciousness. The OA describes the neurophysiological basis (...)
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  24.  5
    Huma Shah & Kevin Warwick (2010). From the Buzzing in Turing’s Head to Machine Intelligence Contests. In TCIT 2010 / AISB 2010 Convention.
    This paper presents an analysis of three major contests for machine intelligence. We conclude that a new era for Turing’s test requires a fillip in the guise of a committed sponsor, not unlike DARPA, funders of the successful 2007 Urban Challenge.
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  25.  3
    Caroline Privault, Jacki O'Neill, Victor Ciriza & Jean-Michel Renders (2010). A New Tangible User Interface for Machine Learning Document Review. Artificial Intelligence and Law 18 (4):459-479.
    This paper describes a tool for assisting lawyers and paralegal teams during document review in eDiscovery. The tool combines a machine learning technology (CategoriX) and advanced multi-touch interface capable of not only addressing the usual cost, time and accuracy issues in document review, but also of facilitating the work of the review teams by capitalizing on the intelligence of the reviewers and enabling collaborative work.
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  26.  16
    Susan G. Sterrett (2012). Bringing Up Turing's 'Child-Machine'. In S. Barry Cooper (ed.), How the World Computes. 703--713.
    Turing wrote that the “guiding principle” of his investigation into the possibility of intelligent machinery was “The analogy [of machinery that might be made to show intelligent behavior] with the human brain.” [10] In his discussion of the investigations that Turing said were guided by this analogy, however, he employs a more far-reaching analogy: he eventually expands the analogy from the human brain out to “the human community as a whole.” Along the way, he takes note of an obvious fact (...)
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  27.  36
    Kevin B. Korb (2004). Introduction: Machine Learning as Philosophy of Science. Minds and Machines 14 (4):433-440.
    I consider three aspects in which machine learning and philosophy of science can illuminate each other: methodology, inductive simplicity and theoretical terms. I examine the relations between the two subjects and conclude by claiming these relations to be very close.
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  28.  29
    José Hernández-Orallo & David L. Dowe (2013). On Potential Cognitive Abilities in the Machine Kingdom. Minds and Machines 23 (2):179-210.
    Animals, including humans, are usually judged on what they could become, rather than what they are. Many physical and cognitive abilities in the ‘animal kingdom’ are only acquired (to a given degree) when the subject reaches a certain stage of development, which can be accelerated or spoilt depending on how the environment, training or education is. The term ‘potential ability’ usually refers to how quick and likely the process of attaining the ability is. In principle, things should not be different (...)
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  29.  16
    Martin Možina, Jure Žabkar, Trevor Bench-Capon & Ivan Bratko (2005). Argument Based Machine Learning Applied to Law. Artificial Intelligence and Law 13 (1):53-73.
    In this paper we discuss the application of a new machine learning approach – Argument Based Machine Learning – to the legal domain. An experiment using a dataset which has also been used in previous experiments with other learning techniques is described, and comparison with previous experiments made. We also tested this method for its robustness to noise in learning data. Argumentation based machine learning is particularly suited to the legal domain as it makes use of the (...)
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  30.  24
    Darren Whobrey (2001). Machine Mentality and the Nature of the Ground Relation. Minds and Machines 11 (3):307-346.
    John Searle distinguished between weak and strong artificial intelligence (AI). This essay discusses a third alternative, mild AI, according to which a machine may be capable of possessing a species of mentality. Using James Fetzer's conception of minds as semiotic systems, the possibility of what might be called ``mild AI'' receives consideration. Fetzer argues against strong AI by contending that digital machines lack the ground relationship required of semiotic systems. In this essay, the implementational nature of semiotic processes posited (...)
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  31.  11
    Valery M. Tsourikov (1993). Inventive Machine: Second Generation. [REVIEW] AI and Society 7 (1):62-77.
    Inventive Machine project is the matter of discussion. The project aims to develop a family of AI systems for intelligent support of all stages of engineering design.Peculiarities of the IM project:deep and comprehensive knowledge base — the theory of inventive problem solving (TIPS)solving complex problems at the level of inventionsapplication in any area of engineeringstructural prediction of engineering system developmentThe systems of the second generation are described in detail.
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  32.  16
    Prakash Mondal (2014). Does Computation Reveal Machine Cognition? Biosemiotics 7 (1):97-110.
    This paper seeks to understand machine cognition. The nature of machine cognition has been shrouded in incomprehensibility. We have often encountered familiar arguments in cognitive science that human cognition is still faintly understood. This paper will argue that machine cognition is far less understood than even human cognition despite the fact that a lot about computer architecture and computational operations is known. Even if there have been putative claims about the transparency of the notion of machine (...)
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  33.  46
    F. Bergadano (1993). Machine Learning and the Foundations of Inductive Inference. Minds and Machines 3 (1):31-51.
    The problem of valid induction could be stated as follows: are we justified in accepting a given hypothesis on the basis of observations that frequently confirm it? The present paper argues that this question is relevant for the understanding of Machine Learning, but insufficient. Recent research in inductive reasoning has prompted another, more fundamental question: there is not just one given rule to be tested, there are a large number of possible rules, and many of these are somehow confirmed (...)
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  34.  18
    Paul Bello & Selmer Bringsjord (2013). On How to Build a Moral Machine. Topoi 32 (2):251-266.
    Herein we make a plea to machine ethicists for the inclusion of constraints on their theories consistent with empirical data on human moral cognition. As philosophers, we clearly lack widely accepted solutions to issues regarding the existence of free will, the nature of persons and firm conditions on moral agency/patienthood; all of which are indispensable concepts to be deployed by any machine able to make moral judgments. No agreement seems forthcoming on these matters, and we don’t (...)
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  35.  15
    Natika Newton (1989). Machine Understanding and the Chinese Room. Philosophical Psychology 2 (2):207-15.
    John Searle has argued that one can imagine embodying a machine running any computer program without understanding the symbols, and hence that purely computational processes do not yield understanding. The disagreement this argument has generated stems, I hold, from ambiguity in talk of 'understanding'. The concept is analysed as a relation between subjects and symbols having two components: a formal and an intentional. The central question, then becomes whether a machine could possess the intentional component with or without (...)
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  36.  23
    Roberto Cordeschi (2010). Which Kind of Machine Consciousness? International Journal of Machine Consciousness 2 (1):31-33.
    Aaron Sloman remarks that a lot of present disputes on consciousness are usually based, on the one hand, on re-inventing “ideas that have been previously discussed at lenght by others”, on the other hand, on debating “unresolvable” issues, such as that about which animals have phenomenal consciousness. For what it’s worth I would make a couple of examples, which are related to certain topics that Sloman deals with in his paper, and that might be useful for introducing some comments in (...)
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  37.  16
    Masaki Kurematsu & Takahira Yamaguchi (1997). A Legal Ontology Refinement Support Environment Using a Machine-Readable Dictionary. Artificial Intelligence and Law 5 (1-2):119-137.
    This paper discusses how to refine a given initial legal ontology using an existing MRD (Machine-Readable Dictionary). There are two hard issues in the refinement process. One is to find out those MRD concepts most related to given legal concepts. The other is to correct bugs in a given legal ontology, using the concepts extracted from an MRD. In order to resolve the issues, we present a method to find out the best MRD correspondences to given legal concepts, using (...)
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  38.  20
    Herbert Simon (1995). Machine Discovery. Foundations of Science 1 (2):171-200.
    Human and machine discovery are gradual problem-solving processes of searching large problem spaces for incompletely defined goal objects. Research on problem solving has usually focused on search of an instance space (empirical exploration) and a hypothesis space (generation of theories). In scientific discovery, search must often extend to other spaces as well: spaces of possible problems, of new or improved scientific instruments, of new problem representations, of new concepts, and others. This paper focuses especially on the processes for finding (...)
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  39.  18
    Edwin J. Beggs, José Félix Costa & John V. Tucker (2010). Physical Oracles: The Turing Machine and the Wheatstone Bridge. Studia Logica 95 (1/2):279 - 300.
    Earlier, we have studied computations possible by physical systems and by algorithms combined with physical systems. In particular, we have analysed the idea of using an experiment as an oracle to an abstract computational device, such as the Turing machine. The theory of composite machines of this kind can be used to understand (a) a Turing machine receiving extra computational power from a physical process, or (b) an experimenter modelled as a Turing machine performing a test of (...)
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  40.  22
    Kuo-Chin Chang, Tzung-Pei Hong & Shian-Shyong Tseng (1996). Machine Learning by Imitating Human Learning. Minds and Machines 6 (2):203-228.
    Learning general concepts in imperfect environments is difficult since training instances often include noisy data, inconclusive data, incomplete data, unknown attributes, unknown attribute values and other barriers to effective learning. It is well known that people can learn effectively in imperfect environments, and can manage to process very large amounts of data. Imitating human learning behavior therefore provides a useful model for machine learning in real-world applications. This paper proposes a new, more effective way to represent imperfect training instances (...)
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  41.  12
    Joachim Quantz & Birte Schmitz (1994). Knowledge-Based Disambiguation for Machine Translation. Minds and Machines 4 (1):39-57.
    The resolution of ambiguities is one of the central problems for Machine Translation. In this paper we propose a knowledge-based approach to disambiguation which uses Description Logics (dl) as representation formalism. We present the process of anaphora resolution implemented in the Machine Translation systemfast and show how thedl systemback is used to support disambiguation.The disambiguation strategy uses factors representing syntactic, semantic, and conceptual constraints with different weights to choose the most adequate antecedent candidate. We show how these factors (...)
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  42.  4
    Furio Di Paola (1988). Human-Oriented and Machine-Oriented Reasoning: Remarks on Some Problems in the History of Automated Theorem Proving. [REVIEW] AI and Society 2 (2):121-131.
    Examples in the history of Automated Theorem Proving are given, in order to show that even a seemingly ‘mechanical’ activity, such as deductive inference drawing, involves special cultural features and tacit knowledge. Mechanisation of reasoning is thus regarded as a complex undertaking in ‘cultural pruning’ of human-oriented reasoning. Sociological counterparts of this passage from human- to machine-oriented reasoning are discussed, by focusing on problems of man-machine interaction in the area of computer-assisted proof processing.
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  43.  5
    Tom Stonier (1988). Machine Intelligence and the Long-Term Future of the Human Species. AI and Society 2 (2):133-139.
    Intelligence is not a property unique to the human brain; rather it represents a spectrum of phenomena. An understanding of the evolution of intelligence makes it clear that the evolution of machine intelligence has no theoretical limits — unlike the evolution of the human brain. Machine intelligence will outpace human intelligence and very likely will do so during the lifetime of our children. The mix of advanced machine intelligence with human individual and communal intelligence will create an (...)
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  44.  5
    Claudio Garola, Jarosław Pykacz & Sandro Sozzo (2006). Quantum Machine and Semantic Realism Approach: A Unified Model. [REVIEW] Foundations of Physics 36 (6):862-882.
    The Geneva–Brussels approach to quantum mechanics (QM) and the semantic realism (SR) nonstandard interpretation of QM exhibit some common features and some deep conceptual differences. We discuss in this paper two elementary models provided in the two approaches as intuitive supports to general reasonings and as a proof of consistency of general assumptions, and show that Aerts’ quantum machine can be embodied into a macroscopic version of the microscopic SR model, overcoming the seeming incompatibility between the two models. This (...)
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  45.  3
    Linda Passarge & Thomas Binder (1996). Supporting Reflection and Dialogue in a Community of Machine Setters: Lessons Learned From Design and Use of a Hypermedia Type Training Material. [REVIEW] AI and Society 10 (1):79-88.
    The debate about experience-based or tacit knowledge has focused much attention on the limits to formalisation of work process knowledge. A main line of argument has been that, for example, industrial work even with highly advanced technical equipment can only be performed adequately when the worker through experience on the job has gained a feel for the functioning of the machinery and the properties and behaviour of the materials. In this debate links tend to be created between on the one (...)
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  46.  7
    Vladimir Pericliev (1999). The Prospects for Machine Discovery in Linguistics. Foundations of Science 4 (4):463-482.
    The article reports the results from the developmentof four data-driven discovery systems, operating inlinguistics. The first mimics the induction methods ofJohn Stuart Mill, the second performs componentialanalysis of kinship vocabularies, the third is ageneral multi-class discrimination program, and thefourth finds logical patterns in data. These systemsare briefly described and some arguments are offeredin favour of machine linguistic discovery. Thearguments refer to the strength of machines incomputationally complex tasks, the guaranteedconsistency of machine results, the portability ofmachine methods to new (...)
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  47.  2
    Masatsugu Tsuji (2003). Technological Innovation and the Formation of Japanese Technology: The Case of the Machine Tool Industry. [REVIEW] AI and Society 17 (3-4):291-306.
    This paper focuses on how “Japanese technology” was formed in the Japanese machine tool industry, and presents how Japanese machine tool builders competed in R&D and the innovation process in the domestic and international markets. During the competition for the innovation of computerised numerically-controlled (CNC) tools, drastic changes occurred in the ranking of individual firms. Prior to the transformation, the traditional “Big 5” companies occupied the largest market share. After the innovation, however, the “Big 3” firms which had (...)
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  48.  1
    Shin’Ichi Warisawa, Chikao Inaba & Yoshimi Ito (2003). The Application of Manufacturing Culture to the Design of Asian Region Oriented Machine Tools. AI and Society 17 (3-4):278-290.
    During the last years the demand for regionally and culturally harmonised machine design is increasingly on the agenda. The problem of localising products like machine tools instantly poses the question for new procedures that allow including the regional and cultural adaptations into the design processes of machine tool companies. How to transform the general insight into the necessity of culture- and region-adapted technologies and how to embed it into a design procedure comprising applicable design attributes is the (...)
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    Peter A. Hancock (2009). Mind, Machine and Morality: Toward a Philosophy of Human-Technology Symbiosis. Ashgate.
    Historically, this work is a modern-day child of Bacon's hope for the 'Great Instauration.' However, unlike its forebear, the focus here is on human-machine systems.
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  50.  50
    Ben Bramble (forthcoming). The Experience Machine. Philosophy Compass.
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