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Summary See the category "Philosophy of Artificial Intelligence."
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130 found
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  1. Revised: From Color, to Consciousness, Toward Strong AI.Xinyuan Gu - manuscript
    This article cohesively discusses three topics, namely color and its perception, the yet-to-be-solved hard problem of consciousness, and the theoretical possibility of strong AI. First, the article restores color back into the physical world by giving cross-species evidence. Secondly, the article proposes a dual-field with function Q hypothesis (DFFQ) which might explain the ‘first-person point of view’ and so the hard problem of consciousness. Finally, the article discusses what DFFQ might bring to artificial intelligence and how it might allow strong (...)
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  2. Probable General Intelligence Algorithm.Anton Venglovskiy - manuscript
    Contains a description of a generalized and constructive formal model for the processes of subjective and creative thinking. According to the author, the algorithm presented in the article is capable of real and arbitrarily complex thinking and is potentially able to report on the presence of consciousness.
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  3. Truth & Understanding: Essays in Honor of John Haugeland.Zed Adams (ed.) - forthcoming
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  4. Short-Circuiting the Definition of Mathematical Knowledge for an Artificial General Intelligence.Samuel Alexander - forthcoming - Lecture Notes in Computer Science.
    We propose that, for the purpose of studying theoretical properties of the knowledge of an agent with Artificial General Intelligence (that is, the knowledge of an AGI), a pragmatic way to define such an agent’s knowledge (restricted to the language of Epistemic Arithmetic, or EA) is as follows. We declare an AGI to know an EA-statement φ if and only if that AGI would include φ in the resulting enumeration if that AGI were commanded: “Enumerate all the EA-sentences which you (...)
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  5. Designing New Intelligent Machines (COMETT European Symposium, Liège April 1992).D. M. Dubois - forthcoming - Communication and Cognition-Artificial Intelligence.
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  6. Explaining Explanations in AI.Brent Mittelstadt - forthcoming - FAT* 2019 Proceedings 1.
    Recent work on interpretability in machine learning and AI has focused on the building of simplified models that approximate the true criteria used to make decisions. These models are a useful pedagogical device for teaching trained professionals how to predict what decisions will be made by the complex system, and most importantly how the system might break. However, when considering any such model it’s important to remember Box’s maxim that "All models are wrong but some are useful." We focus on (...)
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  7. Some Resonances Between Eastern Thought and Integral Biomathics in the Framework of the WLIMES Formalism for Modelling Living Systems.Plamen L. Simeonov & Andree C. Ehresmann - forthcoming - Progress in Biophysics and Molecular Biology 131 (Special).
    Forty-two years ago, Capra published “The Tao of Physics” (Capra, 1975). In this book (page 17) he writes: “The exploration of the atomic and subatomic world in the twentieth century has …. necessitated a radical revision of many of our basic concepts” and that, unlike ‘classical’ physics, the sub-atomic and quantum “modern physics” shows resonances with Eastern thoughts and “leads us to a view of the world which is very similar to the views held by mystics of all ages and (...)
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  8. Measuring Intelligence and Growth Rate: Variations on Hibbard's Intelligence Measure.Samuel Alexander & Bill Hibbard - 2021 - Journal of Artificial General Intelligence 12 (1):1-25.
    In 2011, Hibbard suggested an intelligence measure for agents who compete in an adversarial sequence prediction game. We argue that Hibbard’s idea should actually be considered as two separate ideas: first, that the intelligence of such agents can be measured based on the growth rates of the runtimes of the competitors that they defeat; and second, one specific (somewhat arbitrary) method for measuring said growth rates. Whereas Hibbard’s intelligence measure is based on the latter growth-rate-measuring method, we survey other methods (...)
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  9. Machine Learning and the Future of Scientific Explanation.Florian J. Boge & Michael Poznic - 2021 - Journal for General Philosophy of Science / Zeitschrift für Allgemeine Wissenschaftstheorie 52 (1):171-176.
    The workshop “Machine Learning: Prediction Without Explanation?” brought together philosophers of science and scholars from various fields who study and employ Machine Learning (ML) techniques, in order to discuss the changing face of science in the light of ML's constantly growing use. One major focus of the workshop was on the impact of ML on the concept and value of scientific explanation. One may speculate whether ML’s increased use in science exemplifies a paradigmatic turn towards mere pattern recognition and prediction (...)
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  10. Measuring the Intelligence of an Idealized Mechanical Knowing Agent.Samuel Alexander - 2020 - Lecture Notes in Computer Science 12226.
    We define a notion of the intelligence level of an idealized mechanical knowing agent. This is motivated by efforts within artificial intelligence research to define real-number intelligence levels of compli- cated intelligent systems. Our agents are more idealized, which allows us to define a much simpler measure of intelligence level for them. In short, we define the intelligence level of a mechanical knowing agent to be the supremum of the computable ordinals that have codes the agent knows to be codes (...)
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  11. Self-Referential Theories.Samuel A. Alexander - 2020 - Journal of Symbolic Logic 85 (4):1687-1716.
    We study the structure of families of theories in the language of arithmetic extended to allow these families to refer to one another and to themselves. If a theory contains schemata expressing its own truth and expressing a specific Turing index for itself, and contains some other mild axioms, then that theory is untrue. We exhibit some families of true self-referential theories that barely avoid this forbidden pattern.
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  12. The Archimedean Trap: Why Traditional Reinforcement Learning Will Probably Not Yield AGI.Samuel Allen Alexander - 2020 - Journal of Artificial General Intelligence 11 (1):70-85.
    After generalizing the Archimedean property of real numbers in such a way as to make it adaptable to non-numeric structures, we demonstrate that the real numbers cannot be used to accurately measure non-Archimedean structures. We argue that, since an agent with Artificial General Intelligence (AGI) should have no problem engaging in tasks that inherently involve non-Archimedean rewards, and since traditional reinforcement learning rewards are real numbers, therefore traditional reinforcement learning probably will not lead to AGI. We indicate two possible ways (...)
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  13. Why Attention is Not Explanation: Surgical Intervention and Causal Reasoning About Neural Models.Christopher Grimsley, Elijah Mayfield & Julia Bursten - 2020 - Proceedings of the 12th Conference on Language Resources and Evaluation.
    As the demand for explainable deep learning grows in the evaluation of language technologies, the value of a principled grounding for those explanations grows as well. Here we study the state-of-the-art in explanation for neural models for natural-language processing (NLP) tasks from the viewpoint of philosophy of science. We focus on recent evaluation work that finds brittleness in explanations obtained through attention mechanisms.We harness philosophical accounts of explanation to suggest broader conclusions from these studies. From this analysis, we assert the (...)
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  14. Why is Information Retrieval a Scientific Discipline?Robert W. P. Luk - 2020 - Foundations of Science:1-27.
    It is relatively easy to state that information retrieval is a scientific discipline but it is rather difficult to understand why it is science because what is science is still under debate in the philosophy of science. To be able to convince others that IR is science, our ability to explain why is crucial. To explain why IR is a scientific discipline, we use a theory and a model of scientific study, which were proposed recently. The explanation involves mapping the (...)
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  15. Intelligence Via Ultrafilters: Structural Properties of Some Intelligence Comparators of Deterministic Legg-Hutter Agents.Samuel Alexander - 2019 - Journal of Artificial General Intelligence 10 (1):24-45.
    Legg and Hutter, as well as subsequent authors, considered intelligent agents through the lens of interaction with reward-giving environments, attempting to assign numeric intelligence measures to such agents, with the guiding principle that a more intelligent agent should gain higher rewards from environments in some aggregate sense. In this paper, we consider a related question: rather than measure numeric intelligence of one Legg- Hutter agent, how can we compare the relative intelligence of two Legg-Hutter agents? We propose an elegant answer (...)
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  16. Legg-Hutter Universal Intelligence Implies Classical Music is Better Than Pop Music for Intellectual Training.Samuel Alexander - 2019 - The Reasoner 13 (11):71-72.
    In their thought-provoking paper, Legg and Hutter consider a certain abstrac- tion of an intelligent agent, and define a universal intelligence measure, which assigns every such agent a numerical intelligence rating. We will briefly summarize Legg and Hutter’s paper, and then give a tongue-in-cheek argument that if one’s goal is to become more intelligent by cultivating music appreciation, then it is bet- ter to use classical music (such as Bach, Mozart, and Beethoven) than to use more recent pop music. The (...)
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  17. Gods of Transhumanism.Alex V. Halapsis - 2019 - Anthropological Measurements of Philosophical Research 16:78-90.
    Purpose of the article is to identify the religious factor in the teaching of transhumanism, to determine its role in the ideology of this flow of thought and to identify the possible limits of technology interference in human nature. Theoretical basis. The methodological basis of the article is the idea of transhumanism. Originality. In the foreseeable future, robots will be able to pass the Turing test, become “electronic personalities” and gain political rights, although the question of the possibility of machine (...)
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  18. The Pragmatic Turn in Explainable Artificial Intelligence (XAI).Andrés Páez - 2019 - Minds and Machines 29 (3):441-459.
    In this paper I argue that the search for explainable models and interpretable decisions in AI must be reformulated in terms of the broader project of offering a pragmatic and naturalistic account of understanding in AI. Intuitively, the purpose of providing an explanation of a model or a decision is to make it understandable to its stakeholders. But without a previous grasp of what it means to say that an agent understands a model or a decision, the explanatory strategies will (...)
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  19. Machine Decisions and Human Consequences.Teresa Scantamburlo, Andrew Charlesworth & Nello Cristianini - 2019 - In Karen Yeung & Martin Lodge (eds.), Algorithmic Regulation. Oxford: Oxford University Press.
    As we increasingly delegate decision-making to algorithms, whether directly or indirectly, important questions emerge in circumstances where those decisions have direct consequences for individual rights and personal opportunities, as well as for the collective good. A key problem for policymakers is that the social implications of these new methods can only be grasped if there is an adequate comprehension of their general technical underpinnings. The discussion here focuses primarily on the case of enforcement decisions in the criminal justice system, but (...)
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  20. Building Machines That Learn and Think About Morality.Christopher Burr & Geoff Keeling - 2018 - In Proceedings of the Convention of the Society for the Study of Artificial Intelligence and Simulation of Behaviour (AISB 2018). Society for the Study of Artificial Intelligence and Simulation of Behaviour.
    Lake et al. propose three criteria which, they argue, will bring artificial intelligence (AI) systems closer to human cognitive abilities. In this paper, we explore the application of these criteria to a particular domain of human cognition: our capacity for moral reasoning. In doing so, we explore a set of considerations relevant to the development of AI moral decision-making. Our main focus is on the relation between dual-process accounts of moral reasoning and model-free/model-based forms of machine learning. We also discuss (...)
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  21. Crítica da Razão Positrônica.Sandro Rinaldi Feliciano - 2018 - Dissertation, Universidade Federal Do ABC
    Existe um horizonte à frente. Este horizonte está longe de ser aquele aqui descrito em sua forma, mas talvez o seja em sua essência. O que quero dizer com isso é que existe uma possibilidade de os cérebros positrônicos do título nunca existirem para além das brilhantes mentes que os conceberam na Ficção Científica, mas isto não quer dizer que não existirão sistemas análogos em suas funções, principalmente quanto à racionalidade. A Crítica da Razão Positrônica é um texto que tem (...)
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  22. In Pursuit of the Functional Definition of a Mind: The Inevitability of the Language Ontology.Vitalii Shymko - 2018 - Psycholinguistics 23 (1):327-346.
    In this article, the results of conceptualization of the definition of mind as an object of interdisciplinary applied research are described. The purpose of the theoretical analysis is to generate a methodological discourse suitable for a functional understanding of the mind in the context of the problem of natural language processing as one of the components of developments in the field of artificial intelligence. The conceptual discourse was realized with the help of the author's method of structural-ontological analysis, and developed (...)
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  23. Philosophical Signposts for Artificial Moral Agent Frameworks.Robert James M. Boyles - 2017 - Suri 6 (2):92–109.
    This article focuses on a particular issue under machine ethics—that is, the nature of Artificial Moral Agents. Machine ethics is a branch of artificial intelligence that looks into the moral status of artificial agents. Artificial moral agents, on the other hand, are artificial autonomous agents that possess moral value, as well as certain rights and responsibilities. This paper demonstrates that attempts to fully develop a theory that could possibly account for the nature of Artificial Moral Agents may consider certain philosophical (...)
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  24. Between Language and Consciousness: Linguistic Qualia, Awareness, and Cognitive Models.Piotr Konderak - 2017 - Studies in Logic, Grammar and Rhetoric 48 (1):285-302.
    The main goal of the paper is to present a putative role of consciousness in language capacity. The paper contrasts the two approaches characteristic for cognitive semiotics and cognitive science. Language is treated as a mental phenomenon and a cognitive faculty. The analysis of language activity is based on the Chalmers’ distinction between the two forms of consciousness: phenomenal and psychological. The approach is seen as an alternative to phenomenological analyses typical for cognitive semiotics. Further, a cognitive model of the (...)
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  25. Why Computers Are Not Intelligent: An Argument.Richard Oxenberg - 2017 - Political Animal Magazine.
    Computers can mimic human intelligence, sometimes quite impressively. This has led some to claim that, a.) computers can actually acquire intelligence, and/or, b.) the human mind may be thought of as a very sophisticated computer. In this paper I argue that neither of these inferences are sound. The human mind and computers, I argue, operate on radically different principles.
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  26. From Biological to Synthetic Neurorobotics Approaches to Understanding the Structure Essential to Consciousness (Part 3).Jeffrey White - 2017 - APA Newsletter on Philosophy and Computers 17 (1):11-22.
    This third paper locates the synthetic neurorobotics research reviewed in the second paper in terms of themes introduced in the first paper. It begins with biological non-reductionism as understood by Searle. It emphasizes the role of synthetic neurorobotics studies in accessing the dynamic structure essential to consciousness with a focus on system criticality and self, develops a distinction between simulated and formal consciousness based on this emphasis, reviews Tani and colleagues' work in light of this distinction, and ends by forecasting (...)
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  27. The Case for Government by Artificial Intelligence.Steven James Bartlett - 2016 - Willamette University Faculty Research Website: Http://Www.Willamette.Edu/~Sbartlet/Documents/Bartlett_The%20Case%20for%20Government%20by%20Artifici al%20Intelligence.Pdf.
    THE CASE FOR GOVERNMENT BY ARTIFICIAL INTELLIGENCE. Tired of election madness? The rhetoric of politicians? Their unreliable promises? And less than good government? -/- Until recently, it hasn’t been hard for people to give up control to computers. Not very many people miss the effort and time required to do calculations by hand, to keep track of their finances, or to complete their tax returns manually. But relinquishing direct human control to self-driving cars is expected to be more of a (...)
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  28. A Minimalist Model of the Artificial Autonomous Moral Agent (AAMA).Ioan Muntean & Don Howard - 2016 - In SSS-16 Symposium Technical Reports. Association for the Advancement of Artificial Intelligence. AAAI.
    This paper proposes a model for an artificial autonomous moral agent (AAMA), which is parsimonious in its ontology and minimal in its ethical assumptions. Starting from a set of moral data, this AAMA is able to learn and develop a form of moral competency. It resembles an “optimizing predictive mind,” which uses moral data (describing typical behavior of humans) and a set of dispositional traits to learn how to classify different actions (given a given background knowledge) as morally right, wrong, (...)
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  29. Phenomenology and Science.Jack Alan Reynolds & Richard Sebold (eds.) - 2016 - Palgrave-Macmillan.
    This book investigates the complex, sometimes fraught relationship between phenomenology and the natural sciences. The contributors attempt to subvert and complicate the divide that has historically tended to characterize the relationship between the two fields. Phenomenology has traditionally been understood as methodologically distinct from scientific practice, and thus removed from any claim that philosophy is strictly continuous with science. There is some substance to this thinking, which has dominated consideration of the relationship between phenomenology and science throughout the twentieth century. (...)
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  30. Simulation, Self-Extinction, and Philosophy in the Service of Human Civilization.Jeffrey White - 2016 - AI and Society 31 (2):171-190.
  31. Künstliche Intelligenz: Chancen und Risiken.Mannino Adriano, David Althaus, Jonathan Erhardt, Lukas Gloor, Adrian Hutter & Thomas Metzinger - 2015 - Diskussionspapiere der Stiftung Für Effektiven Altruismus 2:1-17.
    Die Übernahme des KI-Unternehmens DeepMind durch Google für rund eine halbe Milliarde US-Dollar signalisierte vor einem Jahr, dass von der KI-Forschung vielversprechende Ergebnisse erwartet werden. Spätestens seit bekannte Wissenschaftler wie Stephen Hawking und Unternehmer wie Elon Musk oder Bill Gates davor warnen, dass künstliche Intelligenz eine Bedrohung für die Menschheit darstellt, schlägt das KI-Thema hohe Wellen. Die Stiftung für Effektiven Altruismus (EAS, vormals GBS Schweiz) hat mit der Unterstützung von Experten/innen aus Informatik und KI ein umfassendes Diskussionspapier zu den Chancen (...)
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  32. The Species Problem and its Logic: Inescapable Ambiguity and Framework-Relativity.Steven James Bartlett - 2015 - Willamette University Faculty Research Website, ArXiv.Org, and Cogprints.Org.
    For more than fifty years, taxonomists have proposed numerous alternative definitions of species while they searched for a unique, comprehensive, and persuasive definition. This monograph shows that these efforts have been unnecessary, and indeed have provably been a pursuit of a will o’ the wisp because they have failed to recognize the theoretical impossibility of what they seek to accomplish. A clear and rigorous understanding of the logic underlying species definition leads both to a recognition of the inescapable ambiguity that (...)
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  33. Automatic Apple Grading Model Development Based on Back Propagation Neural Network and Machine Vision, and its Performance Evaluation.A. K. Bhatt & D. Pant - 2015 - AI and Society 30 (1):45-56.
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  34. On a Cognitive Model of Semiosis.Piotr Konderak - 2015 - Studies in Logic, Grammar and Rhetoric 40 (1):129-144.
    What is the class of possible semiotic systems? What kinds of systems could count as such systems? The human mind is naturally considered the prototypical semiotic system. During years of research in semiotics the class has been broadened to include i.e. living systems like animals, or even plants. It is suggested in the literature on artificial intelligence that artificial agents are typical examples of symbol-processing entities. It also seems that semiotic processes are in fact cognitive processes. In consequence, it is (...)
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  35. Mind Uploading: A Philosophical Counter-Analysis.Massimo Pigliucci - 2014 - In Russell Blackford & Damien Broderick (eds.), Intelligence Unbound: The Future of Uploaded and Machine Minds. Wiley. pp. 119-130.
    A counter analysis of David Chalmers' claims about the possibility of mind uploading within the context of the Singularity event.
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  36. Levels of Abstraction, Emergentism and Artificial Life.Emanuele Ratti - 2014 - Journal of Experimental & Theoretical Artificial Intelligence:1-12.
    I diagnose the current debate between epistemological and ontological emergentism as a Kantian antinomy, which has reasonable but irreconcilable thesis and antithesis. Kantian antinomies have recently returned to contemporary philosophy in part through the work of Luciano Floridi, and the method of levels of abstraction. I use a thought experiment concerning a computer simulation to show how to resolve the epistemological/ontological antinomy about emergence. I also use emergentism and simulations in artificial life to illuminate both levels of abstraction and theoretical (...)
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  37. Fast-Collapsing Theories.Samuel A. Alexander - 2013 - Studia Logica (1):1-21.
    Reinhardt’s conjecture, a formalization of the statement that a truthful knowing machine can know its own truthfulness and mechanicalness, was proved by Carlson using sophisticated structural results about the ordinals and transfinite induction just beyond the first epsilon number. We prove a weaker version of the conjecture, by elementary methods and transfinite induction up to a smaller ordinal.
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  38. Sobre s História da Paraconsistência e a Obra de Da Costa: A Instauração da Lógica Paraconsistente.Evandro Luis Gomes - 2013 - Dissertation, University of Campinas, Brazil
  39. Inner Speech Generation in a Video Game Non-Player Character: From Explanation to Self?Raúl Arrabales - 2012 - International Journal of Machine Consciousness 4 (2):367-381.
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  40. Automatic Phonetic Segmentation of Hindi Speech Using Hidden Markov Model.Archana Balyan, S. S. Agrawal & Amita Dev - 2012 - AI and Society 27 (4):543-549.
    In this paper, we study the performance of baseline hidden Markov model (HMM) for segmentation of speech signals. It is applied on single-speaker segmentation task, using Hindi speech database. The automatic phoneme segmentation framework evolved imitates the human phoneme segmentation process. A set of 44 Hindi phonemes were chosen for the segmentation experiment, wherein we used continuous density hidden Markov model (CDHMM) with a mixture of Gaussian distribution. The left-to-right topology with no skip states has been selected as it is (...)
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  41. Vagueness Intuitions and the Mobility of Cognitive Sortals.Bert Baumgaertner - 2012 - Minds and Machines 22 (3):213-234.
    One feature of vague predicates is that, as far as appearances go, they lack sharp application boundaries. I argue that we would not be able to locate boundaries even if vague predicates had sharp boundaries. I do so by developing an idealized cognitive model of a categorization faculty which has mobile and dynamic sortals (`classes', `concepts' or `categories') and formally prove that the degree of precision with which boundaries of such sortals can be located is inversely constrained by their flexibility. (...)
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  42. YUTPA as a Design Tool for Public Participation.Maurice Berix - 2012 - AI and Society 27 (1):165-172.
    Engaging the public in decision-making processes is commonly accepted as an effective strategy for a better policy making, a better policy support and for narrowing the gap between government and the public. In today’s digitised society, participation via online media is becoming more important. But is this so-called e-participation being used optimally? Or is a better design possible? In my opinion, the answer to these questions is a ‘yes’. Despite numerous efforts in engaging the public with policy deliberation, the actual (...)
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  43. To Hear—to Say: The Mediating Presence of the Healing Witness. [REVIEW]Sheryl Brahnam - 2012 - AI and Society 27 (1):53-90.
    Illness and trauma challenge self-narratives. Traumatized individuals, unable to speak about their experiences, suffer in isolation. In this paper, I explore Kristeva’s theories of the speaking subject and signification, with its symbolic and semiotic modalities, to understand how a person comes to speak the unspeakable. In discussing the origin of the speaking subject, Kristeva employs Plato’s chora (related to choreo , “to make room for”). The chora reflects the mother’s preparation of the child’s entry into language and forms an interior (...)
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  44. Cybernetic Revelation: Deconstructing Artificial Intelligence.Jd Casten - 2012 - Post Egoism Media.
    Cybernetic Revelation explores the dual philosophical histories of deconstruction and artificial intelligence, tracing the development of concepts like "logos" and the notion of modeling the mind technologically from pre-history to contemporary thinkers such as Slavoj Zizek and Steven Pinker. The writing is clear and accessible throughout, yet the text probes deeply into major philosophers seen by JD Casten as "conceptual engineers." -/- Philosophers covered include: Anaximander, Heraclitus, Parmenides, Plato, Aristotle, Philo, Augustine, Shakespeare, Descartes, Spinoza, Leibniz, Locke, Berkeley, Hume, Kant, Hegel, (...)
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  45. Implications of a Logical Paradox for Computer-Dispensed Justice Reconsidered: Some Key Differences Between Minds and Machines.Joseph S. Fulda - 2012 - Artificial Intelligence and Law 20 (3):321-333.
    We argued [Since this argument appeared in other journals, I am reprising it here, almost verbatim.] (Fulda in J Law Info Sci 2:230–232, 1991/AI & Soc 8(4):357–359, 1994) that the paradox of the preface suggests a reason why machines cannot, will not, and should not be allowed to judge criminal cases. The argument merely shows that they cannot now and will not soon or easily be so allowed. The author, in fact, now believes that when—and only when—they are ready they (...)
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  46. Proper Embodiment: The Role of the Body in Affect and Cognition.Mog Stapleton - 2012 - Dissertation, University of Edinburgh
    Embodied cognitive science has argued that cognition is embodied principally in virtue of grossmorphological and sensorimotor features. This thesis argues that cognition is also internally embodied in affective and fine-grained physiological features whose transformative roles remain mostlyunnoticed in contemporary cognitive science. I call this ‘proper embodiment’. I approach this larger subject by examining various emotion theories in philosophy and psychology. These tend to emphasiseone of the many gross components of emotional processes, such as ‘feeling’ or ‘judgement’ to thedetriment of the (...)
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  47. Studying Laughter in Combination with Two Humanoid Robots.Christian Becker-Asano, Takayuki Kanda, Carlos Ishi & Hiroshi Ishiguro - 2011 - AI and Society 26 (3):291-300.
    To let humanoid robots behave socially adequate in a future society, we started to explore laughter as an important para-verbal signal known to influence relationships among humans rather easily. We investigated how the naturalness of various types of laughter in combination with different humanoid robots was judged, first, within a situational context that is suitable for laughter and, second, without describing the situational context. Given the variety of human laughter, do people prefer a certain style for a robot’s laughter? And (...)
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  48. Algorithms and Arguments: The Foundational Role of the ATAI-Question.Paola Cantu' & Italo Testa - 2011 - In Frans H. van Eemeren, Bart Garssen, David Godden & Gordon Mitchell (eds.), Proceedings of the Seventh International Conference of the International Society for the Study of Argumentation (pp. 192-203). Rozenberg / Sic Sat.
    Argumentation theory underwent a significant development in the Fifties and Sixties: its revival is usually connected to Perelman's criticism of formal logic and the development of informal logic. Interestingly enough it was during this period that Artificial Intelligence was developed, which defended the following thesis (from now on referred to as the AI-thesis): human reasoning can be emulated by machines. The paper suggests a reconstruction of the opposition between formal and informal logic as a move against a premise of an (...)
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  49. Symbiotic Technology for Creating Social Innovation 30 Years in the Future.Shinichi Doi & Keiji Yamada - 2011 - AI and Society 26 (3):197-204.
    This paper discusses a way to create social innovation around 2040. With such innovation, social restrictions that are regarded as being inevitable in the current society can be eliminated. First, it is necessary to determine how to approach the innovation. Symbiotic technology is one of the promising technologies for achieving social innovation. It is the fusion of scientific technology and socio-technology. Its elemental technologies are classified into two categories: technologies for converging the real and cyber worlds and those for integrating (...)
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  50. Don Ross Et Al. (Eds.), Distributed Cognition and the Will. [REVIEW]Federico Faroldi - 2011 - Minds and Machines 21 (1):115-118.
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