Philosophy of Artificial Intelligence

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  1. Varieties of Artifacts: Embodied, Perceptual, Cognitive, and Affective.Richard Heersmink - forthcoming - Topics in Cognitive Science.
    The primary goal of this essay is to provide a comprehensive overview and analysis of the various relations between material artifacts and the embodied mind. A secondary goal of this essay is to identify some of the trends in the design and use of artifacts. First, based on their functional properties, I identify four categories of artifacts co-opted by the embodied mind, namely (1) embodied artifacts, (2) perceptual artifacts, (3) cognitive artifacts, and (4) affective artifacts. These categories can overlap and (...)
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  2. Artifacts and Affordances: From Designed Properties to Possibilities for Action.Fabio Tollon - 2021 - AI and Society 2:1-10.
    In this paper I critically evaluate the value neutrality thesis regarding technology, and find it wanting. I then introduce the various ways in which artifacts can come to influence moral value, and our evaluation of moral situations and actions. Here, following van de Poel and Kroes, I introduce the idea of value sensitive design. Specifically, I show how by virtue of their designed properties, artifacts may come to embody values. Such accounts, however, have several shortcomings. In agreement with Michael Klenk, (...)
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  3. Quem ou o que pensa? Uma busca de aportes para questões filosóficas suscitadas pela revolução informática atual.Lamartine De Hollanda Cavalcanti Neto - 2020 - Dissertation, Universidade Federal de São Paulo
    Taking as an assumption the existence of an informatics revolution nowadays and that the examination of studies and debates related to it may allow the identification of questions of a philosophical nature, the present study aims to identify and formulate some of these questions, as well as to investigate whether the historical controversy about monopsychism, which occurred at the University of Paris in 1270, can be considered a theoretical framework capable of providing contributions to these philosophical questions. The answer to (...)
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  4. Debate: What is Personhood in the Age of AI?David J. Gunkel & Jordan Joseph Wales - forthcoming - AI and Society.
    In a friendly interdisciplinary debate, we interrogate from several vantage points the question of “personhood” in light of contemporary and near-future forms of social AI. David J. Gunkel approaches the matter from a philosophical and legal standpoint, while Jordan Wales offers reflections theological and psychological. Attending to metaphysical, moral, social, and legal understandings of personhood, we ask about the position of apparently personal artificial intelligences in our society and individual lives. Re-examining the “person” and questioning prominent construals of that category, (...)
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  5. Morphing Intelligence: From IQ Measurement to Artificial Brains. [REVIEW]Ekin Erkan - 2020 - Chiasma 6 (1):248-260.
    In her seminal text, What Should We Do With Our Brain? (2008), Catherine Malabou gestured towards neuroplasticity to upend Bergson's famous parallel of the brain as a "central telephonic exchange," whereby the function of the brain is simply that of a node where perceptions get in touch with motor mechanisms, the brain as an instrument limited to the transmission and divisions of movements. Drawing from the history of cybernetics one can trace how Bergson's 'telephonic exchange' prefigures the neural 'cybernetic metaphor.' (...)
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  6. Creativity.Peter Langland-Hassan - 2020 - In Explaining Imagination. Oxford: pp. 262-296.
    Comparatively easy questions we might ask about creativity are distinguished from the hard question of explaining transformative creativity. Many have focused on the easy questions, offering no reason to think that the imagining relied upon in creative cognition cannot be reduced to more basic folk psychological states. The relevance of associative thought processes to songwriting is then explored as a means for understanding the nature of transformative creativity. Productive artificial neural networks—known as generative antagonistic networks (GANs)—are a recent example of (...)
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  7. What Do Technology and Artificial Intelligence Mean Today?Scott H. Hawley & Elias Kruger - forthcoming - In Hector Fernandez (ed.), Sociedad Tecnológica y Futuro Humano, vol. 1: Desafíos conceptuales. Santiago, Chile: pp. 17.
    Technology and Artificial Intelligence, both today and in the near future, are dominated by automated algorithms that combine optimization with models based on the human brain to learn, predict, and even influence the large-scale behavior of human users. Such applications can be understood to be outgrowths of historical trends in industry and academia, yet have far-reaching and even unintended consequences for social and political life around the world. Countries in different parts of the world take different regulatory views for the (...)
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  8. Citizenship as the Exception to the Rule: An Addendum.Tyler L. Jaynes - 2020 - AI and Society Online First Article.
    This addendum expands upon the arguments made in the author’s 2020 essay, “Legal Personhood for Artificial Intelligence: Citizenship as the Exception to the Rule”, in an effort to display the significance human augmentation technologies will have on (feasibly) inadvertently providing legal protections to artificial intelligence systems (AIS)—a topic only briefly addressed in that work. It will also further discuss the impacts popular media have on imprinting notions of computerised behaviour and its subsequent consequences on the attribution of legal protections to (...)
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  9. What is Interpretability?Adrian Erasmus, Tyler D. P. Brunet & Eyal Fisher - 2020 - Philosophy and Technology.
    We argue that artificial networks are explainable and offer a novel theory of interpretability. Two sets of conceptual questions are prominent in theoretical engagements with artificial neural networks, especially in the context of medical artificial intelligence: Are networks explainable, and if so, what does it mean to explain the output of a network? And what does it mean for a network to be interpretable? We argue that accounts of “explanation” tailored specifically to neural networks have ineffectively reinvented the wheel. In (...)
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  10. Self-Building Technologies.François Kammerer - 2020 - AI and Society 35 (4):901-915.
    On the basis of two thought experiments, I argue that self-building technologies are possible given our current level of technological progress. We could already use technology to make us instantiate selfhood in a more perfect, complete manner. I then examine possible extensions of this thesis, regarding more radical self-building technologies which might become available in a distant future. I also discuss objections and reservations one might have about this view.
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  11. Aiming AI at a Moving Target: Health.Mihai Nadin - 2020 - AI and Society 35 (4):841-849.
    Justified by spectacular achievements facilitated through applied deep learning methodology, the “Everything is possible” view dominates this new hour in the “boom and bust” curve of AI performance. The optimistic view collides head on with the “It is not possible”—ascertainments often originating in a skewed understanding of both AI and medicine. The meaning of the conflicting views can be assessed only by addressing the nature of medicine. Specifically: Which part of medicine, if any, can and should be entrusted to AI—now (...)
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  12. Vagueness and Roughness.Bonikowski Zbigniew & Wybranie-Skardowska Urszula - 2008 - In Transactions on Rough Sets IX. Lectures Notes and Computer Science 5290. Berlin-Heidelberg: pp. 1-13.
    The paper proposes a new formal approach to vagueness and vague sets taking inspirations from Pawlak’s rough set theory. Following a brief introduction to the problem of vagueness, an approach to conceptualization and representation of vague knowledge is presented from a number of different perspectives: those of logic, set theory, algebra, and computer science. The central notion of the vague set, in relation to the rough set, is defined as a family of sets approximated by the so called lower and (...)
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  13. Transparency in Complex Computational Systems.Kathleen A. Creel - 2020 - Philosophy of Science 87 (4):568-589.
    Scientists depend on complex computational systems that are often ineliminably opaque, to the detriment of our ability to give scientific explanations and detect artifacts. Some philosophers have s...
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  14. In Defence of the Hivemind Society.John Danaher & Steve Petersen - forthcoming - Neuroethics:1-15.
    The idea that humans should abandon their individuality and use technology to bind themselves together into hivemind societies seems both farfetched and frightening – something that is redolent of the worst dystopias from science fiction. In this article, we argue that these common reactions to the ideal of a hivemind society are mistaken. The idea that humans could form hiveminds is sufficiently plausible for its axiological consequences to be taken seriously. Furthermore, far from being a dystopian nightmare, the hivemind society (...)
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  15. Deleuze’s Postscript on the Societies of Control Updated for Big Data and Predictive Analytics.James Brusseau - 2020 - Theoria 67 (164):1-25.
    In 1990, Gilles Deleuze published Postscript on the Societies of Control, an introduction to the potentially suffocating reality of the nascent control society. This thirty-year update details how Deleuze’s conception has developed from a broad speculative vision into specific economic mechanisms clustering around personal information, big data, predictive analytics, and marketing. The central claim is that today’s advancing control society coerces without prohibitions, and through incentives that are not grim but enjoyable, even euphoric because they compel individuals to obey their (...)
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  16. Carving a Life From Legacy: Frankfurt’s Account of Free Will and Manipulation in Greg Egan’s “Reasons to Be Cheerful”.Taylor W. Cyr - 2018 - Journal of Science Fiction and Philosophy 1:1-15.
    Many find it intuitive that having been manipulated undermines a person's free will. Some have objected to accounts of free will like Harry Frankfurt's (according to which free will depends only on an agent's psychological structure at the time of action) by arguing that it is possible for manipulated agents, who are intuitively unfree, to satisfy Frankfurt's allegedly sufficient conditions for freedom. Drawing resources from Greg Egan's "Reasons to Be Cheerful" as well as from stories of psychologically sophisticated artificial intelligence (...)
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  17. Artificial Intelligence as Art – What the Philosophy of Art Can Offer the Understanding of AI and Consciousness.Hutan Ashrafian - manuscript
    Defining Artificial Intelligence and Artificial General Intelligence remain controversial and disputed. They stem from a longer-standing controversy of what is the definition of consciousness, which if solved could possibly offer a solution to defining AI and AGI. Central to these problems is the paradox that appraising AI and Consciousness requires epistemological objectivity of domains that are ontologically subjective. I propose that applying the philosophy of art, which also aims to define art through a lens of epistemological objectivity where the domains (...)
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  18. Can the G Factor Play a Role in Artificial General Intelligence Research?Davide Serpico & Marcello Frixione - 2018 - In Proceedings of the Society for the Study of Artificial Intelligence and Simulation of Behaviour 2018. pp. 301-305.
    In recent years, a trend in AI research has started to pursue human-level, general artificial intelli-gence (AGI). Although the AGI framework is characterised by different viewpoints on what intelligence is and how to implement it in artificial systems, it conceptualises intelligence as flexible, general-purposed, and capable of self-adapting to different contexts and tasks. Two important ques-tions remain open: a) should AGI projects simu-late the biological, neural, and cognitive mecha-nisms realising the human intelligent behaviour? and b) what is the relationship, if (...)
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  19. The AI Human Condition is a Dilemma Between Authenticity and Freedom.James Brusseau - manuscript
    Big data and predictive analytics applied to economic life is forcing individuals to choose between authenticity and freedom. The fact of the choice cuts philosophy away from the traditional understanding of the two values as entwined. This essay describes why the split is happening, how new conceptions of authenticity and freedom are rising, and the human experience of the dilemma between them. Also, this essay participates in recent philosophical intersections with Shoshana Zuboff’s work on surveillance capitalism, but the investigation connects (...)
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  20. Calibrating Generative Models: The Probabilistic Chomsky-Schützenberger Hierarchy.Thomas Icard - forthcoming - Journal of Mathematical Psychology 95.
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  21. The Cognitive Sciences: A Comment on 6 Reviews of the MIT Encyclopedia of the Cognitive Sciences.Robert A. Wilson - 2001 - Artificial Intelligence 2 (130):223-229.
    As the pluralization in the title of MITECS suggests, and as many reviewers have noted, the stance that we adopted as general editors for this project was ecumenical. We were particularly concerned to generate a volume whose range of topics and perspectives indicated that “cognitive science” was different things to different groups of researchers, and that many even fundamental questions remain open after at least four decades of various interdisciplinary ventures. Implicit in this view is a wariness of any putative (...)
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  22. Why Technoscience Cannot Reproduce Human Desire According to Lacanian Thomism.Christopher Wojtulewicz & Graham J. McAleer - 2019 - Forum Philosophicum: International Journal for Philosophy 2 (24):279-300.
    Being born into a family structure—being born of a mother—is key to being human. It is, for Jacques Lacan, essential to the formation of human desire. It is also part of the structure of analogy in the Thomistic thought of Erich Przywara. AI may well increase exponentially in sophistication, and even achieve human-like qualities; but it will only ever form an imaginary mirroring of genuine human persons—an imitation that is in fact morbid and dehumanising. Taking Lacan and Przywara at a (...)
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  23. Rituals and Algorithms: Genealogy of Reflective Faith and Postmetaphysical Thinking.Martin Beck Matuštík - 2019 - European Journal for Philosophy of Religion 11 (4):163-184.
    What happens when mindless symbols of algorithmic AI encounter mindful performative rituals? I return to my criticisms of Habermas’ secularising reading of Kierkegaard’s ethics. Next, I lay out Habermas’ claim that the sacred complex of ritual and myth contains the ur-origins of postmetaphysical thinking and reflective faith. If reflective faith shares with ritual same origins as does communicative interaction, how do we access these archaic ritual sources of human solidarity in the age of AI?
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  24. La Modellizzazione Computazionale Della Competenza Inferen-Ziale E Della Competenza Referenziale.Fabrizio Calzavarini & Antonio Lieto - forthcoming - Sistemi Intelligenti.
    In philosophy of language, a distinction has been proposed by Diego Marconi between two aspects of lexical competence, i.e. referential and inferential competence. The former accounts for the relation-ship of words to the world, the latter for the relationship of words among themselves. The aim of the pa-per is to offer a critical discussion of the kind of formalisms and computational techniques that can be used in Artificial Intelligence to model the two aspects of lexical competence, and of the main (...)
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  25. From Biological to Synthetic Neurorobotics Approaches to Understanding the Structure Essential to Consciousness (Part 2).Jun Tani & Jeff White - 2016 - APA Newsletter on Philosophy and Computers 2 (16):29-41.
    We have been left with a big challenge, to articulate consciousness and also to prove it in an artificial agent against a biological standard. After introducing Boltuc’s h-consciousness in the last paper, we briefly reviewed some salient neurology in order to sketch less of a standard than a series of targets for artificial consciousness, “most-consciousness” and “myth-consciousness.” With these targets on the horizon, we began reviewing the research program pursued by Jun Tani and colleagues in the isolation of the formal (...)
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  26. From Biological to Synthetic Neurorobotics Approaches to Understanding the Structure Essential to Consciousness, Part 1.Jeffrey White - 2016 - APA Newsletter on Philosophy and Computers 1 (16):13-23.
    Direct neurological and especially imaging-driven investigations into the structures essential to naturally occurring cognitive systems in their development and operation have motivated broadening interest in the potential for artificial consciousness modeled on these systems. This first paper in a series of three begins with a brief review of Boltuc’s (2009) “brain-based” thesis on the prospect of artificial consciousness, focusing on his formulation of h-consciousness. We then explore some of the implications of brain research on the structure of consciousness, finding limitations (...)
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  27. Menschengestützte Künstliche Intelligenz: Über die soziotechnischen Voraussetzungen von "deep learning".Rainer Mühlhoff - 2019 - Zeitschrift Für Medienwissenschaft (ZfM) 21 (2):56–64.
    Die aktuellen Erfolge von Künstlicher Intelligenz beruhen nicht nur auf technologischen Fortschritten, sondern auch auf einem grundlegenden soziotechnischen Strukturwandel. Denn maschinelle Lernverfahren wie Deep Learning benötigen eine große Menge Trainingsdaten, die nur über menschliche Mitarbeit gewonnen werden können. In einer Konvergenz von Methoden der Human-Computer-Interaction und der KI ist in den letzten zehn Jahren eine Fülle von Mensch-Maschine-Interfaces und medialen Infrastrukturen entstanden, durch die menschliche kognitive Ressourcen in hybride Mensch-Maschine-Apparate eingespannt werden. Diese Apparate vollbringen im Ganzen jene Leistung, die als (...)
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  28. Why Be Random?Thomas Icard - forthcoming - Mind:fzz065.
    When does it make sense to act randomly? A persuasive argument from Bayesian decision theory legitimizes randomization essentially only in tie-breaking situations. Rational behaviour in humans, non-human animals, and artificial agents, however, often seems indeterminate, even random. Moreover, rationales for randomized acts have been offered in a number of disciplines, including game theory, experimental design, and machine learning. A common way of accommodating some of these observations is by appeal to a decision-maker’s bounded computational resources. Making this suggestion both precise (...)
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  29. Privacy, Autonomy, and Personalised Targeting: Rethinking How Personal Data is Used.Karina Vold & Jessica Whittlestone - 2019 - In Carissa Véliz (ed.), Report on Data, Privacy, and the Individual in the Digital Age.
    Technological advances are bringing new light to privacy issues and changing the reasons for why privacy is important. These advances have changed not only the kind of personal data that is available to be collected, but also how that personal data can be used by those who have access to it. We are particularly concerned with how information about personal attributes inferred from collected data (such as online behaviour), can be used to tailor messages and services to specific individuals or (...)
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  30. What Does It Mean to Understand? Neural Networks Case.Albert Ierusalem & Aleksandr Senin - manuscript
    We can say that we understand neural networks then and only then if you will come to me and say that the best model ever for some task has a 100 layers, and I will answer "No! 101 layers model is the best!".
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  31. Social intelligence: How to integrate research? A mechanistic perspective.Marcin Miłkowski - 2019 - AI and Society 34 (4):735-744.
    Is there a field of social intelligence? Many various disciplines approach the subject and it may only seem natural to suppose that different fields of study aim at explaining different phenomena; in other words, there is no special field of study of social intelligence. In this paper, I argue for an opposite claim. Namely, there is a way to integrate research on social intelligence, as long as one accepts the mechanistic account to explanation. Mechanistic integration of different explanations, however, comes (...)
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  32. The Bit (and Three Other Abstractions) Define the Borderline Between Hardware and Software.Russ Abbott - 2019 - Minds and Machines 29 (2):239-285.
    Modern computing is generally taken to consist primarily of symbol manipulation. But symbols are abstract, and computers are physical. How can a physical device manipulate abstract symbols? Neither Church nor Turing considered this question. My answer is that the bit, as a hardware-implemented abstract data type, serves as a bridge between materiality and abstraction. Computing also relies on three other primitive—but more straightforward—abstractions: Sequentiality, State, and Transition. These physically-implemented abstractions define the borderline between hardware and software and between physicality and (...)
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  33. Triviality Arguments Reconsidered.Paul Schweizer - 2019 - Minds and Machines 29 (2):287-308.
    Opponents of the computational theory of mind have held that the theory is devoid of explanatory content, since whatever computational procedures are said to account for our cognitive attributes will also be realized by a host of other ‘deviant’ physical systems, such as buckets of water and possibly even stones. Such ‘triviality’ claims rely on a simple mapping account of physical implementation. Hence defenders of CTM traditionally attempt to block the trivialization critique by advocating additional constraints on the implementation relation. (...)
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  34. Stanley B. Klein: The Two Selves—Their Metaphysical Commitments and Functional Independence: Oxford University Press, Oxford, 2014, Xx + 153, £25.00, ISBN: 987-0-19-934996-8.Kourken Michaelian - 2015 - Minds and Machines 25 (1):119-122.
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  35. Machine Intelligence: A Chimera.Mihai Nadin - 2019 - AI and Society 34 (2):215-242.
    The notion of computation has changed the world more than any previous expressions of knowledge. However, as know-how in its particular algorithmic embodiment, computation is closed to meaning. Therefore, computer-based data processing can only mimic life’s creative aspects, without being creative itself. AI’s current record of accomplishments shows that it automates tasks associated with intelligence, without being intelligent itself. Mistaking the abstract for the concrete has led to the religion of “everything is an output of computation”—even the humankind that conceived (...)
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  36. Peeking Inside the Black Box: A New Kind of Scientific Visualization.Michael T. Stuart & Nancy J. Nersessian - 2018 - Minds and Machines 29 (1):87-107.
    Computational systems biologists create and manipulate computational models of biological systems, but they do not always have straightforward epistemic access to the content and behavioural profile of such models because of their length, coding idiosyncrasies, and formal complexity. This creates difficulties both for modellers in their research groups and for their bioscience collaborators who rely on these models. In this paper we introduce a new kind of visualization that was developed to address just this sort of epistemic opacity. The visualization (...)
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  37. Natural Language Understanding: Methodological Conceptualization.Vitalii Shymko - 2019 - Psycholinguistics 25 (1):431-443.
    This article contains the results of a theoretical analysis of the phenomenon of natural language understanding (NLU), as a methodological problem. The combination of structural-ontological and informational-psychological approaches provided an opportunity to describe the subject matter field of NLU, as a composite function of the mind, which systemically combines the verbal and discursive structural layers. In particular, the idea of NLU is presented, on the one hand, as the relation between the discourse of a specific speech message and the meta-discourse (...)
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  38. Ontologies, Mental Disorders and Prototypes.Maria Cristina Amoretti, Marcello Frixione, Antonio Lieto & Greta Adamo - 2019 - In Matteo Vincenzo D'Alfonso & Don Berkich (eds.), On the Cognitive, Ethical, and Scientific Dimensions of Artificial Intelligence. Berlin, Germany: Springer Verlag. pp. 189-204.
    As it emerged from philosophical analyses and cognitive research, most concepts exhibit typicality effects, and resist to the efforts of defining them in terms of necessary and sufficient conditions. This holds also in the case of many medical concepts. This is a problem for the design of computer science ontologies, since knowledge representation formalisms commonly adopted in this field do not allow for the representation of concepts in terms of typical traits. However, the need of representing concepts in terms of (...)
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  39. Cognition as Embodied Morphological Computation.Gordana Dodig-Crnkovic - 2018 - In Vincent C. Müller (ed.), Philosophy and Theory of Artificial Intelligence 2017. Springer.
    Cognitive science is considered to be the study of mind (consciousness and thought) and intelligence in humans. Under such definition variety of unsolved/unsolvable problems appear. This article argues for a broad understanding of cognition based on empirical results from i.a. natural sciences, self-organization, artificial intelligence and artificial life, network science and neuroscience, that apart from the high level mental activities in humans, includes sub-symbolic and sub-conscious processes, such as emotions, recognizes cognition in other living beings as well as extended and (...)
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  40. What an Entangled Web We Weave: An Information-Centric Approach to Time-Evolving Socio-Technical Systems.Markus Luczak-Roesch, Kieron O’Hara, Jesse David Dinneen & Ramine Tinati - 2018 - Minds and Machines 28 (4):709-733.
    A new layer of complexity, constituted of networks of information token recurrence, has been identified in socio-technical systems such as the Wikipedia online community and the Zooniverse citizen science platform. The identification of this complexity reveals that our current understanding of the actual structure of those systems, and consequently the structure of the entire World Wide Web, is incomplete, which raises novel questions for data science research but also from the perspective of social epistemology. Here we establish the principled foundations (...)
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  41. The Art, Poetics, and Grammar of Technological Innovation as Practice, Process, and Performance.Coeckelbergh Mark - 2018 - AI and Society 33 (4):501-510.
    Usually technological innovation and artistic work are seen as very distinctive practices, and innovation of technologies is understood in terms of design and human intention. Moreover, thinking about technological innovation is usually categorized as “technical” and disconnected from thinking about culture and the social. Drawing on work by Dewey, Heidegger, Latour, and Wittgenstein and responding to academic discourses about craft and design, ethics and responsible innovation, transdisciplinarity, and participation, this essay questions these assumptions and examines what kind of knowledge and (...)
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  42. Cognitive Computation Sans Representation.Paul Schweizer - 2017 - In Thomas Powers (ed.), Philosophy and Computing: Essays in epistemology, philosophy of mind, logic, and ethics,. Cham, Switzerland: Springer. pp. 65-84.
    The Computational Theory of Mind (CTM) holds that cognitive processes are essentially computational, and hence computation provides the scientific key to explaining mentality. The Representational Theory of Mind (RTM) holds that representational content is the key feature in distinguishing mental from non-mental systems. I argue that there is a deep incompatibility between these two theoretical frameworks, and that the acceptance of CTM provides strong grounds for rejecting RTM. The focal point of the incompatibility is the fact that representational content is (...)
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  43. Artificial Brains and Hybrid Minds.Paul Schweizer - 2017 - In Vincent C. Müller (ed.), Philosophy and Theory of Artificial Intelligence 2017. Cham, Switzerland: Springer. pp. 81-91.
    The paper develops two related thought experiments exploring variations on an ‘animat’ theme. Animats are hybrid devices with both artificial and biological components. Traditionally, ‘components’ have been construed in concrete terms, as physical parts or constituent material structures. Many fascinating issues arise within this context of hybrid physical organization. However, within the context of functional/computational theories of mentality, demarcations based purely on material structure are unduly narrow. It is abstract functional structure which does the key work in characterizing the respective (...)
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  44. Computers Aren’T Syntax All the Way Down or Content All the Way Up.Cem Bozşahin - 2018 - Minds and Machines 28 (3):543-567.
    This paper argues that the idea of a computer is unique. Calculators and analog computers are not different ideas about computers, and nature does not compute by itself. Computers, once clearly defined in all their terms and mechanisms, rather than enumerated by behavioral examples, can be more than instrumental tools in science, and more than source of analogies and taxonomies in philosophy. They can help us understand semantic content and its relation to form. This can be achieved because they have (...)
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  45. From Computer Metaphor to Computational Modeling: The Evolution of Computationalism.Marcin Miłkowski - 2018 - Minds and Machines 28 (3):515-541.
    In this paper, I argue that computationalism is a progressive research tradition. Its metaphysical assumptions are that nervous systems are computational, and that information processing is necessary for cognition to occur. First, the primary reasons why information processing should explain cognition are reviewed. Then I argue that early formulations of these reasons are outdated. However, by relying on the mechanistic account of physical computation, they can be recast in a compelling way. Next, I contrast two computational models of working memory (...)
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  46. ‘The Action of the Brain’. Machine Models and Adaptive Functions in Turing and Ashby.Hajo Greif - 2018 - In Vincent Müller (ed.), Philosophy and theory of artificial intelligence 2017. Berlin, Germany: Springer. pp. 24-35.
    Given the personal acquaintance between Alan M. Turing and W. Ross Ashby and the partial proximity of their research fields, a comparative view of Turing’s and Ashby’s work on modelling “the action of the brain” (letter from Turing to Ashby, 1946) will help to shed light on the seemingly strict symbolic/embodied dichotomy: While it is clear that Turing was committed to formal, computational and Ashby to material, analogue methods of modelling, there is no straightforward mapping of these approaches onto symbol-based (...)
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  47. Environments of Intelligence. From Natural Information to Artficial Interaction.Hajo Greif - 2017 - London: Routledge.
    What is the role of the environment, and of the information it provides, in cognition? More specifically, may there be a role for certain artefacts to play in this context? These are questions that motivate "4E" theories of cognition (as being embodied, embedded, extended, enactive). In his take on that family of views, Hajo Greif first defends and refines a concept of information as primarily natural, environmentally embedded in character, which had been eclipsed by information-processing views of cognition. He continues (...)
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  48. Ontology.Barry Smith - 2003 - In Luciano Floridi (ed.), Blackwell Guide to the Philosophy of Computing and Information. Oxford: Blackwell. pp. 155-166.
    Ontology as a branch of philosophy is the science of what is, of the kinds and structures of objects, properties, events, processes and relations in every area of reality. ‘Ontology’ in this sense is often used by philosophers as a synonym of ‘metaphysics’ (a label meaning literally: ‘what comes after the Physics’), a term used by early students of Aristotle to refer to what Aristotle himself called ‘first philosophy’. But in recent years, in a development hardly noticed by philosophers, the (...)
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  49. Machine Art or Machine Artists? Dennett, Danto, and the Expressive Stance.Adam Linson - 2016 - In Vincent Müller (ed.), Fundamental Issues of Artificial Intelligence (Synthese Library). Berlin: Springer. pp. 441-456.
    As art produced by autonomous machines becomes increasingly common, and as such machines grow increasingly sophisticated, we risk a confusion between art produced by a person but mediated by a machine, and art produced by what might be legitimately considered a machine artist. This distinction will be examined here. In particular, my argument seeks to close a gap between, on one hand, a philosophically grounded theory of art and, on the other hand, theories concerned with behavior, intentionality, expression, and creativity (...)
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  50. Choosing Your Nonmonotonic Logic: A Shopper’s Guide.Ulf Hlobil - 2018 - In Pavel Arazim & Tomáš Lávička (eds.), The Logica Yearbook 2017. London: College Publications. pp. 109-123.
    The paper presents an exhaustive menu of nonmonotonic logics. The options are individuated in terms of the principles they reject. I locate, e.g., cumulative logics and relevance logics on this menu. I highlight some frequently neglected options, and I argue that these neglected options are particularly attractive for inferentialists.
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