Results for 'Machine'

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  1. Moral Machines:Teaching Robots Right From Wrong: Teaching Robots Right From Wrong.Wendell Wallach & Colin Allen - 2008 - Oxford University Press.
    Computers are already approving financial transactions, controlling electrical supplies, and driving trains. Soon, service robots will be taking care of the elderly in their homes, and military robots will have their own targeting and firing protocols. Colin Allen and Wendell Wallach argue that as robots take on more and more responsibility, they must be programmed with moral decision-making abilities, for our own safety. Taking a fast paced tour through the latest thinking about philosophical ethics and artificial intelligence, the authors argue (...)
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  2. Virtual Machines and Consciousness.Aaron Sloman & Ronald L. Chrisley - 2003 - Journal of Consciousness Studies 10 (4-5):133-172.
    Replication or even modelling of consciousness in machines requires some clarifications and refinements of our concept of consciousness. Design of, construction of, and interaction with artificial systems can itself assist in this conceptual development. We start with the tentative hypothesis that although the word “consciousness” has no well-defined meaning, it is used to refer to aspects of human and animal informationprocessing. We then argue that we can enhance our understanding of what these aspects might be by designing and building virtual- (...) architectures capturing various features of consciousness. This activity may in turn nurture the development of our concepts of consciousness, showing how an analysis based on information-processing virtual machines answers old philosophical puzzles as well enriching empirical theories. This process of developing and testing ideas by developing and testing designs leads to gradual refinement of many of our pre-theoretical concepts of mind, showing how they can be construed as implicitly “architecture-based” concepts. Understanding how humanlike robots with appropriate architectures are likely to feel puzzled about qualia may help us resolve those puzzles. The concept of “qualia” turns out to be an “architecture-based” concept, while individual qualia concepts are “architecture-driven”. (shrink)
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  3. Minds, Machines and Gödel.J. R. Lucas - 2003 - Etica E Politica 5 (1):1.
    In this article, Lucas maintains the falseness of Mechanism - the attempt to explain minds as machines - by means of Incompleteness Theorem of Gödel. Gödel’s theorem shows that in any system consistent and adequate for simple arithmetic there are formulae which cannot be proved in the system but that human minds can recognize as true; Lucas points out in his turn that Gödel’s theorem applies to machines because a machine is the concrete instantiation of a formal system: therefore, (...)
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  4.  25
    Divine Machines: Leibniz and the Sciences of Life.Justin E. H. Smith - 2011 - Princeton University Press.
    Though it did not yet exist as a discrete field of scientific inquiry, biology was at the heart of many of the most important debates in seventeenth-century philosophy. Nowhere is this more apparent than in the work of G. W. Leibniz. In Divine Machines, Justin Smith offers the first in-depth examination of Leibniz's deep and complex engagement with the empirical life sciences of his day, in areas as diverse as medicine, physiology, taxonomy, generation theory, and paleontology. He shows how these (...)
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  5.  69
    Clinical Applications of Machine Learning Algorithms: Beyond the Black Box.David S. Watson, Jenny Krutzinna, Ian N. Bruce, Christopher E. M. Griffiths, Iain B. McInnes, Michael R. Barnes & Luciano Floridi - 2019 - British Medical Journal 364:I886.
    Machine learning algorithms may radically improve our ability to diagnose and treat disease. For moral, legal, and scientific reasons, it is essential that doctors and patients be able to understand and explain the predictions of these models. Scalable, customisable, and ethical solutions can be achieved by working together with relevant stakeholders, including patients, data scientists, and policy makers.
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  6. Virtual Machine Functionalism: The Only Form of Functionalism Worth Taking Seriously in Philosophy of Mind.Aaron Sloman -
    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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  7. Minds, Machines and Gödel.John R. Lucas - 1961 - Philosophy 36 (137):112-127.
    Gödei's Theorem seems to me to prove that Mechanism is false, that is, that minds cannot be explained as machines. So also has it seemed to many other people: almost every mathematical logician I have put the matter to has confessed to similar thoughts, but has felt reluctant to commit himself definitely until he could see the whole argument set out, with all objections fully stated and properly met. This I attempt to do.
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  8.  13
    Discourses of Unity and Purpose in the Sounds of Fascist Music: A Multimodal Approach.David Machin & John E. Richardson - 2012 - Critical Discourse Studies 9 (4):329-345.
    This article, taking a social semiotic approach, analyses two pieces of music written, shared and exalted by two pre-1945 European fascist movements – the German NSDAP and the British Union of Fascists. These movements, both political and cultural, employed mythologies of unity, common identity and purpose in order to elide the realities of social distinction and political–economic inequalities between bourgeois and proletarian groups in capitalist societies. Visually and inter-personally, the fascist cultural project communicated a machine-like certainty about a vision (...)
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  9. Machines Learning Values.Steve Petersen - 2020 - In S. Matthew Liao (ed.), Ethics of Artificial Intelligence. New York, USA: Oxford University Press.
    Whether it would take one decade or several centuries, many agree that it is possible to create a *superintelligence*---an artificial intelligence with a godlike ability to achieve its goals. And many who have reflected carefully on this fact agree that our best hope for a "friendly" superintelligence is to design it to *learn* values like ours, since our values are too complex to program or hardwire explicitly. But the value learning approach to AI safety faces three particularly philosophical puzzles: first, (...)
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  10. Can Machines Read Our Minds?Christopher Burr & Nello Cristianini - 2019 - Minds and Machines 29 (3):461-494.
    We explore the question of whether machines can infer information about our psychological traits or mental states by observing samples of our behaviour gathered from our online activities. Ongoing technical advances across a range of research communities indicate that machines are now able to access this information, but the extent to which this is possible and the consequent implications have not been well explored. We begin by highlighting the urgency of asking this question, and then explore its conceptual underpinnings, in (...)
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  11.  58
    Engineered Wisdom for Learning Machines.Brett Karlan & Colin Allen - forthcoming - Journal of Experimental and Theoretical Artificial Intelligence:1-30.
    We argue that the concept of practical wisdom is particularly useful for organizing, understanding, and improving human-machine interactions. We consider the relationship between philosophical analysis of wisdom and psychological research into the development of wisdom. We adopt a practical orientation that suggests a conceptual engineering approach is needed, where philosophical work involves refinement of the concept in response to contributions by engineers and behavioral scientists. The former are tasked with encoding as much wise design as possible into machines themselves, (...)
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  12. Minds, Machines, and Gödel: A Retrospect.J. R. Lucas - 1996 - In P. J. R. Millican & A. Clark (eds.), Etica E Politica. Clarendon Press. pp. 1.
    In this paper Lucas comes back to Gödelian argument against Mecanism to clarify some points. First of all, he explains his use of Gödel’s theorem instead of Turing’s theorem, showing how Gödel’ theorem, but not Turing’s theorem, raises questions concerning truth and reasoning that bear on the nature of mind and how Turing’s theorem suggests that there is something that cannot be done by any computers but not that it can be done by human minds. He considers moreover how Gödel’s (...)
     
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  13. Organisms ≠ Machines.Daniel J. Nicholson - 2013 - Studies in History and Philosophy of Science Part C: Studies in History and Philosophy of Biological and Biomedical Sciences 44 (4):669-678.
    The machine conception of the organism (MCO) is one of the most pervasive notions in modern biology. However, it has not yet received much attention by philosophers of biology. The MCO has its origins in Cartesian natural philosophy, and it is based on the metaphorical redescription of the organism as a machine. In this paper I argue that although organisms and machines resemble each other in some basic respects, they are actually very different kinds of systems. I submit (...)
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  14. The Machine Question: Critical Perspectives on Ai, Robots, and Ethics.David J. Gunkel - 2012 - MIT Press.
    One of the enduring concerns of moral philosophy is deciding who or what is deserving of ethical consideration. Much recent attention has been devoted to the "animal question" -- consideration of the moral status of nonhuman animals. In this book, David Gunkel takes up the "machine question": whether and to what extent intelligent and autonomous machines of our own making can be considered to have legitimate moral responsibilities and any legitimate claim to moral consideration. The machine question poses (...)
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  15. Why Machines Cannot Be Moral.Robert Sparrow - 2021 - AI and Society:1-9.
    The fact that real-world decisions made by artificial intelligences are often ethically loaded has led a number of authorities to advocate the development of “moral machines”. I argue that the project of building “ethics” “into” machines presupposes a flawed understanding of the nature of ethics. Drawing on the work of the Australian philosopher, Raimond Gaita, I argue that ethical dilemmas are problems for particular people and not problems for everyone who faces a similar situation. Moreover, the force of an ethical (...)
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  16.  84
    Just Machines.Clinton Castro - forthcoming - Public Affairs Quarterly.
    A number of findings in the field of machine learning have given rise to questions about what it means for automated scoring- or decisionmaking systems to be fair. One center of gravity in this discussion is whether such systems ought to satisfy classification parity (which requires parity in accuracy across groups, defined by protected attributes) or calibration (which requires similar predictions to have similar meanings across groups, defined by protected attributes). Central to this discussion are impossibility results, owed to (...)
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  17.  1
    Machine Man and Other Writings.Ann Thomson (ed.) - 1996 - Cambridge University Press.
    Julien Offray de La Mettrie (1709-51), author of Machine Man (1747), was the most uncompromising of the materialists of the eighteenth century, and the provocative title of his work ensured it a succès de scandale in his own time. It was however a serious, if polemical, attempt to provide an explanation of the workings of the human body and mind in purely material terms and to show that thought was the product of the workings of the brain alone. This (...)
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  18.  2
    Signs and Machines: Capitalism and the Production of Subjectivity.Maurizio Lazzarato - 2014 - MIT Press.
    An analysis of how capitalism today produces subjectivity like any other “good,” and what would allow us to escape its hold. “Capital is a semiotic operator”: this assertion by Félix Guattari is at the heart of Maurizio Lazzarato's Signs and Machines, which asks us to leave behind the logocentrism that still informs so many critical theories. Lazzarato calls instead for a new theory capable of explaining how signs function in the economy, in power apparatuses, and in the production of subjectivity. (...)
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  19. The Machine Conception of the Organism in Development and Evolution: A Critical Analysis.Daniel J. Nicholson - 2014 - Studies in History and Philosophy of Biological and Biomedical Sciences 48:162-174.
    This article critically examines one of the most prevalent metaphors in modern biology, namely the machine conception of the organism (MCO). Although the fundamental differences between organisms and machines make the MCO an inadequate metaphor for conceptualizing living systems, many biologists and philosophers continue to draw upon the MCO or tacitly accept it as the standard model of the organism. This paper analyses the specific difficulties that arise when the MCO is invoked in the study of development and evolution. (...)
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  20. Judging Machines: Philosophical Aspects of Deep Learning.Arno Schubbach - forthcoming - Synthese 198 (2):1807-1827.
    Although machine learning has been successful in recent years and is increasingly being deployed in the sciences, enterprises or administrations, it has rarely been discussed in philosophy beyond the philosophy of mathematics and machine learning. The present contribution addresses the resulting lack of conceptual tools for an epistemological discussion of machine learning by conceiving of deep learning networks as ‘judging machines’ and using the Kantian analysis of judgments for specifying the type of judgment they are capable of. (...)
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  21.  8
    Programming Machine Ethics.Luís Moniz Pereira & Ari Saptawijaya - 2016 - Springer Verlag.
    Source: "This book addresses the fundamentals of machine ethics. It discusses abilities required for ethical machine reasoning and the programming features that enable them. It connects ethics, psychological ethical processes, and machine implemented procedures. From a technical point of view, the book uses logic programming and evolutionary game theory to model and link the individual and collective moral realms. It also reports on the results of experiments performed using several model implementations. Opening specific and promising inroads into (...)
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  22. A Critical Multimodal Analysis of the Romanian Press Coverage of Camp Evictions and Deportations of the Roma Migrants From France.David Machin & Petre Breazu - 2018 - Discourse and Communication 12 (4):339-356.
    In this article, we carry out a Multimodal Critical Discourse Analysis of a sample from a larger corpus of Romanian news articles that covered the controversial camp evictions and repatriation of Romanian Roma migrants from France that began in 2010 and continue to the time of writing in 2017. These French government policies have been highly criticized both within France and by international political and aid organizations. However, the analysis shows how these brutal, anti-humanitarian events became recontextualized in the Romanian (...)
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  23.  73
    The Explanation Game: A Formal Framework for Interpretable Machine Learning.David S. Watson & Luciano Floridi - 2020 - Synthese 198 (10):1–⁠32.
    We propose a formal framework for interpretable machine learning. Combining elements from statistical learning, causal interventionism, and decision theory, we design an idealised explanation game in which players collaborate to find the best explanation for a given algorithmic prediction. Through an iterative procedure of questions and answers, the players establish a three-dimensional Pareto frontier that describes the optimal trade-offs between explanatory accuracy, simplicity, and relevance. Multiple rounds are played at different levels of abstraction, allowing the players to explore overlapping (...)
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  24. Machine Mentality and the Nature of the Ground Relation.Darren Whobrey - 2001 - 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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  25.  12
    Machines with a Purpose.H. H. Rosenbrock - 1990 - Oxford University Press.
    There is at present a widespread unease about the direction in which our technology is taking us, apparently against our will. Promising advances seem to carry with them unforeseen negative consequences, including damage to the environment and the reduction of work to the trivial mechanical repetition of actions which have no human meaning. However, attempts to design a better, human-centered technology--one that complements rather than rejects human skills--are all too often frustrated by the prevailing belief that "man is a (...)," and one, moreover, that compares badly in terms of performance and durability. This contentious and stimulating book offers a new approach, one that refutes four centuries of science based on strictly causal explanations. It shows that man and nature can be viewed as "machines with a purpose," and that the "purpose" can be the advancement of technology to the benefit and not the detriment of the human race and its environment. This fascinating work is accessible to a wide range of readers, scientists and nonspecialists alike. It will interest anyone concerned about the impact of technology and the way it is shaping our world. (shrink)
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  26. Machines as Moral Patients We Shouldn’T Care About : The Interests and Welfare of Current Machines.John Basl - 2014 - Philosophy and Technology 27 (1):79-96.
    In order to determine whether current (or future) machines have a welfare that we as agents ought to take into account in our moral deliberations, we must determine which capacities give rise to interests and whether current machines have those capacities. After developing an account of moral patiency, I argue that current machines should be treated as mere machines. That is, current machines should be treated as if they lack those capacities that would give rise to psychological interests. Therefore, they (...)
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  27. Machines and the Moral Community.Erica L. Neely - 2013 - Philosophy and Technology 27 (1):97-111.
    A key distinction in ethics is between members and nonmembers of the moral community. Over time, our notion of this community has expanded as we have moved from a rationality criterion to a sentience criterion for membership. I argue that a sentience criterion is insufficient to accommodate all members of the moral community; the true underlying criterion can be understood in terms of whether a being has interests. This may be extended to conscious, self-aware machines, as well as to any (...)
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  28. Machine Learning and Irresponsible Inference: Morally Assessing the Training Data for Image Recognition Systems.Owen King - 2019 - In Matteo Vincenzo D'Alfonso & Don Berkich (eds.), On the Cognitive, Ethical, and Scientific Dimensions of Artificial Intelligence. Springer Verlag. pp. 265-282.
    Just as humans can draw conclusions responsibly or irresponsibly, so too can computers. Machine learning systems that have been trained on data sets that include irresponsible judgments are likely to yield irresponsible predictions as outputs. In this paper I focus on a particular kind of inference a computer system might make: identification of the intentions with which a person acted on the basis of photographic evidence. Such inferences are liable to be morally objectionable, because of a way in which (...)
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  29. Machine Ethics.Michael Anderson & Susan Leigh Anderson (eds.) - 2011 - Cambridge Univ. Press.
    The essays in this volume represent the first steps by philosophers and artificial intelligence researchers toward explaining why it is necessary to add an ...
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  30.  11
    Are Machines Radically Contextualist?Ryan M. Nefdt - forthcoming - Mind and Language.
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  31.  97
    Machine Consciousness.Owen Holland (ed.) - 2003 - Imprint Academic.
    In this collection of essays we hear from an international array of computer and brain scientists who are actively working from both the machine and human ends...
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  32.  30
    Situating Machine Intelligence Within the Cognitive Ecology of the Internet.Paul Smart - 2017 - Minds and Machines 27 (2):357-380.
    The Internet is an important focus of attention for the philosophy of mind and cognitive science communities. This is partly because the Internet serves as an important part of the material environment in which a broad array of human cognitive and epistemic activities are situated. The Internet can thus be seen as an important part of the ‘cognitive ecology’ that helps to shape, support and realize aspects of human cognizing. Much of the previous philosophical work in this area has sought (...)
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  33.  96
    Machine Models for Cognitive Science.Raymond J. Nelson - 1987 - Philosophy of Science 54 (September):391-408.
    Introduction. During the past two decades philosophers of psychology have considered a large variety of computational models for philosophy of mind and more recently for cognitive science. Among the suggested models are computer programs, Turing machines, pushdown automata, linear bounded automata, finite state automata and sequential machines. Many philosophers have found finite state automata models to be the most appealing, for various reasons, although there has been no shortage of defenders of programs and Turing machines. A paper by Arthur Burks (...)
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  34.  40
    Cybernetics: Or, Control and Communication in the Animal and the MacHine.Norbert Wiener - 1961 - New York: M.I.T. Press.
    This is a new release of the original 1949 edition.
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  35.  60
    Building Machines That Learn and Think Like People.Brenden M. Lake, Tomer D. Ullman, Joshua B. Tenenbaum & Samuel J. Gershman - 2017 - Behavioral and Brain Sciences 40.
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  36.  55
    Machine-Likeness and Explanation by Decomposition.Arnon Levy - 2014 - Philosophers' Imprint 14.
    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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  37.  1
    A Thousand Machines: A Concise Philosophy of the Machine as Social Movement.Gerald Raunig - 2010 - Semiotext(E).
    The machine as a social movement of today's “precariat”—those whose labor and lives are precarious. In this “concise philosophy of the machine,” Gerald Raunig provides a historical and critical backdrop to a concept proposed forty years ago by the French philosophers Félix Guattari and Gilles Deleuze: the machine, not as a technical device and apparatus, but as a social composition and concatenation. This conception of the machine as an arrangement of technical, bodily, intellectual, and social components (...)
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  38.  37
    Crazy Machines.Ronald David Schwartz - 1986 - Telos: Critical Theory of the Contemporary 1986 (70):125-137.
    I want to examine the Habermasian account of modernity from a particular vantage-point: namely the collection of new technologies that are called variously “artificial intelligence,” “knowledge engineering,” “intelligent systems,” “expert systems,” and the like. The significance of these technologies far exceeds whatever role they may come to play in business, government, or the other institutions they penetrate. I suggest that they cast doubt over the entire project of modernity as understood by Habermas — not because they signify the penetration of (...)
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  39. La Machine Univers: Création, Cognition Et Culture Informatique.Pierre Lévy - 1987 - Editions la Découverte.
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  40. Brain Machine Interface and Human Enhancement – An Ethical Review.Karim Jebari - 2013 - 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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  41. Machine Learning Classifiers to Evaluate Data From Gait Analysis With Depth Cameras in Patients With Parkinson’s Disease.Beatriz Muñoz-Ospina, Daniela Alvarez-Garcia, Hugo Juan Camilo Clavijo-Moran, Jaime Andrés Valderrama-Chaparro, Melisa García-Peña, Carlos Alfonso Herrán, Christian Camilo Urcuqui, Andrés Navarro-Cadavid & Jorge Orozco - 2022 - Frontiers in Human Neuroscience 16.
    IntroductionThe assessments of the motor symptoms in Parkinson’s disease are usually limited to clinical rating scales, and it depends on the clinician’s experience. This study aims to propose a machine learning technique algorithm using the variables from upper and lower limbs, to classify people with PD from healthy people, using data from a portable low-cost device. And can be used to support the diagnosis and follow-up of patients in developing countries and remote areas.MethodsWe used Kinect®eMotion system to capture the (...)
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  42. Understanding From Machine Learning Models.Emily Sullivan - 2022 - British Journal for the Philosophy of Science 73 (1):109-133.
    Simple idealized models seem to provide more understanding than opaque, complex, and hyper-realistic models. However, an increasing number of scientists are going in the opposite direction by utilizing opaque machine learning models to make predictions and draw inferences, suggesting that scientists are opting for models that have less potential for understanding. Are scientists trading understanding for some other epistemic or pragmatic good when they choose a machine learning model? Or are the assumptions behind why minimal models provide understanding (...)
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  43.  33
    Using Machine Learning to Predict Decisions of the European Court of Human Rights.Masha Medvedeva, Michel Vols & Martijn Wieling - 2020 - Artificial Intelligence and Law 28 (2):237-266.
    When courts started publishing judgements, big data analysis within the legal domain became possible. By taking data from the European Court of Human Rights as an example, we investigate how natural language processing tools can be used to analyse texts of the court proceedings in order to automatically predict judicial decisions. With an average accuracy of 75% in predicting the violation of 9 articles of the European Convention on Human Rights our approach highlights the potential of machine learning approaches (...)
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  44.  3
    Machine Learning Applications in Healthcare and the Role of Informed Consent: Ethical and Practical Considerations.Giorgia Lorenzini, David Martin Shaw, Laura Arbelaez Ossa & Bernice Simone Elger - forthcoming - Clinical Ethics:147775092210944.
    Informed consent is at the core of the clinical relationship. With the introduction of machine learning in healthcare, the role of informed consent is challenged. This paper addresses the issue of whether patients must be informed about medical ML applications and asked for consent. It aims to expose the discrepancy between ethical and practical considerations, while arguing that this polarization is a false dichotomy: in reality, ethics is applied to specific contexts and situations. Bridging this gap and considering the (...)
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  45.  1
    Grammar, Meaning and the Machine Analysis of Language.Yorick Wilks - 1972 - Routledge & Kegan Paul Books.
  46. Minds and Machines.Hilary Putnam - 1960 - In Sidney Hook (ed.), Dimensions of Minds. New York, USA: New York University Press. pp. 138-164.
  47.  67
    Experience Machines, Conflicting Intuitions and the Bipartite Characterization of Well-Being.Chad M. Stevenson - 2018 - Utilitas 30 (4):383-398.
    While Nozick and his sympathizers assume there is a widespread anti-hedonist intuition to prefer reality to an experience machine, hedonists have marshalled empirical evidence that shows such an assumption to be unfounded. Results of several experience machine variants indicate there is no widespread anti-hedonist intuition. From these findings, hedonists claim Nozick's argument fails as an objection to hedonism. This article suggests the argument surrounding experience machines has been misconceived. Rather than eliciting intuitions about what is prudentially valuable, these (...)
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  48. The Misleading Nature of Flow Charts and Diagrams in Organizational Communication: The Case of Performance Management of Preschools in Sweden.David Machin & Per Ledin - 2020 - Semiotica 2020 (236-237):405-425.
    It has become common to find diagrams and flow-charts used in our organizations to illustrate the nature of processes, what is involved and how it happens, or to show how parts of the organization interrelate to each other and work together. Such diagrams are used as they are thought to help visualization and simplify things in order to represent the essence of a particular situation, the core features. In this paper, using a social semiotic approach, we show that we need (...)
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  49. Why Machine-Information Metaphors Are Bad for Science and Science Education.Massimo Pigliucci & Maarten Boudry - 2011 - Science & Education 20 (5-6):471.
    Genes are often described by biologists using metaphors derived from computa- tional science: they are thought of as carriers of information, as being the equivalent of ‘‘blueprints’’ for the construction of organisms. Likewise, cells are often characterized as ‘‘factories’’ and organisms themselves become analogous to machines. Accordingly, when the human genome project was initially announced, the promise was that we would soon know how a human being is made, just as we know how to make airplanes and buildings. Impor- tantly, (...)
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  50. A Machine That Knows Its Own Code.Samuel A. Alexander - 2014 - Studia Logica 102 (3):567-576.
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