Results for 'Machine'

958 found
Order:
  1. The nonhuman condition: Radical democracy through new materialist lenses.Hans Asenbaum, Amanda Machin, Jean-Paul Gagnon, Diana Leong, Melissa Orlie & James Louis Smith - 2023 - Contemporary Political Theory (Online first):584-615.
    Radical democratic thinking is becoming intrigued by the material situatedness of its political agents and by the role of nonhuman participants in political interaction. At stake here is the displacement of narrow anthropocentrism that currently guides democratic theory and practice, and its repositioning into what we call ‘the nonhuman condition’. This Critical Exchange explores the nonhuman condition. It asks: What are the implications of decentering the human subject via a new materialist reading of radical democracy? Does this reading dilute political (...)
    Download  
     
    Export citation  
     
    Bookmark   1 citation  
  2. 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 (...)
    Download  
     
    Export citation  
     
    Bookmark   53 citations  
  3. Egalitarian Machine Learning.Clinton Castro, David O’Brien & Ben Schwan - 2023 - Res Publica 29 (2):237–264.
    Prediction-based decisions, which are often made by utilizing the tools of machine learning, influence nearly all facets of modern life. Ethical concerns about this widespread practice have given rise to the field of fair machine learning and a number of fairness measures, mathematically precise definitions of fairness that purport to determine whether a given prediction-based decision system is fair. Following Reuben Binns (2017), we take ‘fairness’ in this context to be a placeholder for a variety of normative egalitarian (...)
    Download  
     
    Export citation  
     
    Bookmark   3 citations  
  4. 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 (...)
    Download  
     
    Export citation  
     
    Bookmark   19 citations  
  5. The Machine Conception of the Organism in Development and Evolution: A Critical Analysis.Daniel J. Nicholson - 2014 - Studies in History and Philosophy of Science Part C: 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. (...)
    Download  
     
    Export citation  
     
    Bookmark   25 citations  
  6. Why machines cannot be moral.Robert Sparrow - 2021 - AI and Society (3):685-693.
    The fact that real-world decisions made by artificial intelligences (AI) 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 (just) problems for everyone who faces a similar situation. Moreover, the force of (...)
    Download  
     
    Export citation  
     
    Bookmark   11 citations  
  7. Just Machines.Clinton Castro - 2022 - Public Affairs Quarterly 36 (2):163-183.
    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 (...)
    Download  
     
    Export citation  
     
    Bookmark   5 citations  
  8. 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.
    Download  
     
    Export citation  
     
    Bookmark   17 citations  
  9. Consciousness, Machines, and Moral Status.Henry Shevlin - manuscript
    In light of recent breakneck pace in machine learning, questions about whether near-future artificial systems might be conscious and possess moral status are increasingly pressing. This paper argues that as matters stand these debates lack any clear criteria for resolution via the science of consciousness. Instead, insofar as they are settled at all, it is likely to be via shifts in public attitudes brought about by the increasingly close relationships between humans and AI users. Section 1 of the paper (...)
    Download  
     
    Export citation  
     
    Bookmark   1 citation  
  10. A Machine That Knows Its Own Code.Samuel A. Alexander - 2014 - Studia Logica 102 (3):567-576.
    We construct a machine that knows its own code, at the price of not knowing its own factivity.
    Download  
     
    Export citation  
     
    Bookmark   4 citations  
  11. 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 (...)
    Download  
     
    Export citation  
     
    Bookmark   16 citations  
  12. The experience machine and mental state theories of well-being.Jason Kawall - 1999 - Journal of Value Inquiry 33 (3):381-387.
    It is argued that Nozick's experience machine thought experiment does not pose a particular difficulty for mental state theories of well-being. While the example shows that we value many things beyond our mental states, this simply reflects the fact that we value more than our own well-being. Nor is a mental state theorist forced to make the dubious claim that we maintain these other values simply as a means to desirable mental states. Valuing more than our mental states is (...)
    Download  
     
    Export citation  
     
    Bookmark   28 citations  
  13. (1 other version)Machine Learning and Irresponsible Inference: Morally Assessing the Training Data for Image Recognition Systems.Owen C. 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 (...)
    Download  
     
    Export citation  
     
    Bookmark   2 citations  
  14. 8 Rightful Machines.Ava Thomas Wright - 2022 - In Hyeongjoo Kim & Dieter Schönecker (eds.), Kant and Artificial Intelligence. De Gruyter. pp. 223-238.
    In this paper, I set out a new Kantian approach to resolving conflicts between moral obligations for highly autonomous machine agents. First, I argue that efforts to build explicitly moral autonomous machine agents should focus on what Kant refers to as duties of right, which are duties that everyone could accept, rather than on duties of virtue (or “ethics”), which are subject to dispute in particular cases. “Moral” machines must first be rightful machines, I argue. I then show (...)
    Download  
     
    Export citation  
     
    Bookmark   1 citation  
  15. The Use of Machine Learning Methods for Image Classification in Medical Data.Destiny Agboro - forthcoming - International Journal of Ethics.
    Integrating medical imaging with computing technologies, such as Artificial Intelligence (AI) and its subsets: Machine learning (ML) and Deep Learning (DL) has advanced into an essential facet of present-day medicine, signaling a pivotal role in diagnostic decision-making and treatment plans (Huang et al., 2023). The significance of medical imaging is escalated by its sustained growth within the realm of modern healthcare (Varoquaux and Cheplygina, 2022). Nevertheless, the ever-increasing volume of medical images compared to the availability of imaging experts. Biomedical (...)
    Download  
     
    Export citation  
     
    Bookmark  
  16. Machine morality, moral progress, and the looming environmental disaster.Ben Kenward & Thomas Sinclair - forthcoming - Cognitive Computation and Systems.
    The creation of artificial moral systems requires us to make difficult choices about which of varying human value sets should be instantiated. The industry-standard approach is to seek and encode moral consensus. Here we argue, based on evidence from empirical psychology, that encoding current moral consensus risks reinforcing current norms, and thus inhibiting moral progress. However, so do efforts to encode progressive norms. Machine ethics is thus caught between a rock and a hard place. The problem is particularly acute (...)
    Download  
     
    Export citation  
     
    Bookmark  
  17. Building machines that learn and think about morality.Christopher Burr & Geoff Keeling - 2018 - In Christopher Burr & Geoff Keeling (eds.), 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 (...)
    Download  
     
    Export citation  
     
    Bookmark   2 citations  
  18. 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 (...)
    Download  
     
    Export citation  
     
    Bookmark   56 citations  
  19.  77
    The Machine Speaks: Conversational AIs and the importance of effort to relationships of meaning.Anna Hartford & Dan J. Stein - 2024 - JMIR Mental Health 11.
    The focus of debates about conversational AIs (CAIs) has largely been on social and ethical concerns that arise when we speak to machines. What is gained and what is lost when we replace our human interlocutors—including our human therapists— with AIs? Here, we focus instead on a distinct and growing phenomenon: letting machines speak for us. What is at stake when we replace our own efforts at interpersonal engagement with CAIs? The purpose of these technologies is, in part, to remove (...)
    Download  
     
    Export citation  
     
    Bookmark  
  20. Machine Learning-Based Diabetes Prediction: Feature Analysis and Model Assessment.Fares Wael Al-Gharabawi & Samy S. Abu-Naser - 2023 - International Journal of Academic Engineering Research (IJAER) 7 (9):10-17.
    This study employs machine learning to predict diabetes using a Kaggle dataset with 13 features. Our three-layer model achieves an accuracy of 98.73% and an average error of 0.01%. Feature analysis identifies Age, Gender, Polyuria, Polydipsia, Visual blurring, sudden weight loss, partial paresis, delayed healing, irritability, Muscle stiffness, Alopecia, Genital thrush, Weakness, and Obesity as influential predictors. These findings have clinical significance for early diabetes risk assessment. While our research addresses gaps in the field, further work is needed to (...)
    Download  
     
    Export citation  
     
    Bookmark   1 citation  
  21. Machine Intentionality, the Moral Status of Machines, and the Composition Problem.David Leech Anderson - 2012 - In Vincent C. Müller (ed.), The Philosophy & Theory of Artificial Intelligence. Springer. pp. 312-333.
    According to the most popular theories of intentionality, a family of theories we will refer to as “functional intentionality,” a machine can have genuine intentional states so long as it has functionally characterizable mental states that are causally hooked up to the world in the right way. This paper considers a detailed description of a robot that seems to meet the conditions of functional intentionality, but which falls victim to what I call “the composition problem.” One obvious way to (...)
    Download  
     
    Export citation  
     
    Bookmark   3 citations  
  22. Can machines be people? Reflections on the Turing triage test.Robert Sparrow - 2011 - In Patrick Lin, Keith Abney & George A. Bekey (eds.), Robot Ethics: The Ethical and Social Implications of Robotics. MIT Press. pp. 301-315.
    In, “The Turing Triage Test”, published in Ethics and Information Technology, I described a hypothetical scenario, modelled on the famous Turing Test for machine intelligence, which might serve as means of testing whether or not machines had achieved the moral standing of people. In this paper, I: (1) explain why the Turing Triage Test is of vital interest in the context of contemporary debates about the ethics of AI; (2) address some issues that complexify the application of this test; (...)
    Download  
     
    Export citation  
     
    Bookmark   15 citations  
  23. Machine art or machine artists? Dennett, Danto, and the expressive stance.Adam Linson - 2016 - In Vincent C. Müller (ed.), Fundamental Issues of Artificial Intelligence. Cham: 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, (...)
    Download  
     
    Export citation  
     
    Bookmark  
  24. rethinking machine ethics in the era of ubiquitous technology.Jeffrey White (ed.) - 2015 - Hershey, PA, USA: IGI.
    Table of Contents Foreword .................................................................................................... ......................................... xiv Preface .................................................................................................... .............................................. xv Acknowledgment .................................................................................................... .......................... xxiii Section 1 On the Cusp: Critical Appraisals of a Growing Dependency on Intelligent Machines Chapter 1 Algorithms versus Hive Minds and the Fate of Democracy ................................................................... 1 Rick Searle, IEET, USA Chapter 2 We Can Make Anything: Should We? .................................................................................................. 15 Chris Bateman, University of Bolton, UK Chapter 3 Grounding Machine Ethics within the Natural System ........................................................................ 30 Jared Gassen, JMG Advising, USA Nak Young Seong, Independent Scholar, (...)
    Download  
     
    Export citation  
     
    Bookmark  
  25. Machine Learning, Misinformation, and Citizen Science.Adrian K. Yee - 2023 - European Journal for Philosophy of Science 13 (56):1-24.
    Current methods of operationalizing concepts of misinformation in machine learning are often problematic given idiosyncrasies in their success conditions compared to other models employed in the natural and social sciences. The intrinsic value-ladenness of misinformation and the dynamic relationship between citizens' and social scientists' concepts of misinformation jointly suggest that both the construct legitimacy and the construct validity of these models needs to be assessed via more democratic criteria than has previously been recognized.
    Download  
     
    Export citation  
     
    Bookmark  
  26. “Man-Machines and Embodiment: From Cartesian Physiology to Claude Bernard’s ‘Living Machine’”.Charles T. Wolfe & Philippe Huneman - 2017 - In Justin E. H. Smith (ed.), Embodiment: A History. New York: Oxford University Press.
    A common and enduring early modern intuition is that materialists reduce organisms in general and human beings in particular to automata. Wasn’t a famous book of the time entitled L’Homme-Machine? In fact, the machine is employed as an analogy, and there was a specifically materialist form of embodiment, in which the body is not reduced to an inanimate machine, but is conceived as an affective, flesh-and-blood entity. We discuss how mechanist and vitalist models of organism exist in (...)
    Download  
     
    Export citation  
     
    Bookmark  
  27. Can machines think? The controversy that led to the Turing test.Bernardo Gonçalves - 2023 - AI and Society 38 (6):2499-2509.
    Turing’s much debated test has turned 70 and is still fairly controversial. His 1950 paper is seen as a complex and multilayered text, and key questions about it remain largely unanswered. Why did Turing select learning from experience as the best approach to achieve machine intelligence? Why did he spend several years working with chess playing as a task to illustrate and test for machine intelligence only to trade it out for conversational question-answering in 1950? Why did Turing (...)
    Download  
     
    Export citation  
     
    Bookmark   3 citations  
  28. Machine learning in bail decisions and judges’ trustworthiness.Alexis Morin-Martel - 2023 - AI and Society:1-12.
    The use of AI algorithms in criminal trials has been the subject of very lively ethical and legal debates recently. While there are concerns over the lack of accuracy and the harmful biases that certain algorithms display, new algorithms seem more promising and might lead to more accurate legal decisions. Algorithms seem especially relevant for bail decisions, because such decisions involve statistical data to which human reasoners struggle to give adequate weight. While getting the right legal outcome is a strong (...)
    Download  
     
    Export citation  
     
    Bookmark   3 citations  
  29. 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, (...)
    Download  
     
    Export citation  
     
    Bookmark   19 citations  
  30. Making moral machines: why we need artificial moral agents.Paul Formosa & Malcolm Ryan - forthcoming - AI and Society.
    As robots and Artificial Intelligences become more enmeshed in rich social contexts, it seems inevitable that we will have to make them into moral machines equipped with moral skills. Apart from the technical difficulties of how we could achieve this goal, we can also ask the ethical question of whether we should seek to create such Artificial Moral Agents (AMAs). Recently, several papers have argued that we have strong reasons not to develop AMAs. In response, we develop a comprehensive analysis (...)
    Download  
     
    Export citation  
     
    Bookmark   11 citations  
  31. Manipulation, machine induction, and bypassing.Gabriel De Marco - 2022 - Philosophical Studies 180 (2):487-507.
    A common style of argument in the literature on free will and moral responsibility is the Manipulation Argument. These tend to begin with a case of an agent in a deterministic universe who is manipulated, say, via brain surgery, into performing some action. Intuitively, this agent is not responsible for that action. Yet, since there is no relevant difference, with respect to whether an agent is responsible, between the manipulated agent and a typical agent in a deterministic universe, responsibility is (...)
    Download  
     
    Export citation  
     
    Bookmark   1 citation  
  32. (1 other version)Minds and Machines.Hilary Putnam - 1960 - In Sidney Hook (ed.), Dimensions Of Mind: A Symposium. NY: NEW YORK University Press. pp. 138-164.
    Download  
     
    Export citation  
     
    Bookmark   329 citations  
  33. Machine Advisors: Integrating Large Language Models into Democratic Assemblies.Petr Špecián - forthcoming - Social Epistemology.
    Could the employment of large language models (LLMs) in place of human advisors improve the problem-solving ability of democratic assemblies? LLMs represent the most significant recent incarnation of artificial intelligence and could change the future of democratic governance. This paper assesses their potential to serve as expert advisors to democratic representatives. While LLMs promise enhanced expertise availability and accessibility, they also present specific challenges. These include hallucinations, misalignment and value imposition. After weighing LLMs’ benefits and drawbacks against human advisors, I (...)
    Download  
     
    Export citation  
     
    Bookmark  
  34. Why Machines Can Neither Think nor Feel.Douglas C. Long - 1994 - In Dale W. Jamieson (ed.), Language, Mind and Art. Boston: Kluwer Academic Publishers.
    Over three decades ago, in a brief but provocative essay, Paul Ziff argued for the thesis that robots cannot have feelings because they are "mechanisms, not organisms, not living creatures. There could be a broken-down robot but not a dead one. Only living creatures can literally have feelings."[i] Since machines are not living things they cannot have feelings. In the first half of my paper I review Ziff's arguments against the idea that robots could be conscious, especially his appeal to (...)
    Download  
     
    Export citation  
     
    Bookmark   2 citations  
  35. Consequentialism & Machine Ethics: Towards a Foundational Machine Ethic to Ensure the Right Action of Artificial Moral Agents.Josiah Della Foresta - 2020 - Montreal AI Ethics Institute.
    In this paper, I argue that Consequentialism represents a kind of ethical theory that is the most plausible to serve as a basis for a machine ethic. First, I outline the concept of an artificial moral agent and the essential properties of Consequentialism. Then, I present a scenario involving autonomous vehicles to illustrate how the features of Consequentialism inform agent action. Thirdly, an alternative Deontological approach will be evaluated and the problem of moral conflict discussed. Finally, two bottom-up approaches (...)
    Download  
     
    Export citation  
     
    Bookmark  
  36. Turing Machines and Semantic Symbol Processing: Why Real Computers Don’t Mind Chinese Emperors.Richard Yee - 1993 - Lyceum 5 (1):37-59.
    Philosophical questions about minds and computation need to focus squarely on the mathematical theory of Turing machines (TM's). Surrogate TM's such as computers or formal systems lack abilities that make Turing machines promising candidates for possessors of minds. Computers are only universal Turing machines (UTM's)—a conspicuous but unrepresentative subclass of TM. Formal systems are only static TM's, which do not receive inputs from external sources. The theory of TM computation clearly exposes the failings of two prominent critiques, Searle's Chinese room (...)
    Download  
     
    Export citation  
     
    Bookmark  
  37. Machine vs. Human Translation.Elona Limaj - 2014 - Journal of Turkish Studies 9 (Volume 9 Issue 6):783-783.
    The advantages and disadvantages of machine translation have been the subject of increasing debate among human translators lately because of the growing strides made in the last year by the newest major entrant in the field, Google Translate. The progress and potential of machine translation has been debated much through its history. But this debate actually began with the birth of machine translation itself. Behind this simple procedure lies a complex cognitive operation. To decode the meaning of (...)
    Download  
     
    Export citation  
     
    Bookmark  
  38. The Experience Machine.Ben Bramble - 2016 - Philosophy Compass 11 (3):136-145.
    In this paper, I reconstruct Robert Nozick's experience machine objection to hedonism about well-being. I then explain and briefly discuss the most important recent criticisms that have been made of it. Finally, I question the conventional wisdom that the experience machine, while it neatly disposes of hedonism, poses no problem for desire-based theories of well-being.
    Download  
     
    Export citation  
     
    Bookmark   16 citations  
  39. 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 (...)
    Download  
     
    Export citation  
     
    Bookmark   6 citations  
  40. Time travel and time machines.Chris Smeenk & Christian Wuthrich - 2011 - In Craig Callender (ed.), The Oxford Handbook of Philosophy of Time. Oxford University Press. pp. 577-630.
    This paper is an enquiry into the logical, metaphysical, and physical possibility of time travel understood in the sense of the existence of closed worldlines that can be traced out by physical objects. We argue that none of the purported paradoxes rule out time travel either on grounds of logic or metaphysics. More relevantly, modern spacetime theories such as general relativity seem to permit models that feature closed worldlines. We discuss, in the context of Gödel's infamous argument for the ideality (...)
    Download  
     
    Export citation  
     
    Bookmark   40 citations  
  41. Why machines do not understand: A response to Søgaard.Jobst Landgrebe & Barry Smith - 2023 - Archiv.
    Some defenders of so-called `artificial intelligence' believe that machines can understand language. In particular, Søgaard has argued in his "Understanding models understanding language" (2022) for a thesis of this sort. His idea is that (1) where there is semantics there is also understanding and (2) machines are not only capable of what he calls `inferential semantics', but even that they can (with the help of inputs from sensors) `learn' referential semantics. We show that he goes wrong because he pays insufficient (...)
    Download  
     
    Export citation  
     
    Bookmark  
  42. On machine vision and photographic imagination.Daniel Chávez Heras & Tobias Blanke - 2021 - AI and Society 36:1153–1165.
    In this article we introduce the concept of implied optical perspective in deep learning computer vision systems. Taking the BBC's experimental television programme “Made by Machine: When AI met the Archive” as a case study, we trace a conceptual and material link between the system used to automatically “watch” the television archive and a specific type of photographic practice. From a computational aesthetics perspective, we show how deep learning machine vision relies on photography, its technical regimes and epistemic (...)
    Download  
     
    Export citation  
     
    Bookmark   1 citation  
  43. The mismeasure of machine: Synthetic biology and the trouble with engineering metaphors.Maarten Boudry & Massimo Pigliucci - 2013 - Studies in History and Philosophy of Science Part C: Studies in History and Philosophy of Biological and Biomedical Sciences 44 (4):660-668.
    The scientific study of living organisms is permeated by machine and design metaphors. Genes are thought of as the ‘‘blueprint’’ of an organism, organisms are ‘‘reverse engineered’’ to discover their functionality, and living cells are compared to biochemical factories, complete with assembly lines, transport systems, messenger circuits, etc. Although the notion of design is indispensable to think about adaptations, and engineering analogies have considerable heuristic value (e.g., optimality assumptions), we argue they are limited in several important respects. In particular, (...)
    Download  
     
    Export citation  
     
    Bookmark   18 citations  
  44. Regard time machines.Davide Peressoni - unknown
    Theorem (Will time machines be build?). The probability that in future a time machine that can travel to the past would be build is very low.
    Download  
     
    Export citation  
     
    Bookmark  
  45. Semiotic Machine.Mihai Nadin - unknown
    Download  
     
    Export citation  
     
    Bookmark   8 citations  
  46.  77
    (1 other version)The Machine of the Future (La máquina de futuro): A film and an expanded cinema assemblage.Byron Davies & Bruno Varela - 2024 - Umbrales 3:46-50.
    English version of collaborative text with Bruno Varela on his film La máquina de futuro (2024), published in the catalog of Umbrales experimental film section of the FICUNAM film festival.
    Download  
     
    Export citation  
     
    Bookmark   1 citation  
  47. Machine learning in scientific grant review: algorithmically predicting project efficiency in high energy physics.Vlasta Sikimić & Sandro Radovanović - 2022 - European Journal for Philosophy of Science 12 (3):1-21.
    As more objections have been raised against grant peer-review for being costly and time-consuming, the legitimate question arises whether machine learning algorithms could help assess the epistemic efficiency of the proposed projects. As a case study, we investigated whether project efficiency in high energy physics can be algorithmically predicted based on the data from the proposal. To analyze the potential of algorithmic prediction in HEP, we conducted a study on data about the structure and outcomes of HEP experiments with (...)
    Download  
     
    Export citation  
     
    Bookmark   2 citations  
  48. Reliability in Machine Learning.Thomas Grote, Konstantin Genin & Emily Sullivan - 2024 - Philosophy Compass 19 (5):e12974.
    Issues of reliability are claiming center-stage in the epistemology of machine learning. This paper unifies different branches in the literature and points to promising research directions, whilst also providing an accessible introduction to key concepts in statistics and machine learning – as far as they are concerned with reliability.
    Download  
     
    Export citation  
     
    Bookmark   1 citation  
  49. Machine Learning and Job Posting Classification: A Comparative Study.Ibrahim M. Nasser & Amjad H. Alzaanin - 2020 - International Journal of Engineering and Information Systems (IJEAIS) 4 (9):06-14.
    In this paper, we investigated multiple machine learning classifiers which are, Multinomial Naive Bayes, Support Vector Machine, Decision Tree, K Nearest Neighbors, and Random Forest in a text classification problem. The data we used contains real and fake job posts. We cleaned and pre-processed our data, then we applied TF-IDF for feature extraction. After we implemented the classifiers, we trained and evaluated them. Evaluation metrics used are precision, recall, f-measure, and accuracy. For each classifier, results were summarized and (...)
    Download  
     
    Export citation  
     
    Bookmark  
  50. Fair machine learning under partial compliance.Jessica Dai, Sina Fazelpour & Zachary Lipton - 2021 - In Jessica Dai, Sina Fazelpour & Zachary Lipton (eds.), Proceedings of the 2021 AAAI/ACM Conference on AI, Ethics, and Society. pp. 55–65.
    Typically, fair machine learning research focuses on a single decision maker and assumes that the underlying population is stationary. However, many of the critical domains motivating this work are characterized by competitive marketplaces with many decision makers. Realistically, we might expect only a subset of them to adopt any non-compulsory fairness-conscious policy, a situation that political philosophers call partial compliance. This possibility raises important questions: how does partial compliance and the consequent strategic behavior of decision subjects affect the allocation (...)
    Download  
     
    Export citation  
     
    Bookmark   1 citation  
1 — 50 / 958