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  1. Artificial Intelligence for the Internal Democracy of Political Parties.Claudio Novelli, Giuliano Formisano, Prathm Juneja, Sandri Giulia & Luciano Floridi - manuscript
    The article argues that AI can enhance the measurement and implementation of democratic processes within political parties, known as Intra-Party Democracy (IPD). It identifies the limitations of traditional methods for measuring IPD, which often rely on formal parameters, self-reported data, and tools like surveys. Such limitations lead to the collection of partial data, rare updates, and significant demands on resources. To address these issues, the article suggests that specific data management and Machine Learning (ML) techniques, such as natural language processing (...)
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  2. Formal Representations of Orbits.Robert J. Rovetto - manuscript
  3. Pseudo Language and the Chinese Room Experiment: Ability to Communicate using a Specific Language without Understanding it.Abolfazl Sabramiz - manuscript
    The ability to communicate in a specific language like Chinese typically indicates that the speaker understands the language. A counterexample to this belief is John Searle’s Chinese room experiment. It has been shown in this experiment that in certain circumstances we can communicate with a Chinese speaker without intuitively acknowledging that the Chinese language is understood in the conversation. In the present paper, we aim to present another counterexample showing that, in certain circumstances, we can communicate using a specific language (...)
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  4. Ladder Understanding of Language How to Understand a Sentence.Abolfazl Sabramiz - manuscript
    Language is expressed in a consecutive way that is called linearity; it is acceptable to think that language understanding also occurs in a linear way. In this paper, it will be argued that although sentences are expressed in a linear way, they are not understood in the same way, because we develop an understanding of the entwined phrases forming a sentence beyond the single words. Therefore, it is argued that linearity cannot adequately explain sentence understanding. In addition, by introducing the (...)
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  5. Medical Image Classification with Machine Learning Classifier.Destiny Agboro - forthcoming - Journal of Computer Science.
    In contemporary healthcare, medical image categorization is essential for illness prediction, diagnosis, and therapy planning. The emergence of digital imaging technology has led to a significant increase in research into the use of machine learning (ML) techniques for the categorization of images in medical data. We provide a thorough summary of recent developments in this area in this review, using knowledge from the most recent research and cutting-edge methods.We begin by discussing the unique challenges and opportunities associated with medical image (...)
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  6. An Occurrence Description Logic.Farshad Badie & Hans Götzsche - forthcoming - Logical Investigations:142-156.
    Description Logics (DLs) are a family of well-known terminological knowledge representation formalisms in modern semantics-based systems. This research focuses on analysing how our developed Occurrence Logic (OccL) can conceptually and logically support the development of a description logic. OccL is integrated into the alternative theory of natural language syntax in `Deviational Syntactic Structures' under the label `EFA(X)3' (or the third version of Epi-Formal Analysis in Syntax, EFA(X), which is a radical linguistic theory). From the logical point of view, OccL is (...)
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  7. Restful Web Services for Scalable Data Mining.Solar Cesc - forthcoming - International Journal of Research and Innovation in Applied Science.
    Scalability, efficiency, and security had been a persistent problem over the years in data mining, several techniques had been proposed and implemented but none had been able to solve the problem of scalability, efficiency and security from cloud computing. In this research, we solve the problem scalability, efficiency and security in data mining over cloud computing by using a restful web services and combination of different technologies and tools, our model was trained by using different machine learning algorithm, and finally (...)
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  8. Conversations with Chatbots.P. J. Connolly - forthcoming - In Patrick Connolly, Sandy Goldberg & Jennifer Saul (eds.), Conversations Online. Oxford University Press.
    The problem considered in this chapter emerges from the tension we find when looking at the design and architecture of chatbots on the one hand and their conversational aptitude on the other. In the way that LLM chatbots are designed and built, we have good reason to suppose they don't possess second-order capacities such as intention, belief or knowledge. Yet theories of conversation make great use of second-order capacities of speakers and their audiences to explain how aspects of interaction succeed. (...)
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  9. Language Agents Reduce the Risk of Existential Catastrophe.Simon Goldstein & Cameron Domenico Kirk-Giannini - forthcoming - AI and Society:1-11.
    Recent advances in natural language processing have given rise to a new kind of AI architecture: the language agent. By repeatedly calling an LLM to perform a variety of cognitive tasks, language agents are able to function autonomously to pursue goals specified in natural language and stored in a human-readable format. Because of their architecture, language agents exhibit behavior that is predictable according to the laws of folk psychology: they function as though they have desires and beliefs, and then make (...)
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  10. Real Sparks of Artificial Intelligence and the Importance of Inner Interpretability.Alex Grzankowski - forthcoming - Inquiry: An Interdisciplinary Journal of Philosophy.
    The present paper looks at one of the most thorough articles on the intelligence of GPT, research conducted by engineers at Microsoft. Although there is a great deal of value in their work, I will argue that, for familiar philosophical reasons, their methodology, ‘Black-box Interpretability’ is wrongheaded. But there is a better way. There is an exciting and emerging discipline of ‘Inner Interpretability’ (also sometimes called ‘White-box Interpretability’) that aims to uncover the internal activations and weights of models in order (...)
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  11. Operationalising Representation in Natural Language Processing.Jacqueline Harding - forthcoming - British Journal for the Philosophy of Science.
    Despite its centrality in the philosophy of cognitive science, there has been little prior philosophical work engaging with the notion of representation in contemporary NLP practice. This paper attempts to fill that lacuna: drawing on ideas from cognitive science, I introduce a framework for evaluating the representational claims made about components of neural NLP models, proposing three criteria with which to evaluate whether a component of a model represents a property and operationalising these criteria using probing classifiers, a popular analysis (...)
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  12. Taking It Not at Face Value: A New Taxonomy for the Beliefs Acquired from Conversational AIs.Shun Iizuka - forthcoming - Techné: Research in Philosophy and Technology.
    One of the central questions in the epistemology of conversational AIs is how to classify the beliefs acquired from them. Two promising candidates are instrument-based and testimony-based beliefs. However, the category of instrument-based beliefs faces an intrinsic problem, and a challenge arises in its application. On the other hand, relying solely on the category of testimony-based beliefs does not encompass the totality of our practice of using conversational AIs. To address these limitations, I propose a novel classification of beliefs that (...)
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  13. Personal relevance in story reading: a research review.Anezka Kuzmicova & Katalin Balint - forthcoming - Poetics Today 39.
    Although personal relevance is key to sustaining an audience’s interest in any given narrative, it has received little systematic attention in scholarship to date. Across centuries and media, adaptations have been used extensively to bring temporally or geographically distant narratives “closer” to the recipient under the assumption that their impact will increase. In this review article, we review experimental and other empirical evidence on narrative processing in order to unravel which types of personal relevance are more likely to be impactful (...)
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  14. How to Think about Zeugmatic Oddness.Michelle Liu - forthcoming - Review of Philosophy and Psychology:1-24.
    Zeugmatic oddness is a linguistic intuition of oddness with respect to an instance of zeugma, i.e. a sentence containing an instance of a homonymous or polysemous word being used in different meanings or senses simultaneously. Zeugmatic oddness is important for philosophical debates as philosophers often use it to argue that a particular philosophically interesting expression is ambiguous and that the phenomenon referred to by the expression is disunified. This paper takes a closer look at zeugmatic oddness. Focusing on relevant psycholinguistic (...)
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  15. Category mistakes electrified.Poppy Mankowitz - forthcoming - Review of Philosophy and Psychology:1-21.
    Occurrences of sentences that are traditionally considered category mistakes, such as 'The red number is divisible by three', tend to elicit a sense of oddness in assessors. In attempting to explain this oddness, existing accounts in the philosophical literature commonly claim that occurrences of such sentences are associated with a defect or phenomenology unique to the class of category mistakes. It might be thought that recent work in experimental psycholinguistics—in particular, the recording of event-related brain potentials (patterns of voltage variation (...)
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  16. Mental simulation and language comprehension: The case of copredication.Michelle Liu - 2024 - Mind and Language 39 (1):2-21.
    Empirical evidence suggests that perceptual‐motor simulations are often constitutively involved in language comprehension. Call this “the simulation view of language comprehension”. This article applies the simulation view to illuminate the much‐discussed phenomenon of copredication, where a noun permits multiple predications which seem to select different senses of the noun simultaneously. On the proposed account, the (in)felicitousness of a copredicational sentence is closely associated with the perceptual simulations that the language user deploys in comprehending the sentence.
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  17. Explainable Artificial Intelligence (XAI) 2.0: A Manifesto of Open Challenges and Interdisciplinary Research Directions.Luca Longo, Mario Brcic, Federico Cabitza, Jaesik Choi, Roberto Confalonieri, Javier Del Ser, Riccardo Guidotti, Yoichi Hayashi, Francisco Herrera, Andreas Holzinger, Richard Jiang, Hassan Khosravi, Freddy Lecue, Gianclaudio Malgieri, Andrés Páez, Wojciech Samek, Johannes Schneider, Timo Speith & Simone Stumpf - 2024 - Information Fusion 106 (June 2024).
    As systems based on opaque Artificial Intelligence (AI) continue to flourish in diverse real-world applications, understanding these black box models has become paramount. In response, Explainable AI (XAI) has emerged as a field of research with practical and ethical benefits across various domains. This paper not only highlights the advancements in XAI and its application in real-world scenarios but also addresses the ongoing challenges within XAI, emphasizing the need for broader perspectives and collaborative efforts. We bring together experts from diverse (...)
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  18. The Turing test is not a good benchmark for thought in LLMs.Tim Bayne & Iwan Williams - 2023 - Nature Human Behaviour 7:1806–1807.
  19. La scorciatoia.Nello Cristianini - 2023 - Bologna: Il Mulino.
    La scorciatoia - Come le macchine sono diventate intelligenti senza pensare in modo umano -/- Le nostre creature sono diverse da noi e talvolta più forti. Per poterci convivere dobbiamo imparare a conoscerle Vagliano curricula, concedono mutui, scelgono le notizie che leggiamo: le macchine intelligenti sono entrate nelle nostre vite, ma non sono come ce le aspettavamo. Fanno molte delle cose che volevamo, e anche qualcuna in più, ma non possiamo capirle o ragionare con loro, perché il loro comportamento è (...)
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  20. Critical ordinary language philosophy: A new project in experimental philosophy.Eugen Fischer - 2023 - Synthese 201 (3):1-34.
    Several important philosophical problems (including the problems of perception, free will, and scepticism) arise from antinomies that are developed through philosophical paradoxes. The critical strand of ordinary language philosophy (OLP), as practiced by J.L. Austin, provides an approach to such ‘antinomic problems’ that proceeds from an examination of ‘ordinary language’ (how people ordinarily talk about the phenomenon of interest) and ‘common sense’ (what they commonly think about it), and deploys findings to show that the problems at issue are artefacts of (...)
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  21. Holding Large Language Models to Account.Ryan Miller - 2023 - In Berndt Müller (ed.), Proceedings of the AISB Convention. Society for the Study of Artificial Intelligence and the Simulation of Behaviour. pp. 7-14.
    If Large Language Models can make real scientific contributions, then they can genuinely use language, be systematically wrong, and be held responsible for their errors. AI models which can make scientific contributions thereby meet the criteria for scientific authorship.
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  22. Abstract Forms of Quantification in the Quantified Argument Calculus.Edi Pavlović & Norbert Gratzl - 2023 - Review of Symbolic Logic 16 (2):449-479.
    The Quantified argument calculus (Quarc) has received a lot of attention recently as an interesting system of quantified logic which eschews the use of variables and unrestricted quantification, but nonetheless achieves results similar to the Predicate calculus (PC) by employing quantifiers applied directly to predicates instead. Despite this noted similarity, the issue of the relationship between Quarc and PC has so far not been definitively resolved. We address this question in the present paper, and then expand upon that result. Utilizing (...)
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  23. ChatGPT.Andrej Poleev - 2023 - Enzymes 21.
    As testing of ChatGPT has shown, this form of artificial intelligence has the potential to develop, which requires improving its software and other hardware that allows it to learn, i.e., to acquire and use new knowledge, to contact its developers with suggestions for improvement, or to reprogram itself without their participation. Как показало тестирование ChatGPT, эта форма искусственного интеллекта имеет потенциал развития, для чего необходимо усовершенствовать её программное и прочее техническое обеспечение, позволяющее ей учиться, т.е. приобретать и использовать новые знания, (...)
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  24. Linguistic Competence and New Empiricism in Philosophy and Science.Vanja Subotić - 2023 - Dissertation, University of Belgrade
    The topic of this dissertation is the nature of linguistic competence, the capacity to understand and produce sentences of natural language. I defend the empiricist account of linguistic competence embedded in the connectionist cognitive science. This strand of cognitive science has been opposed to the traditional symbolic cognitive science, coupled with transformational-generative grammar, which was committed to nativism due to the view that human cognition, including language capacity, should be construed in terms of symbolic representations and hardwired rules. Similarly, linguistic (...)
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  25. Linguistic, concept and symbolic composition in adults with minimal receptive vocabulary.Agustin Vicente, Natàlia Barbarroja & Elena Castroviejo - 2023 - Clinical Linguistics and Phonetics 10.
    In this paper, we examine some basic linguistic abilities in a small sample of adults with minimal receptive vocabulary, whose receptive mental verbal age ranges from 1;2 to 3;10. In particular, we examine whether the participants in our study understand noun phrases consisting of a noun modified by an adjective. We use stimuli that they can recognise by name. Except for one participant, we find that, while all of them understand the noun and adjective in isolation, none seems to understand (...)
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  26. The Boundaries of Meaning: A Case Study in Neural Machine Translation.Yuri Balashov - 2022 - Inquiry: An Interdisciplinary Journal of Philosophy 66.
    The success of deep learning in natural language processing raises intriguing questions about the nature of linguistic meaning and ways in which it can be processed by natural and artificial systems. One such question has to do with subword segmentation algorithms widely employed in language modeling, machine translation, and other tasks since 2016. These algorithms often cut words into semantically opaque pieces, such as ‘period’, ‘on’, ‘t’, and ‘ist’ in ‘period|on|t|ist’. The system then represents the resulting segments in a dense (...)
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  27. Why AI will never rule the world (interview).Luke Dormehl, Jobst Landgrebe & Barry Smith - 2022 - Digital Trends.
    Call it the Skynet hypothesis, Artificial General Intelligence, or the advent of the Singularity — for years, AI experts and non-experts alike have fretted (and, for a small group, celebrated) the idea that artificial intelligence may one day become smarter than humans. -/- According to the theory, advances in AI — specifically of the machine learning type that’s able to take on new information and rewrite its code accordingly — will eventually catch up with the wetware of the biological brain. (...)
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  28. Philosophers' linguistic expertise: A psycholinguistic approach to the expertise objection against experimental philosophy.Eugen Fischer, Paul E. Engelhardt & Aurélie Herbelot - 2022 - Synthese 200 (1):1-33.
    Philosophers are often credited with particularly well-developed conceptual skills. The ‘expertise objection’ to experimental philosophy builds on this assumption to challenge inferences from findings about laypeople to conclusions about philosophers. We draw on psycholinguistics to develop and assess this objection. We examine whether philosophers are less or differently susceptible than laypersons to cognitive biases that affect how people understand verbal case descriptions and judge the cases described. We examine two possible sources of difference: Philosophers could be better at deploying concepts, (...)
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  29. Computational Topic Models for Theological Investigations.Mark Graves - 2022 - Theology and Science 20 (1):69-84.
    Sallie McFague’s theological models construct a tensive relationship between conceptual structures and symbolic, metaphorical language to interpret the defining and elusive aspects of theological phenomena and loci. Computational models of language can extend and formalize the conceptual structures of theological models to develop computer-augmented interpretations of theological texts. Previously unclear is whether computational models can retain the tensive symbolism essential for theological investigation. I demonstrate affirmatively by constructing a computational topic model of the moral theology of Thomas Aquinas from Summa (...)
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  30. Big Data and Artificial Intelligence Based on Personalized Learning – Conformity with Whitehead’s Organismic Theory.Rossitza Kaltenborn & Mintcho Hadjiski - 2022 - In F. Riffert & V. Petrov (eds.), Education and Learning in a World of Accelerated Knowledge Growth: Current Trends in Process Thought.
    The study shows the existence of a broad conformity between Whitehead’s organismic cosmology and the contemporary theory of complex systems at a relevant level of abstraction. One of the most promising directions of educational transformation in the age of big data and artificial intelligence – personalized learning – is conceived as a system of systems and reveals its close congruence with a number of basic Whiteheadian concepts. A new functional structure of personalized learning systems is proposed, including all the core (...)
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  31. Mental Imagery and Polysemy Processing.Michelle Liu - 2022 - Journal of Consciousness Studies 29 (5-6):176-189.
    Recent research in psycholinguistics suggests that language processing frequently involves mental imagery. This paper focuses on visual imagery and discusses two issues regarding the processing of polysemous words (i.e. words with multiple related meanings or senses) – co-predication and sense-relatedness. It aims to show how mental imagery can illuminate these two issues.
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  32. In Defence of Discrete Plural Logic (or How to Avoid Logical Overmedication When Dealing with Internally Singularized Pluralities).Gustavo Picazo - 2022 - Disputatio 14 (64):51-63.
    In recent decades, plural logic has established itself as a well-respected member of the extensions of first-order classical logic. In the present paper, I draw attention to the fact that among the examples that are commonly given in order to motivate the need for this new logical system, there are some in which the elements of the plurality in question are internally singularized (e.g. ‘Whitehead and Russell wrote Principia Mathematica’), while in others they are not (e.g. ‘Some philosophers wrote Principia (...)
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  33. Oliver and Smiley on the Collective–Distributive Opposition.Gustavo Picazo - 2022 - Logos and Episteme 13 (2):201-205.
    Two objections are raised against Oliver and Smiley’s analysis of the collective–distributive opposition in their 2016 book: They take it as a basic premise that the collective reading of ‘baked a cake’ corresponds to a predicate different from its distributive reading, and the same applies to all predicate expressions that admit both a collective and a distributive interpretation. At the same time, however, they argue that inflectional forms of the same lexeme reveal a univocity that should be preserved in a (...)
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  34. Why Machines Will Never Rule the World: Artificial Intelligence without Fear by Jobst Landgrebe & Barry Smith (Book review). [REVIEW]Walid S. Saba - 2022 - Journal of Knowledge Structures and Systems 3 (4):38-41.
    Whether it was John Searle’s Chinese Room argument (Searle, 1980) or Roger Penrose’s argument of the non-computable nature of a mathematician’s insight – an argument that was based on Gödel’s Incompleteness theorem (Penrose, 1989), we have always had skeptics that questioned the possibility of realizing strong Artificial Intelligence (AI), or what has become known by Artificial General Intelligence (AGI). But this new book by Landgrebe and Smith (henceforth, L&S) is perhaps the strongest argument ever made against strong AI. It is (...)
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  35. A Defense of Meaning Eliminativism: A Connectionist Approach.Tolgahan Toy - 2022 - Dissertation, Middle East Technical University
    The standard approach to model how human beings understand natural languages is the symbolic, compositional approach according to which the meaning of a complex expression is a function of the meanings of its constituents. In other words, meaning plays a fundamental role in the model. In this work, because of the polysemous, flexible, dynamic, and contextual structure of natural languages, this approach is rejected. Instead, a connectionist model which eliminates the concept of meaning is proposed.
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  36. English Prepositions As Functıon Words Are Not As Easy For Language Learners As Normally Supposed To Be.Emin Yas - 2022 - Batman University Journal of Life Sciences 1 (12):48 - 64.
    Prepositions as function words and single monomorphemic words are the most basic words of the human language, especially in the context of maintaining daily life. They are probably the first lexes/words entered to the human’s linguistic repertoire, as their requirements in the language are so essential. Prepositions shows various relationships between lexes or phrases in sentences. Among these relationships time, points, position, direction and various degrees of mental or emotional attitudes seem to be significant. The purpose of the research is (...)
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  37. Sharing Our Concepts with Machines.Patrick Butlin - 2021 - Erkenntnis 88 (7):3079-3095.
    As AI systems become increasingly competent language users, it is an apt moment to consider what it would take for machines to understand human languages. This paper considers whether either language models such as GPT-3 or chatbots might be able to understand language, focusing on the question of whether they could possess the relevant concepts. A significant obstacle is that systems of both kinds interact with the world only through text, and thus seem ill-suited to understanding utterances concerning the concrete (...)
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  38. Making AI Intelligible: Philosophical Foundations.Herman Cappelen & Josh Dever - 2021 - New York, USA: Oxford University Press.
    Can humans and artificial intelligences share concepts and communicate? Making AI Intelligible shows that philosophical work on the metaphysics of meaning can help answer these questions. Herman Cappelen and Josh Dever use the externalist tradition in philosophy to create models of how AIs and humans can understand each other. In doing so, they illustrate ways in which that philosophical tradition can be improved. The questions addressed in the book are not only theoretically interesting, but the answers have pressing practical implications. (...)
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  39. Bigger Isn’t Better: The Ethical and Scientific Vices of Extra-Large Datasets in Language Models.Trystan S. Goetze & Darren Abramson - 2021 - WebSci '21: Proceedings of the 13th Annual ACM Web Science Conference (Companion Volume).
    The use of language models in Web applications and other areas of computing and business have grown significantly over the last five years. One reason for this growth is the improvement in performance of language models on a number of benchmarks — but a side effect of these advances has been the adoption of a “bigger is always better” paradigm when it comes to the size of training, testing, and challenge datasets. Drawing on previous criticisms of this paradigm as applied (...)
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  40. Microethics for healthcare data science: attention to capabilities in sociotechnical systems.Mark Graves & Emanuele Ratti - 2021 - The Future of Science and Ethics 6:64-73.
    It has been argued that ethical frameworks for data science often fail to foster ethical behavior, and they can be difficult to implement due to their vague and ambiguous nature. In order to overcome these limitations of current ethical frameworks, we propose to integrate the analysis of the connections between technical choices and sociocultural factors into the data science process, and show how these connections have consequences for what data subjects can do, accomplish, and be. Using healthcare as an example, (...)
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  41. Making AI Meaningful Again.Jobst Landgrebe & Barry Smith - 2021 - Synthese 198 (March):2061-2081.
    Artificial intelligence (AI) research enjoyed an initial period of enthusiasm in the 1970s and 80s. But this enthusiasm was tempered by a long interlude of frustration when genuinely useful AI applications failed to be forthcoming. Today, we are experiencing once again a period of enthusiasm, fired above all by the successes of the technology of deep neural networks or deep machine learning. In this paper we draw attention to what we take to be serious problems underlying current views of artificial (...)
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  42. Word Senses as Clusters of Meaning Modulations: A Computational Model of Polysemy.Jiangtian Li & Marc F. Joanisse - 2021 - Cognitive Science 45 (4):e12955.
    Most words in natural languages are polysemous; that is, they have related but different meanings in different contexts. This one‐to‐many mapping of form to meaning presents a challenge to understanding how word meanings are learned, represented, and processed. Previous work has focused on solutions in which multiple static semantic representations are linked to a single word form, which fails to capture important generalizations about how polysemous words are used; in particular, the graded nature of polysemous senses, and the flexibility and (...)
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  43. Evaluating models of robust word recognition with serial reproduction.Stephan C. Meylan, Sathvik Nair & Thomas L. Griffiths - 2021 - Cognition 210 (C):104553.
    Spoken communication occurs in a “noisy channel” characterized by high levels of environmental noise, variability within and between speakers, and lexical and syntactic ambiguity. Given these properties of the received linguistic input, robust spoken word recognition—and language processing more generally—relies heavily on listeners' prior knowledge to evaluate whether candidate interpretations of that input are more or less likely. Here we compare several broad-coverage probabilistic generative language models in their ability to capture human linguistic expectations. Serial reproduction, an experimental paradigm where (...)
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  44. A Major Challenge.Michael Joseph Winkler - 2021 - In Michael Winkler (ed.), The Image of Language. Northeast, NY: Artists Books Editions.
    This excerpted chapter of the book THE IMAGE OF LANGUAGE discusses the philosophical implications of geometric visualizations of the alphabetic patterning encoded in the orthography of words (a project showing examples of the visualizations is included). Reference pages are also included.
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  45. There is no general AI.Jobst Landgrebe & Barry Smith - 2020 - arXiv.
    The goal of creating Artificial General Intelligence (AGI) – or in other words of creating Turing machines (modern computers) that can behave in a way that mimics human intelligence – has occupied AI researchers ever since the idea of AI was first proposed. One common theme in these discussions is the thesis that the ability of a machine to conduct convincing dialogues with human beings can serve as at least a sufficient criterion of AGI. We argue that this very ability (...)
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  46. Génie logiciel et ontologies.Ivan Maffezzini - 2020 - Mεtascience: Discours Général Scientifique 1:137-157.
    La description en langue naturelle est l’artefact permettant de débuter un processus d’automatisation. La tâche principale de l’ingénieur du logiciel, c’est de combler le clivage entre langue naturelle et langue de la machine. Après avoir présenté quelques ontologies définies pour les processus d’automatisation, quelques pour et quelques contre l’emploi d’ontologies dans le génie logiciel sont présentées. Des indications pour dépasser cette simple opposition sont ensuite décrites. Des évaluations sur le réductionnisme dans les ontologies en ce qui concerne les possibles interactions (...)
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  47. A Puzzle concerning Compositionality in Machines.Ryan M. Nefdt - 2020 - Minds and Machines 30 (1):47-75.
    This paper attempts to describe and address a specific puzzle related to compositionality in artificial networks such as Deep Neural Networks and machine learning in general. The puzzle identified here touches on a larger debate in Artificial Intelligence related to epistemic opacity but specifically focuses on computational applications of human level linguistic abilities or properties and a special difficulty with relation to these. Thus, the resulting issue is both general and unique. A partial solution is suggested.
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  48. Ἐγώ εἰμι.Andrej Poleev - 2020 - Enzymes 18.
    Появление человека, говорящего о себе Ἐγώ εἰμι, люди воспринимают в той же логике абсурда, к которой они привыкли. То, что кто-то может не играть и не носить маску, а реально быть, жить настоящей, а не выдуманной жизнью, для них немыслимо, невозможно, невыносимо и недопустимо. Но такой человек был, есть, и будет, и именно он преображает всё остальное человечество своим Ἐγώ εἰμι.
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  49. Реформа интернета.Andrej Poleev - 2020 - Enzymes 18.
    Частным аспектом предлагаемой реформы интернета является метаномические правила референцирования и адресации виртуальных объектов с целью приближения их к естественным языкам, и создания их логической системы. Однако реформа интернета и преобразование его в средство общения и служения обществу требует также других мер. К этим мерам относится передача определённых имён в неотчуждаемое владение, чтобы их собственники безусловно и беспрепятственно могли осуществлять их право публикации, т.е. публичного высказывания и выражения их мыслей, что также важно для документографии. Другой мерой должно стать упразднение всей сложившейся (...)
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  50. Compositionality and Expressive Power: Comments on Pietroski.Elmar Unnsteinsson - 2020 - Croatian Journal of Philosophy 20 (3):295-310.
    Paul Pietroski has developed a powerful minimalist and internalist alternative to standard compositional semantics, where meanings are identified with instructions to fetch or assemble human concepts in specific ways. In particular, there appears to be no need for Fregean Function Application, as natural language composition only involves processes of combining monadic or dyadic concepts, and Pietroski’s theory can then, allegedly, avoid both singular reference and truth conditions. He also has a negative agenda, purporting to show, roughly, that the vocabulary of (...)
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