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  1. The Exploratory Status of Postconnectionist Models.Miljana Milojevic & Vanja Subotić - 2020 - Theoria: Beograd 2 (63):135-164.
    This paper aims to offer a new view of the role of connectionist models in the study of human cognition through the conceptualization of the history of connectionism – from the simplest perceptrons to convolutional neural nets based on deep learning techniques, as well as through the interpretation of criticism coming from symbolic cognitive science. Namely, the connectionist approach in cognitive science was the target of sharp criticism from the symbolists, which on several occasions caused its marginalization and almost complete (...)
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  • Deep learning and cognitive science.Pietro Perconti & Alessio Plebe - 2020 - Cognition 203:104365.
    In recent years, the family of algorithms collected under the term ``deep learning'' has revolutionized artificial intelligence, enabling machines to reach human-like performances in many complex cognitive tasks. Although deep learning models are grounded in the connectionist paradigm, their recent advances were basically developed with engineering goals in mind. Despite of their applied focus, deep learning models eventually seem fruitful for cognitive purposes. This can be thought as a kind of biological exaptation, where a physiological structure becomes applicable for a (...)
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  • Seven properties of self-organization in the human brain.Birgitta Dresp-Langley - 2020 - Big Data and Cognitive Computing 2 (4):10.
    The principle of self-organization has acquired a fundamental significance in the newly emerging field of computational philosophy. Self-organizing systems have been described in various domains in science and philosophy including physics, neuroscience, biology and medicine, ecology, and sociology. While system architecture and their general purpose may depend on domain-specific concepts and definitions, there are (at least) seven key properties of self-organization clearly identified in brain systems: 1) modular connectivity, 2) unsupervised learning, 3) adaptive ability, 4) functional resiliency, 5) functional plasticity, (...)
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  • Transparency in Complex Computational Systems.Kathleen A. Creel - 2020 - Philosophy of Science 87 (4):568-589.
    Scientists depend on complex computational systems that are often ineliminably opaque, to the detriment of our ability to give scientific explanations and detect artifacts. Some philosophers have s...
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  • The Rhetoric and Reality of Anthropomorphism in Artificial Intelligence.David Watson - 2019 - Minds and Machines 29 (3):417-440.
    Artificial intelligence has historically been conceptualized in anthropomorphic terms. Some algorithms deploy biomimetic designs in a deliberate attempt to effect a sort of digital isomorphism of the human brain. Others leverage more general learning strategies that happen to coincide with popular theories of cognitive science and social epistemology. In this paper, I challenge the anthropomorphic credentials of the neural network algorithm, whose similarities to human cognition I argue are vastly overstated and narrowly construed. I submit that three alternative supervised learning (...)
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  • The Fallacy of the Homuncular Fallacy.Carrie Figdor - 2018 - Belgrade Philosophical Annual 31 (31):41-56.
    A leading theoretical framework for naturalistic explanation of mind holds that we explain the mind by positing progressively "stupider" capacities ("homunculi") until the mind is "discharged" by means of capacities that are not intelligent at all. The so-called homuncular fallacy involves violating this procedure by positing the same capacities at subpersonal levels. I argue that the homuncular fallacy is not a fallacy, and that modern-day homunculi are idle posits. I propose an alternative view of what naturalism requires that reflects how (...)
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  • Objections to Computationalism: A Survey.Marcin Miłkowski - 2018 - Roczniki Filozoficzne 66 (3):57-75.
    In this paper, the Author reviewed the typical objections against the claim that brains are computers, or, to be more precise, information-processing mechanisms. By showing that practically all the popular objections are based on uncharitable interpretations of the claim, he argues that the claim is likely to be true, relevant to contemporary cognitive science, and non-trivial.
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  • Automated patent landscaping.Aaron Abood & Dave Feltenberger - 2018 - Artificial Intelligence and Law 26 (2):103-125.
    Patent landscaping is the process of finding patents related to a particular topic. It is important for companies, investors, governments, and academics seeking to gauge innovation and assess risk. However, there is no broadly recognized best approach to landscaping. Frequently, patent landscaping is a bespoke human-driven process that relies heavily on complex queries over bibliographic patent databases. In this paper, we present Automated Patent Landscaping, an approach that jointly leverages human domain expertise, heuristics based on patent metadata, and machine learning (...)
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  • 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.
    Recent progress in artificial intelligence has renewed interest in building systems that learn and think like people. Many advances have come from using deep neural networks trained end-to-end in tasks such as object recognition, video games, and board games, achieving performance that equals or even beats that of humans in some respects. Despite their biological inspiration and performance achievements, these systems differ from human intelligence in crucial ways. We review progress in cognitive science suggesting that truly human-like learning and thinking (...)
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  • Why think that the brain is not a computer?Marcin Miłkowski - 2016 - APA Newsletter on Philosophy and Computers 16 (2):22-28.
    In this paper, I review the objections against the claim that brains are computers, or, to be precise, information-processing mechanisms. By showing that practically all the popular objections are either based on uncharitable interpretation of the claim, or simply wrong, I argue that the claim is likely to be true, relevant to contemporary cognitive (neuro)science, and non-trivial.
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  • (1 other version)Three Generations of Complexity Theories: Nuances and ambiguities.Michel Alhadeff-Jones - 2008 - Educational Philosophy and Theory 40 (1):66-82.
    The contemporary use of the term ‘complexity’ frequently indicates that it is considered a unified concept. This may lead to a neglect of the range of different theories that deal with the implications related to the notion of complexity. This paper, integrating both the English and the Latin traditions of research associated with this notion, suggests a more nuanced use of the term, thereby avoiding simplification of the concept to some of its dominant expressions only. The paper further explores the (...)
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  • (2 other versions)The Rise of Cognitive Science in the 20th Century.Carrie Figdor - 2017 - In Amy Kind (ed.), Philosophy of Mind in the Twentieth and Twenty-First Centuries: The History of the Philosophy of Mind, Volume 6. New York: Routledge. pp. 280-302.
    This chapter describes the conceptual foundations of cognitive science during its establishment as a science in the 20th century. It is organized around the core ideas of individual agency as its basic explanans and information-processing as its basic explanandum. The latter consists of a package of ideas that provide a mathematico-engineering framework for the philosophical theory of materialism.
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  • A cerebellar long-term depression update.David J. Linden - 1996 - Behavioral and Brain Sciences 19 (3):482-487.
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  • Cerebellar rhythms: Exploring another metaphor.Patrick D. Roberts, Gin McCollum & Jan E. Holly - 1996 - Behavioral and Brain Sciences 19 (3):471-472.
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  • No more news from the cerebellum.Steven R. Vincent - 1996 - Behavioral and Brain Sciences 19 (3):490-492.
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  • Q: Is the cerebellum an adaptive combiner of motor and mental/motor activities? A: Yes, maybe, certainly not, who can say?W. Thomas Thach - 1996 - Behavioral and Brain Sciences 19 (3):501-528.
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  • Smolensky's theory of mind.Paul F. M. J. Verschure - 1990 - Behavioral and Brain Sciences 13 (2):407-407.
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  • Value, variable, and coarse coding by posterior parietal neurons.Richard A. Andersen - 1986 - Behavioral and Brain Sciences 9 (1):90-91.
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  • What's the connection?Leif H. Finkel & George N. Reeke - 1986 - Behavioral and Brain Sciences 9 (1):94-95.
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  • Direct Associations or Internal Transformations? Exploring the Mechanisms Underlying Sequential Learning Behavior.Todd M. Gureckis & Bradley C. Love - 2010 - Cognitive Science 34 (1):10-50.
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  • More on climbing fiber signals and their consequence(s).J. I. Simpson, D. R. W. Wylie & C. I. De Zeeuw - 1996 - Behavioral and Brain Sciences 19 (3):496-498.
    Several themes can be identified in the commentaries. The first is that the climbing fibers may have more than one function; the second is that the climbing fibers provide sensory rather than motor signals. We accept the possibility that climbing fibers may have more than one function consequence(s)’ in the title. Until we know more about the function of the inhibitory input to the inferior olive from the cerebellar nuclei, which are motor structures, we have to keep open the possibility (...)
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  • Resilient cerebellar theory complies with stiff opposition.Allan M. Smith - 1996 - Behavioral and Brain Sciences 19 (3):499-501.
    In response to several requests from commentators, an unambiguous definition of time-varying joint stiffness is provided. However, since a variety of different operations can be used to measure stiffness, a problem for quantification admittedly still exists. Several commentaries pointed out the advantage of controlling joint stiffness in optimizing the speed-accuracy trade-off known as Fittss law. The deficit in rapid reciprocal movements and the impact on joint stiffness inhibition caused by cerebellar lesions is clarified here, as the target article was apparently (...)
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  • Nitric oxide is involved in cerebellar long-term depression.Daisuke Okada - 1996 - Behavioral and Brain Sciences 19 (3):468-469.
    The involvement of nitric oxide in cerebellar long-term depression is supported by the observation that nitric oxide is released by climbing fiber stimulation and by pharmacological tool usage. Two forms of long-term depression should be distinguished by their physiological relevance. [CRÉPEL et al.; LINDEN; VINCENT].
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  • Computational psychiatry.P. Read Montague, Raymond J. Dolan, Karl J. Friston & Peter Dayan - 2012 - Trends in Cognitive Sciences 16 (1):72-80.
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  • Computational Modeling in Cognitive Science: A Manifesto for Change.Caspar Addyman & Robert M. French - 2012 - Topics in Cognitive Science 4 (3):332-341.
    Computational modeling has long been one of the traditional pillars of cognitive science. Unfortunately, the computer models of cognition being developed today have not kept up with the enormous changes that have taken place in computer technology and, especially, in human-computer interfaces. For all intents and purposes, modeling is still done today as it was 25, or even 35, years ago. Everyone still programs in his or her own favorite programming language, source code is rarely made available, accessibility of models (...)
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  • Information processing, computation, and cognition.Gualtiero Piccinini & Andrea Scarantino - 2011 - Journal of Biological Physics 37 (1):1-38.
    Computation and information processing are among the most fundamental notions in cognitive science. They are also among the most imprecisely discussed. Many cognitive scientists take it for granted that cognition involves computation, information processing, or both – although others disagree vehemently. Yet different cognitive scientists use ‘computation’ and ‘information processing’ to mean different things, sometimes without realizing that they do. In addition, computation and information processing are surrounded by several myths; first and foremost, that they are the same thing. In (...)
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  • Enaction-based artificial intelligence: Toward co-evolution with humans in the loop. [REVIEW]Pierre De Loor, Kristen Manac’H. & Jacques Tisseau - 2009 - Minds and Machines 19 (3):319-343.
    This article deals with the links between the enaction paradigm and artificial intelligence. Enaction is considered a metaphor for artificial intelligence, as a number of the notions which it deals with are deemed incompatible with the phenomenal field of the virtual. After explaining this stance, we shall review previous works regarding this issue in terms of artificial life and robotics. We shall focus on the lack of recognition of co-evolution at the heart of these approaches. We propose to explicitly integrate (...)
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  • From pixels to insights: Machine learning and deep learning for bioimage analysis.Mahta Jan, Allie Spangaro, Michelle Lenartowicz & Mojca Mattiazzi Usaj - 2024 - Bioessays 46 (2):2300114.
    Bioimage analysis plays a critical role in extracting information from biological images, enabling deeper insights into cellular structures and processes. The integration of machine learning and deep learning techniques has revolutionized the field, enabling the automated, reproducible, and accurate analysis of biological images. Here, we provide an overview of the history and principles of machine learning and deep learning in the context of bioimage analysis. We discuss the essential steps of the bioimage analysis workflow, emphasizing how machine learning and deep (...)
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  • 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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  • An Alternative to Cognitivism: Computational Phenomenology for Deep Learning.Pierre Beckmann, Guillaume Köstner & Inês Hipólito - 2023 - Minds and Machines 33 (3):397-427.
    We propose a non-representationalist framework for deep learning relying on a novel method computational phenomenology, a dialogue between the first-person perspective (relying on phenomenology) and the mechanisms of computational models. We thereby propose an alternative to the modern cognitivist interpretation of deep learning, according to which artificial neural networks encode representations of external entities. This interpretation mainly relies on neuro-representationalism, a position that combines a strong ontological commitment towards scientific theoretical entities and the idea that the brain operates on symbolic (...)
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  • Philosophers Ought to Develop, Theorize About, and Use Philosophically Relevant AI.Graham Clay & Caleb Ontiveros - 2023 - Metaphilosophy 54 (4):463-479.
    The transformative power of artificial intelligence (AI) is coming to philosophy—the only question is the degree to which philosophers will harness it. In this paper, we argue that the application of AI tools to philosophy could have an impact on the field comparable to the advent of writing, and that it is likely that philosophical progress will significantly increase as a consequence of AI. The role of philosophers in this story is not merely to use AI but also to help (...)
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  • The Future Will Not Be Calculated: Neural Nets, Neoliberalism, and Reactionary Politics.Orit Halpern - 2022 - Critical Inquiry 48 (2):334-359.
    This article traces the relationship between neoliberal thought and neural networks through the work of Friedrich Hayek, Donald O. Hebb, and Frank Rosenblatt. For all three, networked systems could accomplish acts of evolution, change, and learning impossible for individual neurons or subjects—minds, machines, and economies could therefore all autonomously evolve and adapt without government. These three figures, I argue, were also symptoms of a broader reconceptualization of reason, decision making, and “freedom” in relation to the state and technology that occurred (...)
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  • Autonomous Systems and the Place of Biology Among Sciences. Perspectives for an Epistemology of Complex Systems.Leonardo Bich - 2021 - In Gianfranco Minati (ed.), Multiplicity and Interdisciplinarity. Essays in Honor of Eliano Pessa. Springer. pp. 41-57.
    This paper discusses the epistemic status of biology from the standpoint of the systemic approach to living systems based on the notion of biological autonomy. This approach aims to provide an understanding of the distinctive character of biological systems and this paper analyses its theoretical and epistemological dimensions. The paper argues that, considered from this perspective, biological systems are examples of emergent phenomena, that the biological domain exhibits special features with respect to other domains, and that biology as a discipline (...)
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  • Book: Cognitive Design for Artificial Minds.Antonio Lieto - 2021 - London, UK: Routledge, Taylor & Francis Ltd.
    Book Description (Blurb): Cognitive Design for Artificial Minds explains the crucial role that human cognition research plays in the design and realization of artificial intelligence systems, illustrating the steps necessary for the design of artificial models of cognition. It bridges the gap between the theoretical, experimental and technological issues addressed in the context of AI of cognitive inspiration and computational cognitive science. -/- Beginning with an overview of the historical, methodological and technical issues in the field of Cognitively-Inspired Artificial Intelligence, (...)
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  • What Do Technology and Artificial Intelligence Mean Today?Scott H. Hawley & Elias Kruger - forthcoming - In Hector Fernandez (ed.), Sociedad Tecnológica y Futuro Humano, vol. 1: Desafíos conceptuales. pp. 17.
    Technology and Artificial Intelligence, both today and in the near future, are dominated by automated algorithms that combine optimization with models based on the human brain to learn, predict, and even influence the large-scale behavior of human users. Such applications can be understood to be outgrowths of historical trends in industry and academia, yet have far-reaching and even unintended consequences for social and political life around the world. Countries in different parts of the world take different regulatory views for the (...)
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  • (1 other version)Rethinking Causation for Data‐intensive Biology: Constraints, Cancellations, and Quantized Organisms.Douglas E. Brash - 2020 - Bioessays 42 (7):1900135.
    Complex organisms thwart the simple rectilinear causality paradigm of “necessary and sufficient,” with its experimental strategy of “knock down and overexpress.” This Essay organizes the eccentricities of biology into four categories that call for new mathematical approaches; recaps for the biologist the philosopher's recent refinements to the causation concept and the mathematician's computational tools that handle some but not all of the biological eccentricities; and describes overlooked insights that make causal properties of physical hierarchies such as emergence and downward causation (...)
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  • The Curious Case of Connectionism.Istvan S. N. Berkeley - 2019 - Open Philosophy 2 (1):190-205.
    Connectionist research first emerged in the 1940s. The first phase of connectionism attracted a certain amount of media attention, but scant philosophical interest. The phase came to an abrupt halt, due to the efforts of Minsky and Papert (1969), when they argued for the intrinsic limitations of the approach. In the mid-1980s connectionism saw a resurgence. This marked the beginning of the second phase of connectionist research. This phase did attract considerable philosophical attention. It was of philosophical interest, as it (...)
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  • A Cognitive Computation Fallacy? Cognition, Computations and Panpsychism.John Mark Bishop - 2009 - Cognitive Computation 1 (3):221-233.
    The journal of Cognitive Computation is defined in part by the notion that biologically inspired computational accounts are at the heart of cognitive processes in both natural and artificial systems. Many studies of various important aspects of cognition (memory, observational learning, decision making, reward prediction learning, attention control, etc.) have been made by modelling the various experimental results using ever-more sophisticated computer programs. In this manner progressive inroads have been made into gaining a better understanding of the many components of (...)
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  • Modeling language and cognition with deep unsupervised learning: a tutorial overview.Marco Zorzi, Alberto Testolin & Ivilin P. Stoianov - 2013 - Frontiers in Psychology 4.
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  • Parallel Distributed Processing at 25: Further Explorations in the Microstructure of Cognition.Timothy T. Rogers & James L. McClelland - 2014 - Cognitive Science 38 (6):1024-1077.
    This paper introduces a special issue of Cognitive Science initiated on the 25th anniversary of the publication of Parallel Distributed Processing (PDP), a two-volume work that introduced the use of neural network models as vehicles for understanding cognition. The collection surveys the core commitments of the PDP framework, the key issues the framework has addressed, and the debates the framework has spawned, and presents viewpoints on the current status of these issues. The articles focus on both historical roots and contemporary (...)
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  • Long-term changes of synaptic transmission: A topic of long-term interest.Paolo Calabresi, Antonio Pisani & Giorgio Bernardi - 1996 - Behavioral and Brain Sciences 19 (3):439-440.
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  • Connectionism: Self-abuse is improper treatment.Gregg C. Oden - 1990 - Behavioral and Brain Sciences 13 (2):402-402.
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  • Cortical architectures and value unit encoding.Charles D. Gilbert - 1986 - Behavioral and Brain Sciences 9 (1):96-97.
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  • Saccades and the adjustable pattern generator.Paul Dean - 1996 - Behavioral and Brain Sciences 19 (3):441-442.
    The adjustable pattern generator (APG) model addresses physiological detail in a manner that renders it eminently testable. However, the problem for which the APG was developed, namely, limb control, may be computationally too complex for this purpose. Instead, it is proposed that recent empirical and theoretical advances in understanding the role of the cerebellum in low-level saccadic control could be used to refine and extend the APG. [HOUK et al.].
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  • Limitations of PET and lesion studies in defining the role of the human cerebellum in motor learning.D. Timmann & H. C. Diener - 1996 - Behavioral and Brain Sciences 19 (3):477-477.
    PET studies using classical conditioning paradigms are reported. It is emphasized that PET studies show and not in learning paradigms. The importance of dissociating motor performance and learning deficits in human lesions studies is demonstrated in two exemplary studies. The different role of the cerebellum in adaptation of postural reflexes and learning of complex voluntary arm movements is discussed, [THACH].
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  • The b-I-c-a of biologically inspired cognitive architectures.Andrea Stocco, Christian Lebiere & Alexei V. Samsonovich - 2010 - International Journal of Machine Consciousness 2 (2):171-192.
    Recent years have seen a gradual convergence of seemingly distant research fields over a single goal: understanding and replicating biological intelligence in artifacts. This work presents a general overview on the origin, the state-of-the-art, scientific challenges and the future of Biologically Inspired Cognitive Architecture (BICA) research. Our perspective decomposes the field into the four principal semantic components associated with the BICA challenge that together call for an integration of efforts of researchers across disciplines. Areas and directions of study where new (...)
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  • On the Opacity of Deep Neural Networks.Anders Søgaard - forthcoming - Canadian Journal of Philosophy:1-16.
    Deep neural networks are said to be opaque, impeding the development of safe and trustworthy artificial intelligence, but where this opacity stems from is less clear. What are the sufficient properties for neural network opacity? Here, I discuss five common properties of deep neural networks and two different kinds of opacity. Which of these properties are sufficient for what type of opacity? I show how each kind of opacity stems from only one of these five properties, and then discuss to (...)
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  • 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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  • Picturing, signifying, and attending.Bryce Huebner - 2018 - Belgrade Philosophical Annual 1 (31):7-40.
    In this paper, I develop an empirically-driven approach to the relationship between conceptual and non-conceptual representations. I begin by clarifying Wilfrid Sellars's distinction between a non-conceptual capacity to picture significant aspects of our world, and a capacity to stabilize semantic content in the form of conceptual representations that signify those aspects of the world that are relevant to our shared practices. I argue that this distinction helps to clarify the reason why cognition must be understood as embodied and situated. Drawing (...)
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  • Opinion: Reproducibility failures are essential to scientific inquiry.A. David Redish, Erich Kummerfeld, Rebecca Morris & Alan Love - 2018 - Proceedings of the National Academy of Sciences 115 (20):5042-5046.
    Current fears of a “reproducibility crisis” have led researchers, sources of scientific funding, and the public to question both the efficacy and trustworthiness of science. Suggested policy changes have been focused on statistical problems, such as p-hacking, and issues of experimental design and execution. However, “reproducibility” is a broad concept that includes a number of issues. Furthermore, reproducibility failures occur even in fields such as mathematics or computer science that do not have statistical problems or issues with experimental design. Most (...)
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