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  1. From Implausible Artificial Neurons to Idealized Cognitive Models: Rebooting Philosophy of Artificial Intelligence.Catherine Stinson - 2020 - Philosophy of Science 87 (4):590-611.
    There is a vast literature within philosophy of mind that focuses on artificial intelligence but hardly mentions methodological questions. There is also a growing body of work in philosophy of scie...
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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 State Space of Artificial Intelligence.Holger Lyre - forthcoming - Minds and Machines:1-23.
    The goal of the paper is to develop and propose a general model of the state space of AI. Given the breathtaking progress in AI research and technologies in recent years, such conceptual work is of substantial theoretical interest. The present AI hype is mainly driven by the triumph of deep learning neural networks. As the distinguishing feature of such networks is the ability to self-learn, self-learning is identified as one important dimension of the AI state space. Another dimension is (...)
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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, 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 offered (...)
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  • How Abstract (Non-Embodied) Linguistic Representations Augment Cognitive Control.Nikola A. Kompa & Jutta L. Mueller - 2020 - Frontiers in Psychology 11.
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  • Throwing light on black boxes: emergence of visual categories from deep learning.Ezequiel López-Rubio - forthcoming - Synthese:1-21.
    One of the best known arguments against the connectionist approach to artificial intelligence and cognitive science is that neural networks are black boxes, i.e., there is no understandable account of their operation. This difficulty has impeded efforts to explain how categories arise from raw sensory data. Moreover, it has complicated investigation about the role of symbols and language in cognition. This state of things has been radically changed by recent experimental findings in artificial deep learning research. Two kinds of artificial (...)
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  • Fodor on Imagistic Mental Representations.Daniel C. Burnston - 2020 - Rivista Internazionale di Filosofia e Psicologia 11 (1):71-94.
    : Fodor’s view of the mind is thoroughly computational. This means that the basic kind of mental entity is a “discursive” mental representation and operations over this kind of mental representation have broad architectural scope, extending out to the edges of perception and the motor system. However, in multiple epochs of his work, Fodor attempted to define a functional role for non-discursive, imagistic representation. I describe and critique his two considered proposals. The first view says that images play a particular (...)
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  • Exploring Minds: Modes of Modelling and Simulation in Artificial Intelligence.Hajo Greif - forthcoming - Perspectives on Science.
    The aim of this paper is to grasp the relevant distinctions between various ways in which models and simulations in Artificial Intelligence (AI) relate to cognitive phenomena. In order to get a systematic picture, a taxonomy is developed that is based on the coordinates of formal versus material analogies and theory-guided versus pre-theoretic models in science. These distinctions have parallels in the computational versus mimetic aspects and in analytic versus exploratory types of computer simulation. This taxonomy cuts across the traditional (...)
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  • Experience-Based Intuitions.Tiffany Zhu - unknown
    In this thesis, I argue that many identification intuitions, such as one that helps you identify the authorship of a painting you are seeing for the first time, fall under the class of experience-based intuitions. Such identification intuitions cannot arise without intuition generating systems that are shaped by experiences accumulated during one’s life. On my view, experience-based intuitions are produced by domain-general learning systems of hierarchical abstraction which may be modeled by deep convolutional neural networks. Owing to the mechanism of (...)
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  • Language and Embodiment—Or the Cognitive Benefits of Abstract Representations.Nikola A. Kompa - forthcoming - Mind and Language.
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  • Solving the Black Box Problem: A Normative Framework for Explainable Artificial Intelligence.Carlos Zednik - forthcoming - Philosophy and Technology:1-24.
    Many of the computing systems programmed using Machine Learning are opaque: it is difficult to know why they do what they do or how they work. The Explainable AI research program aims to develop analytic techniques that render opaque computing systems transparent, but lacks a normative framework with which to evaluate these techniques’ explanatory contributions. The aim of the present discussion is to develop such a framework, paying particular attention to different stakeholders’ distinct explanatory requirements. Building on an analysis of (...)
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  • Understanding From Machine Learning Models.Emily Sullivan - forthcoming - British Journal for the Philosophy of Science:axz035.
    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 misguided? In (...)
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  • Deep Learning: A Philosophical Introduction.Cameron Buckner - 2019 - Philosophy Compass 14 (10).
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  • Connectionism.James Garson - 2008 - Stanford Encyclopedia of Philosophy.
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