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  1. Machine understanding and deep learning representation.Elay Shech & Michael Tamir - 2023 - Synthese 201 (2):1-27.
    Practical ability manifested through robust and reliable task performance, as well as information relevance and well-structured representation, are key factors indicative of understanding in the philosophical literature. We explore these factors in the context of deep learning, identifying prominent patterns in how the results of these algorithms represent information. While the estimation applications of modern neural networks do not qualify as the mental activity of persons, we argue that coupling analyses from philosophical accounts with the empirical and theoretical basis for (...)
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  • Idealization, representation, and explanation in the sciences.Melissa Jacquart, Elay Shech & Martin Zach - 2023 - Studies in History and Philosophy of Science Part A 99 (C):10-14.
    A central goal of the scientific endeavor is to explain phenomena. Scientists often attempt to explain a phenomenon by way of representing it in some manner—such as with mathematical equations, models, or theory—which allows for an explanation of the phenomenon under investigation. However, in developing scientific representations, scientists typically deploy simplifications and idealizations. As a result, scientific representations provide only partial, and often distorted, accounts of the phenomenon in question. Philosophers of science have analyzed the nature and function of how (...)
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