Results for 'Jamuna Krishnan'

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  1. The Shaken Realist: Bernard Williams, the War, and Philosophy as Cultural Critique.Nikhil Krishnan & Matthieu Queloz - 2022 - European Journal of Philosophy 31 (1):226-247.
    Bernard Williams thought that philosophy should address real human concerns felt beyond academic philosophy. But what wider concerns are addressed by Ethics and the Limits of Philosophy, a book he introduces as being ‘principally about how things are in moral philosophy’? In this article, we argue that Williams responded to the concerns of his day indirectly, refraining from explicitly claiming wider cultural relevance, but hinting at it in the pair of epigraphs that opens the main text. This was Williams’s solution (...)
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  2. Williams’s Debt to Wittgenstein.Matthieu Queloz & Nikhil Krishnan - forthcoming - In Marcel van Ackeren & Matthieu Queloz (eds.), Making Sense of the Past: Bernard Williams and the History of Philosophy. Oxford: Oxford University Press.
    This chapter argues that several aspects of Bernard Williams’s style, methodology, and metaphilosophy can be read as evolving dialectically out of Wittgenstein’s own. After considering Wittgenstein as a stylistic influence on Williams, especially as regards ideals of clarity, precision, and depth, Williams’s methodological debt to Wittgenstein is examined, in particular his anthropological interest in thick concepts and their point. The chapter then turns to Williams’s explicit association, in the 1990s, with a certain form of Wittgensteinianism, which he called ‘Left Wittgensteinianism’. (...)
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  3. Widening Access to Applied Machine Learning With TinyML.Vijay Reddi, Brian Plancher, Susan Kennedy, Laurence Moroney, Pete Warden, Lara Suzuki, Anant Agarwal, Colby Banbury, Massimo Banzi, Matthew Bennett, Benjamin Brown, Sharad Chitlangia, Radhika Ghosal, Sarah Grafman, Rupert Jaeger, Srivatsan Krishnan, Maximilian Lam, Daniel Leiker, Cara Mann, Mark Mazumder, Dominic Pajak, Dhilan Ramaprasad, J. Evan Smith, Matthew Stewart & Dustin Tingley - 2022 - Harvard Data Science Review 4 (1).
    Broadening access to both computational and educational resources is crit- ical to diffusing machine learning (ML) innovation. However, today, most ML resources and experts are siloed in a few countries and organizations. In this article, we describe our pedagogical approach to increasing access to applied ML through a massive open online course (MOOC) on Tiny Machine Learning (TinyML). We suggest that TinyML, applied ML on resource-constrained embedded devices, is an attractive means to widen access because TinyML leverages low-cost and globally (...)
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