Buckner, Cameron
(2023)
From Deep Learning to Rational Machines -- What the History of Philosophy Can Teach Us about the Future of Artificial Intelligence -- Sample Chapter 1 -- "Moderate Empiricism and Machine Learning".
[Preprint]
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Abstract
This book provides a framework for thinking about foundational philosophical questions surrounding the use of deep artificial neural networks (“deep learning”) to achieve artificial intelligence. Specifically, it links recent breakthroughs in deep learning to classical empiricist philosophy of mind. In recent assessments of deep learning’s current capabilities and future potential, prominent scientists have cited historical figures from the perennial philosophical debate between nativism and empiricism, which primarily concerns the origins of abstract knowledge. These empiricists were generally faculty psychologists; that is, they argued that the extraction of abstract knowledge from perceptual experience involves the active engagement of general psychological faculties—such as perception, memory, imagination, attention, and empathy. This book explains how recent headline-grabbing deep learning achievements were enabled by adding functionality to these networks that model forms of processing attributed to these faculties by philosophers such as Aristotle, Ibn Sina (Avicenna), John Locke, David Hume, William James, and Sophie de Grouchy. It illustrates the utility of this interdisciplinary connection by showing how it can provide benefits to both philosophy and computer science: computer scientists can continue to mine the history of philosophy for ideas and aspirational targets to hit on the way to building more robustly rational artificial agents, and philosophers can see how some of the historical empiricists’ most ambitious speculations can now be realized in specific computational systems.
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