Buckner, Cameron (2019) Deep Learning: A Philosophical Introduction. [Preprint]

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Abstract

Deep learning is currently the most prominent and widely successful method in artificial intelligence. Despite having played an active role in earlier artificial intelligence and neural network research, philosophers have been largely silent on this technology so far. This is remarkable, given that deep learning neural networks have blown past predicted upper limits on artificial intelligence performance—recognizing complex objects in natural photographs, and defeating world champions in strategy games as complex as Go and chess—yet there remains no universally-accepted explanation as to why they work so well. This article provides an introduction to these networks, as well as an opinionated guidebook on the philosophical significance of their structure and achievements. It argues that deep learning neural networks differ importantly in their structure and mathematical properties from the shallower neural networks that were the subject of so much philosophical reflection in the 1980s and 1990s. The article then explores several different explanations for their success, and ends by proposing ten areas of research that would benefit from future engagement by philosophers of mind, epistemology, science, perception, law, and ethics.

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Item Type:	Preprint
Creators:	Creators Email ORCID Buckner, Cameron cjbuckner@uh.edu 0000-0003-0611-5354
Additional Information:	Preprint, pre-referee version of a paper forthcoming in Philosophy Compass
Keywords:	deep learning neural networks explanation connectionism adversarial examples
Subjects:	Specific Sciences > Computer Science General Issues > Explanation Specific Sciences > Artificial Intelligence > Machine Learning Specific Sciences > Neuroscience General Issues > Technology
Depositing User:	Dr. Cameron Buckner
Date Deposited:	15 Aug 2019 02:04
Last Modified:	15 Aug 2019 02:04
Item ID:	16326
DOI or Unique Handle:	10.1111/phc3.12625
Subjects:	Specific Sciences > Computer Science General Issues > Explanation Specific Sciences > Artificial Intelligence > Machine Learning Specific Sciences > Neuroscience General Issues > Technology
Date:	2019
URI:	https://philsci-archive.pitt.edu/id/eprint/16326

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