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Towards a common theory of explanation for artificial and biological intelligence

Thompson, Jessica A. F. (2018) Towards a common theory of explanation for artificial and biological intelligence. [Preprint]

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Abstract

Much of the confusion that occurs when working at the intersection of cognitive science, artificial intelligence, and neuroscience stems from disagreement about what it means to explain intelligence. I claim that to integrate these fields, we must reconcile their different theories of explanation. I briefly review theories of scientific explanation in neuroscience and recontextualize the stated views of several prominent cognitive computational neuroscientists in terms of the theories of explanation they espouse. Finally, I describe some of the challenges of forging a new theory of explanation that would apply equally to artificial and biological intelligence. As a first step towards an integration of research on biological and artificial intelligence, my goal in writing this paper is to equip scientists of intelligence to interrogate and justify the theories of explanation that underlie their definitions of scientific progress.


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Item Type: Preprint
Creators:
CreatorsEmailORCID
Thompson, Jessica A. F.j.thompson@umontreal.ca0000-0003-0468-5097
Additional Information: An earlier version of this paper was presented at the 2018 Cognitive Computational Neuroscience Meeting (CCN2018).
Keywords: philosophy of science; philosophy of mind; explanation; neuroscience; artificial intelligence; deep learning
Subjects: Specific Sciences > Neuroscience > Cognitive Neuroscience
Specific Sciences > Cognitive Science
Specific Sciences > Artificial Intelligence
General Issues > Explanation
Specific Sciences > Artificial Intelligence > Machine Learning
Depositing User: Jessica Thompson
Date Deposited: 18 Feb 2020 02:15
Last Modified: 18 Feb 2020 02:15
Item ID: 16920
Subjects: Specific Sciences > Neuroscience > Cognitive Neuroscience
Specific Sciences > Cognitive Science
Specific Sciences > Artificial Intelligence
General Issues > Explanation
Specific Sciences > Artificial Intelligence > Machine Learning
Date: 6 September 2018
URI: https://philsci-archive-dev.library.pitt.edu/id/eprint/16920

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