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Hualin International Journal of Buddhist Studies 3.2 (2020): 38–61; https://dx.doi.org/10.15239/hijbs.03.02.03
(This article belongs to the Special Issue Buddhism and Technology, and Epigraphy)
Machine Learning, Plant Learning, and the Destabilization of Buddhist Psychology
Charles GOODMAN
Binghamton University
cgoodman@binghamton.edu
Abstract: Recent developments in artificial intelligence and the nascent scientific literature on ‘plant learning’ pose serious challenges to Buddhist philosophy of mind and to Buddhist practical ethics. These challenges are of two general types. First, the empirical results threaten to extend the reach of mind more broadly than premodern South Asian and Tibetan Buddhists were willing to allow, calling into question the rational defensibility of a range of Buddhist moral commitments.
But the discovery of learning in non-animals also threatens to destabilize the crucial Buddhist distinction between ‘sentient beings’ and the ‘receptacle world,’ and raises the possibility of a separation between intelligence and consciousness. The emergence of such a separation could require a basic rethinking of the traditional framework of the five aggregates. These developments should also sharpen our attention to AI safety by making the prospect of existential AI risk even more threatening than it would otherwise have been.
Keywords: AI safety, existential risk, Buddhist psychology, five aggregates, vegetarianism, plant minds
About the Author: Charles Goodman is a Professor in the Philosophy Department and the Department of Asian and Asian-American Studies at Binghamton University. He has published articles on Buddhist philosophy and on applied ethics, as well as translations from Sanskrit. He is the author of Consequences of Compassion: An Interpretation and Defense of Buddhist Ethics (2009), and the translator of The Training Anthology of Śāntideva (2016) and The Tattvasaṃgraha of Śāntarakṣita: Selected Metaphysical Chapters (forthcoming), all from Oxford University Press.
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
