The selfish machine? On the power and limitation of natural selection to understand the development of advanced AI

Philosophical Studies:1-24 (forthcoming)
  Copy   BIBTEX

Abstract

Some philosophers and machine learning experts have speculated that superintelligent Artificial Intelligences (AIs), if and when they arrive on the scene, will wrestle away power from humans, with potentially catastrophic consequences. Dan Hendrycks has recently buttressed such worries by arguing that AI systems will undergo evolution by natural selection, which will endow them with instinctive drives for self-preservation, dominance and resource accumulation that are typical of evolved creatures. In this paper, we argue that this argument is not compelling as it stands. Evolutionary processes, as we point out, can be more or less Darwinian along a number of dimensions. Making use of Peter Godfrey-Smith’s framework of Darwinian spaces, we argue that the more evolution is top-down, directed and driven by intelligent agency, the less paradigmatically Darwinian it becomes. We then apply the concept of “domestication” to AI evolution, which, although theoretically satisfying the minimal definition of natural selection, is channeled through the minds of fore-sighted and intelligent agents, based on selection criteria desirable to them (which could be traits like docility, obedience and non-aggression). In the presence of such intelligent planning, it is not clear that selection of AIs, even selection in a competitive and ruthless market environment, will end up favoring “selfish” traits. In the end, however, we do agree with Hendrycks’ conditionally: If superintelligent AIs end up “going feral” and competing in a truly Darwinian fashion, reproducing autonomously and without human supervision, this could pose a grave danger to human societies.

Other Versions

No versions found

Links

PhilArchive



    Upload a copy of this work     Papers currently archived: 103,401

External links

Setup an account with your affiliations in order to access resources via your University's proxy server

Through your library

Analytics

Added to PP
2024-09-27

Downloads
14 (#1,321,670)

6 months
14 (#181,413)

Historical graph of downloads
How can I increase my downloads?

Author Profiles

Maarten Boudry
University of Ghent
Simon Friederich
University of Groningen

Citations of this work

No citations found.

Add more citations