Does the No Miracles Argument Apply to AI?

Darrell P. ROWBOTTOM*, William PEDEN, André CURTIS-TRUDEL

*Corresponding author for this work

Research output: Journal PublicationsJournal Article (refereed)peer-review

Abstract

According to the standard no miracles argument, science’s predictive success is best explained by the approximate truth of its theories. In contemporary science, however, machine learning systems, such as AlphaFold2, are also remarkably predictively successful. Thus, we might ask what best explains such successes. Might these AIs accurately represent critical aspects of their
targets in the world? And if so, does a variant of the no miracles argument apply to these AIs? We argue for an affirmative answer to these questions. We conclude that if the standard no miracles argument is sound, an AI-specific no miracles argument is also sound.
Original languageEnglish
Article number173
JournalSynthese
Volume203
Issue number5
Early online date13 May 2024
DOIs
Publication statusPublished - 13 May 2024

Bibliographical note

We are grateful for feedback on earlier versions of the paper from audiences at Cambridge University, Kyoto University, Technical University Berlin, and Zhejiang University.

Publisher Copyright: © The Author(s) 2024.

Keywords

  • Artificial intelligence
  • Scientific realism
  • Machine learning
  • Scientific progress
  • Scientific representation
  • AlphaFold

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