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 language | English |
|---|---|
| Article number | 173 |
| Number of pages | 20 |
| Journal | Synthese |
| Volume | 203 |
| Issue number | 5 |
| DOIs | |
| Publication status | Published - May 2024 |
Fields of science
- 509017 Social studies of science
- 603 Philosophy, Ethics, Religion
- 603102 Epistemology
- 603103 Ethics
- 603109 Logic
- 603113 Philosophy
- 603114 Philosophy of mind
- 603119 Social philosophy
- 603120 Philosophy of language
- 603122 Philosophy of technology
- 603124 Theory of science
- 502027 Political economy
JKU Focus areas
- Digital Transformation
- Sustainable Development: Responsible Technologies and Management