A Moonshot for AI Oracles in the Sciences
Bryan Kaiser, Tailin Wu, Maike Sonnewald, Colin Thackray, Skylar, Callis

TL;DR
This paper explores the potential for AI to generate revolutionary scientific theories by proposing necessary conditions for such breakthroughs, framing it as a challenging moonshot goal in AI and scientific discovery.
Contribution
It introduces a set of necessary conditions for AI to produce revolutionary scientific theories and defines a heuristic for the intelligibility of mathematical theories.
Findings
AI advancements suggest the plausibility of meeting the necessary conditions
Proposes a moonshot challenge for AI to create scientific revolutions
Defines a heuristic for understanding mathematical theory intelligibility
Abstract
Nobel laureate Philip Anderson and Elihu Abrahams once stated that, "even if machines did contribute to normal science, we see no mechanism by which they could create a Kuhnian revolution and thereby establish a new physical law." In this Perspective, we draw upon insights from the philosophies of science and artificial intelligence (AI) to propose necessary conditions of precisely such a mechanism for generating revolutionary mathematical theories. Recent advancements in AI suggest that satisfying the proposed necessary conditions by machines may be plausible; thus, our proposed necessary conditions also define a moonshot challenge. We also propose a heuristic definition of the intelligibility of mathematical theories to accelerate the development of machine theorists.
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Taxonomy
TopicsBig Data and Business Intelligence · Scientific Computing and Data Management
