Year: 2026
Type: editorial
Abstract: Progress in the use of artificial intelligence (AI) to advance scientific discovery has made it increasingly realistic to envision automated "end-to-end science" (ETES) systems: integrated pipelines that could generate hypotheses, run experiments (in silico or robotic), analyze results, and produce publishable outputs with minimal human intervention. The critical question is not whether AI can "do" science but whether science-as a social, evolutionary system that generates trustworthy knowledge-survives the way AI does it.
Source: Science
Authors: Julio M. Ottino, Brian Uzzi
Institution: Northwestern University
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: bronze
OpenAlex ID: W7162032241