没有进步的进展

rogress in the use of artificial intelligence (AI) to ad- vance 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. The consequence is not merely institutional; it is epistemic. The scientific system thrives on inefficiency: redundant ef- forts, failed attempts, and divergent paths. These are not costs to be eliminated but sources of discovery. …