A round 2,000 years ago, the Greek biographer Plutarch wrote about a 30-oared ship that was said to have carried the hero Theseus and a group of young Athenians home from Crete. To preserve the vessel, its Athenian caretakers removed the old wood as it decayed and replaced it with stronger timber. They continued, plank by plank, until the repairs produced a philosophical problem: If none of the original material remained, was it still the Ship of Theseus?
The ship became the namesake of a thought experiment, applied to everything from bands that replace their members to classic cars rebuilt with new components. I have found a new application in the growing role of artificial intelligence in knowledge production: In this version, science is the ship, its human-driven practices are the planks, and AI provides the replacement material. Science’s silhouette stays recognizable even as the activity inside it becomes increasingly automated.
Much of the anxiety surrounding an AI takeover assumes a cinematic event in which an autonomous machine emerges overnight and outperforms every living scientist, maybe in the form of a Nobel-worthy discovery. This picture fits our science fiction imagination. It also distracts from the likelier scenario, in which AI displaces features of the scientific process incrementally.
Each step that gets transferred to AI appears to offer a meaningful gain in efficiency. No committee has to vote to hand over science to the robots; a sequence of quiet software updates can get us there just as fast. The first plank sits in education, where future scientists are trained.
In a large field experiment, high school mathematics students with access to a standard generative AI interface (based on GPT-4) performed better on practice problems than students who didn’t use it; however, they scored 17 percent worse on the subsequent exam. Students who used a more specialized AI math tutor for the practice problems largely avoided the decline. The finding captures a basic distinction between completing an assignment and learning how to do it well.
Training depends on working through difficulty: Students read challenging texts, make mistakes, and revise their reasoning. This is the process by which we learn how to be discerning, how to reason, and make good inferences. When AI removes these challenges, students lose one of the most critical steps to the process.
Extract — continue reading at the source.
