Understanding Science with Large Language Models? - Potentials for the History, Philosophy, and Sociology of Science

Adrian Wüthrich is a guest professor for the History and Philosophy of Modern Science at Technische Universität Berlin. He leads the project Network Epistemology in Practice (NEPI), funded by a European Research Council Consolidator Grant, which investigates collective knowledge generation in large collaborations. His earlier work addressed interpretational debates in quantum mechanics, the role of images in science, causal reasoning in discovery processes, and the history of concepts in physics. His research often combines historical, philosophical, and computational approaches.

Michael Zichert is a PhD candidate in the project Network Epistemology in Practice at Technische Universität Berlin, where he studies how scientific knowledge is produced in large-scale collaborations. He holds a BA in History of Science from Technische Universität Berlin and an MA in Digital Humanities from Universität Leipzig. His research combines computational methods with the history of science, focusing on diachronic semantic change, social network analysis, and the application of large language models in the field.

Gerd Graßhoff is a professor for the History of Ancient Science at Humboldt-Universität zu Berlin and former director of the Excellence Cluster Topoi. He pioneered computational approaches to the history of science, applying machine learning and causal reasoning to ancient astronomical texts and scientific discovery processes. A Max Planck Fellow and member of Deutsche Akademie der Naturforscher Leopoldina, his research spans from Babylonian astronomy to digital humanities and AI methods for historical research.