Eberle Oliver, Büttner Jochen, El-Hajj Hassan, Montavon Grégoire, Müller Klaus-Robert, Valleriani Matteo
Machine Learning Group, Technische Universität Berlin, Marchstr. 23, 10587 Berlin, Germany.
BIFOLD-Berlin Institute for the Foundations of Learning and Data, 10587 Berlin, Germany.
Sci Adv. 2024 Oct 25;10(43):eadj1719. doi: 10.1126/sciadv.adj1719. Epub 2024 Oct 23.
Understanding the evolution and dissemination of human knowledge over time faces challenges due to the abundance of historical materials and limited specialist resources. However, the digitization of historical archives presents an opportunity for AI-supported analysis. This study advances historical analysis by using an atomization-recomposition method that relies on unsupervised machine learning and explainable AI techniques. Focusing on the "Sacrobosco Collection," consisting of 359 early modern printed editions of astronomy textbooks from European universities (1472-1650), totaling 76,000 pages, our analysis uncovers temporal and geographic patterns in knowledge transformation. We highlight the relevant role of astronomy textbooks in shaping a unified mathematical culture, driven by competition among educational institutions and market dynamics. This approach deepens our understanding by grounding insights in historical context, integrating with traditional methodologies. Case studies illustrate how communities embraced scientific advancements, reshaping astronomic and geographical views and exploring scientific roots amidst a changing world.
由于历史资料丰富而专业资源有限，理解人类知识随时间的演变和传播面临挑战。然而，历史档案的数字化为人工智能支持的分析提供了机会。本研究通过使用一种基于无监督机器学习和可解释人工智能技术的原子化 - 重组方法推进历史分析。以“萨克罗博斯科藏书”为重点，该藏书由欧洲大学（1472 - 1650年）的359种早期现代天文学教科书印刷版组成，共计76000页，我们的分析揭示了知识转化中的时间和地理模式。我们强调了天文学教科书在塑造统一数学文化方面的相关作用，这是由教育机构之间的竞争和市场动态驱动的。这种方法通过将见解置于历史背景中，并与传统方法相结合，加深了我们的理解。案例研究说明了各群体如何接受科学进步，重塑天文和地理观点，并在不断变化的世界中探索科学根源。
