PythiaPlus · Machine Learning for the Study of Ancient Epigraphic Cultures

Machine Learning for the Study of Ancient Epigraphic Cultures

The PythiaPlus research project aims to investigate ancient Mediterranean written (epigraphic) cultures using Machine Learning (ML), revolutionising our ability to access, analyse and interpret the epigraphic data. State-of-the-art ML models will be built to analyse Greek and Roman epigraphic habits on an unprecedented large scale, revealing new insights in linguistic and cultural interactions. Context Computational approaches have come to feature prominently in the Humanities, thus defining a unique opportunity to write an interdisciplinary history of the Graeco-Roman world in the Digital Age. Specifically, ML's transformative role in data-driven research can impact how historical data is collected, analysed, and interpreted. Inscribed texts (inscriptions) are primary evidence for reconstructing the history and thought of the Ancient World. ML models could now reveal patterns in this data which historians were previously unable to identify in such detail and on such scale: PythiaPlus will enable the first "big data" study of epigraphic cultures over circa 1,500 years of ancient Mediterranean history using ML. Objectives a) Develop educational and research tools for tracking textual connections and making machine-readable data accessible for future research. b) Advance contextualisation of written evidence and reconstruction of ancient epigraphic habits. c) Pioneer a machine learning approach to analysing textual material cultures, applying technological advances to ancient inscriptions. Action plan 1) Dataset building: Gather, sample and prepare the epigraphic digital data to be used by ML models. 2) Model training: Train models, evaluate performance, and tune parameters for improved statistical performance and explainability. 3) Result interpretation: Interpret new patterns discovered by models in line with scholarly approaches to distinctive epigraphic habits. Impact The PythiaPlus project has introduced cutting-edge ML tools for analysing ancient Greek inscriptions, focused on collaboration and interpretability. It significantly advances the burgeoning field of ML in the study of ancient languages, meticulously documented as part of the project's outcomes. Through the development of real-world epigraphic case studies, PythiaPlus unlocks the cooperative potential between Artificial Intelligence and Ancient History. Additionally, it implements a robust communication strategy and addresses its integration in the education and industry sectors.

Data: CORDIS, © European Union