Descripción del proyecto
Estudio con aprendizaje automático de las culturas epigráficas del Mediterráneo antiguo
El proyecto PythiaPlus, financiado con fondos europeos, examinará la naturaleza de las culturas epigráficas de los mundos griego y romano mediante el empleo de los avances recientes en inteligencia artificial. Las inscripciones ofrecen pruebas de primera mano del pensamiento, el lenguaje, la sociedad y la historia de la Antigüedad. Con todo, el número y la variedad de estos documentos dificultan sobremanera el estudio de los matices y los cambios en las inscripciones. El equipo de PythiaPlus revolucionará nuestra capacidad para acceder y analizar los datos a través del uso de modelos de aprendizaje automático, lo que permitirá interpretar los patrones epigráficos descubiertos por estos modelos en los textos y metadatos de miles de inscripciones griegas y latinas y transformar nuestra comprensión de la comunicación epigráfica en el Mediterráneo antiguo.
Objetivo
PythiaPlus proposes to explore and interpret the nature of the epigraphic cultures of the ancient Mediterranean using Artificial Intelligence. Specifically, it will use Machine Learning (ML) models to trace distinctiveness and change in the Greek and Roman epigraphic evidence on an unprecedented large scale and in unparalleled detail, revealing new insights in linguistic and cultural interactions.
Inscriptions are primary evidence for reconstructing the history and thought of the ancient world, due to their large number and variety in content. However, the chronological development and regional diffusion of inscriptions are not uniform. No print or digital resources exist allowing a precise quantification of inscriptions by time and place, and current approaches are generally confined to specific languages or localised case studies. Recent advances in ML can overcome these limitations: ML is a field of Artificial Intelligence that allows statistical models to discover patterns in large datasets, and learn meaningful representations of them. Because such models can train over vast amounts of data, they can overcome the limitations in quantification and breadth of analysis of current resources and approaches.
By revolutionising our ability to access and analyse the epigraphic data through the implementation of advanced digital technologies, this research will enable and undertake the interpretation of the epigraphic patterns and parallels discovered by ML models across the texts and metadata of thousands of Greek and Latin inscriptions. PythiaPlus will transform our understanding of the use of epigraphic communication and the nature of cultural interference within the written and indirectly spoken languages of the ancient world, making a substantial contribution to the study of Epigraphy and the Historical Sciences.
Ámbito científico
Palabras clave
Programa(s)
Régimen de financiación
MSCA-IF - Marie Skłodowska-Curie Individual Fellowships (IF)Coordinador
30123 Venezia
Italia