Humanités numériques, IA et études asiatiques / Digital humanities, AI, and Asian studies
Humanités numériques, IA et études asiatiques
Digital humanities, AI, and Asian studies
Lundi 2 décembre 2024, 14h-17h | Campus Condorcet, Bâtiment EHESS, Salle A327 (3e étage), cours des Humanités, 93300 Aubervilliers | Et en visioconférence : https://bbb.ehess.fr/b/mar-0ev-tdf-cdp
Demi journée d’études organisée par des membres du CCJ : Isabelle Sancho (CNRS), Marie-Paule Hille (EHESS), Alain Arrault (EFEO) et Florence Galmiche (UPC).
La table ronde s’articulera autour des deux présentations suivantes :
– Cécile Armand (ENS Lyon, associée CECMC), Baptiste Blouin (Aix-Marseille Université), Christian Henriot (Aix-Marseille Université) : “From Tools to Insight: New Computational Approaches for Chinese Historical Texts in the Age of AI”
In this presentation, we share our experiences with an interdisciplinary collaboration between historians of modern China and Natural Language Processing (NLP) specialists to address the challenges of handling massive, multilingual text corpora —such as periodicals, newspapers, diaries, and directories—in studying elites in modern China. Our presentation will proceed in three parts: 1. A brief introduction to AI and an overview of how we integrate various AI techniques into our workflow. 2. A demonstration of the Corpus Creator and HistText tools we developed to build and analyze text corpora. 3. Case Studies showcasing how we have used these tools to introduce new insights into the study of elites in modern China.
– Javier Cha (Assistant Professor of Digital Humanities, The University of Hong Kong) : “Lean AI for Digital Humanities: Sustainable Innovation and Inclusive Access”
Lean AI presents a responsible, democratized vision for AI in the digital humanities, reducing dependence on Big Tech. Commercial AI solutions often lack support for historical or low-resource languages and raise privacy concerns when handling sensitive or protected data. Although open-source alternatives exist, AI-powered humanities research typically requires substantial computational power and advanced hardware. Lean AI addresses this barrier through heterogeneous clusters of repurposed or low-power systems running optimized models with minimal precision loss. By resolving technical
challenges and establishing multilingual benchmarks, we create workflows suited to diverse cultural contexts. In addition, educational initiatives and community grants promote broader (3e étage)adoption of a sustainable, inclusive AI ecosystem for digital humanities worldwide.
OpenEdition vous propose de citer ce billet de la manière suivante :
Monique Abud (19 novembre 2024). Humanités numériques, IA et études asiatiques / Digital humanities, AI, and Asian studies. Carnets du Centre Chine (CNRS/EHESS). Consulté le 18 janvier 2025 à l’adresse https://doi.org/10.58079/12pnv
