Programme > Conférenciers invités

IA-MA pour l'histoire et la philologie :

Speaker : Mathieu Aubry (École des Ponts Paris Tech)

Exposé : Computer Vision for Historians: examples

Préface :   In this presentation, I will give an overview of recent projects my team did on Historical document analysis and outline the main associated challenges and approaches. Applications will include works artwork auction price prediction [1], historical document segmentation [2], historical watermark recognition [3], repeated patterns discovery in artworks [4,5,6], scientific illustration analysis [7], handwriting analysis for paleography [8]. I will discuss in particular methods to avoid relying on large-scale human annotation of the datasets - the use of synthetic data, weak supervision, self or unsupervised approaches - but also the importance of annotations to define tasks and for evaluation.

IA-MA pour l'art et l'archéologie du bâti :

Speaker :  Aleksandra Pizurica (Ghent University)

Exposé : Artificial Intelligence in digital painting analysis

Information processing in support of art investigation is an emerging and rapidly growing cross-disciplinary field of research. Museums are routinely digitizing their collections in multiple imaging modalities. Next to standard digital images of artwork, such as digital photographs, infrared and X-ray images, new sensing technologies are being developed and employed together with advanced techniques for analysing multimodal data. The progress in artificial intelligence (AI) and particularly in deep learning opens new, unprecedented possibilities for supporting technical study of art works, their conservation, and even the process of creation and interactive immersive presentation. AI-driven techniques are increasingly employed not only to authenticate artworks, but also to uncover their hidden details and connections to other works, to enhance viewing experience and to improve understanding of various layers of a masterpiece.   In this talk I will present our results in the analysis of digitized paintings, and how they are employed to support art conservation and restoration, as well as art historical study. The focus will be on the case study of the Ghent Altarpiece and on the experience gained from a multidisciplinary collaboration in the framework of the recent major research and conservation campaign conducted on the Van Eyck brothers’ masterpiece.

IA-MA pour les matérieux du passé :

Speaker : Gabriele Gattiglia (University of Pisa)

Exposé: Managing AI in Archaeology: opportunity and challenges

IA-MA pour la modélisation des systèmes socio-environnementaux :

Speaker : Andreas Angourakis (Rühr Universitat Bochum (RUB))

Exposé : Simulation, Machine Learning, and their Synergy in Addressing Past Socio-Environmental Systems

Simulation and machine learning are two powerful approaches to understanding complex phenomena. Simulation modelling, including but not only system dynamics and agent-based modelling, is to design models to reproduce patterns and trends in empirical data as outcomes of postulated mechanisms under controlled conditions. Machine learning is a family of data-based or descriptive approaches that involve the creation of models able to recognize patterns and make predictions based on large datasets. Despite their shared background and fundamental relationship to AI, they differ in several aspects, among which the most essential is how causality is considered. More recently, both epistemological and pragmatical differences between these approaches have contributed to the unfortunate distancing between communities of practice, particularly accentuated in the study of past complex systems.
However, acknowledging these differences also reveal an opportunity to combine them and bring a more powerful paradigm to light [1]. Simulation can be used for machine learning (generating training datasets and hypotheses sets, serving as learning algorithms, validating final hypotheses) and, conversely, machine learning can be used for simulation (model generation, selecting and processing input data, detecting patterns in simulation output, calibrating parameters, serving as a simulation model component). Deep learning, and particularly the latest generations of large language models, bring the potential of mixed workflows to a new, unforeseen level.
This lecture will present an epistemological outline of simulation and machine learning and emphasize the potential of hybrid methodologies to address complex socio-environmental systems. Although there are still a few examples of deliberate applications, I will overview a selection of explored and unexplored use cases, including insights from previous and current work in modelling ancient agriculture and foodways [3,4,5].

[1] Rueden, Laura von, Sebastian Mayer, Rafet Sifa, Christian Bauckhage, and Jochen Garcke. 2020. ‘Combining Machine Learning and Simulation to a Hybrid Modelling Approach: Current and Future Directions’. In Advances in Intelligent Data Analysis XVIII, edited by Michael R. Berthold, Ad Feelders, and Georg Krempl, 12080:548–60. Lecture Notes in Computer Science. Cham: Springer International Publishing. https://doi.org/10.1007/978-3-030-44584-3_43.

[2] Carrignon, Simon, Tom Brughmans, and Iza Romanowska. 2020. ‘Tableware Trade in the Roman East: Exploring Cultural and Economic Transmission with Agent-Based Modelling and Approximate Bayesian Computation’. Edited by Sergi Lozano. PLOS ONE 15 (11): e0240414. https://doi.org/10.1371/journal.pone.0240414.

[3] Angourakis, A., Santos, J. I., Galán, J. M., & Balbo, A. L. (2015). Food for all: An agent-based model to explore the emergence and implications of cooperation for food storage. Environmental Archaeology, 20(4), 349–363. https://doi.org/10.1179/1749631414Y.0000000041

[4] Angourakis, A., Bates, J., Baudouin, J.-P., Giesche, A., Walker, J. R., Ustunkaya, M. C., Wright, N., Singh, R. N., & Petrie, C. A. (2022). Weather, Land and Crops in the Indus Village Model: A Simulation Framework for Crop Dynamics under Environmental Variability and Climate Change in the Indus Civilisation. Quaternary, 5(2), Article 2. https://doi.org/10.3390/quat5020025

[5] Angourakis, A., Alcaina-Mateos, J., Madella, M., & Zurro, D. (2022). Human-Plant Coevolution: A modelling framework for theory-building on the origins of agriculture. PLOS ONE, 17(9), e0260904. https://doi.org/10.1371/journal.pone.0260904