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AI, ML, and ABMS for Historical Sciences. Opportunities and Limits
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Date of publication
16.10.2021
Public year
2021
DOI
10.31696/S000000000016816-7
AI, ML, and ABMS for Historical Sciences. Opportunities and Limits
Annotation

Once Fernand Braudel expressed the persuasion that the historian of tomorrow will have to create a common language with programmers, which will save the treasures of human experience with the help of information technology.In highly globalized world of today, societies risk losing traditional means of transmitting their heritage before they could master modern methods to keep their most significant knowledge and treasured human experiences alive. Today, hopes are pinned on emerging digital history technologies to address these challenges. However, digital history still operates in a knowledge limbo, because it needs universal ontologies (cultural-dependent ontologies) to store and analyze its knowledge. Historical records need to be comprehensively decomposed into unambiguous fields to become machine-readable.

The research team that experiments on Engineering Historical Memory (EHM, www.engineeringhistoricalmemory.com) is introducing into historical sciences the experience of using machine-readable primary- and secondary-sources datasets with further processing using AI, ML and ABMS.

About authors
Andrea Nanetti
Founding Director of LIBER (Laboratory for Interdisciplinary Bookish and Experiential Research), Founding Editor-in-Chief of Engineering Historical Memory
Nanyang Technological University Singapore
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