Rivista | magazén
Fascicolo monografico | 2 | 1 | 2021
Articolo | Scholarly Editing and AI: Machine Predicted Text and Herculaneum Papyri
Scholarly Editing and AI: Machine Predicted Text and Herculaneum Papyri
Abstract
In 2016 the Digital Restoration Initiative (DRI) at the University of Kentucky, under the direction of Professor Brent Seales, virtually unrolled a carbonized parchment scroll from Ein Gedi, revealing a copy of Leviticus written in iron gall ink. In 2019 the DRI applied a new machine learning method to reveal a Greek character written in carbon ink from an actual Herculaneum papyrus fragment. Virtual unwrapping of cultural heritage objects is a reality. The application of machine and deep learning methods to enhance difficult-to-detect ink signals in tomography will continue to evolve. This raises an important question. How will the process of editing texts that are ‘true-born virtual’ (the object can never be opened to verify the results) change to reflect the presence and dependency on AI? This paper produces a theoretical model for how a critical edition of a virtually unwrapped papyrus text must document the role of the machine. It also engages the possible requirements, in terms of Data Science, that this new type of text compels in order to ensure transparency at the level of its ‘birth’. Put simply, a new virtual edition model that is a fusion of humanities and science is needed.
Presentato: 12 Febbraio 2021 | Accettato: 04 Maggio 2021 | Pubblicato 30 Giugno 2021 | Lingua: en
Keywords Herculaneum • Born-virtual text • Tomography • Scholarly editions • Papyri • AI • Textual criticism
Subtags Afterlife Textual data Monuments Born digital artefacts Processing Publication Documents Modelling Analysis Technology
Copyright © 2021 James H. Brusuelas. This is an open-access work distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction is permitted, provided that the original author(s) and the copyright owner(s) are credited and that the original publication is cited, in accordance with accepted academic practice. The license allows for commercial use. No use, distribution or reproduction is permitted which does not comply with these terms.
Permalink http://doi.org/10.30687/mag/2724-3923/2021/03/002
