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AI, ML, and ABMS for Historical Sciences. Opportunities and Limits
 
PIIS000000000016816-7-1
DOI10.31696/S000000000016816-7
Publication type Article
Status Published
Authors
Andrea Nanetti 
Occupation: Founding Director of LIBER (Laboratory for Interdisciplinary Bookish and Experiential Research), Founding Editor-in-Chief of Engineering Historical Memory
Affiliation: Nanyang Technological University Singapore
Address: Singapore
Journal nameDigital Orientalia
EditionVolume 1 № 1
Pages12-18
Abstract

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.

KeywordsHistorical Sciences, Digital History, Treasure of Human Experiences data, Cultural-dependent ontologies, Artificial Intelligence, Machine learning, Agent-based modeling and simulation, Engineering Historical Memory
Received16.09.2021
Publication date16.10.2021
Number of characters16325
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1 From a human sciences perspective, the dreaming about a Robot Scientist like Adam — the one created in the United Kingdom for Genomics by an interdisciplinary research team of computer scientist and biologists to identify “genes encoding orphan enzymes in Saccharomyces cerevisiae” [1] — or powerful AI algorithms — like the one developed by DeepMind to predict with unprecedented precision the 3-D structure of nearly all the proteins made by the human body [2, p. 635] — pose a series of reflections. Indeed, automation for human sciences has its own peculiarities. However, the discourse on formalisation through ontology and language can be instructive because the creation of learning algorithms needs conceptual solutions. In 2015 the computer scientist Pedro Domingos justifies his use, with poetic license, of the term master algorithm in the Prologue to his homonymous book and explains it in the context of the advancement of learning.
2 If it exists, the Master Algorithm can drive all knowledge in the world-past, present, and future-from data. Inventing it would be one of the greatest advances in the history of science… The Master Algorithm is to machine learning what Standard Model is to particle physics or the Central Dogma to molecular biology: a unified theory that makes sense of everything we know to date, and lays the foundation for… future progress… What it requires is stepping back from the mathematical arcana to see the overarching pattern of learning phenomena… Once we have the conceptual solution, we can fill in the mathematical details… convinced that learning is the key to solving AI… nothing could have more impact that teaching computers to learn [3, pp. xviii–xix].
3 To approach this titanic task, a robust method — for the identification of key academic disciplines and the definition of their major contributions to a new interdisciplinary theory or science — is provided by the work published in September 1959 by John H. Holland as Willow Run Laboratories Technical Memorandum 2900-52-R. The work surveys how four academic disciplines contribute to the foundation of “Automata theory (presently more formally named, at M.I.T. and The University of Michigan, The Communication Sciences)”: logic and meta-mathematics provide the axiomatic method and decision procedures (formal system), effective definition, and computability (Turing Machines); the studies on electronic computers contribute programming and simulations; information theory (Shannon’s mathematical theory of communication) is used for measure of information and channel capacity; neurophysiology offers theories on the formation of concepts in the nervous system, and in particular on the recognition of macroscopic patterns by a sequential process [4].
4 Fernand Braudel’s persuasion that the historian of tomorrow, to be able to survive as a human scientist, must become able and build up a common language, which can be used by programmers across all human sciences to write their computer programs, is clear from a historian’s perspective. From a computation and machine learning perspective, one key question needs to be addressed and answered, before taking any action. Why should the historian oversee such a critical task? [see 5; 6].
5 Referring to Fernand Braudel’s 1969 call for unity, it seems that human sciences need to build up a grammar for the computational language that will allow the reloading of the treasure of human experiences through artificial intelligence. Big human sciences data are embedded and embodied in all sorts of artefacts and media that over time and across space have been created by humans to encode their knowledge and values, to make a better use of them in the present and transmit them to the future generations as well. In this way, artefacts and media are considered and treated as knowledge aggregators ante litteram. These big data can be named “THE data”, where THE stays for Treasure of Human Experiences, and easily identified as the most valuable component of what is generally called heritage (i.e., what may be inherited) [7, pp. 83–84, 89].
6 Societies have always used their heritage (i.e., the treasure of human experiences) to remain resilient and to express their cultural identities. Now, it seems that the rapid development of our society is endangering this spontaneous cultural process of adaptation to change. Part of this social adaptation is enabled by the human knowledge and values that we transmit from one generation to the next. In the past, this transmission is passed down through artefacts, oral traditions, social rituals, and cultural practices. But given the increasing pace of technological development of our times, many traditional modes of knowledge and value transmission have become obsolete or at risk of vanishing. New media and non-conventional communications have arisen, creating new possibilities for cultural expressions. However, we may not have mastered effective ways to harness these channels to transmit our cultural heritage, especially in the areas of transmitting knowledge and its associated values to enrich human experiences and augment the resilience of our society [see 8].

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1. King R.D. e.a. The Automation of Science // Science. 2009. 324. Pp. 85–98.
2. Callaway E. DeepMind’s AI Predicts Structures for a Vast Trove of Proteins // Nature. 595. 2021, 29 July.
3. Domingos P. The Master Algorithm. How the Quest for the Ultimate Learning Machine Will Remake Our World. New York: Basic Books, 2015.
4. Holland J. Survey of Automata Theory. Ann Arbor (MI): The University of Michigan Willow Run Laboratories, 1959; reprint: Vasbinder J.W., Gao H. (eds.) Selected Papers of John H. Holland. A Pioneer in Complexity Science. Singapore: World Scientific, 2018. Pp. 1–24.
5. Para Limes. http://www.paralimes.ntu.edu.sg  (accessed on 28.08.2021).
6. Nanetti A. Overcoming Linguistic and Cultural Obstacles in the Transcultural (Re)-Reading of Primary Sources and Secondary Literature for Afro-Eurasian Pre-Modern History (1205–1533) // Mukherjee, R. (ed.) Order/Disorder in Asia: Historical Perspectives. Kolkata (India): The Asiatic Society, 2021. Ch. 16.
7. Nanetti A., Simpson A. Sharing our Heritage to Shape our Future. How Effective Are Multi-User Sharing Platforms in Supporting Collaborative Visioning for the Future, and why is Heritage Centre-Stage? // SOTICS 2015. The Fifth International Conference on Social Media Technologies, Communication, and Informatics (Barcelona, Spain, 15–20 Nov 2015). Wilmington (DE): IARIA XPS Press, 2015. Pp. 82–90.
8. Nanetti A. Defining Heritage Science. A Consilience Pathway to Treasuring the Complexity of Inheritable Human Experience through Historical Method, AI and ML // Complexity 2021 = Chen S.-H., Alfarano S., Shen D. (eds.) Tales of Two Societies: On the Complexity of the Coevolution between the Physical Space and the Cyber Space. Article ID 4703820.
9. Eco, U. From the Tree to the Labyrinth. Cambridge (MA) & London: Harvard University Press, 2014.
10. Lechte J. Key Contemporary Concepts. From Abjection to Zeno’s Paradox. New York: SAGE Publications, 2003.
11. Nanetti A., Cheong S.A., Filippov M. Interactive Global Histories. For a new information environment to increase the understanding of historical processes // Proceedings of the International Conference on Culture and Computing 2013 (Ritsumeikan University, Kyoto, 16–18 Sep. 2013). Los Alamitos (CA): IEEE Computer Society, 2013. Pp. 104–110.
12. Nanetti A., Cheong S.A. Computational History: From Big Data to Big Simulations // Chen S. (ed.), Big Data in Computational Social Science and Humanities. Cham (Switzerland): Springer International Publishing AG, 2018. (Springer Series on Computational Social Sciences). Pp. 337–363.
13. Young T., Hazarika D., Poria S., Cambria E. Recent Trends in Deep Learning Based Natural Language Processing // IEEE Computational Intelligence Magazine 2018. 13/3. Pp. 55–75.
14. Selected Works of Ramon Llull (1232–1316), translated by Anthony Bonner. Princeton: Princeton University Press, 1985.
15. Die Philosophischen Schriften von Gottfried Wilhelm Leibniz, herausgegeben von Carl Immanuel Gerhardt. Bd. VII. Berlin: Weidmannsche Buchhandlung, 1890.
16. Oeuvres Philosophiques Latines et Françoises de feu Mr de Leibnitz, par Rud. Eric Raspe. Amsterdam & Leipzig: Jean Schreuder, 1765.

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