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Nanetti outlines a methodology for deploying artificial intelligence and machine learning to enhance historical research.
List of contents
List of Figures
List of Abbreviations
Foreword
Preface
Acknowledgements
1 Computational Engineering of Historical Memories
1.1 Vision, Mission, and Motivation from a Human Sciences Perspective
1.2 Reloading the Treasure of Human Experiences into the Digital Time Machine
1.3 The Online System Engineering Historical Memory (EHM): Methods and Tools
2 Historians and Computers
2.1 Computers in the Historian’s Craft. Opportunities and Limits
2.2 Reflections on the Training of Machine Learning Algorithms for the Next Generation of Historians
2.3 Towards a Computational Approach to History. The Principle of Computational Equivalence and the Phenomenon of Computational Irreducibility in Historical Sciences
3 History, Films, and Online Video Streaming
3.1 Communicating History with Films
3.1.1 Documentary Films
3.1.2 Docudramas
3.1.3 Feature Films
3.1.4 Historical Dramas
3.1.5 Compilation Films
3.1.6 User-Generated Content
3.2 Animated Picture as a Privileged Medium to Screen Historical Narratives in Films
3.2.1 Using Animation to Adapt Historical Narratives in Films
3.2.2 Significant Examples of History- Driven Animations
3.3 Validating Historical Narratives in Films
3.3.1 Acquiring Knowledge from History- Based Films. Opportunities and Challenges for the Audience
3.3.1.1 Netflix, Amazon Prime Video, and Disney+
3.3.1.2 MUBI and Curiosity Stream
3.3.1.3 YouTube and TikTok
3.3.1.4 Vimeo
3.3.1.5 History Channel
3.3.1.6 TED
3.3.2 The EHM Approach to Computational Validation of Historical Information in Films
4 Languages and Cultures at the Computational Turn
4.1 Gazing at the World as Seen from the Others
4.2 A New Tower of Babel?
4.3 Computational Approaches as Tools to Overcome Linguistic Obstacles and Cultural Barriers in the Historian’s Craft
5 EHM Showcase on Afro-Eurasia (ca 1100–1500 CE)
5.1 EHM Computational Engineering of Afro-Eurasian Communication Networks with a Focus on Waterways
5.2 Venetian State-Run Galley Convoys as a Testbed to Design ABMs and Run Simulations
5.3 Framing EHM in the Silk Road Discourse
5.4 Epilogue without Conclusion
Index
About the author
Dr. Andrea Nanetti is an award-winning and internationally recognised expert in Digital Humanities. He has carried out trailblazing research in Europe, the United States, China, Africa, and South-East Asia for over 30 years. Since 2013, he has been a Professor at Nanyang Technological University, Singapore. Using the history of Venice as contextualised within late medieval Afro-Eurasian trade systems, he achieved international standing within a broad research spectrum that spans from critical editions of primary historical sources to computational applications and web-based media. As a result, several world’s top-level institutions, including Harvard University, Princeton University, Shanghai Jiao Tong University, Brown University, Johns Hopkins University, and Ca’ Foscari University of Venice, invited him to be a visiting fellow.
Summary
Nanetti outlines a methodology for deploying artificial intelligence and machine learning to enhance historical research.
