主題演講

Frédéric Kaplan

Associate Professor, Digital Humanities Laboratory, EPFL (École polytechnique fédérale de Lausanne)

Frédéric Kaplan is Professor of Digital Humanities at EPFL, where he directs the Digital Humanities Laboratory, and President of the Time Machine Organisation. For more than fifteen years, he has pioneered research on transforming large-scale historical archives, maps, images and newspapers into structured spatiotemporal representations, forming what he calls the Big Data of the Past. His current work investigates how this historical infrastructure can serve as a training ground for predictive artificial intelligence, opening what he describes as the age of computational futures. His research argues that large-scale computational representations of the past will become a core infrastructure for artificial intelligence in the twenty-first century.

演講者頁面

Time Machines at World Scale: From the Big Data of the Past to Computational Futures

Over fifteen years, the digitisation of archives, maps, images, newspapers and administrative records has turned the past into data at an unprecedented scale. Using methodologies developed within the Time Machine project, a network of hundreds of institutions has demonstrated that the scattered traces of history can be progressively reassembled into a structured patiotemporal representation, forming what we call the Big Data of the Past.

Today, around eighty Local Time Machines already reconstruct and computationally represent the past of individual cities. We can reach world scale through a fully distributed system of representation and computing, where each city or region keeps and runs its own Time Machine while interoperability emerges from shared protocols, much as the web connected independent servers into a single information space. The mission of the Time Machine Project is to make this global distributed infrastructure a reality.

Once a sufficient fraction of the world's past has been organised this way, the system undergoes a phase transition. The past becomes a testable laboratory for prediction: because its outcomes are already known, we can learn to forecast from incomplete evidence and measure how well we did, then turn the same models toward the open future. We thus enter the age of computational futures, in which Time Machines, as a detailed spatiotemporal memory from which predictive models of the future can be trained, become crucial strategic assets. This talk is about the world that emerges once enough historical data is gathered and that threshold is crossed, and why Time Machines may become one of the defining infrastructures of the twenty-first century.

Miguel Escobar Varera

Associate Professor, English, Linguistics and Theatre Studies, University of Singapore

Miguel Escobar Varela is an Associate Professor at the National University of Singapore and Co-director of the Centre for Computational Social Science and Humanities. He also serves as an Associate Editor of Computational Humanities Research and co-convenes the minor in Human-AI Systems. He studies the changing landscape of Southeast Asian cultural heritage by combining fieldwork with computational methods (such as NLP, computer vision and network analysis). His aim is to understand how the production and reception of cultural forms have changed over time, in areas such as the performing arts and print media.

演講者頁面

Harnessing AI for Culturally Specific Research Tasks in the Humanities

“Harnessing AI” carries two meanings. The first is the ordinary sense of the word, where we put something to work, as in harnessing a tool or resource. The second is the technical sense, used in software engineering and machine learning, where a harness is the scaffolding built around a system to test it. To harness AI for culturally specific tasks we need to do both. Whether we are developing our own models, or relying on existing ones, we need to ascertain the limits of a model through benchmarks, and find ways to constrain its outputs when they drift beyond these limits. In this talk, I will explore a wide range of harnessing practices by drawing on two examples from Southeast Asia. The first is segmenting videos of Javanese wayang kulit (all-night puppetry shows) according to the tradition’s own narrative conventions. The second example concerns historical newspapers in Jawi, the Perso-Arabic script widely used to write Malay until the 1970s. As is common in the digital humanities, both cases are concerned with the empirical study of material at a scale no single scholar could process by hand, with video and print archives running into the thousands of hours or pages. Both cases also involve significant internal variation, whether in performance style and narrative pacing, or in typeface, layout, and orthography. In both cases the materials are of immense significance to Southeast Asian cultural life.

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