Would it be possible to travel through time as easily as we travel through space?
Could you see how your street looked like 500 years ago?
Could we browse social networks of the middle ages?
Could we bring the past back as a common resource for our future?
Our common past is the Next Frontier.
The Time Machine FET Flagship builds a Large Scale Simulator mapping 2000 years of European History, transforming kilometres of archives and large collections from museums into a digital information system. These Big Data of the Past are common resources for the future that will have a huge cultural, economical and societal impact. Researchers from all over the world are now joining forces to bring the past back in one of the most ambitious project ever on European culture and identity.
Towards a new paradigm
Cultural Heritage is one of Europe’s most precious political, economic and social assets. Today, Science and Technology can profoundly transform the conservation and experience of the cultural heritage with an impact on research, education, new applications and, in turn, on the economy and society at large. Computer and Data Sciences, Physics and Chemistry, Material Sciences and Robotics, can join forces with the Humanities to open a new paradigm for Historical Sciences.
Mapping 2000 years of European History
The Time Machine FET Flagship aims at building a Large Scale Historical Simulator mapping 2000 years of European History. Extending on the proposal submitted to the attention of the European Commission in April 2016, Time Machine is a program that brings together research teams from all over Europe and the participation of about 200 institutions. The goal of this consortium is to develop new technologies for the scanning, analyzing, accessing, preserving and communicating of cultural heritage at a massive scale. Data extracted from this digital patrimony are the basis for the reconstruction of the historical evolution of most European cities and the economical, cultural and migration networks between these urban nodes.
Writing a common history of Europe
This is something of complexity and scale unseen to date. To obtain the necessary data for such a reconstruction, Time Machine has to develop new technologies for a scanning infrastructure able to digitize massive amounts of fragile documents from the European heritage that would be the basis of the largest database ever created for European archival documents. Meanwhile, high performance computing clusters are used to process this mass of documents using increasingly accurate machine vision algorithms, segmenting, indexing and transcribing their content, ultimately making them searchable like any other documents we search on the web. The information networks extracted from the documents constitute a massive semantic graph of linked data – probably the largest ever built about the past - unfolding in space and time as part of an historical geographical information system.
Big Data of the Past
These Big Data of the Past are expected to lead to data-driven historical simulations, making the past de facto as easily accessible as the present. New families of historical search engines, as well as immersive and augmented reality interfaces and other tools, will generate what one could describe as time capsules to seamlessly navigate 2000 years of European history. Thousands of time travellers are already ready to engage in the project, for curating the data, design algorithms and ultimately, to a certain extent, writing a common history of Europe. That approach in turn could apply to other cities, communities and regions of the world.
The Venice Time Machine
Time Machine is anchored in the technologies and methodologies pioneered by the Venice Time Machine, an on-going 10-year seed project focusing on the city of Venice and its 1000 years of history and featuring EPFL, Università Ca’ Foscari, Archivio di Stato di Venezia, Fondazione Giorgio Cini, as well as an international board including scholars from Princeton, Stanford, Columbia and London Universities. Venice Time Machine provides a proof of concept of archival digitization and machine learning to reconstruct the shape of the city over its history, along with the evolution of its continental and Mediterranean Venetian networks over time. The project maps circulation of news, money, commercial goods, as well as migration of artistic patterns along the roads from Venice to the Netherlands and Germany or down to ports of the Black sea, reconstructing, through the history of Venice, a united story of the construction of Europe. Time Machine is a spatial and temporal extension of this ambition, the potential of which the Venice chapter already highlights.
A shared patrimony
All technological development of the Time Machine FET Flagship are open source, in line with the notion of a shared patrimony and cost effectiveness for other institutions in following the same methodologies and encouraging the creation of start-up and services based on similar approaches and standards. That can lead to local initiatives for fostering tourism, cultural entertainment services and new approaches to urban planning.
Europe’s cultural heritage to the future
We believe Time Machine and the projects it will generate represent a unique opportunity to take Europe’s cultural heritage to the future, enhanced as a shared patrimony and a common history, for the next generations.
- The project has a large European coverage with already 173 partners in 32 countries
- It pushes the research in the most advanced Artificial Intelligence and Machine Learning technologies to extend the realm of Big Data to the past
- It develops a unique 4D IT technology enabling navigation through time as easily as we navigate through space
- It is based on Europe's unique asset: its long history, its multi-linguism and multiculturalism
- It has a large expected impact on the sector of tourism (600 million tourists per year), media industries (new forms of virtual reality) and for the development of start-ups exploiting long data series analysis for making prediction using Artificial Intelligence techniques. It is not unlikely that the next Google will emerge out of these technologies.