Unleashing Big Data of the Past – Europe builds a Time Machine
Time Machine project overcomes important hurdle for EU large-scale research project
The European Commission has chosen Time Machine as one of the six proposals retained for preparing large-scale research initiatives to be strategically developed in the next decade. €1 million in funding has been granted for preparing the detailed roadmaps of this initiative that aims at extracting and utilising the Big Data of the past. Time Machine foresees to design and implement advanced new digitisation and Artificial Intelligence (AI) technologies to mine Europe’s vast cultural heritage, providing fair and free access to information that will support future scientific and technological developments in Europe.
The Time Machine Project, which involves FAU as well as several other institutions, will create advanced AI technologies to make sense of vast amounts of information from complex historical data sets. This will enable the transformation of fragmented data – with content ranging from medieval manuscripts and historical objects to smartphone and satellite images – into useable knowledge for industry. In essence, a large-scale computing and digitisation infrastructure will map Europe’s entire social, cultural and geographical evolution. Considering the unprecedented scale and complexity of the data, The Time Machine’s AI even has the potential to create a strong competitive advantage for Europe in the global AI race.
‘Time Machine is likely to become one of the most advanced Artificial Intelligence systems ever built, trained on data from wider geographical and temporal horizons‘, explains Frederic Kaplan, Professor of Digital Humanities at the Ecole Polytechnique Fédérale de Lausanne (EPFL) and Coordinator of the Time Machine Project.
At FAU, more than 30 scientists are involved in the Time Machine Project, including historians, archaeologists and computer scientists. The head of this effort is Prof. Dr. Andreas Maier, Chair of Computer Science 5 (Pattern Recognition), who is also member of the consortium’s Executive Team. “Our aim is to digitise, as far as possible, all documents, pictures, sculptures, archaeological finds and artefacts,” explains Prof. Maier. In order to handle such heterogeneous and unstructured data, completely new tools in computer science are needed, such as the combination of classical artificial intelligence and deep learning. “‘Evaluation by scientists is, however, also a crucial factor, for example in order to avoid spurious correlations.” FAU scientists are involved in the Time Machine Project in a number of important ways, in particular regarding the fusion of symbolic AI and methods of deep learning in addition to their application to build a Nuremberg Time Machine.
Cultural Heritage as a valuable economic asset
Cultural Heritage is one of our most precious assets, and the Time Machine’s ten-year research and innovation programme will strive to show that rather than being a cost, cultural heritage investment will actually be an important economic driver across industries. This constant source of new knowledge will be an economic motor, giving rise to new professions, services and products in areas such as education, creative industries, policy making, smart tourism, smart cities and environmental modelling. For example, services for comparing territorial configurations across space and time will become an essential tool in developing modern land use policy or city planning. Likewise, the tourism industry will be transformed by professionals capable of creating and managing newly possible experiences at the intersection of the digital and physical worlds. These industries will have a pan-European platform for knowledge exchange which will add a new dimension to their strategic planning and innovation capabilities.
Time Machine will mark a new age for Social Sciences and Humanities, as it will offer open access to Europe’s past via unified data and new AI services. This will give ’super powers’ to researchers by revolutionising the individual researcher’s search capabilities, drastically raising the overall scale and scope of social sciences and humanities research. The resulting knowledge will enable the field to effectively contribute to the development of strategic answers to major pan-European challenges such as sustainable growth, social welfare, migration and integration of migrants, and the safeguarding of European democracy.
Education is a crucial factor for social and economic well-being in Europe and the world, and Time Machine will help transform it by creating a dynamic new industry for the production of educative digital material based on aligned massive datasets. The resulting online courses, materials, simulations and other experiences will promote active engagement with our combined cultural heritage and make continuous learning more accessible and inclusive.
A unique alliance and a network of cities
Time Machine promotes a unique alliance of leading European academic and research organisations, cultural heritage institutions and private enterprises that are fully aware of the huge potential of digitisation and the very promising new paths for science, technology and innovation that can be opened through the information system that will be developed, based on the Big Data of the past. In addition to the 33 core institutions that will be funded by the European Commission, more than 200 organisations from 33 countries are participating in the initiatives, including seven national libraries (Austria, Belgium, France, Israel, Netherlands, Spain, Switzerland), 19 state archives (Belgium, Bulgaria, Croatia, Czech Republic, Denmark, Estonia, Finland, Germany, Hungary, Lithuania, Malta, Norway, Poland, Romania, Slovenia, Spain, Slovakia, Sweden, and Switzerland), famous museums (Louvre, Rijkmuseum), 95 academic and research institutions, 30 European companies and 18 governmental bodies.
Time Machine is also a growing network of cities. The project is based on a ’franchise‘ operation model grouping scholars, cultural heritage organisations, government bodies and large groups of volunteers around specific integrated projects focusing on cities. The engagement of a large number of volunteers, often local citizens, in these Local Time Machine initiatives is another key element to ensure the long-term sustainability of the project. Local Time Machines are currently being developed in Venice, Amsterdam, Paris, Jerusalem, Budapest, Regensburg, Nuremberg, Dresden, Antwerp, Ghent, Bruges, Naples, Utrecht, Limburg and more. In the next 12 months, Time Machine is expected to grow as a large community of communities, sharing a standardised platform, with more empowering tools.
In early 2016, the European Commission held a public consultation of the research community to gather ideas on science and technology challenges that could be addressed through future FET Flagships. At the end of 2016, Commissioner Oettinger hosted a round-table event with high-level representatives from the Member States, industry and academia. They agreed on three major areas where promising grand science and technology challenges could be addressed by the FET Flagships: ‘ICT and connected society’, ’Health and the life sciences’ and ’Energy, environment and climate change’. As a result, a call for preparatory actions for future research initiatives was launched in October 2017 as part of the Horizon 2020 FET Work Programme 2018. From the 33 proposals submitted, six were selected after a two-stage evaluation by independent high-level experts.
The 33 institutions receiving parts of the €1 million to develop Time Machine: Ecole Polythechnique Federale de Lausanne, TU Wien, International Centre for Archival Research, Koninklijke Nederlandse Akademie van Wetenschappen, Naver France, Universiteit Utrecht, FAU Erlangen-Nürnberg, Ecole Nationale des Chartes, Universita di Bologna, Institut National de l’Information Geographique et Forestiere, Universiteit van Amsterdam, Uniwersytet Warszawski, Universite du Luxemburg, Bar-Ilan University, Universita Venezia, Universiteit Antwerpen, Qidenus Group, TU Delft, Centre National de la Recherche Scientifique, Stichting Nederlands Instituut voor Beeld en Geluid, FIZ Karlsruhe, Fraunhofer Gesellschaft, Universiteit Gent, TU Dresden, TU Dortmund, Österreichische Nationalbibliothek, Iconem, Institute of Bioorganic Chemistry, Polish Academy of Sciences, Picturae, Centre des Visio per Computador, Europeana Foundation, Indra, Ubisoft
Prof. Dr. Andreas Maier