While several Member States have or are in the process of developing their AI strategies, smart cities and communities could well be the main beneficiaries of AI. With the increasing use of digital devices, sensors and Internet of Things (IoT), smart cities have seen a tremendous growth in data generated, including real-time and Big Data. This wealth of data, combined with machine and deep learning, can recognise patterns, help predict events, allow for more personalised services, optimise the use of resources as well as strengthen evidence-based analytical capability for policy-making and implementation. In order however, to benefit from the possibilities of AI at local level, there is a need for large amounts of high quality data, with relevance in the urban context (economic, urban, geographical, technical, climatic, health, etc.). Smart cities and communities are however, not yet fully exploiting the value of data and AI. Beyond defining their own specific urban data, cities need to implement the necessary processes for data provisioning and data management, build a powerful data infrastructure to support and automate these processes, and ensure their own municipal data sovereignty. There is a need for appropriate data governance models and a vibrant ecosystem, whereby all stakeholders can capitalise on the value of data and AI, to ensure sustainability.
Digital Innovation Hubs (DIHs) have and important role in accelerating the take-up of AI by helping public administrations identify necessary datasets, develop algorithms, train AI and link to computing facilities, building on the “AI-on on-demand” platform. To enable this support mechanism, DIHs could also take an active role in helping cities get access to and re-use the data, relevant at urban level, regardless of its source. While the creation of common EU dataspaces for various domains is promising (energy, mobility, health etc.), smart cities could also benefit from data generated by persons, companies and machines as well as behavioural data. DIHs could also have an important role in bringing together or develop the necessary ecosystem of relevant stakeholders to make this happen.
The previous webinar, held on 30/04/2020 introduced the idea of DIHs helping in the transition towards smart cities and highlighted measures that could help develop strategies, implement urban platforms, re-use existing digital tools and even arrive at providing urban digital services.
The webinar on 25/05/2020 aimed to complement these aspects with that of data and AI, trying to address the following questions:
- What European tools / knowledge can DIHs use to help smart cities experiment with AI?
- What data sets are most relevant for smart city services and what governance mechanisms would be most useful?
- What kinds of AI-enabled solutions and services would cities benefit from the most?
- What role can DIHs take to help cities implement AI-enabled urban services or urban digital twins?
Agenda - 25/5/2020 - 14:00-16:00 CET
Moderator: Andrea Halmos, DG CONNECT H5
Setting the scene
- AI Watch / AI for public services, Gianluca Misuraca, JRC, Unit B6
European tools and support measures for using AI in smart cities
- AI-on-demand platform (Cecile Huet, DG CONNECT A1)
- AI TEFs for smart cities, mobility, energy and environment (Svetoslav Mihaylov, DG CONNECT H5)
- AI DIH Network (Giovanna Galasso, PwC)
- AI4CITIES (Hugo Goncalves, Forum Virium)
Showcasing AI-enabled solutions in smart cities
- AMLab and Szombathely, Balazs Barta, Hungary:
- Digital twin for the city of Antwerp, Stefan Lefever, IMEC, Belgium
- SmartCityTech, Mark De Colvenaer, Belgium
- Fondazione Bruno Kessler, Paolo Traverso, Trentino, Italy
- TeraLab, Natalie Cernecka, France
- AI Digital Innovation Hub, Luleå Technical University, Anna Ståhlbröst, Sweden
Discussion (questions around: data, governance, challenges, enablers)
Conclusions
Related content
Event report | 25 May 2020
Digital Innovation Hubs (DIHs) have and important role in accelerating the take-up of Artificial Intelligence (AI) by helping public administrations identify necessary datasets, develop algorithms, train AI and link to computing facilities, building on the “AI-on on-demand” platform.