European strategic research agenda in artificial intelligence

ELISE vision for the next generation of AI for Europe - path towards trustworthy AI technologies

Motivated by the ambition to establish European leadership in AI and create a new generation of trustworthy AI systems, ELISE aims to build a network of the continent’s leading AI researchers. Together, this network will pursue pan-European research collaborations that tackle issues of pressing scientific and social concern. ELISE Strategic Research Agenda outlines a path towards the next generation AI needed to solve burning global challenges, inspired by European values, in service of the diverse European society.

AI already has and will have even more profound and deep economic and societal impacts on the global scale. Machine learning systems are already successfully deployed in a range of applications, from car driver assistance to language translation and in fields from climate science to drug development. Further advances in AI have the potential to transform economies and society, but also bring new risks.ELISE has produced a Strategic Research Agenda (SRA), outlining the consortium’s vision for the next generation of AI for Europe - trustworthy AI technologies that can boost economic growth while effectively serving the diverse European society. The SRA also outlines how technological advances can contribute to European policy ambitions for AI, as well as the support needed to maintain European leadership in AI.

The SRA aims to answer to the demand for AI to be developed and implemented in ways that ensure it respects applicable laws, upholds ethical principles and values, and takes into account the changing demands of both the technical and social environment. The ELISE approach combines the technical advances coming from ELISE and ELLIS research programmes with five cross-cutting themes that connect these programmes to wider scientific or social issues. These themes cover:

■ Security and privacy;
■ Explainable and transparent AI;
■ Trustworthiness and AI certification;
■ AI integration across systems;
■ AI ethics and societal impact.

Together, these lay the foundation for a new generation of AI methods and applications that benefit all in society.

Read the full Strategic Research Agenda (SRA).

Read the press release on the SRA here.

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ELLIS & ELISE Research programs

To achieve the vision set in the SRA, ELISE supports ELLIS research programmes - a European network of top researchers working at locations throughout Europe. Directed by outstanding European researchers, Research Programms focus on high-impact problem areas that have the potential to move the needle in modern AI. The programs aim to increase the power of today’s AI methods and promote their deployment in areas that can boost economic growth and societal wellbeing. Together, these research programmes map out a pathway to achieving trustworthy AI – AI that reflects European values and benefits all in society.

Implementing the Strategic Research Agenda: research programmes
Machine Learning for Health 
Program aim
To create AI systems that can be used to monitor patient health, using complex datasets to inform decision-support systems and to foster breakthrough applications in healthcare and biomedicine.
Gunnar Rätsch (ETH Zürich)
Mihaela van der Schaar (U Cambridge, ATI)
Oliver Stegle (German Cancer Research Center and EMBL)

Robot Learning: Closing the Reality Gap!
Program aim
To create robotic systems that can interact intelligently with the world around them.
Aude Billard (EPFL)
Jan Peters (TU Darmstadt) 
Tamim Asfour (Karlsruhe Institute of Technology)

Geometric Deep Learning 
Program aim
To improve the performance of deep learning algorithms in non-Euclidean spaces, and in so doing identify new applications, efficient implementations and symmetries in data that can be used to advance the use of deep learning methods.
Max Welling (University of Amsterdam, Qualcomm AI Research) 
Michael Bronstein (Imperial College London)

Human-centric Machine Learning 
Program aim
To develop novel machine learning algorithms that are better aligned with human needs and societal interests, for example taking into account concerns around fairness, privacy, accountability, transparency and autonomy.
Nuria Oliver (DataPop Alliance, Royal Academy of Engineering, University of Alicante)
Plamen Angelov (Lancaster University)
Adrian Weller (Alan Turing Institute)

Interactive Learning and Interventional Representations 
Program aim
To explore the role of causal modelling in bridging the gap between observational and interventional learning and understand the principles underlying interactive learning systems.
Nicolò Cesa-Bianchi (Università degli Studi di Milano)
Andreas Krause (ETH Zürich)
Bernhard Schölkopf (MPI-IS Tübingen)

Machine Learning and Computer Vision 
Program aim
To build bridges between classical algorithms and machine learning to unlock further advances in computer vision.
Cordelia Schmid (INRIA)
Yair Weiss (Hebrew University)
Bernt Schiele (MPI Informatics)

Machine Learning for Earth and Climate Sciences
Program aim
To create AI tools that can contribute to humanity’s response to the climate crisis, increasing understanding of climate extremes, changes to earth systems and potential areas for intervention.
Gustau Camps-Valls (Universitat de València)
Markus Reichstein (MPI for Biogeochemistry)

Multimodal Learning Systems 
Program aim
To push the boundaries of the foundational aspects of this field, to build bridges between researchers and practitioners currently active in multiple unimodal communities, as well as expanding and exploring the applications of multimodal learning systems.
Cees Snoek (UvA)
Nicu Sebe (University of Trento)

Natural Intelligence 
Program aim
To advance the science of artificial intelligence by better understanding the intelligent behaviour of living systems and how this emerges.
Matthias Bethge (University of Tübingen)
Y-Lan Boureau (Facebook AI Research)
Peter Dayan (MPI for Biological Cybernetics)

Natural Language Processing 
Program aim
To build systems for general-purpose natural language understanding and generation.
Ivan Titov (University of Edinburgh)
Andre F.T. Martins (Instituto Superior Técnico, Lisboa)
Iryna Gurevych (Technical University Darmstadt)

Quantum and Physics-Based Machine Learning
Program aim
To design new, energy-efficient machine learning algorithms and hardware implementations, drawing from concepts in quantum physics and statistical physics to develop more powerful machine learning systems.
Bert Kappen (Radboud University Nijmegen)
Riccardo Zecchina (Bocconi University Milan)

Robust Machine Learning 
Program aim
To understand the principles and develop the techniques for machine learning that reliably performs well. 
Yee Whye Teh (University of Oxford and DeepMind)
Chris Holmes (University of Oxford and Alan Turing Institute)
Samuel Kaski (Aalto University, Finnish Center for Artificial Intelligence and University of Manchester)

Semantic, Symbolic and Interpretable Machine Learning 
Program aim
To become a cumulation point of like-minded researchers and we expect fruitful interactions with closely related programs covering, e.g., NLP, vision, and geometric deep learning.
Volker Tresp (Siemens)
Kristian Kersting (TU Darmstadt)
Paolo Frasconi (University of Florence)

Theory, Algorithms and Computations of Modern Learning Systems
Program aim
To advance the theoretical underpinnings and algorithmic capabilities of machine learning, creating more reliable, efficient and usable machine learning systems.
Francis Bach (INRIA)
Philipp Hennig (University Tübingen/MPI Tübingen)
Lorenzo Rosasco (Universita’ di Genova and MIT)

Find out more about the work of Research programs in the
ELISE Strategic Research Agenda and on ELLIS website!
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