ELISE AI roadmap
ELISE is driving a new wave of research and development to deliver AI ‘made in Europe’. Together, its 14 research programmes create AI methods, techniques, and toolkits that are technically innovative, safe, and effective in deployment, while being aligned with social needs. By combining our research agenda with initiatives to attract top talent to Europe, train the next generation of AI researchers, and enhance local start-up and innovation networks, ELISE is creating a European AI ecosystem of excellence and trust.
AI technologies that are technically advanced
Methods and tools to analyse real-world, multi-modal data; Strengthen core machine learning capabilities, through methodological and theoretical advances, such as techniques to bridge between data-driven and domain knowledge; Interrogate workings of complex systems through advances in simulation, emulation, and causality.
AI technologies that are robust in deployment
AI that is robust under dynamic or uncertain conditions; Human-centric tools that are effective as decision-support; Methods to enhance explainability in decision-making.
AI technologies that align with societal interests
Techniques for trustworthy AI; Deployed AI that is integrated into areas of critical need; AI research and development that engages stakeholder perspectives.
- Advance the science of artificial intelligence by better understanding the intelligent behaviour of living systems and how this emerges.
- Strengthen the theoretical underpinnings and algorithmic capabilities of machine learning, creating more reliable, efficient and usable machine learning systems.
- 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.
- Build bridges between classical AI methods and machine learning to advance further progress in computer vision.
- Explore the role of causal modelling as a bridge between observational and interventional learning, identifying the principles for interactive learning systems.
- Push forward the foundations of multimodal learning systems and expand their application.
- Improve the performance of deep learning systems.
- Understand the principles for robustness in deployment and develop techniques for machine learning that reliably performs well.
- Build systems for general-purpose natural language understanding and generation.
- Improve core machine learning functions, for example through enhanced methods for deep learning, computer vision, natural language understanding and generation, and semantic, symbolic, and interpretable machine learning.
- Create robotic systems that can interact intelligently with the world around them by combining robot learning approaches with machine learning methods, such as reinforcement learning; and information systems that can better understand human behaviour.
- Create AI systems to support the delivery of effective public services, for example creating AI systems for healthcare that can monitor patient health, using complex datasets to develop decision-support systems and to foster breakthrough applications in healthcare and biomedicine.
- Develop 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.
- Design 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.
