Research Challenges
FAIR research is organized around two Grand Challenges. These are translated into research activities through a set of interconnected Work Packages. Research results are validated in real-world use cases and disseminated through publications, demonstrators, software, tools and innovation activities.
AI-Driven Data Science
Supporting complex decision-making and actionable insight creation through powerful and intuitive AI-driven data science. Research focuses on making data science more powerful while also automating parts of the data science process itself.
Situated AI
Supporting complex task execution in dynamic environments through autonomous and semi-autonomous AI systems. Research focuses on AI systems that can perceive, reason, interact and collaborate with people and other intelligent systems in real-world environments.
Research Areas
Alongside the two Grand Challenges and their Work Packages, FAIR’s One Program approach brings researchers from across the consortium together around six Research Areas. These areas highlight shared expertise and collective research efforts across FAIR, connecting research challenges and use cases with tangible results, including publications, toolboxes and real-world adoption.
Situated AI Agents
Multimodal AI
Neurosymbolic AI
Resource-Efficient AI: Energy and data
Responsible and Trustworthy AI
AI on Time-Series