About us
AI is a powerful technology reshaping society, but it also presents complex challenges. The appliedAI Institute for Europe stands for Trustworthy AI by focusing on fairness, transparency, and accountability to ensure AI driven innovation while respecting ethical standards. As a central harbour in the European AI ecosystem, we provide knowledge, resources, and connections to empower stakeholders in responsibly developing and applying AI technologies. Our impact is measured through the strategic roles we fulfill like agenda setting, knowledge brokering, enabling, scouting, experimenting, tool providing and network building. Over the past year, we have contributed through policy insights, compliance tools, educational programs, and open-source solutions. We actively foster collaboration, host events, and support AI startups to strengthen the ecosystem. The appliedAI Institute for Europe plays a crucial role in shaping the future of AI in Europe.
The TransferLab aims to identify, test and disseminate established and emerging techniques in machine learning in order to provide practitioners and businesses with the best tools for their applications. We constantly survey multiple fields in AI/ML with the goal of distilling useful knowledge for practitioners in industry and academia. Research at the TransferLab is not measured in publications or conference appearances but by its long-term impact on the people and companies applying machine learning. Our work is not done when the paper is published, but when we have created software, trainings, and other resources that help practitioners to benefit from it.
The TransferLab aims to identify, test and disseminate established and emerging techniques in machine learning in order to provide practitioners and businesses with the best tools for their applications. We constantly survey multiple fields in AI/ML with the goal of distilling useful knowledge for practitioners in industry and academia. Research at the TransferLab is not measured in publications or conference appearances but by its long-term impact on the people and companies applying machine learning. Our work is not done when the paper is published, but when we have created software, trainings, and other resources that help practitioners to benefit from it.