The project is based on two key hypotheses:
- Unsupervised Learning: Concepts reflecting the properties of the natural world, such as object geometry and physical attributes, can be learned without manual supervision while remaining interpretable to humans.
- Efficient Skill Acquisition: Machines can acquire new skills based on minimal manually labeled data, making AI applications more versatile and tailored to specific needs.
Dr. Joanna Kulesza Joins UNION Advisory Board: Contribution to AI Due Diligence and Ethical Assessments
We are delighted to announce that Dr. Joanna Kulesza, Director of the LCH Center, has joined the Advisory Board of the Unsupervised Perception (UNION) research project, funded by the ERC. Dr. Kulesza brings extensive expertise in international law and ethics, supporting due diligence assessments in AI implementation. Her unique perspective on the legal and ethical dimensions of emerging technologies strengthens UNION's mission to deliver innovative and responsible AI solutions.
Project Details:
- Start Date: January 1, 2022
- End Date: May 31, 2027
- Total Cost: €2,311,847.00
- Funding Program: H2020-EU.1.1. - EXCELLENT SCIENCE - European Research Council (ERC)
- Topic: ERC-2020-COG - ERC Consolidator Grants
- Coordinating Institution: The Chancellor, Masters and Scholars of the University of Oxford, United Kingdom
Fields of Science:
- Natural Sciences
- Computer Science
- Artificial Intelligence
For more details, visit the UNION Website.