SOURCE-LINKED INTELLIGENCE

ROBUSTIFYING GENERATIVE AI THROUGH HUMAN-CENTRIC INTEGRATION OF NEURAL AND SYMBOLIC METHODS

CORDIS · observation · Publication date unknown

ROBUSTIFYING GENERATIVE AI THROUGH HUMAN-CENTRIC INTEGRATION OF NEURAL AND SYMBOLIC METHODS Generative AI (GenAI), such as foundation models, represents a powerful and transformative class of AI capable of learning patterns from data and generating new content. However, GenAI has notable shortcomings that can lead to misuse or hinder its widespread adoption and positive societal and economic impact. These shortcomings stem from its lack of robustness in three key areas: technical, operational, and user robustness. Addressing these challenges in foundation models, especially in the context of human cyber-physical systems (HCPS)—the most demanding GenAI applications in terms of robustness—will pave the way for solutions applicable across various domains, unlocking GenAI's full potential.Building on the EU’s competitiveness in constructing and assuring dependable complex systems, RobustifAI, a 3-year pr

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recordType

award

status

SIGNED

region

EU

value

7362490

unit

EUR