ERC Starting Grant · 2024
Continual and Sequential Learning for Artificial Intelligence
Machine Learning systems, while promising, lack the autonomy needed for many real-world applications beyond mere proof-of-concept stages. The key challenge lies in enabling AI to continuously adapt to shifting data distributions and proactively seek information under high uncertainty. Fields such as drug discovery and micro-chemistry are expecting breakthroughs from AI, given the vast and intricate search spaces they deal with, coupled with expensive data acquisition. It is vital for algorithms to steer this search, assimilate new data, and strategically explore promising zones. Reinforcement Learning (RL) offers tools and methods for agents to autonomously learn from their actions, but its…
From the public funding record at EU CORDIS. Describes the funded project, not the reviews below.