SOURCE-LINKED INTELLIGENCE

Continual and Sequential Learning for Artificial Intelligence

CORDIS · observation · Publication date unknown

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 efficacy has been largely confined to stationary, single-task settings. ConSequentIAL's vision is a Continual and Sequential Learning AI that marries supervised and unsupervi

Read original source ↗ Open in workspace

recordType

award

status

SIGNED

region

EU

value

1259375

unit

EUR