Representation Drift

Representation drift describes the phenomenon where learned representations in artificial intelligence models change over time, even when presented with consistent inputs or tasks. Current research focuses on mitigating this drift to improve continual learning, where models must adapt to new tasks without forgetting previously learned information; this involves developing algorithms and architectures (like those based on transformers and prototype learning) that maintain stable representations across multiple tasks. Addressing representation drift is crucial for building more robust and adaptable AI systems, impacting fields like lifelong learning, natural language processing, and robotics by enabling more efficient and effective knowledge acquisition and retention.