Inactive learning?

Abstract

Despite the tremendous level of adoption of machine learning techniques in real-world settings, and the large volume of research on active learning, active learning techniques have been slow to gain substantial traction in practical applications. This reluctance of adoption is contrary to active learning's promise of reduced model-development costs and increased performance on a model-development budget. This essay presents several important and under-discussed challenges to using active learning well in practice. We hope this paper can serve as a call to arms for researchers in active learning--an encouragement to focus even more attention on how practitioners might actually use active learning.