  Although information access systems have long supported people in
accomplishing a wide range of tasks, we propose broadening the scope of users
of information access systems to include task-driven machines, such as machine
learning models. In this way, the core principles of indexing, representation,
retrieval, and ranking can be applied and extended to substantially improve
model generalization, scalability, robustness, and interpretability. We
describe a generic retrieval-enhanced machine learning (REML) framework, which
includes a number of existing models as special cases. REML challenges
information retrieval conventions, presenting opportunities for novel advances
in core areas, including optimization. The REML research agenda lays a
foundation for a new style of information access research and paves a path
towards advancing machine learning and artificial intelligence.
