  Despite the advancement of machine learning techniques in recent years,
state-of-the-art systems lack robustness to "real world" events, where the
input distributions and tasks encountered by the deployed systems will not be
limited to the original training context, and systems will instead need to
adapt to novel distributions and tasks while deployed. This critical gap may be
addressed through the development of "Lifelong Learning" systems that are
capable of 1) Continuous Learning, 2) Transfer and Adaptation, and 3)
Scalability. Unfortunately, efforts to improve these capabilities are typically
treated as distinct areas of research that are assessed independently, without
regard to the impact of each separate capability on other aspects of the
system. We instead propose a holistic approach, using a suite of metrics and an
evaluation framework to assess Lifelong Learning in a principled way that is
agnostic to specific domains or system techniques. Through five case studies,
we show that this suite of metrics can inform the development of varied and
complex Lifelong Learning systems. We highlight how the proposed suite of
metrics quantifies performance trade-offs present during Lifelong Learning
system development - both the widely discussed Stability-Plasticity dilemma and
the newly proposed relationship between Sample Efficient and Robust Learning.
Further, we make recommendations for the formulation and use of metrics to
guide the continuing development of Lifelong Learning systems and assess their
progress in the future.
