  The past two decades have witnessed the great success of the algorithmic
modeling framework advocated by Breiman et al. (2001). Nevertheless, the
excellent prediction performance of these black-box models rely heavily on the
availability of strong supervision, i.e. a large set of accurate and exact
ground-truth labels. In practice, strong supervision can be unavailable or
expensive, which calls for modeling techniques under weak supervision. In this
comment, we summarize the key concepts in weakly supervised learning and
discuss some recent developments in the field. Using algorithmic modeling alone
under a weak supervision might lead to unstable and misleading results. A
promising direction would be integrating the data modeling culture into such a
framework.
