  In the current landscape of deep learning research, there is a predominant
emphasis on achieving high predictive accuracy in supervised tasks involving
large image and language datasets. However, a broader perspective reveals a
multitude of overlooked metrics, tasks, and data types, such as uncertainty,
active and continual learning, and scientific data, that demand attention.
Bayesian deep learning (BDL) constitutes a promising avenue, offering
advantages across these diverse settings. This paper posits that BDL can
elevate the capabilities of deep learning. It revisits the strengths of BDL,
acknowledges existing challenges, and highlights some exciting research avenues
aimed at addressing these obstacles. Looking ahead, the discussion focuses on
possible ways to combine large-scale foundation models with BDL to unlock their
full potential.
