Position: Bayesian Deep Learning is Needed in the Age of Large-Scale AI
This is an incremental position paper advocating for BDL to solve broader problems in AI research beyond predictive accuracy.
The paper argues that Bayesian deep learning (BDL) is essential for addressing overlooked metrics like uncertainty and active learning in large-scale AI, proposing its integration with foundation models to enhance capabilities.
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.