Deep Learning Is Not Good Enough, We Need Bayesian Deep Learning for Safe AI

These results show that when we train on less data, or test on data which is significantly different from the training set, then our epistemic uncertainty increases drastically. However, our aleatoric uncertainty remains relatively constant, which it should because it is tested on the same problem with the same sensor. Next I'm going to discuss an interesting application of these ideas for multi-task learning. Multi-task learning aims to improve learning efficiency and prediction accuracy by learning multiple objectives from a shared representation. It is prevalent in many areas of machine learning, from NLP to speech recognition to computer vision.