Overcome model déjà vu by leveraging diverse datasets with deep multi-task learning - Artificial Intelligence

"When we try to pick out anything by itself, we find it hitched to everything else in the universe" – John Muir Often in real-world tasks, there isn't enough data to take full advantage of deep learning. However, it is possible to leverage other datasets to reach a critical mass. Sharing knowledge across diverse datasets leads to more general knowledge, deeper insights and more well-informed decisions. This is especially true in domains like healthcare, where data for any particular task can be expensive or dangerous to collect. Modeling datasets separately wastes useful structure that could be shared between them.