Driven by progress in deep learning, the machine learning community is now able to tackle increasingly more complex problems—ranging from multi-modal reasoning to dexterous robotic manipulation—all of which typically involve solving nontrivial combinations of tasks. Thus, designing adaptive models and algorithms that can efficiently learn, master, and combine multiple tasks is the next frontier. AMTL workshop aims to bring together machine learning researchers from areas ranging from theory to applications and systems, to explore and discuss: * advantages, disadvantages, and applicability of different approaches to learning in multitask settings, * formal or intuitive connections between methods developed for different problems that help better understand the landscape of multitask learning techniques and inspire technique transfer between research lines, * fundamental challenges and open questions that the community needs to tackle for the field to move forward.