Scientists studying aquatic ecosystems are increasingly able to collect big data; large and complex datasets necessitating more computing intensive analyses. While the data (e.g., from acoustics or omics) themselves can be quite different, the methods to analyze them are often rather similar. In many cases, artificial intelligence (AI; e.g., machine learning, deep learning) can expedite analyses by limiting the amount of human interaction needed. Furthermore, AI-based analyses are often able to detect patterns that traditional statistics don’t pick up on. AI research has begun to surface in all corners of aquatic sciences. Researchers dealing with in situ imagery, and passive and active acoustic data have made particularly rapid progress, but other research areas are also pushing boundaries by applying AI techniques. Examples of such research include ocean -omics research and eDNA, autonomous sampling, fisheries research and management, as well as satellite imagery processing and the automated identification of sea surface features. We invite practitioners from various oceanographic disciplines to submit abstracts highlighting their research on big data and AI at all levels of biological organization (individual, population, ecosystems) and spatio-temporal scales. Given the nascent nature of this field, submissions that focus on methodological innovations are equally welcome to those delving into using AI to address ecological questions.