Techniques grouped under the umbrella term of Artificial Intelligence (AI) are being applied across various fields of human activity, with their applications successfully tackling real-life challenges. Evidence of this is seen in the presence of approaches such as, search and optimisation, statistical learning, probabilistic modelling, and uncertain reasoning, which are increasingly used in web search engines, image and speech recognition modules, recommender systems, route planners, automated timetabling / rostering engines, etc. The marine environment is no exception to this, as state-of-the-art AI developments are already having transformational effects in several sectors – for example, environmental monitoring, logistics, resource management, planning and governance.

However, marine scientists might not be in a position to fully benefit from the potential of AI solutions as: they might be unaware of AI techniques specialised in tackling their type of problem / data; they might achieve sub-optimal results when applying/fine-tuning a complex AI solution; or they might struggle to find the right collaborators to work on AI tasks (e.g., given high-demand from other non-marine sectors). This is especially problematic as promising new technologies in marine research and industry produce large amounts of data (images, videos, signals) which either require AI processing or would benefit from it – e.g.: oceanographic data from gliders, high throughput sequencing and eDNA sequencing, active acoustics for biomass and abundance estimation, automated image/video monitoring.