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The ocean’s sheer size and dynamic nature make it a challenging environment to observe. While we continue to increase our observational capacity, the scale of the problem remains immense: all told, biological records of any kind exist for only about 5% of the ocean's volume. These knowledge gaps are both scientifically unsatisfying and leave us without key data to inform the management of a resource that contributes $2.6 trillion annually to the global economy.

How can society go about understanding, modelling, and managing such an underexplored ecosystem? We cannot exhaustively sample the entire ocean. Our moment of unavoidable environmental change and continued resource extraction demands new approaches to effectively use the data we have and guide resources to maximize our time in the field. By reframing marine sampling as both an ocean and computer science problem, we can develop novel approaches to efficiently monitor biodiversity and rapidly explore under observed regions.

In this talk, Dr Eric Orenstein will discuss two promising approaches: using AI models to propose geospatial locations to explore and integrating AI-based decision making on autonomous underwater vehicles for adaptive sampling.

This event is part of the King’s Institute for Artificial Intelligence’s AI Frontiers series.

Meet the speaker

Dr Eric Orenstein is an AI+ Senior Fellow working at the intersection of computer science and ecology to provide novel perspectives on marine population dynamics, how they are impacted by their environment, and how they in turn influence the rest of the ecosystem. Eric spends lots of time thinking about sampling strategies for robots and how to get AI models to recognize population changes.

Before arriving at King's, Eric was a Senior Research Scientist at the National Oceanography Centre leading the institution's AI Team. Eric was previously a Research Engineer at the Monterey Bay Aquarium Research Institute where he worked on real-time data processing on autonomous vehicles, studied particles in the deep ocean, and co-led the FathomNet project. He was a postdoctoral scholar at the Sorbonne’s Villefranche Oceanographic Laboratory and earned his PhD at UC San Diego for his work on fine-grained image classification of marine plankton.