The UK government have announced plans to ‘unleash AI’ across the UK to boost economic growth. However, this has raised questions about AI’s high energy and water demands, and whether this can be balanced with the UK’s climate and environmental targets. Here we highlight how PML is exploring ways in which AI is being used for our environmental research and how we are reducing our energy demands.
PML’s artificial intelligence (AI) capabilities have been expanding rapidly in recent years as its scientists have been leveraging AI to accelerate understanding from huge amounts of environmental data, which continues to grow. This includes identifying features in satellite images, detecting species in drone footage or autonomous camera systems, and identifying complex relationships within large datasets. With petabytes of data, an ever-increasing archive of field samples and surveys, and data sharing becoming easier, in-house AI and machine-learning expertise is essential to effectively optimise the value of these data.
Prof. Matthew Palmer, Head of Science for Digital Innovation & Marine Autonomy at PML, said: “AI is an inevitable and necessary component of modern environmental research, rapidly advancing efforts to elucidate the complex connections across vast global datasets, helping analyse an ever-increasing range of image data and to provide sustainable solutions for marine spatial planning. PML is ensuring we remain at the leading edge of such technologies through targeted investment in infrastructure and people, and by establishing strong strategic partnerships across academic, industry and government sectors”.
Recent projects include:
Harmful Algal Bloom (HAB) Forecasting
PML has studied algal blooms for decades by looking for colour signatures in optical satellite images. Now with AI we can build more complex models incorporating other data like temperature, chlorophyll levels, etc. to try and detect different species, and to predict when and where they will occur.
Pacific Oyster Detection
The team are training machine learning models to automatically detect invasive Pacific oysters along the Devon coast from drone imagery and with AI, they can identify and count them in a fraction of the time, making the species much easier to monitor.
Benthic Species Detection
We have been using UK based seabed imagery collected with a range of different camera systems to automatically identify and categorise a range of seabed life. This work is vital to understand the health and biodiversity of the UK coastal waters and would not be possible to process the quantity of data without the use of AI.
Plankton Identification
An interdisciplinary team at PML are setting up the state-of-the-art automated, in-situ Plankton Imaging and Classification System (APICS), that uses AI to process significant amount of plankton data in near real-time, to create a more responsive and comprehensive system for monitoring marine ecosystems.
With all the amazing advancements AI brings, it is a resource-hungry activity and there are widespread concerns that the environmental cost could significantly lessen the benefits. This is something about which we are particularly aware at PML.
Dr David Moffat, Artificial Intelligence and Machine Learning Data Scientist specialist at Plymouth Marine Laboratory, said:
“PML is not only dedicated to addressing environmental challenges around the world but also to ensuring its own environmental footprint is as neutral as possible. With increasing AI activities, we have been keen to investigate approaches to reduce the resource use of our AI systems. These activities include the installation of additional solar panels, ensuring that the datacentres we operate at PML are as energy efficient as possible and employing best practices in training AI models. We will typically pilot our AI development on smaller datasets, use smaller AI models, and transfer AI models from other domains, to speed up the training process, using less computer power and therefore, energy”.
Dr Dan Clewley, Lead Research Software Engineer within the Digital Innovation and Marine Autonomy group at Plymouth Marine Laboratory and manager of the NERC Earth Observation Data Analysis and Artificial-Intelligence Service (NEODAAS), said:
“Energy and water usage of AI is something that environmental scientists are concerned about. In particular, balancing these against the benefits AI can bring when applied to improve our understanding of the environment and address some of the challenges faced by the biodiversity and climate crisis. As well as making our use of AI as efficient as possible we also look at everything around applying AI, from preparing the data needed to train models to how we make outputs available to support policymakers. The team of Research Software Engineers at PML work with scientists at PML and externally to support them using AI in their research”.
PML are also investigating where the use of AI can reduce energy usage. For example, modelling our ocean requires solving a set of very complicated equations, which must be done multiple times for different areas, times and scenarios, such as different emissions pathways for climate change. Using AI offers an exciting opportunity to take what we know from existing models and produce ‘Digital Twins’ and ‘emulators’ that can run much faster and use much less energy than current models. PML have been working on this approach for some of the biochemical models of the ocean.
Find out more about PML’s biochemical Digital Twins >>
Dr Jozef Skakala, study lead and Ecosystem Modeller at PML, commented:
“In a recent study we were successful in building computationally-inexpensive and efficient machine learning emulators to replicate computationally-expensive and complex physical-biogeochemical models.”
“Our vision is that these emulators, acting as ‘digital twins of the ocean’, would eventually democratize the access to modelling, enabling developing countries and other end-users without access to high-performance computing facilities, to investigate a range of real-world scenarios for management and policy-making decisions. Plans are to develop similar tools to explore future climate scenarios for ocean ecosystem health”.
