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Biodiversity in lakes: multimodal AI crunches eADN data to monitor pollution

• Pollution and land-use change pose a major threat to biodiversity in lakes and other natural ecosystems.
• A team of scientists from the University of Birmingham is pioneering an innovative approach involving explainable machine learning to study these complex dynamics.
• Their research, which highlights hitherto unforeseen environmental impacts, will pave the way for better management of complex ecosystems.
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Young woman wearing gloves conducts environmental research by a lake. She uses equipment including a laptop and test kits. Trees and water in the background.
A man in a safety vest reviews documents in front of a row of colorful shipping containers at a port.

Contraband: AI efficiently detects anomalies in shipping containers

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“I lost track of time”: how we get caught up in digital applications?

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A User-Centred Approach to AI Explainability

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How to make AI explainable?

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Explainability of artificial intelligence systems: what are the requirements and limits?

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Far far edge: The opportunities and challenges

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