4 papers
How Hyper-Datafication Impacts the Sustainability Costs in Frontier AI
Sophia N. Wilson, Sebastian Mair, Mophat Okinyi +3
Large-scale data has fuelled the success of frontier artificial intelligence (AI) models over the past decade. This expansion has relied on sustained efforts by large technology co…
Position: Stop Preaching and Start Practising Data Frugality for Responsible Development of AI
Sophia N. Wilson, Andrew Millard, Guðrún Fjóla Guðmundsdóttir +2
This position paper argues that the machine learning community must move from preaching to practising data frugality for responsible artificial intelligence (AI) development. For t…
Performance-Carbon Trade-Offs across Architectural Biases in Shear Flow Forecasting
Sophia N. Wilson, Jens Hesselbjerg Christensen, Raghavendra Selvan
Development of modern deep learning methods has been driven primarily by the push for improving model efficacy (accuracy metrics), leading to large-scale models that require massiv…
Characterizing Learning in Deep Neural Networks using Tractable Algorithmic Complexity Analysis
Pedram Bakhtiarifard, Sophia N. Wilson, Mahmoud Afifi +2
Training large-scale deep neural networks (DNNs) is resource-intensive, making model compression a practical necessity. The widely accepted ''learning as compression'' hypothesis p…