Energy and environmental impacts of deep learning

Abstract : In recent years, artificial intelligence based on deep learning models has become increasingly widespread across a wide range of applications, achieving ever-greater performance with ever-increasing computational cost. This growth drives higher energy consumption and carbon emissions, running counter the emission reduction pathways recommended by the IPCC. In this presentation, I will present and explore the environmental impacts associated with developing, training, and deploying deep learning models using the Life-Cycle Assessment methodology. I aim to provide useful methods for estimating the impacts and to raise awareness of the environmental challenges posed by the rapid growth of AI, thereby encouraging more efficient and responsible practices.

‼️ Le séminaire peut être suivi en ligne via le lien : https://univ-amu-fr.zoom.us/j/95011527683?pwd=bTeNvaaZY75buaa7bRL0pcucXSASm7.1

Le mardi 10 février 2026 à 11h00 / Amphithéâtre François Canac, LMA

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