4 citations · 4 across the 6 of their papers we have counts for
8 papers
The Rising Unsustainability of AI Graphics Cards Production
Clément Morand, Aurélie Névéol, Anne-Laure Ligozat
The rapid advancement of Artificial Intelligence (AI) has been accompanied by significant increases in computational and environmental costs, driven by large-scale investments in A…
Environmental Footprint of GenAI Research: Insights from the Moshi Foundation Model
Marta López-Rauhut, Loic Landrieu, Mathieu Aubry +1
New multi-modal large language models (MLLMs) are continuously being trained and deployed, following rapid development cycles. This generative AI frenzy is driving steady increases…
Is Knowledge Distillation Actually Greener? A Case Study in Machine Translation
Joseph Attieh, Timothee Mickus, Anne-Laure Ligozat +2
Knowledge distillation (KD) is a technique to compress a larger teacher system into a smaller student. In machine translation, KD is commonly evaluated through translation quality…
The Environmental Impacts of Machine Learning Training Keep Rising Evidencing Rebound Effect
Clément Morand, Anne-Laure Ligozat, Aurélie Névéol
Recent Machine Learning (ML) approaches have shown increased performance on benchmarks but at the cost of escalating computational demands. Hardware, algorithmic and carbon optimiz…
How Green Can AI Be? A Study of Trends in Machine Learning Environmental Impacts
Clément Morand, Anne-Laure Ligozat, Aurélie Névéol
The compute requirements associated with training Artificial Intelligence (AI) models have increased exponentially over time. Optimisation strategies aim to reduce the energy consu…
Estimating The Carbon Footprint Of Digital Agriculture Deployment: A Parametric Bottom-Up Modelling Approach
Pierre La Rocca, Gaël Guennebaud, Aurélie Bugeau +1
Digitalization appears as a lever to enhance agriculture sustainability. However, existing works on digital agriculture's own sustainability remain scarce, disregarding the environ…