Green

By Denise Handlarski. 2007

Subfields: AI Ethics, Fairness & Society

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The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In AI Ethics, Fairness & Society

Citations in the corpus

Language Models are Few-Shot Learners (2020, methodology): “This means we should be cognizant of the cost and efﬁciency of such models, as advocated by [SDSE19].”
Efficient Large Scale Language Modeling with Mixtures of Experts (2021, neutral): “1https://github.com/pytorch/fairseq/ tree/main/examples/moe_lm Unfortunately, the corresponding growth in computational resources required to train state-of-theart language models is a barrier for many in the research community (Schwartz et al., 2019).”
Quantifying the Carbon Emissions of Machine Learning (2019, methodology): “This trend has been the subject of recent studies aiming to evaluate the climate impact of AI, which have predominantly put the focus on the environmental cost of training large-scale models connected to grids powered by fossil fuels [1, 2].”
Avalanche: an End-to-End Library for Continual Learning (2021, background): “Standard deep learning libraries such as TensorFlow [1] or PyTorch [39] already focus on efﬁciency and scalability as two fundamental designing principles, since modern research experiments can take months to complete [48].”
Towards the Systematic Reporting of the Energy and Carbon Footprints of Machine Learning (2020, background): “Floating Point Operations (FPOs) are the de facto standard for reporting “eﬃciency” of a deep learning model (Schwartz et al., 2019), and intuitively they should be correlated with energy eﬃciency—after all, fewer operations should result in faster and more energy eﬃcient processing.”
Ionic liquids for high performance lithium metal batteries (2020, background): “The uniform, relatively thin, compact, SEI film with high ion conductivity and high elastic strength can prevent the parasitic reactions consuming the lithium metal material and the electrolyte, and leading to reduced coulombic efficiency [7,8].”
Cisplatin: The first metal based anticancer drug. (2019, background): “ROS together with Bax (Bcl2 associated X) and Ca2+ cause mtDNA damage and fall in MPT (Mitochondrial permeability transition) [53] which facilitate rupture of mitochondria [54].”
On Warm-Starting Neural Network Training (2019, background): “This is an example of “Red AI” [5], disregarding resource consumption in pursuit of raw predictive performance.”
Green Synthesis of Highly Luminescent Carbon Quantum Dots from Lemon Juice (2019, background): “In addition to fruit juices, plant wastes such as pomelo peel [9], willow bark [10], and watermelon peel [11] can be used for synthesizing C-dots.”
Distilling Neural Networks for Greener and Faster Dependency Parsing (2020, background): “4There exist a number of packages for computing the FPO of a model but, to our knowledge, as of yet they do not include the capability of dealing with LSTMs. some treebanks suffer more when compressed to 20% than others when compared to the full baseline model, e.g. Finnish-TDT and Ancient-GreekPers”
bert2BERT: Towards Reusable Pretrained Language Models (2021, background): “Therefore, how to reduce the training cost of PLM is of great importance to Green AI (Schwartz et al., 2019).”