A Systematic Review of Green AI
- URL: http://arxiv.org/abs/2301.11047v3
- Date: Fri, 5 May 2023 07:49:02 GMT
- Title: A Systematic Review of Green AI
- Authors: Roberto Verdecchia and June Sallou and Lu\'is Cruz
- Abstract summary: Green AI is the study of AI environmental sustainability.
The topic experienced a considerable growth from 2020 onward.
From this review emerges that the time is suitable to adopt other Green AI research strategies.
- Score: 8.465228064780744
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: With the ever-growing adoption of AI-based systems, the carbon footprint of
AI is no longer negligible. AI researchers and practitioners are therefore
urged to hold themselves accountable for the carbon emissions of the AI models
they design and use. This led in recent years to the appearance of researches
tackling AI environmental sustainability, a field referred to as Green AI.
Despite the rapid growth of interest in the topic, a comprehensive overview of
Green AI research is to date still missing. To address this gap, in this paper,
we present a systematic review of the Green AI literature. From the analysis of
98 primary studies, different patterns emerge. The topic experienced a
considerable growth from 2020 onward. Most studies consider monitoring AI model
footprint, tuning hyperparameters to improve model sustainability, or
benchmarking models. A mix of position papers, observational studies, and
solution papers are present. Most papers focus on the training phase, are
algorithm-agnostic or study neural networks, and use image data. Laboratory
experiments are the most common research strategy. Reported Green AI energy
savings go up to 115%, with savings over 50% being rather common. Industrial
parties are involved in Green AI studies, albeit most target academic readers.
Green AI tool provisioning is scarce. As a conclusion, the Green AI research
field results to have reached a considerable level of maturity. Therefore, from
this review emerges that the time is suitable to adopt other Green AI research
strategies, and port the numerous promising academic results to industrial
practice.
Related papers
- Hype, Sustainability, and the Price of the Bigger-is-Better Paradigm in AI [67.58673784790375]
We argue that the 'bigger is better' AI paradigm is not only fragile scientifically, but comes with undesirable consequences.
First, it is not sustainable, as its compute demands increase faster than model performance, leading to unreasonable economic requirements and a disproportionate environmental footprint.
Second, it implies focusing on certain problems at the expense of others, leaving aside important applications, e.g. health, education, or the climate.
arXiv Detail & Related papers (2024-09-21T14:43:54Z) - Towards Green AI: Current status and future research [0.3749861135832072]
We aim to broaden the discourse on Green AI by investigating the current status of approaches to both environmental assessment and ecodesign of AI systems.
We conduct an exemplary estimation of the carbon footprint of relevant compute hardware and highlight the need to further investigate methods for Green AI.
We envision that AI could be leveraged to mitigate its own environmental challenges, which we denote as AI4greenAI.
arXiv Detail & Related papers (2024-05-01T08:10:01Z) - Now, Later, and Lasting: Ten Priorities for AI Research, Policy, and Practice [63.20307830884542]
Next several decades may well be a turning point for humanity, comparable to the industrial revolution.
Launched a decade ago, the project is committed to a perpetual series of studies by multidisciplinary experts.
We offer ten recommendations for action that collectively address both the short- and long-term potential impacts of AI technologies.
arXiv Detail & Related papers (2024-04-06T22:18:31Z) - On the Opportunities of Green Computing: A Survey [80.21955522431168]
Artificial Intelligence (AI) has achieved significant advancements in technology and research with the development over several decades.
The needs for high computing power brings higher carbon emission and undermines research fairness.
To tackle the challenges of computing resources and environmental impact of AI, Green Computing has become a hot research topic.
arXiv Detail & Related papers (2023-11-01T11:16:41Z) - Artificial intelligence adoption in the physical sciences, natural
sciences, life sciences, social sciences and the arts and humanities: A
bibliometric analysis of research publications from 1960-2021 [73.06361680847708]
In 1960 14% of 333 research fields were related to AI, but this increased to over half of all research fields by 1972, over 80% by 1986 and over 98% in current times.
In 1960 14% of 333 research fields were related to AI (many in computer science), but this increased to over half of all research fields by 1972, over 80% by 1986 and over 98% in current times.
We conclude that the context of the current surge appears different, and that interdisciplinary AI application is likely to be sustained.
arXiv Detail & Related papers (2023-06-15T14:08:07Z) - Towards Sustainable Artificial Intelligence: An Overview of
Environmental Protection Uses and Issues [0.0]
This paper describes the paradox of an energy-consuming technology serving the ecological challenges of tomorrow.
It draws on numerous examples from AI for Green players to present use cases and concrete examples.
The environmental dimension is part of the broader ethical problem of AI, and addressing it is crucial for ensuring the sustainability of AI in the long term.
arXiv Detail & Related papers (2022-12-22T14:31:48Z) - Eco2AI: carbon emissions tracking of machine learning models as the
first step towards sustainable AI [47.130004596434816]
In eco2AI we put emphasis on accuracy of energy consumption tracking and correct regional CO2 emissions accounting.
The motivation also comes from the concept of AI-based green house gases sequestrating cycle with both Sustainable AI and Green AI pathways.
arXiv Detail & Related papers (2022-07-31T09:34:53Z) - Position: Tensor Networks are a Valuable Asset for Green AI [7.066223472133622]
This position paper introduces a fundamental link between tensor networks (TNs) and Green AI.
We argue that TNs are valuable for Green AI due to their strong mathematical backbone and inherent logarithmic compression potential.
arXiv Detail & Related papers (2022-05-25T14:02:49Z) - Metaethical Perspectives on 'Benchmarking' AI Ethics [81.65697003067841]
Benchmarks are seen as the cornerstone for measuring technical progress in Artificial Intelligence (AI) research.
An increasingly prominent research area in AI is ethics, which currently has no set of benchmarks nor commonly accepted way for measuring the 'ethicality' of an AI system.
We argue that it makes more sense to talk about 'values' rather than 'ethics' when considering the possible actions of present and future AI systems.
arXiv Detail & Related papers (2022-04-11T14:36:39Z) - Systematic Mapping Study on the Machine Learning Lifecycle [4.4090257489826845]
The study yields 405 publications published from 2005 to 2020, mapped in 5 different main research topics, and 31 sub-topics.
We observe that only a minority of publications focus on data management and model production problems, and that more studies should address the AI lifecycle from a holistic perspective.
arXiv Detail & Related papers (2021-03-11T11:44:23Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.