Towards Green AI: Current status and future research

• URL: http://arxiv.org/abs/2407.10237v1
• Date: Wed, 1 May 2024 08:10:01 GMT
• Title: Towards Green AI: Current status and future research
• Authors: Christian Clemm, Lutz Stobbe, Kishan Wimalawarne, Jan Druschke,
• Abstract summary: 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.
• Score: 0.3749861135832072
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The immense technological progress in artificial intelligence research and applications is increasingly drawing attention to the environmental sustainability of such systems, a field that has been termed Green AI. With this contribution 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 propose a life-cycle-based system thinking approach that accounts for the four key elements of these software-hardware-systems: model, data, server, and cloud. We conduct an exemplary estimation of the carbon footprint of relevant compute hardware and highlight the need to further investigate methods for Green AI and ways to facilitate wide-spread adoption of its principles. We envision that AI could be leveraged to mitigate its own environmental challenges, which we denote as AI4greenAI.

Related papers

• Green Federated Learning: A new era of Green Aware AI [11.536626349203361]
  Federated Learning (FL) presents new opportunities to address this need. It's crucial to furnish researchers, stakeholders, and interested parties with a roadmap to navigate and understand existing efforts and gaps in green-aware AI algorithms. This survey primarily aims to achieve this objective by identifying and analyzing over a hundred FL works.
  arXiv  Detail & Related papers  (2024-09-19T09:54:18Z)
• Innovating for Tomorrow: The Convergence of SE and Green AI [2.013374581642707]
  Machine learning is changing the frontiers of existing software engineering processes. We reflect on the impact of adopting environmentally friendly practices to create AI-enabled software systems.
  arXiv  Detail & Related papers  (2024-06-26T07:47:04Z)
• A Synthesis of Green Architectural Tactics for ML-Enabled Systems [9.720968127923925]
  We provide a catalog of 30 green architectural tactics for ML-enabled systems. An architectural tactic is a high-level design technique to improve software quality. To enhance transparency and facilitate their widespread use, we make the tactics available online in easily consumable formats.
  arXiv  Detail & Related papers  (2023-12-15T08:53:45Z)
• 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)
• Predictable Artificial Intelligence [77.1127726638209]
  This paper introduces the ideas and challenges of Predictable AI. It explores the ways in which we can anticipate key validity indicators of present and future AI ecosystems. We argue that achieving predictability is crucial for fostering trust, liability, control, alignment and safety of AI ecosystems.
  arXiv  Detail & Related papers  (2023-10-09T21:36:21Z)
• AI Maintenance: A Robustness Perspective [91.28724422822003]
  We introduce highlighted robustness challenges in the AI lifecycle and motivate AI maintenance by making analogies to car maintenance. We propose an AI model inspection framework to detect and mitigate robustness risks. Our proposal for AI maintenance facilitates robustness assessment, status tracking, risk scanning, model hardening, and regulation throughout the AI lifecycle.
  arXiv  Detail & Related papers  (2023-01-08T15:02:38Z)
• 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)
• An interdisciplinary conceptual study of Artificial Intelligence (AI) for helping benefit-risk assessment practices: Towards a comprehensive qualification matrix of AI programs and devices (pre-print 2020) [55.41644538483948]
  This paper proposes a comprehensive analysis of existing concepts coming from different disciplines tackling the notion of intelligence. The aim is to identify shared notions or discrepancies to consider for qualifying AI systems.
  arXiv  Detail & Related papers  (2021-05-07T12:01:31Z)
• Empowering Things with Intelligence: A Survey of the Progress, Challenges, and Opportunities in Artificial Intelligence of Things [98.10037444792444]
  We show how AI can empower the IoT to make it faster, smarter, greener, and safer. First, we present progress in AI research for IoT from four perspectives: perceiving, learning, reasoning, and behaving. Finally, we summarize some promising applications of AIoT that are likely to profoundly reshape our world.
  arXiv  Detail & Related papers  (2020-11-17T13:14:28Z)

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.