Position: Tensor Networks are a Valuable Asset for Green AI
- URL: http://arxiv.org/abs/2205.12961v2
- Date: Thu, 30 May 2024 09:53:16 GMT
- Title: Position: Tensor Networks are a Valuable Asset for Green AI
- Authors: Eva Memmel, Clara Menzen, Jetze Schuurmans, Frederiek Wesel, Kim Batselier,
- Abstract summary: 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.
- Score: 7.066223472133622
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: For the first time, this position paper introduces a fundamental link between tensor networks (TNs) and Green AI, highlighting their synergistic potential to enhance both the inclusivity and sustainability of AI research. We argue that TNs are valuable for Green AI due to their strong mathematical backbone and inherent logarithmic compression potential. We undertake a comprehensive review of the ongoing discussions on Green AI, emphasizing the importance of sustainability and inclusivity in AI research to demonstrate the significance of establishing the link between Green AI and TNs. To support our position, we first provide a comprehensive overview of efficiency metrics proposed in Green AI literature and then evaluate examples of TNs in the fields of kernel machines and deep learning using the proposed efficiency metrics. This position paper aims to incentivize meaningful, constructive discussions by bridging fundamental principles of Green AI and TNs. We advocate for researchers to seriously evaluate the integration of TNs into their research projects, and in alignment with the link established in this paper, we support prior calls encouraging researchers to treat Green AI principles as a research priority.
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) - 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) - Green Edge AI: A Contemporary Survey [46.11332733210337]
The transformative power of AI is derived from the utilization of deep neural networks (DNNs)
Deep learning (DL) is increasingly being transitioned to wireless edge networks in proximity to end-user devices (EUDs)
Despite its potential, edge AI faces substantial challenges, mostly due to the dichotomy between the resource limitations of wireless edge networks and the resource-intensive nature of DL.
arXiv Detail & Related papers (2023-12-01T04:04:37Z) - Igniting Language Intelligence: The Hitchhiker's Guide From
Chain-of-Thought Reasoning to Language Agents [80.5213198675411]
Large language models (LLMs) have dramatically enhanced the field of language intelligence.
LLMs leverage the intriguing chain-of-thought (CoT) reasoning techniques, obliging them to formulate intermediate steps en route to deriving an answer.
Recent research endeavors have extended CoT reasoning methodologies to nurture the development of autonomous language agents.
arXiv Detail & Related papers (2023-11-20T14:30:55Z) - 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) - The Ethics of AI Value Chains [0.6138671548064356]
Researchers, practitioners, and policymakers with an interest in AI ethics need more integrative approaches for studying and intervening in AI systems.
We review theories of value chains and AI value chains from the strategic management, service science, economic geography, industry, government, and applied research literature.
We recommend three future directions that researchers, practitioners, and policymakers can take to advance more ethical practices across AI value chains.
arXiv Detail & Related papers (2023-07-31T15:55:30Z) - A Systematic Review of Green AI [8.465228064780744]
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.
arXiv Detail & Related papers (2023-01-26T11:41:46Z) - The Role of AI in Drug Discovery: Challenges, Opportunities, and
Strategies [97.5153823429076]
The benefits, challenges and drawbacks of AI in this field are reviewed.
The use of data augmentation, explainable AI, and the integration of AI with traditional experimental methods are also discussed.
arXiv Detail & Related papers (2022-12-08T23:23:39Z) - 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)
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.