Survey on Efficient Training of Large Neural Networks

Gusak, Julia; Cherniuk, Daria; Shilova, Alena; Katrutsa, Alexandr; Bershatsky, Daniel; Zhao, Xunyi; Eyraud-Dubois, Lionel; Shliazhko, Oleh; Denis, Dimitrov,; Oseledets, Ivan; Beaumont, Olivier

doi:10.24963/ijcai.2022/769

Cited by 5 publications

(3 citation statements)

References 2 publications

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“…Since understanding these differences is crucial for identifying future challenges and opportunities, it was important to cross-check findings with previous papers. Therefore, it can be concluded that the findings from this paper are in line with [58,59], where it was previously discovered that companies with well-established AI strategies are likely to have a competitive advantage in terms of efficient AI integration.…”

Section: Key Findings and Insightssupporting

confidence: 77%

Strategic Integration of Artificial Intelligence for Sustainable Businesses: Implications for Data Management and Human User Engagement in the Digital Era

Jankovic,

Curovic

2023

Sustainability

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This research paper delves into the pivotal role of strategic integration of artificial intelligence (AI) concepts across sustainability efforts in for-profit businesses. As organizations are increasingly starting to rely on AI-driven solutions, this study examines the profound implications of AI integration for two critical facets: impact on data management in companies and diversification of human engagement during interactions in the digital ecosystem. The main goal of this research is to analyze the AI adoption index within a sample of 240 medium and large-sized companies (therefore excluding new companies, small startups, and low-scale AI applications). Firstly, the paper scrutinizes how AI technologies enhance data management by enabling efficient data collection, analysis, and utilization. It emphasizes the importance of AI-driven data analytics in improving decision-making processes, resource optimization, and overall operational efficiency for sustainable practices. Secondly, this research explores how AI-driven personalization, omnichannel interactions, and recommendation systems significantly impact user experiences, satisfaction, and loyalty, ultimately contributing to sustainable business growth. Findings show that there are three separate profiles of companies (low, moderate, and high), distinguished by AI adoption index and other important dimensions. Future research should focus on determining preconditions for successful planning of AI adoption index improvement, using a data-driven approach.

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Section: Key Findings and Insightssupporting

confidence: 77%

Strategic Integration of Artificial Intelligence for Sustainable Businesses: Implications for Data Management and Human User Engagement in the Digital Era

Jankovic,

Curovic

2023

Sustainability

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“…These materials pose a direct risk to human health and are also reported to increase the chances of cancer later in life. The above drawbacks can be partially solved by the implementation of the so-called "green AI", which uses specially designed neural networks with lower power consumption and, consequently, lesser ecological impact [94].…”

Section: Sustainable Aimentioning

confidence: 99%

Practical AI Cases for Solving ESG Challenges

Burnaev,

Mironov,

Shpilman

et al. 2023

Sustainability

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Artificial intelligence (AI) is a rapidly advancing area of research that encompasses numerical methods to solve various prediction, optimization, and classification/clustering problems. Recently, AI tools were proposed to address the environmental, social, and governance (ESG) challenges associated with sustainable business development. While many publications discuss the potential of AI, few focus on practical cases in the three ESG domains altogether, and even fewer highlight the challenges that AI may pose in terms of ESG. The current paper fills this gap by reviewing practical AI applications with a main focus on IT and engineering implementations. The considered cases are based on almost one hundred publicly available research manuscripts and reports obtained via online search engines. This review involves the study of typical business and production problems associated with each ESG domain, gives background details on several selected cases (such as carbon neutrality, land management, and ESG scoring), and lists challenges that the smart algorithms can pose (such as fake news generation and increased electricity consumption). Overall, it is concluded that, while many practical cases already exist, AI in ESG is still very far away from reaching its full potential; however, one should always remember that AI itself can lead to some ESG risks.

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“…Such neural networks often do not fit a single GPU. To address this limitation, different approaches and strategies can be applied in order to train large neural networks effectively [81]. To deal with overfitting and to enhance model generalization, the dropout technique is usually implemented.…”

Section: Deep Learning Algorithmsmentioning

confidence: 99%

A Survey of Computer Vision Techniques for Forest Characterization and Carbon Monitoring Tasks

et al. 2022

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Estimation of terrestrial carbon balance is one of the key tasks in the understanding and prognosis of climate change impacts and the development of tools and policies according to carbon mitigation and adaptation strategies. Forest ecosystems are one of the major pools of carbon stocks affected by controversial processes influencing carbon stability. Therefore, monitoring forest ecosystems is a key to proper inventory management of resources and planning their sustainable use. In this survey, we discuss which computer vision techniques are applicable to the most important aspects of forest management actions, considering the wide availability of remote sensing (RS) data of different resolutions based both on satellite and unmanned aerial vehicle (UAV) observations. Our analysis applies to the most occurring tasks such as estimation of forest areas, tree species classification, and estimation of forest resources. Through the survey, we also provide a necessary technical background with a description of suitable data sources, algorithms’ descriptions, and corresponding metrics for their evaluation. The implementation of the provided techniques into routine workflows is a significant step toward the development of systems of continuous actualization of forest data, including real-time monitoring. It is crucial for diverse purposes on both local and global scales. Among the most important are the implementation of improved forest management strategies and actions, carbon offset projects, and enhancement of the prediction accuracy of system changes under different land-use and climate scenarios.

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Survey on Efficient Training of Large Neural Networks

Cited by 5 publications

References 2 publications

Strategic Integration of Artificial Intelligence for Sustainable Businesses: Implications for Data Management and Human User Engagement in the Digital Era

Strategic Integration of Artificial Intelligence for Sustainable Businesses: Implications for Data Management and Human User Engagement in the Digital Era

Practical AI Cases for Solving ESG Challenges

A Survey of Computer Vision Techniques for Forest Characterization and Carbon Monitoring Tasks

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