Version 1
: Received: 12 September 2024 / Approved: 12 September 2024 / Online: 14 September 2024 (05:24:03 CEST)
How to cite:
Durmagambetov, A. The Energy Consumption Problem in Cryptocurrency Mining and AI: Analysis, Future, and Breakthrough Mathematical Solutions. Preprints2024, 2024091012. https://doi.org/10.20944/preprints202409.1012.v1
Durmagambetov, A. The Energy Consumption Problem in Cryptocurrency Mining and AI: Analysis, Future, and Breakthrough Mathematical Solutions. Preprints 2024, 2024091012. https://doi.org/10.20944/preprints202409.1012.v1
Durmagambetov, A. The Energy Consumption Problem in Cryptocurrency Mining and AI: Analysis, Future, and Breakthrough Mathematical Solutions. Preprints2024, 2024091012. https://doi.org/10.20944/preprints202409.1012.v1
APA Style
Durmagambetov, A. (2024). The Energy Consumption Problem in Cryptocurrency Mining and AI: Analysis, Future, and Breakthrough Mathematical Solutions. Preprints. https://doi.org/10.20944/preprints202409.1012.v1
Chicago/Turabian Style
Durmagambetov, A. 2024 "The Energy Consumption Problem in Cryptocurrency Mining and AI: Analysis, Future, and Breakthrough Mathematical Solutions" Preprints. https://doi.org/10.20944/preprints202409.1012.v1
Abstract
The growing energy consumption in the cryptocurrency and artificial intelligence (AI) industries is becoming an increasingly critical issue. This paper discusses methods for addressing this problem using data compression algorithms and the optimization of computational processes. Specifically, we examine research results that demonstrate the potential for significantly reducing energy consumption through mathematical solutions, such as the Riemann Hypothesis. We also present the notarized test results of an image compression program developed by the author. The work was presented at a UN panel session and an ECO webinar, receiving recommendations for implementation in ECO member countries. Additionally, modern technologies for accelerating cryptographic methods and algorithms are considered.The growing energy consumption in the cryptocurrency and artificial intelligence (AI) industries is becoming an increasingly critical issue. This paper discusses methods for addressing this problem using data compression algorithms and the optimization of computational processes. Specifically, we examine research results that demonstrate the potential for significantly reducing energy consumption through mathematical solutions, such as the Riemann Hypothesis. We also present the notarized test results of an image compression program developed by the author. The work was presented at a UN panel session and an ECO webinar, receiving recommendations for implementation in ECO member countries. Additionally, modern technologies for accelerating cryptographic methods and algorithms are considered.
Keywords
energy consumption; cryptocurrency; artificial intelligence; data compression; Riemann Hypothesis; cryptography; algorithm acceleration
Subject
Computer Science and Mathematics, Computational Mathematics
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
