Applied and Computational Engineering

- The Open Access Proceedings Series for Conferences


Proceedings of the 4th International Conference on Signal Processing and Machine Learning

Series Vol. 52 , 27 March 2024


Open Access | Article

Analysis of computational power as a potential breakthrough in advancing AI technology

Yezhen Chen * 1
1 University of Washington Seattle

* Author to whom correspondence should be addressed.

Applied and Computational Engineering, Vol. 52, 109-112
Published 27 March 2024. © 27 March 2024 The Author(s). Published by EWA Publishing
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Citation Yezhen Chen. Analysis of computational power as a potential breakthrough in advancing AI technology. ACE (2024) Vol. 52: 109-112. DOI: 10.54254/2755-2721/52/20241443.

Abstract

The term Artificial Intelligence(AI) has become more common in view recently. The performance and fame of ChatGPT brought a new AI fever to present industries with multiple big companies announcing their upcoming “AI plans”. People are arguing about whether AI will help them or steal their jobs. It seems that AI will be, if not have been, walking into people’s daily lives. As a result, this article analyzes the current challenges and potential future breakthroughs of Artificial intelligence by focusing on one of the most fundamental factors that support the development and operation of AI—computational power. This article analyzes the relationship between AI performance and computer performance from different eras particularly. It summarizes and analyzes several sources published in related fields. The primary purpose of this article is to provide people with a better overview of present AI technology by taking a close look at the history, present difficulties, and potential solutions of AI and computational power and concludes with possibilities of each solution and expected futures of the AI industry. This paper concludes that the development of AI relies on the computer performance acquired by the industry. Finding a way to obtain better computer performance at a lower cost might be the next breakthrough in the AI industry.

Keywords

Artificial intelligence, Computational power, History, Deep learning

References

1. Haenlein, Michael, and Andreas Kaplan. “A Brief History of Artificial Intelligence: On the Past, Present, and Future of Artificial Intelligence.” California Management Review, vol. 61, no. 4, 2019, pp. 5–14, https://doi.org/10.1177/0008125619864925.

2. Campbell-Kelly, Martin, and William Aspray. Computer: A History Of The Information Machine. 2nd ed., Westview Press, 2009, pp. xviii–xviii.

3. Rajaraman, V. “From ELIZA to ChatGPT.” Resonance, vol. 28, no. 6, 2023, pp. 889–905, https://doi.org/10.1007/s12045-023-1620-6.

4. 7094 Data Processing System. IBM Archives: 7094 Data Processing System. (n.d.) Accessed 18 October 2023. https://www.ibm.com/ibm/history/exhibits/mainframe/mainframe_PP7094.html

5. Janiesch, C., Zschech, P. & Heinrich, K. Machine learning and deep learning. Electron Markets 31, 685–695 (2021). https://doi-org.offcampus.lib.washington.edu/10.1007/s12525-021-00475-2

6. Jouppi, N. J. (2016, May 18). Google Supercharges machine learning tasks with TPU Custom Chip | Google Cloud Blog. https://cloud.google.com/blog/products/ai-machine-learning/google-supercharges-machine-learning-tasks-with-custom-chip

7. IBM. (n.d.). Brief history of RISC, the IBM RS/6000 and the IBM eServer pSeries. Accessed 18 October 2023. https://www.ibm.com/ibm/history/documents/pdf/rs6000.pdf

Data Availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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Volume Title
Proceedings of the 4th International Conference on Signal Processing and Machine Learning
ISBN (Print)
978-1-83558-349-4
ISBN (Online)
978-1-83558-350-0
Published Date
27 March 2024
Series
Applied and Computational Engineering
ISSN (Print)
2755-2721
ISSN (Online)
2755-273X
DOI
10.54254/2755-2721/52/20241443
Copyright
27 March 2024
Open Access
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited

Copyright © 2023 EWA Publishing. Unless Otherwise Stated