Investigations into the Evolution of Generative AI

Xueyi Cheng

doi:10.5281/zenodo.14003350

OPEN ACCESS |Research Article ||2 November 2024

Investigations into the Evolution of Generative AI

Xueyi Cheng

Duke University

Frances.cheng17@gmail.com

Researcher at Duke University.

* Corresponding Author¹: Xueyi Cheng, E-Mail: Frances.cheng17@gmail.com

Publication

Accepted Unknow ; Published 2024 November 2

Journal of Computer Technology and Applied Mathematics, 2024, 1(4), 3007-4126.

Abstract

Machine Learning, a pivotal technology within the realm of artificial intelligence, has experienced remarkable progress in recent times. This research offers a thorough and structured presentation of machine learning. It begins with a comprehensive look at the evolution of machine learning throughout history, then zeroes in on dissecting the foundational algorithms that underpin the field. Following this, the study sheds light on the cutting-edge developments in machine learning, with the goal of thoroughly examining its applications across different sectors and contemplating the prospective trajectories for its future.

Keywords

Machine Learning , Artificial Neural Networks , Genrative AI .

Metadata

DOI:

10.5281/zenodo.14003350

ARK:

ark:/40704/JCTAM.v1n4a14

Pages: 117-122

References: 7

Disciplines: Artificial Intelligence

Subjects: Artificial Neural Networks

Cite This Article

APA Style

Cheng, X. (2024). Investigations into the evolution of generative ai. Journal of Computer Technology and Applied Mathematics, 1(4), 117-122. https://doi.org/10.5281/zenodo.14003350

Acknowledgments

The authors thank the editor and anonymous reviewers for their helpful comments and valuable suggestions.

FUNDING

Not applicable.

INSTITUTIONAL REVIEW BOARD STATEMENT

Not applicable.

DATA AVAILABILITY STATEMENT

The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author.

INFORMED CONSENT STATEMENT

Not applicable.

CONFLICT OF INTEREST

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

AUTHOR CONTRIBUTIONS

Not applicable.

References

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Huang, Z., Che, C., Zheng, H., & Li, C. (2024). Research on Generative Artificial Intelligence for Virtual Financial Robo-Advisor. Academic Journal of Science and Technology, 10(1), 74-80.

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Che, C., Lin, Q., Zhao, X., Huang, J., & Yu, L. (2023, September). Enhancing Multimodal Understanding with CLIP-Based Image-to-Text Transformation. In Proceedings of the 2023 6th International Conference on Big Data Technologies (pp. 414-418).

PUBLISHER'S NOTE

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Copyright © 2025 The Author(s). Published by Southern United Academy of Sciences.
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