Investigations into the Evolution of Generative AI
DOI:
https://doi.org/10.5281/zenodo.14003350ARK:
https://n2t.net/ark:/40704/JCTAM.v1n4a14Disciplines:
Artificial IntelligenceSubjects:
Artificial Neural NetworksReferences:
7Keywords:
Machine Learning, Artificial Neural Networks, Genrative AIAbstract
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.
References
Che, C., Hu, H., Zhao, X., Li, S., & Lin, Q. (2023). Advancing Cancer Document Classification with R andom Forest. Academic Journal of Science and Technology, 8(1), 278-280.
Che, C., Li, C., & Huang, Z. (2024). The Integration of Generative Artificial Intelligence and Computer Vision in Industrial Robotic Arms. International Journal of Computer Science and Information Technology, 2(3), 1-9.
Lin, Q., Che, C., Hu, H., Zhao, X., & Li, S. (2023). A Comprehensive Study on Early Alzheimer’s Disease Detection through Advanced Machine Learning Techniques on MRI Data. Academic Journal of Science and Technology, 8(1), 281-285.
Che, C., Huang, Z., Li, C., Zheng, H., & Tian, X. (2024). Integrating generative AI into financial market prediction for improved decision making. Applied and Computational Engineering, 64, 155-161.
Liu, H., Wang, C., Zhan, X., Zheng, H., & Che, C. (2024). Enhancing 3D Object Detection by Using Neural Network with Self-adaptive Thresholding. arXiv preprint arXiv:2405.07479.
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.
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).
Downloads
Published
How to Cite
Issue
Section
ARK
License
Copyright (c) 2024 The author retains copyright and grants the journal the right of first publication.
This work is licensed under a Creative Commons Attribution 4.0 International License.