Advanced Techniques in Artificial Intelligence and Machine Learning: A Comprehensive Review
Keywords:
Artificial Intelligence (AI), Machine Learning (ML), Deep Learning, Reinforcement LearningAbstract
In recent years, the fields of Artificial Intelligence (AI) and Machine Learning (ML) have undergone significant advancements, leading to a wide array of innovative techniques and applications. This comprehensive review aims to provide an in-depth analysis of the latest developments in AI and ML, highlighting the most promising methodologies and their practical implications. We explore various advanced techniques, including deep learning, reinforcement learning, generative adversarial networks, and transfer learning, among others. Additionally, this review examines the application of these techniques across diverse domains such as healthcare, finance, autonomous systems, and natural language processing. We discuss the strengths and limitations of each method, providing insights into their suitability for different types of problems. Furthermore, ethical considerations and the potential societal impact of AI and ML advancements are critically evaluated. This review serves as a valuable resource for researchers and practitioners seeking to understand the current state of AI and ML and to identify future research directions that can address existing challenges and drive further innovation in these dynamic fields.
References
Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., ... & Bengio, Y. (2014). Generative adversarial nets. In Advances in neural information processing systems (pp. 2672-2680).
LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436-444.
Silver, D., Schrittwieser, J., Simonyan, K., Antonoglou, I., Huang, A., Guez, A., ... & Hassabis, D. (2017). Mastering the game of Go without human knowledge. Nature, 550(7676), 354-359.
Devlin, J., Chang, M. W., Lee, K., & Toutanova, K. (2018). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. arXiv preprint arXiv:1810.04805.
Ruder, S., Peters, M. E., Swayamdipta, S., & Wolf, T. (2019). Transfer learning in natural language processing. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Tutorials (pp. 15-18).
Esteva, A., Kuprel, B., Novoa, R. A., Ko, J., Swetter, S. M., Blau, H. M., & Thrun, S. (2017). Dermatologist-level classification of skin cancer with deep neural networks. Nature, 542(7639), 115-118.
Schmidhuber, J. (2015). Deep learning in neural networks: An overview. Neural networks, 61, 85-117.
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). ImageNet classification with deep convolutional neural networks. In Advances in neural information processing systems (pp. 1097-1105).
Sutton, R. S., & Barto, A. G. (2018). Reinforcement learning: An introduction. MIT press.
Karras, T., Laine, S., & Aila, T. (2019). A style-based generator architecture for generative adversarial networks. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 4401-4410).
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2024 Well Testing Journal
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
This license requires that re-users give credit to the creator. It allows re-users to distribute, remix, adapt, and build upon the material in any medium or format, for noncommercial purposes only.
