A Comprehensive Review of Innovations in Artificial Intelligence and Enhancing Machine Learning Model Precision and Efficiency
Jamnesh Patel · 2023
This paper presents a comprehensive review of recent innovations in artificial intelligence and algorithm development aimed at enhancing machine learning precision and efficiency. The review covers various techniques, including deep learning, reinforcement learning, and transfer learning, and their applications in different domains. The paper also discusses recent advancements in hardware and software infrastructure that have improved the efficiency of machine learning algorithms. The goal of this paper is to provide a comprehensive overview of the current state of the art in machine learning and to identify promising directions for future research. Furthermore, the paper highlights the challenges and limitations of existing techniques and provides insights into possible solutions. It also discusses the ethical implications of using machine learning in various domains, such as healthcare, finance, and social media. The review identifies the need for more transparent and explainable algorithms and emphasizes the importance of addressing bias and fairness issues in machine learning.