ADVANCING ARTIFICIAL INTELLIGENCE: TRENDS, CHALLENGES, AND INNOVATIONS IN MACHINE LEARNING ALGORITHMS

Abstract

Abstract

Artificial Intelligence (AI) and Machine Learning (ML) have emerged as transformative forces, driving innovation across diverse industries such as healthcare, finance, transportation, and education. This research explores the latest trends, key challenges, and groundbreaking innovations in ML algorithms. The study examines advancements in deep learning, reinforcement learning, and transfer learning, highlighting their applications in solving complex, real-world problems. It also delves into the persistent challenges of scalability, interpretability, and data efficiency, as well as ethical concerns surrounding bias and privacy. Furthermore, the paper discusses cutting-edge developments such as federated learning, explainable AI, and AI's integration with quantum computing, which are reshaping the future of the field. By synthesizing these insights, this research provides a comprehensive perspective on the current state of AI and ML, offering a roadmap for researchers and practitioners to address pressing challenges and capitalize on emerging opportunities.