FROM FOUNDATIONS TO FRONTIERS: A CRITICAL ANALYSIS OF ADVANCED AI AND MACHINE LEARNING SYSTEMS

Abstract

Artificial Intelligence (AI) and Machine Learning (ML) have gone from being technological concepts to the mainstay of modern digital enterprises. This article analyses critical aspect of AI from the core principle to the systems of AI. It starts by examining the background of AI with statistical learning, optimisation, and the early algorithms used in AI. The discussion then covers recent developments like deep learning, transformer architectures, reinforcement learning, and generative models as applied in a variety of spheres.In addition, the paper discusses the emerging domains like explainable AI (XAI), federated learning, and artificial general intelligence (AGI), their scope and limitations. The assessment of Ethical and social implications particularly bias, transparency and governance examine the fold impact of the deployment of AI.Advanced AI systems can or could perform really well. The problem, however, remains that they are not interpretable that is, what they do is fairly obscure, they cannot be scaled and they do not appear to follow what appear to be ethical criteria. It concludes by pointing out significant research gaps and suggesting next steps to strengthen future AI systems so they are more robust, transparent and human-aligned. This study aids in comprehending the transition of AI from theory to technology in a holistic manner.