UNDERSTANDING ARTIFICIAL INTELLIGENCE: PATHWAYS AND ETHICAL CONSIDERATIONS

Abstract

As Artificial Intelligence (AI) continues to expand into various fields, it is important that both specialists and beginners have a clear understanding of its diverse approaches, future objectives, potential drawbacks, and issues related to information overload. This paper presents a concise AI approach accessible to both experts and newbies alike, to prevent confusion in the wake of new AI developments. Using comprehensive diagrams, we provide concrete explanations of supervised learning, unsupervised learning, reinforcement learning, and deep learning in machine learning (ML), and show how these technologies have paved the way for current AI. We also demonstrate how theories of intelligence and AI concepts match technological advances and future objectives in AI. Ethical concerns related to AI and risks are also discussed, alongside biases that arise from human data input. This study aims to provide valuable and easily understandable insights to anyone interested in AI, including teachers, decision-makers, developers, students, and practitioners. The analysis of empirical studies and the illustrations of AI concepts in this study contribute to scientific literature and offer practical implications for the global impact of AI.