Artificial intelligence, machine learning, and deep learning are interconnected yet distinct concepts. AI aims to simulate human intelligence, encompassing abilities such as learning, inference, and reasoning. Machine learning, a subset of AI, focuses on enabling computers to learn from data, while deep learning, a further specialization, utilizes neural networks to automate feature extraction and improve performance on complex tasks. With the recent rise of generative AI, including large language models and chatbots, understanding these technologies is increasingly relevant. Historically, AI research began with basic concepts and languages in the mid-20th century, evolving through various stages, including expert systems in the 1980s and 1990s. Today, AI technologies are becoming more mainstream, prompting discussions regarding their definitions and applications. As generative AI continues to develop, it serves to illustrate the practical implications of AI, machine learning, and deep learning in everyday life. This simplification aids in grasping the relationships among these technologies, as the field continues to grow and impact various industries.