Artificial intelligence AI seeks to simulate human intelligence, encompassing abilities such as learning, inferring, and reasoning. Machine learning, a subset of AI, focuses on algorithms that allow computers to learn from and make predictions based on data. Deep learning, an even narrower subset, employs neural networks with many layers to analyze complex data patterns. Recently, generative AI has gained prominence, evident in technologies like large language models and chatbots, as well as deep fakes, expanding the scope of AI applications. This evolution has raised questions about the interrelation of AI, machine learning, and deep learning, prompting discussions to clarify common misconceptions. AI's history traces back to early research projects that utilized programming languages like Lisp and Prolog, which laid the foundations for modern expert systems that emerged in the 1980s and 1990s. These systems marked a significant development in AI, transitioning from theoretical concepts to practical implementations used in various fields. The overall goal remains to create systems that can perform tasks traditionally requiring human intelligence, evolving with advancements in technology and an increased understanding of data-driven methodologies.