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AI has been making significant strides in recent years, and one of the areas that has seen considerable growth is generative AI. Generative AI is a subfield of AI and DL that focuses on generating new content, such as images, text, music, and video, by using algorithms and models that have been trained on existing data using ML techniques. In order to better understand the relationship between AI, ML, DL, and generative AI, consider AI as the foundation, while ML, DL, and generative AI represent increasingly specialized and focused areas of study and application: AI represents the broad field of creating systems that can perform tasks, showing human intelligence and ability and being able to interact with the ecosystem. ML is a branch that focuses on creating algorithms and models that enable those systems to learn and improve themselves with time and training. ML models learn from existing data and automatically update their parameters as they grow. DL is a sub-branch of ML, in the sense that it encompasses deep ML models. Those deep models are called neural networks and are particularly suitable in domains such as computervision or Natural Language Processing (NLP). When we talk about ML and DL models, we typically refer to discriminative models, whose aim is that of making predictions or inferencing patterns on top of data. And finally, we get to generative AI, a further sub-branch of DL, which doesn’t use deep Artificial Neural Networks to cluster, classify, or make predictions on existing data: it uses those powerful Artificial Neural Network models to generate brand new content, from images to natural language, from music to video.