Abstract
This work reviews the concept of Generative Artificial Intelligence (GAI), its main characteristics, and its classification inside the Artificial Intelligence (AI) field. In this way, the main GAI foundational algorithmic architectures for data generation are presented; each considers a previously gathered data set and generates novel and synthetic data. Subsequently, use cases for generative models reported in the literature are presented, ranging from applications in healthcare, education, and customer experiences. The deployment of GAI models still needs to overcome a series of challenges inspected in this document, considering the presence of bias, lack of transparency, and hallucinations presented by the models. Finally, the risks and misuse of GAI are reviewed since widespread use of this type of technology is expected in society.
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