Generative AI and data augmentation share some similarities, but they are not the same thing.
Data augmentation is a technique used to artificially increase the size of a dataset by applying various transformations to the existing data. These transformations could include flipping, rotating, cropping, or adding noise to images, or modifying the text in various ways. The goal of data augmentation is to improve the performance of a machine learning model by providing it with more diverse training examples.
On the other hand, generative AI refers to a set of techniques used to generate new data samples that are similar to the existing data. These techniques could include generative adversarial networks (GANs), variational autoencoders (VAEs), or other similar models. The goal of generative AI is to create new data samples that are similar to the original dataset, but not identical, in order to help the model learn more generalizable features and avoid overfitting.
In summary, while both techniques aim to improve the performance of a machine learning model by providing it with more diverse examples, data augmentation involves applying transformations to existing data, while generative AI involves generating new data that is similar to the existing data.