A dataset is an organized collection of data used to
training and testing an
AI model. The quality and quantity of this data determine the model's ability to identify
patterns and perform specific tasks.
Datasets can contain different types of information: texts, images, sounds, numbers or a combination of these. For example, for an automatic translation system, you need a dataset with millions of correctly translated phrases in different languages, while for facial recognition you need thousands of photographs of faces with their respective identifications.
The quality and diversity of this data is fundamental to successful learning. If a dataset is not varied enough or contains
biases, the AI will learn incorrectly. For example, if a voice dataset only includes male voices, the system might fail to recognize female voices. That's why creating good datasets is one of the most important challenges in AI: they need to be broad, diverse and representative of the real world.