Datasets play a crucial role in the development and evaluation of machine learning models, serving as the foundation upon which algorithms learn and make predictions. They can range from simple, tabular formats to complex, multimodal collections involving text, images, and audio. The quality, diversity, and size of a dataset significantly impact the performance of machine learning models. Datasets are often split into training, validation, and testing subsets to facilitate model training, tuning, and evaluation, respectively. The process of preparing a dataset, including cleaning, normalization, and augmentation, is critical for removing biases and ensuring that the model learns relevant patterns.

The concept of datasets in computing predates the modern era of machine learning, with early datasets being used for statistical analyses and basic computer programming exercises. However, the use of large, complex datasets for machine learning purposes gained momentum in the late 20th and early 21st centuries, coinciding with the availability of more powerful computing resources and the development of more sophisticated algorithms.

While it's challenging to attribute the concept of datasets to specific contributors, several organizations and individuals have played significant roles in popularizing their use in AI. The creation and publication of benchmark datasets by universities, research institutions, and competitions (e.g., ImageNet by Fei-Fei Li and her team at Stanford University) have been pivotal in advancing the field of machine learning by providing common grounds for training and evaluating models.