Description: The SYNTHETIC function creates synthetic data based on specified parameters. This data is artificially generated and can simulate characteristics of real data, allowing researchers and developers to work with datasets that do not contain sensitive or private information. The generation of synthetic data is especially useful in the field of artificial intelligence and machine learning, where a large amount of data is required to train models. By using synthetic data, privacy issues can be avoided and regulations such as GDPR can be complied with, while providing a controlled environment for testing and experimentation. This technique also allows for the creation of test scenarios that may not be available in real data, thus facilitating the validation of algorithms and systems. In summary, the SYNTHETIC function is a powerful tool for data generation that enables technology professionals to work more efficiently and ethically.
History: The generation of synthetic data began to gain attention in the 1990s when the need to create datasets for testing and simulations without compromising individual privacy was recognized. With advancements in computing and the development of more sophisticated algorithms, the technique has been refined and become more accessible. In the last decade, the rise of machine learning and artificial intelligence has further propelled its use, as models require large volumes of data to train effectively.
Uses: Synthetic data is used in various fields, such as medical research, where patient data can be simulated to test new treatments without risking the privacy of real data. It is also employed in software development, allowing engineers to test applications under controlled conditions. In the field of artificial intelligence, synthetic data is essential for training machine learning models, especially when real data is scarce or difficult to obtain.
Examples: An example of using synthetic data is in creating datasets to train facial recognition models, where images of faces that meet certain characteristics are generated without using photographs of real people. Another case is in simulating financial transactions to test fraud detection systems, where data that mimics behavioral patterns is created without compromising real information.
