In the evergrowing realm of Artificial Intelligence (AI) and Machine Learning (ML), the existing methods to acquire and utilize data are undergoing a significant transformation. As the demand for more optimized and sophisticated algorithms continues to rise, the need for high-quality datasets to train the AI/ML modules also keeps increasing. However, using real-world data to train comes with its complexities, such as privacy and regulatory concerns and the limitations of available datasets. These limitations have paved the way for a counter approach: synthetic data generation. This article navigates through this groundbreaking paradigm shift as the popularity and demand for synthetic data keep growing exponentially, exhibiting great potential in reshaping the future of intelligent technologies.
The Need for Synthetic Data Generation
The need for synthetic data in AI and ML stems from several challenges associated with real-world data. For instance, obtaining large and diverse datasets to train the intelligent machine is a formidable task, especially for industries where data is limited or subjected to privacy and regulatory restrictions. Synthetic data helps generate artificial datasets that replicate the characteristics of the original dataset.