Synthetic training data is a term from the fields of Artificial Intelligence, Big Data and Smart Data, as well as Industry and Factory 4.0. It describes the artificial creation of data that is used to train AI models. Instead of relying exclusively on real, often hard-to-obtain or sensitive data, new, artificial training data is generated using specific algorithms.
The aim of synthetic training data is to make the development of Artificial Intelligence easier and safer. Real-world data is often too expensive, difficult to obtain, or contains personal information that is protected by data privacy regulations. Synthetic data can circumvent these problems. At the same time, it can also be used to simulate rare or dangerous situations that are unlikely to occur in reality.
A clear example: A company wants to develop an AI for quality control in a car factory. Instead of collecting thousands of images of actual faulty car parts, the company uses training data synthesis to generate artificial images showing various types of defects. This allows the AI to learn more quickly and efficiently, without the need for real defects to occur in the factory.
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