How does synthetic data work?

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Synthetic data is information that's artificially generated rather than produced by real-world events. It is typically created using algorithms and can be used to validate mathematical models and to train machine learning models1. Synthetic data can be seen as the output of computer simulations, approximating real-world scenarios while being fully algorithmically generated1. Synthetic data is used in a variety of fields as a filter for information that would otherwise compromise the confidentiality of particular aspects of the data. In many sensitive applications, datasets theoretically exist but cannot be released to the general public due to privacy concerns. Synthetic data sidesteps these privacy issues by not using real consumer information without permission or compensation1. Synthetic data is generated to meet specific needs or certain conditions that may not be found in the original, real data. This can be useful when designing any type of system because the synthetic data are used as a simulation or as a theoretical value, situation, etc. This allows us to take into account unexpected results and have a basic solution or remedy, if the results prove to be unsatisfactory1. There are different types of synthetic data that currently exist: text, media (video, image, sound), and tabular synthetic data3. Synthetic data can also be used as a drop-in replacement for any type of behavior, predictive, or transactional analysis3. Synthetic data offers several advantages over real-world data. It is customizable, cost-effective, and respects data privacy. For example, in artificial training data, uncommon patterns and occurrences can be upsampled4. Synthetic data also enables data scientists to abide by data privacy regulations4. Synthetic data is used in various industries and applications. For instance, in the field of natural language processing, Amazon's Alexa AI team uses synthetic data to complete the training data of its natural language understanding (NLU) system3. The self-driving car industry embraced synthetic data early on because collecting samples of all potential scenarios on the road, including rare, so-called edge cases, would be impractical to impossible. Synthetic data makes it possible to create customized data to fill the gaps7. In conclusion, synthetic data is a powerful tool that allows for the creation of artificial data that closely mimics real-world data. It is particularly useful in situations where real-world data is difficult to obtain, sensitive, or privacy-protected. It is widely used in various fields, including machine learning, AI training, and various industries for predictive analysis and system design.
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