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Machine learning algorithms need examples of data from which they can learn, especially supervised machine learning algorithms. However, one big challenge for those looking to put machine learning into practice is the lack of a sufficient quantity of good quality data examples from which to train systems. To address the needs for large quantities of high quality data, vendors have emerged to provide computer-generated data, known as synthetic data, that matches the range and quality of data needed to train systems. In this episode of the AI Today podcast hosts Kathleen Walch and Ron Schmelzer provide an overview of the synthetic data market, the difference between Simulated vs. Synthetic Data, examples where synthetic data is applied, and some of the challenges synthetic data may introduce.
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