Training AI models requires mountains of data. Acquiring large sets of training data can be difficult, time-consuming, and expensive. Also, the data collected may not be able to cover various corner cases, preventing the AI model from accurately predicting a wide variety of scenarios. Synthetic data offers an alternative to real-world data, enabling AI researchers and engineers to bootstrap��
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