What is the main goal of Generative AI in creating synthetic data?

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The main goal of Generative AI in creating synthetic data is to enhance training datasets for better model performance. This involves generating new data points that enrich the existing datasets, particularly in scenarios where access to real-world data may be limited, difficult, or costly to obtain. By synthesizing data that mimics the characteristics of real data, Generative AI can help improve the robustness and accuracy of machine learning models. This can be especially valuable in areas like healthcare or autonomous driving, where data privacy or rare events create challenges in data collection.

Utilizing synthetic data allows for more diverse and balanced training datasets, which can lead to enhanced generalization of the models to unseen data. The goal is not to replace real-world data entirely but to supplement it strategically so that models can learn more effectively from representative examples.

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