What is essential about the training data used in AI models?

The requirement that the training data should be independent of test sets is crucial in AI model development because it ensures the validity and reliability of the model's performance evaluation. When the training data and test sets are separate, it allows for an unbiased assessment of how well the model generalizes to unseen data. This independence helps to avoid overfitting, where the model learns to memorize the training data rather than understanding underlying patterns. By evaluating the model on a separate test set, practitioners can better understand its predictive power and robustness in real-world applications.