What issue does model tuning help prevent in AI systems?

Model tuning is crucial in AI systems as it specifically addresses the issues of overfitting and underfitting. Overfitting occurs when a model becomes too complex and learns not only the underlying patterns in the training data but also the noise, which makes it perform poorly on unseen data. Underfitting, on the other hand, happens when a model is too simple to capture the underlying trends in the data, leading to poor performance even on the training set.

Through model tuning, practitioners adjust various parameters and configurations to find the right balance that optimizes the model’s performance. This process involves techniques such as cross-validation, regularization, and hyperparameter optimization, which help ensure that the model generalizes well to new, unseen data rather than just memorizing the training set.

By effectively tuning a model, it can achieve a state where it fits the training data adequately while maintaining its ability to perform well on test data, thus preventing both overfitting and underfitting scenarios. This is essential for creating robust AI systems in any field, including dentistry, where optimal AI performance can lead to better diagnostic and treatment results.