What is a potential drawback of overfitting in AI models?

Overfitting occurs when an AI model learns not only the underlying patterns in the training data but also the noise and outliers. This means the model performs exceptionally well on the training dataset, capturing every detail, but struggles to generalize to new, unseen data. As a result, while the model may show high accuracy and low error rates during training, its performance tends to deteriorate when applied to different datasets. This failure to generalize leads to poorer overall performance, particularly in real-world applications where new data may differ from the training set.

The concept of overfitting highlights the importance of balancing model complexity and generalization capability. A well-tuned model should be able to understand the key patterns sufficient for making accurate predictions while still being flexible enough to adapt to new inputs.

In contrast, the other options suggest effects that are not true outcomes of overfitting. Improving generalization, simplifying model structure, and causing faster training are generally associated with well-regularized models or simpler structures, rather than with the negative implications of overfitting.