Over-fitting in artificial intelligence (AI) occurs when a model is too closely aligned to a specific set of training data, including its noise and anomalies, which hinders its ability to perform well on new, unseen data. Imagine training a dog to respond only to a very specific tone of voice; while the dog may perform excellently in that familiar setting, it might struggle to obey commands in any other tone.

In technical terms, over-fitting is like drawing a zigzag line through every data point in a scatter plot, rather than finding a smooth, general curve that captures the overall trend. This overly complex model fits the training data perfectly but fails to predict new data accurately because it's too tailored to the peculiarities of the training data.

Avoiding over-fitting is crucial for developing AI systems that are effective and reliable in real-world applications, beyond the controlled conditions of training. Methods to prevent overfitting include simplifying the model, using fewer parameters, incorporating regularization techniques (which add a penalty for complexity), or using more varied and extensive training data. Additionally, techniques like cross-validation, where the training data is split into multiple mini sets to validate the model multiple times, can help ensure the model's robustness and general applicability.