The difference between truly understanding something versus plainly memorizing it is often regarded as true intelligence . Slightly modifying the tests or structuring the questions in an unseen way will throw off those who haven’t truly learnt the subject matter. This phenomenon translates almost seamlessly to AI models as well. AI models can at times end up memorizing everything but learning too little, and this phenomenon is known as overfitting.

Overfitting occurs when a model learns the training data very closely. The model absorbs the noise and random fluctuations in the training data but fails to make sense of the key underlying pattern. As a result the model performs with almost perfect accuracy on the training set but very poorly when exposed to unseen data.  Overfitting models exhibit low bias (the model does not make any assumptions) and high variance (predictions vary dramatically when shown new information).

Overfitting models can create a false sense of confidence but can be hazardous when deployed, particularly in sensitive domains such as medical diagnostics or financial predictions. 

Managing overfitting is vital to building robust and reliable AI systems. Techniques such as better regularization, data augmentation , the use of larger and more diverse datasets are employed to prevent a model from overfitting.