AI is becoming a big part of our everyday lives. Two cool uses of AI are image recognition and natural language processing. But, even though they are useful, they can face some challenges, like overfitting and underfitting.

Overfitting happens when a model learns too much from the training data. This means it might memorize the data instead of understanding it. For example, a facial recognition program might do a great job at recognizing faces in the pictures it has seen before. But when it sees new pictures, it could struggle and get confused.

Underfitting is the opposite. This is when the model is too simple and doesn't capture important patterns. For instance, a spam filter that is too basic might think that all emails about shopping are spam, even if some of them are real and important messages.

To fix these problems, AI experts use techniques like regularization. This helps find a good balance between being too specific and too general. With the right adjustments, AI can perform better and get things right!