Under-fitting in an AI model is when the model is unable to produce a good output because the problem is more complex than the model is capable of solving, this can be caused by many factors some of them may be: The model may not have enough training time, this can be solved without much effort by just further training the model, another cause for under-fitting may be that the training data is simply too complex for the model to solve and the number of parameters may need to be increased.

Over-fitting is the opposite problem to under-fitting where the model may have too many parameters compared to the amount of data, this can cause the model to memorize the solution to the training data instead of learning how to find the solution, this problem can be solved by increasing the amount of training data or decreasing the number of parameters.
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