What is meant by "ground truth" in the context AI?

It really depends on what words you put after "ground truth". Sometimes people will talk about "ground truth labels", for example in the context of classification or regression problems. The "ground truth labels" in such a case would refer to the true labels of instances; the labels that we use as target labels for instances from a training set, or the labels that we expect our models to output (and "punish" them for if they fail to do so) when evaluating/testing a trained model. This basically follows razvanc92's answer.

"Ground truth" can also refer to something more abstract though, something that we know exists in some form or another, but we may not even know how to express it. For example, there may be "ground truth laws of physics", the laws of physics that our world "follows". We may build or train a simulator trying to approximate those ground truth functions / laws, but we may not actually know how to explicitly express all of them.

In the context of ML, ground truth refers to information provided by direct observation (empirical evidence). If you're training an algorithm to classify your data, then the ground truth will be the actual, true labels which could for example be manually annotated by an domain expert. Please note that the models prediction or the inferred labels, are not considered ground truth.