Aren't a lot of these a kind of over fitting the data?
The learning agent discovers something that is true of the training set, but does not generalize to other examples of the problem outside of the training set.
The article talks about much of the problem being in data set construction, because it is very tricky to design data sets without accidental biases that the learner can use to correctly categorize the examples that have nothing to do with the actual problem you want the learner to solve. The traditional techniques to avoid over fitting, like holding out part of the training data, don't do any good if the entire data set is not representative of the real world in some systematic way.
The learning agent discovers something that is true of the training set, but does not generalize to other examples of the problem outside of the training set.
The article talks about much of the problem being in data set construction, because it is very tricky to design data sets without accidental biases that the learner can use to correctly categorize the examples that have nothing to do with the actual problem you want the learner to solve. The traditional techniques to avoid over fitting, like holding out part of the training data, don't do any good if the entire data set is not representative of the real world in some systematic way.