We discuss relationships of attribute implications to various tools in computer science and artificial intelligence: functional dependencies, horn theories, emergent patterns, disjunctive version spaces, and concept-based hypotheses. The intractability of computing implication bases seems to be the main challenge for the use of implications in analyzing large data collections. Alternatives to generation of implication bases such as lazy-learning classification, target-driven generation of classifiers, and sampling are considered.