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In addition, the examples that provide the most information are those where the result is close to (or defines) the decision boundary: the marginal cases. These also take a lot longer to annotate. Finally, it is usually necessary to come back to those examples which are the most surprising to the algorithms that are learning from them to ensure that the tagging of these examples is correct—many of the more powerful machine learning algorithms can be thrown off by a relatively small amount of noise as they weight examples non-uniformly. These restrictions completely disassociate the dataset from its problem domain (information retrieval or “Learning to Rank”), and in doing so reduce the value to something approaching zero. In effect, Yahoo is outsourcing (or “crowdsourcing” if you will) the work of discovering which Machine Learning paradigm is most suitable for their data, information which is only really useful to Yahoo themselves as only they have this kind of data. nature; far from it. But at some point, as a society, when we decide to allocate resources to Machine Learning instead of (say) to growing potatoes or curing cancer, we’re making some kind of a judgement about the value of one activity over the other.