My supervisor, Alan Blair, and I were talking the other day about problem domains and tasks used for assessing the performance and capability of machine learning and artificial intelligence methods/approaches/algorithms/models (let's just refer to these collectively as MAAMs, shall we). Traditionally, when someone introduces a new MAAM (e.g. a new neural network model, neural network encoding scheme, evolutionary algorithm, etcetera) they will report the results of experiments which test the new MAAM on one or two tasks. (Also typically only positive results are reported, but that's another topic.) Sometimes the new MAAM is designed for some specific task, so performance for other tasks is irrelevant. But often we do care about how well a new MAAM will perform on many kinds of tasks.