Current formal learning theory is a lively field, to which philosophers of statistics also contribute (e.g., Kelly 1996, Kelly et al 1997). Particularly salient for the present concerns is that the systems of formal learning are set up to achieve some notion of adequate universal prediction, without confining themselves to a specific set of hypotheses, and hence by imposing minimal constraints on the set of possible patterns in the data. It is a matter of debate whether this is at all possible, and to what extent the predictions of formal learning theory thereby rely on, e.g., implicit assumptions on structure of the sample space. Philosophical reflection on this is only in its infancy.