Another direction of inductive inference is based on E. Mark Gold's model of learning in the limit from 1967 and has developed since then more and more models of learning. The general scenario is the following: Given a class "S" of computable functions, is there a learner (that is, recursive functional) which for any input of the form ("f"(0),"f"(1)...,"f"("n")) outputs a hypothesis (an index "e" with respect to a previously agreed on acceptable numbering of all computable functions; the indexed function may be required consistent with the given values of "f"). A learner "M" learns a function "f" if almost all its hypotheses are the same index "e", which generates the function "f"; "M" learns "S" if "M" learns every "f" in "S". Basic results are that all recursively enumerable classes of functions are learnable while the class REC of all computable functions is not learnable.