  Transfer learning, multi-task learning, and meta-learning are well-studied
topics concerned with the generalization of knowledge across learning tasks and
are closely related to general intelligence. But, the formal, general systems
differences between them are underexplored in the literature. This lack of
systems-level formalism leads to difficulties in coordinating related,
inter-disciplinary engineering efforts. This manuscript formalizes transfer
learning, multi-task learning, and meta-learning as abstract learning systems,
consistent with the formal-minimalist abstract systems theory of Mesarovic and
Takahara. Moreover, it uses the presented formalism to relate the three
concepts of learning in terms of composition, hierarchy, and structural
homomorphism. Findings are readily depicted in terms of input-output systems,
highlighting the ease of delineating formal, general systems differences
between transfer, multi-task, and meta-learning.
