  Like any field of empirical science, AI may be approached axiomatically. We
formulate requirements for a general-purpose, human-level AI system in terms of
postulates. We review the methodology of deep learning, examining the explicit
and tacit assumptions in deep learning research. Deep Learning methodology
seeks to overcome limitations in traditional machine learning research as it
combines facets of model richness, generality, and practical applicability. The
methodology so far has produced outstanding results due to a productive synergy
of function approximation, under plausible assumptions of irreducibility and
the efficiency of back-propagation family of algorithms. We examine these
winning traits of deep learning, and also observe the various known failure
modes of deep learning. We conclude by giving recommendations on how to extend
deep learning methodology to cover the postulates of general-purpose AI
including modularity, and cognitive architecture. We also relate deep learning
to advances in theoretical neuroscience research.
