  Theoretical analysis of machine intelligence (MI) is useful for defining a
common platform in both theoretical and applied artificial intelligence (AI).
The goal of this paper is to set canonical definitions that can assist
pragmatic research in both strong and weak AI. Described epistemological
features of machine intelligence include relationship between intelligent
behavior, intelligent and unintelligent machine characteristics, observable and
unobservable entities and classification of intelligence. The paper also
establishes algebraic definitions of efficiency and accuracy of MI tests as
their quality measure. The last part of the paper addresses the learning
process with respect to the traditional epistemology and the epistemology of MI
described here. The proposed views on MI positively correlate to the Hegelian
monistic epistemology and contribute towards amalgamating idealistic
deliberations with the AI theory, particularly in a local frame of reference.
