  This paper introduces a new metamodel-based knowledge representation that
significantly improves autonomous learning and adaptation. While interest in
hybrid machine learning / symbolic AI systems leveraging, for example,
reasoning and knowledge graphs, is gaining popularity, we find there remains a
need for both a clear definition of knowledge and a metamodel to guide the
creation and manipulation of knowledge. Some of the benefits of the metamodel
we introduce in this paper include a solution to the symbol grounding problem,
cumulative learning, and federated learning. We have applied the metamodel to
problems ranging from time series analysis, computer vision, and natural
language understanding and have found that the metamodel enables a wide variety
of learning mechanisms ranging from machine learning, to graph network analysis
and learning by reasoning engines to interoperate in a highly synergistic way.
Our metamodel-based projects have consistently exhibited unprecedented
accuracy, performance, and ability to generalize. This paper is inspired by the
state-of-the-art approaches to AGI, recent AGI-aspiring work, the granular
computing community, as well as Alfred Korzybski's general semantics. One
surprising consequence of the metamodel is that it not only enables a new level
of autonomous learning and optimal functioning for machine intelligences, but
may also shed light on a path to better understanding how to improve human
cognition.
