  Can artificial intelligence systems exhibit superhuman performance, but in
critical ways, lack the intelligence of even a single-celled organism? The
answer is clearly 'yes' for narrow AI systems. Animals, plants, and even
single-celled organisms learn to reliably avoid danger and move towards food.
This is accomplished via a physical knowledge preserving metamodel that
autonomously generates useful models of the world. We posit that preserving the
structure of knowledge is critical for higher intelligences that manage
increasingly higher levels of abstraction, be they human or artificial. This is
the key lesson learned from applying AGI subsystems to complex real-world
problems that require continuous learning and adaptation. In this paper, we
introduce the Deep Fusion Reasoning Engine (DFRE), which implements a
knowledge-preserving metamodel and framework for constructing applied AGI
systems. The DFRE metamodel exhibits some important fundamental knowledge
preserving properties such as clear distinctions between symmetric and
antisymmetric relations, and the ability to create a hierarchical knowledge
representation that clearly delineates between levels of abstraction. The DFRE
metamodel, which incorporates these capabilities, demonstrates how this
approach benefits AGI in specific ways such as managing combinatorial explosion
and enabling cumulative, distributed and federated learning. Our experiments
show that the proposed framework achieves 94% accuracy on average on
unsupervised object detection and recognition. This work 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.
