A Metamodel and Framework for Artificial General Intelligence From Theory to Practice

TL;DR

This paper presents a novel metamodel-based knowledge representation that enhances autonomous learning and adaptation, enabling diverse AI systems to operate synergistically and achieve unprecedented accuracy and generalization, with potential insights into human cognition.

Contribution

Introduces a comprehensive metamodel framework for knowledge representation that addresses symbol grounding, cumulative learning, and federated learning, unifying various AI approaches.

Findings

Achieved high accuracy and performance across multiple AI domains.

Enabled diverse learning mechanisms to operate synergistically.

Provided insights into improving human cognition.

Abstract

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.