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

02/11/2021
by   Hugo Latapie, et al.
0

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.

READ FULL TEXT

page 8

page 10

page 11

research
08/28/2020

A Metamodel and Framework for AGI

Can artificial intelligence systems exhibit superhuman general intellige...
research
05/15/2019

Neural-Symbolic Computing: An Effective Methodology for Principled Integration of Machine Learning and Reasoning

Current advances in Artificial Intelligence and machine learning in gene...
research
07/11/2019

Solving Hard Coreference Problems

Coreference resolution is a key problem in natural language understandin...
research
11/22/2022

Differentiable Fuzzy 𝒜ℒ𝒞: A Neural-Symbolic Representation Language for Symbol Grounding

Neural-symbolic computing aims at integrating robust neural learning and...
research
03/09/2020

Neuro-symbolic Architectures for Context Understanding

Computational context understanding refers to an agent's ability to fuse...
research
09/10/2022

Symbolic Knowledge Extraction from Opaque Predictors Applied to Cosmic-Ray Data Gathered with LISA Pathfinder

Machine learning models are nowadays ubiquitous in space missions, perfo...

Please sign up or login with your details

Forgot password? Click here to reset