The General Theory of General Intelligence: A Pragmatic Patternist Perspective
arXiv Preprint Archive March 28, 2021 via arXiv
Summary
AI-generated from the abstractA multi-decade theoretical exploration of artificial and natural general intelligence is reviewed, covering patternist philosophy of mind, formal definitions of intelligence, and a proposed high-level architecture for AGI systems. The review details how cognitive processes like logical reasoning, program learning, clustering, and attention allocation can be implemented within this architecture, emphasizing a common knowledge representation (typed metagraph) to enable cognitive synergy between processes. Human-like cognitive architecture is presented as a manifestation of these general principles, with discussions of machine consciousness and machine ethics. Practical lessons for implementing advanced AGI in frameworks like OpenCog Hyperon are briefly considered.
Study at a glance
| Characteristics | Theoretical or philosophical paper Peer reviewed |
|---|---|
| Keywords | Cs.ai Artificial intelligence Machine intelligence Computational intelligence Agi |
| Key finding | A proposed high-level architecture for AGI systems, grounded in patternist philosophy and formal intelligence definitions, uses a typed metagraph knowledge representation to enable cognitive synergy between diverse cognitive processes. |
Abstract
A multi-decade exploration into the theoretical foundations of artificial and natural general intelligence, which has been expressed in a series of books and papers and used to guide a series of practical and research-prototype software systems, is reviewed at a moderate level of detail. The review covers underlying philosophies (patternist philosophy of mind, foundational phenomenological and logical ontology), formalizations of the concept of intelligence, and a proposed high level architecture for AGI systems partly driven by these formalizations and philosophies. The implementation of specific cognitive processes such as logical reasoning, program learning, clustering and attention allocation in the context and language of this high level architecture is considered, as is the importance of a common (e.g. typed metagraph based) knowledge representation for enabling "cognitive synergy" between the various processes. The specifics of human-like cognitive architecture are presented as manifestations of these general principles, and key aspects of machine consciousness and machine ethics are also treated in this context. Lessons for practical implementation of advanced AGI in frameworks such as OpenCog Hyperon are briefly considered.