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Unifying AI Algorithms with Distributed Knowledge Metagraphs
The integration of neural, symbolic, and evolutionary methods involves creating a large, distributed knowledge metagraph, which goes beyond a traditional graph by allowing links that span multiple nodes and pulling the general subgraphs. This distributed knowledge metagraph represents neural nets, logic engines, evolutionary learning, and static knowledge. It is a self-modifying, self-rewriting, and self-evolving graph, with the initial state representing various types of knowledge that can be fed in from databases, language models, or pattern recognition. This unification is achieved through a common representation using Galois connections, which allows AI algorithms to be represented as fold and unfold operations over the metagraph.
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