Towards Neural Knowledge DNA
This addresses the problem of scalable and reusable knowledge representation for AI systems, but it appears incremental as it builds on existing neural network ideas.
The paper introduces Neural Knowledge DNA, a framework for knowledge representation that adapts neural network principles to enable machines to discover, store, reuse, improve, and share knowledge, structured around four elements: Networks, Experiences, States, and Actions.
In this paper, we propose the Neural Knowledge DNA, a framework that tailors the ideas underlying the success of neural networks to the scope of knowledge representation. Knowledge representation is a fundamental field that dedicate to representing information about the world in a form that computer systems can utilize to solve complex tasks. The proposed Neural Knowledge DNA is designed to support discovering, storing, reusing, improving, and sharing knowledge among machines and organisation. It is constructed in a similar fashion of how DNA formed: built up by four essential elements. As the DNA produces phenotypes, the Neural Knowledge DNA carries information and knowledge via its four essential elements, namely, Networks, Experiences, States, and Actions.