Neural Network Node Instructions for Knowledge Base Updates
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Solution Overview
Problem
Existing knowledge base systems require extensive reevaluation and resource-intensive recalculations whenever inputs change, leading to delays and inefficiencies as they update outputs, even when only portions of the knowledge base are affected.
Innovation Solution
A neural network architecture that transmits node instructions, allowing only affected portions of the knowledge base to be updated, using logical nodes and edges to generate and propagate output change values, thereby conserving computing resources and reducing processing time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the entire knowledge base is reevaluated whenever inputs change, then the outputs are updated comprehensively, but computing resources and processing time are excessively consumed
Solution Approach 1:
The knowledge base is divided into multiple independent logical nodes that can be individually identified and updated. When an input change occurs, only the specific logical nodes affected by that change are selected for update, rather than reevaluating the entire knowledge base. This segmentation enables selective processing that maintains output completeness while reducing computational overhead.
2Reliability
If the entire knowledge base is reevaluated whenever inputs change, then all affected outputs are captured, but the processing time increases significantly
Solution Approach 1:
The system pre-establishes the logical structure of the knowledge base with defined relationships between logical nodes before processing occurs. This preliminary organization allows the system to quickly identify which specific nodes need updating when an input change happens, eliminating the need for time-consuming full reevaluations while ensuring all affected outputs are captured.
3Measurement precision
If comprehensive knowledge base reevaluation is performed, then accurate outputs are generated, but computing resources are excessively consumed
Solution Approach 1:
The system applies different processing qualities to different parts of the knowledge base based on their relevance to the input change. Affected logical nodes receive full processing attention to maintain accuracy, while unaffected nodes are excluded from processing entirely. This local quality approach ensures accurate outputs for affected areas without wasting computing resources on unrelated portions of the knowledge base.
Data Source
AI summary
In some examples, transmitting node instructions can include a logical node of a neural network to: receive a first node instruction, generate an output change value based on the received first node instruction, and transmit the output change value and the first node instruction to a logical edge, and the logical edge of the neural network to: receive the output change value and the first node instruction from the logical node, generate a second node instruction based on the output change value and the first node instruction, and transmit the second node instruction.