Distributed Neural Network With Multiple Injection Points for AI Customization
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Solution Overview
Problem
Existing AI models operate as single nodes, requiring high costs and specialized expertise for custom designs, limiting their applicability and accessibility to only large companies, and failing to meet diverse user objectives effectively.
Innovation Solution
A distributed neural network architecture with multiple injection points for influence data, including query, pattern, cross-node, and control data, allowing flexible and tailored output generation through separate processing pathways and training scenarios.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If single node AI models are used, then implementation is simple, but customization capability and adaptability are limited
Solution Approach 1:
The AI system is segmented into multiple independent nodes, each capable of processing specific tasks. This allows the system to handle diverse customization requirements by routing different data types through specialized nodes, thereby improving adaptability while maintaining manageable complexity through modular architecture
Solution Approach 2:
Multiple injection points are provided that accept different types of influence data (query, pattern, cross-node, control). These injection points serve universal purposes by allowing the same architectural framework to accommodate various customization needs through different data inputs, enabling one system to serve multiple functions
2Manufacturing precision
If single node AI models are used, then deployment cost is low, but quality of AI output and discrimination are insufficient
Solution Approach 1:
Multiple AI nodes are merged into a distributed network where each node contributes specialized processing capabilities. By combining the outputs and processing paths of multiple nodes, the system achieves higher quality AI output and better discrimination than single node models, while the modular nature keeps the architecture complexity manageable
3Adaptability or versatility
If custom AI designs are prepared, then adaptability to user needs is high, but cost and skill requirements are high
Solution Approach 1:
The system provides self-service capabilities through automated influence data processing at multiple injection points. Different types of influence data (query, pattern, cross-node, control) are automatically processed and integrated without requiring extensive manual customization, allowing the system to adapt to user needs while reducing deployment cost and skill requirements
Solution Approach 2:
The system achieves adaptability by changing parameters such as the type, source, and weighting of influence data injected at different points. Rather than requiring custom design from scratch, the system adapts to user needs by adjusting these parameters, thereby reducing both adaptability cost and the skill level required for deployment
Data Source
AI summary
An artificial intelligence infrastructure comprises a first circuitry configured to offer multiple injection points. The circuitry is configured to automatically distribute neural network processing across a local computing device and at least one remote computing device while receiving and processing influence data at the multiple injection points. Furthermore, the first circuitry is configured to inject influences at a plurality of injection points to serve an overall objective. The circuitry is also configured to automatically distribute neural network nodes across one local user's computing device and at least one remote host computing device to accomplish, using artificial intelligence, the overall objective.