Distributed Neural Network with Multi-Point Influence Injection
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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 needs 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 divided into multiple independent nodes, each capable of processing specific types of influence data (query, pattern, cross-node, control). This segmentation allows the system to handle complex customization tasks by distributing functionality across specialized nodes while maintaining overall system manageability.
Solution Approach 2:
The distributed neural network architecture enables a single system to perform multiple functions by routing different types of influence data through appropriate nodes. The system can simultaneously handle query processing, pattern matching, cross-node coordination, and control operations, providing universal customization capability without requiring separate specialized systems.
2Adaptability or versatility
If custom AI designs are implemented, then adaptability improves, but cost and skill requirements increase
Solution Approach 1:
The system enables users to inject their own influence data directly into the neural network at multiple points, allowing customizations to be made without requiring specialized AI expertise. Users can provide query data, pattern data, cross-node data, and control data that the system automatically processes through the distributed architecture, eliminating the need for expensive custom model training by experts.
Solution Approach 2:
Instead of requiring complete redesign of AI models for customization, the system allows users to modify parameters by injecting influence data at specific nodes. This parameter-based customization approach enables adaptability through data injection rather than through complex model retraining, significantly reducing implementation costs and skill requirements.
3Adaptability or versatility
If multiple injection points are used, then adaptability and customization improve, but device complexity increases
Solution Approach 1:
The architecture segments the neural network into distinct nodes, each responsible for processing specific types of influence data. This segmentation organizes the complexity by creating clear functional boundaries, making the multi-injection-point architecture more manageable and easier to implement despite the increased number of connection points.
Solution Approach 2:
The distributed neural network acts as an intermediary layer between the user's raw influence data and the final output generation. This intermediary architecture absorbs the complexity of multiple injection points by providing standardized processing pathways, shielding users from architectural complexity while maintaining high flexibility in output generation.
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.