Hierarchical SLP Sequencing for Concurrent Training and Prediction
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Solution Overview
Problem
Supervised learning processors face challenges in achieving accurate predictions and responses due to the complexity of natural language domains, the need for extensive training data, and the time and cost associated with training, especially in flexible and unstructured environments.
Innovation Solution
A network of supervised learning processor subsystems is employed, utilizing a first and second order subsystems to distribute training and processing, where higher order subsystems enhance lower order subsystems, allowing concurrent training and response generation, with dynamic sequencing based on environmental data to optimize resource utilization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If supervised learning processors undergo extensive training to improve prediction accuracy, then prediction accuracy improves, but training time and cost increase
Solution Approach 1:
The system segments the supervised learning processor into multiple subsystems (first order, second order, third order) with different specialization levels. This segmentation allows concurrent training and operation of different subsystems, reducing overall training time while maintaining prediction accuracy through the hierarchical structure where higher-order subsystems provide guidance to lower-order ones.
Solution Approach 2:
Higher-order subsystems perform preliminary training and generate training data in advance that is then used to train lower-order subsystems. This preliminary action allows lower-order subsystems to be trained more quickly and efficiently, reducing the total training time required while maintaining high prediction accuracy.
2Adaptability or versatility
If supervised learning processors are trained extensively to handle flexible and unstructured natural language domains, then adaptability improves, but training cost increases
Solution Approach 1:
The system divides the adaptability requirement across multiple specialized subsystems rather than training one general-purpose system. Each subsystem focuses on specific aspects of natural language processing, reducing the training cost for each while collectively achieving high overall adaptability to diverse natural language domains.
Solution Approach 2:
Higher-order subsystems generate synthetic training data that is copied and used to train lower-order subsystems. This copying approach reduces the need for extensive manual training data collection and annotation, lowering training costs while maintaining adaptability to flexible natural language domains.
3Device complexity
If a single supervised learning processor is used for both training and operation, then system complexity is reduced, but productivity decreases due to sequential processing
Solution Approach 1:
The system segments the processor into multiple subsystems that can operate concurrently - some subsystems are trained while others handle operational tasks. This segmentation enables parallel processing of training and operation, significantly improving productivity without requiring a single complex processor to handle both sequentially.
Solution Approach 2:
The system adds the dimension of hierarchical ordering (first order, second order, third order subsystems) to enable concurrent training and operation. This dimensional organization allows the system to perform training and operational tasks simultaneously across different subsystems, improving productivity while managing complexity through structured hierarchy.
4Measurement precision
If higher order subsystems are used to enhance lower order subsystems, then prediction accuracy improves, but device complexity increases
Solution Approach 1:
The system implements a nested hierarchy where higher-order subsystems contain and guide lower-order subsystems. This nesting structure allows prediction accuracy to improve through multiple levels of processing while managing complexity through a organized hierarchical framework where each level builds upon the previous one.
Solution Approach 2:
Higher-order subsystems act as intermediaries that process and refine information before passing it to lower-order subsystems. This intermediary role improves prediction accuracy by adding layers of processing while managing complexity through a clear mediator structure that organizes the flow of information between subsystems.
Data Source
AI summary
A supervised learning processing (SLP) system and method provide cooperative operation of a network of supervised learning processors to concurrently distribute supervised learning processor training, generate predictions, provide prediction driven responses to input objects, and provide operational sequencing to concurrently control and distribute supervised learning processor training and provide predictive responses to input data. The SLP system can dynamically sequence SLP subsystem operations to improve resource utilization, training quality, and/or processing speed. A system monitor-controller can dynamically determine if process environmental data indicates initiation of dynamic subsystem processing sequencing. Concurrently training SLP's provides accurate predictions of input objects and responses thereto and enhances the network by providing high quality value predictions and responses and avoiding potential training and operational delays. The SLP system can enhance the network of SLP subsystems by providing flexibility to incorporate multiple SLP models into the network and train with concurrent commercial operations.