Real-Time AI Module Communication with Self-Learning Classifiers
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Solution Overview
Problem
Existing software and artificial intelligence-based Website technologies struggle to efficiently detect and initiate real-time communications between artificial intelligence modules, leading to delayed responses and suboptimal user experiences in web searches.
Innovation Solution
A method and system that utilize a hardware device with a processor to generate models and classifiers for real-time artificial intelligence module communications, detect a master AI module, and initiate communications with multiple AI modules, generating updated self-learning software for improved query execution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If existing software and AI-based Website technologies are used to detect and initiate communications between AI modules, then the system can perform basic web search functions, but the detection and communication initiation is inefficient leading to delayed responses
Solution Approach 1:
The system pre-generates multiple classifier models before actual AI module detection is needed. These classifiers are ready in advance to immediately identify and categorize AI modules when communications need to be initiated, eliminating the time-consuming process of creating classifiers during runtime and thus reducing response delays while improving detection efficiency
2Reliability
If real-time communications are initiated between multiple AI modules with updated classifiers and models, then the web search capabilities and result relevance are improved, but the system complexity increases
Solution Approach 1:
The system divides the AI communication infrastructure into distinct modular components: master AI module, slave AI modules, classifier models, and self-learning software. Each component has a specific function and can be independently developed, tested, and updated. This segmentation manages system complexity by creating manageable modules while enabling sophisticated real-time communications that improve web search result quality through coordinated AI collaboration
3Productivity
If updated self-learning software is generated and executed based on communication results, then the AI module communication efficiency is enhanced, but the software development and update process becomes more complex
Solution Approach 1:
The system implements self-learning software that automatically generates updated classifiers and communication protocols based on its own operational experience and communication results. The software continuously improves its own performance without requiring manual intervention for updates, thereby enhancing AI module communication efficiency while the automated self-updating mechanism manages the complexity of software evolution
Data Source
AI summary
A system, method, and computer program product for implementing artificial intelligence module communication is provided. The method includes generating models associated with communications between real-time artificial intelligence modules. Classifiers associated with the models are generated and a master real-time artificial intelligence module associated with the modules and classifiers is detected. Real-time artificial intelligence modules are detected and communications between the master real-time artificial intelligence module and the real-time artificial intelligence modules are initiated. Updated classifiers, updated models, and updated self learning software are generated. The updated self learning software is executed and a resulting query associated with a Web search is executed.