LRI-DecNN AI Model Selection for Nuanced Task Automation
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Solution Overview
Problem
Current Generative AI technologies face challenges in automating complex, nuanced tasks due to limited applicability, manual and isolated development processes, expertise-dependent workflow identification, high iterative nature, integration issues, lack of control-based guardrails, and experimental Agent-Based Modeling, leading to inefficiencies and barriers in widespread adoption.
Innovation Solution
The use of a Logic and Rule Integrated Decoder Neural Network (LRI-DecNN) for analyzing tasks in natural language, integrating business logic and rules, and employing a meta-mixture of experts technique to create agent chains, fine-tuning AI models with KL divergence and Q-learning, and utilizing an Actor-Critic framework for optimization, enabling precise and adaptive task execution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If Generative AI is used to automate complex nuanced tasks, then task automation capability is improved, but model selection complexity and integration difficulty increase
Solution Approach 1:
The system performs self-service by automatically analyzing task requirements, selecting appropriate AI models, and integrating them into workflows without requiring extensive manual configuration or expert intervention. The automated model selection and integration process enables the system to serve itself in resolving the complexity it introduces.
Solution Approach 2:
The patent introduces an intermediary layer between the complex AI model ecosystem and the end-user task automation needs. This intermediary system handles model analysis, selection, and integration, shielding users from the underlying complexity while enabling effective task automation.
2Measurement precision
If AI models are fine-tuned for specific nuanced tasks, then task execution precision is improved, but training time and computational resources increase
Solution Approach 1:
The system performs preliminary actions by pre-analyzing task requirements and pre-selecting appropriate base models before fine-tuning. This preliminary preparation reduces the scope and time required for subsequent fine-tuning processes while maintaining high execution precision.
Solution Approach 2:
Instead of globally fine-tuning all AI models for all tasks, the system applies local quality by selectively fine-tuning only the specific portions of models that are relevant to particular nuanced tasks. This targeted approach preserves precision while reducing training time and computational resources.
3Device complexity
If traditional automation tools are used for complex tasks, then system simplicity is maintained, but task execution effectiveness decreases
Solution Approach 1:
The system introduces dynamics by adaptively selecting and configuring AI models based on the specific requirements of each nuanced task. Rather than using a static, one-size-fits-all approach, the system dynamically adjusts its automation strategy to maintain effectiveness while managing complexity.
Solution Approach 2:
The patent implements universality by creating a unified automation framework that can handle diverse nuanced tasks through selective AI model deployment. This multi-functional system maintains relative simplicity by providing a single interface for various task types while achieving high effectiveness through appropriate model selection.
Data Source
AI summary
Currently task automation is in its initial stage. Understanding task complexity/requirements, effectiveness/efficiency of automation using best models to automate the task and performance of such task automation approaches are being explored. A method and system for selection-optimization-fine-tuning of Artificial Intelligence (AI) models for nuanced task by analyzing the nuanced task using Logic and Rule Integrated Decoder Neural Network (LRI-DecNN) is disclosed. The system performs tasks analysis using the LRI-DecNN, AI augmentation suitability check and constructs agent chain of subtasks using meta-mixture technique tailored to specific enterprise needs. Thereafter, the most appropriate generative AI model is selected and fine-tuned for the task at hand using Blackbox estimation. The task is executed and monitored for optimal performance.