Dual-Level Input Processing for Adaptive AI Classification
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Solution Overview
Problem
Existing artificial intelligence systems, particularly neural networks, are typically designed for specialized tasks and struggle to adapt to different domains without extensive retraining, leading to inefficiencies and the inability to provide real-time responses in high-dimensional spaces.
Innovation Solution
A method involving a working level and an evaluation level of artificially learning systems, where input values are processed through a first and second classification, with the evaluation level influencing the working level to meet predetermined conditions, incorporating non-technical aspects like moral and ethical considerations, and using modulation functions to adjust neural network parameters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If neural networks are designed for specialized tasks with fixed architectures, then they achieve high accuracy in specific domains, but they cannot adapt to different domains without extensive retraining
Solution Approach 1:
The patent implements dynamic neural networks where the architecture can be modified at runtime through the addition, removal, or modification of nodes and connections. This allows the network to adapt its structure to different classification tasks and domains without complete retraining, resolving the contradiction between specialized accuracy and general adaptability
Solution Approach 2:
The neural network is divided into independent, modifiable components (nodes and connections) that can be individually added, removed, or adjusted. This segmentation enables flexible reconfiguration of the network architecture for different domains while preserving useful existing structures, balancing specialization and adaptability
2Adaptability or versatility
If neural networks process high-dimensional input spaces, then they achieve comprehensive classification coverage, but real-time response becomes impossible due to computational complexity
Solution Approach 1:
The patent pre-calculates and stores distance metrics and similarity measures between data points during an offline phase. This preliminary computation allows the online classification phase to operate rapidly by simply comparing pre-computed values rather than performing complex high-dimensional calculations in real-time
Solution Approach 2:
The system computes classification results for a subset of relevant neighbors or data points rather than processing the entire high-dimensional space. By focusing computational effort on the most relevant portions of the data space, the system achieves real-time response while maintaining comprehensive classification coverage
3Reliability
If AI systems are trained extensively for specialized tasks, then they achieve high performance, but they become useless in other domains and require complete retraining
Solution Approach 1:
The system incorporates feedback mechanisms that allow it to learn from classification results and adjust its architecture accordingly. Performance feedback triggers automatic modifications to the neural network structure, enabling the system to maintain high reliability across different domains by continuously adapting to new tasks while preserving useful learned patterns
Data Source
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AI summary
The invention relates to a method implemented in a controller of a machine for processing input values in an overall system having a working level and a rating level, comprising inputting first input values to the working level and determining first output values; forming first situation data; inputting the first situation data to the rating level and determining first ratings that indicate whether the first situation data meet predefined first conditions; influencing the determination of the first output values based on the first ratings; inputting second input values to the working level and determining second output values, wherein the determination of the second output values is influenced by the first output values; forming second situation data; inputting the second situation data to the rating level and determining second ratings that indicate whether the second situation data meet predefined second conditions, wherein the determination of the second ratings is influenced by the first ratings; influencing the determination of the second output values based on the second ratings; wherein the first and/or the second output values are used as overall output values of the overall system.