Transparent AI Pattern Encapsulation for Human-Aligned Processing
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Solution Overview
Problem
Existing artificial intelligence systems, particularly those based on neural networks, suffer from lack of transparency, difficulty in selective modification, lack of alignment with human objectives, and intensive resource consumption, limiting their scalability and reliability.
Innovation Solution
A system that identifies significant patterns in data and encapsulates them transparently, allowing human oversight and control, while maintaining alignment with predefined objectives through hierarchical data banks and pattern identification, and employing parallel and distributed computing techniques.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If neural networks are used for AI processing, then processing power and speed are improved, but transparency and interpretability deteriorate
Solution Approach 1:
The system segments the AI processing into distinct functional modules: data collection, pattern identification, encapsulation, and execution. Each module operates independently with defined interfaces, allowing transparent inspection of individual components while maintaining overall processing speed through parallel execution of segmented tasks.
Solution Approach 2:
The patent introduces an intermediary encapsulation layer between the neural network processing components and the external environment. This intermediary structure provides transparent interfaces that expose necessary information and control mechanisms while allowing the underlying neural network to operate efficiently without direct exposure of its complex internal state.
2Power
If neural networks are used for AI processing, then computational power is improved, but ease of modification deteriorates
Solution Approach 1:
The system divides the AI processing into separable functional units with well-defined interfaces. This segmentation allows individual components to be modified, updated, or replaced independently while maintaining the overall system functionality, thus improving ease of modification without sacrificing computational power.
Solution Approach 2:
The patent implements a dynamic architecture where the encapsulation structure can adaptively configure the underlying neural network components. This dynamic configuration capability allows the system to modify processing parameters and component connections without requiring complete retraining or redesign, facilitating easy modification while preserving computational power.
3Productivity
If neural networks are used for AI processing, then processing capability is improved, but alignment with human objectives deteriorates
Solution Approach 1:
The system incorporates feedback mechanisms where human-defined objectives and rules are continuously fed back into the encapsulation structure. This feedback loop enables the neural network to adjust its processing to align with human objectives while maintaining high processing capability through efficient neural network computations.
Solution Approach 2:
The encapsulation structure serves multiple functions simultaneously: it provides transparent interfaces for human oversight, enforces alignment with human objectives, and maintains the computational efficiency of neural networks. This multi-functionality allows the system to achieve both high processing capability and reliable alignment without requiring separate systems for each function.
4Speed
If neural networks are used for AI processing, then processing speed is improved, but resource consumption increases
Solution Approach 1:
The system applies partial action by selectively activating neural network components and processing only the necessary portions of data through the encapsulation structure. This partial processing approach reduces overall resource consumption while maintaining processing speed for critical tasks by avoiding unnecessary computational operations.
Solution Approach 2:
The encapsulation structure implements self-service mechanisms that automatically optimize resource allocation based on task requirements. The system self-regulates computational resource consumption by dynamically configuring neural network components according to the complexity and urgency of processing tasks, reducing waste while maintaining high processing speed when needed.
Data Source
AI summary
Artificial Intelligence systems, methods, and products identify significant patterns in data and persist those patterns and/or their constituents as data entries in databases or as transitory patterns of activation. The constituents associated with significant patterns include data constructs representing concepts or filter/exemplars. Those concepts or filters/exemplars themselves represent significant patterns. Recursive identification of significant patterns of significant patterns, and significant patterns of those patterns, and so on to any degree of recursiveness desired, facilitates the leveraging of combinatorial expansion to categorize, recognize, associate, and predict complex combinations of information while maintaining practical control over resources. In preferred implementations, the elements of the systems, methods, and products described herein are transparently encapsulated, and their behaviors are monitored and enforced, to provide alignment with human-determined objectives and rules.