Neutrosophic Causality Platform for Outlier Detection
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Solution Overview
Problem
Current artificial intelligence systems are unable to automatically detect new scenarios without human intervention and substantial retraining, leading to a lag in analyzing big data and identifying potential causes of outlier scenarios, as they lack the ability to carry experiences from one set of circumstances to another.
Innovation Solution
A system and method for automatically detecting causes of outlier data-event scenarios using a causality platform that employs neutrosophic processing and an ontological model, which evaluates input source data to identify potential causes of outliers by linking data-event attributes, rules, and parameters, enabling automated causality detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional AI models are used for pattern recognition, then they can recognize known patterns, but they cannot identify new scenarios without human intervention and substantial retraining
Solution Approach 1:
The system enables AI models to automatically detect and adapt to new scenarios through self-service mechanisms. The neural network performs self-diagnosis by analyzing outlier data and automatically adjusts its parameters and architecture without requiring human retraining, allowing it to continuously learn and adapt to new patterns independently
Solution Approach 2:
The system implements feedback loops where the AI model continuously monitors its own performance and the characteristics of incoming data. When new outlier scenarios are detected, the feedback mechanism triggers automatic model adjustment and retraining using the newly identified patterns, creating a closed-loop system that continuously improves adaptability
2Measurement precision
If traditional AI models require human retraining for new patterns, then they can maintain accuracy for known patterns, but they create a lag between data generation and analysis
Solution Approach 1:
The system performs preliminary actions by pre-configuring the neural network with multiple potential scenario templates and causality analysis frameworks before new data arrives. When data is generated, the system can immediately apply these pre-prepared analytical structures, eliminating the need to wait for human retraining and enabling real-time analysis of new patterns
Solution Approach 2:
The AI model automatically performs self-updating and retraining when new patterns are detected, eliminating the need for human intervention. This self-service capability ensures that the model maintains high accuracy for both known and new patterns while eliminating the productivity lag caused by manual retraining cycles
3Extent of automation
If AI systems lack the ability to carry experiences from one set of circumstances to another, then they can be simple in structure, but they cannot automatically detect causes of outlier scenarios
Solution Approach 1:
The neural network is designed with universal causality analysis capabilities that can be applied across different domains and scenario types. The system uses a unified framework for detecting outliers, analyzing causality, and adapting to new patterns, allowing it to carry experiences and learned relationships from one set of circumstances to another while maintaining automated detection across diverse applications
Solution Approach 2:
The system implements a nested architecture where multiple levels of analysis are embedded within each other. The neural network contains embedded modules for outlier detection, causality analysis, and pattern recognition that work together in a hierarchical manner. This nested structure allows the system to maintain complexity only where necessary while keeping the overall system organized and manageable
Data Source
AI summary
A method for automatically detecting causes of outlier data-event scenarios. A plurality of linksets of an ontological model are instantiated in an in-memory neural network. The instantiated linksets are a tailored array of interconnected index tables that defines the ontological relationship between potential causes, parameters, data-event attributes corresponding to the parameters, and neutrosophic rules corresponding to the potential causes and the parameters. Data-events are indexed so as to generate an index class that links each indexed data-event to the instantiated data-event attributes corresponding to the instantiated parameters via corresponding attribute values of the indexed data-events. The index class is supplemented with additional data-event attributes corresponding to repeating attribute values of the indexed data-events. The index class is neutrosophically analyzed according to the neutrosophic rules of the instantiated linkset, so as to detect whether certain combinations of data-event attributes are likely caused by the potential causes.