AI Classification Model Self-Improvement via Hypothesis Graph
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Solution Overview
Problem
Existing AI-based classification models require human intervention and data scientists for improvement, limiting their autonomy and accuracy in detached environments, and are constrained by the quality of training data and configurations.
Innovation Solution
An AI system that iteratively improves classification models through self-motivated learning by generating and executing hypotheses to achieve predetermined performance goals, using reinforcement learning and pre-defined interfaces to enhance model accuracy without external data exposure or human expert involvement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If human intervention and data scientists are used for model improvement, then model accuracy can be improved through expert knowledge, but system autonomy is reduced and Time To Market increases
Solution Approach 1:
The system performs self-improvement by automatically generating hypotheses, selecting them through traversal of the hypotheses graph, and executing them to update the classification model. This self-service mechanism eliminates the need for continuous human intervention while maintaining high accuracy through iterative autonomous enhancement.
Solution Approach 2:
The system pre-generates a list of hypotheses and constructs a hypotheses graph in advance, allowing autonomous decision-making about which improvements to apply. This preliminary preparation enables the system to act independently without waiting for human expert input when improvements are needed.
2Measurement precision
If iterative improvement with multiple hypotheses is implemented, then model accuracy improves through incremental enhancements, but system complexity increases
Solution Approach 1:
The improvement process is segmented into discrete hypotheses, each representing a specific strategy or parameter change. The hypotheses graph organizes these segmented improvements in a structured manner, making the complex iterative process manageable and systematic rather than chaotic.
Solution Approach 2:
The system dynamically selects which hypothesis to execute based on traversal of the hypotheses graph, allowing flexibility in the improvement path. This dynamic approach enables the system to adapt to different model states and select the most promising improvements at each iteration without following a rigid predetermined path.
3Reliability
If training data quality is constrained, then data security and privacy are maintained in detached environments, but model accuracy is limited
Solution Approach 1:
The system uses feedback from model performance measurements to guide hypothesis generation and selection. By continuously monitoring accuracy and using this feedback to iteratively improve the model through autonomous hypothesis execution, the system can enhance accuracy without requiring additional external training data, thus maintaining data security.
Solution Approach 2:
The system creates and evaluates hypothetical improvements (copies of potential changes) before applying them to the actual model. This copying mechanism allows extensive experimentation and validation of improvement strategies in isolation, ensuring that only verified improvements are applied while maintaining data security in detached environments.
Data Source
AI summary
An Artificial Intelligence system, an apparatus and, a computer program product and a method for automatic improvement of artificial intelligence classification models. A model-performance measurement of the classification model is iteratively improved by at least a predetermined target goal in each iteration. The iterative improvement comprises generating a hypotheses graph for improving the classification model, based on a list of hypotheses and scores thereof. Each hypothesis relates to a strategy for potentially improving the classification model, and is associated with a score indicating a likelihood that an application thereof improves the model-performance measurement. Each node of the hypotheses graph comprises a hypothesis of the list of hypotheses. The iterative improvement further comprises selecting a selected hypothesis from the hypotheses graph based on a traversal thereof; and executing the selected hypothesis thereby updating the classification model and improving the model-performance measurement by at least the predetermined target goal.