Identifying Information Assignment for Machine Learning Traceability
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Solution Overview
Problem
In machine learning, particularly deep learning, it is challenging to identify and manage the relationships between learning processes and their resulting capabilities when different machines perform learning, as the learning apparatus and data used are often distinct, making it difficult to trace the learning data and processes that led to specific outcomes.
Innovation Solution
An identifying information assignment system that generates and assigns specific identifying information to learning results, allowing for the identification of the learning process and conditions that influenced the outcome, while excluding irrelevant information and ensuring security through encryption or compression.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If all learning conditions are recorded as identifying information, then the completeness of learning process identification is improved, but the information amount and storage requirements increase
Solution Approach 1:
The patent extracts only the essential learning conditions that actually influence learning results, separating them from irrelevant conditions. This is achieved by identifying and recording only parameters such as learning algorithm type, data characteristics, and model architecture that have direct impact on learning outcomes, while excluding redundant information.
Solution Approach 2:
The patent applies different levels of detail to different aspects of learning identification. For example, high-precision identification is applied to critical parameters like learning algorithm and data characteristics, while less detailed identification is applied to secondary parameters like hardware environment, optimizing the balance between completeness and information volume.
2Measurement precision
If detailed identifying information is assigned to all learning results, then the ability to distinguish different learning processes is improved, but the system complexity increases
Solution Approach 1:
The patent segments identifying information into hierarchical levels: essential identification information (learning algorithm, data characteristics), secondary identification information (model parameters, training configurations), and optional detailed information (hardware environment, timing). This segmentation allows the system to manage complexity by only requiring essential information for basic differentiation while enabling detailed differentiation when needed.
3Reliability
If identifying information includes all learning conditions, then the traceability of learning results is improved, but the processing and management overhead increases
Solution Approach 1:
The patent extracts and records only the essential traceability information needed to reproduce and verify learning results, such as learning algorithm type, data characteristics, and key model parameters. This extraction approach ensures adequate traceability for scientific rigor while minimizing processing overhead by excluding redundant details.
Data Source
AI summary
An identifying information assignment system includes: a generating portion configured to generate, for a learning result obtained by attaining a predetermined capability through a predetermined learning process by machine learning, identifying information for identifying the predetermined learning process; and an assignment portion configured to assign the generated identifying information to the learning result.