Companion Analysis Network for Deep Learning Introspection
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Solution Overview
Problem
Current machine learning systems, particularly deep learning systems based on neural networks, lack introspection and understanding of their own behavior, making it difficult for human developers to comprehend their inner workings.
Innovation Solution
A second machine learning system is used to analyze the internal properties and behavior of a first machine learning system by collecting computed properties such as node activations and partial derivatives, and training on a specified analysis objective, which also back propagates derivatives to the first system, enabling it to learn and provide information for better performance and human interpretability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a deep learning system is made more complex to achieve better performance, then the system's accuracy improves, but the difficulty of understanding its inner workings increases
Solution Approach 1:
The patent introduces a second machine learning system as an intermediary to analyze and interpret the first system's internal properties. This intermediary system processes computed properties like node activations and partial derivatives, transforming complex internal states into interpretable insights without altering the original system's performance capabilities.
Solution Approach 2:
The patent segments the analysis function from the performance function by creating separate machine learning systems. The first system handles performance tasks while the second system handles analysis tasks, allowing each to be optimized independently. This segmentation enables complex performance systems to be analyzed through dedicated interpretation mechanisms.
2Loss of information
If a machine learning system's internal workings are made more transparent for human understanding, then interpretability improves, but the system's performance may deteriorate
Solution Approach 1:
The patent adds a new dimension to the machine learning ecosystem by introducing a separate analysis system that operates in parallel to the performance system. This dimensional separation allows the performance system to maintain full complexity for accuracy while the analysis system provides interpretability from a different computational perspective, without compromising either function.
3Loss of information
If a second machine learning system is added to analyze the first system, then introspection capability improves, but the overall system complexity increases
Solution Approach 1:
The second machine learning system is designed with multi-functionality, serving both as an analysis tool for introspection and as a potential training target through backpropagation. This universal design allows the same system structure to provide both interpretability insights and performance improvement mechanisms, reducing the need for additional specialized components.
4Loss of information
If backpropagation is used to train the first system on the analysis objective, then interpretability improves, but the training complexity and computational cost increase
Solution Approach 1:
The patent merges the training objectives by combining the primary performance objective with the secondary analysis objective through backpropagation. This integration allows both performance optimization and interpretability improvement to occur within a unified training framework, avoiding the need for separate training processes and reducing overall computational overhead compared to independent training approaches.
Data Source
AI summary
Systems and methods analyze training of a first machine learning system with a second machine learning system. The first machine learning system comprises a neural network with a first inner layer node. The method includes connecting the first machine learning system to an input of the second machine learning system. The second machine learning system comprises a second objective function for analyzing an internal characteristic of the first machine learning system and which is different from a first objective function for the first machine learning system. The method further includes providing a training data item to the first machine learning system, collecting internal characteristic data from the first inner layer node of the first machine learning system associated with the internal characteristic, computing partial derivatives of the first objective function through the first machine learning system with respect to the training data item, and computing partial derivatives of the second objective function through both the second machine learning system and the first machine learning system with respect to the collected internal characteristic data.