Self-Explanatory AI Pattern Identification Transformer
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Solution Overview
Problem
Existing deep neural networks are difficult to explain, making it challenging to understand the underlying causes of specific outcomes, which leads to resource-intensive efforts to recreate processes and lacks self-explanation capabilities.
Innovation Solution
A method for creating an AI-based pattern identification transformation neural network that directly maps training data to neurons, using a simplex representation in a Reproducing Kernel Hilbert Space, allowing the network to be self-explanatory and attribute outcomes to important inputs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If traditional deep neural networks are used, then prediction capability is achieved, but explainability of the process is lost
Solution Approach 1:
The patent segments the neural network into distinct functional modules: input layer with feature extraction, hidden layers with transformation operations, and output layer with prediction. Each module processes specific aspects of the input data, making the overall process more interpretable while maintaining predictive capability.
Solution Approach 2:
The patent introduces intermediate representation layers that act as mediators between raw inputs and final predictions. These intermediate layers provide interpretable feature representations that bridge the gap between complex neural processing and human-understandable explanations.
2Loss of information
If human intervention is used to recreate the process, then understanding of input-outcome relationship is achieved, but resource consumption increases
Solution Approach 1:
The neural network is designed to automatically generate explanatory information about its own decision-making process. The system self-documented the input-outcome relationships through its intermediate representations and feature extraction mechanisms, eliminating the need for external human intervention to recreate the process.
Solution Approach 2:
The patent implements feedback mechanisms where the network's intermediate states and feature activations are fed back to provide explanatory information. This allows the system to self-explain its predictions by examining and reporting on its own internal processing states.
3Measurement precision
If more layers and neurons are added to the network, then prediction accuracy improves, but explainability deteriorates
Solution Approach 1:
The patent organizes the multi-layer network into segmented functional blocks where each layer has a specific interpretability purpose. This segmentation allows maintaining high accuracy through multiple layers while preserving explainability by clearly defining the role of each segment.
Solution Approach 2:
Different layers of the network are designed with different local qualities optimized for specific tasks: early layers focus on interpretable feature extraction, middle layers on transformation, and later layers on prediction. This local optimization maintains overall explainability while achieving high accuracy.
Data Source
AI summary
Apparatus and methods for a pattern identification transformer neural network is provided. The pattern identification transformer neural network may be able to learn from relatively small numbers of data elements. The pattern identification transformer neural network may function in similar method to the way humans transform data points. As such, the pattern identification transformer neural network may be able to learn patterns from a small number of examples and determine what attributes are helpful from a single experience. The pattern identification transformer neural networks may include a multi-head attention module, a normalize module and a feed forward neural network. The multi-head attention module may receive vectors that correspond to experiences. The normalize module may normalize the received vectors. The feed forward neural network may incorporate the received vectors into the neural network.