Neural Network Verification via Cognitive Trajectories
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Solution Overview
Problem
Neural networks' opaque decision-making processes make it difficult for developers to debug, test, and evaluate characteristics like resiliency against adversarial attacks and accuracy, as the internal adaptations during training make the output generation process non-interpretable.
Innovation Solution
A computing architecture that maps a neural network's space into a cognitive space, using a cognitive space encoder, trajectory generator, and decoder to generate and evaluate trajectories, allowing for human-readable interpretation of the reasoning process and identifying inefficiencies or vulnerabilities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If neural networks autonomously adapt internal weights and processes during training, then the network's learning capability and task performance are improved, but the output generation process becomes opaque and non-interpretable to developers
Solution Approach 1:
The patent introduces cognitive trajectories as an intermediary representation that bridges the gap between neural network internal states and human interpretable reasoning processes. By mapping activations through cognitive space encoders and decoders, the system creates a mediating layer that makes the otherwise opaque decision-making process visible and analyzable while preserving the network's autonomous learning capability
Solution Approach 2:
The patent transforms the high-dimensional activation space of the neural network into a lower-dimensional cognitive space that is more amenable to human interpretation. This dimensionality reduction through cognitive space encoding allows developers to visualize and analyze reasoning processes in a compressed representation that retains essential information while improving interpretability
2Reliability
If the neural network process is made opaque through independent adaptations, then the network can achieve higher accuracy and efficiency, but it becomes difficult to debug and test for resiliency against adversarial attacks
Solution Approach 1:
Cognitive trajectories serve as an intermediary that enables debugging and testing of neural networks without altering their core learning process. By providing a interpretable view of the reasoning process through cognitive space mappings, developers can identify and repair issues related to adversarial vulnerability and logical inconsistencies while the network maintains its high accuracy performance
Solution Approach 2:
The patent implements feedback mechanisms where cognitive trajectories are analyzed to generate insights about network behavior, which then inform debugging and improvement efforts. This feedback loop allows developers to systematically identify vulnerabilities to adversarial attacks and refine the network's reasoning processes while preserving accuracy
Data Source
AI summary
Systems, apparatuses and methods may provide for technology that identifies a cognitive space that is to be a compressed representation of activations of a neural network, maps a plurality of activations of the neural network to a cognitive initial point and a cognitive destination point in the cognitive space and generates a first cognitive trajectory through the cognitive space, wherein the first cognitive trajectory traverses the cognitive space from the cognitive initial point to the cognitive destination point.