Hybrid AI System for Pattern Recognition and Reasoning
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Solution Overview
Problem
Current deep learning methods lack the capability for commonsense reasoning, causal relationship identification, and integration of abstract knowledge, limiting their ability to induce and deduce knowledge, and are unable to perform logical inferences or map input vectors to their trained definitions effectively.
Innovation Solution
The implementation of transformational randomization, which involves obtaining left-hand side and right-hand side equivalence transformations, randomizing contexts and action sequences, and determining valid actions based on probability values, enables the machine learning system to automatically determine actions in a computer-implemented application, thereby enhancing artificial intelligence capabilities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If deep learning methods are used for pattern recognition, then pattern recognition performance is improved, but the capability for commonsense reasoning and logical inference deteriorates
Solution Approach 1:
The system segments AI capabilities into distinct modules: deep learning components for pattern recognition tasks and symbolic reasoning components for commonsense reasoning and logical inference. This segmentation allows each component to excel at its specialized function while working together through structured interfaces, resolving the contradiction between pattern recognition performance and reasoning capability
Solution Approach 2:
The patent merges deep learning systems with symbolic AI frameworks into a hybrid architecture. The deep learning module processes patterns and perceptions, while the symbolic reasoning module handles logical inference and commonsense knowledge. These merged components exchange information through structured representations, enabling both high pattern recognition accuracy and robust reasoning capabilities simultaneously
2Device complexity
If neural networks with hidden layers are used to map input vectors, then mapping capability is improved, but the ability to integrate fundamental memories and perform logical inferences deteriorates
Solution Approach 1:
The system introduces symbolic representations as intermediary structures between neural network processing and memory integration. These symbolic intermediaries serve as a bridge that allows fundamental memories to be integrated in a structured, interpretable manner while preserving the mapping capabilities of hidden layers. The symbolic layer enables logical operations on memory contents without losing the pattern recognition strengths of the neural network
3Measurement precision
If manual feature extraction is used for conventional computer vision, then interpretation accuracy is improved, but labor efficiency deteriorates
Solution Approach 1:
The system employs self-service mechanisms where the AI system automatically performs feature extraction and interpretation without manual intervention. Deep learning models learn features directly from data, and symbolic reasoning components automatically interpret these features in context. This self-service approach maintains high accuracy through learned feature representations while dramatically improving productivity by eliminating manual labor
Solution Approach 2:
The patent transforms the feature extraction process by changing parameters from manual specification to automated learning. Instead of manually defining feature parameters, the system learns optimal feature representations through training data. This parameter change enables automatic feature extraction that achieves comparable or superior accuracy to manual methods while vastly improving efficiency and scalability
Data Source
AI summary
Actions may be automatically determined by a machine learning system using transformational randomization. A situation set and an action sequence associated with contexts of a computer-implemented application may be obtained. Left-hand side (LHS) equivalence transformations and right-hand side (RHS) equivalence transformations are obtained based on a set of a plurality of rules for the application. LHS randomizations are obtained based on combining the plurality of LHS equivalence transformations. RHS randomizations are obtained based on combining the plurality of RHS equivalence transformations. A randomized context is obtained based on the LHS randomizations, and an action sequence is determined based on the context randomization. A randomized action sequence is obtained based on the RHS randomizations. A valid action is determined based on a probability value of a randomized rule associated with the randomized action sequence.