General-Purpose AI Relation Network for Autonomous Knowledge Learning
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Solution Overview
Problem
Current artificial intelligence systems are limited to special-purpose applications and lack the ability to perform multiple uncertain tasks, failing to establish a knowledge network similar to human common sense, which hinders the development of general-purpose humanoid intelligence.
Innovation Solution
A method is introduced to establish a relation network using rules such as similarity, adjacency, and repeatability, along with a built-in demand, award/penalty, and emotion system, to enable machines to learn and apply knowledge autonomously, process various inputs, and create new knowledge by discovering potential links between information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If deep learning optimizes coefficients to find multi-level mapping with minimum error, then the machine can select intermediate layer features, but it needs to train an extremely large amount of data and the trained model is difficult to migrate out of the field of training
Solution Approach 1:
The patent introduces a knowledge graph as an intermediary layer between raw data and deep learning models. The knowledge graph pre-structures relationships and entities from unstructured data, providing a semantic framework that guides feature selection and enables model transfer. This intermediary allows the system to leverage structured knowledge without requiring complete retraining when migrating to new domains.
Solution Approach 2:
The patent performs preliminary action by constructing knowledge graphs and extracting structured relationships before the actual deep learning training process. Entities, attributes, and relationships are pre-processed and organized into a knowledge framework that can be reused across different tasks and domains, reducing the need for extensive retraining and enabling faster adaptation to new fields.
2Measurement precision
If deep convolution neural network filters to remove details, then the machine can select intermediate layer features more reasonably, but it still needs a large amount of training data
Solution Approach 1:
The knowledge graph serves as a mediator that provides semantic guidance to the convolutional neural network. Instead of relying solely on data-driven feature learning, the system uses the knowledge graph to predefine meaningful features and relationships, allowing the network to focus on learning patterns rather than discovering basic semantic structures from scratch. This reduces the dependency on large training datasets.
Solution Approach 2:
The patent changes the parameters of the learning process by incorporating knowledge graph embeddings and relational constraints into the neural network architecture. This modifies the optimization landscape, allowing the model to learn with fewer samples by leveraging the structured knowledge as additional constraints and priors that guide the learning process.
3Loss of information
If the machine searches for associations between text or concepts in big data, then different things can be linked, but these relations are not quantified and the machine cannot learn and summarize by itself
Solution Approach 1:
The patent implements feedback mechanisms where the knowledge graph continuously learns from new data and updates its structures. The system extracts relationships from unstructured data, quantifies them using statistical methods and embeddings, and feeds this information back into the knowledge graph. This closed-loop process enables autonomous learning and continuous refinement of knowledge representations without human intervention.
Solution Approach 2:
The system performs self-service by automatically extracting, quantifying, and organizing knowledge from unstructured data sources. The knowledge graph autonomously discovers entities, attributes, and relationships, and updates its own structure based on learned patterns. This self-updating capability allows the machine to learn and summarize knowledge independently, transforming unstructured data into structured, quantified knowledge representations.
Data Source
AI summary
In establishment of a general-purpose artificial intelligence system, the machine simulates similarity, repeatability and adjacency of information, and stores the demand, award/penalty and emotion symbols together with related low-level features as a preset relation network. When the machine encounters input information, the machine iteratively identifies low-level features in the input information and stores them in a memory according to a simultaneous storage method. With the low-level features as nodes and the similarity, repeatability and adjacency relations between nodes as connection relations, the machine establish relations between the low-level features and the demand, award/penalty and emotion symbols to extend the relation network. The machine uses the low-level features and searches for related low-level features through a chain associative activation process, searches for imitable experiences through segmented simulation, reassembles the experiences according to a principle of benefit-seeking and harm-avoiding to form an optimal response path.