Graph-Based AI Training Using Manifold Traversal for Edge Cases
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Solution Overview
Problem
Conventional data processing methods, such as table-based databases, hinder the progress of AI and ML systems by imposing rigid schema that prevent them from leveraging and navigating data in less restricted ways, and training data quality and size issues, particularly with complex behaviors like autonomous vehicles, where common scenarios overwhelm high-risk edge cases.
Innovation Solution
Encoding training data into a graph structure to generate a manifold that exhaustively encodes possible risk scenarios, allowing AI systems to identify and traverse areas of interest, using a graph interface system to manipulate and train neural networks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If table-based databases with rigid schema are used to process training data, then data processing is simplified and human understanding is improved, but AI system performance and ability to navigate complex data relationships deteriorate
Solution Approach 1:
The patent transitions from traditional table-based databases to graph-based data structures, adding a new dimensional perspective to data organization. This allows AI systems to traverse relationships between data points in multiple directions (nodes and edges) rather than being constrained by rigid row-column schemas, thereby improving both data navigation capabilities and training effectiveness while maintaining operational simplicity through intuitive graph interfaces
2Ease of manufacture
If conventional training data structures are used, then implementation is easier, but the ability to encode and traverse complex behavior spaces deteriorates
Solution Approach 1:
The patent implements dynamic graph structures where nodes and edges can be flexibly created, modified, and traversed during AI training. This dynamic architecture allows the system to adapt to complex behavior spaces by creating new data relationships on-the-fly, while maintaining ease of implementation through standardized graph operation interfaces and automated graph generation from existing training datasets
3Quantity of substance
If common training scenarios are prioritized in data collection, then data volume increases, but coverage of high-risk edge cases deteriorates
Solution Approach 1:
The patent performs preliminary encoding of training data into graph structures that explicitly represent scenario relationships and risk categories before AI training begins. This preliminary organization allows the system to identify and prioritize edge cases through graph traversal algorithms, ensuring that high-risk scenarios are adequately represented and traversed during training even when they constitute a small portion of the overall data volume
Solution Approach 2:
The graph structure acts as an intermediary layer between raw training data and the AI system. This intermediary encoding enables the system to weight and prioritize different types of scenarios based on their risk characteristics, allowing edge cases to be properly emphasized during training without requiring them to dominate the overall data distribution
Data Source
AI summary
Graphs are powerful structures made of nodes and edges. Information can be encoded in the nodes and edges themselves, as well as the connections between them. Graphs can be used to create manifolds which in turn can be used to efficiently train more robust AI systems. Systems and methods for graph-based AI training in accordance with embodiments of the invention are illustrated. In one embodiment, a graph interface system including a processor, and a memory configured to store a graph interface application, where the graph interface application directs the processor to obtain a set of training data, where the set of training data describes a plurality of scenarios, encode the set of training data into a first knowledge graph, generate a manifold based on the first knowledge graph, and train an AI model by traversing the manifold.