Embedding Space Query System for ML Transparency
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Solution Overview
Problem
Conventional machine-learning models lack transparency in their decision-making processes, leading to 'black box' issues, and are often inflexible, inefficient, and unable to scale with increasing demands, particularly in handling various data types and model types.
Innovation Solution
The embeddings relationship system explores the embedding space of machine-learning models using various query models like object pair analogy, object similarity, relationship preservation, and emergent semantics to reveal encoded relationships, providing insights into model operations and improving accuracy and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional machine-learning models are used for automated decision-making, then prediction capability is achieved, but transparency and understanding of decision logic are lost
Solution Approach 1:
The patent introduces an embedding space as an intermediary layer between the machine-learning model and the external system. This embedding space contains encoded relationships that serve as a mediator to explain the model's decision-making process, allowing the system to maintain prediction capability while providing transparent insights into the logic behind decisions through relationship queries and explorations
2Measurement precision
If machine-learning models process increasing data volumes and complexity, then prediction accuracy improves, but system efficiency and scalability deteriorate
Solution Approach 1:
The patent extracts the essential relationships from the training data and stores them in the embedding space during the training phase. This extraction allows the model to maintain high prediction accuracy by referencing pre-computed relationships during inference, significantly improving system efficiency and scalability without reprocessing the entire training dataset for each prediction
3Loss of information
If conventional systems attempt to provide transparency in machine-learning operations, then understanding of model behavior improves, but system complexity and computational overhead increase
Solution Approach 1:
The patent performs preliminary action by pre-computing and storing encoded relationships in the embedding space during the model training phase. This preliminary computation of relationships allows the system to provide transparency through simple relationship queries during inference, avoiding the need for complex computational overhead at prediction time while maintaining model behavior transparency
Data Source
AI summary
The present disclosure relates to utilizing an embedding space relationship query exploration system to explore embedding spaces generated by machine-learning models. For example, the embedding space relationship query exploration system facilitates efficiently and flexibly revealing relationships that are encoded in a machine-learning model during training and inferencing. In particular, the embedding space relationship query exploration system utilizes various embeddings relationship query models to explore and discover the relationship types being learned and preserved within the embedding space of a machine-learning model.