Self-Learning Neural Knowledge Artifactory for Limited Data
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Solution Overview
Problem
Artificial intelligence systems face challenges in making accurate decisions when limited by the lack of large training and test data, as they heavily rely on historic data that may not be exhaustive or relevant for specific applications, leading to inefficiencies in initial deployment and ongoing operations.
Innovation Solution
The implementation of a self-learning neural knowledge artifactory that extracts knowledge artifacts from various data sources, encapsulates them in knowledge neurons, and continuously updates these neurons using natural language processing and multi-stage processing techniques, allowing for decision-making without extensive data sets.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If machine learning models are trained with large training data sets, then prediction accuracy and robustness are improved, but data availability and deployment time are worsened
Solution Approach 1:
The system implements feedback loops where predictions are continuously evaluated against actual outcomes, and this feedback is used to automatically update and refine knowledge artifacts. This allows the system to improve prediction accuracy over time without requiring large training datasets, as the feedback mechanism enables continuous learning from limited data.
Solution Approach 2:
The system performs self-learning by automatically extracting knowledge from data sources, creating knowledge artifacts, and updating its own knowledge base without external intervention. This self-service capability enables the system to develop accurate predictions independently, reducing dependence on large pre-prepared training datasets.
2Reliability
If machine learning models are trained with large training data sets, then prediction robustness is improved, but deployment time and resource investment are worsened
Solution Approach 1:
The system pre-extracts and stores knowledge artifacts from various data sources before they are needed for predictions. This preliminary action of knowledge extraction and storage allows the system to make robust predictions immediately when deployed, without requiring time-consuming training on large datasets at deployment time.
Solution Approach 2:
The system automatically builds and maintains its knowledge base through self-learning processes, eliminating the need for manual data preparation and model training. This self-service approach significantly reduces deployment time while maintaining prediction robustness through continuously updated knowledge artifacts.
3Ease of manufacture
If historic data is used for training, then model development is simplified, but relevance to specific applications is worsened
Solution Approach 1:
The system extracts only the most relevant knowledge artifacts from data sources that are specifically pertinent to each application domain. This selective extraction process maintains simplicity in model development while ensuring high relevance to specific applications by focusing on domain-specific knowledge rather than generic historic data.
Solution Approach 2:
The system creates application-specific knowledge artifacts with localized quality tailored to each domain. Different knowledge artifacts are generated for different applications based on their specific requirements, allowing the system to maintain ease of development through standardized processes while achieving high adaptability and relevance to each specific application.
Data Source
AI summary
Approaches, techniques, and mechanisms are disclosed for generating, enhancing, applying and updating knowledge neurons for providing decision making information to a wide variety of client applications. Domain keywords for knowledge domains are generated from domain data of selected domain data sources, along with keyword values for the domain keywords, and are used to generate knowledge artifacts for inclusion in knowledge neurons. These knowledge neurons may be enhanced by domain knowledge data sets found in various data sources and used to generate neural responses to neural queries received from the client applications. Neural feedbacks may be used to update and/or generate knowledge neurons. Any ML algorithm can use, or operate in conjunction with, a neural knowledge artifactory comprising the knowledge neurons to enhance or improve baseline accuracy, for example during a cold start period, for augmented decision making and/or for labeling data points or establishing ground truth to perform supervised learning.