Neural Knowledge Artifactory for Data-Scarce AI Decision Making
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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 depend on historic data sets, which 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 even with limited data, and incorporating feedback mechanisms for ongoing learning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional machine learning approaches are used that rely on large training and test data sets, then prediction accuracy and robustness can be improved, but the system requires significant time, resources, and investment before deployment, and is not feasible for companies without access to big data
Solution Approach 1:
The system performs preliminary actions by continuously collecting and processing feedback data in the background before actual deployment is needed. The feedback processing system proactively builds knowledge bases and updates models using accumulated feedback, so that when deployment is required, the system is already prepared with refined predictions and reduced uncertainty, eliminating the need for lengthy pre-deployment data collection phases
Solution Approach 2:
The system implements continuous feedback loops where predictions are monitored, actual outcomes are captured, and this feedback is automatically processed to refine future predictions. This ongoing feedback mechanism allows the system to continuously improve accuracy during operation rather than requiring extensive pre-deployment training, enabling rapid deployment with subsequent incremental learning
2Reliability
If large data sets are used for training machine learning models, then model robustness and accuracy are improved, but the cost and resource requirements increase significantly
Solution Approach 1:
The system extracts only the most relevant and valuable features from feedback data rather than processing entire raw data sets. The feedback processing system identifies and extracts key patterns, metrics, and signals from user interactions, device data, and outcome information, converting vast amounts of raw feedback into concentrated, high-value training signals that maintain model robustness while requiring minimal data storage and processing resources
Solution Approach 2:
The system changes parameters by transforming raw feedback data into standardized, normalized features with optimized distributions for training. The feedback processing system applies parameter transformations including feature engineering, scaling, and selection to convert heterogeneous feedback sources into consistent numerical representations that maximize information density per data point, reducing the total volume needed while maintaining or improving model performance
3Ease of operation
If historic data sets are used for training, then initial model training can be performed, but the data may not be exhaustive or relevant for specific applications, leading to inefficiencies in decision-making
Solution Approach 1:
The system transitions from static historic data sets to dynamic, continuously updating feedback data that adapts to specific applications. The feedback processing system continuously monitors and processes application-specific feedback, automatically adjusting training data and model parameters to match the particular needs and characteristics of each deployment context, ensuring ongoing relevance without requiring manual data curation
Solution Approach 2:
The system performs self-service by automatically collecting, processing, and utilizing feedback specific to its own deployment context without external intervention. The feedback processing system autonomously identifies application-specific patterns, extracts relevant features from actual usage data, and continuously refines models based on real-world performance, ensuring the system learns and adapts to its specific application domain independently
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.