Inference Model Attention Updates for Anomaly-Contextualized Predictions
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Solution Overview
Problem
Existing technologies fail to effectively address the impact of anomaly conditions on the reliability and quality of predictions generated by inference models, leading to unreliable outcomes when anomaly conditions such as war, inflation, famine, or supply chain failures are not considered in the inferencing process.
Innovation Solution
Implementing an anomaly classifier to detect and classify anomaly conditions, and using an attention mechanism to update inference models by modifying their weights based on anomaly conditions, thereby generating contextualized predictions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If anomaly conditions are not considered in the inferencing process, then the inference model operates simply and quickly, but the reliability and quality of predictions deteriorate
Solution Approach 1:
An anomaly classifier is introduced as an intermediary component that detects and classifies anomaly conditions, providing this information to the inference model. This mediator enables the model to account for anomaly conditions without directly processing complex anomaly data, thus improving prediction reliability while managing system complexity through modular architecture
Solution Approach 2:
The anomaly classifier performs preliminary detection and classification of anomaly conditions before the inference model generates predictions. By preparing anomaly condition labels in advance, the system enables the inference model to focus on generating accurate predictions without being overwhelmed by complex anomaly processing during the prediction generation step
2Reliability
If anomaly conditions are detected and classified, then the reliability of predictions improves, but the processing time and computational resources increase
Solution Approach 1:
The system segments the inferencing process into two independent stages: anomaly condition detection (performed by the anomaly classifier) and prediction generation (performed by the inference model). This segmentation allows each component to optimize its performance independently, with the anomaly classifier handling anomaly detection efficiently and the inference model focusing on accurate prediction without redundant processing
3Adaptability or versatility
If the inference model is updated with anomaly condition information, then the adaptability to anomaly conditions improves, but the model complexity and training requirements increase
Solution Approach 1:
Instead of fundamentally changing the inference model's architecture to handle anomaly conditions, the system updates model parameters by providing anomaly condition information as input features or attention mechanisms. This allows the model to adapt to anomaly conditions through parameter adjustment rather than structural complexity, maintaining model simplicity while improving adaptability
Data Source
AI summary
Methods and systems for managing an inference model are disclosed. Input data usable to generate a prediction using the inference model may be obtained from one or more data sources. An anomaly condition associated with the input data may be identified using a classification model. The anomaly condition may be ingested into an attention mechanism of the inference model to obtain an updated inference model. Using the updated inference model and the input data, the generated prediction may be contextualized with respect to the anomaly condition. The prediction may be used, at least in part to provide a computer-implemented service.