Adaptive Preprocessor Selection for Multi-Modal Classification Cost
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Solution Overview
Problem
Existing artificial intelligence systems face inefficiencies in processing multi-modal data due to high computational and storage costs, particularly in devices with limited resources, and struggle with noisy, inconsistent, or missing data formats, leading to inaccurate classifications.
Innovation Solution
A system that triages preprocessors based on an initial classification's confidence measure, using less computationally expensive preprocessors first and escalating to more complex ones only when necessary, to minimize computational and storage costs while ensuring accurate classification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If advanced preprocessors with higher processing capability are used for multi-modal data classification, then classification accuracy is improved, but computational cost and processing time increase significantly
Solution Approach 1:
The system dynamically selects preprocessors based on the complexity and characteristics of the input data. Instead of using a fixed high-cost preprocessor for all inputs, the system adapts its processing approach by evaluating data properties and selecting the most appropriate preprocessor from multiple options with varying computational costs, thereby optimizing the balance between accuracy and resource consumption
Solution Approach 2:
The preprocessor selection is segmented into multiple levels or tiers. The system divides the preprocessor pool into different categories (e.g., low-cost, medium-cost, high-cost) and selectively applies them based on data requirements. This segmentation allows the system to avoid consistently using expensive preprocessors while maintaining the capability to do so when necessary for accurate classification
2Measurement precision
If advanced preprocessors with higher processing capability are used for multi-modal data classification, then classification accuracy is improved, but processing speed decreases
Solution Approach 1:
The system dynamically adjusts processing speed and accuracy based on real-time data characteristics. By selecting preprocessors matched to the actual complexity of each input, the system achieves fast processing for simple cases while maintaining high accuracy for complex cases, creating a dynamic balance between speed and precision
Solution Approach 2:
The system changes processing parameters (such as preprocessor complexity level) based on input data properties. When data is simple or confidence is already high, less intensive processing parameters are used to maximize speed. When data is complex or ambiguous, more intensive parameters are applied to ensure accuracy, thus optimizing the speed-accuracy tradeoff
3Measurement precision
If the same level of advanced processing is applied to every input, then classification accuracy is maintained, but computational cost increases and real-time processing is prevented
Solution Approach 1:
The system applies advanced processing partially rather than universally. It performs basic processing on all inputs and only applies advanced, computationally expensive processing to specific cases where it is truly needed (e.g., when initial classification confidence is low or data complexity warrants it). This partial application maintains accuracy for critical cases while enabling real-time processing for the majority of inputs
Solution Approach 2:
The processing intensity dynamically adjusts based on input characteristics and system state. The system can scale processing resources up or down depending on the complexity of incoming data streams, allowing it to maintain real-time processing capability during normal operation while escalating to advanced processing when accuracy requirements demand it
4Measurement precision
If multiple distinctly trained models are used for different modalities, then classification accuracy is improved, but device complexity and resource requirements increase
Solution Approach 1:
The system uses a universal preprocessor selection framework that can handle multiple data modalities (text, image, audio, etc.) through a common decision-making process. Instead of requiring separate management systems for each modality-specific model, a single multi-functional preprocessor selection mechanism evaluates all inputs and routes them to appropriate processing pipelines, thereby reducing overall system complexity while maintaining modality-specific accuracy
Data Source
AI summary
Methods and systems are described herein for minimizing the computational and/or storage cost of computer resources when analyzing multi-modal data. A system may receive a multi-modal input using one or more sensors. The system inputs the multi-modal input into a first preprocessor to generate a first output of a single modal data format. The system then inputs the first output into an artificial intelligence (AI) model trained to determine classifications of inputted data and confidence measures for each classification. The output from the AI model indicates a first classification and a first confidence measure for the first classification. In response to determining that the first confidence measure does not correspond to a threshold confidence measure, the system selects a second preprocessor having a greater computational cost than the first preprocessor. Then the system may input the multi-modal input into the second preprocessor to generate a second output.