AI Module Adaptation Using Randomized Idle-Time Data Processing
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Solution Overview
Problem
Current AI systems for autonomous driving and other applications face challenges in developing efficient strategies and adapting to unforeseen situations within limited time frames, as they rely on existing data and algorithms without the ability to generate novel solutions effectively.
Innovation Solution
The method involves determining when an AI module is not in use for its primary purpose, processing stored data by adding a random component, evaluating the results, and adapting the AI module accordingly, utilizing 'artificial random thoughts' (ART) to create new strategies and reduce failures by simulating earlier situations and detecting correlations in data sets.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If AI systems rely on existing data and algorithms for autonomous driving, then they can operate with current infrastructure, but they cannot generate novel solutions or adapt to unforeseen situations effectively
Solution Approach 1:
The system performs preliminary processing of sensor data during normal operation to create compressed representations and store them for later use. This preliminary action allows the AI module to work with pre-processed data during critical decision-making moments, reducing real-time computational complexity while maintaining adaptability to unforeseen situations through offline analysis and correlation detection.
2Measurement precision
If AI systems process large amounts of sensor data in real-time, then they can make informed decisions, but they cannot do so within limited time frames
Solution Approach 1:
The system segments sensor data processing into distinct phases: real-time acquisition and compression during normal operation, and deeper analysis during idle periods. By dividing the processing task into manageable segments with different depths of analysis, the system maintains high-quality decision-making through comprehensive offline processing while ensuring rapid response during critical real-time moments.
Solution Approach 2:
The system performs preliminary compression and preprocessing of sensor data during normal operation, creating condensed representations that capture essential information. This preliminary action reduces the computational burden during real-time decision-making, allowing the system to maintain high measurement precision without exceeding time constraints for critical responses.
3Manufacturing precision
If AI systems use extensive sensor data for complex analysis, then they can achieve accurate results, but they require more resources and time
Solution Approach 1:
The system applies different processing qualities to different data segments based on their importance and timing. Critical real-time data receives rapid compressed processing with sufficient quality for immediate decisions, while less time-sensitive data undergoes more thorough analysis during idle periods. This local differentiation of processing quality maintains accuracy where needed while improving overall processing efficiency.
Data Source
AI summary
The present disclosure relates to a method, a computer program with instructions, and a device for operating an AI module. The disclosure also relates to an AI module suitable for the method and to a locomotion means that has an AI module or a device according to the teachings herein or is configured to carry out a method according to the teachings herein for operating an AI module. In a first step, a state is determined in which a system operated by the AI module is not in use for its primary purpose. Subsequently, data stored by the AI module are processed by adding a random component. A result of the processing is evaluated and the AI module is adapted depending on the evaluation.