Option Selector for Machine Learning Resource Allocation
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Solution Overview
Problem
Modern machine learning systems face significant computational challenges due to the need to process large amounts of data efficiently, particularly in allocating finite resources effectively when uncertainty is involved, such as selecting the best advertisements to show to customers or determining the most informative features for classification tasks.
Innovation Solution
The implementation of an option selector component within machine learning systems that uses scoring logic and racing logic to evaluate and allocate resources efficiently by calculating scores and confidence intervals for options, allowing for accurate selection within a specified error tolerance, even with limited samples, thereby speeding up the decision-making process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If machine learning systems process large amounts of data to improve decision accuracy, then measurement precision is improved, but productivity deteriorates due to significant computational burden
Solution Approach 1:
The patent segments the data processing task by dividing the large dataset into smaller batches or samples that can be processed incrementally. The option selector processes data in manageable chunks, calculating scores and confidence intervals for subsets of options rather than requiring complete data processing before making decisions.
Solution Approach 2:
The patent changes the parameter of data processing by introducing confidence interval thresholds and error tolerances. Instead of processing all data to achieve maximum accuracy, the system adjusts processing parameters (confidence levels, sample sizes) to achieve sufficient accuracy with reduced computational effort, allowing early termination when confidence thresholds are met.
2Measurement precision
If machine learning systems allocate more computing resources to process more data, then measurement precision is improved, but loss of energy increases
Solution Approach 1:
The patent applies partial action by processing only the necessary portion of data required to achieve sufficient decision accuracy. The option selector calculates scores and confidence intervals for a limited number of samples rather than processing complete datasets, consuming energy proportionally to the actual decision-making needs rather than maximum possible accuracy.
Solution Approach 2:
The system performs self-service by automatically adjusting the amount of data processing based on confidence interval calculations. When confidence thresholds are met, the system autonomously terminates further processing for that decision, eliminating the need for external control or wasteful continued computation, thereby reducing energy consumption while maintaining accuracy.
3Measurement precision
If machine learning systems use more samples to reduce error tolerance, then measurement precision is improved, but loss of time increases
Solution Approach 1:
The patent implements feedback through confidence interval calculations that continuously monitor decision quality. As samples are processed, the system calculates confidence intervals and compares them against error tolerance thresholds, providing feedback that determines when sufficient accuracy has been achieved. This feedback mechanism allows the system to stop processing when confidence levels are adequate, reducing time loss while maintaining required precision.
Solution Approach 2:
The system dynamically adjusts the number of samples processed based on real-time confidence interval calculations. Rather than using a fixed sample size, the option selector adapts the processing amount to the actual decision context, increasing samples only when confidence intervals indicate insufficient accuracy and stopping early when error tolerance is satisfied, thereby optimizing the time-precision tradeoff dynamically.
Data Source
AI summary
Resource allocation for machine learning is described such as for selecting between many possible options, for example, as part of an efficient training process for random decision tree training, for selecting which of many families of models best describes data, for selecting which of many features best classifies items. In various examples samples of information about uncertain options are used to score the options. In various examples, confidence intervals are calculated for the scores and used to select one or more of the options. In examples, the scores of the options may be bounded difference statistics which change little as any sample is omitted from the calculation of the score. In an example, random decision tree training is made more efficient while retaining accuracy for applications not limited to human body pose detection from depth images.