Authentication Challenges for Low-Cost Human Training Data
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Solution Overview
Problem
The high cost of evaluating collected user data to identify a high-quality subset for training generative machine learning models is a significant challenge, as it typically requires human evaluators, making the process expensive.
Innovation Solution
Utilize authentication challenges to automatically collect training data by requesting user responses that indicate human analysis and intuition, which are then used to train generative machine learning models, reducing the need for human intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If human evaluators are used to evaluate collected user data to identify high-quality training data, then the quality of training data is improved, but the cost increases significantly
Solution Approach 1:
The system uses authentication challenges to automatically collect and evaluate user responses, allowing the system to self-service the data collection and initial filtering process without requiring human evaluators for every data point, thereby reducing costs while maintaining quality through automated evaluation metrics
Solution Approach 2:
The patent replaces the mechanical system of human evaluators with an automated system using machine learning models and authentication challenges to evaluate user data, substituting human labor with computational processes that can scale without proportionally increasing costs
2Measurement precision
If human evaluators are employed to evaluate and filter user data, then the quality of training subset is improved, but the hourly cost increases
Solution Approach 1:
The authentication challenge system automatically performs data collection and preliminary evaluation, making the system self-sufficient in generating training data without requiring continuous human intervention, thereby improving productivity while controlling hourly costs
Solution Approach 2:
The system performs preliminary evaluation of user data through authentication challenges before human review, pre-filtering and pre-processing data to reduce the workload on human evaluators and improve overall process efficiency
Data Source
AI summary
A method for using authentication challenges to automatically obtain training data to train a machine learning model (MLM). The method includes identifying a generative MLM to be trained using training data reflecting analytical responses of humans, and automatically collecting the training data from a plurality of users by providing an authentication challenge for each user attempting to access a resource. The authentication challenge requests a set of responses from a respective user of the plurality of users. The set of responses include a first response to a first sample which indicates whether the respective user is a human, and a second response to a second sample which indicates an analytical response of the respective user. Responsive to determining that the respective user is a human, the second response is used as part of the training data for the generative MLM.