Gamified Annotations for Video Game Training Data
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Solution Overview
Problem
There is a lack of robust training data for machine learning-based artificial intelligence models to perform video game-related functions, limiting the realization of practical applications.
Innovation Solution
An apparatus and method that utilize non-player user input, such as votes and comments on video game clips, to generate labels for training models, which can then provide auto-generated comments and classify video game segments, and award participants, facilitating the training of models to make inferences related to video game videos.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If non-player user input is utilized to generate training data, then the quantity of training data increases, but the quality and accuracy of labels may deteriorate
Solution Approach 1:
The system implements feedback loops where model predictions are continuously refined based on user interactions. User votes and comments on video clips provide feedback that adjusts model parameters and improves label accuracy over time, transforming initial low-quality crowd-sourced data into high-quality training labels through iterative refinement
Solution Approach 2:
The system enables self-service by allowing the model to automatically generate initial labels from non-player user input, which then serve as training data for subsequent iterations. The system serves itself by continuously improving its own training data quality through automated processing of user-generated content without requiring manual expert annotation for every clip
2Productivity
If gamified incentives are provided to non-players, then user engagement and data contribution increase, but system complexity increases
Solution Approach 1:
The system introduces an intermediary layer that automatically processes user votes and comments into structured training data. This intermediary processing layer includes automated filtering, aggregation, and validation mechanisms that translate chaotic user inputs into organized training datasets, reducing the complexity burden on the overall system while maintaining high user engagement
Solution Approach 2:
The system creates simplified copies of complex annotation tasks by allowing users to provide simple votes and comments rather than requiring detailed manual labeling. These simplified user inputs are then processed and transformed into comprehensive training labels, reducing both user effort and system complexity while maintaining data quality
3Adaptability or versatility
If models are trained to classify video game segments, then the functionality and versatility of the system improves, but the computational resources and training time increase
Solution Approach 1:
The system segments the video game content into distinct clips with specific focus areas, allowing the model to be trained on targeted portions rather than entire games. This segmentation enables efficient training by breaking down complex classification tasks into manageable segments that can be processed independently and then integrated
Solution Approach 2:
The system performs preliminary actions by pre-processing video clips into standardized formats and pre-generating initial labels from user input before actual model training begins. This preliminary preparation organizes data in advance, reducing the computational burden and training time during the actual model training phase
Data Source
AI summary
Non-players can vote and comment on uploaded video clips or livestreams of video games played by video game players. The video game players that upload the clips or do the livestreaming and that then receive the most votes can be awarded digital awards. The non-players providing the best comments can also be awarded digital awards. This incentivizes these behaviors, providing ample training data in the process so that the votes and comments can be used to train a model to make inferences related to video game video. For instance, the model may be trained to provide auto-generated comments in real time as a second video game is played, where the comments are in video game domain-specific language.