Live Stream Concept Association for AI Model Training
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Solution Overview
Problem
Existing methods for training artificial intelligence models are time-consuming and expensive, requiring extensive data collection and human input for model training.
Innovation Solution
A system and method for training prediction models, such as neural networks, via live stream concept association, where a user interface receives a live video stream and user selections to associate concepts with frames, allowing the model to learn from user feedback and improve predictions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional data collection methods are used to train AI models, then model training accuracy can be improved, but training time and cost increase significantly
Solution Approach 1:
The system implements feedback by capturing user selections and corrections made during live video stream viewing. When users select objects or correct model predictions in real-time, this feedback is immediately used to update and retrain the prediction model, creating a continuous improvement loop that accelerates training without sacrificing accuracy
Solution Approach 2:
The system enables self-service training by allowing end-users to participate in model training through their natural interactions with the live video stream. Users automatically provide training data through their selections and corrections, eliminating the need for specialized data collectors or annotators, thereby reducing both time and cost while maintaining high prediction accuracy
2Measurement precision
If traditional data collection methods are used to train AI models, then model training accuracy can be improved, but training cost increases significantly
Solution Approach 1:
The system enables self-service training by allowing end-users to participate in model training through their natural interactions with the live video stream. Users automatically provide training data through their selections and corrections, eliminating the need for specialized data collectors or annotators, thereby reducing both time and cost while maintaining high prediction accuracy
Solution Approach 2:
The system uses copying by leveraging existing live video streams and user interaction patterns as training data sources. Instead of creating new datasets from scratch, the system copies and utilizes the rich visual information already present in live streams, combined with user selections, to generate training data that maintains high accuracy while minimizing costs
3Measurement precision
If extensive data collection is performed for model training, then prediction accuracy improves, but system complexity increases
Solution Approach 1:
The system applies extraction by isolating only the essential training data elements from the live video stream - specifically, the frames where user selections or corrections occur. Instead of processing entire datasets, the system extracts and utilizes only the relevant portions containing user feedback, simplifying the training process while preserving prediction accuracy
Solution Approach 2:
The system implements preliminary action by pre-processing live video streams to identify and prepare potential training frames before user interaction. The system loads and displays video frames in advance, preparing them for potential user selections, which streamlines the data collection process and reduces the complexity of real-time processing during training
Data Source
AI summary
In certain embodiments, training of a neural network or other prediction model may be facilitated via live stream concept association. In some embodiments, a live video stream may be loaded on a user interface for presentation to a user. A user selection related to a frame of the live video stream may be received via the user interface during the presentation of the live video stream on the user interface, where the user selection indicates a presence of a concept in the frame of the live video stream. In response to the user selection related to the frame, an association of at least a portion of the frame of the live video stream and the concept may be generated, and the neural network or other prediction model may be trained based on the association of at least the portion of the frame with the concept.