AI Agent Data Flow for Low-Label Multimodal Software Automation
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Solution Overview
Problem
Existing deep learning models require large amounts of labeled data for training, which is laborious and time-consuming, and there is a high degree of coupling between tasks and data, limiting performance improvements.
Innovation Solution
Integrate human-in-the-loop (HITL) methods to incorporate human knowledge and experience, using core set construction and active learning to select key samples for labeling, and develop a system for automating multimodal agentic workflows with AI agents trained on diverse datasets.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If large amounts of labeled data are used for training deep learning models, then model performance is improved, but annotation cost and time consumption increase significantly
Solution Approach 1:
The system performs preliminary actions by automatically generating synthetic training data and pre-processing data before human annotation is needed. The data generation module creates synthetic samples that can be used for model pre-training, reducing the amount of manually annotated data required later in the training process.
Solution Approach 2:
The system creates synthetic copies of training data through the data generation module, which produces artificial samples that replicate the characteristics of real data. These synthetic copies serve as substitute training data, reducing the need for expensive manual annotation of real-world data.
2Reliability
If more training data is collected to improve model performance, then model accuracy increases, but the data growth rate lags behind model parameter growth
Solution Approach 1:
The data generation module creates synthetic data copies that can be generated at any scale without the constraints of collecting real-world data. This allows the training data quantity to grow exponentially to match model parameter growth, as synthetic data can be generated in parallel and at unlimited scale.
Solution Approach 2:
The system replaces the mechanical process of collecting and annotating real data with an automated synthetic data generation system. This substitution enables rapid, scalable data production that keeps pace with model development, eliminating the bottleneck between data collection and model parameter growth.
3Adaptability or versatility
If new tasks are added to expand model capabilities, then model versatility improves, but the speed of data updates cannot keep up with task emergence
Solution Approach 1:
When new tasks are introduced, the system can preliminarily generate synthetic training data for these tasks using the data generation module before real data becomes available or before manual annotation can be performed. This allows the model to be quickly adapted to new tasks using pre-generated synthetic data.
Solution Approach 2:
The system replaces slow manual data collection and annotation processes with automated synthetic data generation. This mechanical substitution enables rapid data updates for new tasks, as synthetic data can be generated instantly based on task definitions without requiring physical data collection or human annotators.
4Reliability
If complete labeling of all samples is performed to ensure high precision, then model performance improves, but the process becomes laborious and time-consuming
Solution Approach 1:
The training process is segmented into different stages: pre-training using synthetic data generated by the data generation module, and fine-tuning using selectively annotated real data. This segmentation allows the system to achieve high precision without requiring complete labeling of all samples, as the bulk of training can be done with synthetic data.
Solution Approach 2:
The system uses synthetic data copies to replace the need for complete real data labeling. The data generation module creates artificial samples that replicate the necessary characteristics for training, eliminating the laborious task of manually labeling every sample while maintaining training effectiveness and model precision.
Data Source
AI summary
A system for providing artificial intelligence agents that automate software usage includes training servers configured to train agents during training, production servers configured to execute the trained agents during inference, a plurality of training datasets, and data flow logic. The data flow logic is configured to, provide, during the training, the agents and the plurality of training datasets to the training servers to cause the training servers to train the agents on the plurality of training datasets and thereby produce the trained agents, configure the production servers with the trained agents for use during the inference, provide, during the inference, prompts issued by clients to the production servers to cause the production servers to translate the prompts into agent calls to the trained agents that in turn cause the trained agents to generate outputs that are responsive to the prompts, and make the outputs available to the clients.