Active AI Agent for Physical Interaction Modeling
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Solution Overview
Problem
Current graphics processing units (GPUs) are limited in their ability to perform complex operations such as modeling physical interactions, and existing artificial intelligence agents operate passively, requiring large amounts of annotated data for training and lacking the ability to actively collect data.
Innovation Solution
An advanced artificial intelligence agent is implemented using a GPU to actively collect and model physical interactions through a training framework that automatically generates weak annotations, utilizing a master program unit to jointly approximate behaviors of individual program units.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If passive AI agents are used with traditional training methods, then large amounts of annotated data are required for training, but this increases data collection cost and time
Solution Approach 1:
The AI agent performs self-supervised learning by automatically generating its own training data through active collection processes. The system uses a training framework that enables the agent to collect and annotate data autonomously without requiring external human annotation, thereby eliminating the time-consuming manual data collection process while maintaining training accuracy
Solution Approach 2:
The system pre-trains individual program units before combining them into a master program unit. This preliminary training phase allows each component to learn from synthetically generated data, preparing them for subsequent joint training and reducing the need for extensive manually annotated data in later stages
2Productivity
If complex physical interaction modeling is performed using traditional methods, then processing capability is insufficient, but increasing computational resources increases system complexity
Solution Approach 1:
The system divides the complex physical interaction modeling task into multiple individual program units, each responsible for specific aspects of the modeling. These modular units can be trained and processed independently, then combined into a master program unit, reducing overall system complexity while maintaining high processing capability through parallel processing
Solution Approach 2:
The patent replaces traditional mechanical physics simulation systems with an AI-based software system running on GPU hardware. This substitution enables complex physical interactions to be modeled through neural network computations rather than traditional physics engines, significantly increasing processing capability while keeping the system architecture manageable through software abstraction
3Measurement precision
If manual data annotation is performed for AI training, then training data quality is high, but this increases labor cost and reduces automation
Solution Approach 1:
The AI agent performs self-supervised learning by automatically generating its own training data through active collection processes. The system uses a training framework that enables the agent to collect and annotate data autonomously without requiring external human annotation, thereby eliminating the time-consuming manual data collection process while maintaining training accuracy
Solution Approach 2:
The training framework acts as an intermediary between raw data and the AI model, automatically generating weak annotations and refining them through the training process. This intermediary layer eliminates the need for manual annotation while still providing sufficient signal for effective learning through automated refinement mechanisms
Data Source
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AI summary
Described herein are advanced artificial intelligence agents for modeling physical interactions. An apparatus to provide an active artificial intelligence (AI) agent includes at least one database to store physical interaction data and compute cluster coupled to the at least one database. The compute cluster automatically obtains physical interaction data from a data collection module without manual interaction, stores the physical interaction data in the at least one database, and automatically trains diverse sets of machine learning program units to simulate physical interactions with each individual program unit having a different model based on the applied physical interaction data.