Modular AI System with Sub-Agent Spawning for Scalable Adaptability
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Solution Overview
Problem
Current AI systems face challenges in scalability, adaptability, and overfitting when learning new tasks, particularly due to their inflexibility and computational demands, which limits their ability to quickly adapt to diverse tasks and retain previously learned knowledge.
Innovation Solution
A modular AI processing system with sub-agent modules that communicate through a shared or diverse communication algorithm, allowing for the generation of new sub-agent modules to enhance processing capacity and adaptability, and a method for training that iteratively refines the communication algorithm to improve task-solving efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a traditional AI system is trained for a specific task, then it achieves high performance for that task, but it struggles to adapt to new tasks
Solution Approach 1:
The AI system is divided into multiple independent sub-agent modules, each capable of learning and processing tasks separately. This segmentation allows the system to adapt to new tasks by activating relevant sub-agents while maintaining the functionality of existing ones, thus improving adaptability without compromising performance consistency.
Solution Approach 2:
The system dynamically adjusts its architecture by spawning new sub-agent modules when encountering unfamiliar tasks. This dynamic expansion enables the system to adapt to new tasks while maintaining stable performance on known tasks through the persistent operation of existing sub-agents.
2Adaptability or versatility
If the AI system is designed to learn multiple tasks, then it becomes more versatile, but computational demands increase significantly
Solution Approach 1:
By segmenting the system into specialized sub-agent modules, each sub-agent can be trained independently and efficiently. This allows the system to handle multiple tasks without requiring a monolithic architecture that would be computationally expensive, as each sub-agent processes only its specific task domain.
Solution Approach 2:
The system trains sub-agents to handle specific tasks partially, and only activates the necessary sub-agents for each task. This partial action approach reduces overall computational demand compared to training a single comprehensive model for all possible tasks.
3Ease of manufacture
If the AI system uses fixed architecture, then it is simple to implement, but it cannot scale to handle increasing complexity
Solution Approach 1:
The fixed architecture is segmented into standardized sub-agent modules that can be independently implemented and scaled. This segmentation maintains implementation simplicity for each module while enabling system-wide scalability through the addition of more modules as needed.
Solution Approach 2:
The sub-agent modules are designed with universal interfaces and communication protocols that allow them to function together in various configurations. This universality enables the system to scale to increasing complexity while maintaining ease of implementation through standardized components.
4Productivity
If the AI system learns from batches of data offline, then it is efficient for training, but it cannot continuously adapt to new data
Solution Approach 1:
The system transitions from static batch processing to dynamic continuous learning by enabling sub-agents to update their models continuously as new data becomes available. This dynamic approach maintains training efficiency through targeted updates of relevant sub-agents while enabling continuous adaptation to new data streams.
Solution Approach 2:
The system maintains continuous learning capability by allowing sub-agents to process and learn from incoming data continuously rather than in discrete batches. This continuity ensures the system adapts to new data patterns while maintaining the efficiency of incremental learning compared to retraining entire models.
Data Source
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AI summary
A modular Artificial Intelligence (AI) processing system is provided, comprising: an input module configured to receive input data; an output module configured to output data processed by the system; a first agent module operatively connected to the input module and to the output module, the first agent module being configured in use to process the input data and to generate the output data for the output module, and comprising two or more first sub-agent modules, each sub-agent module comprising an internal memory state and being operatively connected via a communication channel to at least one other sub-agent module within the first agent; each first sub-agent module being configured with a communication algorithm, the communication algorithm defining how the sub-agent module communicates with other sub-agent modules to enable the first agent module to process the input data, and in use at least one of the two or more first sub-agent modules is configured to process the input data in dependence on its internal memory state; and a sub-agent spawning module configured in use to generate a new sub-agent module by replicating an existing one of the first sub-agent modules, to increase a first performance metric of the system.