Modular Machine Learning Architecture for Multi-Task Efficiency
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Solution Overview
Problem
Current machine learning systems require separate architectures for each task, leading to redundancy and inefficiencies in resource utilization and incremental improvement.
Innovation Solution
A modular machine learning architecture with multiple neural networks that can be modified and rearranged to perform multiple tasks without modifying existing modules, allowing for incremental enhancement and reduced resource expenditure.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If separate machine learning architectures are used for each task, then task-specific performance is improved, but device complexity and resource consumption increase
Solution Approach 1:
The patent implements a universal machine learning architecture where a single base model can perform multiple tasks by loading different task-specific head modules. The base model contains shared features that can be applied across various tasks, eliminating the need for separate architectures for each task while maintaining task-specific performance through modular task heads.
Solution Approach 2:
The architecture is segmented into a shared base model and separate task-specific head modules. This segmentation allows the system to maintain a common computational backbone while providing specialized processing for different tasks, resolving the contradiction between universality and task-specific optimization.
2Reliability
If separate machine learning architectures are used for each task, then task specialization is improved, but resource utilization efficiency deteriorates
Solution Approach 1:
The patent merges multiple task-specific models into a single unified architecture by combining a shared base model with multiple task heads. This merging eliminates redundant computations across tasks, as the base model processes features once and shares them across all tasks, significantly reducing resource consumption while maintaining task specialization.
Solution Approach 2:
The shared base model provides universal feature extraction that serves all tasks, reducing the overall computational burden. Instead of each task having its own complete model, the universal base model extracts features once, and task-specific heads provide the necessary specialization, optimizing resource utilization.
3Stability of the object's composition
If a fixed machine learning architecture is used, then system stability is improved, but adaptability to new tasks deteriorates
Solution Approach 1:
The architecture transitions from a fixed structure to a dynamic, extensible system. New tasks can be added by introducing new task head modules without modifying the stable base model. This dynamic structure allows the system to adapt to new tasks while maintaining the stability and proven performance of the established base model.
Solution Approach 2:
By segmenting the architecture into a stable base model and interchangeable task heads, the system achieves both stability and adaptability. The base model remains unchanged and stable, while task heads can be dynamically added, removed, or swapped to adapt to new tasks, enabling flexible expansion without compromising system stability.
4Adaptability or versatility
If existing modules are modified to add new functionality, then system versatility is improved, but risk of introducing errors increases
Solution Approach 1:
The patent extracts task-specific functionality into separate task head modules that are independent of the base model. This extraction allows new tasks to be added by introducing new modules rather than modifying existing ones, reducing the risk of introducing errors into the core system while maintaining system versatility.
Solution Approach 2:
The system evolves dynamically through the addition of new task heads rather than through modification of existing components. This dynamic approach to adding functionality isolates changes to specific modules, reducing the risk of cascading errors and maintaining the reliability of the established base model while increasing versatility.
Data Source
AI summary
In an example method, a system accesses first input data and a machine learning architecture. The machine learning architecture includes a first module having a first neural network, a second module having a second neural network, and a third module having a third neural network. The system generates a first feature set representing a first portion of the first input data using the first neural network, and a second feature set representing a second portion of the first input data using the second neural network. The system generates, using the third neural network, first output data based on the first feature set and the second feature set.