Local Command Construction Matching for Lower-Latency AI Interaction
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Solution Overview
Problem
The significant computational cost, energy consumption, and latency associated with server-based generative models for conversational interfaces pose barriers to the widespread adoption and usage of human-computer interaction systems, particularly with the increasing complexity introduced by multimodal inputs.
Innovation Solution
A computing system that utilizes a local constructions database to match natural language inputs to command constructions, generating commands locally when possible, and employs a trained generative model on a server only when necessary, while refining the database with command explanations to improve accuracy and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If server-based generative models are used for conversational interfaces, then response quality and capability are improved, but computational cost, energy consumption, and latency increase significantly
Solution Approach 1:
The system segments the command interpretation task into two parts: a local constructions database handles common and simple commands, while the server-based generative model only processes complex or novel commands. This segmentation reduces the frequency of server calls and associated energy consumption while maintaining high response quality for all command types.
Solution Approach 2:
The constructions database is pre-populated with common command patterns and interpretations before user interaction begins. This preliminary action allows the system to handle frequent commands locally without requiring real-time server processing, thereby reducing energy consumption and latency for the majority of interactions.
2Reliability
If server-based generative models are used for conversational interfaces, then response quality and capability are improved, but processing latency and computational load increase
Solution Approach 1:
The system segments command processing into local database lookup for common commands and server-based generative processing only when necessary. This segmentation eliminates the latency penalty for all commands that can be resolved locally, while maintaining the ability to handle complex commands with acceptable latency.
Solution Approach 2:
By pre-building the constructions database with common command patterns, the system performs preliminary processing work offline, so that during actual use, simple commands can be resolved instantly from the database without waiting for server processing, significantly reducing perceived latency.
3Adaptability or versatility
If server-based generative models are used for conversational interfaces, then adaptability to complex inputs is improved, but device complexity and infrastructure requirements increase
Solution Approach 1:
The system segments the complexity burden: the local constructions database handles straightforward commands with simple infrastructure, while the server-based generative model handles complex and novel inputs. This segmentation allows the client to operate with minimal infrastructure while maintaining the ability to access advanced capabilities when needed.
Solution Approach 2:
The constructions database acts as an intermediary layer between the user interface and the server-based generative model. It filters and pre-processes commands, only passing complex or novel inputs to the server, thereby reducing the infrastructure burden on the client device while maintaining adaptability to complex inputs.
Data Source
AI summary
A computing system stores a constructions database comprising a plurality of command constructions and executes an orchestrator, which receives a request including a message as natural language input from an interaction interface, performs a matching operation that attempts to match the message to one of the plurality of command constructions, and responsive to successfully matching the message to the one of the plurality of command constructions, generates a command based on the one of the plurality of command constructions, and executes the generated command.