Multimodal Code Interpretation With Secure Execution and Real-Time Data
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Solution Overview
Problem
Conventional multimodal machine learning models are limited by their reliance on localized data, lack of programming language integration, and inability to manipulate files or images, leading to outdated and contextually restricted functionality.
Innovation Solution
A tool is provided that enhances multimodal machine learning models with access to current information, expanded libraries, and computer programming capabilities, including a code generator and interpreter, allowing them to execute and manipulate code in a secure environment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional multimodal machine learning models use localized training data, then the models can be trained and deployed, but the data becomes out-of-date and contextually restricted
Solution Approach 1:
The patent introduces an intermediary component that connects the machine learning model to real-time data sources and execution environments. This intermediary enables the model to access current information and execute code without requiring retraining on localized data, thus maintaining data currency while improving contextual adaptability.
2Device complexity
If multimodal machine learning models only emit text, then the model architecture remains simple, but the models cannot manipulate files or images directly
Solution Approach 1:
The patent implements a universal execution environment that enables the machine learning model to perform multiple functions beyond text generation. The model can execute code, manipulate files, process images, and interact with external systems through a standardized interface, achieving multi-functionality without fundamentally changing the core model architecture.
3Adaptability or versatility
If multimodal machine learning models integrate programming language capabilities, then the models can execute code and manipulate data, but the execution environment becomes less secure
Solution Approach 1:
The patent creates a secure, isolated execution environment that acts as an inert atmosphere for code execution. The model can run programming code and manipulate files within this controlled environment, which prevents unauthorized access and ensures security while maintaining full programming capabilities.
4Ease of manufacture
If conventional models rely on localized data, then deployment is straightforward, but the models lack access to real-time information and expanded libraries
Solution Approach 1:
The patent segments the system into distinct components: the machine learning model, the execution environment, and external data sources. This segmentation allows the model to be deployed independently while maintaining connections to real-time data sources and expanded libraries, preserving deployment simplicity while eliminating information loss.
Data Source
AI summary
Disclosed herein are methods, systems, servers, and computer-readable media for interpreting computer code with a multimodal machine learning model. In an embodiment, this comprises: receiving, an input comprising at least one of a text prompt, file prompt, or data object, determining, using a multimodal machine learning model, that the input requires implementing computer code, and in response to determining the input requires implementing computer code: generating computer code based on the input, executing the generated computer code using a code interpreter, and providing, through an interface, an output based on the generated computer code.