Multimodal Code Interpreter With Sandboxed Execution for Current Data
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Solution Overview
Problem
Conventional multimodal machine learning models are limited by their reliance on localized data, inability to utilize programming languages for data manipulation, and inability to process files or images, leading to outdated and provincial applications.
Innovation Solution
A tool is provided that enhances multimodal machine learning models by allowing access to current information, expanded libraries, and enabling safe, constrained actions through a code generator and interpreter, which can write, execute, and correct computer code within a sandboxed 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machine learning models use localized training data, then the models can be trained and deployed, but the data becomes out-of-date and out-of-context with limited applicability
Solution Approach 1:
The patent introduces an intermediary component that connects the machine learning model to external data sources and code execution environments. This intermediary enables the model to access current, contextualized data beyond its training set while maintaining system coherence and controlled interaction between the model and external resources.
2Adaptability or versatility
If multimodal machine learning models only emit text, then the model architecture remains simple, but the models cannot process files or images or perform actionable tasks
Solution Approach 1:
The patent implements a universal interface layer that handles multiple output types (text, files, images) and action types (code execution, data processing) through a unified architecture. This multi-functional design allows the model to perform diverse tasks while sharing common infrastructure for code generation, execution, and result processing.
Solution Approach 2:
The patent employs a nested architecture where code generation and execution capabilities are embedded within the multimodal model framework. The model generates code, which is then executed in a nested execution environment, with results fed back to the model for further processing or output as files, images, or text.
3Productivity
If users can execute code with multimodal machine learning models, then data manipulation capabilities improve, but security risks increase without proper constraints
Solution Approach 1:
The patent implements preliminary anti-action through a sandboxed execution environment that pre-establishes security constraints before code execution. The system proactively prevents harmful actions by configuring the execution environment with approved libraries, restricted file system access, and controlled network permissions, thereby neutralizing security risks before they can manifest.
Solution Approach 2:
The patent creates an inert execution environment (sandbox) that isolates code execution from the host system. This controlled environment provides necessary capabilities for data manipulation while maintaining security through environmental boundaries that prevent unauthorized access or harmful operations.
4Adaptability or versatility
If multimodal machine learning models access expanded libraries and packages, then programming capabilities improve, but system complexity and resource requirements increase
Solution Approach 1:
The patent applies preliminary action by pre-configuring and pre-installing necessary programming libraries and packages in the execution environment before code execution. This advance preparation ensures that the model has access to expanded programming capabilities (pandas, numpy, matplotlib, etc.) without requiring complex real-time configuration or increasing host system complexity.
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.