AI Model GUI with Pedagogical Language and Compiler
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Software developers face challenges in working with AI due to complex toolkits, limited APIs, and constrained black-box solutions, making it difficult for them to solve real-world problems, and the scarcity of data science experts limits accessibility for the broader software development community.
Innovation Solution
A computing system with a processor and storage device that uses a graphical user interface (GUI) to enable authors to define AI models through a pedagogical programming language, generate assembly code, and build executable AI models with neural-network layouts, making AI more accessible and user-friendly.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If developers use complex toolkits and limited APIs to work with AI, then AI functionality can be achieved, but the ease of operation deteriorates and accessibility is reduced
Solution Approach 1:
The system segments the AI development process into distinct components: a high-level domain-specific language for model definition, a separate compilation stage that translates to neural network architecture, and modular training execution. This segmentation allows developers to work at different levels of abstraction without being exposed to underlying complexity.
Solution Approach 2:
The patent introduces an intermediary compilation stage that acts as a mediator between the high-level AI model definition and the low-level neural network implementation. This intermediary automatically translates conceptual models into executable neural network architectures, shielding developers from complex API interactions and toolkit complexities.
2Measurement precision
If specialized data science experts build AI models at the lowest levels, then model performance and precision are improved, but the quantity of accessible AI solutions decreases due to scarcity of experts
Solution Approach 1:
The system enables self-service AI model building by providing developers with automated tools that handle the complex tasks previously requiring expert intervention. The compilation and automatic neural network generation allow developers to create production-ready AI models without needing deep expertise in neural network architecture design, thereby increasing the quantity of available AI solutions.
3Ease of manufacture
If developers use constrained black-box solutions for AI, then implementation simplicity is improved, but adaptability and versatility deteriorate
Solution Approach 1:
The system provides dynamic adaptability by allowing developers to define custom AI models with flexible architectures and training configurations through the domain-specific language. While maintaining simple high-level model definition, the system dynamically adapts to different problem domains by generating specialized neural network architectures tailored to each specific application requirements.
Data Source
AI summary
A computing system includes a processor, and a storage device holding instructions executable by the processor. The instructions are executable to receive a source code through an application programming interface (“API”) exposed to a graphical user interface (“GUI”). The GUI is configured to enable an author to define a proposed model with a pedagogical programming language, the proposed model including an input, one or more concept nodes, and an output. The GUI is further configured to enable the author to provide a program annotation indicating an execution behavior for the source code, to generate an assembly code from the source code with a compiler of an artificial intelligence (“AI”) engine configured to work with the GUI; and to build an executable, trained AI model including a neural-network layout having one or more layers derived from the assembly code.