Constraint-Reduced Combinatoric Code Generation for AI Training
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Solution Overview
Problem
Modern artificial intelligence systems face challenges in training due to the complexity and difficulty in generating and obtaining large, structured training data sets, particularly when data is not freely available and requires licensing or permission, leading to inefficiencies in the training process.
Innovation Solution
Implement combinatoric code generation methods to reduce a plurality of code portion combinations to a subset that satisfy constraints, generate synthetic programs, and train artificial intelligence systems using these programs, leveraging techniques such as combinatorial reduction and binary decision diagrams to optimize the training process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If all code portion combinations are used for training AI systems, then the training data comprehensiveness is improved, but the computational burden and training time increase significantly
Solution Approach 1:
The patent segments the codebase into discrete code portions (functions, classes, methods) that can be independently combined. This segmentation allows the system to generate training data by combining individual code portions in various ways, rather than using complete existing programs, thereby reducing the computational burden while maintaining training data comprehensiveness
Solution Approach 2:
The patent performs preliminary action by pre-processing the codebase to extract and organize code portions into a structured format with metadata. This preliminary organization enables efficient combination and synthesis of code portions during training data generation, avoiding the need to process entire codebases repeatedly and thus reducing training time
2Reliability
If licensed or permission-required data is obtained for training, then the data quality and reliability are improved, but the ease of obtaining and maintaining training data deteriorates
Solution Approach 1:
The patent implements self-service by enabling the system to generate its own training data from existing open-source codebases. Instead of relying on externally obtained licensed data that requires permission and maintenance agreements, the system autonomously synthesizes training programs by combining code portions from publicly available sources, thereby improving ease of obtaining training data while maintaining reliability through controlled synthesis processes
3Adaptability or versatility
If structured non-text data is used for training, then the applicability of AI systems to different data types is improved, but the limitations on data generation and usability increase
Solution Approach 1:
The patent introduces an intermediary layer of code portion synthesis that bridges the gap between raw code and structured training data. By combining code portions with metadata and generating synthetic training programs, the system creates an intermediary representation that can be adapted to various data types and training requirements, thereby improving data type adaptability while maintaining data generation flexibility
Data Source
AI summary
Systems and methods for combinatoric code generation for training artificial intelligence systems, comprising reducing a plurality of code portion combinations to a subset of code portion combinations that satisfy one or more constraints using a combinatorial reduction, generating one or more synthetic programs using the subset of code portion combinations, and training an artificial intelligence system using the synthetic programs.