Self-creating AI Model Code Evolution
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Solution Overview
Problem
Current AI models are limited by pre-defined data and actions, restricting their ability to advance beyond their initial capabilities and solve new problems efficiently.
Innovation Solution
The system generates AI models using self-creating scripts that evolve at runtime, allowing for the expansion of source code and the creation of new actions through reinforcement learning and code combination.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If pre-defined training data and actions are used to train AI models, then the models can be trained efficiently with clear boundaries, but the models are limited in their ability to advance beyond their pre-defined starting point and solve new problems
Solution Approach 1:
The AI system generates its own action code and expands its capabilities autonomously through self-service mechanisms. The model creates new code snippets, combines existing actions, and evaluates outcomes without external intervention, enabling continuous adaptation and capability expansion beyond initial training boundaries.
Solution Approach 2:
The system transitions from static pre-defined actions to dynamic self-generating actions. The action space evolves over time as the AI model creates new code snippets and combines existing actions in novel ways, allowing the system to adapt to new problems and expand its capabilities dynamically rather than being constrained by fixed initial boundaries.
2Productivity
If manual code creation and pre-defined actions are used, then the system has clear control and structure, but it requires continuous manual input and cannot solve new problems efficiently
Solution Approach 1:
The system replaces manual mechanical code creation with automated AI-driven code generation. Instead of human programmers manually writing code for each new problem, the AI model automatically generates action code snippets, combines existing actions, and evaluates outcomes, significantly improving productivity and problem-solving efficiency while reducing manual input requirements.
Solution Approach 2:
The AI model acts as an intermediary between the problem statement and the code execution environment. It automatically translates new problems into actionable code by generating new action snippets or combining existing actions, serving as a mediator that eliminates the need for continuous manual code creation while maintaining system control and structure.
3Adaptability or versatility
If the AI model is given a pre-defined set of actions, then the model has clear boundaries for decision-making, but the model cannot create new capabilities or actions beyond what was pre-defined
Solution Approach 1:
The system implements feedback loops where the AI model evaluates the outcomes of generated actions and uses this information to refine future code generation. By monitoring execution results and incorporating feedback into the learning process, the system maintains reliability while gradually expanding its capability to create new actions, ensuring that new capabilities are developed through iterative validation rather than uncontrolled exploration.
Solution Approach 2:
The system performs preliminary validation and evaluation of generated code before full deployment. New action snippets are tested in controlled environments, and only after successful validation are they integrated into the main system. This preliminary action approach ensures code correctness and safety while enabling the model to progressively create new capabilities beyond its initial action set.
Data Source
AI summary
An example computer implemented method for generating an artificial intelligence (AI) model from a self-creating script coded in a programming language includes receiving an objective for the AI model and obtaining at least first action code corresponding to a first action, where the first action is associated with an action objective similar to the objective. The method further includes generating at least a second action code based on one of the first action code and a specification of the programming language and comparing a first outcome of the first action code and a second outcome of the second action code. The method further includes inserting one of the first action code and the second action code into the self-creating script based on the comparing the first outcome of the first action code and the second outcome of the second action code and executing the self-creating script including the one of the first action code and the second action code to satisfy the objective.