AI Feedback Learning Using 2D Pseudolinear Truth Extraction
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Solution Overview
Problem
Existing AI learning methods require extensive data unilaterally injected from the outside, leading to exponentially large calculations and long learning times due to reliance on regression analysis and classification.
Innovation Solution
An AI feedback method involving a script extractor and executor that generates a 2D information table with key words and key information as axes, followed by pseudolinear transformations to derive a 2D unique characteristic table, efficiently identifying deep truth values through random ON/OFF coordinates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If regression analysis and classification through enormous amounts of information are used, then measurement precision is improved, but calculation time increases exponentially
Solution Approach 1:
The patent extracts only the essential features and patterns from the input data rather than processing all information. The AI system identifies and extracts key characteristics that are sufficient for accurate classification, eliminating the need to process enormous amounts of redundant information while maintaining learning accuracy.
Solution Approach 2:
The patent segments the learning process into distinct stages: initial condition setting, 2D information table generation, pseudolinear transformation, and characteristic vector extraction. Each stage processes a specific aspect of the data, breaking down the complex regression analysis into manageable segments that reduce overall calculation time.
2Loss of information
If conventional AI learning methods with unidirectional data injection are used, then knowledge accumulation is achieved, but learning time becomes excessively long
Solution Approach 1:
The patent implements a feedback mechanism where the AI system's output is fed back into the learning process. The pseudolinear transformation and characteristic vector extraction create a feedback loop that refines knowledge accumulation iteratively, allowing the system to learn from its own predictions and adjust accordingly, significantly reducing learning time compared to unidirectional data injection.
Solution Approach 2:
The patent performs preliminary actions by pre-processing input data into structured 2D information tables and establishing initial conditions before the main learning process. This preliminary organization of data reduces the computational burden during actual learning, enabling faster knowledge accumulation without sacrificing completeness.
3Manufacturing precision
If exponentially large amounts of information are processed, then manufacturing precision of knowledge is improved, but productivity decreases
Solution Approach 1:
The patent changes the parameters of information processing by transforming data from raw format into structured 2D information tables with specific mathematical properties. The pseudolinear transformation modifies the parameter representation of data, allowing the system to achieve high knowledge precision with fewer computational operations, thereby improving learning speed.
Solution Approach 2:
The patent introduces a new dimensional approach by organizing information into 2D tables and applying pseudolinear transformations that operate in this expanded dimensional space. This dimensional change allows the system to process information more efficiently by exploiting geometric and algebraic properties of the transformed space, achieving both precision and productivity.
Data Source
AI summary
An artificial intelligence (AI) feedback method includes: providing input1 and input2 to a script extractor and executor, providing, by the script extractor and executor, an initial condition to an AI, generating, by the AI, a two-dimensional (2D) information table from the initial condition, adding the popularity frequency value or ranking value to corresponding coordinates in the 2D information table and matching knowledge by an interaction between the AI and the script extractor and executor, generating, by the AI, a 2D pseudolinear transformation table from the 2D information table, and performing, by the AI, pseudolinear transformation multiple times to form a 2D unique characteristic table and deriving information of coordinates whose characteristic vector is not changed as a deep truth value.