Automatic Theorem Solver for Natural Language Hypothesis Verification
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Solution Overview
Problem
Artificial Intelligence (AI) systems, particularly deep neural networks, face explainability issues as 'black boxes' that do not provide reasons for their directives and predictions, leading to hesitation among traditional decision-makers in adopting AI-based tools due to a lack of domain knowledge among engineers and statisticians.
Innovation Solution
An automatic theorem solver is developed to verify natural language sentences by converting data into morphisms and equations, determining chains of morphisms, and using probabilistic functions to assess the degree of truth in sentences, providing a method for verifying natural language hypotheses through a system that processes and presents data in a user-friendly format.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If deep neural networks and predictive algorithms are used to improve AI system performance, then accuracy is improved, but explainability deteriorates as they act like black boxes
Solution Approach 1:
The patent introduces probabilistic logic and categorical grammar as intermediary frameworks that bridge the gap between black-box neural network predictions and human-understandable explanations. These intermediaries translate algorithmic outputs into structured logical forms that preserve both the predictive accuracy and the explanatory clarity, allowing decision-makers to understand the reasoning behind AI predictions without sacrificing performance.
2Productivity
If engineers and statisticians develop AI algorithms to improve performance, then productivity is improved, but domain knowledge is lost leading to lack of trust
Solution Approach 1:
The patent segments the AI development process into distinct modular components: probabilistic logic modules, categorical grammar modules, and verification modules. This segmentation allows domain knowledge to be embedded in specific modules rather than being lost in the overall system, enabling traditional decision-makers to understand and verify individual components while maintaining high productivity through automated processing.
3Productivity
If traditional decision-makers adopt AI-based tools to improve efficiency, then productivity is improved, but hesitation increases due to lack of understandability
Solution Approach 1:
The patent implements feedback mechanisms where the system provides structured explanations and verification results back to traditional decision-makers. This feedback loop includes presenting probabilistic logic derivations, categorical grammar analyses, and confidence measures that enhance understandability while maintaining efficiency, thereby reducing hesitation and building trust in AI-based tools.
Data Source
AI summary
Some embodiments of the present disclosure provide a manner for an automatic theorem solver to answer a query. Ahead of time, data that supports columns is received. The data is converted to a data structure. Sets of univariate and multivariate morphisms are then determined and the numbers of morphisms in the sets may be reduced in accordance with various metrics. Additionally, the morphisms may be used to generate chains of morphisms. A plurality of equations may be selected for a category. Upon receiving the morphisms, chains of morphisms and selected equations, the automatic theorem solver may be ready to receive a query. The automatic theorem solver may then determine an answer to the query and present the answer. In other embodiments of the present disclosure, an input probability distribution, obtained from a sentence, may be passed to a probabilistic function to obtain a first output probability distribution. A second output probability distribution may be obtained from the sentence. A degree of truth of the sentence may be presented to a user, determined on the basis of a distance between the two output probability distributions.