AI evaluation of ChatGPT and human generated image/textual contents by bipolar generalized fuzzy hypergraph

Abstract

Artificial Intelligence (AI) tools, i.e., ChatGPT (Chat Generative Pre-Trained Transformer), are positively and negatively revolutionizing the culture of industries, science, and education. The main objectives of this study are to address uncertainty and vagueness in ChatGPT systems, apply bipolarity as two-sided states of data, model generalized graph-based network with derivations, develop bipolar multi-dimensional fuzzy relation, advance entropy metrics for quantifying ambiguity, cluster entities based on level cuts, present pattern recognition in terms of statistical correlation coefficient, analyze speech recognition framework, and schedule online surgeries on the basis of blockchain technology. The outlined innovation pinpoints on the self-evaluation of ChatGPT systems, merging the bipolarity and generalized fuzzy hypergraph approach, developing the interpretation of graph-based patterns, and benchmarking the AI analysis and metrics advancement. To assess the efficiency of AI bipolar generalized fuzzy hypergraph (BGFH) model, the key conceptual benchmarks are clustering technique for detecting patterns and similar groups of data, statistical methods for the analysis of pattern recognition, and entropy metrics for quantifying the fuzziness within a system. This layout furnishes important characteristics such as union, intersection, complement, homomorphism, isomorphism, verifying the overlapping (intersection) and complement of two strong BGFHs as a strong BGFH. In addition, certain specifications of reflexive, symmetric, transitive, overlapping and integration, are defined using bipolar multi-dimensional fuzzy relation. Eleven classes are derived based on different values within \(t\in [0,1]\) and \(s\in [-1,0],\) classifying analogous data that aids the similarity detection of generated outputs. Through this approach, a new pattern recognition is used as a data evaluation technique to intelligently facilitate the process in terms of correlation coefficient. It is revealed that the highest magnitude of 0.145 is adopted for patterns \(C_{1}\) and D, indicating the most positive correlation between patterns, while patterns \(C_{4}\) and D with the value of \(-0.35\) are negatively correlated. The results verify that the entropy measure of visual data (0.75) is higher than the entropy measure of textual data with the value of 0.68, indicating more vagueness and ambiguity in visual generated systems. The corresponding textual data \(E^{P}(U)\) and \(E^{N}(U)\) are, respectively, calculated as 0.62 and 0.45 for human-created contents and ChatGPT-generated contents, whilst for visual data, the entropy measures \(E^{P}(U)\) and \(E^{N}(U)\) are, respectively, 0.25 and 0.66, showing the higher values for the entropy measure of ChatGPT-generated visual data compared to the ChatGPT-generated textual data. In relation to the speech recognition analysis, the highest human performance degree is affiliated to word “a” (0.89), while the lowest degree belongs to word “i” (0.81). The highest AI performance degree is allocated to word “it” \((-0.7),\) and the lowest degree is affiliated to word “the” \((-0.88).\) The overall entropy measure is calculated by 0.23, and the entropy measure of AI-based data is 0.35, on the other hand, the entropy measure of human-based data is equal to 0.29, representing higher vagueness for AI-based data. According to the obtained results in surgical case scheduling, the bipolar value of \((0.9,-0.1)\) is allocated to the surgeon who has the highest positive performance (0.9) and the lowest negative performance \((-0.1);\) this indicates the superior overall performance \((\lambda _{a}=0.8)\) of the leader during the AI blockchain robotic colon surgery. The worst overall performance (0.22) is allotted to the surgeon, who is required to be removed from the surgery team by the leader physician. The outcomes are validated by a comparative analysis with respect to the classical bipolar fuzzy graph and bipolar fuzzy hypergraph, and NLP (natural language processing) approaches.