Build your own GPTZero and Turnitin like App: AI Plagiarism Detector Tool

The speaker introduces an engaging project aimed at developing an AI application to detect calligrism in language, specifically to discern whether text is generated by AI. The project involves creating a Streamlit app inspired by tools like GPT-3 and Turnitin, which are known for detecting AI-generated content. The speaker emphasizes the importance of understanding NLP techniques and metrics such as perplexity and burstiness to build the app.

The speaker provides an in-depth explanation of perplexity, a metric that measures a language model's ability to predict a sequence of words. They discuss the concept of entropy and its relation to perplexity, explaining that a lower perplexity indicates the model is more confident in its predictions. The mathematical formula for perplexity is introduced, highlighting its significance in the AI calligrism detection process.

A practical example is given to illustrate how to calculate perplexity using a simple sentence. The speaker demonstrates the process of tokenizing the sentence, calculating the negative log probability for each word, and then determining the entropy. They explain how these calculations are used to compute perplexity, which is essential for the AI application being developed.

The concept of burstiness is introduced as a metric that measures the deviation of word occurrences from their average frequency in a text. The speaker explains how burstiness can help identify AI-generated content by analyzing the distribution of word occurrences. They provide a step-by-step example of calculating burstiness, emphasizing its importance alongside perplexity in the detection process.

The speaker transitions into the coding phase of the project, discussing the creation of a Streamlit application. They outline the necessary libraries and dependencies, such as NLTK and Transformers, and begin writing the code to calculate perplexity and burstiness scores. The aim is to provide users with insights into whether their text is AI-generated based on these metrics.

Detailed coding of the functions to calculate perplexity and burstiness is presented. The speaker writes Python functions that utilize the GPT-2 model to process input text and compute the metrics. They explain the code, focusing on the use of the GPT-2 tokenizer and model from Hugging Face, and how the functions will be integrated into the Streamlit app.

The speaker continues the development of the Streamlit app by integrating the perplexity and burstiness functions. They discuss the logic for determining whether content is AI-generated based on threshold values of the metrics. Additionally, they introduce a function to visualize the top repetitive words in the text using Plotly Express, enhancing the app's analytical capabilities.

The speaker emphasizes the limitations of AI calligrism detection tools, cautioning against their use as the sole basis for decision-making. They stress the importance of manual verification and critical thinking when evaluating content authenticity. A disclaimer is added to the app to inform users of these limitations and to encourage responsible use of the tool.

The speaker tests the AI calligrism detection tool using content from various sources, including Wikipedia and an article from McKinsey, to demonstrate the app's functionality. They also test the tool with content generated by ChatGPT to show how it can detect AI-generated text. The results are discussed, highlighting the app's ability to provide insights based on perplexity and burstiness scores.

In conclusion, the speaker summarizes the video's exploration into developing an AI calligrism detection tool using perplexity and burstiness metrics. They reiterate the importance of using such tools responsibly and invite feedback and suggestions for improvement. The speaker expresses openness to collaboration and further development of the project.