The challenge of differentiating between human and machine-generated text is not just an academic puzzle; it's a real-world necessity with profound implications. With the sophistication of AI models like Generative Pre-trained Transformers (GPT), we're at a crossroads where the authenticity of written content is more crucial than ever.
The ability of these tools to generate human-like text opens Pandora's box of ethical concerns.
Copyright infringement becomes a minefield, as AI could convincingly reproduce copyrighted material. Academic integrity is also at stake, with the potential for AI-generated essays and responses to undermine the fairness of assessments and job interviews.
Perhaps most alarmingly, the spread of misinformation could escalate dramatically. Malicious actors could exploit these models to generate and disseminate false narratives, fake news, and propaganda unprecedentedly, sowing confusion and distrust in our information ecosystems.
This blog post delves into exploring and developing a tool designed to navigate this complex landscape. It aims to illuminate the challenges and propose potential solutions to uphold the integrity of written content in an AI-powered world.
The Essence of Detection
At the heart of our quest lies a simple yet profound goal: to create a mechanism capable of identifying the nuances that distinguish AI-generated text from that penned by humans. This endeavour is not about undermining AI's incredible capabilities. On the contrary, it leverages these very tools to detect material produced by AI.
The Toolkit
Our tool is akin to a detective specialising in digital authorship. It scrutinises patterns, styles, and the intricacies of language to differentiate between our creativity and the precision of an algorithm's output.
This digital detective operates on the frontline of technology, equipped with machine learning models trained on a vast corpus of text. From literary works to the latest blog posts, it learns the subtle signatures characterising human and AI writers. It's a journey that goes beyond mere words, delving into the very fabric of creativity.
Behind the Scenes
The proposed approach combines the art of language with the science of algorithms. The system is fed a diverse set of text/paragraphs, enabling it to learn and recognise the distinct flavours of human and AI-generated content. This process is similar to teaching a connoisseur to distinguish between varieties of wine, where each sip reveals information about its origin, preparation, and essence.
But how does one teach a computer to appreciate the nuances of language? We analyse the structure of text using tokenisation, vectorisation, and machine learning. The models look for word frequency and syntax peculiarities that indicate AI authorship.
Detecting AI-generated text requires tokenising and vectorising the input data. Tokenisation breaks down the text into smaller units like words or subwords, while vectorisation converts the tokenised text into numerical vectors that can be processed by machine learning algorithms.
Hence, the code employs the Byte Pair Encoding (BPE) algorithm for tokenisation. BPE starts with individual characters and iteratively merges the most frequent pairs of consecutive bytes to create longer subword tokens. This allows for the effective handling of out-of-vocabulary words.
After tokenising the text, TF-IDF vectorisation is applied. This technique considers the frequency of each token (TF) and weighs it by the inverse document frequency (IDF) across the text. Tokens that appear frequently in a document but rarely across others get higher weights, helping differentiate authorship styles.
The vectorised text samples are then fed into an ensemble of classifiers - Multinomial Naive Bayes and Stochastic Gradient Descent models. Ensembling helps capture different patterns and improves prediction accuracy.
The Journey Forward
This exploration is not a technical feat; it’s a step towards understanding the evolving relationship between humans and machines. The aim was to distinguish between AI and human-generated text so that we can open new possibilities for verifying authenticity, protecting intellectual property, and preventing the spread of misinformation.
This blog post is merely the beginning of our journey. We invite you to check the project's GitHub page for those intrigued by the technical intricacies and to see our approach and accuracy.