In the context of AI detectors, embedding representations play a crucial role in the detection process. But first, let’s understand what it is all about. Word meanings are connected to human experiences through context and the five senses. Since context is a relative term, it can sometimes refer to a singular incident or even one that happens in a different region of the world. Pop culture allusions are frequently made in casual conversations, and they can have a funny and lasting effect.
AI can only perceive any form of input through numbers. (I’m sure some of you substituted “words” for the numbers; that’s okay; it happens to the best of us.) Thankfully, AI is quick because the intermediary steps that help it understand words would make it a worthy candidate for the title of “Last person in the room to get the joke.” Theoretically speaking, there is a good set of processes that take place before it comes into the format, which we understand and prefer.
To understand these procedures in the context of AI detectors, let’s map them out one by one.
To start off, let’s take note of how an AI detector works. When the content to be scanned is provided to the tool, it analyzes the text. What exactly does ‘analyze the text’ mean? For AI, words need to be represented numerically to help understand their meanings, how the meanings of words vary according to context, and so on. And in the field of artificial intelligence, this is where embedded representations come into play. AI ‘learns’ everything with the help of machine learning models and semantic search algorithms. By means of embedded representations, machine learning models consume text, videos, values, and images.
Here, each of these data formats is transformed into a mathematical format that will be covered in the following section.
During the embedding process, the text is split into individual words or sub-words, and this process is called tokenization. Now each of these tokens is converted into a vector using different techniques like Word2Vec, GloVe, and BERT (the explanation of which is outside the purview of this article; for the time being, consider them as methods employed by machine learning to assist in associating the words to obtain an enriched understanding of the same in different contexts), and this is called vectorization.
These vectors capture the semantic meaning and the context of the words. Machine learning models derive and identify patterns from the analysis of these vectors. Using pre-existing data of the patterns created by AI-generated content, the newly supplied text is flagged.
Well, this tunnel goes much deeper than this, but just so veer back into the topic of discussion, with the help of embeddings, AI detectors can distinguish between the subtleties between human and AI-generated content. AI detectors can easily classify text with the added information of the nuanced meaning and the relationship between words. This adds to the detection reliability.
The predictability of the words is high, and the length of the sentences created by AI is more or less fixed as opposed to human content. The length and complexity of the sentences created by humans vary across the body of text. Moreover, the vocabulary and the variety of text that human beings tend to use are also large, and the pattern varies from person to person as opposed to AI. This is how AI-generated content can be detected.
To even understand the variety and complexity of the word’s associations with different meanings, it is important to calibrate the accuracy through the embedding process. Although the technical bit is harder to visualize, you can see AI detectors in action. Try out HireQuotient’s AI detector. It is free, with no sign-up required, you can input up to 25,000 words, and you can read more particulars on AI detectors on the page itself.
