How did Undetectable AI become so popular in just two years?
One example is the recent debut of generative artificial intelligence (AI) chatbots, such as ChatGPT, followed by the release of detection tools that identify the content produced by these chatbots.
While this article will introduce you to the development of an award-winning AI detector system, Undetectable.ai, it’s important to look at the environment that led to its creation.
Therefore, we’ll begin by taking a look at the events of late 2022.
Generative AI Takes Off
The release of ChatGPT in November 2022 introduced the world’s internet users to generative artificial intelligence (AI).
Although earlier AI models like IBM’s Watson were designed with limited data, ChatGPT was trained with considerably more, which is estimated at around 570 gigabytes.
As the number of ChatGPT users grew, its competition grew. User numbers exploded as they discovered the versatility of AI…but some users were passing off AI-generated content as their own, and many of these were students.
This dilemma fueled the research that introduced AI detection systems to the public.
Academics and Plagiarism
While tech-savvy educators began to suspect plagiarism soon after the release of ChatGPT, the news spread quickly, together with the realization that there were two problems to solve instead of one.
In addition to students’ use of generative AI to write essays, term papers, and book reports, teachers and their assistants were discovering that separating cheaters from honest students was a time-consuming, difficult task.
Fortunately, help arrived soon with the introduction of the first AI detection tool. GPT Zero went live just two months after ChatGPT, developed by Princeton student Edward Tian. Others AI detectors followed, some adopting academic-sounding names such as Turnitin.
However, instead of solving the original problem, AI detection tools caused a new one: sometimes incorrectly labeling student-authored work as AI-generated.
To understand why this happens, let’s look at the nuts and bolts of these tools.
How AI Detectors Detect
While data training is part of a detector developer’s toolkit, different models work in different ways.
They may utilize one or more of the following:
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Zero-shot learning models work without specific developer training. Unlike supervised learning models that are developed using data labeled with correct answers, ZSL leverages auxiliary information, such as semantic embeddings, to identify content sources.
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Statistical analysis tools look at word frequency distributions, sentence structures, and vocabulary usage, tagging those that are seen in AI writing more than human-generated content.
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Some detector tools incorporate a perplexity measurement that evaluates unpredictable text within content. Since AI-generated content often uses predictable phrases, this is often tagged by the perplexity measurement.
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Model-specific detectors are optimized to work best when detecting content from one chatbot. This limits their usefulness when detecting possible plagiarism.
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Machine learning classifiers are trained with datasets of both human and AI-generated writing, enabling them to identify words and phrases more common in one type of content.
While detectors that relied on perplexity measuring were often “fooled” when AI-generated content was edited with less predictable phrases, later models’ abilities improved.
These often identified sections that were AI-generated, instead of providing an estimated percentage of plagiarized text.
Since these methods are well-known and currently in use by many detection tools, you may be wondering what would be required to develop a tool that could outperform the competition.
Undetectable’s Path to Maximum Accuracy
While developers of other AI detection systems may create hybrid models for increased accuracy, Undetectable.ai developers have taken an all-encompassing approach by integrating their platform with multiple AI models.
This translates into a detection system that provides detailed analysis of AI-generated content from a variety of sources.
Undetectable’s advanced algorithms, sophisticated techniques and constant developer updates provide users with fast, reliable diagnostics, identifying AI-generated content across diverse applications and scenarios.
In addition to five-star reviews from both business and casual users, Undetectable has earned some prestigious awards, including a #1 rating from Forbes magazine.
Educators at Blockchain Council praised Undetectable’s AI detecting and humanizing performance as follows:
“The essence of Undetectable AI lies in its ability to rephrase, adjust syntax, and alter sentence structures of AI-generated content so that it mirrors the complexity and variability of human writing. This not only helps in avoiding detection by AI content checkers but also aims to preserve the original intent and meaning of the text.”
Christian Perry, the co-founder of Undetectable AI, wants to make sure people can use AI freely.
Perry says, “Because of our research in adversarial machine learning, our AI detector has capabilities that others don’t. We prioritize user privacy and don’t want to release any products that could violate a user’s private use of artificial intelligence.”
With just one click, Undetectable users see results from the most popular AI detectors at the same time. According to an article on ZDNet, Undetectable’s AI detector scored 100% accuracy.
