OpenAI Discontinued Their AI Classifier For Identifying AI-Written Text
<p><em>I’m currently the </em><a href="https://www.linkedin.com/in/cobusgreyling" rel="noopener ugc nofollow" target="_blank"><em>Chief Evangelist</em></a><em> @ </em><a href="https://www.humanfirst.ai/" rel="noopener ugc nofollow" target="_blank"><em>HumanFirst</em></a><em>. I explore & write about all things at the intersection of AI & language; ranging from LLMs, Chatbots, Voicebots, Development Frameworks, Data-Centric latent spaces & more.</em></p>
<p>AS seen in the extract below, a paragraph was added to the document which announced the classifier, barely 5 months after launching. This article considers why this type of classification is hard, and how inaccurate it was in the first place.</p>
<p><img alt="" src="https://miro.medium.com/v2/resize:fit:1000/1*SSrbRtrtIgMaVpz94KYWeQ.png" style="height:393px; width:1000px" /></p>
<p><a href="https://openai.com/blog/new-ai-classifier-for-indicating-ai-written-text" rel="noopener ugc nofollow" target="_blank">Source</a></p>
<p>LLMs are flexible and highly responsive to requests. An LLM can be asked to respond in such a way that the response seems human and not machine written.</p>
<p>An LLM might also be asked to write in such a way to fool an AI detector in believing it's human written, or sound like a particular personality or type.</p>
<p>Hence the system is based on word sequences and choices.</p>
<p>Any stringent approach like watermarking the LLM output somehow by hashing and storing every produced output section, along side generated date and location, then let institutions query this with a final doc based on a geo-code and a time window is completely unfeasible. Also considering the advent of open-source LLMs and the extent to which models can be fine-tuned.</p>
<p><a href="https://cobusgreyling.medium.com/openai-discontinued-their-ai-classifier-for-identifying-ai-written-text-7133a927ee7b"><strong>Read More</strong></a></p>