https://aibussin.com/tags/xgboost/Recent content in Xgboost on AIBussin — AI applications, systems and booksHugoen-USFri, 10 Jul 2026 11:32:58 +0100https://aibussin.com/post/future/Fri, 10 Jul 2026 11:32:58 +0100https://aibussin.com/post/future/<h2 id="we-trained-a-model-on-editorial-choices-to-see-whether-it-learned-what-happened-next">We Trained a Model on Editorial Choices to See Whether It Learned What Happened Next</h2> <p>Most preference-learning systems use a choice to change the future.</p> <p>A model produces two responses. A human selects one. The chosen response becomes positive evidence, the rejected response becomes negative evidence, and training makes outputs resembling the chosen response more likely.</p> <p>The preference acts as an instruction:</p> <blockquote> <p>Produce more things like this.</p> </blockquote> <p>I wanted to know whether the same choice could also function as evidence.</p>https://aibussin.com/post/aitext/Thu, 13 Mar 2025 13:28:24 +0000https://aibussin.com/post/aitext/<h2 id="summary">Summary</h2> <p>Artificial Intelligence (AI) has revolutionized the way we generate and consume text. From chatbots crafting customer responses to AI-authored articles, artificial intelligence is reshaping how we create and consume content. As AI-generated text becomes indistinguishable from human writing, distinguishing between the two has never been more critical. Here are some of the reasons it is important to be able to verify the source of information:</p> <ul> <li>Preventing plagiarism</li> <li>Maintaining academic integrity</li> <li>Ensuring transparency in content creation</li> <li>If AI models are repeatedly trained on AI-generated text, their quality may degrade over time.</li> </ul> <p>In this blog post, we’ll explore the current most effective methods for detecting AI-generated text.</p>