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blog post<\/strong>.<\/a><\/p>\n<p>Instead, it has produced false answers like telling a user to glue cheese to pizza if it gets unstuck, to eat rocks to help with your health, or that former US President Barack Obama is Muslim, which is a conspiracy theory that has been debunked.<\/p>\n<p>The AI Overview answers are the latest in a series of examples of where chatbot models respond incorrectly.<\/p>\n<p>One <a href=https://www.euronews.com/"https:////vectara.com//blog//cut-the-bull-detecting-hallucinations-in-large-language-models///">study by Vectara<\/strong><\/a>, a generative AI startup, found that AI chatbots invented information anywhere from three to 27 per cent of the time.<\/p>\n<h2><strong>What are AI hallucinations?<\/strong><\/h2><p>Large language models (LLMs), which power chatbots such as OpenAI\u2019s ChatGPT and Google\u2019s Gemini, learn to predict a response based on the patterns they observe.<\/p>\n<p>The model calculates the most likely next word to answer your question based on what\u2019s in their database, according to Hanan Ouazan, partner and generative AI lead at Artefact.<\/p>\n<p>\u201cThat\u2019s exactly how we work as human beings, we think before we talk,\u201d he told Euronews.<\/p>\n<p>But sometimes, the model\u2019s training data can be incomplete or biased, leading to incorrect answers or \u201challucinations\u201d being made by the chatbot.<\/p>\n<p>To Alexander Sukharevsky, a senior partner at QuantumBlack at McKinsey, it\u2019s more accurate to call AI \u201chybrid technology\u201d because the chatbot answers provided are \u201cmathematically calculated\u201d based on the data that they observe.<\/p>\n<p>There\u2019s no one reason why hallucinations happen, according to Google: it could be insufficient training data used by the model, incorrect assumptions, or hidden biases in the information the chatbot is using.<\/p>\n<p>{{related align=\"center\" size=\"fullwidth\" ratio=\"auto\" storyIdList=\"8461182\" data=https://www.euronews.com/news/europe/'