The term "hallucination" is metaphorical — AI models do not actually suffer from delusions as a mentally unwell human might. Instead they produce unexpected outputs that do not correspond to reality in response to prompts. They may misidentify patterns, misunderstand context, or draw from limited ...
AI hallucination examples Hallucination is a pretty broad problem with AI. It can range from simple errors to dramatic failures in reasoning. Here are some of the kinds of things that you're likely to find AIs hallucinating (or at least the kinds of things referred to as hallucinations): Com...
Both developers and users must test and validate AI tools to ensure reliability. Developers must systematically evaluate the model’s outputs against known truths, expert judgments, and evaluation heuristics to identify hallucination patterns. Not all hallucinations are the same; a complete fabrication di...
Developers of leading AI systems understand that even a slight hallucination rate is unacceptable. What can be done to improve accuracy and prevent AI hallucinations? Success depends heavily on making sure AI models are trained on large, diverse, balanced and high-quality data sets. This helps mod...
Examples of AI hallucinations One infamous example of an AI hallucination event occurred in February 2023 when Google's chatbot, Gemini, made an incorrect claim about the James Webb Space Telescope (JWST). In a promotional video, Gemini was prompted, "What new discoveries from the James Webb Sp...
What is an example of an AI hallucination? Examples of AI hallucinations include when a chatbot gives an answer that is factually inaccurate, or when an AI content generator fabricates information but presents it as truth. Why are AI hallucinations a problem? AI hallucinations are problematic bec...
While the above examples of AI hallucination are concerning, developers are by no means ignoring the issue. As new AI chatbot versions continue to be released, the ability of the system to process prompts tends to improve. By boosting the quality of training data, providing more recent training...
While AI hallucination is certainly an unwanted outcome in most cases, it also presents a range of intriguing use cases that can help organizations leverage its creative potential in positive ways. Examples include: Resources ReportWhy AI governance is a business imperative for scaling enterprise artif...
What Is an AI Hallucination? Causes and Prevention Tips AI hallucinations happen when an AI tool provides irrelevant, false, or misleading information. Luckily, there are ways to manage this.On this page What are AI hallucinations? 3 components of AI systems What causes AI hallucinations? 5 tips...
AI hallucination can occur due to adversarial examples—input data that trick an AI application into misclassifying them. For example, when training AI applications, developers use data (image, text, or others); if the data is changed or distorted, the application interprets the input differently...