AI hallucination refers to a phenomenon where the AI model or LLM generates factually incorrect or out-of-context responses to user queries. According to a recent Microsoft Global Online Safety Survey 2024, 66% of users believe AI hallucination to be a big threat that can arise by using AI tools and technologies.
While there can be several reasons behind why AI hallucinates, including inaccurate and biased training data, overfitting, inaccurate AI models, foundation models being used for tasks that they are not training for, and so on, it becomes the responsibility of AI engineers and developers to take necessary steps to prevent AI hallucination as it can have a bigger impact on real-world application.
For example, a lot of users now trust generative AI Chatbots and their responses for the maximum of their work share. Realizing the AI model is giving inaccurate output can disturb user trust. Not just that, factually incorrect information can also lead to real-world consequences misleading users to take wrong actions, for example, incorrect home medicinal remedies, wrong recipes, etc.
In the following infographic, we talk more about AI hallucination, its causes, examples, and its prevention strategies that AI scientist, and developers must take care of. Looking to enhance your AI career in 2025? Check out the infographic to learn more about this phenomenon.
