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What is AI hallucination?

AI hallucination refers to instances in which an AI system generates responses or predictions that do not accurately reflect reality. This happens when the AI lacks sufficient training data or misunderstands the context of a conversation.

How does AI hallucination occur?

AI hallucination can occur due to biases in the training data, a lack of diverse data, or the AI trying to fill in gaps in understanding by making unsupported guesses. Overfitting on limited data is another common cause.

What are some examples of AI hallucination?

Some examples of AI hallucination include chatbots or voice assistants making up facts, image generators creating images of things that don’t exist, and language models producing text irrelevant to the prompt. These hallucinations may be minor or quite absurd and noticeable.

What are the risks associated with AI hallucination?

The risks associated with AI hallucination include providing users with false information, performing incorrectly on real-world tasks, propagating harmful stereotypes from biased data, and losing user trust in the system. Additionally, more advanced AI roles like self-driving vehicles could have dangerous failures.

How can developers avoid or mitigate AI hallucination?

The best ways to prevent AI hallucination include using larger, higher-quality training datasets; testing for coherence; balancing data biases; limiting overfitting; building in uncertainty estimates; and having human oversight for catching hallucinations before deployment. Continued progress in AI research to build more robust models will also help.

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