How can we Detect AI Hallucinations?
Users can cross-check the facts generated by the model with some authentic sources. However, it is troublesome (in time and complexity) and practically not feasible.
The above solution does not apply to computer vision based AI applications. Recently, an image showing similarities between chihuahuas and muffins (refer to Figure 2) appeared on different channels. Suppose we want to identify images of Chihuahuas without any wrong hits. A human can recognize most of them If not all. This might be a tricky question for an AI model to differentiate between them. This is where AI models lack, common sense. The model might have gotten wrongly labeled image(s) in the training set, or trained with insufficient data.
Artificial Intelligence Hallucinations
The term “hallucination” takes on a new and exciting meaning in artificial intelligence (AI). Unlike its meaning in human psychology, where it relates to misleading sensory sensations, AI hallucination refers to AI systems generating imaginative novel, or unexpected. These outputs frequently exceed the scope of training data.
In this post, we will look into the concept of AI hallucination problems, causes, detections, and prevention in the field of AI.
Table of Content
- What is Artificial Intelligence Hallucinations or AI Hallunication?
- Real-World Example of an Artificial Intelligence (AI) Hallucination
- Causes of Artificial Intelligence (AI) Hallucinations
- How Can Hallucination in Artificial Intelligence (AI) Impact Us?
- How can we Detect AI Hallucinations?
- How to Prevent Artificial Intelligence (AI) Hallucinations?
- Conclusion