AI or Artificial Intelligence has become an important tool for organizations and household chores. While this advanced form of intelligence can help humans sort their tasks, it can also make some mistakes. All is not rosy in the world of AI; there are some cons too, which must be dealt with sophistication. One such con is AI hallucination. Much like its original concept, AI hallucination also deals with generating misleading results by AI tools caused by multiple factors like insufficient data in training, incorrect assumptions made by the system, and inherent biases present in the training data. Such hallucinations can lead to disasters in the medical industry and financial trading.
How do AI Hallucinations Arise?
AI models are trained on data and taught to make predictions by tracking patterns in the data. Now, the accuracy of these predictions often depends on the quality and completeness of the data used for training. If the data is flawed incomplete, or even biased, the AI model will catch on to incorrect patterns and create hallucinations instead of accurate results. However, this is not the only reason for AI hallucinations. Another reason could be AI’s incapability to comprehend real-world knowledge, factual information, and physical properties. This lack of grounding can cause the model to produce outputs that are factually inaccurate or irrelevant, despite appearing reasonable. In more severe cases, it may even generate links to web pages that do not exist.
Some of the most common examples of AI hallucination include:
- Incorrect Predictions – When an AI model predicts an event that will not happen, it is an example of AI hallucination.
- False Positives – This might lead to a lot of confusion because AI can identify a threat that is not nonexistent, generating false alarms.
- False Negatives – The opposite might happen when AI fails to recognize a threat and misleads people.
- Preventing AI Hallucinations – Preventing AI hallucinations is necessary to protect the sanity of human society. Quite a few things can be done to stop this blunder. They are:
- Limit Possible Outcomes – While training an AI model, limiting the number of possible outcomes can be done. Use the ‘regularization’ technique to restrict the number of predictions. This technique penalizes the AI model for making too many predictions, thereby helping to reduce the generation of incorrect outputs.
- Train AI with Relevant Sources – Train the AI model using relevant images related to the task to help prevent incorrect predictions.
- Create a Template for AI – Creating a template for the AI to follow is always a good idea, as it helps guide the AI in generating accurate outputs.
- AI Must Know the Dos and Don’ts – When using an AI model, it is important to clearly specify what you do and do not want. This can also be achieved by providing feedback on the generated output.
Therefore, preventing AI hallucinations is essential for industries to operate effectively.