AI Hallucinations Explained: When Artificial Intelligence Sees Things Differently

We often hear about human hallucinations in extreme scenarios such as prolonged isolation, health conditions, or drug use, but can machines hallucinate too? Although it seems technically implausible, AI is defying these norms.?

To put it simply, this revolutionary innovation of the millennium is not only behaving like humans but also malfunctioning like us. AI hallucination is one such condition that is currently impacting both humans and machines alike.

Diagnosis of the Learning Disorder:

AI hallucination refers to the phenomenon that generates apparently plausible information but is incorrect or fabricated, and unreliable.

As diagnosed by the experts, the ultimate cause behind AI hallucination is as same as the humans- the brain.?

Humans experience hallucinations due to chemical reactions in the brain. Likewise, LLM-based AI models also malfunction (generate wrong information) because of glitches in their neural network.?

Just as neurons in the human brain facilitate seamless communication between brain cells, neural networks in AI train computers to handle data. This method, known as deep learning, employs interconnected nodes in a layered format, making computers highly intelligent and capable of solving complex problems with exceptional accuracy.?

However, any disruption during the deep learning process can result in inaccurate answers, resulting in AI hallucinations.?

AI hallucination in Simple Terms:

This learning deficiency happens when LLM-trained models like ChatGPT generate inaccurate answers and information due to biases in training data and algorithms. These biases lead to the lack of understanding of the underlying reality, making these LLM models to generate answers based on probability but not accuracy.

Impact of AI hallucination:

Inherently a disaster, hallucination abets several other problems - and so is the case with AI.?

AI hallucination is responsible for the following glitches, such as:

• Factual Inaccuracies?

These inaccuracies can confuse masses who lack time to research in-depth and verify. Naturally, these unverified inaccuracies lead to the spreading of false information.

Example- In 2023, Google’s chatbot Bard claimed that the first image of the solar system was captured by the James Webb Space Telescope, which was factually dismissed.

• Weird or Creepy Answers?

Unrealistic prompts that have nothing to do with the real world could lead to whacky outputs. In the worst cases, AI generates imaginative answers and ideas just to address your queries.

• Pollution of Information Ecosystem

Generating too much inaccurate information due to AI hallucination leads to the rapid spreading of misinformation that results in “ pollution of the information ecosystem”.

Tips to Prevent AI hallucination:

1. To avoid biases, AI models must be trained on diverse well-structured data.?
2. Instead of bombarding the LLM models with diverse queries and tasks, organizations & teams must have specific sets of responsibilities defined for AI.?
3. Relying more on human oversight than machine output is a proven way to prevent data hallucination.

Final words

AI hallucinations can bring devastating consequences. The issue highlights the importance of continuous monitoring and refining of AI systems. By understanding and addressing these inaccuracies, we can enhance the reliability and trustworthiness of AI applications, ensuring they provide more accurate and useful outputs while minimizing the risks of misleading information.