Three years ago, I visited a rather interesting art exhibition. “Machine Memoirs” by Refik Anadol piqued my interest from the start.
He is a popular name among those who are interested in the intersection of art and AI. But don’t worry, this blog is not about art. We will delve into the deep “perceptions” of AI.
In this exhibition, Anadol was experimenting with NASA’s space exploration imagery. The exhibition was inspired by the idea that telescopes could “dream” using their visual archives, blurring the barriers between fact and imagination.
By investigating the relationships between data, memory, and history on a cosmic scale, Anadol was asking us to consider the potential of artificial intelligence to observe and comprehend the world around us. And even AI to have its own dreams…
So, why is this relevant to us?
Consider this: much as Anadol investigated the concept of telescopes dreaming from their data, AI systems have their own type of dream—or rather, hallucinations—within their digital memory banks.
These hallucinations, like the visualizations in Anadol’s exhibition, can help us learn more about data, AI, and their limits.
What exactly are AI hallucinations?
When a large language model, such as a generative AI chatbot, produces outputs with patterns that are either nonexistent or invisible to human observers, we call these “AI hallucinations.”
These outputs, which differ from the expected answer based on the input given to the AI, can be completely erroneous or nonsensical.
In the context of computers, the term “hallucination” may seem unusual, but it accurately describes the bizarre character of these incorrect outputs. AI hallucinations are caused by a range of variables, including overfitting, biases in training data, and the complexity of the AI model.
To understand better, this is conceptually similar to how humans see shapes in clouds or faces on the moon.
An Example:
In this example, I asked a very easy question to ChatGPT. I was supposed to get an answer like, “The author of the Dune book series is Frank Herbert.”.
Why Does This Happen?
Despite being built to write content that is coherent and fluid, large language models are actually unable to comprehend what they are saying. This is very critical in determining the credibility of AI-generated content.
While these models can generate reactions that mimic human behavior, they lack the contextual awareness and critical thinking skills that underpin actual intelligence.
As a result, AI-generated outputs run the danger of being misleading or wrong since they favor matching patterns over factual correctness.
What could be some other cases of hallucinations?
Dangerous Misinformation: Let’s say a generative AI chatbot fabricates evidence and testimonies to falsely accuse a public figure of criminal conduct. This misleading information has the potential to damage the person’s reputation and cause unjustified retaliation.
Weird or Creepy Answers: To give a humorous example, picture a chatbot giving a user a weather question and replying with a forecast that says it will rain cats and dogs, along with pictures of raindrops that look like cats and dogs. Even though they are funny, this would still be a “hallucination.”.
Factual Inaccuracies: Assume a language model-based chatbot falsely states that the Great Wall of China may be viewed from space without explaining that it is only visible under specific conditions. While the remark may appear plausible to some, it is inaccurate and can mislead people about the wall’s sight from space.
How Do You Avoid AI Hallucinations as a User?
Make Explicit Prompts
You need to communicate with AI models explicitly.
Think about your goals and design your prompts before writing.
For example, give specific instructions like “Explain how the Internet works and write a paragraph about its significance in modern society” instead of posing a general inquiry like “Tell me about the Internet.”
Explicity helps the AI model interpret your intent.
Example: Ask the AI questions such as these:
“What is cloud computing, and how does it work?”
“Explain the impact of data drift on model performance.”
“Discuss the impact and potential future of VR technology on the IT business.”
Embrace the Power of Example
Providing examples in your prompts helps AI models understand the context and generate precise replies. Whether you’re looking for historical insights or technical explanations, providing examples can help enhance the accuracy of AI-generated content.
For example, you can say, “Mention fantasy novels such as Harry Potter.”
Break Down Complex Tasks
Complex prompts overload AI algorithms, and they may lead to irrelevant results. To prevent this, divide complex activities into smaller, more manageable pieces. By organizing your prompts sequentially, you allow the AI to focus on each component independently, resulting in more logical replies.
For example, rather than asking the AI to “explain the process of creating a neural network” in a single query, break the assignment down into discrete phases like problem definition and data collection.
Validate the Outputs and Provide Feedback
Always double-check the results produced by AI models, particularly for fact-based or crucial activities. Compare the replies to reliable sources and note any differences or errors.
Provide input to the AI system to enhance future performance and reduce hallucinations.
Strategies for Developers to Avoid AI Hallucinations
Implement Retrieval-Augmented Generation (RAG).
Integrate retrieval-augmented generation techniques into AI systems to base replies on factual facts from reliable databases.
Retrieval-augmented generation (RAG) combines standard natural language generation with the capacity to obtain and incorporate relevant information from a huge knowledge base, resulting in more contextually rich output.
By merging AI-generated content with validated data sources, you can improve the dependability and trustworthiness of AI results.
Validate and Monitor AI outputs Continuously
Set up rigorous validation procedures to verify the correctness and consistency of AI outputs in real-time. Monitor AI performance attentively, look for potential hallucinations or mistakes, and iterate on model training and prompt optimization to increase dependability over time.
For example, use automated validation routines to check AI-generated content for factual correctness and highlight instances of possible hallucinations for manual assessment.
Check For Data Drifts
Data drift is a phenomenon in which the statistical features of the data used to train an AI model vary with time. If the AI model meets data that differs considerably from its training data during inference, it can provide false or illogical results, resulting in hallucinations.
For example, if an AI model is trained on past data that is no longer relevant or indicative of the current environment, it may make incorrect conclusions or predictions.
As a result, monitoring and resolving data drifts is critical to ensuring AI system performance and dependability while also reducing the possibility of hallucinations.
Conclusion
According to IBM Data, AI hallucinations occur in around 3% to 10% of answers from AI models.
So, one way or another, you will probably observe them as well. I believe this is an incredibly interesting topic because it is a fascinating reminder of the continuous road toward enhancing AI’s capabilities.
We get to observe and experiment with the reliability of AI, the intricacies of data procesing, and human-AI interactions.
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