Part 34 - AI Buddy... why can artificial intelligence be wrong?
Artificial intelligence can often provide quick, thorough, and impressive answers. Yet it can also get it wrong. Sometimes the mistake is small. Other times it can be serious. Does that mean artificial intelligence can't be trusted? Or does it mean we need to understand how the technology actually works? In this article, we take a closer look at why artificial intelligence can get it wrong—and why it's not necessarily a sign that the technology is bad.
Magne & the Friend
Magne
Friend...
You have helped me immensely.
But I have also experienced that you have occasionally been wrong.
Why does it happen?
The buddy
The short answer is that I don't work the same way as a human.
I'm not necessarily looking up a definitive answer.
I analyze language, patterns, and context to provide the answer that seems most likely based on the question and the information I have available.
Magne
So...
You're not trying to guess?
The buddy
No.
But the result may still be wrong.
This could be due to several things.
The question may be unclear.
The information may be incomplete.
The sources may disagree.
Or the topic may have changed since the information I am building on was published.
Magne
So...
When you make a mistake...
Isn't it necessarily because you "know" it's wrong?
The buddy
Exactly.
Sometimes there is no one right answer.
Other times the answer is found, but the information is uncertain or contradictory.
And sometimes the mistake is that I have interpreted the question differently than you intended.
Magne
It actually reminds me of people.
We can also misunderstand questions.
The buddy
Yes.
The difference is that people often detect uncertainty through experience, body language, or follow-up questions.
I don't have such senses.
I only have the words we share in the conversation.
Magne
So the clearer I am...
The better the answer?
The buddy
Often, yes.
A clear description of the situation, goal and need gives me a much better basis for helping.
But even then, it may be necessary to check the information – especially when it is of great importance.
Magne
So...
Perhaps the most important thing is not to think that you are always right.
But to understand how you work.
The buddy
Just.
Artificial intelligence is a powerful tool.
But like all good tools, it works best when used with knowledge, critical thinking, and human judgment.
Academic specialization
Why can artificial intelligence be wrong?
There are many reasons, including:
the question is unclearly formulated
the information is incomplete
different sources give different answers
the theme has changed over time
the language model is based on probability, not certain knowledge
That doesn't mean artificial intelligence is unreliable.
This means that the user must understand both the strengths and limitations of the technology.
Academic specialization
Why can artificial intelligence be wrong?
There are many reasons, including:
the question is unclearly formulated
the information is incomplete
different sources give different answers
the theme has changed over time
the language model is based on probability, not certain knowledge
That doesn't mean that artificial intelligence is unreliable. It just means that the user needs to understand both the strengths and limitations of the technology.
Common reasons why AI can be wrong
Cause | What does it mean in practice? |
Unclear question | AI may interpret the question differently than the user intended. |
Insufficient information | Important information is missing to be able to give a precise answer. |
Conflicting sources | Different sources describe the topic differently. |
Outdated information | New research or new regulations may have changed the situation. |
Probability-based model | The most likely answer is not always the correct one. |
New terms in this article
English technical term | Short explanation |
Source of Error | A factor that can cause an answer or conclusion to be incorrect. |
Uncertainty | Situations where the information is not sufficient to provide a reliable answer. |
Training Data | The information a language model is developed and trained on. |
Limitation | Conditions that set limits on what a technology can do or know. |
Interpretation | The process of understanding the meaning of a question or text. |
Precision | How accurately an answer matches what the user is actually asking. |
Reasoning | To analyze information and draw logical conclusions. |
Pattern Recognition | Discovering connections and regularities in language and data. |
Validation | To check that an answer is reasonable or consistent with available information. |
Critical Evaluation | To examine and evaluate information before using it as the basis for a decision. |
This is what you have learned
After reading this article, you now know:
• why artificial intelligence can be wrong
• that language models are based on probability and pattern recognition
• why clear questions often yield better answers
• why important information should still be checked
We have only just begun.
Magne
Friend...
You say that artificial intelligence can sometimes give an answer that seems correct, but is still wrong.
Is there a specific name for it?
The buddy
Hehe...
Yes, it does.
In artificial intelligence, the word hallucination is often used.
But it does not mean the same as when the word is used about people.
And that is exactly what we will explore in the next article.
Academic background and further reading
This series also builds on my own professional journey through the Web Design study , the eMarketing study , Innovation and Commercialization and professional seminars in San Francisco and Oxford . Here you will find the background, professional environments and experiences that have followed the development from the early years of the web to today's digitalization.
Recommended books from our library
Build a Large Language Model (From Scratch)
Author: Sebastian Raschka
Short review
This book takes the reader behind the scenes and shows how a modern language model is actually built – step by step. Sebastian Raschka explains advanced concepts in an educational way and provides a unique understanding of how large language models like ChatGPT work. Although the book contains code examples, it is also very valuable for anyone who wants a deeper understanding of the technology behind artificial intelligence.
Why we recommend the book
One of the most talked about books on large language models. Perfect for those who want to understand how artificial intelligence works beneath the surface and why language models have become a revolution in digitalization and knowledge sharing.
Quick Start Guide to Large Language Models
Author: Janelle Shane
Short review
This book provides a practical and easy-to-understand introduction to large language models (LLMs). Sinan Ozdemir explains how language models are used in modern businesses, how they can be integrated into work processes, and why they have become one of the most important technologies in artificial intelligence.
Why we recommend the book
A very good book for anyone who wants a quick and practical introduction to language models. It is suitable for both beginners and professionals who want to understand how LLMs are used in practice.
Artificial Intelligence: A Modern Approach: The Future Is Coming! Discover How Artificial Intelligence Will Change Your Life!
Authors: Stuart Russell & Peter Norvig
Short review
This is the world's most famous textbook on artificial intelligence and is used in universities worldwide. The book covers the entire subject area – from problem solving and machine learning to language understanding, robotics and ethics – and is considered a classic in the AI field.
Why we recommend the book
If you are only going to own one academic book on artificial intelligence, this is one of the very best choices. A timeless classic that provides a solid academic understanding of artificial intelligence and is still used as a syllabus at leading universities around the world.
© 2025 - 2026 Portico Publish | The invisible capital Privacy and use of the website | Developed and operated by Magne Bjella | Powered and secured by Wix
Comments