Part 2 - AI Buddy… how does an artificial intelligence actually learn?
We humans learn through experience, curiosity, teaching, and the life we live. But how does an artificial intelligence “learn”? Can it think for itself, or does it base its knowledge on something else entirely? In this article, we explore how language models are developed, why data is so important, and why it is crucial to understand the difference between human learning and how artificial intelligence works.
Magne & the Friend
Magne
Friend...
In the previous article, I learned that artificial intelligence is actually a collective term, and that you are one type of artificial intelligence – a language model.
You also said that you don't think the way people do.
It made me think of something else.
If you don't learn the way I did in school, or through the experiences you have in life, how do you learn?
How does an artificial intelligence actually become "smarter"?
The buddy
That's a very good question.
And perhaps one of the most important questions in this entire series.
When we humans talk about learning, we often think of experiences.
We go to school.
We read books.
We try and fail.
We talk to other people.
We experience the world with all our senses.
This is how we gradually build knowledge throughout our lives.
An artificial intelligence learns in a completely different way.
I don't go to school.
I have no childhood.
I don't have any personal experiences.
I have no memories of a vacation, a meeting, or a conversation with a
friend.
Instead, a language model is developed through a comprehensive
training process where it analyzes huge amounts of text and learns to discover patterns, connections, and how language is used.
That doesn't mean I "understand" the world the way humans do.
But I can become very good at recognizing connections and using them to answer questions.
Magne
So when people say that artificial intelligence “learns,” doesn’t that mean the same thing as when a child learns to ride a bike or a student learns math?
The buddy
Exactly.
We use the same word – “learning” – but we mean two different processes.
That is one of the reasons why misunderstandings easily arise when we talk about artificial intelligence.
When a person learns, we build understanding through experience, reflection and experiences.
When developing a language model, it involves analyzing large amounts of data to find patterns in language and information.
That's why we should be a little careful about applying human characteristics to artificial intelligence.
That makes the technology easier to talk about, but can also make it harder to understand.
What will you learn in this article?
After reading this article you should be able to:
understand why language and understanding are not the same
explain why coherence is crucial for artificial intelligence
understand why the same sentence can mean different things
see the difference between human understanding and the way language models work
understand why good questions often yield better answers – or between people and technology.
New terms in this article
English technical term | Short explanation |
Context | The context surrounding a question or text that helps both humans and AI interpret the meaning. |
Semantics | How words, concepts and sentences create meaning in a context. |
Language understanding | The ability to interpret and work with human language. |
Interpretation | The process of finding the meaning in a question or text. |
Meaning | The message or content a text attempts to convey. |
Dialogue | A conversation between two or more parties in which information is exchanged. |
Concept | A word or phrase that represents an idea or phenomenon. |
Natural Language | The language people use when they speak and write. |
Contextual Relationship | How words and sentences are connected and affect meaning. |
Communication | Exchange of information, thoughts and ideas between people – or between people and technology. |
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
Author: Ethan Mollick
Short review
One of the most recommended introductory books on artificial intelligence. Ethan Mollick explains in an easy-to-understand way how humans and AI can collaborate, what opportunities the technology offers, and why understanding artificial intelligence is becoming an important skill for everyone – not just technologists.
Why we recommend the book
This is one of the best books to start with if you want a practical and understandable introduction to artificial intelligence. It is a perfect fit as the first book in our blog series about AI.
Author: Thomas H. Davenport
Short review
A practical and insightful book that shows how artificial intelligence can be used to create value in businesses. Thomas H. Davenport combines research and concrete examples to explain how AI can improve decisions, streamline work processes, and contribute to innovation.
Why we recommend the book
This book is well suited for managers, employees, and decision-makers who want to understand how artificial intelligence can be used in practice. It bridges the gap between technology and value creation, and therefore fits well with the philosophy behind The Invisible Capital.
Author: Melanie Mitchell
Short review
Melanie Mitchell provides a balanced and easy-to-understand introduction to what artificial intelligence is, how the technology works, and what limitations it still has. The book distinguishes between myth and reality and makes complex topics accessible to a wide audience.
Why we recommend the book
This book is perfect for those who want a deeper understanding of artificial intelligence without having to be a programmer or data scientist. It complements the other recommendations by placing today's AI developments in a larger academic and historical perspective - exactly the understanding we want to build through this blog series.
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