Lesson 02 — How Does Artificial Intelligence Work?

In the first lesson, we saw that artificial intelligence allows computers to perform tasks that can appear intelligent.

But how does this actually work?

The answer comes down to a few simple ideas: data, algorithms, models, and training.

AI Starts With Data

Data is the information an AI system learns from.

This data can take many forms:

• Text
• Images
• Videos
• Audio
• Numbers
• Documents

For example, if we want to create an AI that can recognize cats, we could show it thousands of images containing cats.

The AI can then look for patterns that appear in those images.

What Is an Algorithm?

An algorithm is simply a set of instructions used to solve a problem or perform a task.

You can think of it as a recipe.

A cooking recipe tells you what steps to follow to prepare a meal. An algorithm tells a computer what steps to follow to process information.

AI uses algorithms to learn patterns from data and produce useful results.

What Is an AI Model?

An AI model is the result of the learning process.

After an AI system has been trained on data, the model can use what it has learned to make predictions or generate answers.

For example, after learning from many pictures of cats and dogs, an AI model can receive a new picture and predict whether it contains a cat or a dog.

What Does “Training” Mean?

Training is the process through which an AI model learns patterns from data.

Imagine showing a student hundreds of examples of different animals. Over time, the student starts to recognize the characteristics that distinguish a cat from a dog.

AI training works in a similar general way: the system processes many examples and adjusts itself to become better at a particular task.

What Happens After Training?

Once a model has been trained, it can be used with new information.

This is sometimes called inference.

For example:

Training: The AI learns from millions of examples.

Inference: You give the AI a new question, image, or piece of information, and the model produces an answer.

A Simple Example

Imagine an AI designed to recognize spam emails.

First, the system is given many examples of emails that are known to be spam and emails that are not spam.

During training, it learns patterns that often appear in spam messages.

Later, when a new email arrives, the trained model can analyze it and estimate whether it is spam.

This basic idea is behind many different AI applications, although real-world systems can be much more complex.

AI Does Not Learn Like a Human

The word “learn” can be slightly misleading.

AI does not learn through personal experiences or emotions like a human does.

Instead, an AI model detects patterns in data using mathematical methods and adjusts its internal parameters during training.

We will explore these concepts in more detail later in the AI Basics Learning Path.

Key Takeaways

• Data provides the information an AI learns from.
• Algorithms provide methods for processing information.
• Training is the process of learning patterns from data.
• An AI model is what the system uses after training to produce results.
• Inference happens when a trained model processes new information.

What’s Next?

Now that you understand the basic ingredients behind AI, it is time to look at where all of this came from.

AI did not appear with ChatGPT. The field has been developing for decades.

In the next lesson, we will take a short journey through the history of artificial intelligence.