Introduction to AI

Artificial Intelligence, commonly called AI, is one of the most important technologies in today’s software industry.

You may have already used AI without realising it.

For example:

• Google Maps suggesting a route
• YouTube recommending videos
• Your phone recognising your face
• Gmail detecting spam emails
• Shopping websites recommending products
• ChatGPT answering questions

All these systems use AI in some form.

But what exactly is AI?

Let’s understand it from the basics.

📌 What is Intelligence?

Before understanding Artificial Intelligence, let’s first understand the word intelligence.

Humans can perform many tasks using their intelligence.

For example, we can:

• Understand a question
• Learn from experience
• Recognise people and objects
• Solve problems
• Make decisions
• Predict what may happen
• Understand languages

For example, if someone asks:

“Should I carry an umbrella today?”

You may look at the weather, think about the possibility of rain, and decide whether you need an umbrella.

This is an example of using intelligence to make a decision.

What is Artificial Intelligence?

Now imagine if a computer could perform some of these tasks.

For example, a computer could:

This is where Artificial Intelligence comes in.

Artificial Intelligence is a technology that enables computers to perform tasks that normally require human intelligence.

These tasks can include understanding language, recognising images, learning patterns, making predictions, and making decisions.

💡 A Simple Example of AI

Let’s take a simple example.

Suppose we want a computer to identify whether a picture contains a cat or a dog.

A computer does not automatically know what a cat or dog looks like.

We can give it many examples:

The AI system can learn patterns from these examples.

Later, we can give it a new picture.

The computer has used what it learned from previous examples to make a prediction.

This is one simple example of AI.

⚖️ AI vs Traditional Programming

To understand AI properly, it is useful to compare it with traditional programming.

Traditional Programming

In traditional programming, the developer writes the rules.

For example, suppose we want to calculate whether a person is allowed to vote.

We could write:

IF age >= 18
    Allow voting
ELSE
    Do not allow voting

The programmer has explicitly defined the rule.

The process looks like this:

The computer simply follows the instructions.

🤖 What happens in AI?

Some problems are much more difficult to solve using manually written rules.

Let’s go back to our cat and dog example.

We could try to write rules:

IF the animal has four legs
AND two eyes
AND two ears
THEN it is a cat

But this doesn’t work.

Dogs also have four legs, two eyes and two ears.

There are many differences between animals that are difficult to describe using simple rules.

Instead, we can provide many examples.

The model learns patterns from the examples and uses those patterns when it sees new data.

This idea is called Machine Learning.

🤖 AI and Machine Learning

You will often hear the terms AI and Machine Learning together.

They are related, but they are not exactly the same.

A simple way to understand their relationship is:

AI is the broader field.

Machine Learning is one approach used to build AI systems.

We will study Machine Learning in detail in the next tutorial.

💡 Examples of AI in Real Life

AI is already used in many applications.

Search Engines

When you search for something, AI can help understand your query and provide relevant results.

Recommendation Systems

Websites such as YouTube, Netflix and shopping platforms can recommend content based on your previous activity.

Spam Detection

Email systems can identify unwanted emails.

Face Recognition

A phone can analyse your face and determine whether it matches the registered user.

Voice Recognition

AI can convert spoken words into text.

This technology is commonly used in voice assistants and speech-to-text applications.

⚙️ AI Can Do More Than Predictions

AI is not limited to recognising or predicting things.

Modern AI systems can also generate new content.

For example:

They can generate:

• Text
• Code
• Images
• Audio
• Video

This area is called Generative AI.

We will study Generative AI later in this course.

🤖 AI Is a Broad Field

One important thing to understand is that AI is not a single technology.

There are many technologies and approaches under AI.

A simplified view is:

This diagram is simplified, but it gives us a good starting point.

As we continue the course, we will understand each of these terms.

⚠️ AI Does Not Mean Human Intelligence

One common misunderstanding is that AI is exactly the same as human intelligence.

It is not.

An AI system can be very good at a particular task while being completely incapable of doing many other things.

For example, an AI model might be excellent at:

Understanding text

but that doesn’t mean it can automatically:

Drive a car
Repair a machine
Cook food
Understand the physical world

unless it has been specifically designed and equipped to perform those tasks.

So, when we say that AI is “intelligent”, we are talking about its ability to perform certain tasks that require intelligent behaviour.

🤖 AI in Software Development

AI is also changing how software is developed.

Modern AI tools can help developers:

• Generate code
• Explain code
• Find bugs
• Write tests
• Generate documentation
• Convert code between languages
• Analyse logs
• Answer questions about a codebase

For example:

This is one of the reasons AI has become very important for software engineers.

🧩 Key Points

Let’s summarise what we learned.

• 1. AI stands for Artificial Intelligence.

• 2. AI allows computers to perform tasks that normally require some level of human intelligence.

• 3. AI can be used for tasks such as:

  • Recognition
  • Prediction
  • Recommendation
  • Language understanding
  • Decision-making
  • Generation

• 4. AI is a broad field.

• 5. Machine Learning is one approach used to build AI systems.

• 6. Generative AI is a type of AI that can generate new content.

📌 Where Do We Go From Here?

We now understand what AI is.

But one important question is still unanswered:

How does a computer actually learn from data?

To answer that, we need to understand Machine Learning.

In the next tutorial:

We’ll start with a very simple example and understand:

Then we’ll gradually move from Machine Learning → Deep Learning → Neural Networks → Transformers → Generative AI → LLMs.