Total Hours: 55 | Marks: 10
What is Artificial Intelligence (AI)?
Artificial Intelligence or AI means making computers or machines smart. These machines can think, learn, and make decisions like humans.
Simple Definition:
AI is a way to make machines work intelligently by learning from data and performing tasks like understanding language, recognizing images, solving problems, or even playing games.
Examples from Daily Life:
- Google Assistant or Siri answering your questions
- YouTube showing you recommended videos
- Online shopping sites suggesting what to buy
- Face detection to unlock your phone
- Spam filter in your email
AI is already a part of our everyday life, even if we don’t always notice it.
Domains of Artificial Intelligence
AI works in different areas. These areas are called domains. There are mainly three important domains of AI that you will learn in this unit:
1. Natural Language Processing (NLP)
This domain helps computers understand human language, both written and spoken.
Examples:
- Chatbots on websites
- Google Translate
- Voice assistants like Alexa or Siri
In simple words: When you talk or type, the computer understands and replies.
2. Computer Vision (CV)
This domain helps computers understand pictures and videos.
Examples:
- Face detection in smartphones
- Self-driving cars detecting traffic signs
- Apps that can identify plants or objects using the camera
In simple words: When a computer can “see” and understand images like humans.
3. Data
This domain is about teaching AI using large amounts of data. AI learns from this data and improves its performance.
Examples:
- YouTube showing recommended videos based on your watch history
- Online stores suggesting products based on what you searched
In simple words: AI learns from data, just like we learn from experience.
Difference Between AI and Traditional Programming
| Traditional Programming | Artificial Intelligence |
|---|---|
| We give rules and data to get the result | We give data and result, AI learns the rules |
| Follows fixed instructions | Learns from examples |
| Example: Calculator | Example: Face recognition |
Explanation:
In traditional programming, we write rules. The computer uses those rules to solve problems.
In AI, we give the computer data and answers. The AI finds out the rules by itself.
The AI Project Cycle
To build an AI project, we follow four main steps. This is called the AI Project Cycle.
Step 1: Problem Scoping
This means understanding the problem. You ask questions like:
- What is the problem?
- Who has this problem?
- What do we want to solve?
Example: Customers are not getting fast replies to their questions on a website.
Step 2: Data Acquisition
This step is about collecting the correct data. Good quality data is very important for AI to work well.
Example: Collect past chat records or common questions asked by customers.
Step 3: Data Exploration and Model Building
In this step, we use the data to train a model. The model finds patterns in the data.
Example: The model learns what replies to give when someone asks a question.
Step 4: Evaluation
Now we check how well the model is working. We test it using new data.
Example: Test the chatbot with new questions and see if it replies correctly.
Ethics in AI
Ethics means understanding what is right or wrong.
Since AI is powerful, it must be used carefully. We must think about its effects on people and society.
Here are some important ethical topics:
1. Bias in AI
If the data used to train AI is unfair, the AI will also be unfair.
Example: If an AI hiring system is trained only on male resumes, it may reject female candidates unfairly.
2. Access to AI
Not everyone gets equal access to AI tools or internet. This creates a gap between people.
Example: Rural schools may not have the same AI tools that city schools have.
3. Privacy
AI collects and uses data. This data may include personal details. If misused, it can harm people’s privacy.
Example: Smart assistants like Alexa may hear private conversations.
4. Responsibility
If AI makes a mistake, who is responsible?
Example: If a self-driving car causes an accident, who is to blame? The developer? The owner? Or the AI?
We need clear rules to answer such questions.
Suggested Classroom Activities and Practicals
These activities can help students understand the concepts of AI in a fun way:
- Semantris Game – A fun word game that shows how AI understands meaning (NLP)
- Quick Draw Game – A drawing game where AI guesses what you are drawing (CV)
- Rock-Paper-Scissors with AI – A game to show how AI learns from patterns
- Moral Machine Activity – Students decide how an AI in a self-driving car should act in emergency situations. This helps them understand AI ethics.
Summary of the Unit
- AI is the science of making machines smart.
- It works in three major domains: NLP (language), CV (images), and Data.
- AI is different from traditional programming because it learns from data.
- The AI Project Cycle has four steps: Problem, Data, Model, and Evaluate.
- Ethics is an important part of AI – we must use AI in a fair and responsible way.