Definition
Your phone unlocks when it sees your face. Is that intelligence?
Artificial Intelligence (AI) is the field of computer science that builds systems able to do tasks which normally need human intelligence, such as understanding language, recognising images, making decisions and learning from experience.
In simple words
A normal program follows exact steps a person wrote. An AI system handles situations nobody wrote steps for. A calculator does exactly what its rules say. A spam filter looks at an email it has never seen before and still decides whether it's spam. That ability to deal with something new is the heart of AI.
Why it matters
AI now decides which posts you see, flags fraud on UPI payments, types what you speak, and helps doctors read scans. To use it well, build it, or just not be fooled by it, you first need to know what it is and what it isn't. Almost every topic in this course depends on this one.
How it works
What makes a system count as AI?
There is no single test, but most AI systems share three abilities. They take in information from the world, they work out what it means, and they act on it to reach a goal. Many also improve with experience.
| Ability | What it means | Example |
|---|---|---|
| Perceive | Take in images, sound or text | A camera spots a pedestrian |
| Reason | Work out what follows from what it knows | Maps finds the fastest route |
| Learn | Get better from data or experience | A spam filter improves as users mark emails |
| Act | Do something that affects the world | A robot picks up a box |
What is not AI?
Fixed automation isn't AI. An alarm clock, a washing machine programme and a plain if-else rule all do only what was written in advance. Software isn't AI just because a company's advert says so, either. Ask one question: can it handle a case that nobody wrote a rule for? If not, it's probably ordinary software.
Two ways to build AI
- Write the knowledge by hand: experts turn what they know into rules. This is symbolic AI, and it powered the expert systems of the 1980s.
- Learn from data: show the system many examples and let it find the patterns. This is machine learning, and it powers nearly all of today's AI, including ChatGPT.
Try it yourself
A calculator and a spam filter meet something new
Watch what each one does with a case nobody planned for.
AI or not AI?
For each product, decide whether it uses AI. Remember the test: can it handle a case nobody wrote a rule for?
Face unlock on a phone
A maps app that reroutes around a traffic jam
A spam filter that catches new kinds of spam
A chatbot that answers your questions
A digital clock
A ceiling fan with a regulator
A washing machine on a fixed programme
A calculator
Common mistakes
- Thinking AI means robots. Most AI is software with no body at all.
- Thinking AI understands like a person. Today's systems find patterns. They have no proven understanding or feelings.
- Using AI and machine learning as if they mean the same thing. ML is one way of doing AI.
- Trusting an answer because it sounds confident. AI can be confidently wrong.
Check yourself
Summary
- AI builds systems that do tasks normally needing human intelligence.
- Key abilities: perceive, reason, learn and act.
- Fixed rules aren't AI. The test is whether it can handle cases nobody wrote a rule for.
- Today's AI is narrow, and mostly learns from data.
Finished reading?
Sources: Russell & Norvig, Artificial Intelligence: A Modern Approach