What is AI

Artificial Intelligence is the branch of computer science dedicated to creating systems that can perform tasks typically requiring human intelligence. These tasks include learning, reasoning, problem-solving, natural language understanding, and perception. AI systems can be as simple as rule-based expert systems or as complex as deep neural networks that power modern applications like voice assistants, self-driving cars, and medical diagnostics.

What Is Intelligence?

Intelligence is the ability of an entity to perceive its environment, learn from experience, reason about information, make decisions, and adapt its behavior to achieve goals.

This definition highlights five core capabilities:

1. Perception – Gathering information from the environment.
2. Learning – Improve performance using experience.
3. Reasoning – Drawing conclusions and solving problems.
4. Decision-making – Choosing actions to achieve goals.
5. Adaptation – Adjusting behavior when conditions change.

Human exhibit all five, but intelligence is not exclusive to humans.

A Formal Definition of AI

A widely accepted definition is:

Artificial Intelligence is the field of computer science concerned with designing systems that can perform tasks that normally require human intelligence.

These tasks include:

• Understanding language
• Recognizing images
• Solving problems
• Playing games
• Making decisions
• Learning from data
• Planning actions
• Generating text, images, or code

How AI Has Evolved

The history of AI can be viewed as a progression of paradigms.

Rule-Based AI

Humans manually wrote rules.

Examples:

IF fever AND cough
THEN flu

Suppose you want to determine whether an email is spam.

A traditional program might use rules such as:

IF contains "FREEE MONEY"
THEN spam

IF sender unknown
THEN spam

IF too many links
THEN spam

While this works in some cases, spam evolves. New tricks appear constantly, and manually updating rules becomes impractical.

Machine Learning

Instead of writing rules, algorithms learn patterns from data.

Example:

Thousands of medical records are used to train a disease classifier.

Millions of emails are used to train spam checker.

A Practical Example

Suppose you want a system to recognize cats.

Traditional Programming

Write rules:

Has whiskers

AND

Has pointed ears

AND

Has fur

 ↓

Cat

This quickly becomes impractical because real images vary enormously.

Machine Learning

Instead:

Thousands of cat images
 ↓
Training
 ↓
Learn patterns automatically
 ↓
Recognize new cats

This model infers the decision process form data.