WHAT IS Artificial Intelligence (AI).pdf.

Artificial Intelligence (AI) is a branch of computer science that focuses on creating systems or machines capable of performing tasks that typically require human intelligence.

1. WHAT IS Artificial Intelligence (AI)?
A branch of computer science focused on creating
systems or machines that can perform tasks that
typically require human intelligence. These tasks
include learning, reasoning, problem-solving,
perception, understanding language, and even
creativity.
Let’s go step-by-step to understand AI in detail :-

1. Definition
Artificial Intelligence is the simulation of human
intelligence in machines that are programmed to
think, learn, and act like humans.

2. It aims to make computers capable of performing
cognitive functions such as:
• Learning: Acquiring information and rules for
using it.
• Reasoning: Using rules to reach conclusions.
• Self-correction: Improving performance based
on experience.
2. Types of Artificial Intelligence
AI can be categorized in several ways. The most
common classifications are:
A. Based on Capability
1. Narrow AI (Weak AI):
o Designed for a specific task (e.g., Siri,
Google Assistant, ChatGPT).
o Cannot perform tasks outside its training.
2. General AI (Strong AI):
o Would have intelligence comparable to
humans.
o Can understand, learn, and apply
knowledge across different tasks.
o Still theoretical — not yet achieved.

3. 3. Super AI:

o Surpasses human intelligence in all aspects
(creativity, reasoning, emotional
intelligence).
o Currently fictional — only exists in theory or
sci-fi.

B. Based on Functionality
1. Reactive Machines:
o Basic AI systems that react to specific
inputs.
o No memory or learning.
o Example: IBM’s Deep Blue (the chess-
playing computer).
2. Limited Memory:
o Can learn from historical data to make
decisions.
o Most modern AI (e.g., self-driving cars,
chatbots).

4. 3. Theory of Mind:
o Future AI that understands human
emotions, beliefs, and social interactions.
4. Self-Aware AI:
o Theoretical AI that has consciousness and
self-awareness.
3. Subfields of AI
AI includes many specialized areas, such as:
1. Machine Learning (ML):
Algorithms that allow computers to learn from
data and improve over time without explicit
programming.
o Example: Spam filters, recommendation
systems.

5. 2. Deep Learning (DL):
A subset of ML using neural networks with
many layers to analyze complex patterns.
o Example: Image and speech recognition.
3. Natural Language Processing (NLP):
Helps computers understand, interpret, and
generate human language.
o Example: ChatGPT, translation apps.
4. Computer Vision:
Enables machines to interpret and make
decisions based on visual data (images, videos).
o Example: Facial recognition, medical
imaging.
5. Robotics:
Integrates AI to allow robots to perform physical
tasks autonomously.
o Example: Industrial robots, drones.
6. Expert Systems:
AI programs that use knowledge and rules to
make decisions in specialized fields.
o Example: Medical diagnosis systems.

6. 4. How AI Works
AI systems work by processing large amounts of
data and identifying patterns. The main steps
include:
1. Data Collection: Gathering information from
various sources.
2. Data Preparation: Cleaning and organizing
data.
3. Model Training: Teaching the AI using
algorithms.
4. Testing and Evaluation: Checking performance
and accuracy.
5. Deployment: Applying AI in real-world
scenarios.
5. Applications of AI
AI is used in almost every sector today, such as:
• Healthcare: Disease prediction, diagnostics,
personalized medicine.
• Finance: Fraud detection, algorithmic trading.
• Transportation: Self-driving cars, traffic
prediction.

7. • Education: Intelligent tutoring systems.
• Entertainment: Content recommendations
(Netflix, YouTube).
• Customer Service: Chatbots and virtual
assistants.
• Security: Facial recognition and surveillance.
6. Advantages and Disadvantages
Advantages:-
• Automation of repetitive tasks
• Faster decision-making
• Reduced human error
• 24/7 availability
• Data-driven insights
Disadvantages:-
• Job displacement
• High development cost
• Data privacy concerns
• Lack of creativity/emotion
• Risk of misuse (e.g., deepfakes, autonomous
weapons)

8. 7. Future of AI
The future of AI lies in ethical, explainable, and
human-centric development.
Research is ongoing in :-
• General AI (human-like cognition)
• Explainable AI (transparent decision-making)
• AI safety and regulation



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