Artificial Intelligence (AI)
1. Introduction to Artificial Intelligence
1.1 Definition of Artificial Intelligence
Artificial Intelligence (AI) refers to the branch of computer science concerned with the creation
of systems capable of performing tasks that typically require human intelligence. These tasks
include learning, reasoning, problem-solving, perception, language understanding, and decision-
making.
Key idea: AI aims to simulate or augment human intelligence using machines.
1.2 Historical Background of AI
1950 – Alan Turing proposes the Turing Test to evaluate machine intelligence.
1956 – John McCarthy coins the term Artificial Intelligence at the Dartmouth
Conference.
1960s–1970s – Early symbolic AI and expert systems emerge.
1980s – Knowledge-based systems and rule-based expert systems gain popularity.
1990s–2000s – Growth of machine learning and data-driven approaches.
2010s–present – Deep learning, big data, and high-performance computing drive rapid
AI advancement.
1.3 Goals of Artificial Intelligence
Build systems that can think and act rationally
Automate intelligent tasks
Enhance human decision-making
Solve complex real-world problems efficiently
2. Types of Artificial Intelligence
2.1 Based on Capability
2.1.1 Narrow AI (Weak AI)
Designed to perform a specific task
Examples: voice assistants, recommendation systems, facial recognition
2.1.2 General AI (Strong AI)
, Possesses human-like intelligence across a wide range of tasks
Currently theoretical and not yet achieved
2.1.3 Super AI
Intelligence that surpasses human capabilities
Hypothetical and subject to ethical debate
2.2 Based on Functionality
Reactive Machines – No memory, respond only to current input
Limited Memory – Learn from past data (most modern AI systems)
Theory of Mind – Understand emotions and intentions (research stage)
Self-Aware AI – Conscious machines (hypothetical)
3. Core Components of Artificial Intelligence
3.1 Data
Foundation of AI systems
Can be structured, semi-structured, or unstructured
3.2 Algorithms
Step-by-step procedures used to solve problems
Enable learning, reasoning, and decision-making
3.3 Computing Power
CPUs, GPUs, TPUs
Cloud computing and edge computing enhance AI scalability
3.4 Models
Mathematical representations trained on data
Examples: decision trees, neural networks, regression models
4. Machine Learning (ML)
4.1 Definition of Machine Learning
1. Introduction to Artificial Intelligence
1.1 Definition of Artificial Intelligence
Artificial Intelligence (AI) refers to the branch of computer science concerned with the creation
of systems capable of performing tasks that typically require human intelligence. These tasks
include learning, reasoning, problem-solving, perception, language understanding, and decision-
making.
Key idea: AI aims to simulate or augment human intelligence using machines.
1.2 Historical Background of AI
1950 – Alan Turing proposes the Turing Test to evaluate machine intelligence.
1956 – John McCarthy coins the term Artificial Intelligence at the Dartmouth
Conference.
1960s–1970s – Early symbolic AI and expert systems emerge.
1980s – Knowledge-based systems and rule-based expert systems gain popularity.
1990s–2000s – Growth of machine learning and data-driven approaches.
2010s–present – Deep learning, big data, and high-performance computing drive rapid
AI advancement.
1.3 Goals of Artificial Intelligence
Build systems that can think and act rationally
Automate intelligent tasks
Enhance human decision-making
Solve complex real-world problems efficiently
2. Types of Artificial Intelligence
2.1 Based on Capability
2.1.1 Narrow AI (Weak AI)
Designed to perform a specific task
Examples: voice assistants, recommendation systems, facial recognition
2.1.2 General AI (Strong AI)
, Possesses human-like intelligence across a wide range of tasks
Currently theoretical and not yet achieved
2.1.3 Super AI
Intelligence that surpasses human capabilities
Hypothetical and subject to ethical debate
2.2 Based on Functionality
Reactive Machines – No memory, respond only to current input
Limited Memory – Learn from past data (most modern AI systems)
Theory of Mind – Understand emotions and intentions (research stage)
Self-Aware AI – Conscious machines (hypothetical)
3. Core Components of Artificial Intelligence
3.1 Data
Foundation of AI systems
Can be structured, semi-structured, or unstructured
3.2 Algorithms
Step-by-step procedures used to solve problems
Enable learning, reasoning, and decision-making
3.3 Computing Power
CPUs, GPUs, TPUs
Cloud computing and edge computing enhance AI scalability
3.4 Models
Mathematical representations trained on data
Examples: decision trees, neural networks, regression models
4. Machine Learning (ML)
4.1 Definition of Machine Learning
