Lecture 1: Introduction to Artificial Intelligence

Course Overview

Welcome to 6.03
- Discussion on the diversity of student names
- Introduction to the course and a brief mention of enrollment turnover

AI involves:
- Thinking, Perception, and Action
- Broad definition, incorporating model making

Common theme across subjects: building models
- Differential equations
- Probability
- Simulations
AI models targeted at improving understanding of thinking, perception, and action
Representation is crucial for models

Gyroscope Example
- Importance of representation in understanding mechanical concepts
Farmer, Fox, Goose, and Grain Problem
- Visualization and state diagrams to solve logical puzzles
- 16 possible states, refined to acceptable states by constraints

Algorithms: Procedures for solving problems
Generate and Test
- Example: Identifying tree leaves using a field guide
- Key properties: non-redundancy, information absorption
Rumpelstiltskin Principle
- Naming concepts give power over them (e.g., 'aglet')
- Importance of terminology in understanding AI concepts

Difference between simple and trivial
- Simple can be powerful and effective
- Trivial implies lack of value
Encouragement to avoid dismissing simple ideas as unimportant

Early Milestones
- Lady Lovelace (1842): First programmer, skepticism about computers' potential intelligence
- Alan Turing (1950): Introduced Turing Test
Modern Era
- Marvin Minsky (1960): Foundation for modern AI research
- Symbolic Integration Programs, ELIZA, SHRDLU
Dawn of Expert Systems (Late 70s - Early 80s)
- Rule-based systems like Mycin for medical diagnosis
- Practical applications in industry (e.g., Delta Airlines)
Bulldozer Age
- Massive computational power substituting for some aspects of intelligence
- Deep Blue defeating the world chess champion