CS 331: Introduction to Artificial
Intelligence
Lecture 1: Introduction
Huazheng Wang
AI is Everywhere
Generative AI (GPT, Claude, Stable Diffusion, …)
Self-driving cars
Machine translation
Speech recognition
Game playing (Chess, Go, Poker, StarCraft, Diplomacy, …)
Robotics (e.g., Cassie — 100m in 25 sec)
Bioinformatics (e.g., AlphaFold — protein folding)
Logistics planning (military, airline scheduling)
Generated by DALL-E:
"elephant in a jar"
What is AI?
Historical definition:
"The study of the conjecture that every aspect of learning or any other
feature of intelligence can in principle be so precisely described that a
machine can be made to simulate it."
— Dartmouth Workshop, Summer of 1956
Two key dimensions:
Behavior vs. Process — Should intelligence be defined by external behavior (input-output) or by the
internal process?
Performance standard — Should we judge against human performance or an objective criterion
(rationality)?
Four Definitions of AI
Performance
Standard
Think (Process)
Act (Behavior)
Human
Think Humanly
(Cognitive Modeling)
Act Humanly
(Turing Test)
Rationality
Think Rationally
(Laws of Thought)
Act Rationally
(Rational Agency)
All four seem reasonable but produce different results.
This course focuses on rational agency — acting to maximize goal achievement.
Thinking Humanly: Cognitive Modeling
Systems that think like humans
To build systems that think like humans, we must determine how humans think
The interdisciplinary field of cognitive science brings together AI and psychology to construct
precise and testable theories of the human mind
Acting like Humans: Turing Test (1950)
Can a human interrogator tell if responses come from a human or a
computer?
System needs these capabilities:
Natural language processing — communicating
Knowledge representation — storing information
Automated reasoning — answering questions, drawing conclusions
Machine learning — extrapolating patterns
Vision + Robotics — perceiving & manipulating (for "Total Turing Test")
Alan Turing
Arguments against the Turing Test
Problems with the test itself:
Not reproducible; can't be analyzed mathematically
Tends to focus on human-like errors, linguistic tricks
Does not produce useful computer programs
Philosophical objections:
Mathematical Objection — There are problems with no algorithms (e.g., halting problem). But no evidence
humans can solve them either.
Lack of Originality (Ada Lovelace) — A machine can't do anything original because it needs programming. But
machines can learn to surprise their programmers.
Lack of Consciousness (Searle) — Machines aren't conscious no matter how well they answer. But how do we
know other people are conscious?
AI researchers believe it's more important to study the underlying principles of intelligence than duplicating how
humans act.
Searle's Chinese Room
Imagine a native English speaker who knows no Chinese, locked
in a room.
He has a rule book that allows him to perfectly answer any
question in Chinese by manipulating characters.
He is so fast and good that he answers every question as well as
a native speaker.
Discussion: Does the person in the Chinese Room understand Chinese?
What implications does this have for the different definitions of AI?
Thinking Rationally: Laws of Thought
Systems that think rationally
Normative or prescriptive approach
Long history: from Aristotle to founders of AI who tried to codify "right thinking" — irrefutable
reasoning processes
Problem: not all intelligent behavior is mediated by logical deliberation
Acting Rationally
Systems that act rationally ← Our focus
Current goal: build systems that act rationally
Act rationally → Doing the "right thing"
Right thing: that which is expected to maximize goal achievement, given the available information
Modern AI Goals
Constructing intelligent machines for a wide range of applications
Formalizing knowledge and mechanizing intelligence
Using computational models to understand complex behavior
Making computers as easy to work with as people
AI and Machine Learning are NOT the
same!
There is often confusion between "AI" and "Machine Learning"
They are both useful and often complementary but are not the same!
Machine learning is a subfield of AI that studies the ability to improve performance, detect and
extrapolate patterns, based on experience
The goal of AI is to build intelligent systems that act rationally
Machine learning is a tool that is often useful in this process
Some AI systems use ML methods; some do not
Birth of AI
Dartmouth Workshop (1956) — organized by John McCarthy, who
coined the phrase "Artificial Intelligence"
Key participants:
Marvin Minsky, Allen Newell, Herbert Simon, Oliver Selfridge, Arthur
Samuel
John McCarthy
Marvin Minsky
Early breakthroughs:
Arthur Samuel — checkers program that learns
Newell & Simon — General Problem Solver; proved theorems in
Principia Mathematica
Allen Newell
Herbert Simon
The Interdisciplinary Nature of AI
Philosophy
Engineering
Psychology
Economics
Linguistics
Control Theory
Mathematics
Biology
What is the nature of mind?
How does the human brain work?
How is language acquired and interpreted?
Characterizing abilities of algorithms
How to build intelligent artifacts?
How should a rational agent behave?
How to control a dynamical system?
What can we learn from evolution?
Difficulties for Early AI
1. Combinatorial Explosion
The number of different 40-move games in chess >> atoms in the universe. Exhaustive search is impossible.
2. Common Sense
"The trophy could not fit in the suitcase. It was too big." — What does "it" refer to? These Winograd Schemas
remain challenging. (Human: 94%, Best AI: 79%)
3. Representational Inadequacy
Rosenblatt's Perceptron could learn anything it could represent — but Minsky & Papert showed it could only
represent linearly separable functions.
AI Becomes an Industry: Expert
Systems
Knowledge-based systems (1970s–1980s):
DENDRAL — Inferred molecular structure from mass spectrograms. Domain knowledge manually mapped
to if-then rules.
MYCIN — Rule-based system for medical diagnosis. Pioneered certainty factors for uncertain knowledge.
By late 1980s, hundreds of expert systems were deployed in industry.
But this created the "knowledge acquisition bottleneck" — the design and maintenance of rules proved
labor-intensive and costly. Most times the experts were not even conscious of their own rules.
Rise of Machine Learning
In the last 30 years, machine learning made rapid advances, overcoming the expert-system bottleneck:
Supervised Learning
Learn a function f from labeled examples
e.g., recognize letters, faces, objects
Reinforcement Learning
Learn a mapping from states to actions to maximize performance
e.g., number of wins in Go
Deep Learning
Uses (deep) neural networks to represent knowledge
Applied to both supervised and reinforcement learning
AI Timeline
1943–56
1956
1970s
1980s
Gestation of AI
1950: Turing Test
Birth of "AI"
Dartmouth Workshop
AI Winter
Combinatorial explosion,
funding cuts
Expert Systems
Narrower, well-defined
tasks
1990s
1997
2017
2020+
Pattern Recognition
Learning, intelligent agents
IBM Deep Blue
beats Kasparov at chess
AlphaGo
Deep RL beats world Go
champion
Large Language Models
GPT, Claude, generative AI
revolution
AlphaGo
Developed by Google DeepMind to play Go
Go originated in China over 2,500 years ago — 19×19 grid
First program to beat a professional player on a full-sized board (Oct
2015)
Beat Lee Sedol, 9-dan professional, in a five-game match (Mar 2016)
What we learn:
AI can tackle very large, complex games and discover moves that even
the best players couldn't handle
Combines deep learning with tree search
Attracted enormous attention and investment in AI
Videos: youtube.com/watch?v=uvtRWWzuybo | youtube.com/watch?v=WXHFqTvfFSw
[IMAGE]
AlphaGo game image
Generative AI
Generate text from prompts using Large Language Models
(LLMs)
Generate images given a text prompt (Large Multimodal
Models)
Voice cloning from a small sample
Image completion and editing
Video question-answering
Generating synthetic video (e.g., Sora)
Code generation and assistance
Prompt: "Draw an elephant flying
over the Grand Canyon"
Generative AI has surpassed expectations and created a new level of excitement in industry and the
general public.
Experiments with LLMs
Prompt: Write a Haiku about Mathematics
Numbers softly speak,
Patterns bloom in silent arcs—
Logic breathes in lines.
Prompt: Add a pun → "I can count on you." • Make it about
Calculus → "I derive great joy."
Prompt: Give me two odd numbers that differ by 7.
LLM response: "3 and 10 — but 10 is even. Let's pick 5 and 12 — also not both odd. 7 and 14 — no. I realize: if two
numbers differ by 7, one must be odd and the other even (because 7 is odd, and odd ± odd = even). So there are no
two odd numbers that differ by 7."
Key insight: LLMs can reason through mistakes and self-correct — but may struggle with tasks requiring precise
mathematical reasoning.
Applications in the Real World
Credit card fraud
detection
Search engines
Intelligent assistants (Siri,
Alexa)
Warehouse & delivery
robots
Disease outbreak
detection
Medical diagnosis
programs
Autonomous vehicles
Drug discovery
(AlphaFold)
Traffic violation detection
Spam filtering
Planetary rovers
Code generation
assistants
Surprises in AI Research
Hard for humans → Easy for AI
Easy for humans → Hard for AI
Chess, Checkers, Othello
Logistics planning
Airline scheduling
Fraud detection
Sorting mail
Proving theorems
Speech recognition
Face recognition
Composing music/art
Autonomous navigation
Walking
Language understanding
Crossword puzzles
Common sense reasoning
Why is AI Hard?
Many complex building blocks
Example: an autonomous robot requires vision, NLU, planning, and learning
Requires ability to handle ambiguity
Example: "Milk drinkers are turning to powder"
Computational complexity
Requires optimizing conflicting goals
Example: optimize travel time, cost, departure time for a flight
Requires reasoning under uncertainty
Missing information, probabilistic outcomes, dynamic environments
Artificial General Intelligence (AGI)
Current AI solves specific challenging tasks at human level — each task (driving, translation, vision)
is solved by a distinct specialized system
Artificial General Intelligence (AGI) — systems that solve ALL tasks, from washing dishes to
scientific discovery, at human level or better
Artificial Superhuman Intelligence (ASI) — superhuman performance at all tasks
Discussion: Some researchers predict AGI will be achieved in ~5 years, building on current
technology. Others give a much longer timeline. What do you think?
Intelligent versus Automated
Automated: run within well-defined parameters; very restricted in what tasks they can perform
Intelligent (Autonomous): self-governing; adapts to changes in the environment
System
Classification
ATM
Automated
Disease outbreak detection
Intelligent
Kettle with automatic shut-off
Automated
Self-driving cars
Intelligent
Pre-programmed warehouse robots
Automated
Robots sorting boxes by content
Intelligent
Types of Intelligent Decisions
Single-Step Decisions
Sequential Decisions
Deciding the "best" action for a given input
Deciding which action to select considering future
actions
Output does not affect future input
Examples:
Prediction, Classification
Which lottery to buy?
Answering a query
Current output affects future inputs
Examples:
Game playing
Robot navigation
Resource allocation
This course covers techniques for both — from search and planning to probabilistic reasoning and reinforcement
learning.
Summary
AI can be defined in four ways: Think Humanly, Act Humanly, Think Rationally, Act Rationally
AI has undergone many stages: Early optimism → difficulties → expert systems → machine
learning breakthroughs → deep learning → generative AI
Generative AI has surpassed expectations and created transformative potential in medicine,
engineering, sciences, and many other fields
Key distinctions: AI ≠ ML • Intelligent ≠ Automated • AGI vs. Narrow AI
Intelligent decisions: Single-step (classification, prediction) vs. Sequential (planning, game playing)
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