Artificial Intelligence (AI) fascinates, challenges and disturbs us. There are many voices in society that predict drastic changes that may come as a consequence of AI – a possible apocalypse or Eden on earth. However, only a few people truly understand what AI is, what it can do and what its limitations are.
Understanding Artificial Intelligence explains, through a straightforward narrative and amusing illustrations, how AI works. It is written for a non-specialist reader, adult or adolescent, who is interested in AI but is missing the key to understanding how it works. The author demystifies the creation of the so-called "intelligent" machine and explains the different methods that are used in AI. It presents new possibilities offered by algorithms and the difficulties that researchers, engineers and users face when building and using such algorithms. Each chapter allows the reader to discover a new aspect of AI and to become fully aware of the possibilities offered by this rich field.
Author(s): Nicolas Sabouret
Edition: 1
Publisher: CRC Press
Year: 2020
Language: English
Commentary: Vector PDF
Pages: 174
City: Boca Raton, FL
Tags: Artificial Intelligence; Machine Learning; Algorithms; Neural Networks; Deep Learning; Reinforcement Learning; Popular Science; Graph Algorithms; Exploitation; Strong AI; Elementary; General Artifical Intelligence
Cover
Half Title
Title Page
Copyright Page
Dedication
Table of Contents
Introduction
1. What is Artificial Intelligence?: Understanding What a Computer, an Algorithm, a Program, and, in Particular, an Artificial Intelligence Program Are
Computer Science and Computers
Computers and Algorithms
Algorithms and Computer Science
The All-Purpose Machine
Programs that Make Programs
And Where Does Artificial Intelligence Fit in All This?
A Machine That Learns?
Obey, I Say!
Attention: One Program Can Conceal Another!
So, What Is AI?
Reference
2. The Turing Test: Understanding That It Is Difficult to Measure the Intelligence of Computers
What Is Intelligence?
A Test, But Which One?
And Then There Was Turing!
It Chats...
A Chatbot Named Eliza
It's Controversial Too!
My Computer and I Are Not Alike!
3. Why Is It So Difficult?: Understanding There Are Limitations to What a Machine Can Calculate
Limitations of Computers
A Real Headache
Counting Operations
This Is Getting Complex
Squaring the Circle?
4. Lost in the Woods: Understanding a First Principles of AI Method –Exploration
A Small Walk in Paris
How a GPS Works
Finding the Path
It Is More Difficult Than It Looks
The Adventurers of the Lost Graph
So Easy!
It's a Win-Win
Computer Science in Three Questions
A Clever Algorithm
AI Is Here!
A Head in the Stars
The Best Is the Enemy of the Good
Roughly Speaking, It's That Way!
Follow the Crows
There's a Trick!
5. Winning at Chess: Understanding How to Build Heuristics
A Short History of AI
A New Challenge
An Old Algorithm
From Theory to Practice
Another Limit to Computers?
Checkmate, von Neumann!
The Return of Minimax
A Heuristic to Win at Chess
Easier Said Than Done
A Disappointing Victory
6. The Journey Continues: Understanding That One Graph Can Conceal Another
On the Intelligence of Machines
A Very Particular Traveler
A Healthy Walk
A Really Difficult Problem...
The Greedy Algorithm
At Least, It's Fast!
Come On, We Can Do Better!
The Solution Space
One Graph Can Conceal Another!
Space Exploration
7. Darwin 2.0: Understanding How to Explore Space as a Group
Natural Selection
Computing Herds
You've Got to Start Somewhere
Two Beautiful Children!
A Little More Randomness
So, What Is This Good For?
This Doesn't Solve Everything, However
8. Small but Strong: Understanding How Ants Find Their Way
Multi-Agent Systems
The Algorithm and the Ant
Just Like in the Ant Colony
Calculating a Path With Ants
The More the Merrier!
The Solution Emerges
The Solution to All Our Problems?
9. A Bit of Tidying Up: Understanding How a Machine Learns to Classify
Find the Odd One Out!
From Animals to Genes
Let's Do Some Sorting
It's All a Matter of Distance
Starting Over Again and Again
There Is No Right Solution
To Each Its Own Method
So, Where's the Learning in All This?
Joy and Good Humor!
10. Taking an AI by the Hand: Understanding That a Good Example Is Often Better Than a Long Explanation
Ask the Program!
From Data to Programs
An Example Is Better Than a Long Explanation
The Adventure Begins
Much Ado About Nothing?
From Image to Data
How About We Take the Train?
It's Logic!
Tell Me What You Read, and I'll Tell You Who You Are
From Symbolic to Numeric
11. Learning to Count: Understanding What Statistical Learning Is
New and Old Alike?
Riddle Me This
Trees Again
A Bit of Prediction
Lots of Examples, Lots of Variables
Too Much, It's Too Much!
The Kitties Return
The Mathematics of Artificial Intelligence
Too Much Class!
Keep Straight
The SVM Strikes Back
Intelligent, Did You Say Intelligent?
Careful, Don't Swallow the Kernel!
12. Learning to Read: Understanding What a Neural Network Is
Draw Me a Neuron
More Triangles
Just a Little Fine Tuning
A Long Learning Process
Where Did My Brain Go?
One More Layer!
Network Success
One More Small Break
13. Learning as You Draw: Understanding What Deep Learning Is
Are Video Games Bad for Your Health?
Parallel Computing
The Return of the Neural Networks
Why Add More?
The Crux of the Problem
The Achievements of Deep Learning
Watch Out for Tricks!
To Each His Own Method
14. Winning at Go: Understanding What Reinforcement Learning Is
The Quest for the Grail
A Bit of Calculation
Want to Play Again?
Red, Odd, and Low!
No Need To Be Stubborn
A Bit of Reinforcement
Stronger Than Ever!
Step by Step
Playing Without Risk
Give Me the Data!
15. Strong AI: Understanding AI's Limitations
Strong AI
General Intelligence
Artificial Consciousness
An Uncertain Future
How Far Can We Go?
16. Is There Cause for Concern?: Understanding That AI Can Be Misused
Autonomous Systems?
Misuse of AI
AI Serving People
Explaining, Always Explaining
17. To Infinity and Beyond!: Understanding That AI Has Good Days Ahead
Where Are We Going?
Doing Like Humans?
Even More AI!
James Allen
John McCarthy and Patrick Hayes
Richard Fikes, Nils Nilsson, and Drew McDermott
Christopher Watkins, Leslie Kaelbling, and Michael Littman
Robert Kowalski, Alain Colmerauer, and Philippe Roussel
Edward Feigenbaum
Lotfi Zadeh
Allen Newell
Judea Pearl
Richard Richens, John Sowa, and Ronald Brachman
Deborah McGuinness
Acknowledgments
They made AI
