Artificial Intelligence for Business

Foreword xv

Preface xix

PART I: Thinking Machines: An Overview of Artificial Intelligence 1

Chapter 1: What Is Artificial Intelligence? 3

What Is Intelligence? 4

Testing Machine Intelligence 6

The General Problem Solver 8

Strong and Weak Artificial Intelligence 11

Artificial Intelligence Planning 14

Learning over Memorizing 15

Chapter Takeaways 18

Chapter 2: The Rise of Machine Learning 19

Practical Applications of Machine Learning 22

Artificial Neural Networks 24

The Fall and Rise of the Perceptron 27

Big Data Arrives 30

Chapter Takeaways 33

Chapter 3: Zeroing in on the Best Approach 35

Expert System Versus Machine Learning 35

Supervised Versus Unsupervised Learning 37

Backpropagation of Errors 38

Regression Analysis 41

Chapter Takeaways 43

Chapter 4: Common AI Applications 45

Intelligent Robots 45

Natural Language Processing 48

The Internet of Things 50

Chapter Takeaways 51

Chapter 5: Putting AI to Work on Big Data 53

Understanding the Concept of Big Data 54

Teaming Up with a Data Scientist 54

Machine Learning and Data Mining: What's the Difference? 55

Making the Leap from Data Mining to Machine Learning 56

Taking the Right Approach 57

Chapter Takeaways 59

Chapter 6: Weighing Your Options 61

Chapter Takeaways 64

PART II: Machine Learning 65

Chapter 7: What Is Machine Learning? 67

How a Machine Learns 71

Working with Data 74

Applying Machine Learning 77

Different Types of Learning 79

Chapter Takeaways 81

Chapter 8: Different Ways a Machine Learns 83

Supervised Machine Learning 83

Unsupervised Machine Learning 86

Semi-Supervised Machine Learning 89

Reinforcement Learning 91

Chapter Takeaways 93

Chapter 9: Popular Machine Learning Algorithms 95

Decision Trees 99

k-Nearest Neighbor 101

k-Means Clustering 104

Regression Analysis 108

Naive Bayes 110

Chapter Takeaways 113

Chapter 10: Applying Machine Learning Algorithms 115

Fitting the Model to Your Data 119

Choosing Algorithms 120

Ensemble Modeling 121

Deciding on a Machine Learning Approach 123

Chapter Takeaways 124

Chapter 11: Words of Advice 125

Start Asking Questions 125

Don't Mix Training Data with Test Data 127

Don't Overstate a Model's Accuracy 127

Know Your Algorithms 128

Chapter Takeaways 128

PART III: Artificial Neural Networks 129

Chapter 12: What Are Artificial Neural Networks? 131

Why the Brain Analogy? 133

Just Another Amazing Algorithm 133

Getting to Know the Perceptron 135

Squeezing Down a Sigmoid Neuron 138

Adding Bias 141

Chapter Takeaways 142

Chapter 13: Artificial Neural Networks in Action 143

Feeding Data into the Network 143

What Goes on in the Hidden Layers 145

Understanding Activation Functions 149

Adding Weights 151

Adding Bias 152

Chapter Takeaways 153

Chapter 14: Letting Your Network Learn 155

Starting with Random Weights and Biases 156

Making Your Network Pay for Its Mistakes: The Cost Function 157

Combining the Cost Function with Gradient Descent 158

Using Backpropagation to Correct for Errors 160

Tuning Your Network 163

Employing the Chain Rule 164

Batching the Data Set with Stochastic Gradient Descent 166

Chapter Takeaways 167

Chapter 15: Using Neural Networks to Classify or Cluster 169

Solving Classification Problems 170

Solving Clustering Problems 172

Chapter Takeaways 174

Chapter 16: Key Challenges 175

Obtaining Enough Quality Data 175

Keeping Training and Test Data Separate 176

Carefully Choosing Your Training Data 177

Taking an Exploratory Approach 177

Choosing the Right Tool for the Job 178

Chapter Takeaways 178

PART IV: Putting Artificial Intelligence to Work 179

Chapter 17: Harnessing the Power of Natural Language Processing 181

Extracting Meaning from Text and Speech with NLU 183

Delivering Sensible Responses with NLG 184

Automating Customer Service 186

Reviewing the Top NLP Tools and Resources 187

NLU Tools 189

NLG Tools 190

Chapter Takeaways 191

Chapter 18: Automating Customer Interactions 193

Choosing Natural Language Technologies 195

Review the Top Tools for Creating Chatbots and Virtual Agents 196

Chapter Takeaways 198

Chapter 19: Improving Data-Based Decision-Making 199

Choosing Between Automated and Intuitive Decision-Making 201

Gathering Data in Real Time from IoT Devices 202

Reviewing Automated Decision-Making Tools 204

Chapter Takeaways 205

Chapter 20: Using Machine Learning to Predict Events and Outcomes 207

Machine Learning Is Really about Labeling Data 208

Looking at What Machine Learning Can Do 210

Predict What Customers Will Buy 210

Answer Questions Before They're Asked 210

Make Better Decisions Faster 212

Replicate Expertise in Your Business 213

Use Your Power for Good, Not Evil: Machine Learning Ethics 214

Review the Top Machine Learning Tools 216

Chapter Takeaways 218

Chapter 21: Building Artificial Minds 219

Separating Intelligence from Automation 221

Adding Layers for Deep Learning 222

Considering Applications for Artificial Neural Networks 223

Classifying Your Best Customers 224

Recommending Store Layouts 225

Analyzing and Tracking Biometrics 226

Reviewing the Top Deep Learning Tools 228

Chapter Takeaways 229

Index 231