Zero to AI: A non-technical, hype-free guide to prospering in the AI era

by Nicolò Valigi, Gianluca Mauro

Overview

Summary
How can artificial intelligence transform your business? In Zero to AI, you’ll explore a variety of practical AI applications you can use to improve customer experiences, optimize marketing, help you cut costs, and more. In this engaging guide written for business leaders and technology pros alike, authors and AI experts Nicolò Valigi and Gianluca Mauro use fascinating projects, hands-on activities, and real-world explanations to make it clear how your business can benefit from AI.

Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.

About the technology
There’s no doubt that artificial intelligence has made some impressive headlines recently, from besting chess and Go grand masters to producing uncanny deep fakes that blur the lines of reality. But what can AI do for you? If you want to understand how AI will impact your business before you invest your time and money, this book is for you.

About the book
Zero to AI uses clear examples and jargon-free explanations to show the practical benefits of AI. Each chapter explores a real-world case study demonstrating how companies like Google and Netflix use AI to shape their industries. You begin at the beginning, with a primer on core AI concepts and realistic business outcomes. To help you prepare for the transition, the book breaks down a successful AI implementation, including advice on hiring the right team and making decisions about resources, risks, and costs.

What's inside

Identifying where AI can help your organization
Designing an AI strategy
Evaluating project scope and business impact
Using AI to boost conversion rates, curate content, and analyze feedback
Understanding how modern AI works and what it can/can’t do

About the reader
For anyone who wants to gain an understanding of practical artificial intelligence and learn how to design and develop projects with high business impact.

About the author
Gianluca Mauro and Nicolò Valigi are the cofounders of AI Academy, a company specializing in AI trainings and consulting.

Table of Contents:

1. An introduction to artificial intelligence

PART 1 - UNDERSTANDING AI

2. Artificial intelligence for core business data

3. AI for sales and marketing

4. AI for media

5. AI for natural language

6. AI for content curation and community building

PART 2 - BUILDING AI

7. Ready—finding AI opportunities

8. Set—preparing data, technology, and people

9. Go—AI implementation strategy

10. What lies ahead

Product Details

ISBN-13:	9781617296062
Publisher:	Manning
Publication date:	05/19/2020
Edition description:	1st Edition
Pages:	264
Product dimensions:	7.30(w) x 9.20(h) x 0.40(d)

About the Author

Nicolò Valigi is a Software Engineer that enjoys making intelligent robots. He has an MBA and also won a Fulbright scholarship for Entrepreneurship. He writes about technology and AI, and is a regular speaker at international conferences.

Gianluca Mauro is a Fulbright Scholar, and an independent consultant who has shared his expertise on innovation and data science with companies ranging from idea-phase startups to multinational corporations. He has been a speaker at venues including universities, multinational corporations, and industry conferences.

Table of Contents

Preface vii

Acknowledgments xv

About this book xvii

About the authors xxi

1 An introduction to artificial intelligence 1

1.1 The path to modern AI 2

1.2 The engine of the AI revolution: machine learning 4

1.3 What is artificial intelligence, after all? 6

1.4 Our teaching method 8

Part 1 Understanding AI 11

2 Artificial intelligence for core business data 13

2.1 Unleashing AI on core business data 14

2.2 Using AI with core business data 15

The real estate marketplace example 16

Adding AI capabilities to FutureHouse 18

The machine learning advantage 22

Applying AI to general core business data 24

2.3 Case studies 25

How Google used AI to cut its energy bill 25

How Square used AI to lend billions to small businesses 29

Case studies lessons 32

2.4 Evaluating performance and risk 33

3 AI for sales and marketing 36

3.1 Why AI for sales and marketing 37

3.2 Predicting churning customers 38

3.3 Using AI to boost conversion rates and upselling 42

3.4 Performing automated customer segmentation 44

Unsupervised learning (or clustering) 45

Unsupervised learning for customer segmentation 49

3.5 Measuring performance 52

Classification algorithms 52

Clustering algorithms 55

3.6 Tying ML metrics to business outcomes and risks 56

3.7 Case studies 58

AI to refine targeting and positioning: Opower 58

AI to anticipate customer needs: Target 64

4 AI for media 68

4.1 Improving products with computer vision 69

4.2 Using AI for image classification: deep learning? 73

4.3 Using transfer learning with small datasets 76

4.4 Face recognition: teaching computers to recognize people 78

4.5 Using content generation and style transfer 81

4.6 What to watch out for 83

4.7 AI for audio 84

4.8 Case study: optimizing agriculture with deep learning 85

Case questions 88

Case discussion 88

5 AI for natural language 91

5.1 The allure of natural language understanding 92

5.2 Breaking down NLP: measuring complexity 93

5.3 Adding NLP capabilities to your organization 96

Sentiment analysis 99

From sentiment analysis to text classification 102

Scoping a NLP classification project 105

Document search 106

Natural conversation 108

Designing products that overcome technology limitations 111

5.4 Case study: Translated 113

Case questions 116

Case discussion 116

6 AI for content curation and community building 119

6.1 The curse of choice 120

6.2 Driving engagement with recommender systems 120

Content-based systems beyond simple features 124

The limitations of features and similarity 126

6.3 The wisdom of crowds: collaborative filtering 127

6.4 Recommendations gone wrong 129

The recommender system dream 130

6.5 Case study: Netflix saves $1 billion a year 131

Netflix's recommender system 131

Recommendations and user experience 133

The business value of recommendations 134

Case questions 135

Case discussion 135

Part 2 Building AI 137

7 Ready-finding AI opportunities 139

7.1 Don't fall for the hype: Business-driven AI innovation 140

7.2 Invention: Scouting for AI opportunities 144

7.3 Prioritization: Evaluating AI projects 146

7.4 Validation: Analyzing risks 150

7.5 Deconstructing an AI product 152

7.6 Translating an AI project into ML-friendly terms 157

7.7 Exercises 161

Improving customer targeting 161

Automating industrial processes 163

Helping customers choose content 164

8 Set-preparing data, technology, and people 167

8.1 Data strategy 168

Where do I get data? 169

How much data do I need? 173

8.2 Data quality 177

8.3 Recruiting an AI team 180

9 Go-AI implementation strategy 185

9.1 Buying or building AI 186

The Buy option: Turnkey solutions 186

The Borrow option: ML platforms 188

The Build option: Roll up your sleeves 190

9.2 Using the Lean Strategy 192

Starting from Buy solutions 193

Moving up to Borrow solutions 195

Doing things yourself: Build solutions 195

9.3 Understanding the virtuous cycle of AI 197

9.4 Managing AI projects 202

9.5 When AI fails 204

Anki 204

Lighthouse AI 205

IBM Watson in Oncology 205

Emotional diary 207

Angry phone calls 208

Underperforming sales 208

10 What lies ahead 210

10.1 How AI threatens society 211

Bias and fairness 211

AI and jobs 214

The AI filter bubble 215

When AI fails: Corner cases and adversarial attacks 217

When the artificial looks real: AI-generated fake content 219

10.2 Opportunities for AI in society 220

Democratization of technology 220

Massive scale 221

10.3 Opportunities for AI in industries 222

Social media networks 223

Health care 224

Energy 226

Manufacturing 228

Finance 229

Education 230

10.4 What about general AI? 231

10.5 Closing thoughts 232

Index 235