With the idea of ‘deep learning’ having now become the key to this new generation of solutions, major technological players in the business intelligence sector have taken an interest in the application of Big Data. In this book, the author explores the recent technological advances associated with digitized data flows, which have recently opened up new horizons for AI. The reader will gain insight into some of the areas of application of Big Data in AI, including robotics, home automation, health, security, image recognition and natural language processing.
Inhoudsopgave
List of Figures ix
Preface xiii
Introduction xxi
Chapter 1. What is Intelligence? 1
1.1. Intelligence 1
1.2. Business Intelligence 2
1.3. Artificial Intelligence 5
1.4. How BI has developed 6
1.4.1. BI 1.0 7
1.4.2. BI 2.0 8
1.4.3. And beyond 11
Chapter 2. Digital Learning 13
2.1. What is learning? 13
2.2. Digital learning 14
2.3. The Internet has changed the game 16
2.4. Big Data and the Internet of Things will reshuffle the cards 18
2.5. Artificial Intelligence linked to Big Data will undoubtedly be the keystone of digital learning 21
2.6. Supervised learning 22
2.7. Enhanced supervised learning 24
2.8. Unsupervised learning 28
Chapter 3. The Reign of Algorithms 33
3.1. What is an algorithm? 34
3.2. A brief history of AI 34
3.2.1. Between the 1940s and 1950s 35
3.2.2. Beginning of the 1960s 36
3.2.3. The 1970s 37
3.2.4. The 1980s 37
3.2.5. The 1990s 38
3.2.6. The 2000s 38
3.3. Algorithms are based on neural networks, but what does this mean? 39
3.4. Why do Big Data and AI work so well together? 42
Chapter 4. Uses for Artificial Intelligence 47
4.1. Customer experience management 48
4.1.1. What role have smartphones and tablets played in this relationship? 50
4.1.2. CXM is more than just a software package 51
4.1.3. Components of CXM 53
4.2. The transport industry 55
4.3. The medical industry 58
4.4. ‘Smart’ personal assistant (or agent) 60
4.5. Image and sound recognition 62
4.6. Recommendation tools 65
4.6.1. Collaborative filtering (a ‘collaborative’ recommendation mode) 66
Conclusion 71
Appendices 75
Appendix 1. Big Data 77
Appendix 2. Smart Data 83
Appendix 3. Data Lakes 89
Appendix 4. Some Vocabulary Relevant to 93
Appendix 5. Comparison Between Machine Learning and Traditional Business Intelligence 101
Appendix 6. Conceptual Outline of the Steps Required to Implement a Customization Solution
based on Machine Learning 103
Bibliography 107
Glossary 111
Index 115
Over de auteur
Fernando Iafrate is head of the Business Intelligence department at Disneyland Paris, France. He is also a lecturer at Paris Dauphine University, Electronic Business Group and Grenoble School of Management in France, and at Vlerick Leuven Gent Management School in Belgium.
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