Details

Artificial Intelligence and Big Data


Artificial Intelligence and Big Data

The Birth of a New Intelligence
1. Aufl.

von: Fernando Iafrate

139,99 €

Verlag: Wiley
Format: EPUB
Veröffentl.: 14.02.2018
ISBN/EAN: 9781119489245
Sprache: englisch
Anzahl Seiten: 162

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Beschreibungen

<p> 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.</p> <p> </p>
<p>List of Figures ix</p> <p>Preface xiii</p> <p>Introduction xxi</p> <p><b>Chapter 1. What is Intelligence? 1</b></p> <p>1.1. Intelligence 1</p> <p>1.2. Business Intelligence 2</p> <p>1.3. Artificial Intelligence 5</p> <p>1.4. How BI has developed 6</p> <p>1.4.1. BI 1.0 7</p> <p>1.4.2. BI 2.0 8</p> <p>1.4.3. And beyond 11</p> <p><b>Chapter 2. Digital Learning 13</b></p> <p>2.1. What is learning? 13</p> <p>2.2. Digital learning 14</p> <p>2.3. The Internet has changed the game 16</p> <p>2.4. Big Data and the Internet of Things will reshuffle the cards 18</p> <p>2.5. Artificial Intelligence linked to Big Data will undoubtedly be the keystone of digital learning 21</p> <p>2.6. Supervised learning 22</p> <p>2.7. Enhanced supervised learning 24</p> <p>2.8. Unsupervised learning 28</p> <p><b>Chapter 3. The Reign of Algorithms 33</b></p> <p>3.1. What is an algorithm? 34</p> <p>3.2. A brief history of AI 34</p> <p>3.2.1. Between the 1940s and 1950s 35</p> <p>3.2.2. Beginning of the 1960s 36</p> <p>3.2.3. The 1970s 37</p> <p>3.2.4. The 1980s 37</p> <p>3.2.5. The 1990s 38</p> <p>3.2.6. The 2000s 38</p> <p>3.3. Algorithms are based on neural networks, but what does this mean? 39</p> <p>3.4. Why do Big Data and AI work so well together? 42</p> <p><b>Chapter 4. Uses for Artificial Intelligence 47</b></p> <p>4.1. Customer experience management 48</p> <p>4.1.1. What role have smartphones and tablets played in this relationship? 50</p> <p>4.1.2. CXM is more than just a software package 51</p> <p>4.1.3. Components of CXM 53</p> <p>4.2. The transport industry 55</p> <p>4.3. The medical industry 58</p> <p>4.4. “Smart” personal assistant (or agent) 60</p> <p>4.5. Image and sound recognition 62</p> <p>4.6. Recommendation tools 65</p> <p>4.6.1. Collaborative filtering (a “collaborative” recommendation mode) 66</p> <p>Conclusion 71</p> <p>Appendices 75</p> <p>Appendix 1. Big Data 77</p> <p>Appendix 2. Smart Data 83</p> <p>Appendix 3. Data Lakes 89</p> <p>Appendix 4. Some Vocabulary Relevant to 93</p> <p>Appendix 5. Comparison Between Machine Learning and Traditional Business Intelligence 101</p> <p>Appendix 6. Conceptual Outline of the Steps Required to Implement a Customization Solution <br />based on Machine Learning 103</p> <p>Bibliography 107</p> <p>Glossary 111</p> <p>Index 115</p>
<strong>Fernando Iafrate</strong> 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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