Preface xv Part I The History of AI 1 1956: The Dartmouth Conference, Where It All Began 1 But Was AI Really Invented in 1956? 2 The Winter of AI: The First One 3 The Expert Systems: AI Is Back! 4 The First "Defeat" of Man Against AI 5 The Rise of Statistical AIs and Machine Learning 5 One of the First Image Recognizers in History 6 The Second "Defeat" of Man Against AI 8 Data: A Major Challenge 9 Tay: The AI That Went Awry 10 "Autonomous" Cars 12 What About GenAI? 16 GenAI for All of Us 18 The "True" AI 18 Part II Is GenAI the Holy Grail of Technology? 21 A Revolution, but Not the One You Think 22 Good Choice of Words This Time 23 The Gartner Hype Cycle at Full Speed! 25 What Are the Concrete Applications for GenAI? 27 Weakness #1: Hallucinations 28 The One Prompt Too Many for Steven Schwartz 28 My Always Evolving Bio 29 Weakness #2: Lack of Accuracy 30 Does Being Wrong One Third of the Time Really Matter? 30 Weakness #3: They Can''t Think 31 Weakness #4: Jailbreaking--A Security Breach 33 What to Make of the Jailbreaking Story? 38 Solution #1: Fine-Tuning and RAG 39 Solution #2: Use the Data We Own and/or Trust 40 Is Data Theft Inherent on Training GenAI? 40 A Marketing Argument 43 Are AIs More and More Stupid? 44 Solution #3: Watermarking Is the Savior 45 Is It Possible to Watermark Text? 46 An AI to Control Another AI: Is It a Good Idea? 47 Solution #4: Open Source--A Source of Creativity 49 Solution #5: Small Language Models and Edge Computing 51 Solution #6: Hybrid AI and the End of GenAI 53 AGI Is Still an Inaccessible Dream 53 Part III The Seven Myths of AI 55 Myth Number 0(Riginal) 56 AI Is Creative 56 What''s Creativity Anyway? 56 There Is "Create" and "create" 57 Can''t We Be Creative with an AI? 58 The Two Dimensions of Creativity 60 AI Will Never Innovate 61 Then AIs Are Useless? 62 Are AIs and Auto-Tune the Same? 63 Myth Number 1: AI Understands What It''s Telling You and Can Reflect Upon It 63 Cognitive Bias Plays Tricks on Us 64 The Characteristics of Intelligence 65 Is AI Really Better Than Humans at Recognizing Speech? 66 Are Humans Really Better Than AI at Understanding Language? 67 The Three Phases of Natural Language Understanding 67 AI Invents a New Language: Too Smart for a Human to Understand? 71 The True Failure of AI 73 Are AIs Better Than Humans at Communicating with Humans? 74 Myth Number 2: AIs Are Inexplicable Black Boxes 75 The Story of Gaston Julia and Fractals 76 The Unexpected and the Inexplicable: The Reason for the Black Box Myth 77 The Three Sources of the Unexpected 78 Example: The Screw-Driving Robot from Tesla 79 Is Explicability an Achievable Goal? 80 Myth Number 3: AI Is Going to Kill Us All 81 Terminator, by James Cameron 82 Avengers: Age of Ultron, by Joss Whedon 83 So, Killer AIs Do Not Exist? 84 Easter Eggs: The Poisoned Chalice of AI 85 The Story of the American Killer Drone 86 Scoop: Socrates Explains Why AIs Aren''t Yet Intelligent 87 GenAIs Need Humans 88 Myth Number 4: AI Is Objective 88 Color Blindness: An Example of a Subjective World 89 War: The Quintessence of Subjectivity 90 Biases Are Inherent in AI 90 AIs: Tools for Profit 91 The Ultimate Proof of AI''s Subjectivity 92 Myth Number 5: AI Will Lead to a Widespread Job Loss 93 There Have Been Other Revolutions Before AI 93 Innovation: Less Lethal Than We Think 94 Will Gen AIs Soon Be Executives'' Only Staff? 94 The Two Levels of AI''s Mastery 95 What Are the Consequences for the Organization of Companies? 96 AI: An Asset for Employability Rather Than a Hindrance 97 AI: The Least Job-Destroying Revolution 98 Myth Number 6: AI Can Learn Anything (Acquired versus Innate) 98 The Foundation of the Discussion 99 Descartes and the Beginning of Reconciliation 100 The Age of Enlightenment and the End of the Debate 100 And What About AI in All This? 101 Is Incomplete AI an Issue? 102 Myth Number 7: AI Cares and Does Everything Right 102 Ethics and Its Multiple Dimensions 102 Are GenAIs Built to Be Unethical? 103 Who''s Really in Charge of AI''s Ethics? 104 Is a Hammer Ethical? 105 Part IV What AI Will Change in Our Lives 107 The Ecological Disaster of GenAI 108 The Three Energy-Hungry Activities 108 ChatGPT: Pandora''s Box of AI 112 Drying Up Humanity to Feed the Machine? 114 How to Meet AI Resource Demands 115 Data Centers: The Sine Qua Non Condition for GenAI 116 Some Shocking Figures on Data Centers 117 Greed Is a Bad Thing 118 There Is No Plan(et) B 120 AI Washing and the Gold Rush of GenAI 120 Getting Rich Thanks to AI or by Lying About It 121 AI Washing: Out of Greed or Fear? 122 The Tortoise and the Hare of GenAI 124 But What Does the AI Police Do? 126 "Out of Sight, Out of Mind" or Paying the Full Price 128 AI Forcing and the Obsolescence of Classic AI 130 The End of the GenAI Bubble? 132 Banning AI: A Necessary Evil? 133 Regulate, Yes; Ban, No 134 GDPR''s Shortcoming 135 What Does the AI Act Look Like in Detail? 137 Allow Technology but Ban Some Applications 140 Data Theft and AI 141 Who Is Really Responsible for AI-Generated Content? 143 AI and the Data Industry 145 Regulation''s Big Miss: The Ecology 147 Malicious Uses of GenAI 147 Fake News 148 What About Deepfakes? 149 Emotion Detectors 151 Hacking and Cybersecurity 152 What About GenAI? 153 A Quick Discussion on Quantum Computers 153 The Hidden Flaws of GenAI 154 Being Manipulated Without Realizing It 154 Cultures Cancelling 155 Evolution of Teaching and Working Methods 156 AI and Teaching: An Explosive Cocktail? 156 Is AI Going to Perform Tasks on Our Behalf? 157 An AI Monopoly? 157 Part V Our Future with AI 159 Solution #1: Make AIs More Frugal 159 Solution #2: Regulate to Encourage Less Consumption 160 Solution #3: Limit Unnecessary Uses 161 AI and IoT 161 Conclusion 163 About the Author 165 Index 167.