INDEX
INTRODUCTION, DEFINITION AND HISTORY. 16
INTRODUCTION.. 16
DEFINITION.. 16
HISTORY. 20
CHAPTER 1. 36
THEORETICAL FOUNDATIONS: 36
1.1. DATA. 37
1.1.1. TEXTUAL DATA. 39
1.2. LOGIC. 41
1.2.1. MACHINE LEARNING.. 44
1.2.2. KNOWLEDGE REPRESENTATION.. 44
1.2.3. AUTOMATED REASONING.. 45
1.2.4. NATURAL LANGUAGE PROCESSING.. 46
1.3. PROBABILITY. 48
1.3.1. BAYESIAN NETWORK. 48
1.3.2. MACHINE LEARNING.. 49
1.3.3. KNOWLEDGE REPRESENTATION.. 50
1.3.4. CLASSICAL PROBABILITY: 52
1.3.5. CONDITIONAL PROBABILITY: 53
1.3.6. BAYESIAN PROBABILITY: 54
1.3.7. FREQUENTIST PROBABILITY. 56
1.3.8. CLASSIFICATION: 58
1.3.9. REGRESSION.. 58
1.3.10. GENERATIVE MODELING.. 58
1.3.11. BAYESIAN INFERENCE. 58
1.4. KNOWLEDGE REPRESENTATION.. 64
1.5. LINEAR ALGEBRA IN AI 66
1.5.1. LINEAR REGRESSION.. 66
1.5.2. CLASSIFICATION.. 67
1.5.3. DIMENSIONALITY REDUCTION.. 68
1.5.4. IMAGE RECOGNITION.. 70
1.5.5. LINEAR ALGEBRA IN THE CONTEXT OF NATURAL LANGUAGE PROCESSING (NLP) 71
1.5.6. SPEECH RECOGNITION.. 72
1.5.7. MACHINE LEARNING.. 74
1.5.8. NEURAL NETWORKS. 75
1.5.9. RECOMMENDER SYSTEMS. 76
1.5.10. SOCIAL NETWORK ANALYSIS. 77
1.5.11. TIME SERIES ANALYSIS. 79
1.5.12. MEDICAL IMAGE ANALYSIS. 80
1.5.13. SIMULATION OF COMPLEX SYSTEMS. 81
1.6. CALCULUS, OR “COMPUTATION” IN AI 82
1.6.1. NUMERICAL COMPUTATION.. 82
1.6.2. SYMBOLIC COMPUTATION.. 83
1.6.3. STATISTICAL COMPUTATION.. 83
1.6.4. OPTIMIZATION.. 83
1.6.5. PARALLEL COMPUTATION.. 84
1.6.6. MACHINE LEARNING ALGORITHMS. 84
1.6.7. CLASSIFICATION.. 84
1.6.8. REGRESSION.. 86
1.6.9. CLUSTERING.. 87
1.6.10. DIMENSIONALITY REDUCTION.. 88
1.6.11. NATURAL LANGUAGE PROCESSING (NLP) 90
1.6.12. COMPUTER VISION.. 91
1.6.13. COMPUTATION AND NEURAL NETWORKS. 92
1.7. PROGRAMMING LANGUAGES. 93
1.7.1. PYTHON.. 94
1.7.2. R. 94
1.7.3. JAVA. 94
1.7.4. MATLAB. 94
1.7.5. LISP. 95
1.7.6. TENSORFLOW... 95
1.7.7. KERAS. 95
1.7.8. PYTORCH. 95
1.7.9. SCIKIT-LEARN.. 96
1.7.10. OPENCV. 96
CHAPTER 2. 101
MACHINE LEARNING: Supervised and Unsupervised Learning Algorithms, Decision Trees, Neural Networks, And Support Vector Machines. 101
2.1. INTRODUCTION.. 102
2.2. PRINCIPLES OF MACHINE LEARNING.. 102
2.2.1. CLASSIFICATION.. 102
2.2.2. REGRESSION.. 102
2.2.3. CLUSTERING.. 103
2.3. TYPES OF MACHINE LEARNING.. 103
2.3.1. SUPERVISED LEARNING.. 103
2.3.1.1. VOICE RECOGNITION.. 106
2.3.1.2. AI VOICE RECOGNITION AND NATURAL LANGUAGE PROCESSING 107
2.3.1.3. CHALLENGES IN USING AI FOR VOICE RECOGNITION.. 110
2.3.2. FACIAL RECOGNITION AND SUPERVISED LEARNING.. 111
2.3.3. NON SUPERVISED LEARNING.. 119
2.3.4. REINFORCEMENT LEARNING.. 120
2.3.5. DECISION TREES: 122
2.3.6. NEURAL NETWORKS. 129
2.3.6.1. CONVOLUTIONAL NEURAL NETWORKS (CNNS) 130
2.3.6.2. RECURRENT NEURAL NETWORKS (RNNS) 131
2.3.6.3. GENERATIVE NEURAL NETWORKS (GANS). 132
2.3.7. SUPPORT VECTOR MACHINES (SVM) 136
2.3.7.1. COMPANIES INVOLVED IN SVM DEVELOPMENT. 144
2.4. UNIVERSITIES OFFERING MACHINE LEARNING COURSES: 145
CHAPTER 3. 150
IMAGE AND SPEECH RECOGNITION, DEEP LEARNING: Recognition Techniques, Computer Vision, and Natural Language Processing. 150
3.1. TECHNIQUES FOR IMAGE AND SPEECH RECOGNITION.. 151
3.1.1. DEEP LEARNING.. 151
3.1.1.1. IMAGE RECOGNITION.. 152
3.1.1.2. SPEECH RECOGNITION.. 157
3.1.1.3. HANDWRITING RECOGNITION.. 159
3.1.1.4. TEXT RECOGNITION IN IMAGES. 160
3.1.1.5. AUTOMATIC TRANSCRIPTION.. 161
3.1.1.6. DEEP LEARNING AND NEURAL NETWORKS FOR IMAGE AND TEXT RECOGNITION.. 165
3.2. COMPUTER VISION.. 175
3.3. NATURAL LANGUAGE PROCESSING.. 177
3.3.1. SYNTAX ANALYSIS. 177
3.3.2. SENTIMENT ANALYSIS. 178
3.3.3. MACHINE TRANSLATION.. 179
3.3.4. SPEECH RECOGNITION.. 180
3.3.5. NATURAL LANGUAGE GENERATION (NLG) 181
3.3.6. TEXT CLASSIFICATION: 182
3.3.7. INFORMATION RETRIEVAL. 184
3.3.8. SOCIAL MEDIA SENTIMENT ANALYSIS. 186
3.3.9. CHATBOT. 187
CHAPTER 4. 193
APPLICATIONS OF AI: Automotive, Travel, Healthcare, Manufacturing, Finance, Defense, Journalism, Fashion. 193
4.1. AUTOMOTIVE. 194
4.1.1. AUTONOMOUS VEHICLES. 195
4.1.2. ACCIDENT PREVENTION.. 199
4.1.3. DRIVING DATA ANALYSIS. 200
4.1.4. INTELLIGENT MANUFACTURING.. 201
4.1.5. HOW THE MICROCHIP SHORTAGE IS IMPACTING THE APPLICATION OF AI IN CARS. 203
4.2. BOOKINGS AND TRAVEL PLANNING.. 205
4.2.1. AIRLINES. 205
4.2.2. ONLINE TRAVEL PLATFORMS. 205
4.2.3. CAR RENTAL SERVICES. 206
4.2.4. PUBLIC TRANSPORTATION SYSTEMS. 206
4.2.5. RIDE-SHARING APPS. 206
4.3. AI IN THE HEALTHCARE SECTOR. 206
4.3.1. AI-ASSISTED DIAGNOSIS. 207
4.3.2. PERSONALIZED HEALTHCARE. 208
4.3.3. MONITORING HEALTH AT A DISTANCE. 209
4.3.4. DRUG DISCOVERY. 210
4.3.5. EVIDENCE-BASED HEALTHCARE. 211
4.3.6. PRECISION MEDICINE. 213
4.3.7. AI-ASSISTED SURGERY. 214
4.3.8. SLEEP MONITORING.. 217
4.3.9. REMOTE HEALTHCARE. 220
4.3.10. DISEASE PREVENTION.. 222
4.3.11. MYTH VERSUS REALITY OF AI IN MEDICINE. 223
4.3.12. CHALLENGES AND RISKS OF AI IN MEDICAL SCIENCE. 224
4.3.13. GEN AI TECHNOLOGY IN HEALTHCARE. 225
4.3.14. CONCLUSION.. 236
4.4 MANUFACTURING INDUSTRY. 237
4.4.1. AI CAN HELP SME’S. 237
4.4.2. HOW AI COULD REVOLUTIONIZE THE MANUFACTURING SECTOR: THE “FACTORY IN A BOX”. 239
4.4.3. MACHINE LEARNING IN PRODUCTION.. 241
4.4.3.1. QUALITY CONTROL. 242
4.4.3.2. PRODUCTION PLANNING.. 244
4.4.3.3. PREDICTIVE MAINTENANCE. 245
4.4.3.4. PRODUCTION PROCESS OPTIMIZATION.. 246
4.4.3.5. PRODUCT LIFECYCLE MONITORING.. 247
4.4.3.6. PRODUCTION PERSONALIZATION.. 249
4.4.3.7. SUPPLY CHAIN MANAGEMENT. 250
4.4.3.8. WORKPLACE SAFETY. 252
4.5. AI IN FINANCE. 253
4.5.1. CORPORATE FINANCE. 253
4.5.2. FINANCIAL CREDIT DECISIONS. 254
4.5.3. FORECASTING AND RISK MANAGEMENT. 255
4.6. AI IN THE DEFENSE INDUSTRY. 256
4.6.1. TRAINING.. 257
4.6.2. SURVEILLANCE. 258
4.6.3. AMMUNITIONS AND WEAPONS. 260
4.6.4. CYBERSECURITY. 261
4.6.5. LOGISTICS. 262
4.6.6. AN OVERVIEW OF THE USE OF AI IN ARMED FORCES WORLDWIDE 263
4.7. AI AND JOURNALISM: THE AUTOMATED NEWSROOM.. 266
4.8. THE FASHION INDUSTRY EMBRACES GENERATIVE AI (the case of Gpt 3.5 and DAL-E) 269
4.8.1. DALL-E. 270
CHAPTER 5. 284
IMPACT OF AI ON SOCIETY AND ECONOMY: Employment, Privacy, Security, Regulation. 284
5.1. EMPLOYMENT AND JOBS. 285
5.1.1. HEALTHCARE. 286
5.1.2. AUTOMOTIVE INDUSTRY. 287
5.1.3. CYBERSECURITY. 289
5.1.4. E-COMMERCE. 289
5.1.5. JOB SEARCH. 290
5.1.6. ADVANTAGES OF AI FOR EMPLOYMENT. 291
5.1.7. DISADVANTAGES OF AI FOR EMPLOYMENT. 297
5.2. PRIVACY AND SECURITY. 301
5.2.1. IMPLICATIONS OF AI ON PRIVACY. 303
5.2.2. BIG DATA AND POTENTIAL PRIVACY ABUSES. 304
5.2.3. SECURITY IMPLICATIONS. 305
5.2.4. MANAGING VULNERABILITIES. 308
5.2.5. MANAGING NETWORK SECURITY WITH AI 308
5.2.6. LEGALITY OF DATA COLLECTION.. 310
5.2.7. REGULATION IN AI 311
5.2.7.1. CHALLENGES IN AI REGULATION.. 311
5.2.7.2. AREAS OF INTERVENTION.. 312
5.2.7.3. EXAMPLES OF AI REGULATIONS IN THE WORLD. 314
5.2.7.4. NEED FOR REGULATORY BODIES. 329
5.2.7.5. REGULATING GENERATIVE AI: UNPRECEDENTED CHALLENGES 330
5.2.7.6. INITIAL REGULATION OF AI 331
CHAPTER 6. 337
THE PRESENT FOR AI: GENERATIVE-AI SYSTEMS; ChatGpt, xAI, GCD, BARD…etc 337
6.1. OPENAI - CHATGPT. 342
6.2. XAI 343
6.2.1. THE CHALLENGE TO CHATGPT. 345
6.3. GOOGLE CLOUD DIALOGFLOW (GCD) 347
6.4. IBM WATSON ASSISTANT. 352
6.4.1. KEY FEATURES AND CAPABILITIES. 353
6.4.2. APPLICATIONS OF IBM WATSON ASSISTANT. 354
6.5. BARD. 355
6.6. DIFFERENCES AMONG CHATGPT, XAI AND BARD. 357
6.7. THE AUTONOMOUS AGENTS. 360
6.7.1. AutoGPT. 364
6.7.2. BabyAGI 366
6.7.3. MICROSOFT'S JARVIS. 370
6.7.4. CAMEL AGI 371
6.7.5. HYPERWRITE. 375
6.7.6. AgentGPT. 376
6.8. ADVANTAGES OF USING AI CHATS. 385
6.9. ISSUES OF USING AI CHATS. 386
6.10. FUTURE DEVELOPMENTS OF AI CHATS. 387
6.11. HOW GENERATIVE AI DIFFERS FROM OTHER FORMS OF AI 389
6.12. BUSINESS EXAMPLES OF APPLICATIONS OF GENERATIVE AI 390
6.13. HOW TO ASK THE RIGHT QUESTIONS TO ARTIFICIAL INTELLIGENCE. 393
CHAPTER 7. 401
THE FUTURE OF AI: Quantum Machine, New Job Opportunities, Ethics, Mobility, Sustainability, Art, Weak AI and Strong AI 401
7.1. QUANTUM MACHINE LEARNING.. 402
7.2. EXPLAINABLE AI 409
7.3. FEDERATED AI 409
7.4. AI AND HUMAN-MACHINE INTERACTION.. 411
7.5. AI AND THE CREATION OF NEW FUTURE JOBS. 412
7.6. AI AND ETHICS. 415
7.6.1. ALGORITHMIC DISCRIMINATION.. 415
7.6.2. ETHICS IN PRIVACY AND SECURITY. 417
7.7. AI AND THE FUTURE OF MEDICINE. 419
7.8. AI AND THE FUTURE OF MOBILITY. 420
7.9. AI AND FUTURE SUSTAINABILITY. 421
7.10. AI AND ART IN THE FUTURE. 423
7.11. SOLUTIONS FOR THE FUTURE CHALLENGES OF AI 424
7.12. AI IN 100 YEARS, WEAK AI AND STRONG AI 426
CHAPTER 8. 438
INTEGRATING GEN-AI INTO BUSINESS PROCESSES: how to do it responsibly? 438
8.1. DEFINE CLEAR OBJECTIVES. 439
8.2. DATA PRIVACY AND SECURITY. 441
8.3. BIAS MITIGATION.. 442
8.4. HUMAN OVERSIGHT. 444
8.5. TRANSPARENCY AND EXPLANATION.. 446
8.6. USER FEEDBACK AND IMPROVEMENT. 447
8.7. RESPONSIBLE USE CASES. 449
8.8. REGULATORY COMPLIANCE. 451
8.9. CONTINUED MONITORING AND EVALUATION.. 453
8.10. HOW TO MANAGE RISKS ASSOCIATED WITH AI 454
CONCLUSIONS. 457
APPENDIX: Acronyms used in AI 461
Artificial Intelligence (AI) is revolutionizing the way we live, work, and interact with the world around us. AI has a significant impact on our lives today and in the future. Thanks to AI, we are witnessing great advancements in the field of machine learning, image recognition, and natural language processing. Machines are becoming increasingly skilled at understanding our world. In this captivating book, we explore the depths of this extraordinary technology, from its definition and history to its practical application in various sectors. Perhaps we are not aware that in recent years, AI has already taken on an increasingly central role in our daily lives, influencing sectors such as healthcare, manufacturing, finance, and even defense. The book presents numerous examples of companies successfully using AI: you will discover how some organizations are harnessing the potential of AI in the automotive industry, healthcare, manufacturing, and finance. We then travel through a world of possibilities and discover how AI is changing the face of these industries.
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Starting from the theoretical foundations of AI, we explore crucial concepts such as data, logic, and probability. We also delve into essential mathematical topics such as linear algebra and calculus. Additionally, we analyze neural networks and clustering algorithms, which are fundamental tools for AI. We then delve into machine learning, exploring advanced algorithms like decision trees, neural networks, and Support Vector Machines (SVM). Through the powerful tool of Deep Learning, we explore image and speech recognition. We delve into recognition techniques such as computer vision and natural language processing, enabling machines to interpret and understand our visual and linguistic world.
In addition to examining the practical applications of AI in various sectors, we address the impact of AI on society and the economy. We discuss implications for employment, privacy, security, and the necessity of regulation. Finally, we explore the future of AI. We examine cutting-edge concepts like Quantum Machine, explainable and federated AI. We analyze the new job opportunities that AI offers and discuss ethical issues, mobility, sustainability, and art in the era of AI.
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