Understanding Artificial Intelligence Provides students across majors with a clear and accessible overview of new artificial intelligence technologies and applications Artificial intelligence (AI) is broadly defined as computers programmed to simulate the cognitive functions of the human mind. In combination with the Neural Network (NN), Big Data (BD), and the Internet of Things (IoT), artificial intelligence has transformed everyday life: self-driving cars, delivery drones, digital assistants, facial recognition devices, autonomous vacuum cleaners, and mobile navigation apps all rely on AI to perform tasks. With the rise of artificial intelligence, the job market of the near future will be radically different???many jobs will disappear, yet new jobs and opportunities will emerge. Understanding Artificial Intelligence: Fundamentals and Applications covers the fundamental concepts and key technologies of AI while exploring its impact on the future of work. Requiring no previous background in artificial intelligence, this easy-to-understand textbook addresses AI challenges in healthcare, finance, retail, manufacturing, agriculture, government, and smart city development. Each chapter includes simple computer laboratories to teach students how to develop artificial intelligence applications and integrate software and hardware for robotic development. In addition, this text: Focuses on artificial intelligence applications in different industries and sectors Traces the history of neural networks and explains popular neural network architectures Covers AI technologies, such as Machine Vision (MV), Natural Language Processing (NLP), and Unmanned Aerial Vehicles (UAV) Describes various artificial intelligence computational platforms, including Google Tensor Processing Unit (TPU) and Kneron Neural Processing Unit (NPU) Highlights the development of new artificial intelligence hardware and architectures Understanding Artificial Intelligence: Fundamentals and Applications is an excellent textbook for undergraduates in business, humanities, the arts, science, healthcare, engineering, and many other disciplines. It is also an invaluable guide for working professionals wanting to learn about the ways AI is changing their particular field.

Table of Contents:
1 Introduction 1 1.1 Overview 1 1.2 Development History 3 1.3 Neural Network Model 6 1.4 Popular Neural Network 7 1.4.1 Convolutional Neural Network 7 1.4.2 Recurrent Neural Network 8 1.4.3 Reinforcement Learning 9 1.5 Neural Network Classification 9 1.5.1 Supervised learning 10 1.5.2 Semi-supervised learning 10 1.5.3 Unsupervised learning 11 1.6 Neural Network Operation 11 1.6.1 Training 11 1.6.2 Inference 12 1.7 Application Development 12 1.7.1 Business Planning 14 1.7.2 Network Design 14 1.7.3 Data Engineering 14 1.7.4 System Integration 15 Exercise 16 2 Neural Network 17 2.1 Convolutional Layer 19 2.2 Activation Layer 20 2.3 Pooling Layer 21 2.4 Batch Normalization 22 2.5 Dropout Layer 22 2.6 Fully Connected Layer 23 Exercise 24 3 Machine Vision 25 3.1 Object Recognition 25 3.2 Feature Matching 27 3.3 Facial Recognition 28 3.4 Gesture Recognition 30 3.5 Machine Vision Applications 31 3.5.1 Medical Diagnosis 31 3.5.2 Retail Applications 32 3.5.3 Airport Security 33 Exercise 34 4 Natural Language Processing 35 4.1 Neural Network Model 36 4.1.1 Convolutional Neural Network 36 4.1.2 Recurrent Neural Network 37 4.1.2.1 Long Short-Term Memory Network 38 4.1.3 Recursive Neural Network 39 4.1.4 Reinforcement Learning 40 4.2 Natural Language Processing Applications 41 4.2.1 Virtual Assistant 41 4.2.2 Language Translation 42 4.2.3 Machine Transcription 43 Exercise 45 5 Autonomous Vehicle 46 5.1 Levels of Driving Automation 46 5.2 Autonomous Technology 48 5.2.1 Computer Vision 48 5.2.2 Sensor Fusion 49 5.2.3 Localization 51 5.2.4 Path Planning 52 5.2.5 Drive Control 52 5.3 Communication Strategies 53 5.3.1 Vehicle-to-Vehicle Communication 54 5.3.2 Vehicle-to-Infrastructure Communication 54 5.3.3 Vehicle-to-Pedestrian Communication 55 5.4 Law Legislation 56 5.4.1 Human Behavior 57 5.4.2 Lability 57 5.4.3 Regulation 58 5.5 Future Challenges 58 5.5.1 Road Rules Variation 58 5.5.2 Unified Communication Protocol 58 5.5.3 Safety Standard and Guideline 59 5.5.4 Weather/Disaster 59 Exercise 60 6 Drone 61 6.1 Drone Design 61 6.2 Drone Structure 62 6.2.1 Camera 63 6.2.2 Gyro Stabilization 63 6.2.3 Collision Avoidance 64 6.2.4 Global Positioning System 64 6.2.5 Sensors 64 6.3 Drone Regulation 65 6.3.1 Recreational Rules 65 6.3.2 Commercial Rules 66 6.4 Applications 66 6.4.1 Infrastructure Inspection 66 6.4.2 Civil Construction 67 6.4.3 Agriculture 68 6.4.4 Emergency Rescue 69 Exercise 70 7 Healthcare 71 7.1 Telemedicine 71 7.2 Medical Diagnosis 72 7.3 Medical Imaging 73 7.4 Smart Medical Device 74 7.5 Electronic Health Record 76 7.6 Medical Billing 77 7.7 Drug Development 78 7.8 Clinical Trial 79 7.9 Medical Robotics 80 7.10 Elderly Care 81 7.11 Future Challenges 82 Exercise 84 8 Finance 85 8.1 Fraud Prevention 85 8.2 Financial Forecast 88 8.3 Stock Trading 89 8.4 Banking 91 8.5 Accounting 94 8.6 Insurance 95 Exercise 96 9 Retail 97 9.1 E-Commerce 98 9.2 Virtual Shopping 100 9.3 Product Promotion 102 9.4 Store Management 103 9.5 Warehouse Management 104 9.6 Inventory Management 106 9.7 Supply Chain 108 Exercise 110 10 Manufacturing 111 10.1 Defect Detection 112 10.2 Quality Assurance 113 10.3 Production Integration 114 10.4 Generative Design 115 10.5 Predictive Maintenance 117 10.6 Environment Sustainability 118 10.7 Manufacturing Optimization 119 Exercise 121 11 Agriculture 122 11.1 Crop and Soil Monitoring 123 11.2 Agricultural Robot 125 11.3 Pest Control 126 11.4 Precision Farming 127 Exercise 129 12 Smart City 130 12.1 Smart Transportation 131 12.2 Smart Parking 132 12.3 Waste Management 133 12.4 Smart Grid 134 12.5 Environmental Conservation 135 Exercise 137 13 Government 138 13.1 Information Technology 140 13.2 Human Service 141 13.3 Law Enforcement 144 13.3.4 Augmenting Human Movement 147 13.4 Homeland Security 147 13.5 Legislation 149 13.6 Ethics 152 13.7 Public Perspective 155 Exercise 159 14 Computing Platform 160 14.1 Central Processing Unit 160 14.1.1 System Architecture 161 14.1.2 Advanced Vector Extension 164 14.1.3 Math Kernel Library for Deep Neural Network 165 14.2 Graphics Processing Unit 165 14.2.1 Tensor Core Architecture 167 14.2.2 NVLink2 Configuration 167 14.2.3 High Bandwidth Memory 169 14.3 Tensor Processing Unit 170 14.3.1 System Architecture 170 14.3.2 Brain Floating Point Format 171 14.3.3 Cloud Configuration 172 14.4 Neural Processing Unit 173 14.4.1 System Architecture 173 14.4.2 Deep Compression 174 14.4.3 Dynamic Memory Allocation 174 14.4.4 Edge AI Server 175 Exercise 176 Appendix A Kneron Neural Processing Unit 178 Appendix B Object Detection (Overview) 179 B.1 Kneron Environment Setup 179 B.2 Python Installation 180 B.3 Library Installation 184 B.4 Driver Installation 185 B.5 Model Installation 186 B.6 Image/Camera Detection 186 B.7 Yolo Class List 190 Appendix C Object Detection - Hardware 192 C.1 Library Setup 192 C.2 System Parameters 193 C.3 NPU Initialization 194 C.4 Image Detection 195 C.5 Camera Detection 197 Appendix D Hardware Transfer Mode 199 D.1 Serial Transfer Mode 199 D.2 Pipeline Transfer Mode 201 D.3 Parallel Transfer Mode 203 Appendix E Object Detection – Software (Optional) 205 E.1 Library Setup 205 E.2 Image Detection 207 E.3 Video Detection 208 Reference 211