Artificial intelligence (AI) Books

Book SynopsisMethods and Techniques in Deep Learning Introduces multiple state-of-the-art deep learning architectures for mmWave radar in a variety of advanced applications Methods and Techniques in Deep Learning: Advancements in mmWave Radar Solutions provides a timely and authoritative overview of the use of artificial intelligence (AI)-based processing for various mmWave radar applications. Focusing on practical deep learning techniques, this comprehensive volume explains the fundamentals of deep learning, reviews cutting-edge deep metric learning techniques, describes different typologies of reinforcement learning (RL) algorithms, highlights how domain adaptation (DA) can be used for improving the performance of machine learning (ML) algorithms, and more. Throughout the book, readers are exposed to product-ready deep learning solutions while learning skills that are relevant for building any industrial-grade, sensor-based deep learning solution. A team of authors Table of ContentsPreface Acronyms 1 Introduction to Radar Processing & Deep Learning 1 1.1 Basics of Radar Systems 1 1.1.1 Fundamentals 2 1.1.2 Signal Modulation 2 1.2 FMCW Signal Processing 6 1.2.1 Frequency-Domain Analysis 7 1.3 Target Detection & Clustering 14 1.4 Target Tracking 19 1.4.1 Track Management 21 1.4.2 Track Filtering 22 1.5 Target Representation 28 1.5.1 Image Representation 30 1.5.2 Point-Cloud Maps 34 1.6 Target Recognition 36 1.6.1 Feedforward Network 37 1.6.2 Convolutional Neural Networks (CNN) 37 1.6.3 Recurrent Neural Network (RNN) 43 1.6.4 Autoencoder & Variational Autoencoder 47 1.6.5 Generative Adversial Network 51 1.6.6 Transformer 54 1.7 Training a Neural Network 56 1.7.1 Forward Pass & Backpropagation 57 1.7.2 Optimizers 62 1.7.3 Loss Functions 65 1.8 Questions to the Reader 66 Bibliography 68 2 Deep Metric Learning 75 2.1 Introduction 78 2.2 Pairwise methods 79 2.2.1 Contrastive Loss 79 2.2.2 Triplet Loss 80 2.2.3 Quadruplet Loss 81 2.2.4 N-Pair Loss 82 2.2.5 Big Picture 83 2.3 End-to-end Learning 84 2.3.1 Cosine Similarity 86 2.3.2 Euclidean Distance 95 2.3.3 Big Picture 100 2.4 Proxy methods 103 2.5 Advanced Methods 103 2.5.1 Statistical Distance 104 2.5.2 Structured Metric Learning 108 2.6 Application Gesture Sensing 110 2.6.1 Radar System Design 111 2.6.2 Data Set and Preparation 112 2.6.3 Architecture and Metric Learning Procedure 114 2.6.4 Results 123 2.7 Questions to the Reader 129 Bibliography 130 3 Deep Parametric Learning 135 3.1 Introduction 135 3.2 Radar Parametric Neural Network 140 3.2.1 2D Sinc Filters 142 3.2.2 2D Morlet Wavelets 143 3.2.3 Adaptive 2D Sinc Filters 145 3.2.4 Complex Frequency Extraction Layer 146 3.3 Multilevel Wavelet Decomposition Network 150 3.4 Application Activity Classification 153 3.4.1 Proposed Parametric Networks 155 3.4.2 State-of-art Networks 158 3.4.3 Results & Discussion 160 3.5 Conclusion 167 3.6 Question to Readers 168 Bibliography 168 4 Deep Reinforcement Learning 173 4.1 Useful Notation and Equations 173 4.1.1 Markov Decision Process 173 4.1.2 Solving the Markov Decision Process 174 4.1.3 Bellman Equations 175 4.2 Introduction 175 4.3 On-Policy Reinforcement Learning 179 4.4 Off-Policy Reinforcement Learning 180 4.5 Model-Based Reinforcement Learning 180 4.6 Model-Free Reinforcement Learning 181 4.7 Value-Based Reinforcement Learning 181 4.8 Policy-Based Reinforcement Learning 183 4.9 Online Reinforcement Learning 183 4.10 Offline Reinforcement Learning 184 4.11 Reinforcement Learning with Discrete Actions 184 4.12 Reinforcement Learning with Continuous Actions 185 4.13 Reinforcement Learning Algorithms for Radar Applications 185 4.14 Application Tracker’s Parameter Optimization 189 4.14.1 Motivation 190 4.14.2 Background 192 4.14.3 Approach 202 4.14.4 Experimental 208 4.14.5 Outcomes of the proposed Approach 219 4.15 Conclusion 220 4.16 Questions to the Reader 220 Bibliography 221 5 Cross-Modal Learning 229 5.1 Introduction 229 5.2 Self-Supervised Multi-Modal Learning 233 5.2.1 Generating Audio Statistics 233 5.2.2 Predicting sounds from images 234 5.2.3 Audio Features Clustering 234 5.2.4 Binary Coding Model 235 5.2.5 Training 235 5.2.6 Results 235 5.3 Joint Embeddings Learning 237 5.3.1 Feature Representations 237 5.3.2 Joint-Embedding Learning 238 5.3.3 Matching & Ranking 239 5.3.4 Training Details & Result 239 5.3.5 Discussion 241 5.4 Multi-Modal Input 241 5.4.1 Multi-modal Compact Bilinear Pooling 242 5.4.2 VQA Architecture 243 5.4.3 Training Details & Result 245 5.4.4 Discussion 245 5.5 Cross-Modal Learning 245 5.5.1 Data Acquisition 246 5.5.2 Cross-Modal Learning for Key-Point Detection 246 5.5.3 Training Details & Result 247 5.5.4 Discussion 249 5.6 Application People Counting 250 5.6.1 FMCW Radar System Design 251 5.6.2 Data Acquisition 252 5.6.3 Solution 1 253 5.6.4 Solution 2 262 5.7 Conclusion 265 5.8 Questions to the Reader 265 Bibliography 267 6 Signal Processing with Deep Learning 273 6.1 Introduction 273 6.2 Algorithm Unrolling 274 6.2.1 Learning Fast Approximations of Sparse Coding 275 6.2.2 Learned ISTA in radar processing 279 6.3 Physics-inspired Deep Learning 282 6.4 Processing-specific Network Architectures 284 6.5 Deep Learning-aided Signal Processing 288 6.6 Questions to the Reader 297 Bibliography 297 7 Domain Adaptation 303 7.1 Introduction 303 7.2 Transfer Learning and Domain Adaptaton 304 7.3 Categories of Domain Adaptation 307 7.3.1 Common Data Shifts 307 7.3.2 Methods of Domain Adaptation 308 7.4 Domain Adaptation in Radar Processing 315 7.4.1 Domain Adaptation with a different Sensor Type 316 7.4.2 Domain Adaptation with different Radar Settings 318 7.5 Summary 331 7.6 Questions to the Reader 331 Bibliography 332 8 Bayesian Deep Learning 339 8.1 Learning Theory 341 8.2 Bayesian Learning 343 8.3 Bayesian Approximations 352 8.4 Application VRU Classification 372 8.4.1 VAE as Bayesian 373 xiii 8.4.2 Bayesian Metric Learning 377 8.4.3 Kalman as Bayesian 383 8.4.4 Results 387 8.5 Summary 391 8.6 Questions to the Reader 393 Bibliography 393 9 Geometric Deep Learning 397 9.1 Representation Learning in Graph Neural Network 399 9.1.1 Fundamentals 399 9.1.2 Learning Theory 401 9.1.3 Embedding Learning 406 9.2 Graph Representation Learning 407 9.2.1 Convolution GNN 408 9.2.2 Recurrent Graph Neural Networks (RGNN) 409 9.2.3 Graph Autoencoders (GAE) 409 9.2.4 Spatial–Temporal Graph Neural Networks (STGNN) 410 9.2.5 Attention GNN 410 9.2.6 Message-passing GNN 411 9.3 Applications 413 9.3.1 Application 1 Long-Range Gesture Recognition 413 9.3.2 Application 2 Bayesian Anchor-Free Target Detection 426 9.4 Conclusion 444 9.5 Questions to the Reader 445 Bibliography 446

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Book SynopsisDRONE TECHNOLOGY This book provides a holistic and valuable insight into the revolutionary world of unmanned aerial vehicles (UAV). The book elucidates the revolutionary and riveting research in the ultramodern domain of drone technologies, drone-enabled IoT applications, and artificial intelligence-based smart surveillance. The book explains the most recent developments in the field, challenges, and future scope of drone technologies. Beyond that, it discusses the importance of a wide range of design applications, drone/UAV development, and drone-enabled smart healthcare systems for smart cities. It describes pioneering work on mitigating cyber security threats by employing intelligent machine learning models in the designing of IoT-aided drones. The book also has a fascinating chapter on application intrusion detection by drones using recurrent neural networks. Other chapters address interdisciplinary fields like artificial intelligence, deep learning, the role of drones in healthTable of ContentsPreface xvii 1 Drone Technologies: State-of-the-Art, Challenges, and Future Scope 1 Arun Agarwal, Chandan Mohanta and Saurabh Narendra Mehta 1.1 Introduction 2 1.2 Forces Acting on a Drone 3 1.3 Principal Axes 3 1.4 Broad Classification of Drones 3 1.4.1 Fixed-Wing Drones 3 1.4.1.1 Advantages 4 1.4.1.2 Disadvantages 5 1.4.2 Lighter-Than-Air Systems 5 1.4.2.1 Advantages 5 1.4.2.2 Disadvantages 6 1.4.3 Multi-Rotor Configuration 6 1.4.3.1 Advantages 6 1.4.3.2 Disadvantages 7 1.5 Military Necessity of Drones 7 1.5.1 Features of Sixth-Generation Fighter Planes 7 1.5.1.1 Introduction 7 1.5.1.2 Cyber Warfare and Cyber Security 9 1.5.1.3 Artificial Intelligence 9 1.5.1.4 Drones and Drone Swarms 10 1.5.1.5 Directed Energy Weapons 10 1.5.2 Pseudo Satellite of HAL 11 1.5.3 Surface to Air Missile vs. Modern Fighter Aircraft 13 1.5.4 Drones as Weapons of Mass Destruction 14 1.6 Conclusion and Future Scope 16 References 17 2 Introduction to Drone Flights—An Eye Witness for Flying Devices to the New Destinations 21 S. Venkata Achuta Rao, P. Srilatha, G.V.R.K. Acharyulu and G. Suryanarayana 2.1 Introduction 22 2.1.1 Brief History 23 2.1.2 The Indomitable Significance of Drone Technology 23 2.1.3 Trends 24 2.2 How Drones Work and Their Anatomy 25 2.2.1 Anatomy of a Drone 25 2.2.1.1 Propellers 25 2.2.1.2 Brushless Motors 26 2.2.1.3 Landing Gear 26 2.2.1.4 Electronic Speed Controllers [ESC] 26 2.2.1.5 Flight Controller 26 2.2.1.6 Receiver 27 2.2.1.7 Transmitter 27 2.2.1.8 GPS Module 27 2.2.1.9 Battery 27 2.2.1.10 Camera 28 2.2.2 Types of Drones 28 2.2.2.1 Sub-System of UAVs 29 2.2.2.2 Other Specific Types of Drones 29 2.2.3 Components of Drones 32 2.2.3.1 Hardware 32 2.2.3.2 Software 33 2.2.3.3 Other Specific Components 33 2.3 Salient Features and Important Codes with Public Awareness with Respect to Safety and Necessary Precautionary Points 36 2.3.1 Safety and Legal Note 36 2.3.2 Public Perception 36 2.3.3 Crew 36 2.3.4 Know Before You Fly 36 2.3.5 Simulation Training 37 2.3.6 Mapping Configuration 37 2.3.7 Mapping BFS Camera and Mapping Camera Mount 37 2.3.8 Equipment to Remove 38 2.3.9 Flight Planning 38 2.3.10 Post Processing Data 39 2.4 Top 10 Stunning Applications of Drone Technology 39 2.4.1 Aerial Photography 40 2.4.2 Shipping and Delivery 40 2.4.3 Geographic Mapping 40 2.4.4 Disaster Management 40 2.4.5 Precision Agriculture 40 2.4.6 Search and Rescue 41 2.4.7 Weather Forecast 41 2.4.8 Wildlife Monitoring 41 2.4.9 Law Enforcement 41 2.4.10 Entertainment 42 2.5 Drones in Enterprises: What Value Do They Add? Work Place Safety and Industry Benchmarks 42 2.5.1 Total Workplace Safety with Drones 43 2.5.2 Future of Drones with Idea Forge’s Industry Benchmarks 43 2.6 Advantages and Disadvantages of Drones 44 2.6.1 Significant Advantages 45 2.6.2 Disadvantages of Drones 45 2.6.3 Significant Disadvantages 46 2.6.4 Best Uses for Drones and Its Applications 46 2.7 Drone Technology as Career and Offered Jobs in the Current Industry 47 2.8 Societal Impact—Commercial Drones 47 2.9 Drones Research Challenges and Solutions 48 2.10 Conclusion 49 References 50 3 Drone/UAV Design Development is Important in a Wide Range of Applications: A Critical Review 53 M. V. Kamal, P. Dileep, G. Sharada, V. Suneetha and M. Gayatri 3.1 Introduction 54 3.2 Classification of Various Categories of Air Drones 55 3.2.1 VTOL and HTOL UAVs 57 3.2.2 Tilt-Body, Tilt-Rotor, and Tilt-Wingducted Fan UAVs 57 3.2.3 Heli-Wing and Helicopter UAVs 58 3.3 Drones Acting on Various Industries 58 3.3.1 Military Drones 58 3.3.2 Medical Drones 58 3.3.3 Agricultural Drones 62 3.4 Conclusions and Future Scope 62 References 63 4 A Comprehensive Study on Design and Control of Unmanned Aerial Vehicles 69 P. Venkateshwar Reddy, P. Srinivasa Rao, M. Hrishikesh and C. Satya Kumar 4.1 Introduction 70 4.2 Classification of Drones 72 4.3 Flight Performance Analysis 75 4.4 Dynamics and Design Objectives of Drones 79 4.4.1 Drone Dynamics 79 4.4.2 Design Objectives and Scaling Laws 80 4.4.3 Energy Utilization 81 4.4.4 Agility and Speed 81 4.4.5 Survivability and Robustness 83 4.4.6 Low-Level Control and Stabilization 83 4.5 Design Methods and Challenges 86 4.5.1 Proposed Solutions for Design Challenges 87 4.6 Guidance, Navigation, and Control of Drones 88 4.7 Conclusion 92 References 93 5 Some Studies of the Latest Artificial Intelligence Applications of Drones are Explored in Detail with Application Phenomena 99 G. Vaitheeswaran, B. Sundaravadivazhagan and Karthikeyan 5.1 Introduction 100 5.2 Evolution of the Drone 101 5.2.1 Military Drones 102 5.2.2 Commercial Drones 103 5.3 Drone Features 104 5.4 AI Meets Drones 105 5.5 Use Cases 109 5.5.1 Army 109 5.5.2 Weather Forecast 111 5.5.3 Industry 112 5.5.4 Agriculture 113 5.5.5 Logistics 113 5.6 Conclusion 115 References 115 6 Drone Technologies: Aviation Strategies, Challenges, and Applications 117 Devshri Satyarthi, K.V. Arya and Manish Dixit 6.1 Introduction 118 6.1.1 Categorization of Unmanned Aerial Vehicle (UAV) 119 6.1.1.1 Classification Based on Size 119 6.1.1.2 Classification Based on Range, Endurance, and Altitude 121 6.1.1.3 Classification Based on Weight 121 6.1.1.4 Classification Based on Engine Type 122 6.1.1.5 Classification Based on Configuration 122 6.1.1.6 Classification Based on Mechanical Design and Analysis 123 6.1.2 Specification of Drones 123 6.2 Drone Technology 124 6.2.1 Drone Monitoring Equipment 125 6.2.2 Drone Countermeasure Equipment 127 6.2.3 Collision Avoidance and Obstacle Detection Technology 129 6.2.4 Flight Controllers, Gyroscope Stabilization, and IMU 129 6.2.5 Drone Propulsion Technology 130 6.2.6 Real-Time Telemetry Flight Parameters 130 6.2.7 No Fly Zone Drone Technology 130 6.2.8 LED Flight Indicators 130 6.2.9 Drones with High Performance Camera 131 6.2.10 Remote Control System and Receiver of UAV 131 6.2.11 Range Extender UAV Technology 131 6.2.12 Video Editing Software 131 6.2.13 Operating Systems in Drone 131 6.2.14 Drone Security and Hacking 131 6.2.15 Modern Top Technology (Drones with Camera) 132 6.2.16 Intelligent Flight Systems 133 6.2.17 Drones For Tracking 133 6.3 India 2021: The Drone Policy and Rules 133 6.3.1 India Policy Guideline for Drones 133 6.3.2 Drone Rules 2021 136 6.4 Unmanned Aerial Vehicle (UAV) or Drone Application 137 6.4.1 Precision Agriculture 137 6.4.1.1 Related Work 138 6.4.1.2 Uses of UAV in Precision Agriculture 139 6.4.1.3 Challenges 140 6.4.1.4 Research Trends 140 6.4.1.5 Future Insights 141 6.4.2 Surveillance Applications of UAVs 141 6.4.2.1 Literature Review 141 6.4.2.2 State-of-the-Art Research 142 6.4.2.3 Product Introduction 142 6.4.2.4 Research Trends and Future Insights 142 6.4.3 Search and Rescue (SAR) 142 6.4.3.1 How SAR Operations Utilize UAVs 143 6.4.3.2 Challenges 143 6.4.3.3 Research Trends 143 6.4.3.4 Future Insights 143 6.4.4 Construction and Infrastructure Inspection 144 6.4.4.1 Literature Review 144 6.4.4.2 Deployment of Drone for Construction and Infrastructure Inspection Applications 144 6.4.4.3 Challenges 144 6.4.4.4 Research Trends 144 6.4.4.5 Future Insights 145 6.4.5 Delivery of Goods 145 6.4.5.1 UAVs-Based Goods Delivery System 145 6.4.5.2 Challenges 145 6.4.5.3 Research Trends 146 6.4.5.4 Future Insights 146 6.5 Conclusion 147 References 147 7 AI Applications of Drones 153 LNC Prakash K., Santosh Kumar Ravva, M.V. Rathnamma and G. Suryanarayana 7.1 Introduction 154 7.2 Review of Literature 159 7.3 AI in Drone Navigation 165 7.4 Companies that Use the AI Drone to Solve Big Problems 166 7.5 Drone Applications Using AI 169 7.6 Issues in the Integration of AI with Drones 176 7.7 Conclusion 177 References 179 8 Applications of Drones—A Review 183 Swathi Gowroju and Santhosh Ramchander N. 8.1 Introduction 184 8.2 Drone Hardware 188 8.3 Components of UAV 189 8.4 Literature Survey 190 8.4.1 Applications of Drones in Aerial Systems 190 8.4.2 Applications of Drones in Oil and Gas Industries 194 8.4.3 Applications of Drones in Military 195 8.4.4 Applications of Drones in Mines 195 8.4.4.1 Underground Mine Geotechnical Characterization 196 8.4.4.2 Underground Mine Rock Size Distribution Analysis 196 8.4.4.3 Underground Coal Mine Gas Detection 196 8.5 Analysis and Discussion 196 Conclusion 203 References 204 9 Drone Enables IoT Applications for Smart Cities 207 R. Santosh Kumar, LNC Prakash K. and G. Suryanarayana 9.1 Introduction to Smart Cities 208 9.2 Components and Characteristics of Smart Cities 209 9.2.1 Smart Healthcare 210 9.2.2 Smart Transportation 210 9.2.3 Smart Pollution Monitoring System 211 9.2.4 Smart Infrastructure and Building 212 9.2.5 Smart Building 212 9.3 The Role of IoT in Smart Cities 213 9.3.1 Road Traffic 213 9.3.2 Smart Parking 214 9.3.3 Public Transport 214 9.3.4 Utilities 215 9.3.4.1 Billing and Smart Meters 215 9.3.4.2 Disclosing Consumption Habits 215 9.3.4.3 Remote Surveillance 215 9.3.5 Waste Management 215 9.3.6 Environment 216 9.3.7 Public Safety 216 9.3.8 Security and Privacy for Smart Cities 216 9.4 General Approach to Implement IoT Solutions in Smart City Design 217 9.5 Challenges in IoT Solutions to Use in Smart City Design 219 9.6 Introduction to Unmanned Aerial Vehicles 221 9.7 Opportunities and Challenges of UAV’s in Smart Cities 222 9.8 Drone-Enabled IoT 224 9.8.1 Drone-Enabled IoT for Disaster Management 224 9.8.2 Drone-Enabled IoT for Public Safety 225 9.8.3 Drone-Enabled IoT for Data Collection 226 9.8.4 Drones and IoT for Improving Life Quality 227 9.8.5 Drone-Enabled IoT for Energy Efficiency 227 9.8.6 Privacy and Security Issues in Drone-Enabled IoT 228 9.9 Conclusion and Future Scope 229 References 229 10 AI-Based Smart Surveillance for Drowning and Theft Detection in Beaches Using Drones 243 V. Sakthivel, Suriya E., Jae Woo Lee and P. Prakash 10.1 Introduction 244 10.2 Literature Survey 244 10.3 Proposed Model 245 10.3.1 Drown Detection by Deep Learning Methods 245 10.3.2 People Alert System Using BLE Beacons 250 10.3.3 Abnormal Event Monitoring for Theft Detection 251 10.4 Deep Learning Model Safeties 252 10.5 Performance Evaluation 254 10.6 Conclusion 254 10.7 Conclusion and Future Work 255 Acknowledgements 256 References 256 11 Algorithms to Mitigate Cyber Security Threats by Employing Intelligent Machine Learning Models in the Design of IoT-Aided Drones 257 Devee Siva Prasad, Pyla Jyothi, G. Suryanarayana and Sachi Nandan Mohanty 11.1 Introduction 258 11.2 Research Methodology 260 11.3 Motivation 260 11.4 Machine Learning for Drone Security 262 11.5 Use of AI in Cyber Security 266 11.6 Use of AI in System to Achieve Robustness, Resilience and Response 267 11.7 NIC Algorithms in Cyber Security 271 11.8 Example Systems for AI and ML Applications for Cyber Security Diagnose 272 11.9 Introduction of New Threats 274 11.10 Areas were Malicious Use of Deepfakes is Trending 276 11.11 Model-Aided Deep Reinforcement Learning for Sample- Efficient UAV Trajectory Design in IoT Networks 276 11.12 Model-Aided Deep Q-Learning 278 11.13 Algorithm Model-Aided Deep Q-Learning Trajectory Design 280 11.13.1 Numerical Results 281 11.14 Machine Learning for Drone Security 282 11.15 Surveillance 283 11.16 Technologies Driving Drones’ Success 284 11.17 Related Work 286 11.18 Drones for Public Safety 289 11.19 Securing Drones 292 11.19.1 Machine and Deep Learning Models 293 11.20 Future Work 294 11.21 Contributions 295 Conclusion 295 References 296 12 IoT-Enabled Unmanned Aerial Vehicle: An Emerging Trend in Precision Farming 301 Gayatri Phade, A. T. Kishore, S. Omkar and M. Suresh Kumar 12.1 Introduction to IoT Enabled UAV 302 12.2 Drones in Precision Farming 306 12.2.1 Types of Agriculture Drones for Precision Agriculture 308 12.2.2 Drone Architecture for Precision Farming 310 12.2.3 IoT-Enabled Drone in Precision Farming 311 12.2.4 Safety and Security in IoT-Enabled Drones in Precision Farming 316 12.2.5 IoT Architecture in Drone 316 12.3 Challenges and Future Scope in IoT-Enabled Drone 319 12.4 Results and Discussion 320 Acknowledgement 322 References 322 13 Unmanned Aerial Vehicle for Land Mine Detection and Illegal Migration Surveillance Support in Military Applications 325 C. Anil Kumar Reddy and B.Venkatesh 13.1 Introduction to Military Drones 326 13.1.1 Unmanned Aerial Vehicle (UAV) 326 13.1.2 UAV Types 329 13.1.2.1 Multi-Rotor Drones 330 13.1.3 Problem Statement 330 13.1.4 Objective 330 13.1.5 Previous Work 331 13.2 Literature Review 331 13.2.1 Need of Drones for Indian Borders 334 13.2.2 UAV Technical Specifications 336 13.3 Methodology of UAV’s in Military Applications 336 13.3.1 Proposed System 336 13.3.2 Methodology 337 13.3.2.1 UAV Work Principle 337 13.3.2.2 UAV Controls and Installation 338 13.3.2.3 Drone Material and Frame 342 13.3.2.4 Program Used/Software Used (e.g., Aurdino) and Data Collection 342 13.3.2.5 Illegal Migration Surveillance with Camera 343 13.3.2.6 Data Collection from Mine Detector and Camera 344 13.3.2.7 Testing Conditions Applied for this Drone 344 13.4 Software Implementation 344 13.4.1 Arduino IDE 345 13.4.2 UAV Program/Coding 345 Appendix A 345 13.5 Conclusion 348 References 348 14 Importance of Drone Technology in Agriculture 351 Karuppiah Natarajan, Karthikeyan R. and Rajalingam S. 14.1 Introduction 352 14.2 Components of a Drone 352 14.3 Study of Natural Resources 355 14.3.1 Study of Natural and Manmade Pastures 357 14.3.2 Monitor Water Resources, Floods, and Droughts 357 14.3.3 Study of Weather Patterns 357 14.3.4 Monitoring of Soil Erosion 358 14.3.5 Cloud Seeding 359 14.4 Soil Fertility Management 359 14.4.1 Management of Soils and Their Fertility 360 14.4.2 Variable-Rate Technology for Soil Fertility Management 361 14.5 Irrigation and Water Management 362 14.5.1 Crop Water Stress Index 363 14.5.2 Drones to Monitor Water Resources 363 14.5.3 Drones to Design an Irrigation System 364 14.6 Crop Disease Identification 365 14.6.1 Monitoring and Identification Using Different Drone Platforms and Peripherals 365 14.6.2 Disease Symptoms 366 14.6.2.1 Sheath Blight 366 14.6.2.2 Narrow Brown Leaf Spot 367 14.7 Pest Control Management 368 14.7.1 Drones Offer a Sustainable Pest Control Solution 368 14.8 Agricultural Drones to Improve Crop Yield Management Efficiency 369 14.9 Issues and Challenges 370 14.9.1 Power Source and Flight Time 370 14.9.2 High Capital Cost 371 14.9.3 The Capacity of the Tank to Carry Fertilizer and Water for Spraying 372 14.9.4 Lack of Technical Skills to Operate, Repair, and Service 372 14.9.5 Job Loss of Existing Farm Workers 372 14.10 Conclusion 372 References 373 15 Network Intrusion Detection of Drones Using Recurrent Neural Networks 375 Yadala Sucharitha, Pundru Chandra Shaker Reddy and G. Suryanarayana 15.1 Introduction 376 15.2 Related Works 378 15.3 Drone Intrusion Detection Methodology 380 15.3.1 Drone RNN 381 15.3.2 Data Collector 382 15.3.3 Centralized-RNN 383 15.3.4 Decision-Maker 383 15.4 Results and Discussion 384 15.4.1 Model Assessment 384 15.4.2 Performance Analysis 384 15.4.3 LSTM_RNN Performance over UNSW-NB 15 Dataset 385 15.5 Conclusion 388 References 388 16 Drone-Enabled Smart Healthcare System for Smart Cities 393 Subasish Mohapatra, Amlan Sahoo, Subhadarshini Mohanty and Sachi Nandan Mohanty 16.1 Introduction 394 16.2 Related Works 397 16.3 Applications of Drones 399 16.4 Suggested Framework 411 16.5 Challenges 415 16.6 Conclusion 418 Future Scopes 419 References 420 17 Drone Delivery 425 V. Sakthivel, Sourav Patel, Jae Woo Lee and P. Prakash 17.1 Introduction 426 17.2 History of Drones 427 17.3 Drone Delivery in Healthcare 432 17.4 Drone Delivery of Food 432 17.5 Drone Delivery in Postal Service 433 17.6 Delivery of Goods 433 Acknowledgements 439 References 439 Index 441

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Book Synopsis

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Book SynopsisKevin Wheeler is a HR, talent acquisition and L&D consultant specializing in advising global firms on how to improve their recruitment function. Based in Fremont, California, he is a frequent keynote speaker and industry writer.Bas van de Haterd is a professional speaker, consultant and trainer with a particular focus on using technology to improve recruitment. Based in Soest, The Netherlands, he has more than 20 years' experience and his consulting clients include Dutch Rail, Dutch Post and Twente University.Trade Review"With Talent Acquisition Excellence, Kevin and Bas offer a timely and astute look at how we are in a revolutionary period in recruiting. Blending keen insights with compelling examples, this book illuminates the future for talent leaders seeking to drive innovation and results. Required reading for anyone aiming to master modern recruiting's tools and tactics." * Toby Culshaw, Head of Talent Intelligence, Amazon Stores *"People teams worldwide are wrestling with the implications of divergent yet intersecting trends in the gig economy, shift to remote, cloud & collaboration, profound social and cultural change, the arrival of transformation technology in generative artificial intelligence, all amidst an unstable and increasingly worrisome geopolitical environment. This is a timely book from Kevin Wheeler and Bas van de Haterd, two of the most original and iconoclastic thinkers in the world of TA / HR, who pull together these diverse threads into a concise, imaginative and persuasive narrative." * Hung Lee, Curator Recruiting Brainfood *"This book is an insightful exploration of how the world of work is evolving and how the field of recruiting needs to adapt alongside it. Few authors can bring this to light with the credibility and bonafide of Kevin Wheeler and Bas van de Haterd. The depth of their collective experience is on display throughout each page as they explore the evolving dynamics of the workplace, seamlessly blending insights on technology, human psychology and the art of recruitment. The book covers an impressive spectrum, from exploring the role of algorithms and analytics in shaping recruitment strategies to the human-centric aspects of workforce planning and talent attraction. It is a guide, a reflection of decades of earned wisdom and a strategic tool all rolled into one." * Lars Schmidt, Founder & CEO Amplify Talent *"In Talent Acquisition Excellence, Bas van de Haterd and Kevin Wheeler set out a compelling and powerful template for talent acquisition in the age of AI, analytics and complex talent ecosystems. I particularly enjoyed the scenarios laid out in the book, which provide a compelling treatise on how strategy, technology, process and data can be interwoven together to build a world-class talent acquisition function. A must-read for recruiters, HR professionals and business leaders alike." * David Green, Managing Partner at Insight222, co-author of Excellence in People Analytics, and host of the Digital HR Leaders podcast. *"Talent Acquisition Excellence is a pragmatic guide for a TA or HR leader on what to consider when building a future-proof TA function from scratch. The authors use results and scientific data to understand what's integral to a best practice TA function and are not afraid to conjecture on how this will play out in the future with a mixture of recent real-life examples and near future projections to inspire HR and TA leaders. I welcome the thought-provoking discussion this forward-looking book will inspire and influence Talent Acquisition development for years to come." * Andrea Marston, Senior Director Global Talent Acquisition, VMware *"Kevin and Bas offer a balanced perspective that underscores how the universal themes and best practices of Talent Acquisition transcend industry sectors, languages, and borders across the globe. It's a treasure trove of wisdom, filled with real-world examples and insights. A must-read for TA leaders seeking practical answers and thought-provoking insights." * Shelley Billinghurst & Serge Boudreau, The Recruitment Flex Podcast *"Embark on a transformative odyssey alongside the visionary insights of Bas and Kevin, the oracles of talent acquisition. In their meticulously crafted guide, business leaders will discover the strategic power of talent acquisition, gaining profound insights into the future of recruitment. Peer into the crystal ball of emerging trends, technologies and strategies that will sculpt the hiring landscape. Stay ahead, triumph in the war for talent, elevate your leadership, make informed decisions and propel your organization to unprecedented success!" * Tash Johnston, Talent Acquisition Leader *"A timely and profound exploration of how to attract and retain top talent in today's rapidly evolving work environment. Talent Acquisition Excellence offers a masterclass in recruitment strategy, providing state-of-art perspectives and guidance." * Ross Dawson, Futurist, Keynote Speaker and Author of "Thriving on Chaos", Chairman, Advanced Human Technologies Group *"In an industry typically heavily influenced by vendors setting the tone for what TA excellence means, Kevin & Bas have curated an independent & refreshing take on navigating our ever-evolving landscape of recruiting & hiring practices. Your new 'go-to' recruiting resource is a blueprint for innovative approaches for success in sourcing, engaging, & securing top talent. This book is packed with punchy narratives, cool charts & contemporary insights that have practical application. leveraging real-world scenarios, useful tools and meaningful data it's a comprehensive guide that will resonate and provoke thought, as well as enable recruiters and hiring partners. A must-read for anyone aiming to keep ahead of the curve or elevate their recruitment game in the times ahead!" * Tracy Quinn, APAC Regional Head of Talent Acquisition *"Talent Acquisition Excellence is a masterful orientation to the current state of the art in TA with a big look at what we can expect next. Kevin and Bas combine their broad knowledge of international trends, and Kevin, as always, considers the grand sweep of history before making predictions about the future. It's practical, smart and full of concrete examples. I wish I had this book ten years ago!" * Dart Lindsley, Strategic Advisor, People Experience, Google, Speaker and host of Work For Humans *"This is a vital book for any talent acquisition professional looking to get ahead of the curve. These are incredibly disruptive times for the recruiting function with challenging talent markets, exponential technological advances, and fast-changing business needs that make it impossible to know what skills will be needed and when. Talent Acquisition Excellence provides a roadmap to the radically changed talent acquisition function of the future. The book is visionary but at the same time also accessible, pragmatic and practical." * Matt Alder, Host, The Recruiting Future Podcast *"Talent Acquisition Excellence provides a clear roadmap for building a more effective and efficient talent supply chain that is capable of meeting your organization's current and future talent needs. This book is a must read for any HR leader looking for practical advice and real-world examples for how to transform the recruiting function into a strategic capability." * Thomas Bertels, Founder and President, Purpose Works Consulting *"Talent Acquisition Excellence by Kevin Wheeler and Bas van de Haterd is a game-changer in recruitment literature. It stands out with its clear, no-nonsense approach, directly addressing how digital tools and analytics can revolutionize hiring. The authors, renowned experts in the field, effectively combine recruitment fundamentals with emerging technologies. The book offers unique insights and real-world case studies, making complex concepts accessible and applicable. This book is especially valuable for HR professionals and recruiters looking to leverage technology for smarter, more effective talent acquisition. Readers can expect to gain practical strategies and see tangible improvements in their recruitment outcomes. It's a concise and impactful read, essential for staying ahead in the modern recruitment landscape." * Felix Wetzel, Vice President, Product Management, Cielo *Table of Contents Chapter - 00: Introduction – what’s happening to work?; Chapter - 01: The emerging talent ecosystem; Chapter - 02: Technology and talent acquisition; Chapter - 03: Talent supply chain thinking; Chapter - 04: Talent attraction; Chapter - 05: Selection; Chapter - 06: RPO or in-house?; Chapter - 07: A radical new model for talent acquisition; Chapter - 08: ‘What hath God wrought?’;

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Book SynopsisAndy Pardoe is a leading AI thought leader, consultant, speaker and author. He is the Founder & CEO of the Wisdom Works Group consultancy and is also the Managing Partner of Wisdom Works Ventures, a specialist accelerator for AI startups. He is the Chair of the Deep Tech Innovation Centre at the University of Warwick. He is based in London, UK.

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Book SynopsisBernard Marr is one of the world's leading voices in business and technology. A futurist and strategic performance consultant, he has worked with many of the world's best-known organizations and writes a regular column for Forbes. Marr is also a major influencer with a combined following of 4 million people across his social media channels and was ranked by LinkedIn as one of the top 5 business influencers in the world. He is the author of The Intelligence Revolution, Data Strategy and Data-Driven HR, all published by Kogan Page. He is based in Milton Keynes, UK.

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Book SynopsisBernard Marr is one of the world's leading voices in business and technology. A futurist and strategic performance consultant, he has worked with many of the world's best-known organizations and writes a regular column for Forbes. Marr is also a major influencer with a combined following of 4 million people across his social media channels and was ranked by LinkedIn as one of the top 5 business influencers in the world. He is the author of AI Strategy, The Intelligence Revolution, Data Strategy and Data-Driven HR, all published by Kogan Page. He is based in Milton Keynes, UK.

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Book SynopsisNicole Alexander is a marketing leader and educator with over 25 years of experience driving growth, innovation and transformation. She is an Adjunct Professor at NYU, a Lecturer at Section School and a Board Member at Per Scholas. She has held prominent leadership roles, including Global Head of Marketing at Meta, SVP of Innovation at Ipsos and VP of Innovation for Greater China at Nielsen. She lives in New York, NY.

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Book SynopsisKatie King is CEO of AI in Business, a firm that specializes in AI consultancy and training. With over 30 years' experience, she has advised many of the world's leading brands and business leaders, including Richard Branson/Virgin, o2, Orange and Accenture. Based in East Sussex, UK, she is a member of the UK Government All-Party Parliamentary Group (APPG) task force for the enterprise adoption of AI and an Editorial Board Member of the journal AI and Ethics. A regular international keynote speaker, she has also delivered TEDx talks and is a frequent commentator on BBC TV and radio.

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Book Synopsis1. Introduction to structural analysis by the Finite Element Method. 2. 1D finite elements for axially loaded rods. 3. Advanced 1D rod elements and requirements for the numerical solution. 4. 2D solids. Linear triangular and rectangular elements. 5. 2D solids. Higher order elements. Shape functions and isoparametric formulation. 6. Axisymmetric solids. 7. Three dimensional solids. 8. Bending of slender beams. Euler-Bemouilli theory. 9. Thick/slender beams. Timoshenko theory. 10. Thin plates. Kirchhoffs theory. 11. Thick/thin plates. Reissner-Mindlin theory. 12. Analysis of shells using flat elements. 13. Axisymmetric shells. 14. Analysis of arbitrary shape shells using degenerate solid elements. 15. Three-dimensional rods and shell stiffness. 16. Prismatic structures. Finite strip and finite prism methods. 17. Miscellaneous: inclined supports, displacements, constrains, nodal condensation error estimation and mesh adaptivity etc. 18. Pre and post-processing. Mesh generation and visuTable of Contents1. Introduction to structural analysis by the Finite Element Method. 2. 1D finite elements for axially loaded rods. 3. Advanced 1D rod elements and requirements for the numerical solution. 4. 2D solids. Linear triangular and rectangular elements. 5. 2D solids. Higher order elements. Shape functions and isoparametric formulation. 6. Axisymmetric solids. 7. Three dimensional solids. 8. Bending of slender beams. Euler-Bemouilli theory. 9. Thick/slender beams. Timoshenko theory. 10. Thin plates. Kirchhoffs theory. 11. Thick/thin plates. Reissner-Mindlin theory. 12. Analysis of shells using flat elements. 13. Axisymmetric shells. 14. Analysis of arbitrary shape shells using degenerate solid elements. 15. Three-dimensional rods and shell stiffness. 16. Prismatic structures. Finite strip and finite prism methods. 17. Miscellaneous: inclined supports, displacements, constrains, nodal condensation error estimation and mesh adaptivity etc. 18. Pre and post-processing. Mesh generation and visualization of computer results. 19. Introduction to FEM programming.

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Book SynopsisArtificial intelligence is part of our daily lives. How can we address its limitations and guide its use for the benefit of communities worldwide?Artificial intelligence (AI) has evolved from an experimental computer algorithm used by academic researchers to a commercially reliable method of sifting through large sets of data that detect patterns not readily apparent through more rudimentary search tools. As a result, AI-based programs are helping doctors make more informed decisions about patient care, city planners align roads and highways to reduce traffic congestion with better efficiency, and merchants scan financial transactions to quickly flag suspicious purchases. But as AI applications grow, concerns have increased, too, including worries about applications that amplify existing biases in business practices and about the safety of self-driving vehicles. In Can We Trust AI?, Dr. Rama Chellappa, a researcher and innovator with 40 years in the field, recounts the evolution of AI,Trade ReviewDrawing on interviews with researchers pushing the boundaries of AI for the world's benefit and working to make its applications safer and more just, Can We Trust AI? responds with a qualified affirmative.—Inside Higher EdIn Can We Trust AI?, Chellappa explores both the promise and peril of AI. For readers searching for an understanding how AI came to be...Chellappa situates AI in an historical context that is thorough, and thoroughly fascinating. Most refreshing is his current assessment of AI that dispels the hype of AI's world takeover....Chellappa gracefully moves among AI's past, present, and future.—Technical CommunicationTable of ContentsPrefaceChapter 1. The Birth and Growth of AIChapter 2. Saving Lives with Artificial IntelligenceChapter 3. The Complexities and Contributions of Facial RecognitionChapter 4. The Promise of Autonomous VehiclesChapter 5. AI's FuturescapeAcknowledgmentsGlossaryNotesIndex

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Book SynopsisWith contributions by numerous expertsTrade ReviewThird Edition J.H. Conway and N.J.A. Sloane Sphere Packings, Lattices and Groups "This is the third edition of this reference work in the literature on sphere packings and related subjects. In addition to the content of the preceding editions, the present edition provides in its preface a detailed survey on recent developments in the field, and an exhaustive supplementary bibliography for 1988-1998. A few chapters in the main text have also been revised."—MATHEMATICAL REVIEWSTable of Contents1 Sphere Packings and Kissing Numbers.- 2 Coverings, Lattices and Quantizers.- 3 Codes, Designs and Groups.- 4 Certain Important Lattices and Their Properties.- 5 Sphere Packing and Error-Correcting Codes.- 6 Laminated Lattices.- 7 Further Connections Between Codes and Lattices.- 8 Algebraic Constructions for Lattices.- 9 Bounds for Codes and Sphere Packings.- 10 Three Lectures on Exceptional Groups.- 11 The Golay Codes and the Mathieu Groups.- 12 A Characterization of the Leech Lattice.- 13 Bounds on Kissing Numbers.- 14 Uniqueness of Certain Spherical Codes.- 15 On the Classification of Integral Quadratic Forms.- 16 Enumeration of Unimodular Lattices.- 17 The 24-Dimensional Odd Unimodular Lattices.- 18 Even Unimodular 24-Dimensional Lattices.- 19 Enumeration of Extremal Self-Dual Lattices.- 20 Finding the Closest Lattice Point.- 21 Voronoi Cells of Lattices and Quantization Errors.- 22 A Bound for the Covering Radius of the Leech Lattice.- 23 The Covering Radius of the Leech Lattice.- 24 Twenty-Three Constructions for the Leech Lattice.- 25 The Cellular Structure of the Leech Lattice.- 26 Lorentzian Forms for the Leech Lattice.- 27 The Automorphism Group of the 26-Dimensional Even Unimodular Lorentzian Lattice.- 28 Leech Roots and Vinberg Groups.- 29 The Monster Group and its 196884-Dimensional Space.- 30 A Monster Lie Algebra?.- Supplementary Bibliography.

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Book Synopsis"Make a Mind Controlled Arduino Robot" shows you how to build your own. You learn to measure attention level with a NeuroSky headband and send this information into Arduino. You will also build a line-avoiding system into the bot. And, of course, you will build the chassis of your robot from scratch.

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Book SynopsisWritten by international experts in their respective fields, the book summarizes research in the many important research communities pertinent for Socially Interactive Agents (SIAs), while discussing current challenges and future directions. The handbook provides easy access to modeling and studying SIAs for researchers and students.

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Book SynopsisWritten by international experts in their respective fields, the book summarizes research in the many important research communities pertinent for Socially Interactive Agents (SIAs), while discussing current challenges and future directions. The handbook provides easy access to modeling and studying SIAs for researchers and students.

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Book SynopsisProfessor Judea Pearl won the 2011 Turing Award for fundamental contributions to artificial intelligence through the development of a calculus for probabilistic and causal reasoning. This book contains the original articles that led to the award, as well as other seminal works, divided into four parts: heuristic search, probabilistic reasoning, causality, first period (1988-2001), and causality, recent period (2002-2020). Each of these parts starts with an introduction written by Judea Pearl. The volume also contains original, contributed articles by leading researchers that analyze, extend, or assess the influence of Pearl''s work in different fields: from AI, Machine Learning, and Statistics to Cognitive Science, Philosophy, and the Social Sciences. The first part of the volume includes a biography, a transcript of his Turing Award Lecture, two interviews, and a selected bibliography annotated by him.

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Book SynopsisThis technique is an example of one facet of the PLNLP approach: the use of natural language itself as a knowledge representation language -- an innovation that permits a wide variety of online text materials to be exploited as sources of semantic information.Table of Contents1. Introduction; K. Jensen, G. Heidorn, S. Richardson. 2. Towards Transductive Linguistics; A.M. Ramer. 3. PEG: The PLNLP English Grammar; K. Jensen. 4. Experience with an Easily Computed Metric for Ranking Alternative Parses; G. Heidorn. 5. Parse Fitting and Prose Fixing; K. Jensen, G. Heidorn, L. Miller, Y. Ravin. 6. Grammar Errors and Style Weaknesses in a Text-Critiquing System; Y. Ravin. 7. The Experience of Developing a Large-Scale Natural Language Processing System: Critique; S. Richardson, L. Braden-Harder. 8. A Prototype English-Japanese Machine Translation System; T. Tsutsumi. 9. Broad-Coverage Machine Translation; D. Santos. 10. Building a Knowledge Base from Parsed Definitions; J. Klavans, M. Chodorow, N. Wacholder. 11. A Semantic Expert Using an Online Standard Dictionary; J.-L. Binot, K. Jensen. 12. Structural Patterns versus String Patterns for Extracting Semantic Information from Dictionaries; S. Montemagni, L. Vanderwende. 13. SENS: The System for Evaluating Noun Sequences; L. Vanderwende. 14. Disambiguating and Interpreting Verb Definitions; Y. Ravin. 15. Tailoring a Broad-Coverage Systems for the Analysis of Dictionary Definitions; S. Montemagni. 16. PEGASUS: Deriving Argument Structures after Syntax; K. Jensen. 17. A Two-Stage Algorithm to Parse Multi-Lingual Argument Structures; J.-P. Chanod, B. Harriehausen, S. Montemagni. 18. C-SHALT: English-to-Chinese Machine Translation Using Argument Structures; Ee Ah Choo, Koh Mui Koong, Low Hwee Boon, Tong Loong Cheong, Wan Kwee Ngim, Wee Li Kwang. 19. Sense Disambiguation Using Online Dictionaries; L. Braden-Harder. 20. Word-Sense Disambiguation by Examples; T. Tsutsumi. 21. Nominalization of Semantic Graphs; F. Segond. 22. The Paragraph as a Semantic Unit; W. Zadrozny, K. Jensen. References. Index.

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Book SynopsisIn Killer Apps Jeremy Packer and Joshua Reeves provide a detailed account of the rise of automation in warfare, showing how media systems are central to building weapons systems with artificial intelligence in order to more efficiently select and eliminate military targets. Drawing on the insights of a wide range of political and media theorists, Packer and Reeves develop a new theory for understanding how the intersection of media and military strategy drives today's AI arms race. They address the use of media to search for enemies in their analyses of the history of automated radar systems, the search for extraterrestrial life, and the development of military climate science, which treats the changing earth as an enemy. As the authors demonstrate, contemporary military strategy demands perfect communication in an evolving battlespace that is increasingly inhospitable to human frailties, necessitating humans' replacement by advanced robotics, machine intelligence, and media systems.Trade Review“In this crucial new book, Jeremy Packer and Joshua Reeves offer a provocative, media-centric analysis of automated killing machines. Engaging with an armada of flying sensors, robotic submarines, and AI weapons already in use, they show that big data, computer vision, and super intelligence emerge not just to order and organize the battlefield, but to produce new enemies. Clever and incisive, the book provides a haunting look at warfare of the near future.” -- Lisa Parks, coeditor of * Life in the Age of Drone Warfare *“This is an excellent book: well designed, thoroughly engaging, informative and, unfortunately, extremely topical and timely. The authors have gone to great lengths to make Killer Apps relentlessly up to date, providing readers with the latest in weapons developments, including AI drones and ‘swarmanoid’ robotics. With its impressive grounding in theory and hardware, it will become the go-to book for critical understandings of the intersection of warfare, media, and enmity.” -- Geoffrey Winthrop-Young, author of * Kittler and the Media *“By focusing first and foremost on the epistemological function of military media, Packer and Reeves have produced a range of rigorous and highly engaging analyses, with broad applicability across a range of possible fields of research.” -- Malcolm Ogden * Critical Studies in Media Communication *"The book is a tour de force regarding the rise of automation in warfare. I recommend Killer Apps to anyone interested in media technology studies as well as political science and international security studies." -- José de Arimatéia da Cruz * International Social Science Review *"Through a careful presentation of technological developments in the domain of military affairs, coupled with a rigorous historical analysis, an effective application of media theory, and a vast array of case studies, Jeremy Packer and Joshua Reeves convincingly present an account of how we arrived where we are today in a world on the cusp of embracing new forms of executing war that will be largely dependent on AI." -- Joseph Michael Gratale * European Journal of American Studies *“For the national security, intelligence, and defense communities, Killer Apps presents both a valuable scholarly resource and a deeply ambiguous set of questions. . . . [Packer and Reeves] force readers to transcend the humanist epistemological orientation in order to understand what the machine age has truly ushered in.” -- Zac Rogers * Parameters *Table of ContentsAcknowledgments vii Preface to an Inauthentic Document ix Introduction. Event Matrix (DoD) 1 1. Identification Friend or Foe (DoD) 29 2. Centralized Control/Decentralized Execution (DoD) 48 3. Hostile Environment (DoD) 61 4. In Extremis (DoD) 89 5. Intelligence, Surveillance, and Reconnaissance (DoD) 109 6. Autonomous Operation (DoD) 124 7. Vital Ground (DoD) 139 8. Escalation (DoD) 159 9. Unidentified Flying Objects (USAF) 175 Conclusion. Armistice (DoD) 198 Notes 217 References 235 Index 261

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Book SynopsisJeremy Packer and Joshua Reeves provide a critical account of the history and future of automation in warfare by highlighting the threats posed by the latest advances in media technology and artificial intelligence.Trade Review“In this crucial new book, Jeremy Packer and Joshua Reeves offer a provocative, media-centric analysis of automated killing machines. Engaging with an armada of flying sensors, robotic submarines, and AI weapons already in use, they show that big data, computer vision, and super intelligence emerge not just to order and organize the battlefield, but to produce new enemies. Clever and incisive, the book provides a haunting look at warfare of the near future.” -- Lisa Parks, coeditor of * Life in the Age of Drone Warfare *“This is an excellent book: well designed, thoroughly engaging, informative and, unfortunately, extremely topical and timely. The authors have gone to great lengths to make Killer Apps relentlessly up to date, providing readers with the latest in weapons developments, including AI drones and ‘swarmanoid’ robotics. With its impressive grounding in theory and hardware, it will become the go-to book for critical understandings of the intersection of warfare, media, and enmity.” -- Geoffrey Winthrop-Young, author of * Kittler and the Media *“By focusing first and foremost on the epistemological function of military media, Packer and Reeves have produced a range of rigorous and highly engaging analyses, with broad applicability across a range of possible fields of research.” -- Malcolm Ogden * Critical Studies in Media Communication *"The book is a tour de force regarding the rise of automation in warfare. I recommend Killer Apps to anyone interested in media technology studies as well as political science and international security studies." -- José de Arimatéia da Cruz * International Social Science Review *"Through a careful presentation of technological developments in the domain of military affairs, coupled with a rigorous historical analysis, an effective application of media theory, and a vast array of case studies, Jeremy Packer and Joshua Reeves convincingly present an account of how we arrived where we are today in a world on the cusp of embracing new forms of executing war that will be largely dependent on AI." -- Joseph Michael Gratale * European Journal of American Studies *“For the national security, intelligence, and defense communities, Killer Apps presents both a valuable scholarly resource and a deeply ambiguous set of questions. . . . [Packer and Reeves] force readers to transcend the humanist epistemological orientation in order to understand what the machine age has truly ushered in.” -- Zac Rogers * Parameters *Table of ContentsAcknowledgments vii Preface to an Inauthentic Document ix Introduction. Event Matrix (DoD) 1 1. Identification Friend or Foe (DoD) 29 2. Centralized Control/Decentralized Execution (DoD) 48 3. Hostile Environment (DoD) 61 4. In Extremis (DoD) 89 5. Intelligence, Surveillance, and Reconnaissance (DoD) 109 6. Autonomous Operation (DoD) 124 7. Vital Ground (DoD) 139 8. Escalation (DoD) 159 9. Unidentified Flying Objects (USAF) 175 Conclusion. Armistice (DoD) 198 Notes 217 References 235 Index 261

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Book SynopsisThis issue provides an overview of the emerging interdisciplinary field of Critical AI, which seeks to demystify artificial intelligence; counter its mythologizing as a marvelous and impenetrable black box; and translate, interpret, and critique its operations, from data collection and model architecture to decision making. Artists and researchers are developing new methods, practices, and concepts for this critical project, which is both historicist and attentive to the institutional, technological, and epistemic transformations still underway. Contributors to this special issue collectively articulate and evince just such a critical approach to AI, one that combines humanistic and technical inquiry in its exploration of disciplinary and epistemological questions on the one hand, and the techniques of machine learning on the other. Featured contributions articulate some of the social, cultural, and ethicopolitical dimensions of machine learning in domains such as ecologies, art, poeti

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Table of Contents1. Getting Started2. The Inventory File3. Installing Wordpress4. Ansible Roles5. Parameterising Playbooks6. Writing Your Own Modules7. Orchestrating AWS8. Testing with Test Kitchen9. Advanced AnsibleAppendix A. Installing AnsibleAppendix B. YAML Files

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Book SynopsisChapter 1: Logic & AI.- Chapter 2: Automated Theorem Proving & First Order Logic.- Chapter 3: Agents.- Chapter 4: Mars Rover.- Chapter 5: Multi-Agent Systems.- Chapter 6: Communication in a Multi-Agent System using WCF.- Chapter 7: Cleaning Agents: A multi-Agent System Problem.- Chapter 8: Simulation.- Chapter 9: Support Vector Machines.- Chapter 10: Decision Trees.- Chapter 11: Neural Networks.- Chapter 12: Handwritten Digit Recognition. - Chapter 13: Clustering & Multi-Objective Clustering.- Chapter 14: Metaheuristics.-  Chapter 15: Game Programming.- Chapter 16: Game Theory - Adversarial Search & Othello Game.- Chapter 17: Reinforcement Learning.Table of ContentsPractical Artificial IntelligenceChapter 1: Logic & AIChapter 2: Automated Theorem Proving & First Order LogicChapter 3: AgentsChapter 4: Mars RoverChapter 5: Multi-Agent SystemsChapter 6: Communication in a Multi-Agent System using WCFChapter 7: Cleaning Agents: A multi-Agent System ProblemChapter 8: SimulationChapter 9: Support Vector MachinesChapter 10: Decision TreesChapter 11: Neural NetworksChapter 12: Handwritten Digit RecognitionChapter 13: Clustering & Multi-Objective ClusteringChapter 14: MetaheuristicsChapter 15: Vines & CopulasChapter 16: Game ProgrammingChapter 17: Sliding Tiles Puzzle & Othello GameChapter 18: Reinforcement Learning

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Book SynopsisGain a deep understanding of data science and the thought process needed to solve problems in that field using the required techniques, technologies and skills that go into forming an interdisciplinary team. This book will enable you to set up an effective team of engineers, data scientists, analysts, and other stakeholders that can collaborate effectively on crucial aspects such as problem formulation, execution of experiments, and model performance evaluation. You'll start by delving into the fundamentals of data science classes of data science problems, data science techniques and their applications and gradually build up to building a professional reference operating model for a data science function in an organization. This operating model covers the roles and skills required in a team, the techniques and technologies they use, and the best practices typically followed in executing data science projects. Building an Effective Data Science Practiceprovides a common base of reTable of ContentsPart One: Fundamentals1. Introduction: The Data Science Process2. Data Science and your business 3. Monks vs. Cowboys: Data Science CulturesPart Two: Classes of Problems4. Classification 5. Regression6. Natural Language Processing 7. Clustering8. Anomaly Detection9. Recommendations10. Computer Vision11. Sequential Decision Making Part Three: Techniques & Technologies12. Overview13. Data Capture14. Data Preparation15. Data Visualization16. Machine Learning17. Inference18. Other tools and services19. Reference Architecture20. Monks vs. Cowboys: PraxisPart Four: Building Teams and Executing Projects21. The Skills Framework22. Building and structuring the team23. Data Science Projects Appendix FAQs

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Book SynopsisHarness the power of MATLAB for deep-learning challenges. Practical MATLAB Deep Learning, Second Edition, remains a one-of a-kind book that provides an introduction to deep learning and using MATLAB''s deep-learning toolboxes. In this book, you''ll see how these toolboxes provide the complete set of functions needed to implement all aspects of deep learning. This edition includes new and expanded projects, and covers generative deep learning and reinforcement learning.Over the course of the book, you''ll learn to model complex systems and apply deep learning to problems in those areas. Applications include: Aircraft navigation An aircraft that lands on Titan, the moon of Saturn, using reinforcement learning Stock market prediction Natural language processing Music creation usng generative deep learning Plasma control Earth sensor processing for spacecraft MATLAB Bluetooth data acquisition applied to danTable of Contents1. What is deep learning? – no changes except editoriala. Machine learning vs. deep learningb. Approaches to deep learningc. Recurrent deep learningd. Convolutional deep learning2. MATLAB machine and deep learning toolboxesa. Describe the functionality and applications of each toolboxb. Demonstrate MATLAB toolboxes related to Deep Learningc. Include the text toolbox generative toolbox and reinforcement learning toolboxd. Add more detail on each3. Finding Circles – no changes except editorial.4. Classifying movies – no changes except editorial.5. Tokamak disruption detection – this would be updated.6. Classifying a pirouette – no changes except editorial.7. Completing sentences - This would be revamped using the MATLAB Text Processing Toolbox.8. Terrain based navigation-The example in the original book would be changed to a regression approach that can interpolate position. We would switch to a terrestrial example applicable to drones.9. Stock prediction – this is a very popular chapter. We would improve the algorithm.10. Image classification – no changes except editorial.11. Orbit Determination – add inclination to the algorithm.12. Earth Sensors – a new example on how to use neural networks to measure roll and yaw from any Earth sensor.13. Generative deep learning example. This would be a neural network that generates pictures after learning an artist’s style.14. Reinforcement learning. This would be a simple quadcopter hovering control system. It would be simulation based although readers would be able to apply this to any programmable quadcopter.

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Book SynopsisIn introducing the National Security Commission on AI''s final report, Eric Schmidt, former Google CEO, and Robert Work, former Deputy Secretary of Defense, wrote: The human talent deficit is the government''s most conspicuous AI deficit and the single greatest inhibitor to buying, building, and fielding AI-enabled technologies for national security purposes. Drawing upon three decades of leading hundreds of advanced analytics and AI programs and projects in government and industry, Chris Whitlock and Frank Strickland address in this book the primary variable in the talent deficit, i.e., large numbers of qualified AI leaders.The book quickly moves from a case for action to leadership principles and practices for effectively integrating AI into programs and driving results in AI projects. The chapters convey 37 axioms - enduring truths for developing and deploying AI - and over 100 leader practices set among 50 cases and examples, 40 of which focus on AI iTable of ContentsForewordIntroduction Chapter 1. The Three Imperatives to Develop AI Leaders Chapter 2. How Leaders Should Think and Talk About AI Chapter 3. Leading the Program Chapter 4. Government Programming and Budgeting for AI Leaders Chapter 5. Leading the Project Chapter 6. Data Science for AI Leaders Chapter 7. Leading the People Chapter 8. Leading the Technology Endnotes About AI Leaders

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Book SynopsisThis book is for those who wish to understand how Python debugging is and can be used to develop robust and reliable AI, machine learning, and cloud computing software. It will teach you a novel pattern-oriented approach to diagnose and debug abnormal software structure and behavior. The book begins with an introduction to the pattern-oriented software diagnostics and debugging process that, before performing Python debugging, diagnoses problems in various software artifacts such as memory dumps, traces, and logs. Next, you'll learn to use various debugging patterns through Python case studies that model abnormal software behavior. You'll also be exposed to Python debugging techniques specific to cloud native and machine learning environments and explore how recent advances in AI/ML can help in Python debugging. Over the course of the book, case studies will show you how to resolve issues around environmental problems, crashes, hangs, resource spikes, leaks, and performancedegradatioTable of ContentsChapter 1: Fundamental Vocabulary.- Chapter 2: Pattern-Oriented Debugging.- Chapter 3: Elementary Diagnostics Patterns.- Chapter 4: Debugging Analysis Patterns.- Chapter 5: Debugging Implementation Patterns.- Chapter 6: IDE Debugging in Cloud.- Chapter 7: Debugging Presentation Patterns.- Chapter 8: Debugging Architecture Patterns.- Chapter 9: Debugging Design Patterns.- Chapter 10: Debugging Usage Patterns.- Chapter 11: Case Study: Resource Leaks.- Chapter 12: Case Study: Deadlock.- Chapter 13: Challenges of Python Debugging in Cloud Computing.- Chapter 14: Challenges of Python Debugging in AI and Machine Learning.- Chapter 15: What AI and Machine Learning Can Do for Python Debugging.- Chapter 16: The List of Debugging Patterns.

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Book SynopsisFrom understanding various technologies as an enabler to marketing efforts and its impact on decision making and mapping of various facets of customer experience, this book is recommended for marketers and learners to understand the advantages of using technology.Table of Contents1. From Data to Action: Leveraging AI in marketing1.1 AI & Marketing: Core Elements 1.2 Unleashing AI driven competitive advantage through IoT and Big Data Analytics1.3 Challenges of using AI technologies in the area of Marketing1.4 Core benefits of AI Marketing1.5 AI and future of Marketing 2. Informed Data driven decision making 2.1 Using Big data analytics for market intelligence2.2 Application of Big data analytics to marketing mix elements2.3 AI led Cognitive Data Quality Management2.4 AI-enabled marketing decisions 3. AI Marketing & Predicting Consumer Choices3.1 The value of social media for Improving Customer Engagement3.2 Optimizing marketing value, retention, customer satisfaction and loyalty3.3 Strategic applications of AI in different stages of customer journey3.4 AI in segmentation, targeting and positioning3.5 Internet trends and customer sentiment analysis 4. Unlocking Data in understanding Customers4.1 Customer Analytics4.1.1 Descriptive Customer Analytics4.1.2 Predictive Customer Analytics4.1.3 Prescriptive Customer Analytics4.2 Marketing Analytics: AI for Data Driven Marketing4.3 Customer Data Visualization & Information Management4.4 Mapping Customer Journey through big data analytics 5. Improving Experiences and Customer Satisfaction with AI5.1 AI and Product Life Cycle Management (PLM)5.2 Opportunities and Challenges of applying AI for PLM5.3 AI and granular personalization5.4 Use of AI to provide each segment of a target with tailored content 6. Value Creation & Value Capture with Artificial Intelligence6.1 Role of AI in optimizing Pricing6.2 Optimizing marketing value, retention and loyalty6.3 XR on value co-creation and customer engagement6.4 Creating value with data analytics6.5 Customer Value Modelling6.6 Marketing intelligence for optimal marketing return6.7 Creating value with data analytics 7. Reliable & Profitable AI driven Distribution7.1 Using AI for Distribution Process Management7.2 Smart Distribution7.3 Prediction of consumer behavior and improving lead generation7.4 Optimizing sales territory design with AI7.5 AI based delivery system7.6 AI integrated Logistics, inventory management, warehousing and transportation 8. Artificial Intelligence driven Promotions and Social Networking8.1 Network Modelling, Visualization and Analyzing Tools8.2 Role of Centrality in Social Networks: Influencer Marketing8.3 Sentiment Analysis and Public Opinion Mining8.4 Review Mining and Rating8.5 Big Data & scalability in Social Networks8.6 AI powered Chatbots and conversational experiences8.7 Propensity modelling for advertisement targeting and lead scoring8.8 Advertising Optimization & Viral Effects8.9 Fake News, Misinformation & Rumor Detection 9. Optimizing the future of Digital Marketing with A.I.9.1 Enhancing Interactive User Experience with AI9.2 Content Creation & Curation with AI9.3 Aligning marketing metrics with business goals9.4 Web analytics for digital marketing 10. Ethics of Artificial Intelligence for Marketing10.1 Dark side of AI in Marketing10.1.1 Consumers’ data protection rights10.1.2 Concerns about AI-enabled marketing decisions 10.1.3 Legal Concerns and Compliance issues10.2 Piracy, Security and Consumerism10.3 Ethical, Moral & Societal Challenges of AI 11. Case Studies on applications of AI11.1 AI driven cyber security and privacy11.2 Applications of AI in health care11.3 Applications of AI in tourism11.4 Applications of AI in manufacturing11.5 Applications of AI in finance

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Book SynopsisChapter 1. An Overview of Machine Learning.- Chapter 2. Data Representation.- Chapter 3. MATLAB Graphics.- Chapter 4. Kalman Filters.- Chapter 5. Adaptive Control.- Chapter 6. Neural Aircraft Control.- Chapter 7. Fuzzy Logic.- Chapter 8. Classification with Neural Nets.- Chapter 9. Simple Neural Nets.- Chapter 10. Data Classification. - Chapter 11. Neural Nets with Deep Learning.- Chapter 12. Multiple Hypothesis Testing.- Chapter 13.  Autonomous Driving with MHT.- Chapter 14. Case-Based Expert Systems.- Chapter 15. Spacecraft Attitude Determination Using Neural Nets. -Appendix A Brief History of Autonomous Learning.- Appendix B. Software for Autonomous Learning.

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Book SynopsisAre you using SAP ERP and eager to unlock the enormous value of its data? With this practical guide, SAP veterans Greg Foss and Paul Modderman show you how to use several data analysis tools to solve interesting problems with your SAP data.

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Book SynopsisWhile several market-leading companies have successfully transformed through data- and AI-driven approaches to business, the vast majority have yet to reap the benefits. This practical guide presents a battle-tested method to help you translate business decisions into tractable descriptive, predictive, and prescriptive problems.

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Book SynopsisBeing ethical takes constant diligence, and in many situations identifying the right choice can be difficult. In this in-depth book, contributors from top companies in technology, finance, and other industries share experiences and lessons learned from collecting, managing, and analyzing data ethically.

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Book SynopsisFairness is becoming a paramount consideration for data scientists. This practical book covers basic concerns related to data security and privacy to help data and AI professionals use code that's fair and free of bias.

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Book SynopsisRecent startling successes in machine intelligence using a technique called ‘deep learning’ seem to blur the line between human and machine as never before. Are computers on the cusp of becoming so intelligent that they will render humans obsolete? Harry Collins argues we are getting ahead of ourselves, caught up in images of a fantastical future dreamt up in fictional portrayals. The greater present danger is that we lose sight of the very real limitations of artificial intelligence and readily enslave ourselves to stupid computers: the ‘Surrender’. By dissecting the intricacies of language use and meaning, Collins shows how far we have to go before we cannot distinguish between the social understanding of humans and computers. When the stakes are so high, we need to set the bar higher: to rethink ‘intelligence’ and recognize its inherent social basis. Only if machine learning succeeds on this count can we congratulate ourselves on having produced artificial intelligence.Trade Review“In an age when heady promises and dark warnings from advocates of a fast-approaching “Technological Singularity” regularly make front-page news, this book offers timely words of caution.”J. Mark Bishop, Director of the Tungsten Centre for Intelligent Data Analytics, Goldsmiths, University of London“By highlighting artificial intelligence’s fundamental failures, Professor Collins provides an overdue correction to "the market-driven urge to advertise its successes”. Authoritative and technically accurate, this book will be essential for students of AI, policy makers, business innovators and the broader public for many years.”Alan Blackwell, Computer Laboratory, University of Cambridge“[Harry Collins examines] pervasive existential fears over artificial intelligence and its perceived threat in the ‘deep learning’ era. Collins probes this idea trenchantly and in considerable detail. Pointing to computers’ inability to factor in social context, master natural language use well enough to pass a severe Turing test, or wield embodied cognition, he argues that the real danger we face is not a takeover by superior computers, but slavery to stupid ones.”Barbara Kiser, Nature“[E]ven as a non-industry expert, Collins has still read deeply in this area, and consequently is posing some important, challenging questions. Having already experienced long periods of AI winters this book provides a robust challenge to those techno solutionist optimists who see AI-delivered solutions through overly rose-tinted glasses.”Simon Cocking, Irish Tech News“If you are looking for a balanced debate on artificial intelligence, or are engaged in a critique of deep learning, concerned with the implications of singularity on society, intrigued by the notion of equivalence of human and machine intelligence, a critical observer of automation vs augmentation debate, perplexed by the ongoing interest in Turing test, or curious about what AI narratives attract AI research funding, then this book, by a critical scholar, a reflective narrator and a far-sighted teacher, Harry Collins, is for you.”Karamjit S. Gill, AI & Society“Collins has provided a distinctive perspective to the conversation on AI.”Metascience“[P]resents some interesting questions, most notably about how an embeddedness based on layers of data abstraction may or may not map onto embeddedness in social context. […] Collins's frameworks often prove useful for questioning and analyzing what tends to be very messy data, and the book is sure to produce lively discussion among students and established scholars alike.”Sarah E. Sachs, Contemporary SociologyTable of Contents Chapter 1. Computers in Social Life and the Danger of the ‘Surrender’ Chapter 2. Expertise and Writing about AI: Some Reflections on the Project Chapter 3. Language and ‘Repair’ Chapter 4. Humans, Social Contexts and Bodies Chapter 5. Six Levels of Artificial Intelligence Chapter 6. Deep Learning: Precedent-Based, Pattern-Recognising Computers Chapter 7. Kurzweil’s Brain and the Sociology of Knowledge Chapter 8. How Humans Learn What Computers Can’t Chapter 9. Two Models of Artificial Intelligence and the Way Forward Chapter 10. The Editing Test and Other New Versions of the Turing Test Appendix 1: How the Internet Works Today Appendix 2: Little Dogs

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Book SynopsisRecent startling successes in machine intelligence using a technique called ‘deep learning’ seem to blur the line between human and machine as never before. Are computers on the cusp of becoming so intelligent that they will render humans obsolete? Harry Collins argues we are getting ahead of ourselves, caught up in images of a fantastical future dreamt up in fictional portrayals. The greater present danger is that we lose sight of the very real limitations of artificial intelligence and readily enslave ourselves to stupid computers: the ‘Surrender’. By dissecting the intricacies of language use and meaning, Collins shows how far we have to go before we cannot distinguish between the social understanding of humans and computers. When the stakes are so high, we need to set the bar higher: to rethink ‘intelligence’ and recognize its inherent social basis. Only if machine learning succeeds on this count can we congratulate ourselves on having produced artificial intelligence.Trade Review“In an age when heady promises and dark warnings from advocates of a fast-approaching “Technological Singularity” regularly make front-page news, this book offers timely words of caution.”J. Mark Bishop, Director of the Tungsten Centre for Intelligent Data Analytics, Goldsmiths, University of London“By highlighting artificial intelligence’s fundamental failures, Professor Collins provides an overdue correction to "the market-driven urge to advertise its successes”. Authoritative and technically accurate, this book will be essential for students of AI, policy makers, business innovators and the broader public for many years.”Alan Blackwell, Computer Laboratory, University of Cambridge“[Harry Collins examines] pervasive existential fears over artificial intelligence and its perceived threat in the ‘deep learning’ era. Collins probes this idea trenchantly and in considerable detail. Pointing to computers’ inability to factor in social context, master natural language use well enough to pass a severe Turing test, or wield embodied cognition, he argues that the real danger we face is not a takeover by superior computers, but slavery to stupid ones.”Barbara Kiser, Nature“[E]ven as a non-industry expert, Collins has still read deeply in this area, and consequently is posing some important, challenging questions. Having already experienced long periods of AI winters this book provides a robust challenge to those techno solutionist optimists who see AI-delivered solutions through overly rose-tinted glasses.”Simon Cocking, Irish Tech News“If you are looking for a balanced debate on artificial intelligence, or are engaged in a critique of deep learning, concerned with the implications of singularity on society, intrigued by the notion of equivalence of human and machine intelligence, a critical observer of automation vs augmentation debate, perplexed by the ongoing interest in Turing test, or curious about what AI narratives attract AI research funding, then this book, by a critical scholar, a reflective narrator and a far-sighted teacher, Harry Collins, is for you.”Karamjit S. Gill, AI & Society“Collins has provided a distinctive perspective to the conversation on AI.”Metascience“[P]resents some interesting questions, most notably about how an embeddedness based on layers of data abstraction may or may not map onto embeddedness in social context. […] Collins's frameworks often prove useful for questioning and analyzing what tends to be very messy data, and the book is sure to produce lively discussion among students and established scholars alike.”Sarah E. Sachs, Contemporary SociologyTable of Contents Chapter 1. Computers in Social Life and the Danger of the ‘Surrender’ Chapter 2. Expertise and Writing about AI: Some Reflections on the Project Chapter 3. Language and ‘Repair’ Chapter 4. Humans, Social Contexts and Bodies Chapter 5. Six Levels of Artificial Intelligence Chapter 6. Deep Learning: Precedent-Based, Pattern-Recognising Computers Chapter 7. Kurzweil’s Brain and the Sociology of Knowledge Chapter 8. How Humans Learn What Computers Can’t Chapter 9. Two Models of Artificial Intelligence and the Way Forward Chapter 10. The Editing Test and Other New Versions of the Turing Test Appendix 1: How the Internet Works Today Appendix 2: Little Dogs

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Book SynopsisCommunication and artificial intelligence (AI) are closely related. It is communication – particularly interpersonal conversational interaction – that provides AI with its defining test case and experimental evidence. Likewise, recent developments in AI introduce new challenges and opportunities for communication studies. Technologies such as machine translation of human languages, spoken dialogue systems like Siri, algorithms capable of producing publishable journalistic content, and social robots are all designed to communicate with users in a human-like way. This timely and original textbook provides educators and students with a much-needed resource, connecting the dots between the science of AI and the discipline of communication studies. Clearly outlining the topic's scope, content and future, the text introduces key issues and debates, highlighting the importance and relevance of AI to communication studies. In lively and accessible prose, David Gunkel provides a new generation with the information, knowledge, and skills necessary to working and living in a world where social interaction is no longer restricted to humans. The first work of its kind, An Introduction to Communication and Artificial Intelligence is the go-to textbook for students and scholars getting to grips with this crucial interdisciplinary topic.Trade Review“Gunkel’s book is an accessible but technically savvy monograph introducing students and scholars of communication and computer science to the intersections between AI and communication. … Gunkel’s book will also be a particularly useful resource to instructors, not only due to its accessible language and wide- reaching scope, but also thanks to the five ‘Maker exercises’ included in the last section. These provide useful entry points for students that are not versed in computer programming for experimenting with simple computer programs.”Communication Theory “An introduction to communication and artificial intelligence aims and succeeds in making sense of AI for students and scholars in social sciences.”CommunicationsTable of ContentsPreface Part I: Introduction and Orientation 1 Introduction 2 Communication and AI 3 Basic Concepts and Terminology Part II: Applications 4 Machine Translation 5 Natural Language Processing 6 Computational Creativity 7 Social Robots Part III: Impact and Consequences 8 Social Issues 9 Social Responsibility and Ethics Part IV: Maker Exercises Introduction Exercise 1 – Demystifying ELIZA Exercise 2 – Algorithms Exercise 3 – Machine Translation Exercise 4 – Chatbot and Quasi-Loebner Prize Exercise 5 – Template NLG Notes References Index

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Book SynopsisThe world of work is undergoing the most significant change since the Industrial revolution. Cognitive A.I. is driving world change faster than at any time in history. There are massive advantages for employers who act and act quickly. At precisely the same time, COVID has been a wake-up call. Organizations have discovered that they employ too many people, and the realization – many can be more productive working remotely. Productivity increases, reduction in office space and management are all being actioned through home working.A significant study on Homeworkers indicates that worldwide, 1 in 5 will be working from home. Already many Global companies have announced this year plans to reduce office space by 40%. Productivity results that have been realized from remote working have exceeded expectations, which will accelerate.This innovative book will guide you through A.I., how it will affect employment and existing processes, and what the employer and employee can expect in the new and rapidly changing world of work.

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Book SynopsisThis book covers an introduction to convex optimization, one of the powerful and tractable optimization problems that can be efficiently solved on a computer. The goal of the book is tohelp develop a sense of what convex optimization is, and how it can be used in a widening array of practical contexts with a particular emphasis on machine learning.The first part of the book covers core concepts of convex sets, convex functions, and related basic definitions that serve understanding convex optimization and its corresponding models. The second part deals with one very useful theory, called duality, which enables us to: (1) gain algorithmic insights; and (2) obtain an approximate solution to non-convex optimization problems which are often difficult to solve. The last part focuses on modern applications in machine learning and deep learning.A defining feature of this book is that it succinctly relates the “story” of how convex optimization plays a role, via historical examples and trending machine learning applications. Another key feature is that it includes programming implementation of a variety of machine learning algorithms inspired by optimization fundamentals, together with a brief tutorial of the used programming tools. The implementation is based on Python, CVXPY, and TensorFlow. This book does not follow a traditional textbook-style organization, but is streamlined via a series of lecture notes that are intimately related, centered around coherent themes and concepts. It serves as a textbook mainly for a senior-level undergraduate course, yet is also suitable for a first-year graduate course. Readers benefit from having a good background in linear algebra, some exposure to probability, and basic familiarity with Python.Trade ReviewThe topic is surely still of great interest, since courses on Convex Optimization, in conjunction or not with Machine Learning applications, are ubiquitous in Engineering curricula around the world. What appears as somewhat novel here is the juxtaposition of Part I and II on convex optimization and duality with Part III on machine learning applications. The emphasis on Python, TensorFlow etc. is also practically very important and surely appreciated by the students, especially if presented via challenging practical problems. More than completeness, I believe that what is important is that the book gives a meaningful “cut” through these topics, as this books appears to do. It seems important that the author tries to motivate and link together as much as possible part III with the previous parts, explaining why part I and II are important for part III, but also highlighting what the limits of convex models are and at which point they need be superseded by more general models. Giuseppe Carlo Calafiore, Professor at the Politecnico di Torino, Italy, and visiting Professor at UC Berkeley -- Giuseppe Carlo CalafioreI have looked at the manuscript and my impression is positive, the aims and scope are actual and comprehensive. The intended audience is senior undergraduates and early graduate, which differs the book significantly from several competing books , and this should be an advantage. I would say that a good senior undergraduate level textbook on convex optimization would, in my opinion, be very timely. Arkadi Nemirovski, Georgia Tech, USA -- Arkadi NemirovskiTable of Contents Preface 1 Convex Optimization Basics 1.1 Overview of the book 1.2 Definition of convex optimization 1.3 Tractability of convex optimization and gradient descent 1.4 Linear Program 1.5 Least Squares 1.6 Test error, regularization and CVXPY implementation 1.7 Computed tomography 1.8 Quadratic program 1.9 Second-order cone program 1.10 Semi-definite program 1.11 SDP relaxation 1.12 Problem Sets 2 Duality 2.1 Strong duality 2.2 Interior point method 2.3 Proof of strong duality theorem 2.4 Weak duality 2.5 Lagrange relaxation for Boolean problems 2.6 Lagrange relaxation for the MAXCUT problem 2.7 Problem Sets 3 Machine Learning Applications 3.1 Supervised learning and optimization 3.2 Logistic regression 3.3 Deep learning 3.4 Deep learning II 3.5 DL: TensorFlow implementation 3.6 Unsupervised Learning: Generative modeling 3.7 Generative Adversarial Networks (GANs) 3.8 GANs: TensorFlow implementation 3.9 Wasserstein GAN 3.10 Wasserstein GAN II 3.11 Wasserstein GAN: TensorFlow implementation 3.12 Fair machine learning 3.13 A fair classifier and its connection to GANs 3.14 A fair classifier: TensorFlow implementation Appendices

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Book SynopsisThis book delves into the theoretical underpinnings of differential privacy, its use in machine learning systems, practical implementation details, and its broader social and legal ramifications

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Book SynopsisHealthcare and technology are at a convergence point where significant changes are poised to take place. The vast and complex requirements of medical record keeping, coupled with stringent patient privacy laws, create an incredibly unwieldy maze of health data needs. While the past decade has seen giant leaps in AI, machine learning, wearable technologies, and data mining capacities that have enabled quantities of data to be accumulated, processed, and shared around the globe. Transforming Healthcare with Big Data and AI examines the crossroads of these two fields and looks to the future of leveraging advanced technologies and developing data ecosystems to the healthcare field. This book is the product of the Transforming Healthcare with Data conference, held at the University of Southern California. Many speakers and digital healthcare industry leaders contributed multidisciplinary expertise to chapters in this work. Authors’ backgrounds range from data scientists, healthcare experts, university professors, and digital healthcare entrepreneurs. If you have an understanding of data technologies and are interested in the future of Big Data and A.I. in healthcare, this book will provide a wealth of insights into the new landscape of healthcare.

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Book SynopsisHealthcare and technology are at a convergence point where significant changes are poised to take place. The vast and complex requirements of medical record keeping, coupled with stringent patient privacy laws, create an incredibly unwieldy maze of health data needs. While the past decade has seen giant leaps in AI, machine learning, wearable technologies, and data mining capacities that have enabled quantities of data to be accumulated, processed, and shared around the globe. Transforming Healthcare with Big Data and AI examines the crossroads of these two fields and looks to the future of leveraging advanced technologies and developing data ecosystems to the healthcare field. This book is the product of the Transforming Healthcare with Data conference, held at the University of Southern California. Many speakers and digital healthcare industry leaders contributed multidisciplinary expertise to chapters in this work. Authors’ backgrounds range from data scientists, healthcare experts, university professors, and digital healthcare entrepreneurs. If you have an understanding of data technologies and are interested in the future of Big Data and A.I. in healthcare, this book will provide a wealth of insights into the new landscape of healthcare.

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Book SynopsisThere are many myths about Artificial Intelligence (AI) relating to what it is and what it can and cannot do. The people making decisions on AI projects are often not technologically savvy and unable to find easy answers. The spending on and the returns from AI projects are not necessarily straightforward. Part of the reason for this is the lack of understanding of the impact of critical decision criteria. AI touches on many ethical concepts - data privacy, validity, and, more importantly, its potential misuse. AI often replaces human decision-making, as managers do not clearly understand the implications of those choices. This book provides an easy and accessible guide for practitioners without a technological background to understand AI. It guides the reader through the fundamental issues confronting decision-makers. It offers advice on 'how to ask relevant questions' using the 15 decision scales. There is currently no comparable book on the market that acts as a pocketbook management reference guide for the AI layman.Table of Contents What is AI? AI Manager's Dilemma AI Smart Kit Scales Scale 1: AI Expertise Level Scale 2: AI Interoperability Level Scale 3: AI and Global Embeddedness Scale 4: AI Data Types Scale 5: AI Data Management Scale 6: AI and Human Teams Scale 7: AI and Human Productivity Scale 8: AI Onboarding Scale 9: AI and the Sensory Experience Scale 10: AI and the Human Interface Scale 11: AI and Regulations Scale 12: AI and IP Scale 13: AI and Impact on Sustainable Development Scale 14: AI and Accountability Scale 15: AI and Crises

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Book SynopsisThere are many myths about Artificial Intelligence (AI) relating to what it is and what it can and cannot do. The people making decisions on AI projects are often not technologically savvy and unable to find easy answers. The spending on and the returns from AI projects are not necessarily straightforward. Part of the reason for this is the lack of understanding of the impact of critical decision criteria. AI touches on many ethical concepts - data privacy, validity, and, more importantly, its potential misuse. AI often replaces human decision-making, as managers do not clearly understand the implications of those choices. This book provides an easy and accessible guide for practitioners without a technological background to understand AI. It guides the reader through the fundamental issues confronting decision-makers. It offers advice on 'how to ask relevant questions' using the 15 decision scales. There is currently no comparable book on the market that acts as a pocketbook management reference guide for the AI layman.Table of Contents What is AI? AI Manager's Dilemma AI Smart Kit Scales Scale 1: AI Expertise Level Scale 2: AI Interoperability Level Scale 3: AI and Global Embeddedness Scale 4: AI Data Types Scale 5: AI Data Management Scale 6: AI and Human Teams Scale 7: AI and Human Productivity Scale 8: AI Onboarding Scale 9: AI and the Sensory Experience Scale 10: AI and the Human Interface Scale 11: AI and Regulations Scale 12: AI and IP Scale 13: AI and Impact on Sustainable Development Scale 14: AI and Accountability Scale 15: AI and Crises

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Book SynopsisThis book will provide a comprehensive overview of business analytics, for those who have either a technical background (quantitative methods) or a practitioner business background. Business analytics, in the context of the 4th Industrial Revolution, is the "new normal" for businesses that operate in this digital age. This book provides a comprehensive primer and overview of the field (and related fields such as Business Intelligence and Data Science). It will discuss the field as it applies to financial institutions, with some minor departures to other industries. Readers will gain understanding and insight into the field of data science, including traditional as well as emerging techniques. Further, many chapters are dedicated to the establishment of a data-driven team – from executive buy-in and corporate governance to managing and quantifying the return of data-driven projects.

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Book SynopsisThis book will provide a comprehensive overview of business analytics, for those who have either a technical background (quantitative methods) or a practitioner business background. Business analytics, in the context of the 4th Industrial Revolution, is the "new normal" for businesses that operate in this digital age. This book provides a comprehensive primer and overview of the field (and related fields such as Business Intelligence and Data Science). It will discuss the field as it applies to financial institutions, with some minor departures to other industries. Readers will gain understanding and insight into the field of data science, including traditional as well as emerging techniques. Further, many chapters are dedicated to the establishment of a data-driven team – from executive buy-in and corporate governance to managing and quantifying the return of data-driven projects.

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