What is Artificial Intelligence (AI)? Artificial intelligence is a collection of several technologies acting in unity to enable machines to sense, understand, act, and learn with simulated human intelligence. Within AI, a few important concepts must be understood:
Machine Learning : an algorithm that lets a computer learn from examples without being specifically programmed. Deep Learning : A subdivision of machine learning that uses artificial neural networks to carry out data processing. Neural Networks: An interconnected network modeled after the human neuronal network.
In THE AI CITIZEN, I draw on over a decade’s worth of industry experience to explore the dynamic and enigmatic world of Artificial Intelligence. Business professionals, industry leaders, policymakers, and just about anyone with a remote or keen interest in AI will find this book tailored to their need for a relatable exploration of AI. THE AI CITIZEN presents AI as a global nation that cuts across international borders and one which everyone will – directly or indirectly – interact with. In simple and non-technical terms, THE AI CITIZEN deconstructs the world of AI and Industry 4.0.
From examining the origins of AI, the book takes a critical look at its present state and further reflects on the ethical and moral issues to be contended with as the AI nation grows. The book goes on to explain how to put together all of its content to create a winning AI strategy. This knowledge, however, is not only meant for the corporate world. Even children need to learn about Al, so they grow up understanding and stepping into roles that ensure the continuous development of Al for the benefit of humanity.
Who is this Book for? Without compromising quality, this book has been written in language simple enough for anyone to become familiar with core Al principles and concepts. The book will also serve as a reliable guide to business Al (non-technical Al) professionals who need to incorporate Al into their operations. This guidance includes but is not limited to setting up a goal-oriented Al strategy, creating an effective Al team that works with the right mindset, selecting a fitting machine learning algorithm for a particular project, and understanding how and which Al applications impact their sector the most.
I. Preface II. Who is this Book for? 1. Introduction 1.1 Background 1.2 What is Artificial Intelligence (AI)? 1.3 AI is not a Robot 1.4 The AI Race 1.5 AI & The Global Economy 1.6 Why Should You Care? 1.7 What AI Cannot Do Now? 2. Industry 4.0 2.1 The 4th Industrial Revolution 2.2 What is the 4th Industrial Revolution ? 2.3 Robotic Process Automation (RPA) 2.4 Blockchain Technology 2.5 Internet of Things 2.6 Robotics 2.7 Smart City 2.8 Cyber Security 2.9 Cloud Computing 2.10 Cognitive Computing 2.11 Quantum Computation 2.12 Digital Reality (DR) 3. AI 101 3.1 History: The Evolution of AI 3.2 Enablers: Why AI is Booming Now? 3.3 Types of Artificial Intelligence 3.3.1 Stages of development 1.3.2 Functionality-based AI Types 1.3.3 The 4 AI Applications 1.3.4 Technique-based AI 3.4 How it Works? 3.5 Why You need AI? 3.6 Social Robots 3.7 The AI Debate 4. Data 4.1 What is Data? 4.2 Types of Data 4.3 Why Data is the Fuel? 4.4 What is Big Data? 4.5 The 5Vs 4.6 Big Data Life Cycle 4.7 Data Collection 4.8 Data Management & Analytics 4.9 Hadoop Framework 4.10 AI & dаta: Common Misconceptions 4.11 Additional Key Concepts 4.12 Big Data : Risks and Ethics 5. Introduction to Machine Learning 5.1 What is Machine Learning 5.2 Basic Concepts 5.3 How Machines Learn ? 5.4 Types of Machine Learning 6.Supervised Learning 6.1 What is Supervised Learning? 6.2 Types of Machine Learning 6.3 What is Classification ? 6.4 Classification Algorithms 6.4.1 The K-Nearest Neighbor Classifier 6.4.2 Support Vector Machines 6.4.3 Linear Classifiers: Logistic Regression & Naïve Bayes 6.4.4 Decision Tree 6.4.5 Random Forest 6.4.6 Neural Network 6.5 Examples of Classification 6.6 What is Regression? 6.6.1 Understanding Linear Regression 6.6.2 Learning Linear Regression 6.6.3 Visualizing Linear Regression 6.6.4 Applying Regression to Label Predictions 6.7 Regression Algorithms 6.7.1 Simple Linear Regression Model 6.7.2 Lasso Regression 6.7.3 Support Vector Machines 6.8 Examples of Regression 7. Unsupervised Learning 7.1 What is Unsupervised Learning? 7.1.2 Types of Unsupervised Learning 7.2 What is Clustering ? 7.3 Types of Clustering 7.3.1 Methods of Clustering 7.3.2 Types of Clustering 7.4 Dimensionality Reduction 7.4.1 What is Dimensionality Reduction? 7.4.2 Methods of Dimensionality Reduction 7.4.3 Pros and Cons of Dimensionality Reduction 7.5 Examples of Dimensionality Reduction 8. Reinforcement Leaning 8.1 What is Reinforcement Learning? 8.1.1 Difference between Reinforcement Learning and Supervised Learning 8.1.2 How Reinforcement Learning Works 8.2 Reinforcement Learning Algorithms 8.2.1 Classification of Reinforcement Learning Algorithms 8.2.2 Types of Reinforcement Learning 8.2.3 Learning Models of Reinforcement Learning 8.3 Applications of Reinforcement Learning 8.3.1 Self-Driving Cars 8.3.2 Traffic Control 8.3.3 Robotics 8.3.4 Web System Configuration 8.3.5 Chemistry 8.3.6 Personalized Recommendations 8.3.7 Games 9. Deep Learning 9.1 The Fundamentals of Artificial Neural Networks 9.2 The Construction of Artificial Neural Networks 9.2.1 Weights and Inputs 9.2.2 Activations and Outputs 9.2.3 Neuron Activation 9.2.4 The Perceptron 9.2.5 Networking Neurons 9.2.6 Comparing the AI Brain and the Human Brain 9.3 What is Deep Learning ? 9.4 Deep Learning vs Machine Learning 9.5 Types of Deep Learning 9.5.1 Feedforward Neural Networks 9.5.2 Backpropagation 9.6 Convolutional Neural Networks 9.7 Recurrent Neural Networks (RNN) 9.8 Generative Adversarial Networks (GAN) 10. The Machine Learning Process Framework 10.1 The Machine Learning Process Framework 10.2 Problem Formulation 10.3 Data Preprocessing 10.4 Feature Engineering 10.5 Data Splitting 10.6 ML Algorithm Selection 10.7 Model Training 10.8 Model Evaluation 10.9 Model Improvement 10.10 Model Deployment 11. AI Capabilities 11.1 Speech Recognition 11.2 Natural Language Processing 11.2.1 Example 11.3 Computer Vision 11.3.1 What is it? 11.3.2 How Does it Work? 11.3.3 Industry Applications 11.4 Drones, Autonomous Vehicles & Intelligent Robotics 11.4.1 What is it? 11.4.2 How does it work? 11.2.3 The DARPA Challenge 11.2.4 Valkyrie 11.5 Predictive Analytics 11.5.1 What is it? 11.5.2 How does it work? 11.5.3 Example 12. Industry-Based AI Applications 12.1 AI in Education 12.1.1 Student Universal Access 12.1.2 Automate Administrative Tasks 12.1.3 Out-of-School Tutoring and Support 12.2 AI in Healthcare 12.2.1 Staying Healthy 12.2.2 Early Detection 12.2.3 Diagnosis 12.2.4 Improve Decision-Making 12.2.5 Treatment 12.2.6 End-of-Life Care 12.2.7 Research 12.2.8 Training 12.3 AI in Meteorology 12.4 AI in Business Analysis 12.5 AI in Communication 12.6 AI in Industrial Operations 12.7 AI in Pharmaceuticals 12.8 AI in Property Management 12.8.1 Automated Property Management 12.8.2 The Recommendation System 12.8.3 Virtual Assistants 12.9 AI in Financial Services 12.10 AI in Security 12.10.1 AI and Security Applications 12.11 AI in Marketing & Retail 12.12 AI in E-Commerce 12.12.1 Chatbots & Virtual Assistants 12.12.2 Intelligent Product Recommendations 12.12.3 Personalization Services 12.13 AI in Oil & Gas 12.14 AI in Space 12.14.1 Space Exploration 12.14.2 Astronaut Assistants 12.14.3 Applications of AI in Space 12.15 AI in Agriculture 12.16 AI in Politics & Economics 12.17 AI in Logistics & Transportation 12.17.1 Autonomous Vehicles 13. Responsible AI 13.1 AI Governance 13.2 AI Ethics 13.3 Human-AI Collaboration 13.4 AI & Society 13.5 Society-Related AI Projects 13.6 Singularity 13.7 Our Responsibility to Our Children 14. The Future of Jobs 14.1 AI and Job Markets 14.2 What Jobs AI Will Replace 14.3 What Jobs AI Won’t Replace 14.5 The Most In-Demand Future Jobs 15. The AI Economy 15.1 Latest Trends in AI 15.1.1 Computer Vision 15.1.2 Generative Adversarial Networks 15.1.3 Natural Language Processing 15.1.4 Training & Deployment of Powerful Models 15.2 AI: Economic Potential 15.3 The AI Patent Race 15.4 AI & Policy Implications 15.4.1 Policy Implications 15.5 Economic and Societal Cost 15.6 Taxing Robots & Universal Basic Income (UBI) 15.6.1 Taxing Robots 15.6.2 Universal Basic Income (UBI) 15.7 Maximizing the AI Benefits 15.8 Private vs. Government Sector 15.9 The AI Start-Up Economy 16. Business AI 16.1 What Is In It For Enterprises? 16.2 Why You Need to Adopt AI Fast? 16.3 How to Build Your AI Strategy ? 16.4 The Agile Approach 16.5 Methodologies & Tools 16.6 Best Practices in Developing AI 17. Conclusion Bibliography Index
Название: The Artificial Intelligence Citizen