Data Analytics and AI.

Cover -- Half Title -- Series Page -- Title Page -- Copyright Page -- Dedication -- Table of Contents -- Foreword -- Preface -- List of Contributors -- Editor -- Chapter 1 Unraveling Data Science, Artificial Intelligence, and Autonomy -- 1.1 The Beginnings of Data Science -- 1.2 The Beginnings of Artificial Intelligence -- 1.3 The Beginnings of Autonomy -- 1.4 The Convergence of Data Availability and Computing -- 1.5 Machine Learning the Common Bond -- 1.5.1 Supervised Learning -- 1.5.2 Unsupervised Learning -- 1.5.3 Reinforcement Learning -- 1.6 Data Science Today -- 1.7 Artificial Intelligence Today -- 1.8 Autonomy Today -- 1.9 Summary -- References -- Chapter 2 Unlock the True Power of Data Analytics with Artificial Intelligence -- 2.1 Introduction -- 2.2 Situation Overview -- 2.2.1 Data Age -- 2.2.2 Data Analytics -- 2.2.3 Marriage of Artificial Intelligence and Analytics -- 2.2.4 AI-Powered Analytics Examples -- 2.3 The Way Forward -- 2.4 Conclusion -- References -- Chapter 3 Machine Intelligence and Managerial Decision-Making -- 3.1 Managerial Decision-Making -- 3.1.1 What Is Decision-Making? -- 3.1.2 The Decision-Making Conundrum -- 3.1.3 The Decision-Making Process -- 3.1.4 Types of Decisions and Decision-Making Styles -- 3.1.5 Intuition and Reasoning in Decision-Making -- 3.1.6 Bounded Rationality -- 3.2 Human Intelligence -- 3.2.1 Defining What Makes Us Human -- 3.2.2 The Analytical Method -- 3.2.3 "Data-Driven" Decision-Making -- 3.3 Are Machines Intelligent? -- 3.4 Artificial Intelligence -- 3.4.1 What Is Machine Learning? -- 3.4.2 How Do Machines Learn? -- 3.4.3 Weak, General, and Super AI -- 3.4.3.1 Narrow AI -- 3.4.3.2 General AI -- 3.4.3.3 Super AI -- 3.4.4 The Limitations of AI -- 3.5 Matching Human and Machine Intelligence -- 3.5.1 Human Singularity -- 3.5.2 Implicit Bias -- 3.5.3 Managerial Responsibility.

3.5.4 Semantic Drift -- 3.6 Conclusion -- References -- Chapter 4 Measurement Issues in the Uncanny Valley: The Interaction between Artificial Intelligence and Data Analytics -- 4.1 A Momentous Night in the Cold War -- 4.2 Cybersecurity -- 4.3 Measuring AI/ML Performance -- 4.4 Data Input to AI Systems -- 4.5 Defining Objectives -- 4.6 Ethics -- 4.7 Sharing Data-or Not -- 4.8 Developing an AI-Aware Culture -- 4.9 Conclusion -- References -- Chapter 5 An Overview of Deep Learning in Industry -- 5.1 Introduction -- 5.1.1 An Overview of Deep Learning -- 5.1.1.1 Deep Learning Architectures -- 5.1.2 Deep Generative Models -- 5.1.3 Deep Reinforcement Learning -- 5.2 Applications of Deep Learning -- 5.2.1 Recognition -- 5.2.1.1 Recognition in Text -- 5.2.1.2 Recognition in Audio -- 5.2.1.3 Recognition in Video and Images -- 5.2.2 Content Generation -- 5.2.2.1 Text Generation -- 5.2.2.2 Audio Generation -- 5.2.2.3 Image and Video Generation -- 5.2.3 Decision-Making -- 5.2.3.1 Autonomous Driving -- 5.2.3.2 Automatic Game Playing -- 5.2.3.3 Robotics -- 5.2.3.4 Energy Consumption -- 5.2.3.5 Online Advertising -- 5.2.4 Forecasting -- 5.2.4.1 Forecasting Physical Signals -- 5.2.4.2 Forecasting Financial Data -- 5.3 Conclusion -- References -- Chapter 6 Chinese AI Policy and the Path to Global Leadership: Competition, Protectionism, and Security -- 6.1 The Chinese Perspective on Innovation and AI -- 6.2 AI with Chinese Characteristics -- 6.3 National Security in AI -- 6.4 "Security" or "Protection" -- 6.5 A(Eye) -- 6.6 Conclusions -- Bibliography -- Chapter 7 Natural Language Processing in Data Analytics -- 7.1 Background and Introduction: Era of Big Data -- 7.1.1 Use Cases of Unstructured Data -- 7.1.2 The Challenge of Unstructured Data -- 7.1.3 Big Data and Artificial Intelligence -- 7.2 Data Analytics and AI.

7.2.1 Data Analytics: Descriptive vs. Predictive vs. Prescriptive -- 7.2.2 Advanced Analytics toward Machine Learning and Artificial Intelligence -- 7.2.2.1 Machine Learning Approaches -- 7.3 Natural Language Processing in Data Analytics -- 7.3.1 Introduction to Natural Language Processing -- 7.3.2 Sentiment Analysis -- 7.3.3 Information Extraction -- 7.3.4 Other NLP Applications in Data Analytics -- 7.3.5 NLP Text Preprocessing -- 7.3.6 Basic NLP Text Enrichment Techniques -- 7.4 Summary -- References -- Chapter 8 AI in Smart Cities Development: A Perspective of Strategic Risk Management -- 8.1 Introduction -- 8.2 Concepts and Definitions -- 8.2.1 How Are AI, Smart Cities, and Strategic Risk Connected? -- 8.3 Methodology and Approach -- 8.4 Examples of Creating KPIs and KRIs Based on Open Data -- 8.4.1 Stakeholder Perspective -- 8.4.2 Financial Resources Management Perspective -- 8.4.3 Internal Process Perspective -- 8.4.4 Trained Public Servant Perspective -- 8.5 Discussion -- 8.6 Conclusion -- References -- Chapter 9 Predicting Patient Missed Appointments in the Academic Dental Clinic -- 9.1 Introduction -- 9.2 Electronic Dental Records and Analytics -- 9.3 Impact of Missed Dental Appointments -- 9.4 Patient Responses to Fear and Pain -- 9.4.1 Dental Anxiety -- 9.4.2 Dental Avoidance -- 9.5 Potential Data Sources -- 9.5.1 Dental Anxiety Assessments -- 9.5.2 Clinical Notes -- 9.5.3 Staff and Patient Reporting -- 9.6 Conclusions -- References -- Chapter 10 Machine Learning in Cognitive Neuroimaging -- 10.1 Introduction -- 10.1.1 Overview of AI, Machine Learning, and Deep Learning in Neuroimaging -- 10.1.2 Cognitive Neuroimaging -- 10.1.3 Functional Near-Infrared Spectroscopy -- 10.2 Machine Learning and Cognitive Neuroimaging -- 10.2.1 Challenges.

10.3 Identifying Functional Biomarkers in Traumatic Brain Injury Patients Using fNIRS and Machine Learning -- 10.4 Finding the Correlation between Addiction Behavior in Gaming and Brain Activation Using fNIRS -- 10.5 Current Research on Machine Learning Applications in Neuroimaging -- 10.6 Summary -- References -- Chapter 11 People, Competencies, and Capabilities Are Core Elements in Digital Transformation: A Case Study of a Digital Transformation Project at ABB -- 11.1 Introduction -- 11.1.1 Objectives and Research Approach -- 11.1.2 Challenges Related to the Use of Digitalization and AI -- 11.2 Theoretical Framework -- 11.2.1 From Data Collection into Knowledge Management and Learning Agility -- 11.2.2 Knowledge Processes in Organizations -- 11.2.3 Framework for Competency, Capability, and Organizational Development -- 11.2.4 Management of Transient Advantages Is a Core Capability in Digital Solution Launch and Ramp-Up -- 11.3 Digital Transformation Needs an Integrated Model for Knowledge Management and Transformational Leadership -- 11.4 Case Study of the ABB Takeoff Program: Innovation, Talent, and Competence Development for Industry 4.0 -- 11.4.1 Background for the Digital Transformation at ABB -- 11.4.2 The Value Framework for IIoT and Digital Solutions -- 11.4.3 Takeoff for Intelligent Industry: Innovation, Talent, and Competence Development for Industry 4.0 -- 11.4.4 Case 1: ABB Smartsensor: An Intelligent Concept for Monitoring -- 11.4.5 Case 2: Digital Powertrain: Optimization of Industrial System Operations -- 11.4.6 Case 3: Autonomous Ships: Remote Diagnostics and Collaborative Operations for Ships -- 11.5 Conclusions and Future Recommendations -- 11.5.1 Conclusions -- 11.5.2 Future Recommendations -- 11.5.3 Critical Roles of People, Competency, and Capability Development -- References.

Chapter 12 AI-Informed Analytics Cycle: Reinforcing Concepts -- 12.1 Decision-Making -- 12.1.1 Data, Knowledge, and Information -- 12.1.2 Decision-Making and Problem-Solving -- 12.2 Artificial Intelligence -- 12.2.1 The Three Waves of AI -- 12.3 Analytics -- 12.3.1 Analytics Cycle -- 12.4 The Role of AI in Analytics -- 12.5 Applications in Scholarly Data -- 12.5.1 Query Refinement -- 12.5.2 Complex Task and AI Method -- 12.6 Concluding Remarks -- References -- Index.

Two hot topics in recent years are data analytics and AI. Unfortunately, both communities have not done been communicating and collaborating with each other to build the necessary synergies. This book presents theory, applications, and case studies to bridge the gap between these fields.

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Electronic reproduction. Ann Arbor, Michigan : ProQuest Ebook Central, 2024. Available via World Wide Web. Access may be limited to ProQuest Ebook Central affiliated libraries.