Big Data and Analytics Applications in Government: Current Practices and Future Opportunities (Data Analytics Applications)

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