Data analytics and AI

Data analytics and AI edited by Jay Liebowitz.

Analytics and artificial intelligence (AI), what are they good for? The bandwagon keeps answering, absolutely everything! Analytics and artificial intelligence have captured the attention of everyone from top executives to the person in the street. While these disciplines have a relatively long hist...

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Bibliographic Details
Other Authors: Liebowitz, Jay, 1957- (Editor)
Format: eBook
Language:English
Published: Boca Raton : Auerbach, 2020.
Series:Data Analytics Applications Ser.
Subjects:
Statistics > Data processing.
Artificial intelligence.
artificial intelligence.
COMPUTERS / Database Management / Data Mining
COMPUTERS / Artificial Intelligence
COMPUTERS / Database Management / General
Online Access:Click for online access
Table of Contents:
  • 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

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