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Data analytics and AI
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...
| Clasificación: | Libro Electrónico |
|---|---|
| Otros Autores: | |
| Formato: | eBook |
| Idioma: | Inglés |
| Publicado: |
Boca Raton :
Auerbach,
2020.
|
| Colección: | Data Analytics Applications Ser.
|
| Temas: | |
| Acceso en línea: | Texto completo |
MARC
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|---|---|---|---|
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| 003 | OCoLC | ||
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| 082 | 4 | |a 001.4/22028563 |q OCoLC |2 23/eng/20230216 | |
| 049 | |a UAMI | ||
| 245 | 0 | 0 | |a Data analytics and AI |c edited by Jay Liebowitz. |
| 264 | 1 | |a Boca Raton : |b Auerbach, |c 2020. | |
| 300 | |a 1 online resource |b illustrations (black and white). | ||
| 490 | 1 | |a Data Analytics Applications Ser. | |
| 505 | 0 | |a 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 | |
| 505 | 8 | |a 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 | |
| 505 | 8 | |a 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 | |
| 505 | 8 | |a 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 | |
| 505 | 8 | |a 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 | |
| 520 | |a 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 history, within the last ten or so years they have exploded into corporate business and public consciousness. Organizations have rushed to embrace data-driven decision making. Companies everywhere are turning out products boasting that "artificial intelligence is included." We are indeed living in exciting times. The question we need to ask is, do we really know how to get business value from these exciting tools? Unfortunately, both the analytics and AI communities have not done a great job in collaborating and communicating with each other to build the necessary synergies. This book bridges the gap between these two critical fields. The book begins by explaining the commonalities and differences in the fields of data science, artificial intelligence, and autonomy by giving a historical perspective for each of these fields, followed by exploration of common technologies and current trends in each field. The book also readers introduces to applications of deep learning in industry with an overview of deep learning and its key architectures, as well as a survey and discussion of the main applications of deep learning. The book also presents case studies to illustrate applications of AI and analytics. These include a case study from the healthcare industry and an investigation of a digital transformation enabled by AI and analytics transforming a product-oriented company into one delivering solutions and services. The book concludes with a proposed AI-informed data analytics life cycle to be applied to unstructured data. | ||
| 590 | |a ProQuest Ebook Central |b Ebook Central Academic Complete | ||
| 650 | 0 | |a Statistics |x Data processing. | |
| 650 | 0 | |a Artificial intelligence. | |
| 650 | 6 | |a Statistique |x Informatique. | |
| 650 | 6 | |a Intelligence artificielle. | |
| 650 | 7 | |a artificial intelligence. |2 aat | |
| 650 | 7 | |a COMPUTERS / Database Management / Data Mining |2 bisacsh | |
| 650 | 7 | |a COMPUTERS / Artificial Intelligence |2 bisacsh | |
| 650 | 7 | |a COMPUTERS / Database Management / General |2 bisacsh | |
| 700 | 1 | |a Liebowitz, Jay, |d 1957- |e editor. | |
| 758 | |i has work: |a Data analytics and AI (Text) |1 https://id.oclc.org/worldcat/entity/E39PCFPBQbPmxTMdQxGBfD8CFC |4 https://id.oclc.org/worldcat/ontology/hasWork | ||
| 776 | 0 | 8 | |i Print version: |a Liebowitz, Jay |t Data Analytics and AI |d Milton : Auerbach Publishers, Incorporated,c2020 |z 9780367522001 |
| 830 | 0 | |a Data Analytics Applications Ser. | |
| 856 | 4 | 0 | |u https://ebookcentral.uam.elogim.com/lib/uam-ebooks/detail.action?docID=6264228 |z Texto completo |
| 938 | |a Askews and Holts Library Services |b ASKH |n AH37316491 | ||
| 938 | |a Askews and Holts Library Services |b ASKH |n AH37316489 | ||
| 938 | |a ProQuest Ebook Central |b EBLB |n EBL6264228 | ||
| 994 | |a 92 |b IZTAP | ||