Data Analytics and AI

1. Auflage 2020
ISBN: 978-0-367-52200-1
Verlag: Auerbach Publications

Buch, Englisch, 266 Seiten, Format (B × H): 161 mm x 240 mm, Gewicht: 570 g

Reihe: Data Analytics Applications

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.

Zielgruppe

Professional Practice & Development

Autoren/Hrsg.

Liebowitz, Jay

Fachgebiete

• Sozialwissenschaften Soziologie | Soziale Arbeit Soziologie Allgemein Empirische Sozialforschung, Statistik
• Mathematik | Informatik EDV | Informatik EDV & Informatik Allgemein
• Mathematik | Informatik EDV | Informatik Programmierung | Softwareentwicklung Software Engineering
• Mathematik | Informatik EDV | Informatik Daten / Datenbanken Data Mining
• Sozialwissenschaften Pädagogik Pädagogik
• Mathematik | Informatik EDV | Informatik Daten / Datenbanken Automatische Datenerfassung, Datenanalyse
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz

Inhaltsverzeichnis

Contents

Foreword.ix

Preface.xvii

List of Contributors.xix

1 Unraveling Data Science, Artificial Intelligence, and Autonomy.1

JOHN PIORKOWSKI

2 Unlock the True Power of Data Analytics with Artificial

Intelligence.21

RITU JYOTI

3 Machine Intelligence and Managerial Decision-Making.31

LEE SCHLENKER AND MOHAMED MINHAJ

4 Measurement Issues in the Uncanny Valley: The Interaction

between Artificial Intelligence and Data Analytics.53

DOUGLAS A. SAMUELSON

5 An Overview of Deep Learning in Industry.65

QUAN LE, LUIS MIRALLES-PECHUÁN, SHRIDHAR KULKARNI,

JING SU, AND OISÍN BOYDELL

6 Chinese AI Policy and the Path to Global Leadership:

Competition, Protectionism, and Security.99

MARK ROBBINS

7 Natural Language Processing in Data Analytics.117

YUDONG LIU

8 AI in Smart Cities Development: A Perspective of Strategic

Risk Management.133

EDUARDO RODRIGUEZ AND JOHN S. EDWARDS

9 Predicting Patient Missed Appointments in the Academic

Dental Clinic.151

AUNDREA L. PRICE AND GOPIKRISHNAN CHANDRASEKHARAN

10 Machine Learning in Cognitive Neuroimaging.167

SIAMAK ARAM, DENIS KORNEV, YE HAN, MINA EKRAMNIA,

ROOZBEH SADEGHIAN, SAEED ESMAILI SARDARI,

HADIS DASHTESTANI, SAGAR KORA VENU, AND AMIR GANDJBAKHCHE

11 People, Competencies, and Capabilities Are Core Elements

in Digital Transformation: A Case Study of a Digital

Transformation Project at ABB.183

ISMO LAUKKANEN

12 AI-Informed Analytics Cycle: Reinforcing Concepts.211

ROSINA O. WEBER AND MAUREEN P. KINKELA

Index.235

Über Autor(innen)

Dr. Jay Liebowitz is the Distinguished Chair of Applied Business and Finance at Harrisburg University of Science and Technology. He previously was the Orkand Endowed Chair of Management and Technology in the Graduate School at the University of Maryland University College (UMUC). He served as a Professor in the Carey Business School at Johns Hopkins University. He was ranked one of the top 10 knowledge management researchers/practitioners out of 11,000 worldwide, and was rankedsecond in KM Strategy worldwide according to the January 2010 Journal of Knowledge Management. At Johns Hopkins University, he was the founding Program Director for the Graduate Certificate in Competitive Intelligence and the Capstone Director of the MS-Information and Telecommunications Systems for Business Program, where he engaged over 30 organizations in industry, government, and not-for-profits in capstone projects.