256 pages | 62 B/W Illus.
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.
1. Unraveling Data Science, Artificial Intelligence, and Autonomy
2. Unlock the True Power of Data Analytics with Artificial Intelligence
3. Machine Intelligence and Managerial Decision Making
Lee Schlenker and Mohamed Minhaj
4. Measurement Issues in the Uncanny Valley: The Interaction Between Artificial Intelligence and Data Analytics
5. An Overview of Deep Learning in Industry
Quan Le, Luis Miralles-Pechuan, Shridhar Kulkarni, Jing Su, and Oisın Boydell
6. Chinese AI Policy and the Path to Global Leadership: Competition, Protectionism and Security
7. Natural Language Processing in Data Analytics
8. AI in Smart Cities Development: A Perspective of Strategic Risk Management
Eduardo Rodriguez and John S. Edwards
9. Predicting Patient Missed Appointments in the Academic Dental Clinic
Aundrea L. Price and Gopikrishnan Chandrasekharan
10. Machine Learning in Cognitive Neuroimaging
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 Digital Transformation at ABB
12. AI-Informed Analytics Cycle: Reinforcing Concepts
Rosina Weber and Maureen Kinkela