Artificial Intelligence for Business (eBook)

Rajendra Akerkar holds master’s in applied mathematics and doctorate in computer science. He is a professor of Information Technology at Vestlansforsking (Western Norway Research Institute), Norway. He leads big data research group at the institute. His research and teaching experience, in artificial intelligence and related subjects, includes over 26 years in the Academia spanning universities in Asia, Europe and North America. Rajendra received prestigious BOYSCASTS Young Scientist award of Department of Science & Technology, Government of India, in 1997. He was UNESCO‐TWAS Associate Professor from 1998‐2001. He is an editor‐in‐chief of International Journal of Computer Science & Applications, and also an associate editor of International Journal of Metadata, Semantics and Ontologies and Knowledge Management Track Editor of Web Intelligence, an international journal. His research focuses on application of big data methods to business challenges, and social media analysis in a wide set of semantic dimensions. He currently coordinating projects funded by Research Council of Norway and EU-funded Horizon 2020 programme. He is actively involved in several international ICT initiatives for more than 21 years.

Preface 6
Contents 9
Introduction to Artificial Intelligence 12
Data 12
Information 13
Knowledge 13
Intelligence 14
Basic Concepts of Artificial Intelligence 14
Benefits of AI 17
Data Pyramid 17
Property of Autonomy 19
Situation Awareness 20
Business Innovation with Big Data and Artificial Intelligence 21
Overlapping of Artificial Intelligence with Other Fields 22
Ethics and Privacy Issues 24
AI and Predictive Analytics 25
Application Areas 26
Clustering or Segmentation 27
Psychographic Personas 29
Machine Learning 30
Introduction 30
Machine Learning Workflow 32
Learning Algorithms 33
Linear Regression 33
k-Nearest Neighbour 34
Decision Trees 35
Feature Construction and Data Reduction 37
Random Forest 37
k-Means Algorithm 37
Dimensionality Reduction 39
Reinforcement Learning 39
Gradient Boosting 40
Neural Networks 41
Deep Learning 44
Introduction 44
Analysing Big Data 45
Different Deep Learning Models 47
Autoencoders 47
Deep Belief Net 47
Convolutional Neural Networks 48
Recurrent Neural Networks 48
Reinforcement Learning to Neural Networks 49
Applications of Deep Learning in Business 49
Business Use Case Example: Deep Learning for e-Commerce 50
Recommendation Engines 52
Introduction 52
Recommendation System Techniques 55
Content-Based Recommendations 55
Item Representations 56
User Profiles 56
Learning of User Models 56
Collaborative Recommendations 57
Hybrid Approaches 58
Applications of Recommendation Engines in Business 58
Collection of Data 59
Storing the Data 60
Analysing the Data 60
Product Recommendation Algorithm 61
Business Use Case 62
Natural Language Processing 64
Introduction 64
Morphological Processing 66
Syntax and Semantics 66
Semantics and Pragmatics 66
Use Cases of NLP 67
Text Analytics 68
Sentiment Analysis 69
Sentiment Analysis Use Cases 69
Challenges of Sentiment Analysis 70
Applications of NLP in Business 70
Customer Service 70
Reputation Monitoring 71
Market Intelligence 72
Sentiment Technology in Business 72
Employing AI in Business 74
Analytics Landscape 74
Application Areas 75
Complexity of Analytics 75
Descriptive Analytics 76
Predictive Analytics 77
Prescriptive Analytics 81
Embedding AI into Business Processes 81
Implementation and Action 83
Artificial Intelligence for Growth 83
AI for Customer Service 83
Applying AI for Marketing 84
Glossary 86
References 92