This book provides a brief synthesis of the known implementations, opportunities and challenges at the intersection of artificial intelligence (AI) and modern industry beyond the big-four companies that traditionally consume and produce such advanced technology: Facebook, Amazon, Microsoft and Google. With this information, the author also makes some reasonable claims about the role of AI in future industries. The book draws on a broad range of material, including reports from consulting firms, published surveys, academic papers and books, and expert knowledge available to the author due to numerous collaborations in academia and industry on AI. It is rigorous rather than speculative, drawing on known findings and expert summaries, where available. This provides industry leaders and other interested stakeholders with an accessible review of contemporary perspectives on AI’s forward-looking role in industry as well as a clarifying guide on the major issues that companies are likely to face as they commence on this exciting path.
Examines the likely role of AI in industries of the future, both known and unknownPresents use-cases of AI currently being explored across Big Tech, multi-national corporations and start-ups
Explores the regulation of AI and its potential impacts on the workforce
Author(s): Mayank Kejriwal
Series: Future of Business and Finance
Publisher: Springer
Year: 2022
Language: English
Pages: 164
City: Cham
Preface
Acknowledgments
Contents
Acronyms
1 Artificial Intelligence: An Introduction
1.1 Introduction
1.2 Artificial Intelligence (AI)
1.3 AI, Machine Learning, and Deep Learning
1.3.1 Types of Machine Learning
1.4 Industry 4.0 Versus Industries of the Future
1.5 Other (Non-AI) Drivers of Industries of the Future
1.5.1 Quantum Information Science (QIS)
1.5.2 5G and Advanced Communication
1.5.3 Advanced Manufacturing
1.5.4 Biotechnology
1.6 Where Will Industries of the Future Come From?
1.7 The Role of Research
1.8 Future Developments
References
2 AI in Practice and Implementation: Issues and Costs
2.1 Introduction
2.2 Challenges in Implementing AI
2.2.1 Data Acquisition
2.2.2 Data Quality
2.2.3 Privacy and Compliance
2.2.4 AI Quality Metrics
2.3 Guidelines and Practices for Measuring Return on Investment (ROI) of AI Projects
2.3.1 Traditional Valuation Approaches and Their Pitfalls for Valuing AI Projects
2.3.2 Soft Versus Hard Returns and Investments
2.4 Digital Technology and the Productivity Puzzle
2.5 Conclusion
References
3 AI in Industry Today
3.1 Introduction
3.2 AI in Big Tech
3.2.1 Alphabet
3.2.2 Amazon
3.2.3 Meta
3.2.4 Other Big Tech: Microsoft and Apple
3.2.5 Other Large Tech Firms in the United States
3.2.6 The Chinese ``Big Tech''
3.3 Large Firms Outside Big Tech
3.4 Startups and Small/Medium-Sized Enterprises (SBEs)
3.5 Case Study: Neural Language Models
3.5.1 Can Transformers Automate Software Engineers?
3.5.2 Applications Beyond NLP
3.5.3 Potential Ethical Concerns
3.5.4 Summary
3.6 Conclusion
References
4 Augmented Artificial Intelligence
4.1 Introduction
4.2 Augmented AI Versus Complete Automation
4.3 Key Features and Example Applications
4.4 A Case Study in Augmented AI: Radiology
4.5 Changes in the Workforce
4.5.1 How Will Organizations Change?
4.5.2 Demand for Technological Skills
4.5.3 Cognitive Skills and the Future of Work: Is There a Mismatch?
4.5.4 New-Collar Versus White-Collar Jobs
4.5.5 Adaptation in the C-Suite
4.6 Automation and the Future of Work: Examples from Three Industrial Sectors
4.6.1 Banking and Insurance
4.6.2 Manufacturing
4.6.3 Retail
4.7 Conclusion
References
5 AI Ethics and Policy
5.1 Introduction
5.2 AI Versus Digital Ethics
5.3 The Philosophy of Ethics: A Brief Review
5.4 AI Ethics in Policy
5.4.1 Case Study 1: The European Union General Data Protection Regulation (GDPR)
5.4.1.1 Enforcement of GDPR
5.4.2 Case Study 2: The United States National Defense Authorization Act (NDAA)
5.5 AI Ethics in Research and Higher Education
5.6 Conclusion
References
6 What Is on the Horizon?
6.1 Introduction
6.2 Can AI Copyright Its Own Art?
6.3 Legal Issues Around Deepfakes
6.4 AI's Explainability Crisis
6.5 More Vigorous Algorithmic Regulation
6.6 Increasing Convergence of Emerging Technologies
6.7 Concluding Notes
References
Glossary
References
Index
