Confident AI: The Essential Skills for Working With Artificial Intelligence (Confident Series, 16)

Cover
Contents
List of figures
About the author
Bonus for readers
Preface
Acknowledgements
Abbreviations
Introduction
PART ONE Why AI?
	01 Why work in AI?
		The researcher
		The data scientist
		The head of data science
		Other technical roles
		Senior roles
		New roles
		Other key skills and capabilities
		The five success factors for AI
		Fast-forward 10 years
		Why not work in AI?
		Notes
	02 AI myths
		Myth 1: AI is just a fad that will soon fade away
		Myth 2: Only big businesses can afford AI
		Myth 3: Machines will soon replace humans in the workforce
		Myth 4: AI is too difficult to implement
		Myth 5: AI will never be able to replicate human levels of intelligence
		Myth 6: You need to be a technologist to work in the field of AI
		Myth 7: Building AI will become fully automated
		Myth 8: AI is Infallible and always right
		Other myths
		Future AI myths
		Myth busting
		Notes
	03 Applications of AI
		Healthcare
		Finance
		Sales and marketing
		Education
		Industry and manufacturing
		Agriculture and environmental monitoring
		Entertainment and creative industries
		Energy and utilities
		Government and public services
		Legal and compliance
		Supply chain and logistics
		Human resources and talent management
		Non-profit and social impact
		Emerging and cross-industry applications
		Deep tech
		Final thoughts
		Notes
	04 A future perspective
		Emerging trends in AI
		Ethical and societal considerations
		The evolution of AI careers
		Research and innovation
		The future of work
		Challenges and uncertainties
		Final thoughts – the road ahead
		Notes
PART TWO The technology
	05 Understanding AI
		Overview
		The history of AI
		Innovation at the speed of thought
		Overview of AI
		The beauty of intelligence and the human brain
		Artificial general intelligence
		Superintelligence and the singularity
		Measuring intelligence
		Recent advances
		Final thoughts
	06 Overview of machine learning
		The basics of machine learning
		Learning approaches
		Final thoughts
	07 Data and infrastructure
		Data foundations
		Technical infrastructure and architecture
		Final thoughts
	08 Advanced topics
		Deep learning
		Natural language
		Video and image analytics
		Generative algorithms
		The data science toolbox
		Distributed computing
		Quantum computing
		Data privacy and trust
		Regulation
		A new era of computing – generative waves
		Final thoughts
PART THREE The process
	09 Elements of an AI strategy
		The nine elements of an AI strategy
		One: Strategy alignment and business need
		Two: AI roadmap
		Three: Technical infrastructure and data foundations
		Four: Data science frameworks
		Five: Skills and talent
		Six: Culture of innovation
		Seven: Organizational structure and governance
		Eight: Planning, vendors and partners
		Nine: Managing an AI-powered workforce
		Final thoughts
		Note
	10 Implementing AI
		12 challenges of AI adoption
		Overcoming implementation challenges
		Implementation approaches
		Internal AI development
		AI consultancies
		Vendor products and services
		The hybrid approach
		Final thoughts
	11 Governance, ethics and safety
		Governance
		Ethics
		Safety
		Core AI principles
		AI governance management
		Final thoughts
		Note
PART FOUR The people
	12 People: key characteristics
		Key characteristics of a data scientist
		Problem solving
		Communication skills
		Teamwork, office politics and personalities
		Community support
		Final thoughts
	13 Different teams and roles
		Data science pods
		Pod roles
		Supporting roles
		Other roles (outside of the pods)
		Other stakeholders
		Communities of practice
		Organizational structure
		Final thoughts
	14 Getting started
		Students and first job
		Professionals and career change
		Other considerations
		Final thoughts
	15 Career development
		The AI career landscape
		Understanding AI career paths
		AI career options
		Building a strong foundation
		Gaining practical experience
		Networking and mentorship
		Staying informed and adapting
		Landing your first AI job
		Career advancement and growth
		Your AI career journey starts now
		Final thoughts
		Notes
	Conclusion
		The technology
		The process and the people
		Four key takeaways
		Summary
		Epilogue
Appendix
Index