Creators of Intelligence: Industry secrets from AI leaders that you can easily apply to advance your data science career

Cover
Preface
Chapter 1: Introducing the Creators of Intelligence
Chapter 2: Cortnie Abercrombie Wants the Truth
	Getting into the business
	Discussing diversity and leadership
	Implementing an ethical approach to data
	Establishing a strong data culture
	Designing data strategies
	Summary
Chapter 3: Edward Santow vs. Unethical AI
	Developing responsible AI pathways
	Applying ethics in practice
	Considering the broader impact of AI on society
	Responding to the challenges of generative AI
	Summary
Chapter 4: Kshira Saagar Tells a Story
	The path to data science
	Implementing a data-driven approach
	Discussing leadership in data culture
	Storytelling with data
	Getting into the industry now
	Looking to the future of AI
	Summary
Chapter 5: Consulting Insights with Charles Martin
	Getting into AI
	Balancing research and consulting
	Advising companies on their AI roadmap
	Understanding why data projects fail
	Measuring impact
	Integrating data
	Finding the limits of NLP
	Explainable AI and ethics
	Summary
Chapter 6: Petar Veličković and His Deep Network
	Entering the world of AI research
	Discussing machine learning using graph networks
	Applying graph neural networks
	Pushing research boundaries with machine learning
	Using graphs for AGI
	Bridging the gap between academia and industry
	Getting into research
	Summary
Chapter 7: Kathleen Maley Analyzes the Industry
	Pursuing a career in analytics
	Striving for diversity
	Becoming data-driven
	Dealing with dueling datasets
	Overcoming roadblocks
	Establishing an effective data culture
	Learning about analytics
	Looking to the future
	Summary
Chapter 8: Kirk Borne Sees the Stars
	Getting into the field
	Advising a new organization on becoming data-driven
	Structuring teams
	Managing data scientists
	Why do AI projects fail?
	Building an effective data culture
	Teaching data science
	Predicting the future of AI
	Summary
Chapter 9: Nikolaj Van Omme Can Solve Your Problems
	Getting started
	Assessing the progress of AI
	ML and OR
	Becoming data-driven
	Setting your project up to succeed
	Exploring leadership
	Measuring success
	Developing ethical AI in an organization
	Starting out in data
	Looking to the future
	Summary
Chapter 10: Jason Tamara Widjaja and the AI People
	Getting started in data science
	Becoming data-driven
	Managing data science projects
	Why AI projects fail
	Communicating a realistic expectation to clients and partners
	Establishing a data culture
	The importance of data governance
	Discussing leadership
	Advising new entrants to the field
	Generative AI and ChatGPT
	Predicting the future
	Summary
Chapter 11: Jon Whittle Turns Research into Action
	Building a career
	Translating research into real-world impact
	Developing AI that is ethical, inclusive, and trustworthy
	AI in Australia
	Discussing leadership
	Predicting the future of AI
	Entering the industry today
	Summary
Chapter 12: Building the Dream Team with Althea Davis
	Getting into data
	Increasing diversity and inclusion
	Working in consulting
	Establishing a data service and culture
	Managing projects
	Why does AI fail?
	Summary
Chapter 13: Igor Halperin Watches the Markets
	Coming to AI from another field
	Applying ML to problems in finance
	Making AI explainable and trustworthy
	Planning for successful AI
	Navigating hype
	Discussing the role of education
	Considering the future of AI
	Summary
Chapter 14: Christina Stathopoulos Exerts Her Influence
	Becoming a data science leader
	Observing changes in the field
	Increasing diversity and inclusion in the field
	Advising new organizations
	Understanding why projects fail
	Using data storytelling
	Understanding the fundamental skills of data science
	Getting hired in data science
	Progressing into leadership
	Summary
Chapter 15: Angshuman Ghosh Leads the Way
	Getting into AI
	Watching the field evolve
	Becoming data-driven
	Organizing a data team
	Building a good data culture within an organization
	Understanding the value of data storytelling
	Hiring new team members
	Summary
Chapter 16: Maria Milosavljevic Assesses the Risks
	Getting into analytics
	Discussing diversity and inclusion
	AI and analytics
	Becoming data-driven
	Ethical AI
	Establishing a good data culture
	Why do data science projects fail?
	Discussing data leadership
	Looking to the future
	Summary
Chapter 17: Stephane Doyen Follows the Science
	Getting into data science
	Becoming a leader
	Becoming data-driven
	Developing AI solutions for the medical field
	Putting the “science” in “data science”
	Establishing a data culture at an organization
	Building the right team
	Looking to the future of AI
	Summary
Chapter 18: Intelligent Leadership with Meri Rosich
	Becoming a chief data officer
	Improving diversity and inclusion
	Discussing the high failure rates of AI projects
	Becoming a data-driven organization
	Establishing an effective data culture
	What makes a good data leader?
	The importance of data storytelling
	Making AI ethical and trustworthy
	Advice for aspiring data scientists
	Looking forward
	Summary
Chapter 19: Teaming Up with Dat Tran
	Entering the industry
	Discussing the high failure rates of AI projects
	Setting up for success
	Establishing a good data culture
	Being a data leader
	Discussing data storytelling
	Hiring team members
	Advice for beginners
	Looking to the future
	Summary
Chapter 20: Collective Intelligence
	Entering the field and becoming a successful data scientist
	Becoming a CDO and senior data leader
	Developing an effective data strategy
	Establishing a strong data culture
	Becoming data-driven
	Ethical and responsible AI
	Data literacy
	Scaling your data capability
	Structuring and managing data science teams
	Avoiding AI failure
	Measuring Success
	Storytelling with data
	Predicting the future of AI
	Striving for diversity and inclusion
	The changemakers
Index
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