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

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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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