Creators of intelligence : industry secrets from AI leaders that can be easily applied to build and ace your data science career

Creators of intelligence industry secrets from AI leaders that can be easily applied to build and ace your data science career

A Gartner prediction in 2018 led to numerous articles stating that "85% of AI and machine learning projects fail to deliver." Although it's unclear whether a mass extinction event occurred for AI implementations at the end of 2022, the question remains: how can I ensure that my projec...

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Detalles Bibliográficos
Otros Autores: Antic, Alex, author (author), Thompson, John K., author
Formato: Libro electrónico
Idioma:Inglés
Publicado: Birmingham, England : Packt Publishing Ltd [2023]
Edición:1st ed
Materias:
Electronic data processing > Vocational guidance.
Artificial intelligence > Vocational guidance.
Ver en Biblioteca Universitat Ramon Llull:https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009742735606719
Tabla de Contenidos:
  • 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
  • Other Books You May Enjoy.

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