Book Image

AI & Data Literacy

By : Bill Schmarzo
Book Image

AI & Data Literacy

By: Bill Schmarzo

Overview of this book

AI is undoubtedly a game-changing tool with immense potential to improve human life. This book aims to empower you as a Citizen of Data Science, covering the privacy, ethics, and theoretical concepts you’ll need to exploit to thrive amid the current and future developments in the AI landscape. We'll explore AI's inner workings, user intent, and the critical role of the AI utility function while also briefly touching on statistics and prediction to build decision models that leverage AI and data for highly informed, more accurate, and less risky decisions. Additionally, we'll discuss how organizations of all sizes can leverage AI and data to engineer or create value. We'll establish why economies of learning are more powerful than the economies of scale in a digital-centric world. Ethics and personal/organizational empowerment in the context of AI will also be addressed. Lastly, we'll delve into ChatGPT and the role of Large Language Models (LLMs), preparing you for the growing importance of Generative AI. By the end of the book, you'll have a deeper understanding of AI and how best to leverage it and thrive alongside it.
Table of Contents (14 chapters)
12
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13
Index

Thriving with GenAI

GenAI models will reward individuals who can apply knowledge rather than those who can memorize and regurgitate knowledge.

The definition of success is changing. No longer will memorization and regurgitation of knowledge be sufficient. Instead, success will be defined by people who know how to apply knowledge to deliver meaningful, relevant, and ethical business, operational, and societal outcomes.

The roles that will prosper and excel in a world of AI are the roles that integrate and blend an understanding of data and analytics with their areas of expertise by:

  • Identifying (envisioning) where and how AI can be applied to their professions to deliver more meaningful, relevant, and ethical business and operational outcomes.
  • Driving cross-organizational alignment and consensus on the variables, metrics, and desired outcomes against which AI effectiveness will be measured.
  • Defining a comprehensive, healthy AI utility function to avoid...