AI & Data Literacy E-Book

Bill Schmarzo

• Herausgeber: Packt Publishing
• Kategorie: Fachliteratur
• Sprache: Englisch

Seitenzahl: 312

Veröffentlichungsjahr: 2023

Beschreibung

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.

Leseprobe

AI & Data Literacy

Empowering Citizens of Data Science

Bill Schmarzo

BIRMINGHAM—MUMBAI

AI & Data Literacy

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Endorsements

Generative AI has burst onto the scene with great promise and huge hype, and has caused significant concern. Bill’s pragmatic and clear approach to explaining how AI works, what it can and cannot do, and how people are actually in control of the way models work and produce results is a much-needed treatment of the topic. I always look to Bill for an accessible assessment of new technologies and trends. In this book on AI, Bill has delivered an overview and explanation that will put many at ease in their newly found understanding of AI.

—John Thompson, EY, Global Head of AI

Whether you are just starting to explore AI or are a seasoned data scientist looking to power up to the next value level for your enterprise, Schmarzo provides a clear yet efficacious framework to allow you to comprehend, wrestle with, and most importantly, apply AI to unlock its full potential, empowering better decisions for your business. Schmarzo has the rare gift of blending cutting-edge academic research with real-world experience, making him a gifted educator who makes technical concepts accessible and easy to apply to any sector or enterprise. If you are worried the big data revolution will leave you behind, this book shows you exactly how to become a Citizen of Data Science and make AI a tool that works for you.

—David Hayes, President Coe College

Bill Schmarzo’s latest book is a masterful guide for those looking to build their knowledge and literacy at the intersection of data science and AI. With a clear and concise educational framework, Schmarzo guides readers through everything from data privacy and analytics literacy to decision-making and the ethics of AI. This book is an essential read for anyone looking to become a proficient Citizen of Data Science.

—Doug Laney, author of Infonomics and Data Juice

This is the book needed for everyone investing or interested in AI and data. Bill Schmarzo’s book equips one with the skills and competencies they need to be able to use AI technologies and applications as tools, including how they can become “more human” to thrive alongside AI. It further empowers one to be able to critically assess AI tools, understand their context and embedded principles, and question their design and implementation. This includes providing an understanding of what AI is, how it works, and its strengths and limitations.

—Prof. Mark Nasila, Chief Data and Analytics Officer, FNB Risk

He taught you the Economics of Data Analytics and Digital Transformation. He helped you earn your Big Data MBA. Because of him, you mastered The Art of Thinking Like a Data Scientist. Now join Bill Schmarzo, the Dean of Big Data, as he takes you on a trek into the very heart of the AI jungle, wielding his machete of meaning to carve a permanent path of data literacy through the AI thicket – a path that will turn you into a truly enlightened Citizen of Data Science capable of expertly leveraging and managing AI, regardless of your profession, for the benefit of all humanity.

—Brian A. Rudolph, Professor, Iowa State University

If we are to shape AI to benefit all of society, we must develop shared policies, rules, metrics, and measurement practices that make AI more accurate, understandable, transparent, equitable, and safe. AI & Data Literacy: Empowering Citizens of Data Science provides a foundational understanding of AI to empower policymakers and experts in fields outside of technology to have productive dialog about the impact of AI on society and its ethical use.

—Dan Everett, Owner, Insightful Research LLC

As the renowned data scientist and author Bill Schmarzo states in the preface to this book, the emergence of AI is destined to change the ways people consume/use data. Therefore, it is essential that everyone, regardless of their job, becomes familiar with how data is collected, stored, and used. Being “data literate” as a person is critical for all of us since we are all affected by AI and its use of our data.

—Dr. Anne-Marie Smith, Doctoral Program Faculty Member, Capella University

The emergence of ChatGPT has brought a lot of excitement, caution, and suspicion toward AI and data science. This is a momentous opportunity to educate the public on the methods, assumptions, benefits, and risks behind this elusive technology. Bill Schmarzo’s book, AI & Data Literacy, is an important work to help society understand AI’s business, environmental, social, and economic potential. Bill’s ability to relate complex subjects using intuitive explanations sets him apart among the field experts. In addition, his generosity and commitment to share such valuable insights for the benefit of the community is truly inspiring. Bill is a true role model for aspiring data leaders and educators everywhere.

—David Hendrawirawan, Founder, Data Integrity First

Bill’s book on AI and data literacy is a very timely read. With everyone talking about AI replacing almost everything we do, it’s important to be grounded in the fundamentals of AI, data, and how the combination of the two helps us make better personal and professional decisions. The book covers the basics, the in-depth, and the key understandings needed to navigate the conversation today and into the future.

—Howard Miller, CIO at UCLA Anderson School of Management

Mr. Bill Schmarzo’s expertise in data science and artificial intelligence is unmatched, and his passion for sharing knowledge is truly inspiring. Through this book, he demonstrates his unwavering commitment to empowering individuals with the necessary skills and insights to navigate the complex world of data. I wholeheartedly recommend AI & Data Literacy: Empowering Citizens of Data Science to anyone seeking to expand their understanding of data science and its impact on our world. Bill Schmarzo’s expertise, passion, and genuine desire to help others make this book an invaluable resource that will undoubtedly shape the future of data literacy.

—Dr. Mouwafac Sidaoui, Chief Academic Officer and Dean of School of Business, Menlo College

From chatbots and virtual assistants to self-driving cars and predictive sales analytics, AI is changing people’s lives. More than ever, it is imperative for individuals and organizations to understand what it means to become AI and data literate. It is now a requirement to be a Citizen of Data Science to stay competitive, make ethical decisions, and protect data and privacy. This book empowers us to become a Citizen of Data Science with sensibility, humor, and relatable stories, all the while reminding us of our obligation to be better humans.

—Renée B. Lahti, Chief Innovation Officer, Board Advisor & Continuous Learner

Bill Schmarzo is an educator at heart. For the last several decades, he’s focused on lifting the data competency and design thinking literacy of thousands via his various platforms, including teaching and guest lecturing at a number of colleges and universities on multiple continents, publishing multiple academic texts, business workbooks, and kids-style cartoons, and fostering one of the most active data communities on LinkedIn. In his latest book, AI & Data Literacy: Empowering Citizens of Data Science, Bill shifts his focus to the rapidly growing world of artificial intelligence, arguably the most important development in computing since the internet.

—Jeff Frick, Principal and Founder, Menlo Creek Media, Host of the Work 20XX and Turn the Lens podcasts

I have known Bill for a number of years, during which time he used the concepts from his books in workshops tailored for business decision-makers and lectures for postgraduate students in Ireland. This book further demonstrates Bill’s uncanny ability to bridge the chasm between practice and academia. He leverages contemporary academic knowledge and applies it to address complex business challenges encountered by project and management teams that seek to use advanced analytics to create economic value in dynamic business environments. This book will appeal to practitioners keen to continue their professional development, as well as complement executive and postgraduate education programs that challenge their students to grapple with analytical-based decision making and organizational readiness to embed analytical tools and techniques into organizations across all sectors and industries.

—Dr. Denis Dennehy (Ph.D.), Associate Professor, Swansea University, Wales

A must-have book for everyone in the era of artificial intelligence, networks, platforms, and digital ecosystems.

—Gregory D Esau, SmartSwarms Performance Digital Ecosystems

Get ready to stamp your data passport, since the Dean of Big Data is on a mission to be convert us all to Citizens of Data Science. As only an educator, thought leader, and natural storyteller can, Bill Schmarzo adeptly highlights how individuals and corporations alike can leverage the power of AI to deliver transformative benefits without all the fearmongering and technobabble commonplace these days around AI discussions. As Schmarzo teaches us to think more like data scientists using street-tested tools and frameworks acquired through decades of data leadership, we come to see that AI is only as good (or bad) as we instruct it to be. With this knowledge, we become empowered citizens of this brave new world of AI and data science.

—Malcolm Hawker, Head of Data Strategy for Profisee Software

AI & Data Literacy: Empowering Citizens of Data Science is a great read that simplifies the complexity of the all-important topics and nuances of today’s data and AI landscape into everyday language that everyday people can understand. As data and AI continue to evolve to play a bigger part in the lives of both businesses and everyday people, there’s a huge chasm between the knowledge and understanding of the key topics and the reality of how it will change our world, creating fear and resistance. This book bridges that chasm head-on. A fascinating read for the data and technology community and an essential read for the business community.

—Kyle Winterbottom, CEO & Founder of Orbition Group

Bill continues his quest education on data, analytics, and AI – to make sure everyone understands what they are, what they can achieve, how they work, and how to deal with them. His new book, AI & Data Literacy: Empowering Citizens of Data Science, is not only for AI experts and practitioners but provides very didactic and pragmatic content to educate a broad audience of citizens while being very exhaustive and, as always, fun and easy to read. For all data and AI people, this is a highly recommended pick for your colleagues, friends, and family as education on the topic is obviously critical to face the upcoming changes in our society.

—Nicolas Averseng, Founder & CEO at YOOI

Bill’s book comes at a critical time as people now realize that data and AI are part of everyone’s job and part of the fabric of society. He explains in approachable and pragmatic ways why data literacy matters, how your data can be used for and against you, and the critical topic of AI ethics.

—Cindi Howson, Chief Data Strategy Officer at AI-Analytics ThoughtSpot, Host of the Award-Winning The Data Chief Podcast

Bill Schmarzo should get a Nobel prize or equivalent for his brilliant innovation in the data, analytics, and AI space. For the last 3 decades, Bill has innovated practically with his clients and produced a staggering amount of guidance, models, recipes, best practices, and thought leadership, and he shares it all freely. His lessons over the years are too many to mention and exceptionally relevant today. They have greatly matured the CDAO’s arsenal of tools to be successful. His Business Benefit Analysis whitepaper in 1998 paved the way for how we prioritize initiatives today. Then there is the Big Data MBA Video Educational Series, and his two recent books, Thinking Like a Data Scientist and The Economics of Data, Analytics, and Digital Transformation. The CDAO community in the Middle East and Africa sees his approach as essential, and we appreciate the great relationship we have with Bill.

This book, AI & Data Literacy: Empowering Citizens of Data Science, will not only be essential in the offices of all CDAOs and their teams, but will be used by CEOs, board members, managers in the business units, front-line staff, and, well, everybody in business and technology. One of the most well-known sayings these days is “AI will not take over your job. Someone who uses AI will take over your job.” Data literacy is still a very hot topic as businesspeople aspire to get business value from data. Bill has taken brilliant snippets of his repertoire of teachings and has again created a framework to empower us all to use data and AI responsibly and get the most value out of it. The AI and Data Literacy Educational Framework used throughout the book is once again a recipe that will be essential in corporate AI and data literacy programs.

—Debbie Botha, Women in AI Global Chief Partnership Officer, Dalebrook Media Middle East Managing Director

I really like the way this book puts people and the human experience deep at the center of AI, where others are desperately trying to automate everybody out. Bill is enabling everybody to have enough appreciation and knowledge to manage in the new world of ubiquitous AI, and that is how we move everybody forward.

—Jon Cooke, AI and Data Product Specialist

Contributor

About the author

Bill Schmarzo, the Dean of Big Data, is currently the Head of Customer Data Innovation at Dell Technologies. In his spare time, he is a lecturer at Iowa State University and Coe College (Cedar Rapids, IA), a University of San Francisco School of Management Executive Fellow, and an Honorary Professor at the School of Business and Economics at the National University of Ireland-Galway, where he teaches and mentors students in his course Big Data MBA and Thinking Like a Data Scientist. He is the author of four books: Big Data: Understanding How Data Powers Big Business, Big Data MBA: Driving Business Strategies with Data Science, The Art of Thinking Like a Data Scientist, and The Economics of Data, Analytics, and Digital Transformation. He has written over 300 blogs for Data Science Central, appeared on countless podcasts, has given numerous keynote presentations and university lectures, and is an active social media lightning rod on the topics of data science, artificial intelligence, data economics, design thinking, and team empowerment.

This book holds a special significance as it is the first book that I’ve written since moving back to Iowa in August 2022. Both my wife, Carolyn, and I were born in Iowa and met at Coe College in Cedar Rapids, IA. However, returning to Iowa after spending nearly three decades in Palo Alto and Silicon Valley did create a wee bit of apprehension. Nevertheless, I am immensely grateful to the numerous individuals who played a crucial role in supporting me throughout this transition period, enabling me to find new opportunities to flourish and grow.

First, my family members. I’ve already mentioned my wife, Carolyn, who endured my early mornings and restless nights wrestling with the book, and provided motivation (a.k.a. a swift kick in the butt) whenever I left like I’d reached the end of my rope. Thanks to my two sons and their wives who blessed us with our first grandchildren this past year – Alec and Dorian with granddaughter Emerson, and Max and Kelsey with grandson Campbell. Can’t wait to start spoiling them (and helping them master that 3-point shot).

I’d also like to acknowledge my creative daughter, Amelia, who followed in her dad’s footsteps to recently get her graduate degree from Emerson College in Digital Marketing and Data Analytics. Yes, we are a family that believes in the liberating power of AI and data literacy.

There were numerous friends who were instrumental in smoothing our transition back to Iowa, including Steve, David, Lori, Navin, Rod, Dave, Barb, Chantel, Kevin, Dawn, Brian, Jenny, Murugan, Armel, Peg, Dr. Buck, Rick, Marcia, Chuck, Amy, Ranjeetha, Russ, Kiran, Ted, Vrashank, Keri, Greg, Sudhir, Oliver, Daniel, Ant, Rob, Mike, Julie, Teresa, John, Bryan, Rob, Brenda, and Neal. I want to also thank my LinkedIn colleagues who are instrumental in providing me feedback and ideas whenever I engage on LinkedIn, including Samir, Somil, Mark, Jon, Christopher, Fred, Henrik, Dr. Anne-Marie, Dr. Mark, Debbie, Sharon, Malcolm, Jeff, Kyle, Benjamin, Kevin, Anders, J. Brian, Dan, Chris, Michael, Cindi, Mina, Vincent, Kurt, Assaf, Wayne, Randy, Tom, and so many more that I have missed but whose friendship I sincerely appreciate.

Special thanks to my Big Data MBA class lecturers, including John, John (again), Doug, Dan, Josh, Renee, and Brent. Also, thanks to my favorite Ingersoll Avenue Caribou Coffee crew (Alyssa, Fiona, Allison, Rachel, Emily, Kami, Jerzye, Hannah, Bailey, Nevean, Liv, Uat, Sophie, and Kelsey) for keeping my chai tea lattes hot. And, of course, to the absolutely best neighbors and friends with whom one could be blessed in Katie, Michael, Kennedy, Warren, and Allison (by the way, can I borrow your snow blower again?).

I want to express a huge appreciation for the Packt crew of Tanya D’cruz, Tushar Gupta, Aniket Shetty, and Janice Gonsalves, who were both patient and demanding in getting the most out of me in writing this book. If the book makes sense and the chapters flow smoothly, you can thank them for that!

I want to thank my students at Iowa State University and Coe College (all of whom must have watched Tom Hanks, in the movie Big, bravely raise his hand and feel empowered to say, “I don’t get it.”) and the clients with whom I have the good fortune to work. Every conversation leads to new learnings, and that’s great for someone who doesn’t have time for green bananas!

I also want to thank Dr. Wei Lin (my former Chief Data Scientist and the smartest, humblest person that I know) and Maria Parysz (CEO and Founder of three companies: Elephant AI, RecoAI, and LogicAI) for their assistance on my chapter on Generative AI and ChatGPT. Their guidance proved invaluable in simplifying this complex topic and its enabling technologies.

Finally, I want to express my sincere gratitude to Renee Lahti for her invaluable assistance in reviewing the flow and content of this book. Renee’s exceptional skills as a Chief Data Officer, evident in her deep understanding of data science, data economics, and design thinking, make her the best CDO I’ve ever met. Her expertise shines through in every endeavor and team she leads. I feel incredibly fortunate that Renee took the time to review this book.

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Contents

Preface

Who this book is for

What this book covers

To get the most out of this book

Get in touch

Why AI and Data Literacy?

History of literacy

Understanding AI

Dangers and risks of AI

AI Bill of Rights

Data + AI: Weapons of math destruction

Importance of AI and data literacy

What is ethics?

Addressing AI and data literacy challenges

The AI and Data Literacy Framework

Assessing your AI and data literacy

Summary

References

Data and Privacy Awareness

Understanding data

What is big data?

What is synthetic data?

How is data collected/captured?

Sensors, surveillance, and IoT

Third-party data aggregators

Understanding data privacy efforts and their efficacy

Data protection and privacy laws

Data privacy statements

How organizations monetize your personal data

Summary

References

Analytics Literacy

BI vs. data science

What is BI?

What is data science?

The differences between BI and data science

Understanding the data science development process

The critical role of design thinking

Navigating the analytics maturity index

Level 1: Operational reporting

Level 2: Insights and foresight

Statistical analytics

Exploratory analytics

Diagnostic analytics

Machine learning

Level 3: Augmented human intelligence

Neural networks

Regression analysis

Recommendation engines

Federated learning

Level 4: Autonomous analytics

Reinforcement learning

Generative AI

Artificial General Intelligence

Summary

Understanding How AI Works

How does AI work?

What constitutes a healthy AI utility function?

Defining “value”

Understanding leading vs. lagging indicators

How to optimize AI-based learning systems

Understand user intent

Build diversity

Summary

Making Informed Decisions

Factors influencing human decisions

Human decision-making traps

Trap #1: Over-confidence bias

Trap #2: Anchoring bias

Trap #3: Risk aversion

Trap #4: Sunk costs

Trap #5: Framing

Trap #6: Bandwagon effect

Trap #7: Confirmation bias

Trap #8: Decisions based on averages

Avoiding decision-making traps

Exploring decision-making strategies

Informed decision-making framework

Decision matrix

Pugh decision matrix

OODA loop

Critical thinking in decision making

Summary

References

Prediction and Statistics

What is prediction?

Understanding probabilities and statistics

Probabilities are still just probabilities, not facts

Introducing the confusion matrix

False positives, false negatives, and AI model confirmation bias

Real-world use case: AI in the world of job applicants

Summary

References

Value Engineering Competency

What is economics? What is value?

What is nanoeconomics?

Data and AI Analytics Business Model Maturity Index

Stages

Inflection points

Value Engineering Framework

Step 1: Defining value creation

Step 2: Realizing value creation via use cases

Step 3: Scale value creation

What are the economies of learning?

Monetize analytic “insights,” not data

Summary

Ethics of AI Adoption

Understanding ethics

Ethics is proactive, not passive

Redefining ethics in the age of AI

The intersection of ethics, economics, and societal well-being

Ethical behaviors make for good economics

The difference between financial and economic metrics

The role of laws and regulations on ethics

Achieving a responsible and ethical AI implementation

The Ethical AI Pyramid

Ensuring transparent AI

Understanding unintended consequences

Identifying unintended consequences

Mitigating unintended consequences

Summary

References

Cultural Empowerment

A history lesson on team empowerment

Tips for cultivating a culture of empowerment

#1: Internalize your mission

#2: Walk in the shoes of your stakeholders

#3: Nurture organizational improvisation

#4: Embrace an “AND” mentality

#5: Ensure everyone has a voice

#6: Unleash the curiosity-creativity-innovation pyramid

Driving AI and data literacy via cultural empowerment

Reassessing your AI and data literacy

Summary

ChatGPT Changes Everything

What are ChatGPT and GenAI?

How does ChatGPT work?

Beginner level 101

Capable level 201

Proficient level 301

Critical ChatGPT-enabling technologies

LLM

Transformers

Role-based personas

Reinforcement Learning from Human Feedback

ChatGPT concerns and risks

Thriving with GenAI

AI, data literacy, and GenAI

Summary

References

Glossary

Data economics

Design thinking

Data science and analytics

Other Books You May Enjoy

Index

Landmarks

Cover

Index

Preface

Notice the cover of this book. Simple. Straightforward. No hyperbole about the extinction of humankind. No outrageous claims about massive human unemployment. Just a simple cover with a simple title to reflect the simple concept of Artificial Intelligence (AI).

Here is the simple truth about AI: AI will do exactly what you train it to do. Yes, AI can continuously learn and adapt with minimal human intervention, which scares people. However, the actions AI takes will be guided 100% by the user-defined outcomes and the measures against which outcome effectiveness will be measured. And all of these are 100% defined by you.

To design, develop, and manage AI effectively, adopting a holistic approach is paramount, and it calls for the active participation of everyone. The objective of this book is to simplify the discussion around AI and equip everyone with the knowledge to ensure AI is working for our benefit. By empowering everyone as Citizens of Data Science and fostering fundamental AI and data literacy, we can encourage active engagement that ensures AI’s benefits are accessible to all.

I hope you enjoy reading and learning from the book as much as I did researching, testing, learning, relearning, and writing it. I hope you enjoy your AI and data literacy journey of becoming a Citizen of Data Science!

Who this book is for

This book is written for three segments of modern-day citizens:

Segment #1 comprises of individuals who seek a deeper understanding of AI and data, particularly regarding their impact on their everyday life. These individuals aspire to gain enough knowledge about AI and data to engage thoughtfully and respectfully with different perspectives and opinions, enabling them to independently evaluate the advantages and risks associated with AI and data.Segment #2 encompasses individuals who are actively seeking to comprehend how AI and data can enhance both their personal lives and professional careers. With a focus on their careers, these individuals seek to understand future educational and personal development requirements. These folks are motivated to gain a better understanding of how AI and data applies directly to their lives, preparing them to grow and advance personally and professionally.Segment #3 consists of individuals who are actively seeking to engage in the definition and oversight of rules and regulations governing the ethical, responsible, and meaningful design and deployment of AI. These individuals are driven by a desire to ensure that all voices and perspectives are considered, aiming to protect society from the careless and malevolent application of AI. Their goal is to create a safe and inclusive environment, where the potential risks and adverse impacts of AI are mitigated while maximizing its benefits for everyone.

While the book delves into some technical aspects, its goal is to explain these topics in a pragmatic manner. The book seeks to help everyone understand key technologies and concepts so they can participate in the discussions about the future applications and potential ramifications of these technologies and concepts. The book offers different ways of engaging with the content, so that anyone who wants to become a Citizen of Data Science can find a suitable and confident way of participating.

What this book covers

Chapter 1, Why AI and Data Literacy?, sets the groundwork for understanding why AI and data literacy is a conversation that must include everyone. The chapter highlights the rapid growth of AI in our everyday lives that impact society. The chapter also introduces the AI and Data Literacy Educational Framework that we will use throughout the book to guide our AI and data literacy education.

Chapter 2, Data and Privacy Awareness, ensures that everyone has a shared understanding of what we mean by the term big data and why it’s more valuable (and dangerous) than regular data. We also outline new technology developments with the Internet of Things (IoT) and how your data is captured and used in real time to monitor and influence your decisions. Discussing some regulatory efforts to protect your data and preserve your privacy, we will also learn how organizations monetize personal data for their benefit.

Chapter 3, Analytics Literacy, is one of the more technical chapters in the book. But everyone must understand the different levels of analytics and how they can be used to uncover market, society, environmental and economic insights that can lead to better, more informed decisions. If data is the new oil, then analytics is the exploration, mining, extraction, and production tools we use to convert raw oil into products of value.

Chapter 4, Understanding How AI Works, like the title suggests, dives deep into AI and how it works. We will discuss the importance of ascertaining or determining user intent to frame your AI model development and provide a conceptual understanding of the AI utility function – the weighted portfolio of variables and metrics that the AI models will use to guide its relentless optimization efforts.

Chapter 5, Making Informed Decisions, explores the decision-making traps we fall into that lead to suboptimal, bad, and even dangerous choices. As a solution, we will introduce decision-making strategies, like the decision matrix, OODA, and so on, that everyone can and should use to leverage AI and data to make more informed decisions.

Chapter 6, Prediction and Statistics, provides a short primer on statistics, probabilities, predictions, and confidence levels. We will discuss how we can use statistics to help us improve the odds of making more effective and safer decisions in a world of constant economic, environmental, political, societal, and healthcare disruption.

Chapter 7, Value Engineering Competency, will explore how organizations of all sizes can leverage AI and data to engineer or create “value.” We will present a framework for understanding how organizations define value and then identify the KPIs and use to measure their value creation effectiveness. We will also discuss why “economies of learning” are more powerful than “economies of scale” in a digital-centric world.

Chapter 8, Ethics of AI Adoption, describes some leading-edge ideas on how organizations and society can leverage economic concepts to transparently instrument and measure ethics and ensure that AI-based machines are working for humans rather than the other way around.

Chapter 9, Cultural Empowerment, will delve into the power and importance of empowerment to ensure that everyone has a voice in deciding and defining how best to leverage AI and data from a personal perspective. We will discuss how we must become “more human” to thrive alongside AI.

Chapter 10, ChatGPT Changes Everything, will provide a short primer on Generative AI (GenAI) products such as OpenAI ChatGPT, Microsoft Bing, and Google Bard. We will discuss how GenAI products work and the underlying technologies that make GenAI so effective in replicating human intelligence. Finally, we’ll assess how one can apply the 6 components of the AI and Data Literacy Framework to use GenAI to deliver more relevant, meaningful, responsible, and ethical outcomes.

To get the most out of this book

What are the pre-requisites for this book? Simple – an open mind, an insatiable urge to learn, and the passion and fearlessness to get involved in the AI conversation. You must be willing and empowered in ensuring that your voice, and the voices of others, are being heard in this all-important AI conversation.

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Get in touch

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Why AI and Data Literacy?

Artificial Intelligence (AI) is quickly becoming infused into the very fabric of our everyday society. AI already influences decisions in employment, credit, financing, housing, healthcare, education, taxes, law enforcement, legal proceedings, travel, entertainment, digital marketing, social media, news dissemination, content distribution, pricing, and more. AI powers our GPS maps, recognizes our faces on our smartphones, enables robotic vacuums that clean our homes, powers autonomous vehicles and tractors, helps us find relevant information on the web, and makes recommendations on everything from movies, books, and songs to even who we should date!

And if that’s not enough, welcome to the massive disruption caused by AI-powered chatbots like OpenAI’s ChatGPT and Google’s Bard. The power to apply AI capabilities to massive data sets, glean valuable insights buried in those massive data sets, and respond to user information requests with highly relevant, mostly accurate, human-like responses has caused fear, uncertainty, and doubt about people’s futures like nothing we have experienced before. And remember, these AI-based tools only learn and get smarter the more that they are used.

Yes, ChatGPT has changed everything!

In response to this rapid proliferation of AI, STEM (Science, Technology, Engineering,and Mathematics) is being promoted across nearly every primary and secondary educational institution worldwide to prepare our students for the coming AI tsunami. Colleges and universities can’t crank out data science and machine learning curriculums, classes, and graduates fast enough.

But AI and data literacy are more than just essential for the young. 72-year-old congressman Rep. Don Beyer (Democrat Congressman from Virginia) is pursuing a master’s degree in machine learning while balancing his typical congressman workloads to be better prepared to consider the role and ramifications of AI as he writes and supports the legislation.

Thomas H. Davenport and DJ Patil declared in the October 2016 edition of The Harvard Business Review that data science is the sexiest job in the 21st century[1]. And then, in May 2017, The Economist anointed data as the world’s most valuable resource[2].

“Data is the new oil” is the modern organization’s battle cry because in the same way that oil drove economic growth in the 20th century, data will be the catalyst for economic growth in the 21st century.

But the consequences of the dense aggregation of personal data and the use of AI (neural networks and data mining, deep learning and machine learning, reinforcement learning and federated learning, and so on) could make our worst nightmares come true. Warnings are everywhere about the dangers of poorly constructed, inadequately defined AI models that could run amok over humankind.