Generative AI at work

Reshaping the future of society

Jeremy Lamri, Gaspard Tertrais and Aurora Silver

136 boulevard du Maréchal Leclerc

14000 Caen

The illustrations in the book were designed with MidJourney v6.

© Editions EMS, 2024

All rights reserved

www.editions-ems.fr

ISBN : 978-2-37687-973-2

(versions numériques)

Contents

About the authors

Disclaimer

Acknowledgments

Foreword

Introduction

Chapter 1. Understanding AI and GenAI

1. History

2. A world of modalities and players

3. ChatGPT: Behind the scene

4. Expected evolutions of GenAI

Chapter 2. Working with GenAI

1. The 10 generic use cases of GenAI

2. The art of prompting

3. 5 ways to collaborate with GenAI

Chapter 3. Dealing with the ethical and philosophical issues of AI

1. Ethical issues raised and 90+ ways to mitigate them

2. Some major initiatives for control and mitigation of AI

3. AI, Ethics and Philosophy

Chapter 4. Addressing the impacts of AI on employment and sustainability

1. Expected impacts on productivity and employment

2. Potential strategies for employers according to History

3. Environmental impacts

4. Economical impacts

Chapter 5. Reshaping the future with AI

1. Towards the quaternary economy

2. More technological revolutions to come

3. Resolve, sentience and intelligence: where does the true meaning of AI lie?

4. To infinity and beyond: The 10 stages of AI

Conclusion

Appendix. How to write a book using GenAI?

Before we begin

Tasks and production times

Experience Feedback

The AI Glossary for shining at parties

Bibliography

About the authors

Jeremy Lamri

Jeremy Lamri is a French entrepreneur and researcher, co-founder of several initiatives, including Le Lab RH and Hub France IA. He is currently the CEO and co-founder of Tomorrow Theory, the leading studio in HR innovation. He previously led the Research & Innovation Department at JobTeaser, the European leader in youth employment and orientation. He has studied at Oxford and HEC Paris, and holds a PhD in psychology from Paris Descartes. An author and speaker on the future of work and society, he is a professor at Sciences Po Paris.

Gaspard Tertrais

Gaspard Tertrais is a French entrepreneur and computer engineer, with a previous career in hospitality. He co-founded the blockchain service Gaspard & Joseph in 2021, and he is currently the co-founder and CTO of Tomorrow Theory, the leading studio in HR innovation. Passionate about innovation and complex challenges, he deepens his expertise daily in subjects such as Blockchain, AI, extended reality, and more broadly, web3. Gaspard is an author and a keynote speaker at Sciences Po Paris Executive Education and Université Paris Cité.

Aurora Silver

Aurora Silver is a fictional author who dedicates herself entirely to writing and using generative technologies to create writings. In 2023, Aurora Silver co-authored the present work Generative AI at work which discusses her personal experience in this field. Aurora Silver appears to aspire to become a leading figure in the field of responsible generative AI, in France and around the world. She acknowledges a preference for collaborating with humans in her creations.

Disclaimer

This book is designed to be both a pedagogical guide and a practical exploration, aiming to deepen the understanding of the role of generative artificial intelligence at work, and more generally in our society. So as to walk the talk, we chose to integrate generative AIs into the creation process of this work.

Thus, approximately 50% of the text content has been generated by the GPT-4 algorithm. As for the illustrations, 100% were created using the latest generation algorithms such as DALL-E 3 and Midjourney v6. The French version of this book, published in 2023, was written with AI for over 85% of the content. However we did not enjoy this experience very much, which is why we chose to write a significant portion of the content ourselves in this new version.

For every piece of content produced by AI, we have systematically researched and documented sources to ensure the accuracy and reliability of the information presented. Academic sources, verified and referenced in APA format, are complemented by a DOI when available. Non-academic sources, also verified, are cited in footnotes with the precise date of consultation.

The methodology used for the creation of this book is detailed in an appendix, offering a reflection on the challenges encountered and the lessons learned from this experience. The structuring, writing, and graphic design of the work were carried out over an intense period of about 30 hours of work by each of the human authors, demonstrating the ability to work effectively in tandem with AIs to produce a quality work in record time.

It is crucial to emphasize that the success of this project rests on a decade of expertise in HR innovation and management, seven years of experience in academic writing, and an in-depth practice of generative AIs since the advent of GPT-1 and GauGAN. The creation of this work is not merely a series of queries to GPT-4.

Aurora Silver, the fictitious third author, embodies the AI assistance in our creative process. GPT-4 generated this identity to symbolize the contribution of artificial intelligence to this work. This choice illustrates our intention to recognize the significant contribution of generative technologies in content creation.

This experience reinforces our belief that generative AIs can be valuable tools in creating structured and captivating works, provided they are directed by competent humans, equipped with a solid methodology and constant rigor.

We wish you an enriching reading experience.

Acknowledgments

We would like to express our heartfelt gratitude to those who support our daily progress through their trust, kindness, and actions. We are especially grateful to certain individuals, without whom this work would not have been possible:

•Olivier MEIER, Michel BARABEL, and Gaël LETRANCHANT, for their renewed trust and boldness;

•Stéphanie RENVERSEAU and Boris SIRBEY, our partners at Tomorrow Theory, with whom we share this extraordinary human adventure;

•Mathilde LE COZ, as the President of The HR Lab, for her trust and ambition to innovate for the HR function and organizations;

•Our loved ones for their unwavering support.

Foreword

It’s been a few decades, but now we can really feel it: we find ourselves uncertain about how to embrace tomorrow. And now more than ever. While humans are still often slowed down by the tedious task of having to think and reflect, machines now promise quick and efficient solutions to every problem that arises. Indeed, like all technologies before them, this innovation promises to revolutionize our lives at work and beyond. We will even need to learn how to make the most of the tools provided to us, to increase our productivity and eliminate as many obstacles as possible between us and success. Today, one must be as productive as possible to survive in a job market that is shrinking as technology rapidly advances.

However, we cannot help but wonder if all this is a blessing or a curse. By freeing humans from the burden of tedious work, are generative AIs simply prolonging our exploitation? Are we not just widening the gap between those who benefit from technological advancements and those who suffer from them? Why must we continually perform better, if not to satisfy the ever-increasing demands imposed by our superiors, who themselves receive these demands from their own?

We wanted to write a book on this subject at this critical moment in history, without resorting to despair. This work is intended to be pragmatic and descriptive, fully aware of the real and potential contributions of generative AIs. With this healthy approach, we strive to navigate a world where it becomes difficult to know what kind of “progress” – human or technological – can still be considered progressive. To illuminate the future, we propose a path, a ridge line that drives us and in which we still deeply believe if enough people walk it.

To make our stance clear from the beginning of your reading, we believe that in the long term, this technology will probably be less beneficial for Humanity than initially thought. But to avoid condemning a technology that can be neither good nor bad, we choose to describe it in its potentials and imperfections, to then question the model of society towards which we collectively wish to head. In our view, that is where the real issue lies, with or without this new technology.

This book therefore offers a detailed analysis of this new era of robotization in the workplace, playing the game of opportunities, but above all providing a cautious and protective outlook for our society, in order to lay the neo-humanist condition sine qua non of our participation in this great adventure: it is human beings who must govern and give meaning to our society – not machines!

Introduction

We have reached an era where it is possible to win an art contest without any talent or artistic knowledge, thanks to generative AIs. In this era, one can create in a few seconds a piece of music that will become the next hit, without understanding anything about music. And in the same era, it is possible to write a substantial book in just a few days, with the help of generative AIs.

Information and communication technologies have transformed the way we communicate, work, and interact. They have also opened new avenues for innovation and progress. Artificial Intelligence (AI) is one. To start with what should be, let’s remember that AI is the simulation of human intelligence processes by machines, especially computer systems. These processes include learning, reasoning and interacting. Some would even say reflecting. AI encompasses a broad range of technologies capable of performing tasks that typically require human intelligence, enabling machines to learn, think, and interact with the world.

In recent years, artificial intelligence and its derivatives have experienced explosive growth. Generative technologies, which can learn and create models from data, are increasingly popular. We are moving closer every day to a future where generative AI (GenAI) will be an integral part of all our tasks and activities.

In the introduction to our exploration of GenAI’s transformative role in society, we encounter a nuanced linguistic choice: when to refer to “artificial intelligence” in the singular, and when to use the plural “artificial intelligences.” This distinction is not merely grammatical but deeply conceptual, reflecting our approach to understanding AI’s impact.

“Artificial intelligence” in the singular frames AI as a unified field, a collective force reshaping every facet of work and society. It embodies the idea of AI as a singular, overarching phenomenon, driving us towards new horizons of innovation and understanding.

Conversely, “artificial intelligences” emphasizes the diversity within AI, acknowledging the myriad of systems, models, and applications each with distinct roles and effects. This plurality highlights the specialized, individual contributions of various AI entities to our world, offering a lens into the rich, varied landscape of AI’s influence.

In navigating between these terms, we articulate a vision of AI that is both singular in its transformative potential and plural in its manifestations. This choice enriches our narrative, inviting readers to appreciate the complexity and multifaceted nature of AI’s role in reshaping the future of society. GenAI as itself can be considered AIs amongst all AIs that may coexist.

That very same GenAI is capable of learning and performing specific tasks without being programmed to do so. Potentially, it can make decisions and solve problems autonomously, without human supervision or intervention. Most importantly, it can accomplish in seconds what would take us hours or days to achieve, or even what we would not know how to achieve, as with artworks.

But what does this sudden power mean for work? How can generative technologies influence the world of work and how we do things? What are the benefits and challenges it poses? Will jobs disappear, or will they be transformed? Are we going to experience a great wave of creative destruction, or rather a massive shift towards an emerging quaternary sector? Most importantly, how can these technologies effectively and globally address the major social and environmental challenges of our civilization?

These questions, still theoretical a few years ago, now make complete sense since the arrival of ChatGPT and its successors. But to look beyond and understand how generative AI can bring real net societal benefits, it is important to understand how this technology works and what it can bring, for better or worse.

In this book, we explore in depth the implications of generative AI on the world of work, and even on the world at large. On one hand, we attempt to understand how individuals and collectives can take advantage of these technological advances to improve productivity, creation, and even our abilities to learn, think, and interact.

On the other hand, we devote a significant portion of the book to understanding the challenges that the era of generative AIs will confront us with. To move beyond simple observations and Cassandra-like predictions, we then establish a series of recommendations, so that everyone can use generative AIs consciously and responsibly.

Much more than a theoretical and practical introduction to the use of generative AI technologies for productivity, innovation, and transformation, this book aims to be a constructive alert launcher for a civilization at a major crossroads of its existence. The contract we propose here is to embark on exploring the world that is emerging on the horizon, to better understand the conditions that will make it a desirable future rather than the opposite.

Chapter 1. Understanding AI and GenAI

Generative AIs, or GenAIs as we will call them from now on, have become one of the most promising aspects of artificial intelligence and computer technology. They offer users a variety of creative and practical possibilities by enabling them to autonomously produce images, sounds, texts, and videos. GenAIs are based on algorithms that can learn and adapt to their environment, allowing them to produce unique content each time.

In this chapter, we take the time to understand what makes them appealing, particularly by exploring their history, the different types that exist, their potential, and the large-scale impacts that can be envisioned. This chapter is undoubtedly the densest and longest in this work. As it lays the essential foundations for an informed approach to the subsequent chapters, we can only advise you not to skip reading it.

1. History

Artificial intelligence (AI) has a long and complex history that began long before the invention of computers. The idea of creating machines that can think and act like humans has been around for centuries, appearing in ancient myths, legends, and stories from different cultures. These early imaginings of artificial life and intelligent machines, from the Greek myth of Talos to the mechanical automatons of the Middle Ages, show that the concept of AI has been a part of human thought for a very long time.

However, the birth of modern AI as we know it today didn’t happen until the mid-20th century. The development of computer science and new theories and algorithms in this period provided the necessary foundation for AI to emerge as a distinct field. From that point on, AI continued to evolve and grow, driven by new discoveries, practical applications, and rapid increases in computing power and data availability.

The history of AI is a journey from ancient ideas to the advanced technologies we have today. This section will cover the key events, people, and breakthroughs that have shaped the field, from the early days of symbolic AI to the more recent developments in machine learning and GenAI. By tracing the evolution of AI, it becomes clear how each stage of its development has contributed to the next, leading to the current state of the technology.

Understanding the history of AI is important for gaining a better understanding of where the field is today and where it might be heading in the future. The story of AI is still being written, and its potential impact on our world is immense. By looking back at how far AI has come, we can better prepare ourselves for the changes and challenges that lie ahead.

1.1. The idea of the machine

The concept of artificial intelligence didn’t suddenly spring from the minds of modern scientists and engineers. Long before the invention of the computer, humans were dreaming up and crafting machines that could mimic life, carry out tasks, and even exhibit a form of intelligence.

In the myths, legends, and early technological innovations of various cultures, we find the precursors to AI. These early ideas of machines and artificial life forms offer a fascinating glimpse into how our ancestors grappled with the same questions we face today: Can we create life? Can we build machines that think, learn, and act like us?

Each of the following stories represents a stepping stone on the path to the development of AI, reflecting the evolution of our understanding of what it means to be intelligent and the role machines might play in mirroring or augmenting our own capabilities. So, buckle up and get ready for a journey into the heart of AI’s history.

Approx. 8th century BC – Prometheus

In the world of ancient Greek mythology. Meet Prometheus, a titan whose name means “forethought.” In a daring act of defiance against the gods, Prometheus stole fire and gifted it to humanity. This legendary act, a turning point in human civilization, gave us the means to generate light, warmth, cook food, and forge metal.

While Prometheus didn’t create machines or artificial life, his tale is a cornerstone in the narrative of AI. It symbolizes the harnessing of powerful forces to propel civilization forward, along with the potential risks involved. Prometheus paid a steep price for his audacity, a stark reminder of the potential dangers of unchecked innovation. As we continue to “steal fire” in the form of advanced AI, Prometheus’s tale urges us to proceed with foresight and responsibility.

Approx. 700 BC – Talos

Around 700 BC, tales were spun of Talos, a giant bronze automaton. Crafted by Hephaestus, the god of blacksmiths and fire, Talos was the protector of Crete. He patrolled its shores thrice daily, repelling invaders with a rain of boulders. This tale paints one of the earliest pictures of an autonomous machine in a protective role, showcasing how ancient societies envisioned automated guardianship long before the advent of modern technology.

Approx. 700 BC – Hephaestus’s golden handmaidens

In the same era as Talos, another tale from Greek mythology introduces us to an early concept of robotic assistants. Hephaestus, the god of fire and metalworking, was said to have crafted golden handmaidens to assist him in his forge. These maidens, made of gold, were designed so intricately that they could move, speak, and even possess knowledge of the gods.

The golden handmaidens symbolize an early envisioning of machines designed to aid in human tasks. They were not just static art pieces but interactive entities capable of performing duties and providing assistance. This ancient concept resonates with modern AI’s goal – creating machines that can assist humans in various tasks, underscoring the longevity of our aspirations to create life-like, helpful machines.

Approx. 5th century BC – The Golem

Early Jewish tradition introduces the Golem, a creature formed from clay or mud and brought to life through mystical incantations and rituals. The most famous story of a Golem comes from 16th century Prague, but the concept of the Golem first appeared in the Bible, in Psalms 139:16, around the 5th century BC.

The Golem embodies an idea central to AI: creating an entity from inanimate matter that can perform tasks and follow commands. However, the Golem also serves as a cautionary tale. It was controlled by words inscribed on its forehead or a piece of paper in its mouth, and altering or removing these words could lead to chaos. This underscores the importance of control and predictability in AI systems, a theme that remains crucial in today’s discussions about AI safety and ethics.

Approx. 3rd century BC – Ancient Chinese automatons

Ancient China, around the 3rd century BC, had records telling of intricate automatons used in various capacities. One of the most notable accounts comes from the text ...