AI Management System Certification According to the ISOIEC 42001 Standard How to Audit, Certify, and Build Responsible AI (Sid Benraouane) (Z-Library)

The book guides the reader through the certification process of the newly released ISO/IEC Artificial Intelligence standard. It provides tools and best practices on how to put together an AI management system that is certifi- able and sheds light on ethical and legal challenges business leaders strug- gle with to make their AI system comply with existing laws and regulations, and the ethical framework of the organization. The book is unique because it provides implementation guidance on the new certification and conformity assessment regime required by the new ISO/IEC Standard on Artificial Intelligence (ISO/IEC 42001:2023 Artificial Intelligence Management System) published by ISO and IEC in December 2023. This is the first book that addresses this issue. As a member of the US/ISO team who participated in the drafting of this and other standard during the last three years, the author has direct knowl- edge and insights that are critical to the implementation of the standard. He explains the context of how to interpret ISO clauses, gives examples and guidelines, and provides best practices that help compliance managers and senior leadership understand how to put together the AI compliance system to certify their AI system. The reader will find in the book a complete guide to the certification process of AI systems and the conformity assessment required by the standard. It also provides guidance on how to read the new EU AI Act and some of the US regulations enacted by many federal agen- cies, state and local government entities. This is the first book that helps the reader create an internal auditing program that enhances the company’s AI compliance framework. Generative AI has taken the world by storm, and as of the writing of this book, there is no international standard that provides guidance on how to put together a management system that helps business leaders address issues of AI gover- nance, AI structure, AI risk, AI audit, and AI impact analysis. ISO/IEC 42001:2023 is the first international voluntary certifiable standard that pro- vides a comprehensive and well- integrated framework for the issue of AI governance. This book provides a step- by- step process on how to implement the standard so the AI system can pass the required accreditation process. AI Management System Certification According to the ISO/IEC 42001 Standard

AI Management System Certification According to the ISO/IEC 42001 Standard How to Audit, Certify, and Build Responsible AI Systems Sid Ahmed Benraouane

First published 2024 by Routledge 605 Third Avenue, New York, NY 10158 and by Routledge 4 Park Square, Milton Park, Abingdon, Oxon, OX14 4RN Routledge is an imprint of the Taylor & Francis Group, an informa business © 2024 Sid Ahmed Benraouane The right of Sid Ahmed Benraouane to be identified as author of this work has been asserted by them in accordance with sections 77 and 78 of the Copyright, Designs and Patents Act 1988. All rights reserved. No part of this book may be reprinted or reproduced or utilised in any form or by any electronic, mechanical, or other means, now known or hereafter invented, including photocopying and recording, or in any information storage or retrieval system, without permission in writing from the publishers. Trademark notice: Product or corporate names may be trademarks or registered trademarks, and are used only for identification and explanation without intent to infringe. ISBN: 978-1-032-73397-5 (hbk) ISBN: 978-1-032-73394-4 (pbk) ISBN: 978-1-003-46397-9 (ebk) DOI: 10.4324/9781003463979 Typeset in Garamond by SPi Technologies India Pvt Ltd (Straive)

v Contents Foreword �������������������������������������������������������������������������������������������xi Preface ���������������������������������������������������������������������������������������������xiv About the Author and the Contributors �����������������������������������������xxx Introduction and Book Organization ������������������������������������������xxxiii PART 1 ARTIFICIAL INTELLIGENCE AND GENERATIVE AI: FORCES BEHIND THE DIGITAL TRANSFORMATION 1 Artificial Intelligence: A Transformational Technology ����������������3 Introduction ................................................................................................4 Definition of AI ...........................................................................................5 Different Types of AI ..................................................................................5 Artificial Narrow Intelligence (ANI) ...........................................................7 Artificial General Intelligence (AGI) ..........................................................8 Artificial Super Intelligence (ASI) ...............................................................8 2 Generative AI: A “Spark from AGI” �����������������������������������������������9 Introduction ..............................................................................................10 What Is Generative AI ..............................................................................11 Generative AI Added Value and Economic Sectors Impacted ...............13 Generative AI Harm, Risk, and Cost ........................................................14 Emerging (and Unknown) Abilities .....................................................15 Harmful Content ...................................................................................15 Privacy and Data Protection ................................................................15 Cybersecurity Threat ............................................................................16 Hallucinations .......................................................................................16 3 Economic Impact of Artificial Intelligence ��������������������������������19 Introduction ..............................................................................................20 Economic Sectors That Will Be Impacted by AI .....................................21

vi ◾ Contents The Manufacturing Sector ....................................................................21 The Finance Sector ...............................................................................21 The Transportation Industry ................................................................22 National Security and Law Enforcement Sector ..................................22 The Healthcare Sector ..........................................................................22 The Cybersecurity Sector .....................................................................23 Strategy Implications: The Current State of AI Adoption .......................24 AI in Automation: RPA .........................................................................24 AI in Prediction: Gaining Cognitive Insight ........................................24 AI and Cognitive Engagement: Enhancing Customer Relationship Management ....................................................................25 AI and Robotics ........................................................................................25 Industrial Robotics and AI ...................................................................26 Medical Robots .....................................................................................27 Military Robots and AI .........................................................................27 Impact of Automation on Society: How Will Society React to AI and Automation? ....................................................................30 Scenario One: Society Will Accept AI .................................................30 Scenario Two: Society Will Reject AI ..................................................31 Scenario Three: Society Will Accept Automation ...............................31 The Jobs AI Will Create ............................................................................31 Trainer ...................................................................................................32 Explainer ...............................................................................................32 Sustainer................................................................................................32 Countries’ AI National Strategy ............................................................33 4 Digital Transformation: How to Prepare Your Organization for Change ������������������������������������������������������������34 Introduction ..............................................................................................35 Digital Transformation Framework ..........................................................36 Leadership Commitment: Building Digital Leadership .......................36 Reskilling and Upskilling .....................................................................42 Teach Critical Thinking Skills ..............................................................42 Teach Innovation ..................................................................................43 Build a Customer-Centricity Capability ...............................................44 Build an Enterprise Agility ...................................................................46 Self-Directed Team to Manage Collaboration .....................................46 Agile Process: Review Your Decision-Making Process .......................48

Contents ◾ vii PART 2 ARTIFICIAL INTELLIGENCE MANAGEMENT SYSTEM: HOW TO PUT IN PLACE AN AI GOVERNANCE SYSTEM 5 Clause 4: Context of the Organization ���������������������������������������53 Introduction: Why Context Analysis Is Crucial to AI Management System? ................................................................................54 What to Include in the Context Analysis .................................................56 Competitive Landscape and Stakeholders’ Analysis ...........................56 Legal Context Analysis: Laws and Regulations ...................................59 The General Data Protection Regulation ............................................60 The EU AI Act .......................................................................................62 The US AI Regulatory Landscape ........................................................65 The Executive Order on Safe, Secure, and Trustworthy Artificial Intelligence ............................................................................68 Ethical AI: Responsible and Trustworthy AI .......................................69 Do No Harm Principle .........................................................................70 The Principle of Fairness and Non-Discrimination ............................71 Human Oversight and Respect of Human Autonomy Principle ........73 The Principle of Explainability ............................................................74 The Principle of Robustness ................................................................77 ISO Certification Process: How to Conduct an Analysis of the Context ...........................................................................................78 Step 1: Mobilize the Team and Clarify the Mission ............................79 Step 2: Set the Roadmap ......................................................................79 Step 3: Conduct Discovery Sessions....................................................80 Step 4: Start with the External Environment .......................................80 Step 5: Conduct an Internal Analysis ..................................................81 6 Clause 5: Leadership ������������������������������������������������������������������83 Introduction ..............................................................................................84 Set the Vision ........................................................................................84 Set the Vision, Define the Priorities, and the Strategic Direction ...............................................................................................86 Lead with Responsible AI Principles (RAI) .........................................87 Set the Tone and Use Proactive Communication ...............................89 AI Policy: Characteristics and Components .............................................90 What Should Be in the AI Policy? ............................................................90 A Statement on the Scope of the Policy, Its Purpose, and What the Policy Intends to Achieve ....................................................91

viii ◾ Contents Guidelines on the Use of AI in the Organization ...............................91 Show How AI Management System Integrates with Other Management Systems ...........................................................................91 Define the Roles and Responsibilities .................................................91 Data and Privacy ..................................................................................92 AI Compliance ......................................................................................93 AI Talent Management .........................................................................93 Monitoring and Improvement ..............................................................93 Review and Alignments .......................................................................93 How Do You Create an AI Policy? ...........................................................94 Form the Team .....................................................................................94 Engage with Stakeholders ....................................................................94 Conduct Discovery Sessions and Workshop Meetings with Different Stakeholders .................................................................94 Review the Laws, Regulations, and Ethical Framework .....................94 AI Strategy .................................................................................................95 Step 1: Develop AI Use Case ...............................................................96 Step 2: Assess the Competitive Landscape .........................................97 Step 3: Reorganize Internally ...............................................................97 AI Oversight: The Role of Board of Directors ....................................99 7 Clause 6: Planning �������������������������������������������������������������������102 Introduction ............................................................................................103 AI Risk Management, Risk Treatment, and Impact Assessment ..............................................................................................104 The Concept of Risk...........................................................................104 The Concept of Risk Assessment (Clause 6.1.2) ...............................104 The Concept of Risk Treatment (Clause 6.1.3) .................................104 The Concept of Impact Assessment (Clause 6.1.4) ..........................104 A Typology of Risks ...............................................................................105 Performance Risk ...............................................................................106 Security Risk .......................................................................................106 Enterprise Risk ....................................................................................106 Reputational Risk ................................................................................107 Legal and Regulatory Risk .................................................................107 AI Scalability Risk ...............................................................................107 The Black Box Risk ............................................................................108 AI Risk Management Planning: Principles, Framework, and Process .............................................................................................110

Contents ◾ ix AI Risk Framework: A Requirement to Certification ........................111 AI Risk Management Foundations .....................................................111 The Planning of Data Management Risk: An Imperative to AI Management System ...............................................................................116 ISO Standard Data Quality Requirements .............................................118 Data Collection Phase ........................................................................118 Data Preparation Phase ......................................................................119 Problem Framing Phase .....................................................................120 The Planning of Change: AI Management System Change Strategy ......................................................................................120 Create a Sense of Urgency .................................................................121 Build the Guiding Team.....................................................................121 Get the Right Vision ...........................................................................121 Communicate for Buy-In ...................................................................122 Empower Teams .................................................................................122 Perseverance .......................................................................................122 8 Clause 7: Support ���������������������������������������������������������������������123 Introduction ............................................................................................124 Tangible Resources: The AI Infrastructure ............................................125 Computing Performance ....................................................................126 Storage Capacity .................................................................................127 Networking Infrastructure ..................................................................127 Security ...............................................................................................127 Intangible Resources: AI Competence Model .......................................128 What Is a Competence Model? ..........................................................128 AI-Focused Competence Model ........................................................129 Awareness (Section 7.3) .....................................................................134 All Employees Need to Be Aware of the AI Policy ..........................134 How Employees Contribute to a Better Improved AI Management System ......................................................................136 Noncompliance Issues of the AI Management System ....................137 Communication (Clause 7.4) ..................................................................138 Encourage Face-to-Face Communication .........................................138 The Medium Is the Message ..............................................................138 Create Policy Champions ...................................................................139 Documented Information .......................................................................139 Documented Information Required: What Needs to Be Documented .................................................................................140

x ◾ Contents 9 Clause 8: Operation �����������������������������������������������������������������142 Introduction ............................................................................................143 AI Project Life Cycle ...............................................................................143 Design Phase: Process Grouping 1 ...................................................145 ISO/IEC 42001 Requirements for Process Grouping 1 (Design) .....146 Design Phase: Process Grouping 2 ...................................................147 ISO/IEC 42001 Requirements for Process Grouping 2 (Design) .....148 Development Phase: Process Grouping 3 ........................................149 ISO/IEC 42001 Requirements for Process Grouping 3 .....................150 Deployment Phase: Process Grouping 4 ..........................................150 ISO/IEC 42001 Requirements for Process Grouping 4 .....................151 10 Clause 9: Performance Evaluation �������������������������������������������155 Introduction ............................................................................................156 AI Management System Evaluation and Assessment Requirements ..........................................................................................157 AI Management System Assessment and Audit ................................157 Set Up an Internal Audit Program .....................................................162 Management Review ..........................................................................163 11 Clause 10: Improvement ����������������������������������������������������������166 Introduction ............................................................................................167 Corrective Actions and Preventive Actions Framework ........................168 Corrective Actions ...................................................................................168 Preventive Actions ..................................................................................169 Continual Improvement: The PDCA Approach .....................................169 Conclusion ..............................................................................................171 Appendix ����������������������������������������������������������������������������������������174 Bibliography �����������������������������������������������������������������������������������178 Index ����������������������������������������������������������������������������������������������185

xi Foreword As you open this book, the first thought that may come to you is “Why do we spend millions of dollars preparing, publishing, and distributing ISO standards for everything from electric plugs to Artificial Intelligence and back again? Aren’t there more than enough books and papers published on the subjects already?” Well in asking that question, you answered the question. The truth of the matter is that there are so many different ways that some- thing can be done, used, or misused in this international consumer market that a minimum threshold on how to use or implement a new process or a new technology is required. Standards produced by the International Organization for Standardization (ISO) help organizations agree on a set of practices that make the new technology, the new process, the new service, or the new product safe and meet the quality level the consumer aspires for. The mission of ISO is to bring people together to build a consensus around new and emerging business and management practices. It provides a neutral platform where experts from different countries come together to agree on a set of ideas and ways of doing things. ISO’s slogan “Great things happen when the world agrees,” truly captures ISO’s identity in producing frame- works that bridge barriers raised by language and cultural differences, as well as by local regulations and national legislations. In looking back at my experience in the 1980s with ISO standards, first with ISO 9000 family (quality management), then ISO 14000 family (envi- ronmental management) and, more recently, ISO 56000 series (innovation management), I can say that with no doubt ISO standards have had a deep impact on improving the quality of products and services around the world, and have had a significant added value to process improvement. Standards have become a teaching space where entrepreneurs and managers, espe- cially from the small and medium enterprise world, come to learn about how to apply new ideas, how to improve processes, and, ultimately, how to

xii ◾ Foreword embrace business Excellence. Today, ISO standards serve as a benchmark for process improvement, quality control, and innovation. ISO’s work brings clarity, harmony, and agreement resulting in more comprehensive interpre- tations of how to use and implement a new technology or a new process. In my experience as the chair of the American Society for Quality (ASQ) and as a member of the US delegation at ISO, heavily engaged in the draft- ing of ISO standards, I learned many important things about the role of a standard. First, a standard assures that the product or the service has met the quality controls that protect consumers and clarifies requirements and technical specifications to create consistency in the way technology is rolled out. A standard builds the trustworthiness needed for the product to live, grow, and prosper. A standard helps technology overcome interoperability barriers by adjusting the technology architecture to meet different contexts. Standards are a core foundation of the World Trade Organization TBT Agreements (Technical Barriers to Trade). They create the ecosystem that facilitates and promotes global trade and commerce to help the overall market integration. And finally, a standard helps in leveling off our under- standing of technology to create a common language that guides us during our future scientific inquiry. The standard you will be reading about in this book, ISO/IEC 42001 Artificial Management System (AIMS) is a game changer. It is a game changer because it addresses a transformational technology that is moving very fast, faster than we have time to understand it. Artificial Intelligence, and its most recent applications, such as Generative AI and frontier AI models, are set to challenge some of the basic foundations of the way we work and interact with each other. As a General- Purpose Technology, AI will transform everything from education to commerce, to healthcare. It is set to transform the entire global economy by adding more efficiency and productivity output. The risk of this technology going rogue is also high. Misinformation, disinformation, bias, and privacy violations, as well as performance and reputational risks, are some of the common risks listed by experts that need appropriate safeguards and oversight. The recently approved AI Act, by the EU Parliament, and the issuance of directives by federal agencies and local governments in the United States (such as the NYC Local Law 144) are a good step forward to regulate AI. But we need more. We need to go beyond setting up legal requirements. We need gover- nance frameworks to educate managers on how to implement this technol- ogy. We need methodologies that explain to frontline employees how to interact with this technology. We need tools and best practices to help data

Foreword ◾ xiii engineers understand the ethics of producing a responsible AI model. This is what ISO/IEC 42001 brings to the table. The book you will be reading walks you through the different require- ments of the new AI Standard ISO/IEC 42001. The goal is to help you understand these requirements, link them to your business strategy, and connect them with other management systems you may have in your organization. In the first part of the book, the author provides the general context of AI. He defines AI and discusses some of the most recent breakthroughs in AI systems. AI is also part of the broader issue, the issue of the governance of digital transformation that has become a major challenge for organiza- tions to manage. In this part, the author sets up the scene for his analysis of the standard and provides a framework on how to engage people, modify behavior, and create a culture that aligns with the new normal of AI transformation. Next and in the second part, the author gets into the discussion of the different clauses of the standard. He provides guidelines on how to under- stand and implement better the standard. While every manager has his way of implementing requirements, I sensed in the author’s approach compre- hensiveness, simplicity, and straightforwardness that make the implementa- tion an easier one. He goes beyond what the standard provides to offer many options and ways of putting together an AI management system. After all, he participated in the drafting of this standard, so his insights on what fits better average organization are relevant to a good implementation. More importantly, he backs up his explanation of different clauses with examples and research from leading organizations that have set a benchmark in implementing AI management systems. As you read this book, I want you to reflect on how your way of under- standing the standard adds value to the overall AI standard development. There are many ways for you to get involved in helping others understand this new technology to make it work for everyone. I believe that we are spending too much time worrying about winners and losers. Maybe it’s time to stop trying to beat each other and focus on what we can do for mankind by working together to get results. I hope you will enjoy this book. Dr� H� James Harrington Past President and Past Chairman of the Board of the American Society for Quality (ASQ) CEO of Harrington Management Systems Los Gatos, California, USA

xiv Preface Generative AI: The Promise, Risks, Challenges, and Opportunities In November 2022, the world was introduced to generative AI with the launch of ChatGPT. By January 2023, this technology was dominating headlines and blogs. To most, OpenAI had invented generative AI overnight with ChatGPT. The core technology was available for years before ChatGPT’s release. But what exactly is generative AI, and how does it differ from previous AI? What promise and perils does this new wave of artificial intelligence hold? The release of ChatGPT and other generative models represents a seismic shift for AI, showcasing tremendous potential but also risks requiring thoughtful governance. Realizing the benefits of generative AI necessitates addressing core challenges and opportunities while consid- ering the broad impact on society’s digital and socioeconomic divides. Systems like the technology behind ChatGPT – GPT- 4 (Generative Pre- trained Transformer version 4) – promise to revolutionize human- machine interaction. Their ability to generate remarkably human- like text, images, code, and more opens new possibilities for automating rote work, augment- ing creativity, and personalizing content. Applications span nearly every industry, from healthcare to manufacturing to entertainment, reducing costs and driving revenue. Generative models could also help tackle societal problems by uncovering insights from data. Their expansive potential explains the palpable excitement. However, genuine risks require diligent governance, including potential bias and toxicity from imperfect training data, misinformation that appears highly credible, job displacement from rapid automation, security

Preface ◾ xv vulnerabilities like spreading malware or propaganda, and lack of transpar- ency in model decision- making. Mitigating these risks necessitates ongoing research and industry collaboration to improve model architecture, training processes, human oversight, and monitoring systems. Meaningful challenges temper unbridled enthusiasm about generative AI, including limited reasoning and brittle responses, massive data requirements many organizations lack, scarcity of qualified generative AI experts, sub- stantial compute resources required, and building user trust in black box model recommendations. Patience is warranted as models, talent, and best practices mature. Prudent experimentation unlocks momentous opportunities for first movers, including cost savings from automating repetitive tasks, new rev- enue streams with enhanced products and services, competitive advantage from cutting- edge technology, top talent attraction, customer satisfaction gains via hyper- personalization, and faster innovation and creativity. Organizations should start building foundations through small pilots focused on augmenting employees, auditing data/skills, and crafting respon- sible AI principles. Democratizing access to advanced AI has tremendous potential to empower disadvantaged communities through applications like translation and intelligent tutoring. Still, there are risks that benefits accrue dispropor- tionately to the digitally privileged as poorer communities lack infrastruc- ture and expertise. Widespread automation also threatens to displace jobs before at- risk populations can develop new skills, and biases in training data may further sideline marginalized voices. Proactive policies and prac- tices like investing in digital infrastructure/literacy (especially in developing nations), training models on diverse datasets, job retraining initiatives, robust safeguards against bias and misinformation, fostering inclusive inno- vation, and positioning as collaborators rather than replacements are essen- tial. With ethical implementation, generative AI could uplift people worldwide, but neglecting digital and socioeconomic divides amplifies the risks of exacerbating inequality. Stakeholders must make inclusive choices to deliver on generative AI’s transformative potential, unlocking immense progress for business and society. The decisions we make today will reso- nate for years to come. In addition to digital and socioeconomic considerations, the environmen- tal impact of widespread generative AI adoption warrants examination. Training and deploying these models require massive data centers, consum- ing substantial energy, mostly from carbon- emitting and water- consuming

xvi ◾ Preface sources. The computational power needed also necessitates advanced semiconductors and electronics manufacturing, which is also carbon and water- intensive. While AI efficiency improves, unchecked proliferation risks increasing technology’s carbon and water footprints. Environmental stew- ardship requires optimizing energy and water usage, using renewable power where feasible, and weighing benefits versus sustainability costs for AI applications. With mindful model development and computing strategies, generative AI can progress ecologically responsibly. However, overlooking environmental externalities will exacerbate climate change and resource scarcity. The extent to which we align generative AI with environmental values will shape its role in building a sustainable future. The emergence of generative AI represents a turning point laden with both profound opportunities and risks. Realizing the full potential requires inclusive collaboration to develop ethical frameworks for model governance, safety, bias mitigation, job displacement, environmental impact, and other critical factors. A nuanced, thoughtful approach can unlock immense benefits for business and society. But unchecked proliferation or prioritizing narrow interests over collective welfare will undermine public trust and stunt prog- ress. The path forward must align generative AI with shared human values of equity, responsibility, and sustainability. If stewarded judiciously, these models can augment our capabilities, increase the accessibility of information, and provide insights into serving society more broadly. Working together proac- tively can shape an enlightened artificial intelligence future, benefitting com- munities worldwide. But we must start making the right choices today. Understanding Generative AI Generative AI represents a paradigm shift in artificial intelligence capabili- ties. Rather than analyzing existing data to produce insights, generative models create entirely new data from scratch. This enables revolutionary applications across domains like natural language, images, and audio. At its core, generative AI systems are trained on massive datasets to learn general- ized knowledge about the world. This foundational model can then gener- ate or synthesize novel, original content based on prompts and parameters provided by users. The key breakthrough is producing information that may not exist in the initial training data. For example, natural language models like GPT- 4 ingest billions of web pages and books to learn linguistic skills. This training corpus contains only

Preface ◾ xvii some potential articles, stories, or poems. But GPT- 4 can then take a short text prompt and generate coherent continuations in a remarkably human- like manner. The model exhibits creativity, humor, and topical reasoning without needing specific examples of the desired output built into the initial dataset. Similarly, image generation models like DALL- E 2 and Stable Diffusion are only trained on some possible photographs. But they learn visual concepts like objects, scenes, colors, lighting, and styles from millions of images. This allows the generating of realistic and creative pictures from text prompts that were never seen before. Want an image of a unicorn playing chess in a film noir style? Generative models can synthesize it without any such photo in the training data. Generative AI capabilities span: ◾ Text generation – Models like GPT- 4 can produce natural language for articles, stories, emails, reports, code, and more. Prompts can specify topics, tone, length, and other parameters. ◾ Image generation – Create original, photorealistic images from text prompts: control image composition, style, objects, and other attributes. ◾ Video generation – Emerging techniques like Google’s Imagen Video generate short videos from text prompts by leveraging diffusion models. ◾ Audio generation – Models can generate human- like voices and music by learning from large datasets. Useful for text- to- speech, voice clon- ing, and more. ◾ 3D model generation – Generate 3D shapes and scenes from text prompts. Enables rapid 3D concept model creation for gaming, VR, and design. ◾ Multimodal – Models combining language, image, and audio in innova- tive ways. For example, text- to- image- to- video generation. ◾ Data augmentation – Generate synthetic training data to improve downstream AI systems. Expand datasets for better machine learning. The applications span nearly every industry: ◾ Chatbots – Conversational AI agents for customer service, sales, sup- port, and more. ◾ Content creation – Automate writing for marketing copy, reports, news articles, social media posts, and more. ◾ Drug discovery – Generate molecular structures with desired pharma- ceutical properties.

xviii ◾ Preface ◾ Creative design – Enhance ideation and concept generation for prod- ucts and media. ◾ Personalization – Tailor content like marketing messages, newsfeeds, and search results for each user. ◾ Recommendation systems – Improve product and content recommendations. ◾ Game development – Generate game assets like 3D models, textures, and landscapes. Assist narrative design. The critical shift is generative AI’s ability to produce novel, high- quality artifacts rather than categorize existing data. This facilitates creative tasks, personalized experiences, and democratizing access to beneficial AI appli- cations. However, responsible development is crucial to manage risks as generative models become more capable. Generative AI can augment human capabilities and enhance our collective future if cultivated prudently. Generative AI leverages a technique called foundation models that has rapidly advanced capabilities in recent years. But despite impressive demos, this field remains in its early stages with much room for improvement. Foundation models first train a model on a general domain like language or images. This model learns broad skills from the massive dataset without specializing in a narrow task. For example, models like GPT- 3 ingest bil- lions of web pages to acquire general linguistic abilities. The pre- trained foundation model can then be fine- tuned on smaller datasets to create specialized AI applications. This transfer learning approach enables models to learn new tasks faster with less data than training from scratch. The foundation model provides the general knowledge to bootstrap more specific capabilities. GPT- 4 demonstrates the potential of language foundation models. After training on internet text, it can generate remarkably human- like writing on most topics with simple prompting. GPT- 4 powers applications like chat- bots, text auto- completion, and more. Despite lacking purpose- built train- ing, the general linguistic base enables versatile performance. DALL- E 2 illustrates the power of image foundation models. It can gener- ate realistic images from text prompts following pre- training on millions of photos. The general visual knowledge allows for synthesizing novel scenes and objects without explicit training examples. DALL- E fuels applications like marketing creative and product concept design. Figure I.1 provides a timeline of the recent evolution of foundation models. Dr� Seth Dobrin CEO Qantm AI

Preface ◾ xix 2019 - GPT-2 shows generave text modeling generang coherent paragraphs 2020 - GPT-3 leverages massive scale (175 billion parameters) to achieve new performance levels 2021 - Image: DALL-E generates images from text prompts using generave adversarial networks 2022 - Code: GitHub Copilot 2022 – Image: The release of Latent Diffusion, Stable Diffusion open-sources an image generator rivaling DALL-E 2. Midjourney 2022 - 3D Image: Meta's Make-A-Scene models text- to-3D scene generaon 2022 - GPT-3 API & ChatGPT conversaonal agent built on GPT-3 launched 2022 – Audio: AudioLM 2022 – Open Source Language: BLOOM, GPT- NeoX, Falcon 2022 – Closed Source Language: PaLM, GPT-3.5 2023 – Closed Source Language: GPT-4, Jurassic, Bard, Claude 2, LLaMa 2023 – Industry Specific Language: BloombergGPT 2023 – Audio: MusicGen, VoiceBox 2023 – Agent-based Language :AutoGPT 2023/2024 - Claude-Next 2T, Gemi 2T, Industry-specific GenAI, Organizaon-specific GenAI, Task-specific, GenAI, LongNet 2017 - Transformer architecture enables modeling longer text dependencies in language models. 2018 – The BERT foundaon model sets new benchmarks in NLP tasks via pretraining Figure I.1 A Timeline of Foundation Model Evolution.