Generative AI: Revolutionizing the Way Enterprises Work

Generative AI: Revolutionizing The Way Enterprises Work E-book March 23

Table of Contents Preface 3 Chapter 1: An Introduction to Generative AI ........................................................................................... 7 Chapter 2: Generative AI Business Applications .................................................................................. 12 How Information Management is Changing With New Generative AI Tools ..................... 13 How Software Development is Changing With New Generative AI Tools .......................... 15 How Content and Marketing are Changing With New Generative AI Tools ....................... 17 How Design is Changing With New Generative AI tools ............................................................. 19 Generative AI Impact on Banking .......................................................................................................... 22 Chapter 3: Industry Transformation With Generative AI ................................................................. 23 How Film Production is Changing With New Generative AI Tools .......................................... 24 How Game Development is Changing With New Generative AI Tools .................................. 26 Chapter 4: Reflections and Future Trends .............................................................................................. 29 Resources 33 Gen AI: Revolutionizing the Way Enterprises Work 2

Preface Generative AI: Revolutionizing The Way Enterprises Work The goal of this eBook is to provide a comprehensive practical guide for the current applications of Generative AI in the business world. Nevertheless, this field is constantly evolving, with new advancements occurring daily. To keep pace with the latest developments, we intend to publish subsequent editions of this book. The emergence of generative AI is the most significant transformation in the business landscape since the advent of the internet. The potential of generative AI is becoming increasingly evident with the rapid expansion of applications such as ChatGPT, which are revolutionizing content creation, planning, and brainstorming, among other areas. A recent MIT study has shown that businesses can achieve a 50% increase in productivity by leveraging generative AI. Additionally, employees who work with AI tools report higher levels of performance and satisfaction. This finding is a testament to the significant impact that generative AI can have on the business world. Gen AI: Revolutionizing the Way Enterprises Work 3

Figure 1. Generative AI effects on productivity as discovered in a recent MIT study. To put this in perspective, consider the following table that shows the estimated productivity gains from major technological breakthroughs: Table 2. Estimated productivity gain from technological breakthrough. Technology Breakthrough Estimated Productivity Gain Internet 10-15% of productivity growth (between 1995-2005) Steam Power 25% (for a small factory) Electricity 35-40% (between 1900-1940) Generative AI 50% increase in productivity according to a recent MIT Study As shown in Table 2, Generative AI has the potential to deliver the largest gains in productivity of any technology to date. However, it's important to note that we are just scratching the surface of what's possible with this technology. Generative AI is still in its infancy, and we have yet to see the full potential of this revolutionary technology. It is very likely that in the upcoming years this trend will continue, and we’ll see a significant increase in productivity across all sectors of knowledge work. Gen AI: Revolutionizing the Way Enterprises Work 4

ChatGPT's meteoric rise to becoming the fastest-growing application in human history is indicative of a fundamental shift in the business world. Generative AI has the potential to dramatically enhance productivity and reduce costs, ultimately transforming the way businesses operate. Experts believe that this is just the beginning of what is possible with generative AI, and its transformative impact cannot be ignored. Figure 3. The number of months it took popular applications to reach 100 million users. New Generative AI applications, such as ChatGPT, Bing AI, and Jasper, are now capable of performing tasks that were once considered to be exclusive to humans. These tasks include writing blog posts, designing, writing computer code, and even contributing to the creation of video games and movies. Additionally, they can quickly summarize vast amounts of data and make it accessible through language, which experts believe will fundamentally transform how businesses operate. These new Generative AI tools are user-friendly and can be used by anyone, including individuals without any technical background or specialized machine learning skills. This means that business leaders can start utilizing these tools today to gain a competitive advantage. Gen AI: Revolutionizing the Way Enterprises Work 5

Who is this book for? This ebook is a practical and comprehensive guide for businesses and professionals aiming to leverage the full potential of generative AI to gain a competitive advantage. While not overly technical, readers should have some basic knowledge in information technologies and technical tools. The primary target audience for this ebook comprises businesses and professionals involved in creating, manipulating, transforming, and disseminating various forms of digital content, including numbers, text, images, and audio. Such professionals may come from diverse fields, such as executive management, consulting, sales, photography, accounting, podcasting, marketing, law, or software engineering. The concepts presented in this ebook can be easily understood without any coding expertise, and many of the examples provided throughout may be familiar to readers. “We’re at the beginning of an AI computing revolution that will affect every industry and every person on earth.” Jensen Huang CEO, NVIDIA Gen AI: Revolutionizing the Way Enterprises Work 6

Chapter 1: An Introduction to Generative AI Machine learning, deep learning, and artificial intelligence. While these concepts are sometimes used interchangeably, there are distinct differences between them. Generative AI, on the other hand, refers specifically to AI systems that can generate new data based on patterns and examples from existing data. This distinction is important to understand when exploring the capabilities of AI and how they can be applied to various fields. To begin with, let's talk about what is meant by intelligence and learning. Intelligence generally refers to an ability to deal with novel situations, and learning is the process by which that ability is acquired. Learning and intelligence are opposed to a preset, predetermined set of actions and behaviors that are in some sense “hard coded”. For instance, a simple worm can learn how to find food in its environment, while even the most sophisticated calculator only does computations in a predetermined manner. Until very recently, the only form of intelligence that we were aware of was biological intelligence. However, with the advent of various sophisticated computational mechanisms - first mechanical, then electrical, and finally the electronic - it became conceivable to think and talk about non-biological intelligence. Although the term "artificial intelligence" only appeared in the 1950s, the concept behind it was already at least a century or two old. In many respects, the lines between the terms discussed in this section, are rather artificial and fuzzy at best; they often reveal more about the background and professional history of those who use them rather than the content of the terms. Statistics Statistics has been around ever since humans started keeping track of various numerical quantities of major interest to them, especially those involved in work, society, and Gen AI: Revolutionizing the Way Enterprises Work 7

commerce. Keeping track of the number of sheep you have, how much grain your field produces on average, or what’s the population of your town and how much has it been growing, have all been major preoccupations for millennia. However, as a separate discipline, statistics emerged only in the late 18th century, and it has since then become a crucial tool in many areas of research and practice, from natural sciences to social sciences and beyond. Today, statistics is used to analyze and interpret complex datasets, to test hypotheses, and to make informed decisions in the face of uncertainty. Its applications range from predicting weather patterns and stock prices to understanding the effects of policies and treatments, and from designing experiments and surveys to controlling quality and ensuring safety. In short, statistics is a versatile and indispensable field that continues to evolve and grow in importance in our data-driven world. Statistical Learning Statistical learning is a subfield of statistics that deals with finding functional relationships between various data points and using those relationships for prediction and understanding. Machine Learning On the most fundamental level, there is hardly any distinction between machine learning and statistical learning. Conceptually, they both refer to the process of using statistics to extract deep, meaningful relationships from data. Nonetheless, in practical terms machine learning is more reliant on the large-scale computational methods and more sophisticated algorithms than the majority of the more fundamental functions and algorithms involved in Statistical Learning. As the size of the dataset increases, and the number of features grows beyond a dozen or so, it becomes both conceptually and computationally difficult to keep track of the complex statistical relationship between the dependent and independent variables for instance. Simple linear models can still be used in many instances, but their predictive power tends to diminish. Predictive power is the characteristic of the model that tells us how well it can predict something given the data that it used as an input. Artificial Neural Networks Artificial neural networks (ANNs) are a machine learning algorithm that has been inspired by the structure and a general perceived architecture of the animal neural tissue. Animal neural tissues, like all the other biological tissues, consist of a large number of individual cells. In a neural tissue, the particular cells that are responsible for the processing of the biological signals are known as neurons. Individual neurons receive signals from many neurons and transmit a modified signal to other neurons. It is the Gen AI: Revolutionizing the Way Enterprises Work 8

coordination of this signal processing between many, many neurons that characterizes all of the neural processes. Our brains are particularly large agglomerations of neurons, and the complexity of our thoughts is the direct consequence of this extremely large size. Natural neurons are extremely complex cells, and proper simulation of even a single neuron is exceedingly complex. Nonetheless, the early machine learning researchers, in a tried and tested tradition of mathematical modeling of physical phenomena, have been able to reduce this immense complexity to a rather simple set of mathematical functions. Early research with individual neurons hit a snag when it was shown that there were limitations to the kinds of predictive functions it was possible to model with a single neuron. Eventually, it was shown that these setbacks could be overcome when a large combination of such functions was used. Nonetheless, as the number of individual artificial neurons grew, it became increasingly difficult to “train” them properly, and the research on ANNs hit another snag. It is important to understand that even though the inspiration for ANNs came from the natural neural tissue, the analogy should not be taken too literally or too far. Modern ANNs have moved significantly away from the simple original functional representations, and in terms of how they are designed and trained; there is very little remaining similarity with the natural neural networks, except perhaps on a very high level. Deep Learning As mentioned above, training and the expansion of the simple neural networks beyond a few layers of neurons (the so-called “Multi-layer perceptions, MLPs) proved to be a challenge. The need for much more complex neural networks arose as the complexity of data that they needed to be trained on increased. Neural Networks that needed to Artificial Intelligence Thinking machines have been conceived of in some form for almost as long as humans tinkered with machines of any kind, but the modern approach to this field dates from the early 1950s when the term Artificial Intelligence (AI) was conceived. Over the decades there have been many different approaches to the problem of solving intelligence with artificial means, including advanced logic, probabilistic approaches, modeling with knowledge graphs, etc. However, over the years modeling thinking and cognition with Machine Learning and Deep Learning in particular became the prevailing paradigm. AI is still a broad field, and there are still many issues with applying ML across the board (there are many amusing examples where ML-based AI gets even some simple numerical calculations completely wrong), but the advances in Deep Learning over the past 5-10 years have solidified it as the definitive way of tackling the challenge of AI. Gen AI: Revolutionizing the Way Enterprises Work 9

Natural Language Processing Natural Language refers to the regular human language used for communication, and it is the primary mode of human communication. It is also considered the quintessential paradigm of what it means to be intelligent. Effective communication in human language has been deemed as the test of "true" intelligence that any artificial system must pass to be considered intelligent. The famous Turing test was based on this premise, postulating that an AI agent would be considered intelligent if it could converse with a human counterpart in such a manner that the human could not tell if they were conversing with a machine or another human being. One of the main challenges for computers to effectively process language was to turn it into computer-understandable form. This challenge has been addressed by converting written text, primarily in digital form, into some kind of numerical representation. The most commonly used method involves splitting text into small "chunks" called tokens and assigning a string of numbers to each token. Once this "tokenization" has been accomplished, the full range of machine learning tools becomes available to convert the problem of text representation into another machine learning problem. Generative Artificial Intelligence Traditionally, the two most common ways of dividing machine learning problems have been into the so-called supervised and unsupervised problems. Supervised problems are those where we are trying to predict something from the data based on what we already know about each data point. This knowledge is termed “data labels”, and the goal of the supervised learning mechanism is to predict labels for the new data points based on the labels that have been provided for the old data points. For unsupervised learning, however, no such set of data points exists. Unsupervised learning tries to get some kind of insight from the data itself, with any additional information than the structure of the dataset. Over the years yet another paradigm emerged: self-supervised learning. This is sort of a middle ground between supervised and unsupervised learning. The idea behind self- supervised learning is to use select aspects of the dataset as labels for other parts of the dataset. In the ultimate case of this approach, each aspect of the dataset is used as the label for all the other aspects of the dataset. For instance, we can learn the next word in some text based on several other words in the text, or we can try to predict a portion of an image based on all the other aspects of the image, or what an image should look like based on its textual description. Once we build a self-supervised model, we can start using this model to generate new text, images, songs, and so on, based on a small initial “prompt” that we provide. This is the essence of Generative AI. Generative AI has been in development for years, but only in the recent few months has it really captured the world’s attention thanks to the increasing sophistication of the latest generation of models like ChatGPT, Stable Diffusion, and so on. The reason that it took so long to get to this point is that for these Gen AI: Revolutionizing the Way Enterprises Work 10

models to be truly useful, they need to be trained on an enormous amount of data, sometimes on the order of all human knowledge and content ever created. As a result, training these models requires really powerful and really large computational resources, and these resources take many days, and sometimes even months. Gen AI: Revolutionizing the Way Enterprises Work 11

Chapter 2: Generative AI Business Applications A new era of AI-enabled business automation is beginning. As AI continues to evolve, it is becoming increasingly vital to the success of businesses across all industries. Generative AI represents one of the largest opportunities of the century and one of the greatest threats to existing businesses. This section delves into the numerous ways in which businesses can harness the power of Generative AI to drive innovation and enhance their employees’ productivity. From information management to content development, marketing, design, and software development, the many applications of Generative AI in business are examined. Information Management Traditionally, information management has been a time-consuming and laborious process for businesses and organizations. With data being recognized as one of the most valuable assets of a company, it is essential to make the most of it. However, less than half of companies feel they are effectively utilizing their data. Key Issues in Information Management Organizations run into the following key issues: • It takes a lot of time to put together and organize data. • The amount of data created on the internet is increasing at an exponential rate. • It's hard to figure out important business insights from a huge amount of data. • Hiring people to manage and organize data is expensive. Gen AI: Revolutionizing the Way Enterprises Work 12

How Information Management is Changing With New Generative AI Tools Generative AI tools are transforming the way businesses manage and synthesize data. These tools enable businesses to gain insights from massive volumes of data quickly and accurately and free up staff to work on more complex tasks. Popular Gen AI Tools The following tools are used for information management: • ChatGPT • Bing AI • Notion AI Top Use Cases Some of the top use cases are: • Summarizing documents and files into easy-to-digest notes and bullet points. • Automating the creation of notes from meetings. • Organizing and creating data in spreadsheets. • Creating a chatbot that has access to a knowledge base consisting of all of your data. Examples Some examples of applications are: • Bing AI offers quick and accurate summarization of PDFs or websites, making it easier to gain insights from large amounts of data. • Microsoft Teams leverages AI technology to generate intelligent meeting recaps that provide concise and organized summaries of discussion points and action items. • Notion is a powerful tool for brainstorming and summarizing data in a user- friendly way. It can help you capture and organize ideas, as well as summarize important data points for reports and presentations. Future Outlook The potential of generative AI in the field of information management is vast. With the ability to synthesize data quickly and accurately, businesses can focus on creating value from their data rather than managing it. In the future, Generative AI tools will continue to transform the field of information management. Predictions for Generative AI in the coming year are: • Chatbots will be able to comprehend all of a company's data, including internal documents, notes, spreadsheets, and meeting transcripts. Gen AI: Revolutionizing the Way Enterprises Work 13

• This development will enable individuals at all levels of the organization to draw insights from data and make informed, data-driven decisions that positively impact business outcomes. • The ability to make public-facing versions of the same chatbot that exclude confidential information will change the way consumers interact with companies. • Chatbots will reduce the number of redundant meetings, improving productivity across the organization by accurately recalling past discussions. • Overall, this development will revolutionize the way businesses manage and utilize their data. Key Takeaways The key takeaways for the future of information management using Generative AI are: • New Generative AI tools like ChatGPT, Bing AI, and Notion AI can help businesses manage and use large amounts of data quickly and accurately. • This technology lets people focus on more important tasks and make better decisions using data. • Decision-making will be easier and reduce the need for unnecessary meetings in the future, since chatbots will have access to and understand all the data belonging to the company. Additional Resources • Build effective information management tools with NVIDIA Enterprise solutions • Learn more about generative AI information management with Lore Software Development In the modern business landscape, software has become a crucial driving force for all major companies. However, the complexity of code and new frameworks is increasing at an ever-accelerating rate. This poses a significant challenge for companies looking to hire engineers who can become productive quickly. In fact, it can take months or even years for a developer to gain a sufficient understanding of just a part of a large code base, making it harder than ever for businesses to find and onboard the right talent. Key Issues Software development has to deal with the following issues: • Learning fragmented languages and constantly changing frameworks is becoming increasingly difficult. • As the codebase size increases, debugging becomes more challenging. • It takes months to onboard new developers due to the complexity of modern software systems. Gen AI: Revolutionizing the Way Enterprises Work 14

• Documentation can be a productivity killer, and many developers do not enjoy doing it. • Developing and testing new product ideas can take months or even years to determine market demand. How Software Development is Changing With New Generative AI Tools Generative AI (Gen AI) tools are transforming the way developers code. Just in the last few months, new tools and features have been announced almost every week. According to recent data from GitHub Co-pilot, engineers are coding 55% faster and experiencing 75% more fulfillment with the help of Generative AI tools. Feedback from engineers who have used AI for coding suggests that it can even be enjoyable, a noteworthy aspect of AI that is often overlooked. Popular Gen AI Tools Some of the tools for software development are: • Co-pilot by GitHub/Microsoft. • ChatGPT by OpenAI. • Ghostwriter by Replit. Top Use Cases Some of the top use cases are: • Inline code suggestions can be made while coding using Generative AI tools. • Documentation and test cases can be automatically generated by these tools. • The tools can also explain what the code does. • Code can be improved and simplified with the help of Generative AI. • The tools can even translate code from one programming language to another. Examples Some examples of use cases for using Generative AI tools in software development are: 1. Generative AI tools like Github Co-pilot are being used to suggest entire blocks of code, transforming the way developers write code and speeding up the coding process. 2. Using ChatGPT to translate code from one language to another can save developers significant amounts of time and effort, especially when working with multiple programming languages. ChatGPT's natural language processing capabilities can help ensure that the code is accurately translated, reducing the risk of errors, and improving efficiency. 3. Replit, in combination with the Codex model, can automatically explain what code does in natural language. This feature can save time and improve understanding for developers who are working with complex code. Gen AI: Revolutionizing the Way Enterprises Work 15

Future Outlook The future of software development with Generative AI is exciting. The ability to code faster and possibly even instantly create applications, will completely change the way businesses do software development. Predictions for software development with Generative AI in the coming year are: • Developers will become productive in hours, not months, resulting in a faster pace of technological advancement than ever before. • The rise of AI-enhanced "super developers" will occur, making them dramatically more productive than traditional developers. • Business leaders will be able to create entire demo products from text prompts, streamlining the product development process. • Generative AI will continue to evolve rapidly, with new tools and features being introduced almost every week. • As a result of these advancements, the software development industry will undergo a fundamental transformation, with businesses leveraging Generative AI to create new products and drive innovation. Key Takeaways The key takeaways for the future of software development using Generative AI are: • The increasing complexity of code and new frameworks is making it difficult for companies to hire productive software developers. • Generative AI tools, such as Co-pilot, ChatGPT, and Ghostwriter, are changing the landscape of software development by providing real-time suggestions, enabling developers to ask questions about code, and simplifying the development process. • The future of software development is exciting, with the rise of AI-enhanced "super developers" and the possibility of creating entire products with just a text description. This will lead to faster technological advancement, and companies seriously need to consider the implications for their business. Additional Resources • Generative AI for Software Development Content and Marketing For many businesses, content creation and marketing are critical components of their success, serving as foundational pillars for building brand awareness and establishing thought leadership within their industries. However, creating compelling and engaging content can be a complex and resource-intensive process, requiring significant time and effort from content creators. It can take days or weeks to create good content. Gen AI: Revolutionizing the Way Enterprises Work 16

Key Issues in Content and Marketing Key issues are: • Content creation is a very resource-intensive process. • This has created a barrier for smaller companies and startups that could not afford to hire content creators and professional writers. • With mediums like social media and video becoming more important, it’s increasingly difficult to produce content for every popular marketing channel. How Content and Marketing are Changing With New Generative AI Tools Generative AI tools are revolutionizing the way businesses create content by allowing them to focus on the creative work, not the tedious parts of their craft. This shift in content creation has been led by companies such as Jasper, which has over 100,000 customers and claims that its AI technology can write blog posts 10x faster than traditional methods. Popular Gen AI Tools for Content and Marketing The following tools are popular Gen AI tools for managing content and marketing: • Jasper • ChatGPT • Bing AI • Notion AI • D-ID Top Use Cases Some of the top use cases are: • Write SEO-optimized content for blogs and PR. • Create social media content. • Brainstorm content ideas. • Create videos. Examples Some examples of using Gen AI to manage content and marketing are: • Jasper AI Platform: Content creation teams can use Jasper's AI platform to automate tasks like research, writing, and editing, allowing them to accelerate content production and meet tight deadlines while producing high-quality content. • ChatGPT for Content Idea Generation: Content writers can seek assistance from ChatGPT's AI language model to generate new and unique content ideas, helping them save time and avoid writer's block. Gen AI: Revolutionizing the Way Enterprises Work 17

• Bing AI and LinkedIn: Marketers can leverage Bing AI's automation technology to create LinkedIn posts quickly and efficiently, saving time and allowing them to focus on other important tasks, such as analyzing post performance. • Notion AI for Blog Posts and Data Organization: Bloggers can use Notion AI to generate blog posts using AI-generated text and organize data, making it easier to find and use relevant information for their writing. • D-ID for Digital Avatars: Video marketers can use D-ID's digital avatars to create engaging video content that captures their audience's attention, providing a more personalized and interactive experience. “AI-powered content generation is transforming the way businesses create and distribute content. This is only the beginning. I think we'll start to see industries be disrupted and category leaders beat out by AI-first companies if they don’t adapt to this type of workflow from the ground up." Dave Rogenmoser CEO, Jasper Future Outlook The future of AI-generated content and marketing is promising. As AI tools become more advanced, the human role in content creation will become more creative about the topic to cover, and less about picking the perfect word. Predictions for management of content and marketing with Generative AI in the coming year are: • Professional writers who are skilled at using Gen AI tools can become dramatically more productive than traditional writers. • The AI technology will make it possible to create entire books that include contextually relevant charts and illustrations, without human intervention. • Writers can train AI on their past works, such as blog posts and tweets, and use that to create new content. • This development has the potential to revolutionize the quantity and quality of content created by businesses. Gen AI: Revolutionizing the Way Enterprises Work 18

Key Takeaways The key takeaways for the future of content creation and marketing using Gen AI are: • Generative AI tools are transforming the way businesses create and market content. • With the use of AI tools, businesses can generate high-quality content in minutes for free or at a low cost. • The future of AI-generated content is promising. Additional Resources • The Future of Generative AI for content creation • Generative AI for Content and Marketing Design Design serves as a crucial aspect of business operations, encompassing various disciplines, such as branding, advertising, web design, and product design. Previously, creating visually compelling designs required extensive human expertise. However, with the advent of Generative AI, this paradigm is changing. Key Issues in Design Key issues are: • Executives may have limited influence over design as it can be seen as a field that is beyond their expertise. • Design projects tend to exceed the planned timeline. • Typically, design is something that only large businesses can afford to invest in. How Design is Changing With New Generative AI Tools Generative AI tools are revolutionizing the design industry, making it easier for businesses to create high-quality designs and visuals. Popular Gen AI Tools Popular Gen AI tools for design are: • MidJourney • Stable Diffusion • Dall-E 2 Top Use Cases Some of the top use cases are: • Create website design concepts. • Create illustrations. • Create branding such as a logo. Gen AI: Revolutionizing the Way Enterprises Work 19

Examples Some examples of using Gen AI tools for Design are: • Website Design: MidJourney can be used to create website design concepts, helping designers visualize how the website will look and feel. This can be especially helpful in the early stages of a project, where designers are still exploring different design ideas. • Illustrations: MidJourney can also be used to create illustrations for movie posters quickly and efficiently, providing a useful tool for movie studios looking to generate high-quality posters in a short amount of time. • Infographics: Businesses can use MidJourney to create infographics as a helpful tool for brainstorming design layout ideas. Although the infographics may not be immediately usable, improvements to the technology in the future can potentially make them more practical and effective. • Logo Concepts: MidJourney can help graphic designers generate logo concepts efficiently, allowing them to focus on other aspects of the design process, such as fine-tuning the chosen concept. “Interface design has always been constrained by the tools at our disposal. Every time design tools leap forward, so does our imagination. What becomes possible as design tools gain imaginations of their own?” Bobby Goodlatte Co-founder, Form Capital Future Outlook The future of design with Generative AI is promising. As AI tools become more advanced, they will provide companies with a faster and more cost-effective way to produce high- quality designs. Predictions for using Generative AI for Design in the coming year are: • AI-generated illustrations are poised to become increasingly common at major corporations in the near future. • It will become feasible to create a new company or product, complete with branding and website design, in a matter of minutes, thanks to advancements in AI technology. • Designers who acquire expertise in using these AI tools can become more valuable contributors to a business's revenue. Gen AI: Revolutionizing the Way Enterprises Work 20

Key Takeaways The key takeaways for the future of design using Gen AI are: • Generative AI tools are revolutionizing the design industry, making it easier for businesses to create high-quality designs and visuals. • Gen AI tools can be used to create illustrations, logos, infographics, landing page designs, and explore different colors. • In the future, AI-generated illustrations will become commonplace, and it will be possible to create a new company or product, including branding and website design, in minutes. Additional Resources • Creating the future of advertising with AI • Revolutionizing Design Collaboration and Simulation • Generative AI for Design Financial Services Intelligent technology can address critical challenges within the modern financial services industry. With the NVIDIA accelerated computing platform powering deep learning, machine learning, and natural language processing (NLP) models, institutions can boost risk management, improve data-backed decisions, strengthen security, and enhance customer experiences. Generative AI and Large Language Models (LLMs) are changing how banks, insurers and asset managers interact with AI. These transformer models will impact every function within a financial services company, reducing costs, creating operational efficiencies, and improving customer experiences. Current Challenges in Financial Services are: • Consumers demand digitally immersive, personalized, intelligent omnichannel experiences across financial services such as banking, credit card, investing, insurance, and so on. • Market volatility, rising interest rates, regulatory changes, and Environmental Social and Government scoring demand banks analyze thousands of data streams in real-time for to identify new risks and opportunities. • Competition from Fintech, big tech, and large retailers. • Competition is increasing as companies vie for customers and their financial data. • Technologies such as open banking are increasing data portability across banks and expanding access to a rich partner ecosystem offering banking as a service platforms, enabling any company to become a “bank.” • IT leadership and data scientists are burdened by legacy IT systems and budget constraints, that are combined with demands to enable work-from-everywhere, enhance cybersecurity, and improve risk management, further straining bank infrastructure. Gen AI: Revolutionizing the Way Enterprises Work 21

• New regulations, stricter oversight and compliance requirements, combined with new data security, privacy, risk management policies require flexible platforms to serve new demands from all lines of business within a financial institution. Generative AI Impact on Banking Generative AI impacts every function in a bank as follows: • Enterprise Search - Optimizes information retrieval by evaluating multiple sources, and summarizing results. • Anti-Money Laundering and Transaction Fraud - Improves accuracy and generates reports, reducing investigations and compliance risk. • HPC Pricing and Risk - Summarizes news feeds, improving risk management and optimizing reserves. • Algorithmic Trading - Synthesizes audio and test, accelerating time to insight, improving market returns. • Document Management - Summarization and report generation will optimize middle/back office workflows. • Customer Service - Expands knowledge base beyond scripted answers increasing customer satisfaction. • Marketing - Creates personalized copy, and email increasing click to conversion. • Financial Advice - Utilizing various data inputs, generative AI will build wealth management plans to drive assets under management (AUM) Key Takeaways The key takeaways for the future of using Gen AI in financial services are: • Generative AI is transforming the finance industry by streamlining processes and freeing up time for strategic decision-making. • AI tools can effortlessly create financial plans, check spreadsheets for errors, and analyze vast amounts of financial data. • Businesses should choose tools specifically designed for the finance industry, let AI handle repetitive tasks, and use AI to make informed decisions. Additional Resources • State of AI in Financial Services - 2023 Trends • Natural Language Processing for Fraud Prevention • Generative AI for Finance - Lore.com Gen AI: Revolutionizing the Way Enterprises Work 22

Chapter 3: Industry Transformation with Generative AI In the Industry Examples section, we delve into the specific ways in which AI is making a tangible impact on the entertainment industry. From examining real-world applications to quotes from industry leaders, we showcase the limitless potential of Generative AI within this sector. By exploring how companies are utilizing AI to drive innovation and improvement, businesses can gain insights and inspiration for leveraging AI to achieve their goals. Film Production For many years, the film industry has been renowned for its creativity and innovative spirit, but as technology has advanced, filmmakers have started exploring new ways to integrate it into their work to create better end products. One of the most exciting recent developments in this area is the adoption of Generative AI, which has the potential to revolutionize the film industry. For instance, in Avatar 2, directed by James Cameron, the use of Generative AI has enabled the film's producers to create sophisticated and realistic visual effects that were once only achievable through extensive manual labor. By harnessing the capabilities of AI, filmmakers can open up new avenues for creative expression and push the boundaries of what is possible in their craft. The budget for big Hollywood films has increased dramatically, now to around $100-$200M per film. Key Issues in Film Production Key issues are: • Film production is a complex process that encompasses a variety of tasks, such as pre-production planning and post-production editing. • One of the primary obstacles in the industry is the creation of realistic and immersive visual effects that captivate audiences and draw them into the film's universe. • Achieving this requires a significant amount of time and effort from a team of highly skilled professionals, and can often become a bottleneck in the production process. • It’s hard for films to make a profit due to ever-increasing budgets, primarily driven by special effects costs. Gen AI: Revolutionizing the Way Enterprises Work 23

How Film Production is Changing With New Generative AI tools With the help of Generative AI, film production companies are starting to be able to automate many of the tasks that once required manual labor. For example, AI can be used to create realistic visual effects, such as realistic water, fire, and smoke, that would be difficult or impossible to create by hand. Additionally, AI can be used to automate many of the time-consuming tasks involved in post-production editing, such as color grading, compositing, and sound mixing. Popular Gen AI tools for Film Production: • ChatGPT • Sudowrite • Gen-1 by RunwayML • Flawless Top Use Cases: • Scriptwriting: AI can help writers generate new and unique ideas, as well as assist in the writing process by suggesting dialogue, plot points, and character development. • Shot List Creation: AI can analyze a script and automatically generate a shot list, including suggested camera angles and movement, lighting, and more. • Storyboarding: AI can assist in creating storyboards by generating visualizations of scenes based on the script. • Special Effects: AI can be used to create realistic and immersive visual effects, from creating digital environments and characters to enhancing practical effects. • Translations and Dubbing: AI-powered language translation and speech synthesis can make it easier to localize films for international audiences. Examples Some examples for using Generative AI tools for film production are: • SudoWrite Chrome Extension: The SudoWrite Chrome Extension is a tool designed to assist users in brainstorming script ideas and various aspects of their story, including characters, plotlines, and more. Additionally, the extension provides support as users write, offering suggestions and guidance throughout the writing process. • ChatGPT for Detailed Shot Lists: Creators can use ChatGPT to generate detailed shot lists based on a script it helped create, providing a helpful tool for pre- production planning and saving time in the process. • MidJourney Storyboard Creation: Storyboard creation can be streamlined using MidJourney, allowing creators to quickly visualize and plan out scenes without having to spend hours sketching and drawing. • Gen-1 by Runway for Video-to-Animation Conversion: Creators can use Gen-1 by Runway to turn a video clip into an animation, providing a more unique and creative way to present their content. Gen AI: Revolutionizing the Way Enterprises Work 24

• Flawless for Film Scene Profanity Removal: Generative AI from Flawless can help remove profanity from a film scene, allowing filmmakers to convert their film from Rated R to PG-13 and reach a wider audience. Future Outlook As the film industry continues to evolve, it is clear that AI will play an increasingly important role in the production process. With the help of Generative AI, filmmakers can create more realistic and immersive worlds, automate many of the tedious tasks involved in the production process, and focus on delivering a higher-quality end product. Predictions for film production with Generative AI in the coming year are: • The popularity and usage of Runway Gen-1 is expected to continue to grow, allowing indie creators to produce short films with advanced visual effects and animation. • With the accessibility of Generative AI tools, more independent filmmakers will be able to create high-quality films without the need for a large budget or studio backing. • Movie studios will increase their use of Generative AI technology to streamline and automate various aspects of film production, from scriptwriting and storyboarding to special effects and post-production editing. • With the advancements in AI technology, we can expect to see a rise in amateur animation and short films, akin to how Instagram improved the quality of user- generated photos with filters. Key Takeaways The key takeaways for the future of film production using Gen AI are: • The film industry is using Generative AI to create more sophisticated and realistic visual effects, pushing the boundaries of what is possible. • The film production process is complex and requires significant time, effort, and skill to create immersive visual effects. • Generative AI can automate many of the manual labor tasks required for film production, such as creating visual effects and post-production editing. • Tools such as ChatGPT, Sudowrite, Gen-1 by RunwayML, and Flawless are helping automate various film production tasks, such as scriptwriting, shot list creation, storyboarding, special effects, and translations/dubbing. • As AI technology advances, we can expect to see more indie creators and amateur animators using Generative AI to create high-quality films with advanced visual effects. Additional Resources • The year’s most captivating visual effects in film using AI • Bring the future of Media and Entertainment into Focus • Generative AI for Film Production Gen AI: Revolutionizing the Way Enterprises Work 25

Video Game Development Game design is one of the only industries with a more complex visual pipeline than the movie industry. Modern AAA games can cost almost as much as a major film to create. The average AAA game takes about four years to develop. Some games have taken as long as 12 years to develop. Key Issues in Game Development • The complexity of the overall game development pipeline is massive. Ranging from prototypes, modeling, systems, stories, animation, music, game design, and much more. • AAA games are so difficult to make that only major studio that can afford to invest in 3-5 years of development can make them. • Because of the significant investment required, studios aren’t willing to take major risks with new franchises. So a lot of games are sequels, just like in Hollywood. How Game Development is Changing With New Generative AI Tools Generative AI has the potential to automate most parts of the development process, with the potential to dramatically reduce the time it takes to develop a good game. Popular Gen AI Tools for Game Development Popular Gen AI tools for game development are: • Scenario • Leonardo.ai • LumaLabs Top Use Cases Some of the top use cases are: • Create game assets. • Help code in Unreal Engine 5 and Unity. • Create user-generated content. Examples Some examples for using Generative AI tools for game development are: • Scenario for Character Asset Creation: Video game developers can use Scenario to create high-quality character assets quickly and efficiently, providing a helpful tool for streamlining the game development process. Gen AI: Revolutionizing the Way Enterprises Work 26

• Environment Design with Stable Diffusion Model: Developers can use custom Stable Diffusion models created using Leonardo.ai to design environments for their video games. This can provide a more unique and creative approach to environment design. • Roblox Generative AI Chatbot Concept: Roblox is exploring the concept of a generative AI chatbot that would allow anyone to become a game creator. Users could talk to the chatbot to help create games, providing a more accessible and streamlined approach to game development. “Generative AI will be as transformational for game development as Photoshop has been for digital photography, but it cannot get there without the same commitment to stylistic consistency, ease of use, and the rights of the individual artist. The most successful digital artists in the next 10 years will be the ones who adopt and adapt to AI just like any other technology - the key is to make sure their own style, signature, and IP can ride alongside that technology, not be subsumed by it.” Emmanuel De Maistre CEO, Scenario Future Outlook The potential of Generative AI in Game Development is huge. With the ability to generate game assets and automate most parts of the incredibly complex game development process, games could be created in months, not years, opening doors for entirely new forms of games. Predictions for game development with Generative AI in the coming year are: • Major game studios are expected to integrate Generative AI into their development processes, starting with software development and asset creation. • Roblox and other theme-park and sandbox-style games may enable their users to create new experiences custom-designed for them using a ChatGPT-like interface. • With the help of Generative AI tools like Scenario, indie games will be created that are surprisingly high-quality thanks to the time and cost savings. Key Takeaways The key takeaways for the future of game development using Generative AI are: • Game development is a complex process that takes years and costs almost as much as making a blockbuster movie. • Generative AI tools have the potential to automate game development tasks and significantly reduce development time. • New tools like Scenario, Leonardo.ai, and LumaLabs can help create game assets. Gen AI: Revolutionizing the Way Enterprises Work 27

Additional Resources • Generative AI Development for Games and Creative Industries • Learn About Cutting-edge Game Development Solutions • Generative AI for Game Development Gen AI: Revolutionizing the Way Enterprises Work 28

Chapter 4: Reflections and Future Trends Generative is growing exponentially. According to Steph Smith, host of the A16Z podcast, just three years ago, primitive AI art made up of a collage of shapes was considered remarkable. However, today, we have access to a wide range of powerful Generative AI tools, including ChatGPT, DALL-E 2, Stable Diffusion, and MidJourney, that enable people to create incredible and distinctive works of art, among other things. As a result, people's attitudes have changed, and they are already adjusting to thinking it’s normal. Nonetheless, these developments represent an enormous breakthrough, and we are witnessing the emergence of a new era in which Generative AI is playing an increasingly important role. Marc Andreessen recently highlighted Bing AI's advancements as a significant milestone. Despite being factually incorrect and ethically concerning, Bing AI demonstrated creativity, something that AI researchers had only imagined for the past 30 years. As Generative AI continues to develop, ethical considerations surrounding AI and bias will be significant points of debate in the next decade. Despite these concerns, we remain optimistic that AI will ultimately be a force for good. “AI-powered content generation is transforming the way businesses create and distribute content. This is only the beginning. I think we'll start to see industries be disrupted and category leaders beat out by AI-first companies if they don’t adapt to this type of workflow from the ground up." Taizo Son CEO, Mistletoe Generative AI for good in Healthcare and Pharma Gartner predicts that “By 2025, more than 30% of new drugs and materials will be systematically discovered using generative AI techniques, up from zero today.” Gen AI: Revolutionizing the Way Enterprises Work 29

Use of Generative AI is expected to grow significantly in chemistry and biology for molecular generation and drug discovery. Generative AI looks promising for the pharma industry, given the opportunity to reduce costs and time in taking new drugs to market. Dedicated services like BioNemo from NVIDIA will accelerate the journey to AI- powered drug discovery, offering tools to quickly train, customize, deploy, and perform inference on state-of-the-art generative and predictive biomolecular AI models at-scale. Explore how the intersection of generative AI with biology and healthcare is driving breakthrough innovations. Predictions Predictions for the coming year are: • OpenAI is expected to release GPT-4, which is predicted to significantly surpass ChatGPT in performance and surprise users with its capabilities. • GPT-4 is anticipated to be capable of generating entire books in any desired style, complete with unique charts and illustrations. • Many major technology companies will release competitors to GPT-4, and we will see the continued evolution of open-source projects such as the highly anticipated release of Stable Diffusion 3. • AI is predicted to become an integral component of every major technology product in the near future. • The coming year may also see the emergence of new startups that reimagine existing businesses in ways that are presently unforeseeable. Predictions for the next 2-5 years are: • AI will become a ubiquitous part of every business, even those that operate offline. • Employees who receive proper training in AI may become many orders of magnitude more productive than employees today. • AI technology will provide the capability to learn anything and create virtually anything in the digital world, opening up a whole new world of possibilities for creative entrepreneurs. • A new kind of startup with only 1-10 people will emerge, capable of performing like companies with 1,000 employees thanks to the power of AI. Gen AI: Revolutionizing the Way Enterprises Work 30

“The 2020’s would be recorded as the era that AI itself is transforming from a tool to a companion of humans, although it would depend on how we nurture the culture of AI’s.” Atsushi Taira Co-Founder, Mistletoe “AI is going to be as transformative or more as the transistor. First, we taught rocks to do math, and now we have math that can think. If the computer is the bicycle for the mind, we’ve upgraded that bicycle and we’re on a moon mission.” Paul McKellar Founding Team, Square In conclusion, Generative AI is not just a new tool for businesses to innovate, but it has the potential to be a transformative force in society, akin to the internet or the transistor. As AI technology continues to advance and become more accessible, it has the power to revolutionize entire industries and even the way we live our lives. It is crucial for businesses to stay ahead of the curve and incorporate AI into their operations to remain competitive in the years to come. By embracing the possibilities of Generative AI, we can unlock new levels of creativity, productivity, and innovation that will shape our future in ways we can't yet imagine. Businesses and Generative AI Businesses should start investing in the future today and consider the following questions to take advantage of Generative AI: • What is our Generative AI strategy? • How can we empower our team with these new tools? • Are we investing enough in the future of AI? • Which parts of our business could become obsolete in competition with these Gen AI-based new technologies? Gen AI: Revolutionizing the Way Enterprises Work 31

What to do next Businesses should consider the following next steps: • If you haven’t used the following applications, consider the following: o ChatGPT o Jasper o MidJourney o Runway o Stable Diffusion • Create a plan for how your company will become educated and properly trained on the latest generative AI tools and applications. • Subscribe to the Lore newsletter to stay informed about the latest developments in Generative AI. The future is here. The companies that harness AI today, will be the leaders of tomorrow. Gen AI: Revolutionizing the Way Enterprises Work 32

Resources NVIDIA • Generative AI Solutions • Riding the Wave: Generative AI for Startups • AI Solutions for Industries | NVIDIA • Generative AI News • Generative AI Blogs Lore • Weekly AI Newsletter • Generative AI Tools Directory • Generative AI Beginner’s Guide • Generative AI Glossary Gen AI: Revolutionizing the Way Enterprises Work 33

Authors Bojan Tunguz is a highly accomplished data scientist and machine learning expert who currently works at NVIDIA, a leading technology company specializing in artificial intelligence and graphics processing. As a Kaggle Quadruple Grandmaster, Tunguz is widely recognized as one of the world's leading experts in the field of machine learning, having won numerous competitions and received accolades from across the industry. Bojan Tunguz Senior Engineer, NVIDIA Nathan is the founder of Lore.com, an AI media and consulting company. He has a proven track record in entrepreneurship, having previously founded Binded, a San Francisco-based technology company in the copyright sector that received funding from prominent investors Mistletoe, Taizo Son’s fund, and Asahi Shimbun, the oldest newspaper in Japan. Prior to that, Nathan made waves in the crypto world with his creation of QuickCoin, the first Nathan Lands social bitcoin wallet, and was featured in Paul Vigna’s book, Founder, Lore “The Age of Cryptocurrency.” Nathan is also known for coining the term "gamify" and building and selling gamification.org, the leading wiki and resource in the gamification industry. He also helped famed game designer David Perry with early strategy at game- streaming company Gaikai, later acquired by Sony for $380M. With over 20 years of experience in technology and experience across Silicon Valley, Hollywood, and Japan, Nathan is a seasoned expert in using technology to achieve business success. Gen AI: Revolutionizing the Way Enterprises Work 34

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