Artificial Intelligence in Practice Read online

  What Were The Results?

  Key Challenges, Learning Points And Takeaways

  Notes

  39 Salesforce: How Artificial Intelligence Helps Businesses Understand Their Customers What Problem Is Artificial Intelligence Helping To Solve?

  How Is Artificial Intelligence Used In Practice?

  What Technology, Tools And Data Were Used?

  What Were The Results?

  Key Challenges, Learning Points And Takeaways

  Notes

  40 Uber: Using Artificial Intelligence To Do Everything What Problems Is Artificial Intelligence Helping To Solve?

  How Is Artificial Intelligence Used In Practice?

  What Technology, Tools And Data Were Used?

  What Were The Results?

  Key Challenges, Learning Points And Takeaways

  Notes

  Part 5 Manufacturing, Automotive, Aerospace and Industry 4.0 Companies 41 BMW: Using Artificial Intelligence To Build And Drive The Cars Of Tomorrow What Problems Is Artificial Intelligence Helping To Solve?

  How Is Artificial Intelligence Used In Practice?

  What Were The Results?

  What Technology, Tools And Data Were Used?

  Key Challenges, Learning Points And Takeaways

  Sources

  42 GE: Using Artificial Intelligence To Build The Internet Of Energy What Problem Is Artificial Intelligence Helping To Solve?

  How Is Artificial Intelligence Used In Practice?

  What Technology, Tools And Data Were Used?

  What Were The Results?

  Key Challenges, Learning Points And Takeaways

  Notes

  43 John Deere: Using Artificial Intelligence To Reduce Pesticide Pollution In Agriculture What Problem Is Artificial Intelligence Helping To Solve?

  How Is Artificial Intelligence Used In Practice?

  What Technology, Tools And Data Were Used?

  What Are The Results?

  Key Challenges, Learning Points And Takeaways

  Notes

  44 KONE: Using Artificial Intelligence To Move Millions Of People Every Day What Problems Is Artificial Intelligence Helping To Solve?

  How Is Artificial Intelligence Used In Practice?

  What Technology, Tools And Data Are Used?

  What Were The Results?

  Key Challenges, Learning Points And Takeaways

  Sources

  45 Daimler AG: From Luxury Personal Cars To Passenger Drones What Problems Is Artificial Intelligence Helping To Solve?

  How Is Artificial Intelligence Used In Practice?

  What Were The Results?

  What Technology, Tools And Data Were Used?

  Key Challenges, Learning Points And Takeaways

  Sources

  46 NASA: Using Artificial Intelligence To Explore Space And Distant Worlds What Problem Is Artificial Intelligence Helping To Solve?

  How Is Artificial Intelligence Used In Practice?

  What Were The Results?

  What Technology, Tools And Data Were Used?

  Key Challenges, Learning Points And Takeaways

  Sources

  47 Shell: Using Artificial Intelligence To Tackle The Energy Transition What Problem Is Artificial Intelligence Helping To Solve?

  How Is Artificial Intelligence Used In Practice?

  What Technology, Tools And Data Were Used?

  What Were The Results?

  Key Challenges, Learning Points And Takeaways

  Notes

  48 Siemens: Using Artificial Intelligence And Analytics To Build The Internet Of Trains What Problems Is Artificial Intelligence Helping To Solve?

  How Is Artificial Intelligence Used In Practice?

  What Technology, Tools And Data Were Used?

  What Were The Results?

  Key Challenges, Learning Points And Takeaways

  Notes

  49 Tesla: Using Artificial Intelligence To Build Intelligent Cars What Problems Is Artificial Intelligence Helping To Solve?

  How Is Artificial Intelligence Used In Practice?

  What Are The Results?

  What Technology, Tools And Data Were Used?

  Key Challenges, Learning Points And Takeaways

  Sources

  50 Volvo: Using Machine Learning To Build The World's Safest Cars What Problems Is Artificial Intelligence Helping To Solve?

  How Is Artificial Intelligence Used In Practice?

  What Technology, Tools And Data Were Used?

  What Were The Results?

  Key Challenges, Learning Points And Takeaways

  Sources

  Part 6 Final Words and Artificial Intelligence Challenges 51 Final Words And Artificial Intelligence Challenges Approach Artificial Intelligence Strategically

  Develop Artificial Intelligence Awareness And Skills

  Secure The Right Data

  Update Your Technology And IT Systems

  Use Artificial Intelligence Ethically

  Prepare Yourself For Artificial Intelligence Disruption

  Connect To Keep The Conversation Going

  Notes

  About the Author

  Acknowledgments

  Index

  WILEY END USER LICENSE AGREEMENT

  Introduction

  One thing is very clear, artificial intelligence (AI) is going to change our world forever. And the change is likely to be more profound than most people realize today. No matter what job you are in, no matter what business or industry you work in, AI is going to augment, if not completely transform, it.

  AI is giving machines the power to see, hear, taste, smell, touch, talk, walk, fly and learn. This in turn means businesses can develop completely new ways to interact with their customers, offer them much more intelligent products and service experiences, automate processes and boost business success.

  Having said that, we also know there is a massive amount of hype and confusion about AI. Some see it as the ultimate threat to our civilization, while others believe AI is the savior that's going to solve humanity's biggest challenges, from tackling climate change to curing cancer. The aim of this book is to cut through the hype and scare-mongering, and provide a cutting-edge picture of how AI is actually being used by businesses today.

  By sharing some of the latest and most innovative real world use cases from across many industries, we hope to demystify AI while at the same time inspiring you to see the immense opportunities AI is offering. We have written this book for anyone who would like to better understand AI and have therefore tried hard to keep the technical details to a level anyone can understand. At the same time, we have attempted to include just enough techie stuff to make it informative for people who already work in the field of AI.

  In this book, you will of course gain insights into how some of the AI giants such as Google, Facebook, Alibaba, Baidu, Microsoft, Amazon and Tencent use it, but you will also learn how many traditional incumbent companies across most industries as well as innovative start-ups use AI. Our hope is that this will provide a realistic picture of the current state of the art: where the AI trailblazers are rolling full steam ahead, leaving many traditional businesses behind in the starting blocks; where traditional businesses are working hard at reinventing themselves and using AI to stay competitive; and where start-ups are using AI to challenge both the AI trailblazers and traditional businesses.

  The Most Powerful Technology Of Mankind

  AI is the most powerful technology available to mankind today and the biggest mistake anyone can make is to ignore it. Leaders of nations and businesses alike are seeing both the magnitude of opportunities AI brings and the risks of being left behind in the AI goldrush.

  In the United States, the White House has released numerous policy documents that emphasize the strategic significance of AI. In 2016, under President Barack Obama, the White House issued the first report “Preparing for the Future of Artificial Intelligence”,1 which laid the foundation for a US AI strategy. In 2018, under Donald Trump, foll
owing an AI summit at the White House, the administration issued “Artificial Intelligence for the American People”2 in which President Trump states: “We're on the verge of new technological revolutions that could improve virtually every aspect of our lives, create vast new wealth for American workers and families, and open up bold, new frontiers in science, medicine, and communication.” The goal of the US Administration is to maintain American leadership in AI by accelerating AI research and deployment, and by training the future American workforce to take full advantage of the benefits of AI.3

  Russia's President Putin said: “Artificial intelligence is the future, not only for Russia, but for all humankind. […] Whoever becomes the leader in this sphere will become the ruler of the world.”4 China has arguably developed the most ambitious plan to make use of AI with a goal of becoming the world leader in AI by 2030.5 In Europe, the European Commission released its AI strategy in 2018, in which it states: “Like the steam engine or electricity in the past, AI is transforming our world, our society and our industry. Growth in computing power, availability of data and progress in algorithms have turned AI into one of the most strategic technologies of the 21st century. The stakes could not be higher. The way we approach AI will define the world we live in.”6

  Business leaders agree. Amazon CEO Jeff Bezos believes we have entered the “golden age” of AI that allows us to solve problems that once were the realm of sci-fi.7 Google co-founder Sergey Brin said: “The new spring in AI is the most significant development in computing in my lifetime”8 and Microsoft CEO Satya Nadella calls AI the “defining technology of our times”.9 The founder and executive chairman of the World Economic Forum, Klaus Schwab, together with many others, believes that AI (especially when combined with all other technological innovations) has triggered a fourth industrial revolution that is going to transform all parts of business and society.10

  What's Artificial Intelligence? The Rise Of Deep Machine Learning

  AI is nothing new and nothing magical. The first developments in AI date back to the 1950s. AI refers to the ability of computer systems or machines to display intelligent behavior that allows them to act and learn autonomously. In its most basic form, AI takes data, applies some calculation rules (or algorithms) to the data and then makes decisions or predicts outcomes.

  For example, the data could be images of handwritten words, letters or numbers. The algorithm would be a computer program written by a human that contains rules such as the common shapes of each letter and spacing between words. This then allows a computer to analyze scanned images of handwritten text, apply the rules and make predictions about which letters, numbers and words it contains, enabling machines to recognize handwriting. This type of AI has been used, for example, by the US Postal Services to automatically read addresses on letters from as early as 1997. For narrow applications this kind of AI worked well.

  This rule-based AI runs into difficulties when tasks are more complex or when we humans can't easily explain the rules and therefore can't program them into algorithms. Speaking our language, walking around and recognizing a friend in a crowd are all examples of skills that we have acquired through experience but for which we can't easily explain the rules.

  We have learned those skills via a network of neurons in our brain that have been programmed to, for example, recognize a face by looking at the face from lots of different angles over a period of time, or we have learned how to walk and talk through trial and error. In modern AI, we basically replicate this process using artificial neural networks and instead of having humans programming the rules, we let the machines create the rules by themselves, similarly to how our brain learns from experience. We refer to this as machine learning.

  In machine learning, we train AI with data by, for example, feeding it thousands of images that either contain human faces or don't contain human faces. The computer then takes in the information and creates its own algorithm either completely independently (unsupervised machine learning) or with help from humans (supervised or semi-supervised machine learning). When machine learning uses multiple layers of artificial neural networks to learn from training data (which makes them more powerful), we refer to it as deep learning.

  Deep learning has given us many of the recent advances in AI, such as the ability for computers to see and recognize what or who is in an image or in a video (machine vision). Or it has given machines the ability to understand and reproduce written text or spoken words, which we call natural language processing and see in website chatbots or home smart speakers like Amazon's Echo.

  There are two key reasons why deep learning is thriving today:

  We have data: Data is the raw material that is fuelling AI and in today's big data world we are generating more data than ever before. The digitization of our world means that almost everything we do leaves a data trail and we are increasingly surrounded by smart devices that collect and transmit data. This is causing exponential growth in the volume and types of data we can now use to train AI.

  We have computing power: We now have the ability to store and process vast amounts of data. Breakthroughs in cloud computing allow businesses to cheaply store almost unlimited volumes of data and use distributed computing to analyze big data in near real time. What's more, advances in chip technology mean AI computations can now be performed on devices such as smartphones or other smart connected devices. We refer to this as edge computing on Internet of Things devices.

  We humans continuously learn and improve through experience. This “learning by doing” approach can now also be replicated by machine learning algorithms via reinforcement learning. Similarly to how toddlers learn to walk by adjusting actions based on the outcomes they experience, such as taking a smaller step if the previous broad step made them fall, AI uses reinforcement learning algorithms to determine the ideal behavior based upon feedback from the environment. Reinforcement learning gives machines (for example, robots) the ability to walk, drive or fly autonomously. Many leading-edge applications of machine learning combine deep and reinforcement learning techniques.

  If you would like to learn more about any of these fascinating topics, head to www.bernardmarr.com where you can find hundreds of articles and videos explaining and discussing everything you need to know about AI and machine learning.

  Artificial Intelligence Opportunities In Business

  There are three key use cases for AI in business, which can overlap to some degree, but help to segment the opportunities. Businesses can use AI to: (1) change the way they understand and interact with customers, (2) offer more intelligent products and services, and (3) improve and automate business processes.

  Customers: AI can help businesses better understand who their customers are, predict what products or services customers are likely to want, predict market trends and demands and provide more personalized interactions with customers. In this book, we will look at companies like Stitch Fix and Facebook, which use AI to really get to know their customers.

  Products and services: AI can help businesses create more intelligent products and services to offer to their customers. Customers want more intelligent products such as smarter phones, smarter cars and smarter home devices. In this book, we will look at how Apple, Samsung and car companies such as Tesla and Volvo use AI to create smarter products and we explore how others like Spotify, Disney or Uber use AI to deliver more intelligent services to their customers.

  Automate processes: AI can improve and help automate business processes. In this book, we will look at examples such as JD.com that is using autonomous drones, automated fulfilment centers and delivery robots to transform its retail operations. We will also look at how AI can automate medical diagnosis in the Infervision and Elsevier case studies, and even the pizza quality checks at Domino's.

  The Strategic Use Of Artificial Intelligence In Business

  Exploring the applications of AI in any business will often lead to a business model refresh or even a complete transformation of the business
approach. It is important that companies don't use AI to automate and improve a business model that is no longer relevant during the fourth industrial revolution.

  The starting point for any use of AI should be an AI and data strategy that identifies the biggest strategic opportunities and threats for any business and then pinpoints the most impactful applications. It is important to recognize that simply experimenting with AI around the edges is not going to deliver the necessary effects on business success.

  Artificial Intelligence In Practice

  In this book, you will find 50 company use cases and within them even more leading-edge examples of how these companies have used AI in practice to solve real world problems. We have divided the book into five parts.

  Part 1 contains case studies from the AI trailblazers. These tech companies are the ones that have grabbed hold of the AI opportunities and are running with them to transform industries and deliver mouth-watering business results. Most of them have made innovative applications of AI part of all aspects of their business and therefore provide great insights into the art of the possible.

  We could have segmented the remaining case studies in different ways, by AI application or by industry. Based on the feedback we received, we opted for the following industry segmentations.

  In Part 2 we look at retail, consumer goods and food and beverage companies. In Part 3 we explore how media, entertainment and telecom companies use AI. Part 4 looks at the services sector, including financial services and healthcare. Finally, in Part 5 we look at manufacturing, automotive, aerospace and industry 4.0 case studies.