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Unformatted text preview: THE EXECUTIVE GUIDE TO ARTIFICIAL INTELLIGENCE How to identify and implement applications for AI in your organization ANDREW BURGESS The Executive Guide to Artificial Intelligence Andrew Burgess The Executive Guide to Artificial Intelligence How to identify and implement applications for AI in your organization Andrew Burgess AJBurgess Ltd London, United Kingdom ISBN 978-3-319-63819-5    ISBN 978-3-319-63820-1 (eBook) Library of Congress Control Number: 2017955043 © The Editor(s) (if applicable) and The Author(s) 2018 This work is subject to copyright. All rights are solely and exclusively licensed by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, express or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Cover illustration: Ukususha/iStock/Getty Images Plus Printed on acid-free paper This Palgrave Macmillan imprint is published by Springer Nature The registered company is Springer International Publishing AG The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland This book is dedicated to my wonderful wife, Meg, and our two amazing children, James and Charlie. Foreword I remember well the AI work I did whilst at college studying computer science, how different and fascinating it was and still is. We were set a very open challenge to write an AI programme on any subject. I decided to write mine so that it could tell you if the building in a photo was a house, a flat or a bungalow. Somewhat impractical, but a great learning experience for me, particularly in understanding how AI is different from traditional software. Although my college days were a number of years ago, since that time the concept of computers learning has always intrigued me and I have since wondered how long it will take for AI to have a truly widespread impact. In recent years, we’ve seen massive improvements in processing power, big data collection via sensors and the Internet of Things, cloud services, storage, ubiquitous connectivity and much more. These technological leaps mean that this is the right time for AI to become part of the ‘here and now’ and I strongly believe we will see a dramatic increase in the use of AI over the next few years. The AI in use today is known as narrow AI because it can excel at thousands of relatively narrow tasks (e.g. doing internet searches, playing Go or looking for fraudulent transactions). Things will certainly get even more exciting when ‘general AI’ can outperform humans at nearly every task we do, but we simply don’t know when this might be, or what the world will then look like. Until then, what excites me most is how we can apply AI now to solve our day-today problems at home and work. So why is AI important and how can we use it? Firstly, if you are impatient (like I am), doing small manual, repetitive tasks on computers simply takes too much time. I want the computer to do a better job of anticipating my needs and to just get on with it. If I could, I would prefer to talk to Alexa or vii viii  Foreword Google Assistant and just tell the computer what to do. For example, I would love to be able to ask Alexa to buy the most convenient train ticket and take the money out of my account. Compare this to buying a train ticket on any website, where after something like 50 key strokes you might have bought a ticket. I don’t think future generations, who are becoming increasingly impatient, will put up with doing these simple and time-consuming tasks. I see my children and future generations having more ‘thinking time’ and focusing on things that are outside the normal tasks. AI may in fact free up so much of my children’s time that they can finally clean up their bedrooms. In the workplace, how many of the emails, phone calls and letters in a call centre could be handled by AI? At Virgin Trains, we used AI to reduce the time spent dealing with customer emails by 85% and this enabled our people to focus on the personable customer service we’re famous for. Further improvements will no doubt be possible in the future as we get better at developing conversational interfaces, deep learning and process automation. One can imagine similar developments revolutionising every part of the business, from how we hire people to how we measure the effectiveness of marketing campaigns. So, what about the challenges of AI? One that springs to mind at Virgin is how to get the ‘tone of voice’ right. Our people are bold, funny and empathetic, and our customers expect this from us in every channel. Conversational interfaces driven by AI should be no different. Today it may be a nuisance if your laptop crashes, but it becomes all the more important that an AI system does what you want it to do if it controls your car, your airplane or your pacemaker. With software systems that can learn and adapt, we need to understand where the responsibility lies when they go wrong. This is both a technical and an ethical challenge. Beyond this, there are questions about data privacy, autonomous weapons, the ‘echo chamber’ problem of personalised news, the impact on society as increasing numbers of jobs can be automated and so on. Despite these challenges, I am incredibly excited about the future of technology, and AI is right at the heart of the ‘revolution’. I think over the next five to ten years AI will make us more productive at work, make us more healthy and happy at home, and generally change the world for the better. To exploit these opportunities to the full, businesses need people who understand these emerging technologies and can navigate around the challenges. This book is essential reading if you want to understand this transformational technology and how it will impact your business. John Sullivan, CIO and Innovation at Virgin Trains Acknowledgements I would like to thank the following people for providing valuable input, content and inspiration for this book: Andrew Anderson, Celaton Richard Benjamins, Axa Matt Buskell, Rainbird Ed Challis, Re:infer Karl Chapman, Riverview Law Tara Chittenden, The Law Society Sarah Clayton, Kisaco Research Dana Cuffe, Aldermore Rob Divall, Aldermore Gerard Frith, Matter Chris Gayner, Genfour Katie Gibbs, Aigen Daniel Hulme, Satalia Prof. Mary Lacity, University of Missouri-St Louis Prof. Ilan Oshri, Loughborough University Stephen Partridge, Palgrave Mike Peters, Samara Chris Popple, Lloyds Bank John Sullivan, Virgin Trains Cathy Tornbaum, Gartner ix x  Acknowledgements Vasilis Tsolis, Congnitiv+ Will Venters, LSE Kim Vigilia, Conde Naste Prof. Leslie Willcocks, LSE Everyone at Symphony Ventures Contents 1 Don’t Believe the Hype   1 2 Why Now?  11 3 AI Capabilities Framework  29 4 Associated Technologies   55 5 AI in Action   73 6 Starting an AI Journey   91 7 AI Prototyping   117 8 What Could Possibly Go Wrong?   129 9 Industrialising AI  147 10 Where Next for AI?  165 Index  177 xi List of Figures Fig. 2.1 Fig. 2.2 Fig. 2.3 Fig. 3.1 Fig. 3.2 Fig. 3.3 Fig. 4.1 Fig. 4.2 Fig. 4.3 Fig. 6.1 Fig. 6.2 Fig. 6.3 Fig. 6.4 Fig. 9.1 Basic neural network Training a neural network A trained neural network AI objectives Knowledge map The AI framework Human in the loop Training through a human in the loop Crowd-sourced data training Aligning with the business strategy AI maturity matrix AI heat map first pass AI heat map AI Eco-System 21 21 22 30 43 51 68 69 69 93 100 102 104 148 xiii 1 Don’t Believe the Hype Introduction Read any current affairs newspaper, magazine or journal, and you are likely to find an article on artificial intelligence (AI), usually decrying the way the ‘robots are taking over’ and how this mysterious technology is the biggest risk to humanity since the nuclear bomb was invented. Meanwhile the companies actually creating AI applications make grand claims for their technology, explaining how it will change peoples’ lives whilst obfuscating any real value in a mist of marketing hyperbole. And then there is the actual technology itself—a chimera of mathematics, data and computers—that appears to be a black art to anyone outside of the developer world. No wonder that business executives are confused about what AI can do for their business. What exactly is AI? What does it do? How will it benefit my business? Where do I start? All of these are valid questions that have been, to date, unanswered, and which this book seeks to directly address. Artificial Intelligence, in its broadest sense, will have a fundamental impact on the way that we do business. Of that there is no doubt. It will change the way that we make decisions, it will enable completely new business models to be created and it will allow us to do things that we never before thought possible. But it will also replace the work currently being done by many knowledge workers, and will disproportionally reward those who adopt AI early and effectively. It is both a huge opportunity and an ominous threat wrapped up in a bewildering bundle of algorithms and jargon. But this technological revolution is not something that is going to happen in the future; this is not some theoretical exercise that will concern a few businesses. © The Author(s) 2018 A. Burgess, The Executive Guide to Artificial Intelligence, 1 2  1  Don’t Believe the Hype Artificial Intelligence is being used today in businesses to augment, improve and change the way that they work. Enlightened executives are already working out how AI can add value to their businesses, seeking to understand all the different types of AI and working out how to mitigate the risks that it inevitably brings. Many of those efforts are hidden or kept secret by their instigators, either because they don’t want the use of AI in their products or services to be widely known, or because they don’t want to give away the competitive advantage that it bestows. A persistent challenge for executives that want to get to grips with AI is where to find all the relevant information without resorting to fanciful articles, listening to vendor hyperbole or trying to understand algorithms. AI is firmly in the arena of ‘conscious unknowns’—we know that we don’t know enough. People generally experience AI first as consumers. All our smartphones have access to sophisticated AI, whether that is Siri, Cortana or Google’s Assistant. Our homes are now AI enabled through Amazon’s Alexa and Google Home. All of these supposedly make our lives easier to organise, and generally they do a pretty good job of it. But their use of AI is actually pretty limited. Most of them rely on the ability to turn your speech into words, and then those words into meaning. Once the intent has been established, the rest of the task is pretty standard automation; find out the weather forecast, get train times, play a song. And, although the speech recognition and natural language understanding (NLU) capabilities are very clever in what they achieve, AI is so much more than that, especially in the world of business. Artificial Intelligence can read thousands of legal contracts in minutes and extract all the useful information out of them; it can identify cancerous tumours with greater accuracy than human radiologists; it can identify fraudulent credit card behaviour before it happens; it can drive cars without drivers; it can run data centres more efficiently than humans; it can predict when customers (and employees) are going to desert you and, most importantly, it can learn and evolve based on its own experiences. But, until business executives understand what AI is, in simple-enough terms, and how it can help their business, it will never reach its full potential. Those with the foresight to use and exploit AI technologies are the ones that need to know what it can do, and understand what they need to do to get things going. That is the mission of this book. I will, over the course of the ten chapters, set out a framework to help the reader get to grips with the eight core capabilities of AI, and relate real business examples to each of these. I will provide approaches, methodologies and tools so that you can start your AI journey in the most efficient and effective way. I will also draw upon interviews and case studies from business leaders who are already implementing AI, from established AI vendors, and from academics whose work focuses on the practical application of AI.   Introducing the AI Framework    3 Introducing the AI Framework My AI Framework was developed over the past few years through a need to be able to make sense of the plethora of information, misinformation and marketing-­speak that is written and talked about in AI. I am not a computer coder or an AI developer, so I needed to put the world of AI into a language that business people like myself could understand. I was continually frustrated by the laziness in the use of quite specific terminology in articles that were actually meant to help explain AI, and which only made people more confused than they were before. Terms like Artificial Intelligence, Cognitive Automation and Machine Learning were being used interchangeably, despite them being quite different things. Through my work as a management consultant creating automation strategies for businesses, through reading many papers on the subject and speaking to other practitioners and experts, I managed to boil all the available information down into eight core capabilities for AI: Image Recognition, Speech Recognition, Search, Clustering, NLU, Optimisation, Prediction and Understanding. In theory, any AI application can be associated with one or more of these capabilities. The first four of these are all to do with capturing information—getting structured data out of unstructured, or big, data. These Capture categories are the most mature today. There are many examples of each of these in use today: we encounter Speech Recognition when we call up automated response lines; we have Image Recognition automatically categorising our photographs; we have a Search capability read and categorise the emails we send complaining about our train being late and we are categorised into like-minded groups every time we buy something from an online retailer. AI efficiently captures all this unstructured and big data that we give it and turns it into something useful (or intrusive, depending on your point of view, but that’s a topic to be discussed in more detail later in the book). The second group of NLU, Optimisation and Prediction are all trying to work out, usually using that useful information that has just been captured, what is happening. They are slightly less mature but all still have applications in our daily lives. NLU turns that speech recognition data into something useful—that is, what do all those individual words actually mean when they are put together in a sentence? The Optimisation capability (which includes problem solving and planning as core elements) covers a wide range of uses, including working out what the best route is between your home and work. And then the Prediction capability tries to work out what will happen next— if we bought that book on early Japanese cinema then we are likely to want to buy this other book on Akira Kurosawa. 4  1  Don’t Believe the Hype Once we get to Understanding, it’s a different picture all together. Understanding why something is happening really requires cognition; it requires many inputs, the ability to draw on many experiences, and to conceptualise these into models that can be applied to different scenarios and uses, which is something that the human brain is extremely good at, but AI, to date, simply can’t do. All of the previous examples of AI capabilities have been very specific (these are usually termed Narrow AI) but Understanding requires general AI, and this simply doesn’t exist yet outside of our brains. Artificial General Intelligence, as it is known, is the holy grail of AI researchers but it is still very theoretical at this stage. I will discuss the future of AI in the concluding chapter, but this book, as a practical guide to AI in business today, will inherently focus on those Narrow AI capabilities that can be implemented now. You will already be starting to realise from some of the examples I have given already that when AI is used in business it is usually implemented as a combination of these individual capabilities strung together. Once the individual capabilities are understood, they can be combined to create meaningful solutions to business problems and challenges. For example, I could ring up a bank to ask for a loan: I could end up speaking to a machine rather than a human, in which case AI will first be turning my voice into individual words (Speech Recognition), working out what it is I want (NLU), deciding whether I can get the loan (Optimisation) and then asking me whether I wanted to know more about car insurance because people like me tend to need loans to buy cars (Clustering and Prediction). That’s a fairly involved process that draws on key AI capabilities, and one that doesn’t have to involve a human being at all. The customer gets great service (the service is available day and night, the phone is answered straight away and they get an immediate response to their query), the process is efficient and effective for the business (operating costs are low, the decision making is consistent) and revenue is potentially increased (cross-selling additional products). So, the combining of the individual capabilities will be key to extracting the maximum value from AI. The AI Framework therefore gives us a foundation to help understand what AI can do (and to cut through that marketing hype), but also to help us apply it to real business challenges. With this knowledge, we will be able to answer questions such as; How will AI help me enhance customer service? How will it make my business processes more efficient? And, how will it help me make better decisions? All of these are valid questions that AI can help answer, and ones that I will explore in detail in the course of this book.   The Impact of AI on Jobs    5 Defining AI It’s interesting that in most of the examples I have given so far people often don’t even realise they are actually dealing with AI. Some of the uses today, such as planning a route in our satnav or getting a phrase translated in our browser, are so ubiquitous that we forget that there is actually some really clever stuff happening in the background. This has given rise to some tongue-­ in-­cheek definitions of what AI is: some say it is anything that will happen in 20 years’ time, others that it is only AI when it looks like it does in the movies. But, for a book on AI, we do need a concise definition to work from. The most useful definition of AI I have found is, unsurprisingly, from the Oxford English Dictionary, which states that AI is “the theory and development of computer systems able to perform tasks normally requiring h...
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