THE EXECUTIVE GUIDE TO ARTIFICIAL INTELLIGENCE How to identify and implement applications for AI in your organization ANDREW BURGESS 每日免费获取金融报告 1.每日微信群内分享5+重磅金融研究报告； 2.金融，互联网，制造业，服务业，数据模型，研究方法…超过10 万份，每周分享 重点事件； 4.每月200+资料汇总，累计上万份金融报告，给专属用户； 截图 扫一扫二维码 或搜索公众号【seed思得】 点击菜单栏“研究报告”->"资料共享" 加入seed思得共享微信群（1-100群） 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) https://doi.org/10.1007/978-3-319-63820-1 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 informa- tion 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 experi- ence for me, particularly in understanding how AI is different from tradi- tional 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 won- dered 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 collec- tion 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-to- day 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 impa- tient, 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 peo- ple 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 empa- thetic, 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 cham- ber’ problem of personalised news, the impact on society as increasing num- bers of jobs can be automated and so on. Despite these challenges, I am incredibly excited about the future of tech- nology, 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 chal- lenges. This book is essential reading if you want to understand this transfor- mational 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, con- tent 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 S tarting an AI Journey 91 7 AI Prototyping 117 8 What Could Possibly Go Wrong? 129 9 Industrialising AI 147 10 Wher e Next for AI? 165 Index 177 xi List of Figures Fig. 2.1 Basic neural network 21 Fig. 2.2 Training a neural network 21 Fig. 2.3 A trained neural network 22 Fig. 3.1 AI objectives 30 Fig. 3.2 Knowledge map 43 Fig. 3.3 The AI framework 51 Fig. 4.1 Human in the loop 68 Fig. 4.2 Training through a human in the loop 69 Fig. 4.3 Crowd-sourced data training 69 Fig. 6.1 Aligning with the business strategy 93 Fig. 6.2 AI maturity matrix 100 Fig. 6.3 AI heat map first pass 102 Fig. 6.4 AI heat map 104 Fig. 9.1 AI Eco-System 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.