And the United States. Good morning, ladies and gentlemen. It's great to see so many of you here and so many old friends as well. With a lot to do over the next hour or so in terms of me speaking at you so I'm going to speak for about an hour or so and then hopefully the next hour or so. And hopefully the opportunity for some questions and answers. And what I thought I would take you is here. I'm going to talk a little bit about the future that Daniel Susskind I started with. Daniel Susskind I see for the professions, then provide some evidence that we gathered in our preparation for the book, and try. ng to still this down into a model of we regard the evolution of professional service. This, you will see rapidly, is under. It is underpinned by technology. I will devote a little bit of time to expressing our views and how technology is best understood, which will lead me on. o the heart of this conference, artificial intelligence. That will take me as one will see from the line of argument. The question of the future of jobs. And finally, I will close by considering how it is that we share expertise. So let's start with future. In fact, start with two futures. Because the more Daniel Susskind I solve the professions as we explore the future, the more we can solve the future. We explore and investigate it, we recognize there were two futures unfolding. And the first we say is reassuringly familiar. This is where technology comes along and automates our traditional ways of working. It streamlines and optimizes the. So there's not fundamental change here, and broadly speaking we find professionals comfortable with this idea that the role of. echnology is to automate pre-existing practices. But now our travels and I know everyone in this room has simply did with this. We also saw a second future that we believe is unfolding in parallel. And this is a future where many of the problems. To which historically only professionals could provide the answers. Many of these problems are now being solved in a difficult way. So, technology is coming along in this second role, not simply to automate the problem. But to innovate, to give rise to new ways, essentially, of delivering professional services. And what we see is that these two futures will run along in parallel for a while, but in the long run, we believe that. We believe the second will dominate, and then due course, the traditional professions as we know them will come to be dismantled. Now this is not a protection over the next few years, this is over the next few decades, and it might sound horrific in many professions. I'll see what a gloomy message you have, but I want to see the outset that our interest has always been in the recipients of service. While we have sympathy with those who need to adapt, those who currently provide, our overwhelming interest is in people's. This is a very important part of the process of the process. It provides an opportunity to make the expertise of professionals far more widely available than was possible. Indeed, was conceivable in the past. We also thought that we should take a step back and ask a more fundamental question. Why do we have the professions at all? We spent the first chapter of our book looking at this. What basic problem? Do our professions exist to solve? And the one we thought about it, we recognised in some extent we. ake this from Herbert Hart, the Great League of the Lost Turns, book the concept of law, who talks about trussoms of a human nature. And one of them, he says, is that she. says, is that human beings have limited understanding. It's unarguable. We all have limited understanding. And so when we're faced with problems, n our world beyond our knowledge and experience, serious problems, we tend in the print-based industrial society to turn. o these people called professionals, when we have a problem with our health, in relation to law, in relation to our buildings and to the buildings. And so forth, our tax affairs. We turn to these people we call professionals. And they have what we in the book define as practice. Practical expertise. And that's a mix of knowledge and expertise and experience, but also know-how and skills. The whole cluster. attributes that we find in professionals, we call this practical expertise, and the only way in the print-based. ndustrial society that we as non-experts can gain access to this practical expertise is through. these professionals, and they're operating under this grand bargain, and this is essentially the monopoly, the exclusive. It's exclusivity that we grant professionals in many areas and certainly in law as people who are the only ones who are allowed to conduct. Whether it be appearing in a courtroom or conducting an operation. So these professionals, we argue, are gatekeepers. They're the custodian, the curators of knowledge and expertise that dispense. Expanse, they make available to people who need this. And this is the solution of the print-based industrial society. But we're no longer in a print-based industrial society. We're in a technology-based internet society, and if we're really honest around the world, our current society is not a real world. Our professions are creaking, our health service, our legal service, our educational services. Even at advanced economies, there are concerns, complaints, worries. We face a crisis, it seems to us, in how it is that our professions deliver their service. For most people, for the most part, professional services unaffordable. We know this is a law in particular. Many of the practices are antiquated. The ropey egg, sometimes this is intentional obfuscation, but sometimes. It's the jargon, the lingual we all use, to communicate with one another is impenetrable to the known expert. And also the professions underperform in a very specific way. We're not saying the professions do their job badly. But what we are saying is that the expert. se of the very best can, because of the way they make this expertise available, only be made available. And so our professions are unaffordable, antiquated, opaque, and underperforming. We think this is a... A fairly severe indictment. So the question we ask in the book is this, can we solve the problem differently? Can we find a new way of offering access to this practical expertise that doesn't involve. Human beings acting as gatekeepers. Do we really need the gatekeepers any longer? And our argument is that we need to. And our argument, of course, is that through the emergence of a variety of technologies, we're finding new techniques, new ways of making. ractical expertise available. And indeed, at the end of the presentation, I'll suggest to you that there's six different ways we can make. xpertise available. But let's look at the evidence. And this is what largely done by Daniel. It seemed to us that it. would be useful to speak to thought leaders, to market leaders, right across various professions, to get a sense of what was happening at work. So in education, we found at Harvard that more people signed on at one point. To their online courses that had graduated from the university in its 377 years of existence. Then with the Takana Academy, you may be familiar with this. Online and Structural, instructional videos. The Mathematics. Daniel teaches maths and economics at Oxford. He points people towards Khan Academy. These. videos, owned for 10 million unique visitors each month, has more than the Effective Computing, school population in England and England. These are first-rate, traditional videos that can help people both with easy and hard problems and mathematics. In medicine, Web MD, a network of health websites, more than 190 million people. Every month, they're not all hypercontracts. They visit these websites, and this is more people than attend doctor. The FDA has predicted that by 2018, at least one. nd a half billion people on their smartphones will have regularly used medical apps. In journalism, you can think of the Huffington Post next 6th birthday, it'd more unique visitors to its website. The New York Times enjoyed and it had been an existence for 164 years. Our Bleacher Report is for you. We have been working on the same thing as the new report in sports. 2000 sports fans blogging over 22 million unique visitors each month. And after I will see you then in sports. Or associated press to generate their earnings reports traditionally done by financial journalists. They use software. These systems 15 times more productive than the human journalists. In laws we know e Bay. 60 million disputes every year settle the e Bay not through courts and lawyers but by ODR online decision. Online Dispute Resolution presentation. Legal Zitten.com. Online advice online documents for small businesses largely and for individual consumers. The same runs and tax. 48 million people in 2014 use Turbo Tax or similar software to file. 48 million people in 2015 use Turbo Tax or similar software to file their tax returns, instead of using tax consultants. Traditionally, it regard as a conservative organization. They're using advanced software to detect fraud over a billion pieces of data. An architecture we find also here is a swarm of autonomous robots actually building a... Essentially a wall made over about 1500 bricks but you can see already the potential. What about printing a building? Well, there's a firm of architects in Holland are doing precisely that. You see 3D printing? o print and assemble parts that are put together that will constitute a building. Printing parts that are. They are around 3.5 meters in length. In consultancy, look at Accenture, where regard they am I suppose traditionally, is from a minor. The. State Deliant founded 170 years ago as an audit firm. They now have over 200,000 people working within that. They have their own university for goodness sake, 700,000 square feet in Texas. And even when we looked at the clergy, we looked at divinity, we found people doing strange things online, this is second life, where we want their avatar. It's a gloomy looking Anglican Cathedral, but every day, and there's a large attendance, they have global, they're. Daily worship classes, they have weekly Bible discussions, they have counseling as well, but are all time favorite. There's an app called Confession. And the Vatican, believe it or not, the 2011. Granted, it's first digital Imprimatur as the license that's given to. fficial publications and Catholic religion. This is remarkable. This is good tools for tracking Satan. And it has drop-down menus, options for contrition. We saw sometimes it using, sometimes amazing, but certainly alerting instances of the "not only" instances, not simply of automation, that's to say of systems coming along and computerizing our traditional ways of working. But systems that were solving the problems, providing the service that historically had to involve human beings. While human professionals and gatekeepers were saying that systems were taking on a different role. A lot of our book is trying to make sense. And one of the models, the structures we use is this. And it's a development from something that I've done in previous work. In my book, the end of Laura's question, Mark, and to Morris Lawrence, people might see some resemblance there. But what we argue is that we can't do this. What we argue is that the professions are evolving from the traditional marble of some kind of craft. While the professional services delivered as a craft, what I call my allow work of bespoke service, a highly tailored customized service. An assumption that each particular case requires a fresh canvas. You start with a blank sheet of paper. This is a notion of professional service and law, for example, is very much inculcated into us in law school. Where one gets the sense that every case could reach the Supreme Court, or when you read fiction. People are running around looking for loopholes or for smoking guns. But actually, in daily life, in law, and elsewhere, we find in the profession a high degree of standardization. Consultants use methodology. Doctors use protocols and checklists. Lawyers use precedence templates, standard form documents. Teachers use last year's notes. It's not the case that we start from scratch and we craft... Craft every bit of service on this blank sheet metaphor it is speaking. We go further than standardization. We systematize, we use within our institutions our schools, our universities, our hospitals, our professional firms. We use technology. We have technology sometimes to automate checklists and we have workflow systems. In law, for example, automatically to generate. Now this is all within the professions and at this stage in evolution we reach a significant line. Now we call this the line of externalization. Where the content, the systems, that have been used internally, are then made available across the internet to users to recipients of the internet. And significantly, this can happen in three different ways. It can happen when, for example, Allen and Over. One of the world's leading law firms charges for its online legal services. 15 million pounds a year, they generate in revenue. But that's one model where the content you make available is as an online charge or service, and many professional firms. Are rushing into this box. If they can't sell their time, then they should surely... and capsulate their expertise and make it available online. Charities and governments, however, tend to be moving in a distant direction. They're making guidance and content and advice, materials available online, on a non-chargeable basis. There's still control of the. ontent, but the service is not one for which users pay. And the final model, which is alien to many professions. But for us, deep attractions, is the idea of the content being made available on a common spaces. That is to say, it's not owned or controlled by major professional providers, by gatekeepers, by governments, in the spirit of open source software, in the spirit of Wikipedia. This is. ontent that we all can contribute to, can draw from, and in a sense, we create and look at. er it ourselves. These are the three different models, and when one's thinking ahead, one of the big strategic models. The most important thing is to have a strategic question, as to how it is that practical expertise in different fields will be made available online. It's so hard for those who are lawyers to think objectively about this. But I think we can often think more clearly when we think of other professions in medicine, for example. When you think of the billions of people around the world who would benefit from ready availability of the. Medical expertise. Do we really want to evolve why we still have some control into a society or into a. lobal network where that expertise. The expertise can only be made available on a chargeable basis, or when it's rather qualmsily controlled by the state. Should we not be thinking in terms of medicine, of building up this commons of expertise, and the same argument supply? We call this move from left to right, from craft to externalisation, the Commoditization. f professional service. But as you can imagine, much of this is underpinned by Techno. ogy is at the heart of this future, these futures that we identify. And I want to talk a little bit about the future. Talk a little bit about technology to you today. And to some extent, I know many of you are familiar, intimately familiar with. technology. I want to share with you the way that we try to express the developments we see in technology. Because I speak to you, I suspect, as converts, but I also speak to you as ambassadors. One of the things you need to do is to. Go back to your universities, your firms, your state bars, as the case may be. And if you're convinced, you need to plead the case. And I just want to share with you the way that we plead the case, how it is that we unfold the story of a technology in a way that we can do. That we hope is compelling and interesting. And I start by going back to 1996 when I wrote a book called The Future of Law. And bizarre, this most may seem in retrospect. When I wrote that book, one of my main. The main preoccupations, the electronic Commoditization was email. And I was just getting lectures all around the world and so forth. And one of the running themes was. The themes was, of mine, was that the dominant way that lawyers and clients would come to communicate in the future would be by email. Now this is of course seeming entirely unobjectionable and deep but all today. But at the time I joke not the. Law Society of England and Wales said I shouldn't be allowed to speak in public. They said I didn't understand the confidentiality. They argued that I was bringing the legal profession into disrepute by suggesting that I was not a legal professional. I was interested that lawyers and clients would use email together in the future. And that gives us a flavor of where we were just twenty years ago. That wasn't outlandish thinking. That was fairly mainstream. And today we believe that we can understand ten. ology by considering four different phenomena.' The first is the exponential explosive. Growth in the underpinning technologies. Secondly, and to some extent this is a defining phrase of our work. And I suppose it maps quite well onto the notion of AI. But what we argue is our machines are becoming increasingly. We also say that machines are becoming increasingly pervasive. And finally, as human beings, we're becoming increasingly connected. And I want to run you through each of these until I hear the story that we are so sorry. And the law one needs to start with here is not a law of the law. It's Moore's law. Gordon Moore, the man who 51- years ago, in 1965 predicted very approximately that every 18 months or two years that changed slightly over time, processing power. This goes rise to mathematicians and others, an explosive and exponential growth. So it starts off quite shallowly when it doubles every month and then it explodes. And we are! To use the phrase of Ray Kurzweil in my view, the knee of the curve just now. We think we've come a long way in terms of processing power. But actually we're just warming up. What's hard I think as human beings is to grasp the effects of exponential. So let me give you an example. Imagine a piece of paper. A4, just 0.06 millimeter. Imagine you folded over once. You folded over twice. Again, four times. After four folds, it's the thickness of the. Thickness of a credit card. After a living fold, if you keep doubling, it's the height of a cook can. After 21 folds, it's the height. Big Ben. After 43 folds, it's distant from here to the moon. And after 100 folds, it's 8 billion light. This is quite outlandish as I want an Earth that's going on here. He's doubling a bit of paper over 100 times. But this. s what's happening to processing power. And this leads a number of commentators to observe that by 2020, the average destination. The average desktop machine will have the same processing power as the human brain. That's about 10 to the 16th, 10 to 17th calculations for second. Which I think we would all say is remarkable. But as not as remarkable as this, by 2050, if processing power continues to be. Our continues to double every year. There's some controversy here. But most material scientists and computer scientists say is we move away from silicon into other ways. Other techniques for processing, particularly in Effective Computing, is entirely reasonable prediction that doubling will continue. By 2050, if the doubling continues, the average desktop machine will have more processing power than all of humanity put together. This is this unbelievable doubling of years. He must be exaggerating. Well, this in 2001 was the prize lecture, Nobel Prize lecture of Michael Spence, and he said there that roughly there had been a. A 10 billion times reduction in the cost of processing part in the first 50 years of the computer age. And remember he was giving that lecture. The beginning of 2000, so every couple of years I'll have 20 billion, 40 billion, 80 billion, 160 billion, and so on. It's not just processing power though. Think about data. According to Eric Schmidt, Google's German, every two days now. We create as much information as we did from the dawn of Commoditization up until 2003. If you project forward by about 2000 years ago, you will be able to make a difference. If you project forward by about 2021, that will be every hour or so. That's the amount of data we're creating. On a more prosaic level, for those of you who find out more about the future, please visit us at www.mooji.org/mooji. If you find out more about the future, please visit us at www.mooji.org/mooji. We are now at the University of New York, where we are seeing this explosive growth. This is fueling, enabling what we call... This increasing capability of machines. And we look at that currently, but this is going to change over time, under four headings. The idea that machines, using big data techniques, can actually begin to outperform human. beings in a variety of areas. The machines can solve problems. The machines, this is a feel called Effective Computing. Can both express and detect human emotions. And finally, the whole area of robotics. So we think these. are the four areas in which we're currently seeing machines becoming increasingly capable. And if you look at big data, this is the idea that. Data can yield insights, patterns, correlations that we're creating as users of the internet and of technology. A data exhaust, huge bodies of information that actually encapsulate capture human experience. And from that data and information, we can make very valuable predictions. You'll have heard of the system called Lex Machina, which. It's bought recently by Lexus Nexus that helps predict decisions of patent court. You'll be human ledger from Dan Katz, who's working. Supreme Court prediction is fascinating too. The underlying point in these and other projects is fascinating. It's that. o predict the decisions of the court, one can, as we've traditionally have done, engage in legal. Look at the substantive issues and see where we think that he does. But we can also make statistical predictions about the hate-based. But the behavior of the court. We can look at phenomena such as the judges involved, the court involved, the volume of the court. You may know the research that was done in Israel in parole hearings, which showed the difference between hearings before lunch and after lunch. A whole bundle of criteria that we can bring to bear in thinking about court behavior well beyond the substantive. And this fascinates me because the question that all clients ask when they have a dispute is what we can do. Now, if you can come to a more accurate answer to that question, not by reasoning and law, but by the way, the question is what are the reasons why we can't do this. In law, but by undertaking some kind of statistical prediction, you won't find that she's executive having a problem with that. Or let me put it another way, if you get a great case in England, a leading commercial bastard will never say to the Chancellor. The chances of winning are greater than 70%. Never. Why is it you can't be more confident given the merits of the claim? And the barstell will say, "Will there's many other factors involved?" For example, who the judge is? And I would say precisely. They are the criteria that Dan and others are looking at when. they are engaging in the prediction of judicial decisions. And so what we're seeing, and this is just one example, is that from the data, hat we created, the byproduct of users of systems, we actually can identify corally. By correlations, patterns, we can yield insights that sometimes can even outperform us as human beings. And then, the eponymous Watson, one of the reasons we're gathered here today, this computer system that remarkably in 2011. On your TV quiz show, Jeopardy, We Don't Have It in the United Kingdom, beat the two best of our human jeopardy champions. When you think of the. chnologies there, the speech recognition, the forms of natural language processing, the knowledge, storage, manipulation, the inference, the reasoning mechanisms, the speech synthesis, apparently even in an earlier model on mechanical. arm that could press about it, but when you think of what's going on here, this is definitely a move forward in the whole field of artificial intelligence. Our system leaders and gentlemen that frankly, in its own bizarre way, given the nation of program jeopardy, can answer questions about anything. Now, as this often said, I game did not vest in the world. In Watson, because they wanted to be good at Quichelas. And the early signs of there were particularly a medicine where I think they've invested. In terms of the quality of treatment plan, the quality of diagnosis, you can see how these systems are not too distant. We also know that it's early days yet, but what's. oving beyond jeopardy, moving beyond medicine into areas of law as well. So that's a little bit of. problem solving. A third element is what I call, I don't call it, two, Effective Computing. I suspect, this is an area that most of you will be less familiar with. This idea that computers can both detect. nd express human emotions. That a computer could look at your face and tell whether or not you're happy. Surprise. Angry or disgusted. Indeed Apple just beat that just bought a company that specializes in precisely this. idea that there are systems now that more accurately than any human being can look at a smile on a human face. And tell whether or not that smile is a genuine smile or a fake smile. Computers more accurately than any human being can look at. Any human being can listen to two female voices and tell whether or not they belong to a mother and a daughter. By the early 20s, our smart phones will know what kind of mood we're in and they'll respond accordingly. Our Jackpot, our jackets will give us a little hug, which someone sends a friendly message to us. Our machines will begin to interact with us. In a way that reflects their perception of our mood, and also they'll begin to interact with us. They'll begin to express emotions if you believe that makes sense in the context of the narrative machines. In gender in us already do this idea of a cute machine that we can look at a little robot and find that cute. They can actually engender him. And of course all of this leads in due course.?" "It's a little notion of transhumanism, I suppose, where the borders between human beings and machines become blue." But an interesting step in this direction, I suppose, if you look at this man, Hiroshi Ishiguro, in my view, the world's. eading academic roboticist who builds Androids, and this is our android of himself. And it's quite hard to tell which is him and which is the android. And partly this android gives lectures to people. And when you get an android at the podium and you're more than. few rules back. You can't actually tell that it's not Hiroshi Ishiguro himself. You may wonder as I think. It causes hand luggage and the body goes in the hole. I just have this vision of the hand luggage opening up and it's a headline. I think it's also important to say a little bit of a revolting. So what I'm just talking about is the weight of the hand luggage. The ways in which machines are becoming increasingly capable handle the data, solve the problems, even express it and detect emotions. Robotics fascinates me. For all bundle of reasons, how rapidly it's progressed over the last few years. It's interesting if you read a book. It was published in 2004 by a two MIT economist called the New Division of Labor. And in that book, these economists were trying to identify the kind of work that all the human beings could do in the future and those the machines could take on. And they make the opposite of it. They make the observation then and these are leading experts. There are certain things you cannot really imagine a computer doing. You can't imagine they say that a computer is cutting your hair. You can't imagine a computer. ing your garden. And thirdly, they say you can't imagine a computer driving a truck. Now this was in 2004. The leading commentator is on this whole question. And of course we now have fleets of self-driving cars. Google self-driving cars and every serious water manufacturer in the world is investing in this area. I don't think you'll. I don't think it'll be 25 years before we look back and say it's amazing we used to drive cars. We used to sit there behind a wheel and do that. Why and after? What an absurd waste of time. This technology is coming along. I think it's. a good example of how the unimaginable becomes an everyday reality in a decade or so. The machines are becoming increasingly. ervasive as well. Already with 6 billion mobile phone subscriptions. 2 billion smartphones. But beyond that, those are your. nternet of Things. This idea that chips will be embedded in every. The objects 40 to 50 billion devices is anticipated just within the. next five years. And actually, there's a bit of chips also embedded in us. The whole idea of ingestible machines. Little deep. You can see online all sorts of illustrations of this where people actually can swallow pills with chips on them. These. chips roam around the body monitoring, dispensing, communicating. So chips in everything. Not just in our top chains, but human beings too. And in a different way human beings though, fourthly are going to become increasingly how we are becoming. And it's not just the high definition desktop to desktop video conferencing which will transform all our lives. We also notice in our research the development of interesting social networks, that were closed to the general level. Social networks like SIRMOL over 200,000 doctors in the United States. No pharmaceutical companies, no patients. Doctors of the United States. Doctors online, exchanging news and views and research findings about medications therapies and so forth. Same in education and edmodal. Less successful but not more. It's the same idea legal on brand but I think we will, in due course, have the same in law and an architecture too and are connect. Another formal social network which we find fascinating more of which in a minute but this idea of recipient subprofessional service. Coming online and saving experiences. Patients like me, about a third of a million patients, particularly people suffering from chronic illnesses. Who find greater comfort and also better advice in some cases, not by regular visits to the dog. But from their peers. And secondly, the emotional support of the empathy, the sympathy, the one who can gain online from patients who are living in the hospital. Patients are suffering from different disorders. We saw the same though in religion and architecture as well. Even in tax, fascinating. If you're filling out a total tax-neavor problem, you don't pick up a form of traditional taxifies. You go on to Answer Exchange. I don't know how many of you still use windows. I've been a Mac convert for many years. But I remember when you used to use windows, they came up with a new version of the video. There were some completely unintelligible error message. What you would do is cut it into Google and up would come some clever people. Out there who could tell you how to start out the problem. This community of experience, communities of professional experience, were fighting. In areas such as medicine architecture as well. So they are, our underpinning technologies are great. Our machines are becoming increasingly capable. They are becoming increasingly pervasive and we as human beings are capable of. The big news is there's no finishing line. That's the hardest thing for all of us to accept. The technologies that may well transform our lives by turning off our lives. y 2025 probably haven't been invented yet. It's hard for us to grasp, because we can all remember our pre-internet worlds. But this takes. on to AI. And I believe it's important to understand AI in at least two waves. And for me, this is quite a sad thing. This is quite nostalgic. Because in the 1980s, I spent much of my time in AI. And I just thought I'd say a little bit of that. In 1981, when. I was an undergraduate in law in Glasgow University, I wrote this dissertation called Computers in the Judicial Process. And this was the beginning of the video. This was the beginning of my journey. Because part of this work exposed me to the field of artificial intelligence and law, and the night. In 1981, there were 26 publications globally in English on the subject. And I thought that would be quite a good thing to do some research in. So I went to Oxford, edited my doctorate in artificial intelligence and law. It was called Expert Systems and Law, Jurisdepidential and Wire, a snap-out. I wrote my first book in '87, I think about four people read, called Expert Systems and Law. But then from 1986 to 1988, I was involved in a project that I think is of relevance today. Look at this piece of legislation. Section 2 of this act shall not apply to an action to which this section applies. This is serious. Some legislature sat down and thought this was a good job. Well done. So this turns out to be one of the more intelligible clauses or sections rather in the Latent Damage Act. ct 1986. And this was a new piece of legislation written by a man called Philip Capper, who the time was the. dean of the Law School of Oxford University had been one of my examiners, and he wrote this book on this unintelligible piece of legislation all the time. The legislation, all the case law, the previous lane had been developed that was relevant for it. And also, he wrote this book about how he anticipated the field would develop. And he said to me that he thought, "I'd finish my doctor." He said to speak. He said, "This will make good area for some expert systems work. Why don't we build an expert system of his knowledge?" So the time of this project for artificial intelligence is known as "expective." The idea was, all sorts of different areas. You sat down with the human expert, you mined the jewels from their heads, you represented the. knowledge in a computer system, you essentially developed a decision tree around which they could roll. So, I'd expect, I don't want you to say just by with content, and there's two ways that machines can do clever things. On the one hand, we're all the clever things. All the clever stuff is done by human beings outside the system. You develop that complex decision tree, you drop it into a system, and the system becomes as it works. The system becomes as aware of the method for distributing the knowledge, and then there's the systems, the clever systems, that both produce and distribute the knowledge, where the systems operate at each other. All of the systems operate on, say, unstructured data, do clever things themselves. This is very much the first wave. And we develop the system, and I need to explain myself. At the time, ladies and gentlemen, this was as cool as you could imagine. This was state-of-the-art design. We were very proud of that. So this was a system, essentially a big decision tree of Philip's knowledge. It was published, and this was when. loppy disks genuinely were floppy, 5.25" floppy disks. We published a system that was appeared in the back of a book. We wrote. bout the system. The system itself was a kind of standard interactive expert system. It asked questions, yes, no answers, pull.me. And eventually, at a room through this huge decision tree, answering the question, when can my action no longer be raised because it's tight? Because it's time-bought. That belongs to law and limitation. A complex area of law, over two million passes through the system are. Our system reduced research time from two hours to two minutes. And this was done in the late 80s. I don't exaggerate this as exactly as we reported it. And I was heavily involved in the AI of our community indeed in 1991. I chaired this conference, the Third International Conference of the AI Law that was held in Oxford. So it was a big part of my life. And at the time... After the Latent Damage Act system... But maybe as this conference crept along I was becoming less comfortable. But it seemed to be truly inevitable. It was that within the next 25 years everyone would be using this kind of technology. These questions and answers systems outperforming. Philip Capper the system was based on the system. as back to the end. Should everyone be using these systems in 25 years? And it wasn't just in law, it was also in medicine and tax and audit and consulting. But these systems didn't really take off. I want to qualify that in one way they did. If you look at any tax-compliant systems of the big accounting firms. They're all based on rule-based expert system technology. If you look at Automation Document Assembly, it's all based on rule-based expert systems technology. If you look at all of the online legal guidance, it's all based, for example, neotrologics work, for example, all over his work. It's all based in 1980s rule-based expert systems technology. So I won't have it when people say it died away completely. But what it didn't do... When what I anticipated would be pervasive. It seemed to me that's just surely the way that people will feel. People will solve tight their problems. And I think it didn't succeed for three reasons. First, it's very costly to develop these systems. Secondly, this was at a time where they were not. The time where the cost pressure is in the legal profession worked was great. True to other professions too. And everyone was charging by the hour. So why would you really want to develop. If clients weren't clamoring for that kind of reduction in time and your time, and you're not going to. And your competitors weren't doing otherwise. The commercial incentive wasn't there to develop these systems. But thirdly, there was this. The web came along the first public accessible web site just 40 days after the web. And a lot of us just saw immediately that this is a final. This is a far more exciting way to make legal content, legal guidance, legal advice available online. To be true, these systems have. Those of us who then could have evolved with systems in the web, they weren't nearly sophisticated. They didn't answer questions in the way they expressed systems did in the 1980s, but there was an immediacy and there was a... A usability and there was an inexpensive involve that trumped to be honest the development where we had. So I largely left AI in law behind and then this happened as we know in 1997 with the world change. The World Chess Champion, Garry Kasparov, was beaten by Deep Blue, IBM Computer System. Then the 1980s, when I worked in the Office of University. At Effective Computing Laboratory, we talked about this a lot, the computer system ever beat the best chess player. And our answer generally was no. And the reason our answer was no is absolutely pivotal for today's discussion. We thought the answer was no for this reason. In chess playing in as many other areas of expertise, often human. xperts couldn't explain how they performed. Some people call this tacit knowledge. Human experts couldn't. Identify rules for their mundane level of playing, but they're real pieces of genius, they're real creativity, they're real imagination. Seemed to be ineffable, inexplicable, no one can actually reduce that to rules. And remember the model then was... The only way you could get these systems to perform at the level of experts was by representing experts' knowledge in a computer system. So if you couldn't articulate the rules, the systems we're adding were destined to perform at quite a high level, but not at the level of expertism. We're intuition, creativity, genius soon to be required. But what we hadn't banked on is that we had to be able to do that. But the time IBM's deep blue beat Garry Kasparov. This was a system that was built in the United States. This system that could look at 330 million possible moves in a second. Guided by brute Effective Computing by huge amounts of data. Guided by brute Effective Computing by James Bond, It's a phenomenal arrogance that we had in the 80s, thinking that the only way we could develop computer systems to work at the level of... The best lawyers was by representing the way lawyers solve legal problems. And this gives rise to a renewal and I call the E.I. fallacy. We find the E.I. fallacy committed by some of the world's leading E.I. scientists. We find it succinctly in the media and I suspect people in this room. It's the mistaken assumption that the only way to develop systems that perform tasks at the level of expert. The way to replicate the thinking processes of human specialists, that defined AI and expert systems in the 1980s. And much of the 90s as well, that the only way to get these systems performing at a high level was to model. And yet we saw with Capacasparov that he was beaten at a different gaming. He was outperformed. By a system that a huge amounts of data and fingertips and was capable of astounding processing. Now let me give an example of this. Many people say to me, "A.I. is impossible in law because...""Why my clients come to me is because of my judgement." How can a computer ever exercise judgement? There's a little bit of a... If you often say the same with creativity and empathy, just as a psychomite, there's a little bit of a definitional trick there. If you think that judgement is a little bit more difficult, then you can't do it. If you think that judgement creates a little bit more, then you can never exhibit any of these. But let's put that to one side, and to see to you that how can computers exercise judgement in an event is the wrong question. This is the better. estion. Do we call upon the judgment of our human experience? And the answer to that we think is we live in a world of uncertainty. Uncertainty, Facts, Uncertainty, Knowledge. And when we have problems featured by this uncertainty... We go to experts, human experts, and we say to them, in your experience, using your judgment, how. is it you think we should proceed? What's the answer? So what we're calling upon our experts to do, is to manage uncertainty. And so the question we should ask is. not can computers exercise judgment. It's can computers manage. It's the kind of thing again Dan will be talking about later. It's the whole story of the human being. It's the kind of thing that's. The whole story of big data that if you've got tens of millions of data items stored, then. the uncertainty that human beings usually cope with by drawing on what they call their experience a couple of thousand examples. It's well-trumped by the technology. I have a limited question or something else which I love because I'm interested in philosophy. But I think it's not entirely relevant for this discussion. It's can machines think? And my favorite illustration of this, and this is just, I think, simply. Wonderful. The Deafter, Watson I, Jeopardy, John Sarrow, Abertly Philosopher. He addressed it a lot in the eye, and he wrote an alpé that was headed this. Watson doesn't know it one in jeopardy, and that is perfect. It didn't want to go down to the pub to celebrate, it didn't want to phone its family to tell how it felt, but it still wasn't. And what we are finding, therefore, and we call this the second wave of AI, forget the distractions but whether our machines have these emotions. re exercise, judgment and so forth. We're seeing their marriages of increasingly capable non-thinking machines. These are machines that outperform human beings but don't operate like human beings. That's the second way to be. And it amazes me just even over the last few weeks we saw it, we've seen yet another illustration of this. The game goal, the board game. Apparently it's more. parrotations than there are atoms in the universe. And not many months ago, leading commentators were saying it'll be 10 years before a system. It can be the best Go! Champion. And a few weeks ago, 4-1, the world champion was beaten by the system called Alpha Go. Now what's particularly fascinating about this kind of system, which is based on neural networks, not the neural networks, but the neural networks. e use in the 80s is a form of representation language, but neural networks is a way of supporting machine learning. What's particularly interesting is that neural networks are very important to us. The reality in the development of the system is how it played the game with itself. This idea of recursive self-improvement. And I'm not yet sure what that looks like in law. We're servicing machine learning, and that's to say, machines that perform better. I'm not sure if I can answer as they're used. But actually, the idea, I only analogy I can think of just now is, imagine a computer system that could draft a contract. Then correct itself, redraft it, and then correct itself. This kind of recursive self improvement, which is taken... The Deep Mind people, part of Google, in a number of game planks and arrows from systems that could barely play the game, playing them soon. Playing themselves many millions of times to a level that outstrips human beings. Here's another way of looking at the whole area of the eye. Consider this, there's really four faculties of professionals and seems to us. What do we have? When you think of any professionals, we have our cognitive... capacity. Our both think to solve problems. We've manual psycho-motorability. Dexterity. We have effective skills. Emotional. Protecting. Expressing. Human Emotions. And what's a moral capacity? That as professionals, not only do we offer guidance, but in some sense we take responsibility for the advice of a dispense. And our conclusion as we looked at the professions was if you look at the advances in. Effective Computing, big data, so forth. You can see that many of the cognitive tasks are likely to be taken on. By machines that can perform at a higher level than human beings. We can see in robotics the same happening in manual tasks. We can even get a glimpse in the future. We can see in the future that  we can see in the future. The one question we're left answering and we take a view of this is the moral question. That it does seem to us a question we ask in the book that actually it's not a total obvious to us how our machine can take moral responsibility. Do we really want a machine to make an excuse, the decision to turn on the light support system? Do we really want a machine to... Make the decision to pass a life sentence? Do we really want more responsibility, the buck, to stop at a robot? And our inclination to... The inclination is probably not. So we say in the book, we call for immediate, because we think it's urgent, discussion of the moral relevance of technology. As machines. an do more of the cognitive manual and effective tasks, possibly the only boundary over which the machine won't cross, or we ought not to... Let it to cross as an armative question is the moral boundary. What does this mean for jobs? We're asked two questions. All there be any jobs that are in the world. Any jobs left for professionals, and what could humans do that machines cannot? This is a lecture in his own rights, just to give him a flavor. In the 20s, which we call the medium run, here are the roles that we reckon will be taken on by the people who. e solving problems, there used to be the problems of traditional professionals. And finally, older professionals look slightly horrified by this list. Partly because they don't understand some of the words, but partly because they don't sign that excite together. I didn't go to the... I didn't go to law school to be a process analyst. I didn't go to medical school to be a data scientist. But actually, in this time... of greater technological progress......or rapid progress in the world as ever seen......we need to rethink the roles we take on. There will still be roles, certainly in the 20s, for people who are interested in improving access to justice and better health, but they won't look like the traditional. In the long run, though, we're from refer to this is an article review happily. A pleasant review written of our book in The Economist, The Shoopicha Column, and this wonderful cartoon of Professor Dr. Robok Yisie. You can see it's got its wig on, it's got the count of its ledger, its stethoscope and so forth. Now very many people wouldn't think of the future of jobs once they... Once they hear about the AI narrative that I've been unfolding. They haven't made that one day they'll come in and sit in their chair... will be Professor Dr. Robot QC taking on the job. And this just isn't how it's going to unfold. Just a couple of things incidentally, if you were a divine, for example, devising a system to draft. Why on earth would you give arms and legs? This idea, I'm afraid many of you in the room commit. I wish we'd stop talking about robots. It's an amazingly anthropocentric way of representing legal problem solving the future. Why should I be here? Why on earth would we want a machine that looks like a human being to do a lot of these tasks? That's just a side-coming. The bigger cost. this. Then the long run when we look to it. Machines have become, as I said, increasingly capable. And they're going to take on more and more of today's tasks. You shouldn't think so much in terms of jobs for the future. You should unpack jobs. nto the tasks involved. Take Nurse, for example. Nurse 25 years ago, a lot of their work was about bedside conversation, a bed pass. Today, nurses in the UK are writing prescriptions for the D minor surgery. We still call them nurses, but the tasks they take away from us. They take on, have changed. It's not that the job has disappeared, the job has changed. And what we're seeing is a switching of tasks. Many of the tasks that human lawyers used under tape were replaced, they were punished by the rules and tasks I've just mentioned. So... At the same time however, machines are going to be taking on more of those tasks. And as we've shown in the 20s, no doubt new tasks... Will emerge. But here's the key point. It's likely that machines will take on these as well. There's no reason to think that you're. That list of roles, a task I mentioned, won't itself or themselves be susceptible to the same technology. So we find a heart of all the conclusion in the very long run by which we mean the 30s, 40s, 50s. The best steady decline in the need for human profession. Once one unpacks the jobs, you realize many of the tasks will be replaced by machines, some new tasks will emerge. But if the rate at which machines are taking on the new tasks outstrips the rate at which they're emerging, you come, I'm afraid, ladies and gentlemen. tay at where there's less and less human professionals to do. Now we started off writing a book we thought that was asking and answering the question, "What's the future of?" In fact, we're asking and answering a more profound question. How do you produce and distribute practical expectations? Practical expertise in society. You see, asking the question what's the future of lawyers, what's the future of professionals? It is said, assume as an answer. If you will, it assumes there will be lawyers or professionals, or it somehow assumes that they are part of the future landscape. We don't deny it. But the more important question, going back to our analysis of what professionals for is how do we produce and distribute practical expertise? That is the problem for which the traditional professions have been the answer. The traditional models, professionals, infirmers and schools and hospitals. But what we see is there are six alternative models emerging and are run through them very quickly because of conscious of time. Finish it for five minutes. The first is the network experts model. The economists call this workers and tap. Axim is an example. This is this idea that actually professionals don't need to work in traditional institutions. Hospital schools firm. They can be self-employed, they can be organized in different ways. So, still human experts delivering. The traditional institutions that add in their over. The broad-based triangle, generalised at the bottom, deep-pink spreads at the top. And conventional wisdom has it that what will happen with the eye and... If the loss is something with this, we'll see it, certainly, is that the machines will take on tasks from the bottom up. But also what you're seeing with Watson... in terms of what's in the logo there's a different model. Imagine in health, for example, where a diagnostic Watson... what's some system outstrips a human expert, but you still want someone offering empathy. So actually the paraprofessional model... On this view is that the expertise, the diagnostic power comes from the machine and the interpersonal power comes from the machine. The personal service might be delivered by a nurse, so a nurse with Watson will outperform in terms of empathy and in terms of diagnostic. Then there's the knowledge engineering model, the one I mentioned earlier. It's not d by the rule-based expert society. We're in the same-based expert system models. They say it underpins much that we see in tax, much that we see actually in law too. This idea that we do... I think for maybe not world-class performance, but very high performance, we model complex expertise in the form of decision-treat. Fourthly, and I've mentioned this, communities of experience model, where the way in which the knowledge is made is made. In which the knowledge is produced and distributed, actually doesn't really involve experts at all. It's where recipients, patient, client, students, share their experiences themselves, and often people find it more comforting."We believe in communicating with a peer who's somewhere to assemble a problem or face a similar challenge. Now, we believe, in one of the roles, that these communities have begun to be. The communities of experience, we benefit from being moderated. As you see, the experts roam around and perhaps identify areas where perhaps, they. Dangerous or highly misleading guys being offered. But there is no question that already exists. This is a flourishing way in which. roblems to which human beings used to be the only answers in the form of professionals are now being started out. And now you may see, of course, this is terribly dangerous. You need to speak to all the answers. t's remember again, our grave problem of access to justice, that almost no person is going to be able to speak to all the answers. Other than situations of life or death, metaphorically speaking. Can actually afford to seek the advice so that they can understand their entitlements and enforce their entitlements. We need different ways. Then they've made a knowledge model. Think of this game we all play in our handheld machines. When I was younger we played this with atoms. We called it atoms. We call it playing cards, so we call it game patience. Those are your memories to play. Now what used to happen when you played that game with cards? If you tried to put a red 500 or a red 6, what would happen? I don't know why you want to do it's called cheating, in fact. What? But you could do it, that's the point. What happens when you try out the system? The card is flicked away. The rules are embedded in the system. And this is actually crucial, because in the future, particularly in law, rules are going to be embedded in systems. As our clients in manufacturing production, more and more of their process is. It won't be that sudden there's a human being in the loop to look at a regulatory legal issue, the law will be embedded in the workflow. And the same in financial services as well. In the global financial services essentially, huge, they have their pin by technology, high degree of automation and workflow. A knowledge processing. The idea that lawyers are the only human beings left involved to be advising. When you can actually embed the knowledge seems to be unimaginable. Then there's the machine generated model. And this is the one most people fixate on. They say when you're saying machines are replacing human professionals. I suppose in the long run we're saying this is very likely. But really that is the decades from now on. We want to arch people who are thinking they're five alternative models before you get to the idea of big data systems that are performing. I've been performing human beings, or have watched some like systems I perform in human beings, or indeed have alpha-co systems I perform in human beings. Final thoughts. I believe the legal professions are evolving through three stages. The first stage is denial. And that's the stage, and it's lasting up until about this year, where. leaders, both infirms and state bars and so forth, were frankly a denial that the profession was going to change fundamentally. I find that hard now to look at and straighten the face and say we're going to go back to 2004. It seems to me we are where we are and that's a... Stage of greater technological changes for the world's over and over. It's unimaginable we won't change. But over the next five years up until the 2020. The dominant change I believe will be resourcing. Of outsourcing, off-shoring, subcontracting, finding new ways of sexually delivering the. Taking the cost out of the more routine work through sourcing in different ways. Stage 3, the Disruptive. The Disruption is going to occur in the 20s, and this is going to be underpinned ladies and gentlemen by artificial intelligence. The systems that are available. The systems that are evolving that are taking on more and more of the tasks that historically we thought only human beings could undertake. For many lawyers when they hear this, they hope to, they can hold out to retirement before any of this stuff engulfs them. And that's a nervous laugh I hear in the audience. I argue this is a window of opportunity and I love this quotation from Thomas. dison, the opportunity is missed by most people because it's dressed in overalls and looks like work. And I say this because there is a certain. lamour, I think, a certain excitement about AI and machines taking on... Tasks that we used to think required human intelligence, but actually a lot of us going to be about hard work. It's going to be rolling up our sleeves, clapping, clever people thinking imaginatively and creatively about the development of new systems. And my advice to the legal world is from Jack Welch. Well, as this, change before you have to. I think what we've seen in something like Alpha Goal and we're seeing in robotics, we're seeing this dramatic excel. Some lawyers say, "Oh, I don't want to be a pioneer, I want to be a fast second." That's usually our rationalisation for you. I don't think we should be in our back feet. I think, definitely look at the legal world. I have never argued. The need for legal and compliance services shrinking. Quite the opposite is increasing. The only question for legal profession, traditional lawyers, is that the legal profession is not the only way to do it. The law is not the only way to do it. Those things to me who are going to survive and thrive through the 20s in the legal industry are those who invest in the underpinning technologies. And in particular, in the kinds of AI we've been discussing today. Thank you very much. Have a good time for a few questions and then we'll break it. I'll be guided by Lyra as to where we should call a halt. But sir, the question in the middle there. Yes, sir. Thank you, Dr. Sh. Thank you.