Shannon Vallor: The Ethics of AI

[Electronic beat] [Enda:] Welcome to Futures Conversations, the Edinburgh Futures Institute podcast that showcases all the wonderful research taking place at the Edinburgh Futures Institute. Research at the Futures Institute is challenge-led and interdisciplinary addressing many of the greatest challenges we face in the world today. [Electronic beat] I'm your host, Enda Delaney, Director of Research at EFI. And in this episode, I'm joined by Professor Shannon Vallor, who's Baillie Gifford Chair in the Ethics of Data and Artificial Intelligence. Welcome, Shannon. Just to get our discussion going, could you tell us a little bit about your background, where you grew up? What inspired you? Who inspired you as a teacher? What values were important to you, and your wider social group?- [Shannon:] So, I grew up in, a suburb of the San Francisco... area in California and, grew up in a... working class household. My father was a civil servant for a Naval Station nearby, where he calibrated ship instruments. My mother was initially, uh, a stay at home mom. Later, as I got older, she went to work as a PBX operator at a hospital, manning the... the phones. No one in my family had been to college, but my grandfather, who probably had the greatest influence on me growing up, was, a frustrated scientist, shall we say. He had never been to university, but he was a scientific glassblower. And he worked in, the physics labs at UC Berkeley, making cloud chambers and, things that were used for, you know, some of the most innovative experiments of the 20th century. And he was fascinated with science and, always wanted me to love science, be a scientist. So I was encouraged academically from a very young age. I was also just transfixed by science-fiction, by technology. I was obsessed with airplanes. Wanted to be a pilot. Actually, it’s one of the first ambitions that I had. Until I was told that, you had to be in the military to be a pilot, to become a civilian pilot first, that you normally had to get there through- through the military. And that, as a woman, I wouldn't be allowed to fly, which at that time was still probably true, although it wasn't true for much longer after that. So I abandoned that particular goal. But, you know, I had the kind of traditional nerd kid’s ambitions of being, a pilot, an astronaut, which, again, I was dissuaded from, for similar reasons. Um... I was obsessed with Star Wars. I saw it, you know, in the theater when I was six years old, and, Uh... was particularly influential, not just in terms of another fascination with technology and, you know, the first encounter on film with robots and mechanical, minds or- or beings, but also, because the one of the central characters was, a strong woman, who, was a, was a leader and not just a sort of passive victim, as in most kinds of stories of that nature was- was the trope in my early schooling, was very interested in science and mathematics, but I, I began to kind of turn away from that towards the end of, of high school. And I got much more interested in the science of the human mind. I was really interested in psychology, kind of prefiguring my later interest in AI. I was very interested in how the mind works. So I went to community college, which in California is a kind of public, two year path that allows you to get actually a very good first two years of, college education, then transfer to a four year university after, and because it's designed for the kind of student I was. The courses are often offered at night to allow students to work. So I worked full time from the time I was 17 until I went to get my PhD, actually. So I worked full time all the way through my undergraduate degree, transferred to California State University, still a psychology major at that point, but becoming dissuaded because the psychology department there was really more focused on kind of training psychologists and counselors. And it wasn't, you know, they didn't have a strong program in cognitive science, neuroscience, the kinds of things that actually interested me more. But when I got to the four year university, the first thing I did was take a general education requirement course in ethics in the philosophy department. And I didn't have any affection for philosophy at that point. So I took this philosophy course because it just met a requirement that I needed to graduate, but I fell in love with it. It was also a night class. I fell in love with it the first night. I changed my major within two weeks of starting that course, which is very difficult because I was already two years through, so I had to radically refigure my entire schedule and catch up on all of the requirements in philosophy in just two years. So it was- it was really challenging, but I but I managed to do it, but I was also very driven at that point. It took me a long time to realize it, by moral questions. And so philosophy was also the discipline where you could ask moral questions about how we should live and what we owe to one another. I ended up, focusing in the philosophy of science. So kind of combining my- my earlier interests of philosophy of science, philosophy of mind. And then in graduate school in, Boston College, I took a course in the philosophy of technology, which at the time really wasn't... a common offering in philosophy departments. It wasn't an area of study, particularly in the United States or in any English speaking part of the world. That was- that was well developed at that point. There were some places in Europe that focused on philosophy of technology, but in the Netherlands. But, in the U.S., it, it was, very difficult, to explore that area. So I was very lucky that I happened to take that one elective course. And it just brought everything together. You know, once I ended up, at Santa Clara University, back in California teaching, I was teaching the philosophy of science. But I developed a course essentially called Science, Technology and Society. But it was a philosophy course. I taught a unit on the ethics of social media in 2006, and my students just absolutely threw themselves into it, and were desperate to talk at that point. And, so I realised that the ethics of the new digital technologies, the ethics of... social media platforms, the ethics of data were becoming increasingly important. And my- my work focused on that. But of course, I had always had a fascination with AI and robotics, so I was working on that as well. But back in 20-, you know, 2008, 2010, but it made me very well positioned for when AI became a commercial reality, because I'd been working on questions around what would it mean to have machines that perform the tasks that we think of today as intelligent? So that's been that's been my focus really ever since.- [Enda:] Do you think your different paths to academia, do you think that informs the way in which you conduct your research, lead your teams? Is there- [Shannon:] Yeah, absolutely. One of the things that it did was, I think, remove for me the desire to stay within a traditional discipline and earn the kind of status points that you get by following the rules and norms of one discipline in a very rigid way. But because I had that wandering path, I was never fully satisfied with a particular disciplinary approach or a particular set of conversations that would only happen within a single discipline. So, I was always interested in bringing, for example, moral questions and scientific questions together, questions about politics and questions about technology together. And because those questions were always- and those, those combinations of questions were always at the front of my mind, it really drove me towards wanting to explore those questions without being hemmed in. So I stayed within the disciplinary box just enough, to get tenured and quickly promoted to full professor. And then the minute an opportunity came to break out of that disciplinary box, I jumped at it. And that was the opportunity here at the University of Edinburgh to come to the Edinburgh Futures Institute and, create a new- a new interdisciplinary research programme in the ethics of data and artificial intelligence. That was really- that moment was really the fulfilment, of what I'd- what I'd long sought was the opportunity to work on the questions that fascinated me, and pull in whichever kinds of expertise were needed to answer those questions. So to lead research groups that had social scientists, that had computer scientists, that had, um, human computer interactions specialists, that had law scholars that had philosophers, right? Whoever's needed onboard to answer the question, that's who needs to be in the room. And, um, it’s something that's very difficult to do within traditional academic structures. And you have to have a lot of institutional support and resources to make it work. That's what- that's what EFI has been able to accomplish.- [Enda:] I have to ask you, you moved halfway across the world in- in the middle of a global pandemic to- At the new chair, the Baillie Gifford Chair, in the Ethics of Data and AI. What was that like?- [laughs] [Shannon:] Well, the funny thing is, is that I didn't know it was a global pandemic until, actually, I knew it halfway through the trip. So we flew from San Francisco to New York, and it was at JFK airport that things were getting very, very bad, very, very quick. And it was at that point that I realised that we might be heading for a very scary time. But we were, of course, already committed. And yeah, I arrived about, I don't know, 2 or 3 weeks before lockdown. So it was a bit of a frantic period as- as the situation, escalated or devolved, depending upon how you want to look at it. And- and had to very quickly get out of our temporary accommodations and find, find a permanent place to hole up during lockdown. So it was a very stressful period. But it was also in many ways for me, kind of proof that I'd made the right decision because the community here, even though after the first couple of weeks I only saw people online, the community here was so welcoming and supportive from the very beginning. And we had such a wonderful, oddly enough, experience getting the Centre for Technomoral Futures off the ground. And we had our first five PhD students selected, within a few months of- of my arrival. But they came to the university, starting in September of 2020. And from that point forward, you know, it felt like the Centre for Technomoral Futures was a- was an incredibly close knit family because those PhD researchers needed each other very- from the very beginning as a source of mutual aid and support. You know, at the time, there was, of course, no EFI building open, and had there been, we wouldn't have been allowed to be in it. So- so we used our online platforms to try to create a supportive space. So we had students coming in who, you know, were working, across the Business School and Informatics. We had students that were coming in to work across, you know, philosophy and psychology or, or education and sociology. And- and so they all needed to find a way to knit themselves together into- into a group. It was- it was a really wonderful experience to see how quickly that happened. So it was it was- it was a very challenging first year, but one that gave me great confidence that this model would work, and that there was something here to- to build on for the- for the long term- [Enda:] In a reasonably short period of time, less than four years, the Centre's really established itself internationally from fairly much limited reputation in Edinburgh beforehand. How do you account for that?- [Shannon:] Um, a couple of things. I mean, I think it helps that, before coming here, you know, I had developed a pretty wide research community in the US and Europe. So it was something that- bringing that to Edinburgh meant that the Centre immediately had connections to a kind of wider ecosystem of researchers and in AI and data ethics. But the other thing I think that's significant is I came here because the Centre for Technomoral Futures could be the thing that I had always said needed to exist. And my colleagues in the field, we'd all sort have been saying, there needs to be some kind of programme that's actually training the next generation of AI and data ethics researchers, and that does so without being tied to a particular disciplinary lens. So there was a hunger already for the thing that the Centre for Technomoral Futures was created to become, particularly as AI technologies accelerated in their commercial, and social impact.- [Enda:] One of the things I've been really interested in in learning about you and your work is- is the interactions that you have with engineers; you spent time at Google, you- and your databases, you work with people who have quite a different approach to AI, in terms of designing it and- how do you- how would you characterise your interactions with people who are involved in designing these- this type of technology?- [Shannon:] I mentioned that, you know, my my family was very supportive of, of my interest, particularly with respect to science and technology. And even though they had, very little money, I still don't know how they did it. They actually bought me one of the early personal computers and put it in my bedroom. So in the early ‘80s. Right, I- I was programming games in BASIC the, you know, early computer language and learning, the possibilities of- of coding and what you could do with computers. I was always aware that, computing and software engineering were potentially as creative an endeavour as anything that, I would do as a, as a humanist or, or philosopher. That was met with an openness, by the the first, machine learning and AI researchers that I started working with and that, as you, as you mentioned, was at Google, actually. So I had interacted with people in AI and computing before that. But back in 2015, 2016, those communities still hadn't really formed into the really large communities you have now. We have conferences like the FAccT Conference in the AIES Conference that happen every year, that are sponsored by large computing societies. But that bring together computer scientists, machine learning researchers, philosophers, lawyers, social scientists. That's a pretty mature ecosystem right now. But my interactions with computer scientists at that point were, scattered, interesting, but scattered. I was brought into Google by Fei-Fei Li, who at the time was the, senior research scientist in Google Cloud. And Google had just formulated a set of AI principles that in 2018, it had said it would use to kind of govern the responsible development of- of AI internally. And Fei-Fei had met me at a keynote that I gave at her lab conference at Stanford I think the year before. She invited me to come to Google, at first just on a temporary contract basis, to advise them a little bit about how to apply these AI principles in practice. At first, I was only there, you know, maybe one day a week. But very quickly, something really remarkable happened. The first day I arrived at Google, I had people coming up to me that I'd never met saying, we are so glad you're here. We're so excited that you're here. And I had this expectation, as many philosophers did at the time, that, you know, when you walk into a technical space as an ethicist, you're going to be viewed with suspicion. You're an outsider, you're an intruder, and you're there to tell them what not to do, right? But there was something about Google at that time that was so receptive to thinking about how to do this work responsibly, that we had all kinds of machine learning, and AI folks, wanting to engage with the work that we were doing, thinking about how product- AI product development could be done responsibly from the ground up, thinking about how you would allow the engineering teams to work with, the social scientists and the ethicists and, and the legal folks to actually do something that would allow for a trustworthy and socially beneficial product to actually emerge on the other side. That experience was so positive. I remember my husband, commented very shortly after I started, going half-time at Google. He commented that the days I would come home from Google, I always had a smile on my face. It wasn't a perfect experience, and there were struggles and there were challenges and obstacles and- and some of the things that we see today in the tech ecosystem that prevent responsible AI work from really happening in industry the way it needs to, you know, those those barriers were there. But overall, my experience was most technologists and most people in the AI field really want to build something good. They really want to build something that helps people and doesn't hurt them. And that should be obvious, right? Everybody wants to use their talents for the most part, to make the world a slightly better place, at least. So you don't have to have people who want to save the world. You just have to have people who want to be proud of their work. And that's almost everybody. So I think I learned from that experience that working with technical folks is as rewarding as working with other social scientists or humanists. As long as you can cross that barrier of translation where you find a common language to talk about what it means for technology to be good or- or responsible, or beneficial. It's about reaching a shared understanding of what that has to mean. I found it incredibly challenging, but incredibly rewarding to learn to navigate those, uh, translation gaps. That really positive experience that I had at Google for those two years really is what gave me both the desire and the skills to work with technical experts here at the university, to work with the people in the School of Informatics who lead on AI and robotics and- and machine learning, to kind of figure out how to integrate the ethical dimension into technical practice.- [Enda:] Just moving on to your wonderful new book, ‘The AI Mirror’, which is beautifully written and doesn't shy away from difficult concepts. I think some of the concepts in the book, be it virtue, or others are quite difficult concepts. The sort of core argument of the book is that AI is a mirror of our society with all the issues, fault lines, problems and challenges. Could you tell us a little bit more about that sort of central argument?- [Shannon:] Sure. And I would say, you know, the argument is that it's a it's a mirror, but a partial and distorted mirror, right? So one of the one of the key points that I make early in the book is that the mirrors that we are building with these current AI systems don't reflect humanity as a whole. They reflect a particularly privileged subset of humanity that, have their data stored on servers, that have their writings digitised. You know, the people who've historically been given the license to write and create, you know, which are predominantly men, predominantly wealthy, predominantly in the global north. Right? So if you look at the digital corpus that's used to train these models, it's overwhelmingly representative of- of a fairly small subset of humanity as a whole. But these mirrors then are taken to represent us all, which is- which is part of the danger. The mirror metaphor is a way of providing a better way of understanding AI than the default that you often see in media representations of AI today, which is to think of AI as another kind of mind, as a factual claim that's just plainly false. But even as a metaphor, it's a terrible metaphor. So if you use the metaphor of a mind to try to understand what AI systems do, you actually will, end up with a pretty, misinformed view of how AI systems are developed. So the the mirror metaphor provides a much better basis for understanding what AI is, what it's good at, what it's not good at, what it does, what it doesn't do. And what we can expect from it and how we can use it, in ways that are, in the long run, compatible with human flourishing. The book is really focused on the kinds of AI tools we're building today, like the GPT and Gemini models, the large language models and generative AI tools that are built on large volumes of data about us. If you think about the way mirrors work, there's three fundamental components to a mirror. You need a reflective surface, and the physical properties of that reflective surface need to be very specific and those reflective properties, the physics of a mirror coating and how that glass surface is polished, the physics of that surface determine how the mirror performs, how it reflects. So you need an algorithmic surface that's been prepared in a, in a certain way, because that's an, in fact, the part of the of the metaphor for the algorithm. The algorithm is the surface, like the mirror surface. And the properties of the algorithm determine how the model performs. So that's one part. The second thing that you need for a mirror, of course, is light. And because the reflective surface doesn't do anything unless light, is directed towards it. So of course we know what the light is. In the case of AI, it's the data. So instead of shining wide beams of powerful light on glass, we shine vast troves of human generated data on these algorithmic surfaces. And it's the patterns within that data that are processed, analyzed by the algorithmic surface and reflected back. And the third thing, of course, is the image. And that's, of course, the output. It's what you get from the model. So when you talk to ChatGPT, what you're really doing is talking to a mirror. The surface, if you will, of that mirror is the algorithm that is responding to what you say. Your prompts, your questions are light. But of course, the surface of that- that algorithmic mirror has also already been trained on a large volume of light that has determined its shape and function, determines how it will respond to the data that's now coming from you in the form of a prompt. And then what you get back, the answer you get is actually just a reflection, that is a modification of the pattern that has already been analysed in the data that comes back at you, slightly modified with some kind of randomness added to- to make the answer sound a little bit, you know, novel and surprising. But that's how generative AI works. And this is a really important metaphor, because when you look at yourself in a mirror, you know there's no second person on the other side of that mirror. You know it's just a reflection. And reflections have different properties than the things they reflect, right? You know that that reflection doesn't have the warmth of your body. Right? You know that it doesn't have the depth that you can't kind of press on it and have it mold in the way that if you press on your actual face, it will conform to your finger. Whereas if you touch the same spot on the mirror, it won't. So when you're talking to a generative AI model, it really helps to understand it in that way. It's reflections of thoughts. It's reflections of human voices, it's reflections of mind, it's reflections of intelligence. And just as the reflection of a body is not a body and has very different properties, a reflection of intelligence is not intelligent and it has very different properties. So once we understand this metaphor, we are inoculated, as it were, from making a lot of the mistakes that we're led to make when we're manipulated by marketers into thinking that these tools are actually new forms of intelligence cropping up in the world, that we can talk to, that we can fall in love with, that we can get good advice from, that we can be comforted by that we, you know, can be understood by. Those are all incredibly distorted ways of describing what AI does. AI can't understand you. It can speak to you in the way that a mirror can, you know, speak to you, a mirror image, right? You can you can talk to yourself in the mirror, but you know there's nothing on the other side understanding you in the same way an AI tool is providing a kind of, modified composite reflection of human intelligence and understanding rather than something that actually has understanding. I go in the book through a lot of different things that mirrors do they reveal, they magnify, but they also distort, they also occlude, and it turns out that AI mirrors can be described in all the same ways. We can talk about the things that they magnify, and amplify, which can include things like unfair bias or, you know, disinformation. We can talk about what they distort, the ways that, they can, kind of take reality and twist its shape and feed it back to us in a way that can be either entertaining or- or quite harmful and dangerous. And the ways that AI tools can kind of occlude our vision, so they can show us only one side of something, just in the way that a mirror shows you only the front side of what- of what you're looking at. It can't show you what- the other angle, right? In the same way we can- we can see the AI mirrors will- will show us certain perspectives, but not others.- [Enda:] As humans, we've had various different challenges. You cite lots of great historical figures, including Narcissus and Descartes and all these people to say that, you know, we have we've sort of been here before. Obviously, the the particular challenge of the technology is a new one. But we have been here before in terms of under- understanding of ourselves, but also, I think quite a critical interpretation and a sort of sub- sub-commentary on- on the sort of- on the industry. There's a recent report out from MIT, I think, where they talk about the fears of dominance of a very small number of AI, some of which we've- we've mentioned already. Do you think that’s a genuine fear that because the resources and the infrastructure that are needed tend to fall into relatively few hands and perhaps that- that might be a concern for the future as well?- [Shannon:] Yeah. It's a very, very serious concern. And it's one I touch on in the book. So the book is optimistic in the sense that it's trying to- to help you understand that AI itself is not something to fear, is not something to feel, uh, endangered by, is not something to, um, feel like is destined to kind of overtake human intelligence, right? So a lot of what I'm trying to puncture and- and diffuse is- is this kind of marketing hype that says AI is what's next. Humanity is- is over. Humanity is, you know, destined to be replaced, and superseded by these new super intelligent machines. So- so the book is optimistic and sort of trying to restore a sense of human agency and power that we're still in the driver's seat and that these technologies can only replace us if we allow it, and if we accept something lesser than ourselves in in our place. The dark side, though, despite the fact that AI itself does not pose a threat to us, the organisations and powers behind AI, and the economic and political incentives that are determining its current shape and the way it's being used, are, in fact, quite threatening to us. So the the call is, as it were, I say is coming from inside the house, right? It's not- AI is not this external threat, but our own social, economic and political systems and their incentives, which are kind of misaligned with sustainable human flourishing right now. Those are shaping AI's form, and that is the threat. There's been a lot of focus recently, rightly so, on the environmental impact of AI models and the fact that, large AI companies, which, yes, overwhelmingly control the AI landscape right now, are pushing countries and regions to build a huge number of new data centres to power AI systems to an extent that is likely going to prevent those organisations and the countries that they're pushing to to build new data centres away from meeting their climate targets and commitments. We cannot afford that. Humanity cannot afford that. And the idea that somehow this will work out because AI is going to solve climate change for us. That's absurd. AI is a tool, and there are absolutely applications of AI that will help us meet some of the challenges associated with climate change, but only if we don't develop AI in a way which puts our foot on the gas of fossil fuel extraction and and use, which is what's happening right now. There's lots of applications of AI that you don't need large language models for, that you don't need training that's incredibly costly environmentally or economically. You can use smaller, more sustainable models and smaller but higher quality data sets to do a lot of the work that we needed AI to do. But these few large AI companies have doubled down on the strategy of making their biggest, most environmentally damaging models the flagship product that they want everybody to be using and everybody to be buying. And that's an incredibly dangerous and unsustainable strategy right now.- [Enda:] Another point that comes up in the book on a number of occasions is the the representation of non dominant groups within models, you make, I think, a very good point about how manual correction occurs in low wage economies, and how the companies are able to use that, how different races and ethnicities are, rarely acknowledged and... how serious do you think the AI companies are about addressing those concerns? Do they really take these seriously?- [Shannon:] They do, yes, most of them, primarily because when these models produce outputs that are, you know, clearly, expressive of harmful, racist or sexist biases, for example, that- that gives a, a black eye to the company, right? Both because it makes their product look like something you don't want to use in your business, Right? No one- no- no business wants to employ a product that's going to start spewing racial insults or stereotypes at their at their customers, right? So it's- it's a business risk, but also because it makes the companies look like they are endorsing these harmful or toxic viewpoints. But the problem is that it's baked so deep into the models by the training data that you just can't easily pull it out. So you end up having these solutions that are, in a way, like playing a game of Whac-a-Mole, if you're familiar, right? You- you kind of- the problem crops up and you- you build a tweak to the algorithm that will suppress that particular kind of output, but then a different one pops up elsewhere. So it's- this is a a systemic feature of AI mirrors that they're trained on large volumes of human data that have not been curated to remove the harmful biases in our own history, and our own social patterns. And these models will always pick up on and reflect the patterns that run consistently through the data set. And bias is one of those patterns, because our history and certainly our social media, history, which composes a pretty significant chunk of what's been, you know, put into the training data, is rife with this kind of- of- of bias and harmful content. So it's what the model has learned, and you can't easily get the model to unlearn it. I can't pull on one corner of my sheet without that actually being felt in- at the other end of the sheet. Right? The- it's a fabric. And if I- if I yank on one thread it kind of runs through the whole thing. It's- it's similar the way that AI models work, it's very difficult to extract something like a racial bias without distorting the whole sheet. So it becomes a very difficult problem to solve, and one that arguably cannot be solved without addressing the underlying bias or changing the way that we curate, our data and train our models. But what these companies are not supporting is the kind of firm regulatory guidance. Instead, what these companies mostly want is to self-regulate and to invest a little bit in mitigating the worst, but without having to actually build a better product from the ground up.- [Enda:] Do you feel optimistic about the future, taking in around all these different issues that we- that we face in terms of humanity?- [Shannon:] I place myself on the optimistic end of the spectrum only insofar as it's still entirely in our power to change the incentives that are currently driving us towards an unsustainable world. And- and a planet that is simply incapable of supporting flourishing, intelligent life. So I'm not optimistic in the sense that if the status quo continues, the chances for, you know, sustainable human flourishing are slim to none. So that's the dark side. But the positive side is there is no kind of determined outcome. The future is still unwritten, and we actually know what we need to change. It's not like we don't understand why climate change is happening, or what it would take to stop it, or slow it down, or mitigate at least its greatest harms, and not add to the harms that have already, in a sense, been written into the future. We know what needs to be done. It's not like an asteroid coming at the planet where you've got nothing. We have ways to address this challenge. What we don't have is the political will. We've been here before where we needed the political will to shift toward the kind of world that people had always said could never exist. So I'm old enough that I grew up in a world where people believed we would never see gay marriage. In 2014 the- in the United States, and shortly after, many other countries, right, the political will shifted in what felt like a landslide overnight. But it wasn't overnight, right? It was something that had been building in the- in the culture. And in our moral view of the world, I think we're going to have to see the same thing with respect to climate change and the current economic order. And I think you already see it happening. A lot of people are resisting it. A lot of people are saying, look, you know, the status quo is the status quo. This is how the world works. We can't imagine a different kind of future. They're wrong. We can. We have before and we have to now. One of the great dangers that AI does pose is that because it's trained on all of the patterns of the past, and I'm certainly not the first to say this, it is a fundamentally conservative and even regressive technology. If we use it to automate decision making, if we use it to predict and write the future, because all it will do is take the patterns from the past and push those into the present and future. A colleague, a philosopher, of mine, Mark Coeckelbergh, calls AI a time machine. But it's a time machine that, sort of instead of taking you and putting you back in the past, takes the past and, and brings it into the present and future. Because if we simply use AI to set ourselves on autopilot, we'll basically stay on the same unsustainable paths that we’re on. As a roadmap into the future, it's actually, it's actually the end- it really is the end of humanity. If we simply take AI to be the thing that tells us how we should live.- [Enda:] Thank you very much, Shannon, for talk about your fascinating research and most recently in The AI Mirror. If you want to find out more about Shannon's research, check out: www.efi.ed.ac.uk and our social media channels. Thank you. [Electronic beat]