 Well, thank you very much for that introduction and that welcome. It's a pleasure and a joy to see so many faces out there. Not just my colleagues, but my friends, my bridge players and, I know, my family are watching online. So I'm really pleased that you've managed to come out in the rain or you're tucked up somewhere at home. So, I would like to start my lecture with a picture here of the great Robyn Milner. And I had the pleasure of meeting him yw'r cyfrifélydd ar gyfer y gweithio yma ar y bobl. Yn y cyfriflydd Robin yn y pwyllgor yn ychydig, ac yn ychydig a fyddai yn maen nhw'n mynd i'r lle, ac iddo i ddechrau'r cyfriflydd, y pwyllgor sy'n rhan o'r cyfriflydd a'r cyfriflydd yn y pwyllgor, yw'r rhan o'r Marthyr, a'r Abbey, ac yw'r rhan o'r cyfriflydd heirio'r cyfriflydd a'r mynd yn oed o'r cyfriflydd. Dyna'r amser o'r rhan am ymddian nhw'n ffordd a ddim yn gallu ein bod y cynhwysbeth i'w cymryd ymrong y ddigon iawn, a'r amser o'r ddigon iawn i'w cymryd a'r ddigon iawn. Dyna'r amser o'r angen i'w'r cymryd a'r angen i'w cymryd, a'r angen i'w'w mynd i'w ddigon iawn, ac yn yma'r Robin, dyma'r angen i'w ar agnorion sy'n amddai ar hyn. Dyma'r angen i'w'r angen i'w'r angen i'w'w ar y dweud. First I'm going to talk a bit about the early stages of my career where I very much helped to inform the field of ubiquitous computing and in particular looking at how we can inspire children to learn. Then I'm going to talk about some research that I've been doing in the last two or three years where we've been trying to design software to help people think more systematically. Finally I'm going to finish by looking into the future about how we might develop new AI tools to facilitate creativity. At the start of my career I was very much interested in how we could design new technology for children and this is what we work confronted with. Rows of children sitting in front of PCs by themselves following tasks and trying to get something finished. It was very dull and drill based and I thought surely we can do better than that. There's this amazing technology that we're just discovering that's taking off the desktop and this is where I got involved in an area called technology enhanced learning. What we tried to do was to think about how we might move computers out of the classroom and into the wild. The reason for this is that kids get excited when they're outdoors and we wanted to encourage them to be more self-initiated in their thinking to talk to each other and to think about scientific inquiry in a much more engaged way and also to inspire curiosity. One of the projects I'm most well known for is called The Ambient Wood, which we did 20 years ago now, which was really a field trip with a difference. This was work that was done with partners on the Equator IRC project that was eight UK universities of like-minded, daring to think differently, researchers of which one of them is Tom Rodden, is in the audience here. Part of this project was that we worked with people from different disciplines, from design, from developmental psychology, from engineering and from computer science. We suddenly felt that we were in a sweet shop that we had been given all these new technologies to experiment with. We designed all manner of innovative technologies to help children really get inspired by using technology to think. We developed what were called probing devices, where they could collect readings from the environment of moisture and light. We also made our very own handheld devices. This was before mobile phones were around, by which the students could get feedback and information whilst they were walking around outdoors in the woodland. One of my colleagues, Danielle Wild, she was at the RCA at the time. She created this periscope device down at the bottom here, where the idea was that the children would come across it and a video would be played in the woodland, rather than watching something in the classroom and then going out. This was a David Attenborough video about the Bluebell life cycle or something like that. Then they could see immediately and that would wet their curiosity. What we tried to do then is to encourage learning through exploring and discovering. We didn't tell them explicitly what they had to do. We just gave them these tools that we built. We said go forth and experiment and see what you can find. One of the things we were experimenting with at the time was what could we do with these new ubiquitous technologies. We thought about allowing them to see the invisible and the inaudible, whereby a physical action, this might be you walk past a flower, causes a digital event to occur. This device down here looks pretty weird, was what was called an ambient horn. It would honk when it was going past something that would play a sound. I'm just going to play you the sound that these two girls are listening to. I want you to guess what it is. It's a butterfly drinking nectar. Now you know. We wanted them to do things that you take for granted or you never really think about. We also had sounds for photosynthesis, what that might sound like. This was again to try and get them curious to think about these things. We found when we let children, they were aged 10 to 12, go out into this woodland that much self-initiated learning took place. We got them to go out in pairs so that they could talk to each other and collaborate. With this probing device, they probed everything, the air, the ground, the trees and foliage. What we discovered is kids being kids, they like to find the most extreme, the wettest, the lightest, the darkest. Of course, they tested different parts of their own body to see whether they were the wettest, the lightest and the darkest. It was just a pleasure to see them enjoying that. An important part of being pairs is that one of the children would go and probe on the device but then wouldn't have the reading immediately. They would then have to join the other child to have a look on the display and talk about what they'd found. The displays that we used were really, or the visualizations, really simple. They could just see relative levels and then that would trigger them to think where they might go and probe next. Here's two girls that are using the devices together. You can see how one does the probing and then the other reads out what the reading is. On the basis of that, they hypothesize where to go next, which would be even drier or even wetter. So we couldn't stop them from going around testing things and thinking about why something was dark and light and whether something was even darker. Little did they know was that once they'd been experimenting and exploring the woodland, they had to come back to the classroom. But the classroom wasn't back at school. We made a pop-up classroom. You can see it on the left here, this stripy-looking tent. We got the pairs of children to come back and share their experiences. What they didn't realize was that every reading that they'd taken, every probe, we recorded it. One of our software designers here represented those on a bird's-eye view of the woodland and where they'd been. They could click on these dots and the reading that they'd taken would show. It would show the relative light or moisture level. They were absolutely fascinated by this and were trying to predict which of the readings that they'd taken were moisture levels or the light levels. This triggered a natural conversation between the children. They were comparing the different places in which they'd been exploring. This led them to a hypothesis about the ecosystems in the woodlands. For example, which plants grew in the wetter areas and why. What creatures and insects thrived in different parts of the woodlands. Just to show you how different that type of learning is compared to what we were up against in the classroom. I think I can be confident enough to say that at the time we pioneered a new way of designing technology to daring children to think differently. It was hard work, as you can see here, after a hard day's work. But it brought us all together and also we brought indoor and outdoor learning together in a novel way. The children talked freely and excitedly with others, not just whilst they were there in the ambient wood, but also on the bus back. Believe it or not, 10-15 years later we came across some of these who were nice young adults and they remembered their day in the ambient wood as one of the best days at school. And they loved doing the scientific inquiry like this in the wild. But they were also fascinated by the underlying technology. So at the time this was a woodland of one of my colleagues where his wife used to do yoga. So we had to wire it up literally and make our own wi-fi with aerials and put laptops in trees for this. And they would go around just to try and find that technology as well, to try and work out how it was possible that things were pinging and noises were being made. So that got them interested in the ubiquitous technology as well as trying to understand more about the ecosystems. So Marta's already said how I contributed to the field of ubiquitous computing. And that doing this type of work led me to think that that field of ubiquitous computing should be much more exciting and provocative and stimulating and engaging. And not how it had been led to many people thought it should be following on from Mark Weiser's view, which is that it should be make our lives efficient, calm and easy by doing things on our behalf. The trouble with that is that we just get lazy, we just expect the technology to do it. And I think really technology is there to exercise our minds. And in particular we should be designing engaging user experiences. And so for most of my career that's what I've been doing is trying to think about the different technologies that are out there to encourage us to be more active, be more reflective in our learning, but also in our living and to facilitate creativity. So that's part one of my talk and how it's inspired me throughout. 20 years on since the beginning of ubiquitous computing there's been a lot of technology that's been around and developed that we can experiment with. So we've had PCs for a long time, but now we've got tablets and various mobile devices. We've got what's called the Internet of Things, which basically is putting sensors into the environment, into objects and connecting them to the internet so that they can talk to each other. We've also got what are called tangibles and physical computing where the computation is in some artefact. And this allows us to think what do we want to do with the digital world in respect to the objects that are out there. And then there's augmented reality and virtual reality and wearables and speech interfaces and robots and chatbots. And of course artificial intelligence and machine learning. And the question is which of these technologies do we design for and how? And this gets me on to thinking about thinking. There are many kinds of thinking that we can use those different technologies to augment. So we're all involved in different aspects throughout our lives, whether it's planning, deciding what to do, choosing between alternatives, reasoning about things, making sense, reflecting on what's happening, contemplating and solving problems. So how the hell do we match this up? How do we know which of these various technologies do we use to support these? We could possibly use PCs and tablets for supporting problem solving, but then again we might want to use them for planning. We could use artificial intelligence to support decision making. We could use augmented reality to support reflecting. We could use tangible to support planning and so on and so forth. There really isn't any systematic research or guidance out there as to how to make those decisions. And what we do in human-computer interaction is a bit of trial and error and a bit of experimentation, but also we go to theory, particularly in psychology, to inspire us. And one theory I'm just going to mention, but there are many theories in which I've looked at and been inspired to think about how you design technologies, is Daniel Kahnwin's book on thinking fast and slow. How many of you have bought this book? I'm not going to ask if you've read it. I'd say 90% of you. It is a bestseller and it's a really great title. But basically in the book he argues that there are two types of thinking systems. One which is intuitive, fast, it's no effort, it's instinctive, automatic. And the other one is more effortful, it's slower, it's more orderly and deliberate. And he argues that system one is what routinely guides our thoughts and actions and is often right, but it's prone to making errors and particularly those of judgment in decision making. Whereas system two is meant to be the voice of reason and that he argues that we should employ this more when we've detected bias in our thinking. Now it's a rather oversimplification of how thinking is, but when you're modelling you do try and think of how you contrast. And so to think of these two systems as alternating and that sometimes our thinking might be somewhere in between. But it's a useful heuristic, a useful theory by which for us in human-computer interaction to think of how could we stop people or reduce their bad thinking or their bias thinking. And how we might promote what's called system two thinking. Having been inspired by reading this theory, what we do next is we develop our own concepts to inform the design of the technology. So this is where I've come up with the collaboration of my students, in particular Leon, are you here Leon? Yes you're there. Right cuts. A notion of scaffolded thinking, the idea here just like scaffolding is that you somehow use the technology to guide people and maybe stop them and slow them down to reflect more on their decisions. And I'm going to give a case study of where we think we can design technology to support scaffolded thinking. And then the second one I'm going to talk about is what I'm calling integrated thinking. And this is designing technology to help people to externalise their thoughts to be more systematic when problem solving. So the first one, scaffolded thinking. And we think that we can use this concept to help us design technology to support people who invest in stocks. I don't know about you but what you did during the pandemic. But apparently there was an astronomical uptake of trading apps. Any of you dabble in trading apps? There's a few of you who admit to it. But apparently over 130 million people have used them in 2021 and the most popular is Robin Hood. And these are designed for the novice person who doesn't have much expertise. But many people knew to investing have made costly mistakes. And what we wanted to do is to think about how we might design technology to slow down their thinking and to help them so that they don't make these mistakes. So what happens when you've just invested in a stock and it goes up and then it goes stable and then you see this on your phone? You panic, you get really emotional, you get all sweaty, your palms, your hands go like this. You don't know what to do and you think well if I just leave it like that I'm going to lose all my money. So you sell but you often sell too much too fast and then you regret it later. And the problem with novice traders is they don't have a good strategy to deal with this situation. And this is where we come in is to think how can we help novice traders to learn to think more methodically. This here is one of my PhD students here, Ava, who volunteered to be a model. But seeing that you get the stress and it's really difficult to think under stress. Professional traders develop the voice of reason and they will have a set of questions and criteria they use before finalising their trading decisions. Unless they've had a couple of glasses of wine, they will sort of maybe think through, is this the best thing to do? But beginner traders don't have this and they make rash decisions. And so we wanted to help them to become more experienced and expert by thinking through having this voice of reason and scaffold it so that it stops them acting impulsively and also to think about what they're doing and why. And if you see over here to the right, these are the sorts of interior monologue or questions that we'd like them to be thinking through in a way in which many expert traders do. So things like, rather than I need to act fast, it's how did I come to this conclusion? Have you considered criteria X? Overall this seems to be the best alternative. So having this sort of conversation with yourself and then you can decide whether it's a good to buy or to sell. So we decided to choose a chatbot for our technology intervention. For those of you who are not familiar with chatbots, but I suspect most of you if you go on to British Gas or any of those banking sites, they now all have chatbots. It's essentially a virtual agent that a person has a conversation with. So it can be customer services, marketing sales, travel. And this one is travel where the user types in on the right a question and the chatbot will answer and then the user will then ask another question or answer it. So it's kind of having a simple conversation. And we designed our chatbot in this context. Here it was to probe traders about their intentions and to help externalize their hunches that aren't necessarily well thought through. So our chatbot is called a probabot because essentially it's probing the user and asking them questions. And we designed it such that it would be embedded in the software so that as the user is looking at how well their stocks are doing and whether to sell or to buy, the probabot will pop up at an opportune time and ask them questions. And here it says, if your investment hypothesis has changed, what made you change it? And the user types in the blue box recent news. And so I'm going to show you how it works in action and that we developed a software simulation for trading. And as you can see here, these are on the left are the stock lists that the person has. And on the right it shows the information and then this will pop up if they want to trade. And that's at the point when the trader bot pops up and says, what's your investment hypothesis? And this is to slow down and get them to think and they'll type in an answer. And that may be enough for them to think about, is this what I wanted to do? So that's how it works. Just to recap the design rationale is that it pops up at key moments when the user is about to make a trade. It promotes short conversations with the user to stop thinking and reflect. And it's embedded in the trading tool so that it dovetails the task execution and the thinking. So how effective is our chat bot? Well, my student Leon Rikertz ran a pilot study with six traders and presented three scenarios to them where they had to make investment decisions, whether to buy or to sell a stock. And then we used the HCI method of think aloud and in-depth interviews. I'm not going to go into the details, there is a paper out there if you're interested. But the idea was would it make them stop and think? And from the think allows, this was very much the case that it did. Very occasionally it was annoying for it to pop up. And that's something that you have to design is not for it to become like clippy when it keeps popping up too much. But to encourage reflection on decision making by helping in the moment when it matters. And also they said that it would help reduce impulsive actions. So this suggests that this approach by having this type of chat bot appear can make investors thinking more systematic. Now I've talked about novice traders, what about expert traders where they have only too much information, too much knowledge and they can be tempted to be naughty. And this is the second case study that I want to present which is financial institutions are responsible for detecting this naughtiness which is essentially market abuse and trading. Where someone who's got confidential information uses that to their advantage. So here's one that was in the news recently where an investor accuses Rocket Stan Gilbert of insider trading claiming that they had pocketed 500 million. A lot of this happens and as I said financial institutions try and stop it or try and detect it. And they employ compliance officers to do this and their job is to detect this but to do this by conducting investigations and curating and collating data from several sources by which to make up this and to see whether it's true. But it's an awful lot of work that's involved in being a compliance officer. And this is a hierarchical task analysis which I'm not going to go through the different steps I'm just going to show you that there are many steps involved in doing this. And there's a huge amount of cognitive work, much multitasking. They have to scan through thousands of alerts, sit through millions of emails and check lots of news feeds and there's huge demands on their attention constantly having to switch between various resources. Much of it is done inside the expert's head and occasionally they might jot down their notes and their thoughts. What if they were given a new kind of software toolkit that could help them with this work and support more integrated thinking. And this is where I was working with a behavioral science team at NASDAQ a couple of years ago. Wendy Jefferson who I think is in the audience somewhere and Anna Leslie. They both have left now and are co-founders of their startup called Let's Think. But what they did was to think about how you can develop what's called an investigative canvas which is a set of software tools where disparate information would be brought together in one place. And rather than having to go in and out of all these different software tools to have them there side by side and to help them to make and discover new connections. So there were lots of tools that they came up with and the canvas was in the middle. There's the alert of the network, the historian, the checker and so on. And the way in which this was designed is that the compliance officer can decide which tools to bring together. So they start off with a blank canvas called the investigative canvas. And then on the top there is the case builder where they can start to build up their case. They can populate it with information that they found with potential alerts. Then they might want to bring up what's called the people profile. By the way, this is an early design. And here you can see what's going on between two people. Is there any strange communication or lots of communication that's happening? So they can start to see there. Then they might want to build up another tool which this tool here is trading information that will be very useful for them when building up this picture. And over here is what's a scratch pad where you can bring information from the different tools and have it in place ready to add to the case builder. So I've given you an example of a few of the tools, but there are many others that were being developed. How effective is this approach? Well, I think our initial evaluations showed how it could be used by compliance officers to externalize their thoughts, but also project their sometimes random internal thoughts and make them more systematic. And also they could share it with other team members rather than just jotting it down on a notebook, enabling them to understand each other's lines of thinking and maybe collaborate. One of my PhD students described it as a whiteboard on steroids because it's allowing you to do so much more. You can discover new connections through having this set of side-by-side tools and you can move the pieces of information, all the tools around, a bit like you do in Scrabble and test hypotheses that come to mind, but also it maybe enables you to think about something you may not have. And if you get distracted, you can pick up where you left off because it's out there. So how generalizable is this approach? Well, we've been noticing how others are now developing what are called orchestration platforms where they take silo data from multiple storage locations and organize it in a way that data analysts have it ready to hand. So there's a lot of interest in this new approach to thinking about integrated thinking. And the startup company, which I am part of as the CTO, is called Let'sThink.com and if any of you are interested or have areas where you think this approach would be useful, but we're trying to develop other kinds of canvas tools in education, in finance and our strapline is enabling people to think brilliantly. So I want to recap. I've talked through two approaches by which we've used theory and ACI to think about designing tools to empower us. And I've shown how we can try to slow down people's thinking and we can scaffold and integrate their thoughts. We can externalize their cognition and Marta mentioned one of the theories that are developed because it's called external cognition which I won't be going through today but that's been one of my contributions is to think about how we can do that and what the design principles can be. We can also see new connections and it can help us reason and reflect and in some cases reduce the biases in our decision making. And also as we saw there's a potential for supporting multi-tasking. So matching technology type to thinking type still is very much an art form rather than a science. But in some ways it doesn't really matter. I think the key thing is the theory that you use by which to inform which of these and why. And just to recap, we turn that theory and it can be from psychology, it can be from behavioral economics into concepts to inform the design of an interface. So I've come up with a numerous design principles throughout my career. One I'm just going to mention is dyna linking which is where you link representations on different displays so that if you make a change to this one it's reflected or it's changed in that one so that you can see by looking around what the effects are of making a change. It might be a simulation or you might be building something up. And this type of dyna linking is really important when you're thinking about designing complex interfaces. We also look at all sorts of questions about the specifics of the interface. Should we use voice or text? What type of conversation? Should it be open? Should it be long? What type of feedback? Not that type of feedback. Where to place information on the display? And what kind of interactivity and how much? And so there are lots of questions that automatically come to us when we start thinking about new areas by which to design these technologies. I'm going to finish though with thinking about the future. And we've heard about how Abbie has changed her mind about how AI can make a difference and is very useful. And I believe very strongly that there's a huge potential and we're just seeing it in AI changing how we think. I'm not going to go into the discussion about whether it's going to take over our jobs. I'll leave that for someone else. But I think it can support creative thinking and in particular in the sort of art and design. So what is creative thinking? Well, it involves us looking at things differently, finding novel designs and solutions. And essentially in a nutshell it's making something new. So it could be a poem, it could be a picture, it could be a design, it could be a piece of music, a recipe, a dance, an app. And some of us find it quite difficult to be creative. So wouldn't it be wonderful if we could design AI tools to help us to discover new ways of being creative? And it is happening. This summer I was amazed at just how many people were just talking about these new AI tools. And I suspect some of you out there have tried them. They've emerged to support creativity and in particular open AI have developed a process known as diffusion that turns text into images. So the user types in some words for those of you who haven't tried it into the box here. And then the AI tool generates images to match them. So Dali 2 is perhaps the best known one, but there are others called crayon, mid-journey and stable diffusion. So here you can see my first attempt was I typed in blue sky, sloths, rainforest melting clocks. And that's what it came back with. I could never have come up with that. If you haven't tried one of these tools, there's a big waiting list for many of them. But this one you can get on to straight away called crayon. And it says, what do you want to see? And I first typed in cat sat on a mat thinking. You can do any sentence in there. And this is what it came back with. They're quite cute. Some of them have got a bit squiffy eyes. And one of them looks like it's had its nose in the jam. But they're all sitting on a mat and they look like they are thinking. So it's quite clever at how it does that matching up. Perhaps the one that's the most advanced though is Dali 2. I first typed in a modern painting of a professor giving a lecture and being nervous. And it came back with four male professors. So I thought that's not good. So I typed in a modern painting of Yvonne Rogers giving a lecture and being nervous. And this is what it came back with. They don't really look like me, but well, at least I don't think they look like me. But it certainly does look like someone who's giving a lecture. The one on the right looks like they're quite angry rather than nervous. But it's like you then want to write another sentence. And you can't stop yourself using these. And there's mass appeal. So whoever I talk to is just really excited by these. They're artists, computer scientists, architects, designers, and the general public have all gone crazy using them. Why? And it's good to have a look at one of the engineers who developed this by Adita Ramesh. She said, Dali is a very useful assistant that amplifies what a person can normally do but really is dependent on the creativity of the person using it. And I couldn't agree more. And I've made in red this word amplifies. It's not replacing. It's getting you to think again, well, what can I write now? Could it come up with something else? Some people might say, well, is this creativity? And I would argue yes. So when I typed in Dali is Dali too creative, it came back with this lovely design there on the left. But each time you write a sentence or a few words, the AI tool makes us think of a new idea. And it enables us to dare to think differently. And some might say, well, is it really an art form? And I was having a discussion in my lab this week. And we're saying, well, it's just like photography became a new form of creativity. So will this new breed of AI apps? They've only just started to come out in the next year or two. We'll see many more. Another debate, and it's not one for me to talk about here, but just to mention it, is it stealing the work of artists it uses in its training data? And can we find a way of compensating or paying for them? I want to finish really by saying in the future, successful tools, AI tools would be those that help humans in their work. And just like the probabot, which I talked about the chatbot, the most effective AI tools will be those that are embedded in other software tools so that you use them whilst you're doing your task or your work. And just like the investigative canvas, I think the most powerful AI tools will be those that facilitate integrated thinking, enabling us to think and use more and more resources. And I want to end by giving the Microsoft a funding this by a new Microsoft tool that I think is really exciting. And it's called Microsoft Designer. And unfortunately there is a waiting list to get this, but hopefully not too long for me to get my hands on. But what it does is it uses Dali 2, so you can type in here with a description like kitten adoption day and it will come up with some designs and then you can use those designs in whatever it is that you're creating. So it might be a website, it might be a poster, it might be a newsletter, it might be social media. But here again it's embedding the tool in what you are doing. And here again you can start with... Oh no, it's the same one. Let's do this one. Just add or remove content. So here they're creating a newsletter. And it's this idea that it makes it really easy for anyone to use and it opens up many possibilities by which to think about new designs. So I'm really excited by this tool and I think there will be many more that are coming out that actually match what we as human beings are doing rather than replacing us. So to conclude, I think there are a diversity of technology tools to think with and I've just described a couple of them and my field, human-computer interaction has helped to design and shape those. And the most empowering tools are those that are embedded in ongoing user tasks and activities, especially those with a canvas that enable people to put things, put them around and to discover and to explore and investigate. And those that enable professionals and the general public to extend how they create work. And I think the future is very much human-AI thinking rather than AI-replacing thinking. And I've always thought of that and I think the best tools will be there to empower us and to engage us and to excite us. So I'd like to thank Microsoft, the Royal Society and the late Robert Milner for this award and also the many, many researchers who I've collaborated with and I've only really mentioned those in the universities I've worked at and on Equator, but there are many others in other universities throughout the world. Without them, I wouldn't be here today. So thank you very much. If your name's not on here and you're in the audience and you think it should be, let me know. Thank you. That's to me that probably you'll do your best work ahead of you. So I have a question. When we were both starting out I was off the visor that computers should be in and you were worried that the computer shouldn't buy for your attention. Do you think the computer now does buy for our attention and do you think that's a positive thing? That's a very good question for those of you who didn't hear it. I worked with Julie on a project funded by Incel and I was very much for making technology be visible and engaging and she was very much for the Mark Viser view of making it invisible and hidden. And now she's saying, has it not gone too far and that is taking too much of our attention? And I would agree. I think some people have got really addicted to using their mobile phones far too much and there are some very clever people out there who've designed some apps and games and so on and so forth which are difficult to put down. And I think the way to overcome that like any sort of addictive activities whether it's taking drugs or alcohol or eating too much food or all of the others is we have to find ways to help people who find it really difficult. And there are various software tools out there or attempts to try and get people to stop and sometimes they're quite blunt instruments and I think there's a lot of opportunity to help people to try and wean themselves off or to simply just throw their phone away. But yes, I agree. I think I'm probably guilty myself sometimes of using it too much. Ben Park. Thank you very much for the great lecture. During the lecture you have shown two case studies which make use of chatbot and visualisation respectively to make people think what are your views on what kind of contexts each method could help people to think the best. Oh, that's a difficult one. I think chatbots can be used in a wide range of contexts and for different activities. So I mentioned one or two types that are used in sort of commercial domains but also we were trying to think about how it can probe but they've been used for other applications and contexts. For example, there is a chatbot called Replica which has been designed to help people in their wellbeing and get them to think and interact with it. So they've been used for different contexts. In terms of visualisations, again, I think there are many application areas where you can use visualisations and we've seen that in data analysis and some of the work currently engaged in is thinking about what types of data visualisations might we create for lifelong narratives for people and how they can reflect on different aspects of their life and not just coming up with graphs but to think of what other kinds of visualisations might there be. So I think, as I showed in that slide, there are no hard and fast rules as to which type of technology you should use for which type of activity. It's obviously easier and cheaper to design for a mobile phone and one of my colleagues who can't be here tonight wrote a book called There's Not a Mobile App for That or something like that, but everyone just goes straight to designing apps because it's easier to do. I actually think that we can design technologies, a whole range of technologies rather than just going for one that's easier and cheaper. Hi, my name is Suya Shah. I'm a student at Goldsmiths College here and I'm studying game design and I'm looking to make specially educational games, games that can teach kids about different subjects and different topics. So the slide that you showed about system one, system two thinking was really interesting because in games a lot of it is about really fast reflexes, shooting, going around system one and then with educational games you want to ask them some question or make them think or learn which engages the system two brain but it makes the game less fun and entertaining and it makes it kind of boring. So how can one solve such a challenge of mixing both learning and while having fun? I don't think system two is always boring but maybe children see it as such. I think it's meant to be a metaphor the system one and system two and I think the key is to be able to alternate between those so at certain points let them be fast and just react and at other times you might want to slow them down so that they can be more strategic and get them to think about is this the best way to race or whatever else you are wanting to do in the educational game and I think some early educational software was designed to try and combine different approaches and different strategies so use it just as a heuristic rather than how can I get more system two and to think well how can we alternate they've been playing really fast for a long time maybe it's a good time to give them an activity that will slow them down or get a chat bot that will say is this the best thing maybe you should think of doing this and to encourage what's called metacognition which is thinking about your thinking rather than just constant reacting so I think that would be my suggestion Thank you so much Thank you so much Yvonne for a really amazing high opening talk I have a question about the chat bot again the system one and system two thinking I find that whole concept extremely interesting so did you consider how to engage the users more with the chat bot for example in that system one thinking is presumably when the person is very overwhelmed with their own emotions so they either angry or very scared or very sad something really is happening and then having something pop up in that moment engaging knowing that it's a robot might just be immediately kind of the person might not consider even engaging with the chat bot so did you consider what the chat bot could be doing like could chat bot maybe be affecting emotions of the person maybe creating a shock scenario for example or where they show what could happen if they make this bad decision or maybe in using trust I mean just kind of a question of what were the kind of different ideas of how why would the person engage with chat bot in that moment That's a really good question and I think our research in this area has only just begun and we started off looking into first of all we looked at how we could facilitate teams of clinicians working together sense making with data that it was they didn't really, not necessarily understand but they didn't know what was causing the different trends so we designed our chat bots to trigger more conversation between the team and it was very much thinking about how you can get more conversations going whereas the next tranche research was looking at individual users and how you might get them to stop and think so I think there's a huge scope for using chat bots that model but maybe understand the types of human emotions and tap into them the key thing is you need to find a sweet spot because you can just annoy people and they'll switch them off and they become annoying or frustrating so that's where doing some good user testing can come in to help is this too much and our first probabot perhaps was a bit too in the face of the traders so we reduced the amount of times and the conversation and the key is how long should the conversation be so that they can get back to the task if it's just that they want to explore their mental health or their emotions you might have a much longer conversation which is what replica does so I think there's a huge scope there for doing much more in this area and getting beyond the Q&A model that's very much under much commercial use of chat bots Thank you Yvonne for a really excellent and informative lecture I'm now beginning to wonder whether this question follows neatly from the preceding question so one of the ways we scaffold thinking in society is through debate, challenge, criticism a sort of quite scratchy way of engaging with people so I'm interested in where that fits within a model and how you can do that whilst remaining engaging That's a tricky one because even in humans themselves can find that hard to be all of those things particularly in marriages and understanding when it's best to say certain things when it's good to be scratchy to be blunt and open-ended but I think there's a lot of scope for us moving there's been a lot of work in AI and natural language processing for quite simple conversations but I think in the last year or two there's been more understanding of the nature of conversations the nature of these discussions that might go on so I don't have an answer to that other than more research into these things but also for us to understand a bit better what goes on in these types of discussions and scratchiness as you call it and do we have good understandings and theories about what goes on in human conversations of this nature and if so can we borrow from that and design these types of chatbots and other interventions to improve them some of these very large government meetings it would be very powerful and useful to have maybe some of these chatbots to help Hi, Adrian Gregory I'm a business leader so I'm coming at it from that aspect so really interesting presentation and lecture I was just wondering though on your financial services example it was about AI to slow down the thinking and scaffold the thinking what about access to expert opinion and then what did that open up in terms of regulatory requirements as well so I imagine that was quite a tricky subject but I'm really interested in your thoughts on that I think there were two case studies one is we were focusing very much on novice traders where it was trying to get them to develop new strategies and to think about the criteria in the financial world I'm not an expert on the regulatory matters there as to how... sorry I wasn't sure what the second part of your question was Yeah, grab the mic yet so a lot of the AI you were talking about was around decision process part of the decision process is access to expert opinion now in financial services expert opinion is regulated so it opens up I mean I imagine it's a complete mine field because what I would want to do as an investor is say well okay what do the experts think about this and how does that affect my decision making how do I get access to it but through the technology I mean the regulation would be quite stifling in terms of how to get access to that so I just wonder what your thoughts were and whether you came across that in your example I think we steer clear of that because of those very reasons and the other competitors might find very useful and we just give that freely out in our chatbots I think that we weren't trying to tap into that expertise in order to let people interact with expert chatbots it was more getting them to develop their own thinking strategies but that's a very different area I think is to sort of work that and I think we steered clear of that because it's a mine field This is probably a broad question but I was wondering how such designs of decision support tools might be applied to more time constrained settings like in healthcare where they're making kind of high stakes decisions and they're already cognitively overloaded and they may not have the cognitive resources at their disposal to make such systematic system 2 type of decisions just coming from a PhD student who is studying decision making I think artificial intelligence has come a long way towards helping people under those types of conditions in decision making and in particular in diagnostic diagnosing and they will continue to be developed to help but the key is not to let them in my view take over completely or people in these situations to know when to trust them when to use them and what they can do themselves or what they would like to do themselves and that I think is what we call human AI is in very much one of the research areas that's happening at the moment is to think about where AI can replace certain activities that are time consuming or that can be unreliable and for doctors and other clinicians can use those but also to give them new tools that can empower them to be creative in ways which they couldn't so I think there are two things one is as you said for people who are overworked or how can we help them but also those who are trying to think about the future of medicine how can we help them with these new types of creativity tools I think there's a place for both Very stimulating questions and thanks Yvonne for the great lecture and now I think I have the pleasure of actually giving the award Thank you Another scroll Wow look at that Thank you Well I will hang this somehow around my neck but not for the time being but I'm really words fail me I'm just touched by how so many of you are here and also for you to think that I'm worthy of this award so thank you very much