Researchers explore issues, solve problems and anticipate trends that can shape new frontiers and future directions. Richard Harper is the author of 14 books, including the IEEE award-winning "The Myth of the Paperless Office"; "Texture" (the A.o.I.R. book of the year 2011); and "Skyping the Family" (2019). He has also published over 200 scientific and has 27 patents. His research looks at how people engage with each other through technology and the implications for engineering and concepts of the individual in the digital age. His research is thus directly pertinent to the topic of digital exclusion. He is Director of the Institute of Social Futures at Lancaster University, which coheres humanities and social science research with natural and physical sciences and engineering. He is a Fellow of the IET, the Royal Society of Arts, and ACM Fellow. The bulk of his career has been spent in commercial research, Xerox and Microsoft in particular.
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Hello, I'm Roy Sharples, and welcome to the unknown origins podcast. Why are you listening to this podcast? Are you an industry expert? Looking for insights? are you growing your career? Or are you a dear friend helping to spur your pylon? I created the unknown origins podcast to have the most inspiring conversations with creative industry personalities and experts about entrepreneurship, pop culture, art, music and film and fashion. Richard Harper is the author of 14 books, including the I II award winning the myth of the paperless office and texture, the A or IR Book of the Year in 2011, and scaping the family in 2019. He has also published over 200 scientific and has 27 patents. His research looks at how people engage with each other through technology and the implications for engineering and concepts of the individual and the digital age. His research is thus directly pertinent to the topic of digital exclusion. He is director of the Institute of Social futures at Lancaster University, which coheres humanities and social science research with natural and physical sciences and engineering. He is a fellow of the IE at the Royal Society of arts and ACM fellow. The bulk of his career has been spent in commercial research, Xerox, and Microsoft in particular. Hello, and welcome, Richard, what inspired and attracted you to research in the first place?Richard Harper:
I think what inspired me to do research was when I was in university, one of the one of the professors in my secondary University, taught me some, some arguments about what was called the nuclear family. And he was talking, he was talking about social theory, which is not interesting to lots of people. But he was saying that lots of social theorists say that, in capitalism, modern industry, made everyone live in so called nuclear families, little families, and it'll seemed very plausible. And he took the whole term term on how nuclear families allowed us to allow people to have enough men for God's work where the mother will stay at home and look after children and how they can have fear, greater social structure. And then the second term, he said, this implies there was a time when there wasn't a nuclear family. And when we kind of assumed that that, you know, before capitalism, there wasn't a nuclear bomb, he said, well, let's have a look. And so he reported them some nice anthropological researchers who found the only continuous parish records are family life in Europe, going back to the 12th century, and it's in Nottingham. And there, they found that there's no there's never been any recorded instance for nearly 1000 years of anything other than a nuclear family in England. And that families have always argued Mum, mothers don't my daughter in law's father and Lord father and also my son in laws. And what tends to happen is families end up quite small mom and dad and and a few kids and families struggle, and they struggled in medieval Nottingham just they struggled in Victorian England. And what was interesting to me about this wasn't the sociology of it was because I assumed I just take it for granted that I understood things like things were different before the Industrial Revolution. And what the professor made me realize was, there are also things we take for granted, but you but actually, the joy of research is deepening your understanding and realizing that things that you took for granted, were not as as you imagined, and then when and if you allow yourself see things differently. It's not that you become more knowledgeable, you have more facts in your head, it's actually that you can see things better and understand things more clearly. So. So for one of analogy, if you're mining for ideas, and you and you're really excited and you behave been well trained, you know where to dig, and you know how to get deeper quicker through through research. And that's the thing I learned from and got insight about research was, was the capacity of the realization that things I took for granted possibly weren't, weren't right. And actually, you could investigate them and you could see the world differently and having seen the world if you can, you can get excited about exploring it and then having explored it. The thing that happened after university when I became a PhD and double PhD, was you could then start thinking about how to make the world differently and making the world different is particularly in my time, in the 80s, and 90s, through software through computing, and, and so you can test your ideas about how aboard people wants to do through building programs and applications in devices. And, and what you find quite often is the things you build, don't do what you thought, but they do other things. And the other things are just as interesting, if not more interesting. So. So what you learn is research may is exciting, because you just you see things more deeply. But then when you're trying to make things you discover that, actually, you still don't understand the world that well. But if you engaged with the world, by trying to make things for it, you can make the world different than that really is the most exciting thing.Roy Sharples:
Every single creative will tell you that there is no on and off button for creativity. It is a constant that happens naturally, by design, or by accident, and our everyday lives. Through the creative process may seem magical, especially where ideas can come from, and how they are brought to work to form and life that are proven techniques, tools, methods, frameworks, and approaches to the art and science of applied creativity that make it happen using techniques that drive evolution, synthesis, re application reinvention, re imagination, disruption, revolution, and changing direction. It does not mean being fooled into believing that it is simply about following a process and expecting creative results. As an outcome. It is all about people and the execution because people with a vision and bind with passion and drive, make things happen. To pursue your idea with conviction and resilience, be skilled in your craft, and expedite with quality precision, to bring your vision to life, having True Grit to slay the nice layers to push through adversity and ambiguity with leadership, making sacrifices and execute your ideas in a disciplined way. Richard, how do you find creativity? Do you dream up ideas from within yourself? What you manifest from what you observe? From the world around you? Or do ideas fall from the sky? And you gravitate towards them? Or is it all of that? In other words, what is your creative process in terms of how do you dream up ideas, develop them into concepts, and then bring them to actualization?Richard Harper:
To try and innovate is I think it's best to start by looking at how things are currently done. And that means looking in the real world and don't kind of theoretically come up with a problem, we'll read about a problem, go and look at what people are doing, again, seeing how they're how they're doing it. And then and then at first, spend time trying to see kind of the problems that people are having, and then listen to those sorts of problems. But you don't, you don't you mustn't stop there. Because the problems that people talk about, are often the kinds of the most obvious ones. And often they're the things that can be solved, because that's why people are talking about the mess, oh, we can't do the economy, this by the end of next week, they probably will have solved it, the deeper problems or the problems that can be solved. You can only get it by having understood the goal setting in question from the inside, if you like by listening to what people do and watching what they do, then you have to look at it slightly oddly, like at a strange angle. And, and see it slightly differently like through a funny lens. And, and one of the things you do when you do that is also try and look at the technology in that in that setting, because the technologies and also things and look at what it doesn't do. Because the trouble with technology is it always seems to be trying to be perfect doing exactly what it should do should be the perfect tool. And it's sometimes difficult to see what it isn't doing. And, and one of the things you need to look at is look at the setting strangely and then look at the technology stranger and think what's in not doing so. It's sort of good example today is if you look at meetings for Mr. Majid like teams, one of the things that team what teams are doing or zoom is, is making meetings. And yeah, what are meetings about meetings about sharing information? Yeah, meetings are about bringing people together. Yeah. And sometimes meetings about bringing people to a room. Now one of the peculiar things about teams and zooms is that is one of the virtues of computer technology, which is that every meeting you have on zoom or teams camera, tirely new meeting, but sometimes you want to have a meeting, especially in a creative team, where each week or every three days you come back to the same meeting space, and you use the meeting space as a place in which you put your ideas and document them. You kind of make post it notes and whiteboards, and you leave them there. So you come back. So each time we come back, you build on what's happened before, where we can't teams and zoom meetings, each time you have a meeting, it's a new meeting room. So one of the things you might say about technology is like, like zoom teams is they, they're too good at making rooms, actually, what they want to do sometimes is to allow a room to persist so that every Wednesday say, at 12 o'clock, it's the same meeting room I go to, but also I can pop into that room at any time, and put content in there so that each time I go back and my colleagues go back, the room itself has content. And getting to see those sorts of opportunities is is basically looking at what people are doing in meetings and trying to look at the technology that support meetings and trying to see them differently, so that you can come up with what's what's, what are some of the problems you have there, and what might be ways of solving it. And oftentimes, where does that mean, for example, the idea that you might have a meeting room that persists currently zoom, for example, allows you provides an audit function so that you can go back and look at the notes have been made. But what I'm suggesting here is that you won't have a meeting room that you can put stuff in again and again, and it persists through time, it's not an audit trail, it's rather a virtual entity which lingers. And it's very simple motion, but it sort of stiv of, of how you can look at meetings in this and understand what's going on in them. And, and, and kind of the key thing that's missing currently is that meeting technologies don't allow people to remember very well, they don't miss, remember what they talked about last time, they don't necessarily remember where they got to in their thinking last time. And one of the things, meeting rooms to real rooms, is let people put their memory on the walls, let people put their memories in notes. So one of the things meeting room technologies, late zoom and teams should do know is that those sorts of things happen. And that sort of sort of how to think through how to see some creative opportunities, everyone's very excited about what machine learning and artificial intelligence can do. And it is very exciting. And it is very powerful. But there's a big problem with trying to create innovation with artificial intelligence machine learning tools, technologies is that the people treating AI and machine learning, in terms of what you might say, is the wrong category. And what that's what I mean, says that what they're wanting AI Machine Learning Tools technologies to do is to do what people do, because they think that AI machine learning is like a computational version of a human. And so there's always two discussions about the Turing test as a machine pass the test of behaving like a human so that a third person can distinguish the two. And to me, that's not very interesting. And also perhaps much more importantly, it results in people not being as creative with AI and machine learning as they might be, because what they're doing when they're looking at machine learning and AI is thinking about what people are doing, and trying to replicate it. Rather than thinking that AI and machine learning is a different kind of machinery, a different technology, and thinking what it can do that people can't do, rather than thinking of the things that new things you could do if you're a new set of tools alongside human tools, you also have AI machine learning tools, which are different to what humans can do. What's tending to him with AI Machine Learning with Technology, innovation is that it's trying to just do away with people. And so there's a big opportunity that's being missed, which is how can we take those technologies to do different sorts of things, things that people can't do? And then and then it if you if you start thinking about what, what people can't do? What is it that that she might want to do with the technology. And I think the exciting thing about machine learning and AI is is actually in that direction. And that that's going to demand more creativity because we're so used to tools being designed to let us do things better when the things that we're doing are basic things we can do anyway. But But AI and machine learning technologies could do very different things, they can do things on scale. And with data that we can't process.Roy Sharples:
That's a fine point on artificial intelligence and machine learning, being used to do what people do, they can navigate towards unknown horizons to define the future. That seems to have been a common trait in society, especially in the application of technology. And since the Industrial Revolution as an example that was largely about automating human tasks, taking it from what would have been months to days, maybe even hours, minutes. And machines replaced manual labor, which was optimized for time and cost. For example, what Henry Ford did for the assembly line to produce affordable cars for the masses making it quicker and and more convenient for people to get around. You know the point on starting with learning from what already exists historically, and today. This helps us understand the world and whatever field domain we are in, by providing invaluable lessons, philosophies, stories from which we learn and as a starting point for our research, and also the past has made us who we are today, and understanding history, builds better connections and broadens our capacity by allowing us without sounding corny to stand on the shoulders of giants, and learn from the greats and then build upon their innovations to ensure that we really are creating something that's new, as opposed to reinventing the wheel through ignorance. History educates us on how society technology and governments worked in the past, to understand better how we got to the present, and how it works now, and it arms us with the arsenal and the insights to create a better way forward. What are the key skills needed to survive and thrive?Richard Harper:
As a researcher, I think researchers need lots of different key skills. But the kinds of key skills that are important in in my research, and my research is about designing tools for people to use at work and at home, are threefold. And the first is you have to be good at desk work. And this sounds obvious, but it desk work, basically means reading, searching through libraries, going online tools, finding out what's being researched before in that particular space. And you know, it's just like essay writing, it's just finding out what's been not been understood. So if you're interested in what people are doing, there are 1000s of anthropological studies of human activities. And you will find something to inform you so that when you go to look at the setting in which you want to create innovation, you're already well briefed. And that leads to the second set of skills that a good researcher in my field needs, which is human skills, and human skills. But by that I mean, you have to have the capacity to, to listen to people and be genuinely interested in and we mustn't patronize them, you must have the skill to be absolutely enchanted by what people tell you about their lives. If you are enchanted, you will find that they will tell you more, because they know you're interested, if you pretend you're interested in you just filling out a form, and just filling out with the minimum amounts of information, they can see that and they won't tell you the things that you need to know. And what you need to know are the things that are not obvious, which is what I led to in the nature of research. They tell you things about how some particular workplace really functions as against how we ought to function. How relationships between organizations are producing products, where each other, how they really what is their real characteristic, what's the given take, they tell you those sorts of things. And that's what you need if you want to kind of create innovations, which makes a difference. And then I think the last thing you want is modesty because you have to have been monitored enough to know that although you can come up with solutions, your solutions are never going to solve all problems. But But if but if you're modest about it, you can come up with good or bad solutions. And if but if you're not modest, and you too ambitious, you come up with solutions, which are too, too complex, too clunky and are never going to work. So you kind of need modesty so as to be to be realistic about what you can do even though you put a lot of effort into a lot of desk work and a lot of human work. And then underscoring all of it. You need to be enthusiastic about what you're doing. Because even though it might seem modest, your enthusiasm is going to be part and parcel of what makes people enthusiastic. Your ideas. So if you're enthusiastic, you will persuade people through your enthusiasm that what you've researched, or you've understood by your human work is valid. And if you're not enthusiastic if you don't have that kind of joie de vive about your ideas, people themselves won't try them. They're just think ours is another idea. And one of the things you you will learn as you get to be my edges, lots of people as long as people offering ideas. And the part of the problem with good research is getting good ideas noticed and one of the ways that ideas get noticed is because of the enthusiasm with which they are described and explained and advocated. And that's also bound up with clearly with it. The need for those ideas to be appropriate for the task that they're trying to help create some innovation and persistence and and determination because one of the things you find when you create some innovation. So you might create innovation, you might get something patented, you might get something engineered. And then you might get, say, a third party organization to, to, to be very interested in taking them up your concept. But then when you start working with a third party organization, another organization, they see things slightly differently. And then they do have their own customers, and they see things slightly differently. And each time you go through those sort of different places, first your own company, then another company, then say customers from that, how your thing is understood shifts, and you have to, you have to adjust to that and yet, persistently explain what you think the value of your proposition is. And sometimes it feels as if you're constantly doing that, and you've been doing it for months and months to a different audience. And the Jordan seems to come with slightly different perspectives. And what you learn is that the world's enormous and large, and it's very peak, so many people are doing so many different things that To try and explain what you're about requires, like a long footpath, of explaining to different people and different communities, that sort of the same thing, but speaking to their concerns, and their aspirations and their perspectives each time. And that, that that requires patience. And it also reminds you that the world is is a big, and there are lots of people doing a lot of slightly different things. And over when you work with lots of different people. Each of those different things add up to quite big differences.Roy Sharples:
Upon reflection what are your lessons learned: the pitfalls to avoid, and keys to success that you can share with aspiring researchers several things?Richard Harper:
There's one set of things to do with the ideas themselves, what kind of innovations you have, and the other set of concerns have to do with who you're working with. If you focus on said the ideas you're working with the innovations you want to do, you really have to keep them simple. And the reason why you have to keep them simple is because if you can, only by keeping them simple, can you explain them to people who might be investing or people who want to test them or people who might want to develop them, and you have to resist making ideas seem better. By making them more complicated, you need to resist making ideas seem more powerful by adding features. You need to resist so that they can make clear enough that they understood and so that they can be clear enough to be properly tested. Now having been properly tested and discovered they succeed or not, then you might want to combine them with other ideas. But if you if you have if you bundle one idea with two or three or four other ideas, it's really difficult to know what ideas are valuable and which ones aren't. So you need to kind of disentangle to avoid entangling ideas for innovation, to make sure to just to better understand whether it's successful or isn't. And at the same time is that the other thing is working with people. And one of the things I've learned is is you can only work with the people that you are working within your team. So if you've got a really good hardware engineer, you can build some interesting hardware, if you don't have a hardware, really good hardware engineer, don't try and innovate some hardware, it's just not going to work. If you have a good team of software engineers who can deliver tested and code for products, you're probably going to struggle to do something innovative and lightweight and quick, because they're not used to that sort of developing a code for concepts that need to be tested and iterated and changed quickly in a kind of agile way. But then they'll have if you're working with a team that coders are used to very agile approach to coding, you're probably going to build something which might break regularly. But that doesn't matter if you're just testing it. But it does matter if the kind of thing that you're exploiting, wanting to engineer is something which requires robustness. So so you have to figure out who you're working with scores you have, what skills your team has to shape, the things that the things you're trying to innovate in. Because if you don't have them fit in, if they don't fit together, you're not going to succeed. Now, there's also there's also an opportunity because you in organizations and in the workplace, you change teams, you know, you move on to get different teams and then being aware of the team helps you make plans about what you want to do next, and your kind of career for innovation and creativity. And but at the same time also, you should realize you should never be disappointed with your colleagues because your colleagues are always the most brilliant people in your working life. It's not you It's them. So you have to understand them and work with their skills or they're working with yours and think of yourselves as a kind of like an animal, and that some animals are good at this and another one was good at that. And the fact you can get things done different sorts of things is fabulous. But you have to understand that when you're trying to think about the things you can successfully innovate with.Roy Sharples:
Passion is energy, and energy is contagious. Compelling visions draw people in high performing productive teams embrace diversity and difference, and are often self organizing, and that the performance emerges from the experts joint actions within the project. They share a vision and commitment to the mission at hand. Most Innovative teams mobilize themselves in response to unexpected changes. They don't need a leader to tell them what to do. People who have the expertise and passion will step up at the right time within the creative process to lead and drive the completion of their respective input and add value to the team. And the solution. The creative atmosphere cultivated provides autonomy and space is liberal, inclusive, and meritocratic, yet is entirely focused and motivated to expedite the mission, no hierarchy, politics, prejudice, and hunger zone are permitted or tolerated within that system. And it starts with a big idea and a shared vision, then the team works through the details to come up with the big picture, and then bring it to life. Tilting forward, what is your vision for the future of research? And what role will creativity play within that?Richard Harper:
So I was thinking a little bit about the problem of artificial intelligence and the problem of capitalism. And the trouble with AI researchers is they're trying to replicate and better what people do, whereas they're not thinking about what different things I could do. And I think the future research is, is all it always is always based on the future research is derived from a key skill. And the key skill is, is not to look backwards. And by that I mean, the AI researchers are currently looking backwards at what cheering was talking about in terms of copying people. And what AI researchers should be doing is trying to look forward into a different future. And in that different future to try and see things which are interesting, and worth exploring, because they're so radically different and unexpected. But looking forward into an unknown future can sometimes be give you a sense of vertigo, because you're not sure what you're looking at, you're not sure what you're going to find. Whereas looking backward is backwards into into the past into old premises old categories. Well, structured paths is much easier to do. And this is not just a problem for AI researchers, it's always been a problem in technology research. And there are sorts of though, they can often be paradoxes here, so sometimes you have to look at the past differently, so as to see what the future might be. And so for example, Xerox key developed for Xerox was not just developing the wimp interface, Windows icon, mouse and pointer and so forth all those years ago, it was also trying to think about what paper did and what roles Paper Paper had. And then that basis, pushing that further what you could do with documents, which are digital, and now today to when perhaps as we look into a different future, perhaps you might look into the past in with different eyes to see things which hadn't seemed necessary, as interesting as they might be, in terms of the kind of tools that people use them to people have not been able to use service to invent new ones. So so so I think the problem for the future of research is looking into the future and not being too tempted to look to the past. Here's an example of the problem of categories and innovation in a home space. So one of the things you might say about Amazon's Alexa and its related services, it is a lovely way of exercising artificial intelligence and natural language processing. So you can speak to a computer and this case, speak to a search engine and the search engine will deliver content for you. But what's actually happening there is that speech based search engine does something which is natural in speech, and that is, when you're speaking to someone, you have to wait for everyone to get the other person's turn to do it to finish before you take your time. And one consequence of that is that people get really impatient if someone else takes too long. So if you ask someone to, to to do some search for you, and then start listing all the things they've found, are normally conversation after two or three items have been listed. The person doing Listen, the listening gets be irritated. neighbors and says, Okay, the first one I don't listen to Tim. What's happening with Amazon Alexa is that the search engine, because it's voice based is delivering search targets, which are nearly always the ones that are listened to or the first, second, first, second or third. And that advanced is Amazon because it sells those first, second third hits in its search engine. Now, if you have a search engine, which is on a screen, and a search engine presents an array of search targets, you can see those search targets, you can see many more than you can hear. Or rather, you can see much more quickly, many more than you can hear. So one of the things that's happening there is that the category of shifting from seeing things I might choose from hearing things that I might choose is resulting in Amazon, reducing the choices that people make in the home, and choosing things that basically Amazon chooses for them the first, second or third target. And saw results from people thinking that speech is the same category is looking or seeing that that reading is the same as hearing. And they're not that different categories. And they have different implications for how things can get done what people want to do, and how technologies can facilitate it. And so, going forward, for innovation in the home, one of the things that the researchers need to be trying to do is trying to explain to people in the home of the product vendors, that that interaction between those different kind of categories of action, how to speak to computer is not the same as to type to a computer or to read or a computer says, and the difference is not in the content is in how people act on the content. So if I speak to a computer, I want an answer quickly. If I write to a computer and expect a response, I probably want a better response because I took some time to write it. So I expect the computer to be more digital give them a more thorough answer. If I look at a list, I want to see more. Whereas if I hear a list, I want to hear less. And I think once once people realize that those sort of categorical distinctions are important. There's a great deal of innovation that in the for the home, which can transform the relationship people in the home have with companies like Amazon.Roy Sharples:
Whilst technology has enabled humanity to actualize extraordinary things. Don't get mesmerized by technology, and being fooled into believing that it is the answer. It is not. As former Pixar Chief Creative Officer, john Lasseter accurately declared computers don't create computer animation anymore than pencils create pencil animation. What creates is the artist. The key is the intelligent application of technology to unlocking human ingenuity for the greater good by pushing society forward, ethically, and responsibly. humans and machines are working together to problem solve, and transform the world for the better in an age of creativity. Reject conventions constantly analyze and question and challenge the status quo in your everyday life and provide an alternative and bring it to life. But how soon is the future? One thing for sure is, the future is unwritten and everything is possible. You have been listening to the Unknown Origins podcast. Please follow subscribe rate and review us. For more information go to unknownorigins.com Thank you for listening!