 Hydiwyd eich ymgyrchu'r ysgrifetau o'i cyfnod ymgyrch yn y Cymru ar y newydd yw 2016, ac ymmwybodol yn ymwybodol, yn rwyf, yn cynnwys, ac yn cyfnodol ymgyrch ond ymgyrch. Ac mae gennym eu cyfnodol i yn gliriaeth i ddigonol yn y cyfnodol, ac mae'n syniadol, cyfnodol, technologi, a llai. Yn y cyfnod, ac mae'n cyfnod olwgwyrd yn y rhan fwyaf, mae'n cyfnod o'r ffordd yn y Roedd Fforth Yng nghylch. The industrial era we're about to embark on, which is emerging and melding of the whole myriad of new technologies and innovations in business models and ways of engagement. But we're going to ask some of the younger people who've been participating, some of the younger leaders at this meeting. All three of the people I'm joined by today are young scientists, a community that is convened and brought together by the World Economic Forum. So they can participate, so that the youngest, brightest stars of science from around the world and a range of disciplines can participate, can contribute thinking and meet with leaders of business and government and make sure that their voice is heard and that their thinking is heard. And hopefully we can start the process of really putting together the kinds of changes we need if we're going to address the global regional industry challenges that we know we face. My name is Oliver Cann. I'm a member of the media team here at the World Economic Forum. I'm not going to talk for very much longer because we only have half an hour. Issue briefings are all about interchange. We try to keep things as rapid as possible. We only have 30 minutes, so I encourage you all to put up your hand on the panel here and here in the audience. If you have a comment or a question, please do shout. We have microphones around the room. We encourage dissent and disagreement everywhere so we can get the party started. Now, I'm going to just introduce briefly my three panellists here. My immediate left is Gerardo Odessa, Associate Professor, University of Nottingham in the United Kingdom. Amanda Randalls, Assistant Professor, Biomedical Engineering, Duke University, Bjorn Shuller, a reader, Imperial College London in the United Kingdom again. Bjorn, I've found that mistaken. It's your second annual meeting with the new champion. You have a head start on Amanda and Gerardo. I believe it's your first. I'm going to start very, very basically by asking you all what your biggest key takeaway has been from this meeting so far. Gerardo, let's start with you. Thank you very much. Well, for me, this has been a very different event. I normally travel for scientific conferences, but this for me has been a very interesting experience of being exposed to completely different environments. Having the chance to interact with global leaders like politicians, the Chinese Premier, the Canadian Minister for Innovation, and as well as other young scientists in different disciplines, which are far away from mine, but still do stimulating science, and also tech pioneers, entrepreneurs that maybe transition from academia into their own ventures. This reminded me that basically scientists have some responsibility towards society to actually get out of the ivory tower of academia and communicate. I have an impact on the wider public. So we had conversations with the president of the European Research Council, Professor Bourguignon, which is the institution, the organization which funds my research, and the editor-in-chief of Nature in one of our breakfasts with the delegation of young scientists, and it was really interesting to see what are the challenges and the opportunities that we have in order to achieve impact beyond the academia. So we have to disseminate without, let's say, dumbing down, but without over-hyping, clearly communicating to the public. We have a responsibility as stewardship towards the taxpayers who are supporting our work in order to present our plans, our outcomes, and this has been really stimulating for me. So I think we have to try much harder. So in particular I am in the UK, there's been this referendum, and then I see it as a missed opportunity. We failed as scientists to communicate to the public, even to local communities, how important it is for us to have European support, to be integrated into the European community, how important it is for the development and leadership of science in the United Kingdom, and how science is important for society at large. So I will go back with a lot of inspiration about doing more than just being a leader in science. It takes a lot more in order to drive forward and address the challenges of the world. Of course, as young scientists we expect you to be a leader and to work more closely with business and with politics, but we also expect you to be brilliant continuously in your own field as well. So tell us a little bit more about where you are at in quantum computing I believe is your discipline, in layman's terms, and where you hope to go forward in terms of pushing boundaries in that science. Okay, so I am by formation a theoretical physicist, and at the moment I work in the School of Mathematical Sciences, so I am pretty much a basic scientist. My field of specialisation is quantum information theory, which is basically developing the theory for quantum technologies. At the moment, if you look at the description of what is this fourth industrial revolution that we are celebrating and discussing with this meeting, this is not driven by a single breakthrough like, say, steam engines in the mid 18th century, which was the first industrial revolution, but a confluence of several emerging technologies, which overall are going to make a very rapid impact, transformation for our society. One of these technologies is quantum based technologies, and so at the moment already in the last couple of years there have been huge investments, for instance in the country where I live, the United Kingdom, for translation from fundamental science into technologies, and examples can be developing better sensors that can have applications in biomedical sector or navigation. You can have simulators in order to study the physics of complex systems that you cannot do with normal computers. You could do it with quantum simulators, and this can help, for instance, accelerating drug delivery and discovery of new materials, and other applications, for instance, in secure communication. This is something which is already commercial, like cryptography, because at the moment existing schemes for encrypting data are not fundamentally secure, they can be beaten if you have enough computing power, and with quantum cryptography you can develop a system which is protected by the laws of physics, and is fundamentally secure. And this is something already commercial, for instance in Switzerland they use quantum cryptography to secure voting and the elections, they used it also during the World Cup in South Africa. So technologies are moving forward, and at the moment there has been a huge push also with the involvement of different companies like Google and Microsoft and Intel in the Netherlands and so on. For actually bringing these technologies into the real world. So at the moment we still are quite far away in some of the aspects, for instance quantum computing, but we are progressing much faster in quantum communication, and China for instance is a pioneer in this. There has been this announcement that I think by the end of this year there will be a network of satellites that will be put in orbit by China, escordirated by eminent scientists in China, Professor Jean Weipan, and this will serve the purpose of establishing a global network of secure communication based on quantum principles. Now what I do in Nottingham, we still do research that at the moment is on the fundamental side, so investigating what are the resources which allow quantum technologies to be more efficient than the classical ones, and what I expect is that my research will feed into the next generation of quantum technologies, the one that can be more robust and more resilient towards environmental noise. Amanda, we'll come back to this, you raised some really important points here, and a lot of it is second guessing where these technologies are going to be deployed, where possibly we didn't expect them to be, so let's think about that a little bit. But let's first talk about you Amanda, you're a biomedical engineer, so where are you taking your branch of science? Yeah, so I just switched over to Duke from Lawrence Livermore National Lab last year, where I was coming from a high performance computing standpoint, so my background is really in physics, my PhD is from Harvard in physics, and I'm trying to take both the physics approach, the computational approach, and build models to really understand disease. And we're taking patient specific images, so we're getting data from MRCT scans, and getting vascular geometries, and trying to model blood flow through these vascular geometries. So the direction we've gone in is trying to really understand how do you model someone at the human scale, or at large regions of the vasculature, so the latest work we've done is trying to model blood flow in the entire arterial network. So to do that you need to have large scale supercomputers. We had to use the systems at Lawrence Livermore National Lab with 1.6 million processors, so it's kind of building on a lot of what we're seeing and talking about this week of this fusion between biology, physical systems, and the computational technology, and bringing that, I think we've seen that a lot in the narrative throughout all these sessions this week is really just building on the integration of these different parts. And that's where my work is really trying to head towards is how do we leverage the advances in technology as well as in the biomedical to really understand the origin of vascular disease, how it's going to progress and try to help make target better therapies. And it's kind of interesting doing like being here and talking about it this week where like maybe last week China came out with the first, the fastest supercomputer in the world is right now 124 petaflops here in China. And one of the big questions then is like you know now that we have these big systems what can we do with them and how do we actually you know how do we leverage them and make really real breakthroughs from the scientific perspective. And in America we're trying to build a 200 petaflop system in the next few years and a lot of my work is focused on you know what can we do with these, what kind of discoveries can we make with these systems, and how do we use them to target questions like vascular biology, cancer, and target some of the biomedical questions in that area. Bjorn, second year running as a young scientist, how has the experience changed this year and how has your progress and your own research evolved over the past 12 months? So talking about the change was the second year I would say you're much more brave to go out there and really speak to the people that in the first time you're just amazed to be with. I mean this time we had Premier Lee Ke Chiang. I couldn't talk to him. We also were able to talk to Minister of Debates and in fact just this morning I met with him and actually was brave enough to go there and talk with him and his amazing experience to not only talk with people from a distance but really go there and talk with him individually. And also you're getting a whole new perspective because in the first time you're just first confronted with whole new perspective as we've heard from the colleagues like looking at communication with society, communication with decision makers. But now you're more and more getting the feeling that you're really belonging to a world where this matters and not only seeing it. And what about your own work I believe in machine learning if I remember from our conversation last year. How is that going? I mean it was only several hours into this meeting when I was talking to a journalist who was talking about an AI winter that due to the fact that there was so much a lot of hype building up around this particular area. How would you comment on that for example? So the hype was quite useful in a way to me last time because I was lucky enough to have the opportunity to give a talk in the better zone. That was amazing. But of course there's a lot of concern about machine learning artificial intelligence and what it can do to society in particular in the context of the force industrial revolution. So we have big data, we have deep learning as big buzzwords out there, the omnipresence of monitoring of people. To us this interplay in the force industrial revolution of different disciplines is giving a lot of opportunities because we can combine the ever present collection of data, big data from individuals to do some almost miracles in health care and really disruptive changes there, providing ever present health state analysis, collecting knowledge. Collecting data from your speech, from your audio environment, collecting data from your movements on the wrist to predict your next seizure if you're epileptic to predict your level of depression, to predict all sorts of health relevant things. At the same time there's quite some concern that the force industrial revolution will deprive us of our souls, will make us more robotized in society, more addictive to technology and will change our social interaction patterns. At this point we are very much trying to put some emotion and warmth into machines by doing effective computing. So we're landing machines emotional intelligence, social intelligence, giving them the ability to understand our emotion, react to it for the sake of better communication with individuals but maybe also for the sake of giving back some social touch to robots to machines. It's social AI, of course, one of the top 10 emerging technologies of 2016, published by the World Economic Forum last year. Apologies for the pitch. Okay, I've talked enough. We've talked enough. Any questions from you guys? Okay, a gentleman on the front row. Sebastian Coe, Global Shaper from Hong Kong. So we have our own community here. I was just wondering from your young scientist community, do you have any advice for reaching out to other communities to communicate your ideas or to, you know, pollinate and get ideas from, you know, people you don't normally speak with. And yeah, either from a young scientist perspective or just general kind of perspective. Thank you. I think this is a very interesting point. From my experience here we have been more in touch with the tech pioneers. We have met with yours. I like much more in the corridors, say, or at the different other occasions. I think the general idea is to take it more home as well and see if there's a good opportunity to network and find a way to communicate outside also these three, four days and get in touch in other ways of the means we have been seeing and discussing these days. Do you get to that? Yeah, just kind of building on the same ideas. We've had a few of the interactions with some of the other networks. It'd be great to have other ways of interacting with the young global leaders, the global shapers. Yeah, it's hard. It's a little difficult, but I think going back and it seems that the young global leaders have put forward like that jurid collective where they're trying to really help people who want to get involved in the entrepreneurial side. I think having more programs centered around that that are allowing us to communicate once we leave the forum and kind of building and using a lot of the community aspect is something we should try to focus on a bit more. And hopefully, as well, this is just a one meeting throughout a year of collaboration. So we hope you maintain the ties you make at this meeting. Let's just go back to one of the points you made, Amanda, about the supercomputers that are being built here in China and maybe an even more powerful one being built in the US. You raised a very valid point. What do we do with all these things? We're generating and creating semi scientific capacity, but are we really channeling it for the right reasons? So are we really getting an effective use of this great capacity in solving the challenges? Are we aligned? Are we lined up and linked into the same challenges here? Yeah, I think it is kind of building the same idea of a lot of what they're really going after this week of like we're not really in our little silos anymore. We're not just the computer scientists making this really great supercomputer. The problems we're starting to run into is now that we're pushing the bounds of how you can actually program to it. And it takes trying to get the application scientists to be able to understand what the power is, how you can actually leverage it and how to use it is a significant challenge aside from just trying to build the system itself. And I think that's where we're having a lot of international communities trying to really help learn from how have we learned how to program the systems in the states versus the systems in other countries. And there has been a lot more interaction on that side, but the focus now is really on how do we build these applications and what can we do with it. And we're making a lot of progress, but there's definitely a lot more that needs to be done. And from our side, looking at hemodynamics and doing blood flow modeling has been a huge focus in the last few years throughout the supercomputing community. And we've learned a lot more on how do we model the brain and how do we model the heart disease and focusing on different areas. But trying to bring what we're learning from the way we're scaling out is very similar to how people are modeling the weather and modeling other application areas. That unless you have platforms similar to this where you're talking to people from completely different fields and learning how they're using these systems, it's difficult to bring that back together. I think a lot of focus, especially from the policy side, has been trying to create how do we fund these supercomputers, how do we get them, and the focus really needs to shift now on how do we use them and what can we do. Beyond some of the applications in your field are more commercial than others, how do you reckon, how are you reconciling that? So the applications that are most commercial at the moment being is really centered around effective computing. It's really highly novel technology to, as I said, make machines more social again and emotional again. I think in machine learning in the context of the force industrial revolution there's also a huge promise in terms of interplay with complex systems engineering. Because force industrial revolution in particular requires us to make very fast-paced decisions to be extremely fast and very complex decisions. So I think that these kinds of combinations of complex systems modelling and machine learning will be very commercializable soon. If you have more and more governance support, management support driven from big data analysis in real time. We'll take both questions. Thank you. I'm Jocelyn Ford with US Public Radio. There's been a lot of discussion this week about the fears of the speed of which technology is changing the world that we live in, and I'm wondering if you could all talk about what you think is the greatest misplaced fear, and also what you think is the greatest, your own concerns about the advances in your own field, what negative use could that be put to? What is your fear about what is being developed in Europe? The dark side of the force industrial revolution, I love it. I'll take the next one and we'll answer them in turn. Hi everyone, I'm also the global shipper from the Tundall Hubs. My question about, you are both from the university and the professor of the university, how will the education change in the force revolution? Okay, let's do the dark side first. Bjorn, your killer robots are right up your street. Let's start with you. That's a classic for us. Machine learning is always coming with the concern these days of robots and machines taking over, of course. This is a very misplaced fear in my eyes because we're very far, if it ever happens from machines that have this level of consciousness and self-organisation that they will want to take over and are in a position to do this, so we will certainly remain. But a much more realistic fear is of course the one of big data analysis and privacy threat. So we're having a lot of surveillance these days. We're collecting a lot of data. A lot of this data is giving voluntarily to organisations collecting it without putting thought into it what may be derived from this data. So in my eyes that is probably the much more serious threat than the concern to have machines taking over soon. Amanda? You're doing things for the human body. What can go wrong there, huh? I think a lot of the challenge we run into is the question of security and data information. If we're modelling, if we take the imaging data and we model and decide this person is prone to have heart disease I think people are scared. Is that information going to go back to the insurance agencies? Who gets access to that data and how is that going to be used? Is that going to go to your employer? Is that going to go? And that brings out a lot of fears and questions on that side. I think there's two parts that are similar to the machine learning side. We're not really, first off, the technology is not there at that point. We're not ready to feed that kind of information and people get ahead of themselves and what they get afraid of and what they think is going to happen. A lot of the issues we end up running into is also even just getting a level of trust of proving that what we're actually simulating from the computational angle is usable and something that has value because a lot of people just see it as, you know, this is what we're running on the simulation. It may not be actually very real. And a lot of that end we're working very closely with a lot of the experimentalists to really kind of prove that we're 3D printing the geometries that we're running the simulations through and comparing our simulation results to experiments in these 3D printouts. And the challenge we're having is really trying to convince stakeholders that aren't coming from a computational side that this kind of information has value. Okay, so let's just remind historically in the 1920s with the development of quantum mechanics, people just got a more precise understanding of the atoms and then eventually all the founding fathers of quantum mechanics, almost all of them, got together into Manhattan Project and then they did the atomic bomb. So there's always the danger that with some advances in physics when we discover something fundamentally new about the world, this can be used for bad. Now, coming more concretely in the case of the advent of quantum technologies, if you develop a quantum computer tomorrow that is able to factor large products of prime numbers, then all the communications as used today encrypted by the RSA become immediately insecure because they rely on the fact that if you take two numbers and you multiply them, it's easy to get the result, but if you just get the result that you want to go back to the factor, that's hard. But it's hard, but with the quantum computer it's not hard anymore, okay? But the thing is quantum technologies are also advancing with solutions. So at the same time they are bringing new solutions for security and encrypted communication that make all this other insecure communication not needed anymore. So I think in the 14th Industrial Revolution one thing that worries me a lot is this huge boom of the Internet of Things. And I think all this Internet of Things is fundamentally insecure. There will be lots of devices connected, very easy to hack. So I think we have to develop solutions from first principles in order to ensure security. So for instance, one of the most recent developments in quantum information theory in quantum technologies is called device independent approach to security. So you might buy some device, but maybe this can be hacked or maybe there is some use dropper which is trying to still control your communications somehow. And then you have some enforced measures of security that are based on the existence of stronger correlations allowed by quantum physics which allow you to be secure even without trusting your own devices. So sometimes, for instance, if you think about cloud computing, you can imagine that maybe your own receiver, your own apparatus is secure, maybe you built it or you test it, but then you don't trust the server. So you can have situations where you exploit partial or one-sided device independence in order to be able to accomplish computations or communications without having to know the specifics of the server, without trusting it, but we are still measured to identify threats and correct for them. This is something that comes only in quantum theory. The Internet of Insecure Things. What a chilling prospect. Let's get to the ladies' question here about education. It's an interesting one because one of our co-chairs, Shirlianne Jackson, from Rensler Polytechnic, speaks very eloquently and particularly on the need for placing universities and learning at the heart of the Fourth Industrial Revolution. Are you seeing any changes in the way that education throughout the different grades and levels is adapting to meet the changing world we're in? Yeah, for sure. When I was a kid, I didn't have my first computer until I was maybe 10 or 11. That was exceptional. Now children, when they are already two, three years old, they know much better than us how to use tablets and go on the internet. They do these things in school. So I think we have the possibility of accessing a lot of information immediately and being connected globally. This is helping. It has to be somehow still obeys some rules. So we don't have to replace the traditional methods of learning just by this immediate access to information. People have to develop the ability to discover. They have to develop the value of meeting in person, collaboration, discussion. But barriers come down. We become much closer. If used properly, it's a huge advantage. What is education up to the challenge? I think so. I think what we're seeing a lot is similar to the ideas being pushed forth with the fourth industrial revolution where it's all very interdisciplinary. A lot of our programs are really pushing. You have to take the math classes, the life science classes, as well as the computing classes, and it's drawing from the different perspectives. We're changing how we're teaching a bit. You're seeing a lot more problem-driven courses. A lot of the flipped classroom where instead of just lecturing, you're having them do the problems in class together. I think we are starting to try to adapt to bring in these different disciplines in an attractable way. Doing that from not just at the college level, but starting much earlier, at least in the US, there's definitely a lot more interaction where we bring in a lot of the high school students and elementary students to the university and have a lot of programs to really integrate and teach them from that side and expose them early. I think there's been an increased focus on that side, at least. We're running perilously short of time. If I may, I have one last question. It's a double header. We're talking all over the place about interdisciplinary sciences. I want you to put yourself outside of your zone. I'd love you all to tell us what you're outside of your own discipline and what the most exciting science is to you right now. Also, what's been the greatest idea you've heard at this meeting? What a pitch. What can have the greatest change in the world? Let's start with you, Bjorn. That is a little bit of a challenge given how many exciting things we've heard. I think the most exciting single science is a little bit hard to say. It's much more about the interplay, and that's all what force industrial revolution is about. We've heard tremendous things in bioengineering these days. I'll let you have an interplay. It doesn't need to be one particular science, but outside of machine learning. Where is the most exciting discovery taking place, in your view? To me, the exciting thing is really the opportunities we're seeing in health care and data analysis these days for well-being and monitoring health. Any great ideas? Any brain waves that have occurred during this meeting? Anybody pitched you with an idea you thought was truly amazing? Yes, definitely. So there's a lot of ideas around how we can use more sensors, other sensors, other forms of interaction data analysis to identify and pre-diagnose other diseases and conditions. Jon, are you nodding your head? Let's go to you next. I was inspired by many of these ideas lab, and in particular yesterday we had some, which was led by the European Research Council, and it was about antibiotic resistance. I really liked one of the presentations where they were proposing methods to boost or turbocharge our immune system in order to better protect ourselves against these bacteria, which are resistant to antibiotics. This was very interesting. Another one I heard was about navigating the brain in order to identify only the cancer cells and then act on them without damaging the healthy ones. They are developing this at the Imperial College as well. They are developing some technology like the equivalent of a sat nav in order to navigate at the cellular level. This is something which inspires me. I think we can even improve the resolution if we can integrate this with some quantum technology, quantum sensing, but it's very premature. If you weren't in quantum computing right now, where would you be? Well, in quantum computing we didn't hear much here, but I think quantum methodology is one of the perspectives that is stimulating the most. In particular, there are some challenges which are created by the development of other technologies, like, for instance, additive manufacturing, 3D printing. Now everything, very complex structures, can be built. But of course you need to include some control methods in order to make sure that these are grown precisely and they are very complex features. It's very hard for conventional microscopes to go there and image these situations. So these create a challenge, whereas we can use our methods in order to develop some in-line modules for metrology integrated with additive manufacturing, so properly monitor and control the 3D printing of complex structures. I think we have scope for it. We are trying to collaborate with engineers in order to start these developments. Amanda? Yes, and there are a lot of really exciting topics this week. You're not allowed to say that. You've got to get down to one. I really liked the out-of-second imaging techniques. I thought the gravitational waves discussion was fantastic. It's one of the biggest discoveries this year, and getting to hear it from the researchers that actually did that work was pretty exciting from my end. Being able to actually have experiments to validate predictions we've seen for 80 years or so, I think that's a really powerful, really exciting opportunity. A lot of the discussions on the machine learning side, it made me start thinking about, how can I use machine learning in my own work more and go back and create more predictive models that might supplement the simulation data. I feel like I've taken a lot out of this, and it leaves you very excited and inspiring, so it's good. I hope you will be working together on many projects. We'll come to the end of this session, which is always regrettable, but I hope it's given you a glimpse into the future leaders of the Forth Industrial Revolution, shaping the science. Thanks for joining us here in the room, and thanks for watching live online. This session is now over.