>>from around the globe. It's the Cube with digital coverage of AWS Public Sector online brought to you by Amazon Web services. Oven Welcome back to the cubes. Virtual coverage of Amazon Web services. Eight. Of his public sector summit online. We couldn't be there in person, but we're doing remote interviews. I'm John Curry. Your host of the Cube got a great segment from Asia Pacific on the other side of the world from California about social impact, transforming, teaching and learning with cloud technology. Got three great guests. You go. Richard is the CEO and co founder of Guys Tech and Jean Te'o, CEO and founder of Solve Education Founders and CEOs of startups is great. This is squad was the AIPAC regional head. Education, health care, not for profit and research. Ray Ws, he head start big program Vincent. Thanks for coming on, Janine. And you go Thank you for joining. >>Thanks for having us, John. >>We're not there in person. We're doing remote interviews. I'm really glad to have this topic because now more than ever, social change is happening. Um, this next generation eyes building software and applications to solve big problems. And it's not like yesterday's problems there. Today's problems and learning and mentoring and starting companies are all happening virtually digitally and also in person. So the world's changing. So, um, I gotta ask you, Vincent, we'll start with you and Amazon. Honestly, big started builder culture. You got two great founders here. CEO is doing some great stuff. Tell us a little bit what's going on. A pack, >>A lot of >>activity. I mean, reinvent and some it's out. There are really popular. Give us an update on what's happening. >>Thank you. Thank you for the question, John. I think it's extremely exciting, especially in today's context, that we are seeing so much activities, especially in the education technology sector. One of the challenges that we saw from our education technology customers is that they are always looking for help and support in many off the innovation that they're trying to develop the second area off observation that we had waas, that they are always alone with very limited resources, and they usually do not know where to look for in terms, off support and in terms off who they can reach out to. From a community standpoint, that is actually how we started and developed this program called A W s. At START. It is a program specifically for education technology companies that are targeting delivering innovative education solutions for the education sector. And we bring specific benefits to these education technology companies when they join the program. Aws ed start. Yeah, three specific areas. First one is that we support them with technical support, which is really, really key trying to help them navigate in the various ranges off A W S services that allows them to develop innovative services. The second area is leaking them and building a community off like minded education technology founders and linking them also to investors and VCs and lastly, off course, in supporting innovation. We support them with a bit off AWS cop credits promotional credits for them so that they can go on experiment and develop innovations for their customers. >>That's great stuff. And I want to get into that program a little further because I think that's a great example of kind of benefits AWS provides actually free credits or no one is gonna turn away free credits. We'll take the free credits all the time all day long, but really it's about the innovation. Um, Jean, I want to get your thoughts. How would solve education? Born? What problems were you solving? What made you start this company and tell us your story? >>Thank you so much for the question. So, actually, my co founder was invited to speak at an African innovation forum a couple of years back on the topic that he was sharing with. How can Africa skip over the industrialization face and go direct to the knowledge economy? Onda, the discussion went towards in orderto have access to the knowledge economy, unique knowledge. And how do you get knowledge Well through education. So that's when everybody in the conference was a bit stuck right on the advice waas. In order to scale first, we need to figure out a way to not well, you know, engaging the government and schools and teachers, but not depend on them for the successful education initiated. So and that's was what pain walk away from the conference. And when we met in in Jakarta, we started talking about that also. So while I'm Singaporean, I worked in many developing countries on the problem that we're trying to solve this. It might be shocking to you, but UNESCO recently published over 600 million Children and you are not learning on. That is a big number globally right on out of all the SDG per se from U N. Education. And perhaps I'm biased because I'm a computer engineer. But I see that education is the only one that can be solved by transforming bites. But since the other stg is like, you know, poverty or hunger, right, actually require big amount of logistic coordination and so on. So we saw a very, um, interesting trend with mobile phones, particularly smartphones, becoming more and more ubiquitous. And with that, we saw a very, uh, interesting. Fortunately for us to disseminate education through about technology. So we in self education elevate people out of poverty, true, providing education and employment opportunities live urging on tech. And we our vision is to enable people to empower themselves. And what we do is that we do an open platform that provides everyone effected education. >>You could How about your company? What problem you're you saw And how did it all get started? Tell us your vision. >>Thanks, John. Well, look, it all started. We have a joke. One of the co founder, Matthew, had a has a child with severe learning disorder and dyslexia, and he made a joke one day about having another one of them that would support those those kids on Duh. I took the joke seriously, So we're starting sitting down and, you know, trying to figure out how we could make this happen. Um, so it turns out that the dyslexia is the most common learning disorder in the world, with an estimated 10 to 20% off the worldwide population with the disorder between context between 750 million, up to 1.5 billion individual. With that learning disorder on DSO, where we where we sort of try and tackle. The problem is that we've identified that there's two key things for Children with dyslexia. The first one is that knowing that it is dislikes. Yeah, many being assessed. And the second is so what? What do we do about it? And so given or expertise in data science and and I, we clearly saw, unfortunately off, sort of building something that could assess individual Children and adults with dyslexia. The big problem with the assessment is that it's very expensive. We've met parents in the U. S. Specifically who paid up to 6000 U. S. Dollars for for diagnosis within educational psychologist. On the other side, we have parents who wait 12 months before having a spot. Eso What we so clearly is that the observable symptom of dyslexia are reading and everyone has a smartphone and you're smart. Smartphone is actually really good to record your voice. Eso We started collecting order recording from Children and adults who have been diagnosed with dyslexia, and we then trying a model to recognize the likelihood of this lecture by analyzing audio recording. So in theory, it's like diagnosed dyslexic, helping other undiagnosed, dyslexic being being diagnosed. So we have now an algorithm that can take about 10 minutes, which require no priors. Training cost $20. Andi, anyone can use it. Thio assess someone's likelihood off dyslexia. >>You know, this is the kind of thing that really changes the game because you also have learning progressions that air nonlinear and different. You've got YouTube. You got videos, you have knowledge bases, you've got community. Vincent mentioned that Johnny and you mentioned, you know making the bits driver and changing technology. So Jeannine and Hugo, please take a minute to explain, Okay? You got the idea. You're kicking the tires. You're putting it together. Now you gotta actually start writing code >>for us. We know education technology is not you. Right? Um, education games about you. But before we even started, we look at what's available, and we quickly realize that the digital divide is very real. Most technology out there first are not designed for really low and devices and also not designed for people who do not have Internet at hope so way. So with just that assessment, we quickly realized we need toe do something about on board, but something that that that problem is one eyes just one part of the whole puzzle. There's two other very important things. One is advocacy. Can we prove that we can teach through mobile devices, And then the second thing is motivation it again. It's also really obvious, but and people might think that, you know, uh, marginalized communities are super motivated to learn. Well, I wouldn't say that they are not motivated, but just like all of us behavioral changes really hard right. I would love to work out every day, but, you know, I don't really get identity do that. So how do we, um, use technology to and, um, you know, to induce that behavioral change so that date, so that we can help support the motivation to learn. So those are the different things that we >>welcome? >>Yeah. And then the motivated community even more impactful because then once the flywheel gets going and it's powerful, Hugo, your reaction to you know, you got the idea you got, You got the vision you're starting to put. Take one step in front of the other. You got a W s. Take us through the progression, understand the startup. >>Yeah, sure. I mean, what Jane said is very likely Thio what we're trying to do. But for us, there's there's free key things that in order for us to be successful and help as much people as we can, that is free things. The first one is reliability. The second one is accessibility, and the other one is affordability. Eso the reliability means that we have been doing a lot of work in the scientific approach as to how we're going to make this work. And so we have. We have a couple of scientific publications on Do we have to collect data and, you know, sort of published this into I conferences and things like that. So make sure that we have scientific evidence behind us that that support us. And so what that means that we had Thio have a large amount of data >>on and >>put this to work right on the other side. The accessibility and affordability means that, Julian said. You know it needs to be on the cloud because if it's on the cloud, it's accessible for anyone with any device with an Internet connection, which is, you know, covering most of the globe, it's it's a good start on DSO the clock. The cloud obviously allow us to deliver the same experience in the same value to clients and and parent and teacher and allied health professionals around the world. Andi. That's why you know, it's it's been amazing to to be able to use the technology on the AI side as well. Obviously there is ah lot of benefit off being able to leverage the computational power off off the cloud to to make better, argue with them and better training. >>We're gonna come back to both of you on the I question. I think that's super important. Benson. I want to come back to you, though, because in Asia Pacific and that side of the world, um, you still have the old guard, the incumbents around education and learning. But there is great penetration with mobile and broadband. You have great trends as a tailwind for Amazon and these kinds of opportunity with Head Start. What trends are you seeing that are now favoring you? Because with co vid, you know the world is almost kind of like been a line in the sand is before covert and after co vid. There's more demand for learning and education and community now than ever before, not just for education, the geopolitical landscape, everything around the younger generation. There's, um, or channels more data, the more engagement. How >>are you >>looking at this? What's your vision of these trends? Can you share your thoughts on how that's impacting learning and teaching? >>So there are three things that I want to quickly touch on number one. I think government are beginning to recognize that they really need to change the way they approach solving social and economic problems. The pandemic has certainly calls into question that if you do not have a digital strategy, you can't You can find a better time, uh, to now develop and not just developed a digital strategy, but actually to put it in place. And so government are shifting very, very quickly into the cloud and adopting digital strategy and use digital strategy to address some of the key problems that they are facing. And they have to solve them in a very short period of time. Right? We will talk about speed, three agility off the cloud. That's why the cloud is so powerful for government to adult. The second thing is that we saw a lot of schools closed down across the world. UNESCO reported what 1.5 billion students out of schools. So how then do you continue teaching and learning when you don't have physical classroom open? And that's where education, technology companies and, you know, heroes like Janine's Company and others there's so many of them around our ableto come forward and offer their services and help schools go online run classrooms online continue to allow teaching and learning, you know, online and and this has really benefited the overall education system. The third thing that is happening is that I think tertiary education and maybe even catch off education model will have to change. And they recognize that, you know, again, it goes back to the digital strategy that they got to have a clear digital strategy. And the education technology companies like, what? Who we have here today, just the great partners that the education system need to look at to help them solve some of these problems and get toe addressing giving a solution very, very quickly. >>Well, I know you're being kind of polite to the old guard, but I'm not that polite. I'll just say it. There's some old technology out there and Jenny and you go, You're young enough not to know what I t means because you're born in the cloud. So that's good for you. I remember what I t is like. In fact, there's a There's a joke here in the United States that with everyone at home, the teachers have turned into the I T department, meaning they're helping the parents and the kids figure out how to go on mute and how toe configure a network adds just translation. If they're routers, don't work real problems. I mean, this was technology. Schools were operating with low tech zooms out there. You've got video conferencing, you've got all kinds of things. But now there's all that support that's involved. And so what's happening is it's highlighting the real problems of the institutional technology. So, Vincent, I'll start with you. Um, this is a big problem. So cloud solves that one. You guys have pretty much helped. I t do things that they don't want to do any more by automation. This >>is an >>opportunity not necessary. There's a problem today, but it's an opportunity tomorrow. You just quickly talk about how you see the cloud helping all this manual training and learning new tools. >>We are all now living in a cloud empowered economy. Whether we like it or not, we are touching and using services. There are powered by the cloud, and a lot of them are powered by the AWS cloud. But we don't know about it. A lot of people just don't know, right Whether you are watching Netflix, um Well, in the old days you're buying tickets and and booking hotels on Expedia or now you're actually playing games on epic entertainment, you know, playing fortnight and all those kind of games you're already using and a consumer off the cloud. And so one of the big ideas that we have is we really want to educate and create awareness off club computing for every single person. If it can be used for innovation and to bring about benefits to society, that is a common knowledge that everyone needs to happen. So the first big idea is want to make sure that everyone actually is educated on club literacy? The second thing is, for those who have not embarked on a clear cloud strategy, this is the time. Don't wait for for another pandemic toe happen because you wanna be ready. You want to be prepared for the unknown, which is what a lot of people are faced with, and you want to get ahead of the curve and so education training yourself, getting some learning done, and that's really very, very important as the next step to prepare yourself toe face the uncertainty and having programs like AWS EC start actually helps toe empower and catalyzed innovation in the education industry that our two founders have actually demonstrated. So back to you Join. >>Congratulations on the head. Start. We'll get into that real quickly. Uh, head start. But let's first get the born in the cloud generation, Janine. And you go, You guys were competing. You gotta get your APS out there. You gotta get your solutions. You're born in the cloud. You have to go compete with the existing solutions. How >>do you >>view that? What's your strategy? What's your mindset? Janine will start with you. >>So for us, way are very aware that we're solving a problem that has never been solved, right? If not, we wouldn't have so many people who are not learning. So So? So this is a very big problem. And being able to liberate on cloud technology means that we're able to just focus on what we do best. Right? How do we make sure that learning is sufficient and learning is, um, effective? And how do we keep people motivated and all those sorts of great things, um, leveraging on game mechanics, social network and incentives. And then while we do that on the outside way, can just put almost out solved everything to AWS cloud technology to help us not worry about that. And you were absolutely right. The pandemic actually woke up a lot of people and hands organizations like myself. We start to get queries from governments on brother, even big NGOs on, you know, because before cove it, we had to really do our best to convince them until our troops are dry and way, appreciate this opportunity and and also we want to help people realized that in order to buy, adopting either blended approach are a adopting technology means that you can do mass customization off learning as well. And that's what could what we could do to really push learning to the next level. So and there are a few other creative things that we've done with governments, for example, with the government off East Java on top of just using the education platform as it is andare education platform, which is education game Donald Civilization. Um, they have added in a module that teaches Cove it because, you know, there's health care system is really under a lot of strain there, right and adding this component in and the most popular um mitigate in that component is this This'll game called hopes or not? And it teaches people to identify what's fake news and what's real news. And that really went very popular and very well in that region off 25 million people. So tech became not only just boring school subjects, but it can be used to teach many different things. And following that project, we are working with the federal government off Indonesia to talk about anti something and even a very difficult topic, like sex education as well. >>Yeah, and the learning is nonlinear, horizontally scalable, its network graft so you can learn share about news. And this is contextual data is not just learning. It's everything is not like, you know, linear learning. It's a whole nother ballgame, Hugo. Um, your competitive strategy. You're out there now. You got the covert world. How are you competing? How is Amazon helping you? >>Absolutely. John, look, this is an interesting one, because the current competitors that we have, uh, educational psychologist, they're not a tech, So I wouldn't say that we're competing against a competitive per se. I would say that we're competing against the old way of doing things. The challenge for us is to, um, empower people to be comfortable. We've having a machine, you know, analyzing your kids or your recording and telling you if it's likely to be dislikes. Yeah, and in this concept, obviously, is very new. You know, we can see this in other industry with, you know, you have the app that stand Ford created to diagnose skin cancer by taking a photo of your skin. It's being done in different industry. Eso The biggest challenge for us is really about the old way of doing things. What's been really interesting for us is that, you know, education is lifelong, you know, you have a big part in school, but when you're an adult, you learn on Did you know we've been doing some very interesting work with the Justice Department where, you know, we look at inmate and you know, often when people go to jail, they have, you know, some literacy difficulty, and so we've been doing some very interesting working in this field. We're also doing some very interesting work with HR and company who want to understand their staff and put management in place so that every single person in the company are empowered to do their job and and and, you know, achieve success. So, you know, we're not competing against attack. And often when we talk to other ethnic company, we come before you know, we don't provide a learning solution. We provide a assessment solution on e assessment solution. So, really, John, what we're competing against is an old way of doing things. >>And that's exactly why clouds so successful. You change the economics, you're actually a net new benefit. And I think the cloud gives you speed and you're only challenges getting the word out because the economics air just game changing. Right, So that's how Amazon does so well, um, by the way, you could take all our recordings from the Cube, interviews all my interviews and let me know how ideo Okay, so, um, got all the got all the voice recordings from my interview. I'm sure the test will come back challenging. So take a look at that e. I wanna come back to you. But I wanna ask the two founders real quick for the folks watching. Okay on Dhere about Amazon. They know the history. They know the startups that started on Amazon that became unicorns that went public. I mean, just a long list of successes born in the cloud You get big pay when you're successful. Love that business model. But for the folks watching that were in the virtual garages, air in their houses, innovating and building out new ideas. What does Ed start mean for them? How does it work? Would you would recommend it on what are some of the learnings that you have from work with Head Start? >>But our relationship X s start is almost not like client supplier relationship. It's almost like business partners. So they not only help us with protect their providing the technology, but on top of that, they have their system architect to work with my tech team. And they have, you know, open technical hours for us to interact. And on top of that, they do many other things, like building a community where, you know, people like me and Google can meet and also other opportunities, like getting out the word out there. Right. As you know, all of their, uh, startups run on a very thin budget. So how do we not pour millions of dollars into getting out without there is another big benefit as well. So, um definitely very much recommend that start. And I think another big thing is this, right? Uh, what we know now that we have covert and we have demand coming from all over the place, including, like, even a lot of interest, Ally from the government off Gambia, you know? So how do we quickly deploy our technology right there? Or how do we deploy our technology from the the people who are demanding our solution in Nigeria? Right. With technology that is almost frameless. >>Yeah. The great enabling technology ecosystem to support you. And they got the region's too. So the region's do help. I love we call them Cube Region because we're on Amazon. We have our cloud, Hugo, um, and start your observations, experience and learnings from working with aws. >>Absolutely. Look, this is a lot to say, so I'll try and making sure for anyone, but but also for us on me personally, also as an individual and as a founder, it's really been a 365 sort of support. So like Johnny mentioned, there's the community where you can connect with existing entrepreneur you can connect with expert in different industry. You can ask technical expert and and have ah, you know office our every week. Like you said Jenny, with your tech team talking to cloud architect just to unlock any problem that you may have on day and you know, on the business side I would add something which for us has been really useful is the fact that when we when we've approached government being able to say that we have the support off AWS and that we work with them to establish data integrity, making sure everything is properly secured and all that sort of thing has been really helpful in terms off, moving forward with discussion with potential plant and and government as well. So there's also the business aspect side of things where when people see you, there's a perceived value that you know, your your entourage is smart people and and people who are capable of doing great things. So that's been also really >>helpful, you know, that's a great point. The APP SEC review process, as you do deals is a lot easier. When here on AWS. Vincent were a little bit over time with a great, great great panel here. Close us out. Share with us. What's next for you guys? You got a great startup ecosystem. You're doing some great work out there and education as well. Healthcare. Um, how's your world going on? Take a minute, Thio. Explain what's going on in your world, >>John, I'm part of the public sector Team Worldwide in AWS. We have very clear mission statements on by the first is you know, we want to bring about destructive innovation and the AWS Cloud is really the platform where so many off our techs, whether it's a text, healthtech golf text, all those who are developing solutions to help our governments and our education institutions or health care institutions to really be better at what they do, we want to bring about those disruptive innovations to the market as fast as possible. It's just an honor on a privilege for us to be working. And why is that important? It's because it's linked to our second mission, which is to really make the world a better place to really deliver. Heck, the kind of work that Hugo and Janina doing. You know, we cannot do it by ourselves. We need specialists and really people with brilliant ideas and think big vision to be able to carry out what they are doing. And so we're just honored and privileged to be part off their work And in delivering this impact to society, >>the expansion of AWS out in your area has been phenomenal growth. I've been saying to Teresa Carlson, Andy Jassy in the folks that aws for many, many years, that when you move fast with innovation, the public sector and the private partnerships come together. You're starting to see that blending. And you've got some great founders here, uh, making a social impact, transforming, teaching and learning. So congratulations, Janine and Hugo. Thank you for sharing your story on the Cube. Thanks for joining. >>Thank you. Thank >>you, John. >>I'm John Furry with the Cube. Virtual were remote. We're not in person this year because of the pandemic. You're watching a divest Public sector online summit. Thank you for watching

>> Announcer: From around the globe, it's theCUBE with digital coverage of AWS Public Sector Online brought to you by Amazon Web Services. >> Welcome back to theCUBE's Virtual coverage of Amazon Web Services, AWS Public Sector Summit Online. We couldn't be there in person, but we're doing remote interviews. I'm John Furrier, your host of the cube. We've got a great segment from Asia Pacific on the other side of the world from California, about social impact, transforming teaching and learning with Cloud technology we've got three great guests. Hugo Richard is the CEO and co-founder of Dystech and Janine Teo CEO and founder of Solve Education founders and CEOs of startups is great Vincent Quah is the APAC Regional Head of Education, Healthcare Not-For-Profit and Research for AWS. (indistinct) big program. Vincent, thanks for coming on Janine and Hugo thank you for joining. >> Thanks for having us, John. >> Thanks John So, we're not there in person. We're doing remote interviews. I'm really glad to have this topic because now more than ever social change is happening. This next generation is building software and applications to solve big problems. And it's not like yesterday's problems, they're today's problems and learning and mentoring and starting companies are all happening virtually, digitally, and also in person. So the world's changing. So I got to ask you, Vincent we'll start with you Amazon, obviously big (indistinct) culture. You got two great founders here and CEOs doing some great stuff. Tell us a little bit what's going on at APAC, a lot of activity. I mean re-invent and the summits out there are really popular. Give us an update on what's happening. >> Thank you, thank you for the question, John. I think it's extremely exciting, especially in today's context, that we are seeing so much activities, especially in the education technology sector. One of the challenges that we saw from our education technology customers is that they're always looking for help and support in many of the innovation that they're trying to develop. The second area of observation that we had was that they are always alone with very limited resources and they usually do not know where to look for in terms of support and in terms of not who they can reach out to from a community standpoint, that is actually how we started and developed this program called AWS EdStart. It is a program specifically for education technology companies that are targeting, delivering innovative education solutions for the education sector. And we bring specific benefits to these education technology companies when they joined the program, AWS EdStart. Yeah, three specific areas, one is that we support them with technical support, which is really, really key trying to help them navigate in the various ranges of AWS services that allows them to develop innovative services. The second area is leaking them and building a community of like-minded education technology founders, and linking them also to investors and VCs. And lastly, of course, in supporting innovation, we support them with a bit of AWS Cloud credits, promotional credits for them so that they can go and experiment and develop innovations for their customers. >> That's great stuff I want to get into that program a little bit further because I think, you know, that's a great example of kind of benefits AWS provides (indistinct) free credits or, no one is going to turn away free credits. We'll take the free credits all the time, all day long, but really it's about the innovation. Janine I want to get your thoughts. How was Solve Education born? What problems were you solving? What made you start this company and tell us your story. >> Thank you so much for the question. So actually my co-founder was invited to speak at an African Innovation Forum couple of years back, and the topic that he was sharing with, how can Africa skip over the industrialization phase and go direct to the knowledge economy and that discussion went towards, in order to have access to the knowledge commonly you need knowledge and how do you get knowledge well through education. So that's when everybody in the Congress was a bit stuck, right? And the advice was in order to scale fast, we need to figure out a way to not while, you know, engaging the government and schools and teachers, but not depend on them for the success of the education initiative. So, and that's was what (indistinct) walk away from the conference. And when we met in Jakarta, we started talking about that also. So while I'm Singaporean, I worked in many developing countries. And the problem that we're trying to solve is it might be shocking to you, but UNESCO recently published over 600 million children and youth are not learning. And that is a big number globally, right? And out of all the SDGs per se, from UN, education, and perhaps I'm biased, because I'm a computer engineer, but I see that education is the only one that can be solved by transforming (indistinct) versus the other SDGs like, you know, poverty or hunger, right? Actually require big amount of logistic coordination and so on. So we saw a very interesting trend with mobile phones, particularly smart phones becoming more and more ubiquitous. And with that, we saw a very interesting opportunity for us to disseminate education through mobile technology. So we in self-education elevate people on a public through providing education and employment opportunities, (indistinct) on tech. And we.. our vision is to enable people to empower themselves. And what we do is that we build an open platform that provides everyone active education. >> Hugo How about your company? What problem are you solving? How did it all get started? Tell us your vision. >> Thanks, John. Well, look, it all started with a joke, one of the co-founder, Matthew, had a, he has a child who has severe learning disorder and dyslexia, and he made a joke one day about having (indistinct) that could support those kids. And I took the joke seriously. So we started sitting down and, you know, trying to figure out how we can make this happen. So it turns out that dyslexia is the most common learning disorder in the world. We have an estimated 10 to 20% of the worldwide population with the disorder, due to in context, that's between 750 million up to 1.5 billion individuals with that learning disorder. And so where we sort of try and tackle the problem is that we've identified that there's two key things for children with dyslexia. The first one is that knowing that it is dyslexia, meaning being assessed. And the second one is, so what, what do we do about it? And so given all expertise in data science and AI, we clearly saw an opportunity of sort of building something that could assess individual children and adults with dyslexia. The big problem with the assessment is that it's very expensive. We've met parents in the U.S. specifically who paid up to 6,000 U.S. Dollars for a diagnosis with an educational psychologist. On the other side, we have parents who wait 12 months before having a spot. So what we saw clearly is that the observable symptom of dyslexia are reading, and everyone has a smartphone and (indistinct) from smartphone is actually really good to record your voice. So we started collecting audio recordings from children and adults who have been diagnosed with dyslexia. And we then try to model and to recognize the likelihood of dyslexia by analyzing audio recording. So in theory, it's like diagnosed dyslexic, helping other undiagnosed dyslexic being diagnosed. So we have now (indistinct) them. That can take about 10 minutes, which requires no prior training costs, 20 U.S. Dollar, and anyone can use it to assess someone's likelihood of dyslexia. >> You know, this is the kind of thing that really changes the game because you also have learning for questions that are nonlinear and different. You've got YouTube, you've got videos, you have knowledge bases, you've got community. Vincent mentioned that Janine, you mentioned, you know, making the bits of driver and changing technology. This is the kind of thing that seems obvious now as look at it, but now you've got to put it into action. So, you know, one of the benefits of Cloud on AWS, we'll give a plug for Vincent's company here is that you can move faster. And that's something that Andy Jassy always talks about and Teresa Carlson, being builders and moving fast, but you got to build it. So Janine and Hugo, please take a minute to explain, okay, you got the idea, you're kicking the tires, you're putting it together. Now you've got to actually start writing code. What happens next? Janine, we'll start with you. >> Well, what happens next? Okay. So for us, we know education technology is not new, right. And education games are not new, but before we even started, we look at what's available and we quickly realized that the digital divide is very real, most technology out there first are not designed for (indistinct) devices, and also not designed for people who do not have internet at home. so with just that assessment, we quickly realized we need to do something about, and that's something that problem is. One is just one part of the whole puzzle. There's two other very important things. One is advocacy. Can we prove that we can teach through mobile devices? And then the second thing is motivation. And again, it's also really obvious, but, and people might think that, you know, marginalized communities are super motivated to learn. Well, I wouldn't say that they are not motivated, but just like all of us behavioral change is really hard, right? I would love to workout everyday, but you know, I don't really do that. So how do we use technology to, you know, to induce that behavioral change so that we can help support their motivation to learn. So those are the different things that we work on, certainly with it. >> Yeah, and then a motivated community, is even more impactful because then once the flywheel gets going, then it's powerful. Hugo your reaction to, you know, you got the idea, you got the vision, you're starting to put, take one step in front of the other. You got AWS, take us through the progression on the startup. >> Yeah, sure. I mean, what Janine said is, very likely to, to what we're trying to do, but for us, there's three key things that in order for us to be successful and help as much people as we can, it is three things. The first one is reliability. The second one is accessibility and the other one is affordability. So the reliability means that we have been doing a lot of work in the scientific approach as to how are we going to make this work And so we've.. We have a couple of scientific publications and we had to collect data and, you know, sort of publish this into AI conferences and things like that. So it makes sure that we have the scientific evidence behind us that support us. And so what that means is that we have to have a large amount of data and then put this to work, right on the other side of the accessibility and affordability means that Janine said, you know, it needs to be on the Cloud because if it's on the Cloud, it's accessible for anyone with any device, with an internet connection, which is, you know, covering most of the globe. So it's a good start. And so, the Cloud obviously allow us to deliver the same experience and the same value to clients and parent and teacher and (indistinct) professional around the world. And that's why, you know, it's been amazing, to be able to use the technology on the AI side as well obviously there is a lot of benefit of being able to leverage the computational power of the Cloud, to make better algorithm and better training. >> (indistinct) to come back to both of you on the AI question. I think that's super important. Vincent I want to come back to you though, because in Asia Pacific and that side of the world, you still have the old guard, the incumbents around education and learning, but there's great penetration with mobile and broadband. You have great trends as a tailwind for Amazon and these kinds of opportunities EdStart, what trends are you seeing that are now favoring you? Because with COVID, you know, the world is almost kind of like been a line in the sand is before COVID and after COVID, there's more demand for learning and education and community now than ever before, not just for education, the geopolitical landscape, everything around the younger generation is more channels, more data, the more engagement, how are you looking at this? What's your vision of these trends? Can you share your thoughts on how that's impacting learning and teaching? >> So there're three things that I want to quickly touch on. Number one, I think governments are beginning to recognize that they really need to change the way they approach solving social and economic problems. The pandemic has certainly calls into question that if you do not have a digital strategy, you can't find a better time to now develop and not just develop a digital strategy, but actually to put it in place. And so government are shifting very, very quickly into the Cloud and adopting digital strategy and use digital strategy to address some of the key problems that they are facing. And they have to solve them in a very short period of time. Right, We will talk about speed, the agility of the Cloud, and that's why the Cloud is so powerful for government to adopt. The second thing is that we saw a lot of schools close down across the world, UNESCO reported, what 1.5 billion students out of schools. So how then do you continue teaching and learning when you don't have physical classroom open and that's where education technology companies and, you know, heroes like Janine's company and others, there are so many of them around are able to come forward and offer their services and help schools go online, run classrooms online, continue to allow teaching and learning, you know, online. And this has really benefited the overall education system. The third thing that is happening is that I think tertiary education and maybe even (indistinct) education model will have to change. And they recognize that, you know, again, it goes back to the digital strategy that they've got to have a clear digital strategy and the education technology companies like what, who we have here today. Just the great partners that the education system need to look at to help them solve some of these problems and get to addressing giving a solution very, very quickly. >> Well, I know you're being kind of polite to the old guard, but I'm not that polite. I'll just be, say it. There's some old technology out there and Janine and Hugo, you're young enough not to know what IT means because you're born in the Cloud. So that's good for you. I remember what I teach. Like in fact, there's a, there's a joke here in the United States so with everyone at home the teachers have turned into the IT department, meaning they're helping the parents and the kids figure out how to go unmute and how to configure a network address translation if their routers don't work, real problems. I mean, this was technology, schools were operating with low tech Zoom's out there. You've got video conferencing, you've got all kinds of things, but now there's all that support that's involved. And so what's happening is it's highlighting the real problems of the institutional technology. So Vincent, I'll start with you. This is a big problem. So Cloud solves that one, you guys have pretty much helped IT do things that they don't want to do anymore by automation. This is an opportunity, not necessarily.. There's a problem today, but it's an opportunity tomorrow. Could you just quickly talk about how you see the Cloud, helping all this manual training and learning new tools. >> Absolutely. So I want to say and put forth a hypothesis and that hypothesis is simply this. We are all now living in a Cloud empowered economy, whether we like it or not, we are touching and using services that are powered by the Cloud. And a lot of them are powered by the AWS Cloud, but we don't know about it. A lot of people just don't know, right? Whether you are watching Netflix, well in the old days, you're buying tickets and booking hotels on Expedia, or now you're actually playing games on Epic Entertainment, you know, playing Fortnite and all those kinds of games you're already using and a consumer of the Cloud. And so one of the big ideas that we have is we really want to educate and create awareness of top computing for every single person. If it can be used for innovation and to bring about benefits to society that is a common knowledge that everyone needs to have. And so the first big idea is, want to make sure that everyone actually is educated on Cloud literacy. The second thing is for those who have not embarked on a clear Cloud strategy, this is the time don't wait for another pandemic to happen because you want to be ready. You want to be prepared for the unknown, which is what a lot of people are faced with. And you want to get ahead of the curve. And so education, training yourself, getting some learning done. And that's really very, very important as a next step to prepare yourself to face the uncertainty and having programs like AWS EdStart actually helps to empower and catalyze innovation in the education industry that our two founders have actually demonstrated. So back to you, John. >> Congratulation on the EdStart, we'll get into that and real quickly, EdStart but let's first get the born in the Cloud generation Janine and Hugo you guys are competing, you got to get your apps out there. You've got to get your solutions. You're born in the Cloud. You have to go compete with the existing solutions. How do you view that? What's your strategy? What's your mindset, Janine, we'll start with you. >> So for us, we are very aware that we are solving a problem that has never been solved, right? If not, we wouldn't have so many people who are not learning. So this is a very big problem. And being able to leverage on Cloud technology means that we are able to just focus on what we do best, right? How do we make sure that learning is sufficient and learning is effective. And how do we get people motivated and all those sort of great things leveraging on game mechanics, social network, and incentives. And then while we do that on the Cloud side, we can just put that almost ourselves, everything to AWS Cloud technology to help us not worry about that. And you were absolutely right. The pandemic actually woke up a lot of people and has organizations like myself. We start to get queries from governments and other, even big NGOs on, you know, because before COVID we had to really do our best to convince them until (indistinct) are dry >> (indistinct) knock on doors and convince people. >> Yes. And now we don't have to do that. It's the other way around. So we are really, you know, we appreciate this opportunity and also we want to help people realize that in order to.. By adopting either a blended approach or adopting technology means that you can do mass customization of learning as well. And that's, what we could do to really push learning to the next level. So, and, there are a few other creative things that we've done with governments, for example, with the government of East Java on top of just using the education platform, as it is an educational platform, which is education (indistinct) on our civilization, they have added in a module that teaches COVID because, you know, their health care system is really under a lot of strain there, right? And adding this component in and the most popular mini game in that component is this game called Hoax Or Not. And it teaches people to identify what's fake news and what's real news. And that really went very popular and very well in that region of 25 million people. So that became not only just boring school subjects, but it can be used to teach many different things. And following that project, we are working with the Federal Government of Indonesia to talk about (indistinct) and even a very difficult topic like sex education as well. >> Yeah. And the learning is nonlinear, it's horizontally scalable, it's network graph. So you can learn, share about news. And this is contextual data. It's not just learning, it's everything. It's not like, you know, linear learning. It's a whole nother ballgame, Hugo, your competitive strategy. You're out there now, you got the COVID world. How are you competing? How's Amazon helping you? >> Absolutely John, look, this is an interesting one because the common competitor that we have are educational psychologist, they're not at tech. So I wouldn't say that we're competing against a competitor per se. I would say that we are competing against some old way of doing things. The challenge for us is to empower people, to be comfortable with having a machine, you know, analyzing your kid's audio recording and telling you if it's likely to be dyslexia. And this concept obviously is very new. You know, we can see this in other industry with AI, you know, you have the app that Stanford created to diagnose skin cancer by taking a photo of your skin. So it's being done in different industry. So the biggest challenge for us is really about the old way of doing things. What's been really interesting for us is that you know, education is lifelong, you know, you have a big pot in school, but when you're an adult you learn and, you know, we've been doing some very interesting work with the Justice Department where, you know, we look at inmate and, and, you know, often when people go to jail, they have, you know, some literacy difficulty. And so we've been doing some very interesting work in this field. We're also doing some very interesting work with HR and company who want to understand their staff and put management in place so that every single person in the company are empowered to do the job and, you know, achieve success. So, you know, we're not competing against Ed Tech. And often when we talk to other Ed Tech company, we come before, you know, we don't provide a learning solution. We provide an assessment solution, an E assessment solution. So really John, what we competing against is an old way of doing things. >> And that's exactly why the Cloud's so successful. You change the economics. You're actually a net new benefit. And I think the Cloud gives you speed. And your only challenge is getting the word out because the economics are just game changing, right? So that's how Amazon does so well, by the way, you can take all our recordings from theCUBE interviews, all my interviews and let me know how I do, okay. So got all the, got all the voice recordings for my interview. I'm sure the test will come back challenging. So take a look at that. >> Absolutely. >> Vincent I want to come back to you, but I want to ask the two founders real quick for the folks watching okay and hear about Amazon. They know the history, they know the startups that started on Amazon that became unicorns that went public. I mean, just a long list of successes born in the Cloud. You get big pay when you're successful, love that business model. But for the folks watching that are in the virtual garages or in their houses innovating and building out new ideas, what does EdStart mean for them? How does it work? Would you would recommend it? And what are some of the learnings that you have from working with EdStart? Janine We'll start with you. >> For me. So I would, for me, I would definitely highly recommend EdStart. And the reason is because EdStart, our relationship with EdStart, is almost not like a client-supplier relationship it's almost like business partners. So they not only help us with providing the technology. But on top of that, they have their system architects to work with my tech team and they have, you know, open technical hours for us to interact. And on top of that, they do many other things like building a community where, you know, people like me and Google can meet. And also other opportunities like getting out there, right? As you know, all of the startups run on a very thin budget. So how do we not pour millions of dollars into getting all that out there is another big benefit as well. So I'll definitely very much recommend EdStart. And I think another big thing is this, right? Now that we have COVID and we have demands coming from all other places including like, even (indistinct) from the Government of Gambia, you know, so how do we quickly deploy our technology right there? Or how do we deploy our technology from the people who are demanding our solution in Nigeria, right? With technology it is almost brainless. >> Yeah. The great enabling technology ecosystem to support you. I think, at the regions too. So the regions do help. I love we call them cube regions because we're on Amazon, we have our Cloud Hugo, EdStart your observations, experience and learnings from working with AWS. >> Absolutely. Look, there's a lot to say, so I'll try and make it short for anyone, but, so for us and me personally, and also as an individual and as a founder, it's really been a 365 sort of support. So like Janine mentioned, there's the community where you can connect with existing entrepreneur. You can connect with experts in different industry. You can ask technical experts and have a, you know, office hour every week. Like you said, Janine with, your tech team talking to a Cloud architect just to unlock any problem that you may have. And, you know, on the business side, I would add something which for us has been really useful is the fact that when we've approached government, being able to say that we have the support of AWS and that we work with them to establish data integrity, making sure everything is properly secured and all that sort of thing has been really helpful in terms of moving forward with discussion with potential client and government as well. So there's also the business aspect side of things, where when people see you, there's a perceived value that, you know, your entourage is smart people and people who are capable of doing great things. So that's been also really helpful. >> You know, that's a great point. The AppSec review process as you do deals is a lot easier when you're on AWS. Vincent we're a little bit over time. What a great panel here. Close us out, share with us what's next for you guys. You've got a great startup ecosystem and doing some great work out there and education as well, healthcare, how's your world going on? Take a minute to explain what's going on in your world. >> John I'm part of the public sector team worldwide in AWS, we have very clear mission statements. And the first is, you know, we want to bring about disruptive innovation. And the AWS Cloud is really the platform where so many of our Ed Techs, whether it's (indistinct) Health Tech, Gulf Tech, all those who are developing solutions to help our governments and our education institutions, our healthcare institutions to really be better at what they do. We want to bring about those disruptive innovations to the market, as fast as possible. It's just an honor and a privilege for us to be working. And why is that important? It's because it's linked to our second mission, which is to really make the world a better place to really deliver.. The kind of work that Hugo and Janine are doing. We cannot do it by ourselves. We need specialists and really people with brilliant ideas and think big vision to be able to carry out what they are doing. And so we're just honored and privileged to be part of their work. And in delivering this impact to society. >> The expansion of AWS out in your area has been phenomenal growth. I've been saying to Teresa Carlson and Andy Jassy and the folks at AWS for many, many years, that when you move fast with innovation, the public sector and the private partnerships come together, you starting to see that blending. And you've got some great founders here making a social impact, transforming teaching and learning. So congratulations, Janine and Hugo. Thank you for sharing your story on theCUBE. Thanks for joining. >> Thank you for having us >> thanks John >> Thank you, John. I'm John Furrier with theCUBE Virtual we're remote. We're not in person this year because of the pandemic you're watching AWS Public Sector Online Summit. Thank you for watching. (soft music)

>>don't talk mhm, >>Okay, thing is my presentation on coherent nonlinear dynamics and combinatorial optimization. This is going to be a talk to introduce an approach we're taking to the analysis of the performance of coherent using machines. So let me start with a brief introduction to easing optimization. The easing model represents a set of interacting magnetic moments or spins the total energy given by the expression shown at the bottom left of this slide. Here, the signal variables are meditate binary values. The Matrix element J. I. J. Represents the interaction, strength and signed between any pair of spins. I. J and A Chive represents a possible local magnetic field acting on each thing. The easing ground state problem is to find an assignment of binary spin values that achieves the lowest possible value of total energy. And an instance of the easing problem is specified by giving numerical values for the Matrix J in Vector H. Although the easy model originates in physics, we understand the ground state problem to correspond to what would be called quadratic binary optimization in the field of operations research and in fact, in terms of computational complexity theory, it could be established that the easing ground state problem is np complete. Qualitatively speaking, this makes the easing problem a representative sort of hard optimization problem, for which it is expected that the runtime required by any computational algorithm to find exact solutions should, as anatomically scale exponentially with the number of spends and for worst case instances at each end. Of course, there's no reason to believe that the problem instances that actually arrives in practical optimization scenarios are going to be worst case instances. And it's also not generally the case in practical optimization scenarios that we demand absolute optimum solutions. Usually we're more interested in just getting the best solution we can within an affordable cost, where costs may be measured in terms of time, service fees and or energy required for a computation. This focuses great interest on so called heuristic algorithms for the easing problem in other NP complete problems which generally get very good but not guaranteed optimum solutions and run much faster than algorithms that are designed to find absolute Optima. To get some feeling for present day numbers, we can consider the famous traveling salesman problem for which extensive compilations of benchmarking data may be found online. A recent study found that the best known TSP solver required median run times across the Library of Problem instances That scaled is a very steep route exponential for end up to approximately 4500. This gives some indication of the change in runtime scaling for generic as opposed the worst case problem instances. Some of the instances considered in this study were taken from a public library of T SPS derived from real world Veil aside design data. This feels I TSP Library includes instances within ranging from 131 to 744,710 instances from this library with end between 6880 13,584 were first solved just a few years ago in 2017 requiring days of run time and a 48 core to King hurts cluster, while instances with and greater than or equal to 14,233 remain unsolved exactly by any means. Approximate solutions, however, have been found by heuristic methods for all instances in the VLS i TSP library with, for example, a solution within 0.14% of a no lower bound, having been discovered, for instance, with an equal 19,289 requiring approximately two days of run time on a single core of 2.4 gigahertz. Now, if we simple mindedly extrapolate the root exponential scaling from the study up to an equal 4500, we might expect that an exact solver would require something more like a year of run time on the 48 core cluster used for the N equals 13,580 for instance, which shows how much a very small concession on the quality of the solution makes it possible to tackle much larger instances with much lower cost. At the extreme end, the largest TSP ever solved exactly has an equal 85,900. This is an instance derived from 19 eighties VLSI design, and it's required 136 CPU. Years of computation normalized to a single cord, 2.4 gigahertz. But the 24 larger so called world TSP benchmark instance within equals 1,904,711 has been solved approximately within ophthalmology. Gap bounded below 0.474%. Coming back to the general. Practical concerns have applied optimization. We may note that a recent meta study analyzed the performance of no fewer than 37 heuristic algorithms for Max cut and quadratic pioneer optimization problems and found the performance sort and found that different heuristics work best for different problem instances selected from a large scale heterogeneous test bed with some evidence but cryptic structure in terms of what types of problem instances were best solved by any given heuristic. Indeed, their their reasons to believe that these results from Mexico and quadratic binary optimization reflected general principle of performance complementarity among heuristic optimization algorithms in the practice of solving heart optimization problems there. The cerise is a critical pre processing issue of trying to guess which of a number of available good heuristic algorithms should be chosen to tackle a given problem. Instance, assuming that any one of them would incur high costs to run on a large problem, instances incidence, making an astute choice of heuristic is a crucial part of maximizing overall performance. Unfortunately, we still have very little conceptual insight about what makes a specific problem instance, good or bad for any given heuristic optimization algorithm. This has certainly been pinpointed by researchers in the field is a circumstance that must be addressed. So adding this all up, we see that a critical frontier for cutting edge academic research involves both the development of novel heuristic algorithms that deliver better performance, with lower cost on classes of problem instances that are underserved by existing approaches, as well as fundamental research to provide deep conceptual insight into what makes a given problem in, since easy or hard for such algorithms. In fact, these days, as we talk about the end of Moore's law and speculate about a so called second quantum revolution, it's natural to talk not only about novel algorithms for conventional CPUs but also about highly customized special purpose hardware architectures on which we may run entirely unconventional algorithms for combinatorial optimization such as easing problem. So against that backdrop, I'd like to use my remaining time to introduce our work on analysis of coherent using machine architectures and associate ID optimization algorithms. These machines, in general, are a novel class of information processing architectures for solving combinatorial optimization problems by embedding them in the dynamics of analog, physical or cyber physical systems, in contrast to both MAWR traditional engineering approaches that build using machines using conventional electron ICS and more radical proposals that would require large scale quantum entanglement. The emerging paradigm of coherent easing machines leverages coherent nonlinear dynamics in photonic or Opto electronic platforms to enable near term construction of large scale prototypes that leverage post Simoes information dynamics, the general structure of of current CM systems has shown in the figure on the right. The role of the easing spins is played by a train of optical pulses circulating around a fiber optical storage ring. A beam splitter inserted in the ring is used to periodically sample the amplitude of every optical pulse, and the measurement results are continually read into a refugee A, which uses them to compute perturbations to be applied to each pulse by a synchronized optical injections. These perturbations, air engineered to implement the spin, spin coupling and local magnetic field terms of the easing Hamiltonian, corresponding to a linear part of the CME Dynamics, a synchronously pumped parametric amplifier denoted here as PPL and Wave Guide adds a crucial nonlinear component to the CIA and Dynamics as well. In the basic CM algorithm, the pump power starts very low and has gradually increased at low pump powers. The amplitude of the easing spin pulses behaviors continuous, complex variables. Who Israel parts which can be positive or negative, play the role of play the role of soft or perhaps mean field spins once the pump, our crosses the threshold for parametric self oscillation. In the optical fiber ring, however, the attitudes of the easing spin pulses become effectively Qantas ized into binary values while the pump power is being ramped up. The F P J subsystem continuously applies its measurement based feedback. Implementation of the using Hamiltonian terms, the interplay of the linear rised using dynamics implemented by the F P G A and the threshold conversation dynamics provided by the sink pumped Parametric amplifier result in the final state of the optical optical pulse amplitude at the end of the pump ramp that could be read as a binary strain, giving a proposed solution of the easing ground state problem. This method of solving easing problem seems quite different from a conventional algorithm that runs entirely on a digital computer as a crucial aspect of the computation is performed physically by the analog, continuous, coherent, nonlinear dynamics of the optical degrees of freedom. In our efforts to analyze CIA and performance, we have therefore turned to the tools of dynamical systems theory, namely, a study of modifications, the evolution of critical points and apologies of hetero clinic orbits and basins of attraction. We conjecture that such analysis can provide fundamental insight into what makes certain optimization instances hard or easy for coherent using machines and hope that our approach can lead to both improvements of the course, the AM algorithm and a pre processing rubric for rapidly assessing the CME suitability of new instances. Okay, to provide a bit of intuition about how this all works, it may help to consider the threshold dynamics of just one or two optical parametric oscillators in the CME architecture just described. We can think of each of the pulse time slots circulating around the fiber ring, as are presenting an independent Opio. We can think of a single Opio degree of freedom as a single, resonant optical node that experiences linear dissipation, do toe out coupling loss and gain in a pump. Nonlinear crystal has shown in the diagram on the upper left of this slide as the pump power is increased from zero. As in the CME algorithm, the non linear game is initially to low toe overcome linear dissipation, and the Opio field remains in a near vacuum state at a critical threshold. Value gain. Equal participation in the Popeo undergoes a sort of lazing transition, and the study states of the OPIO above this threshold are essentially coherent states. There are actually two possible values of the Opio career in amplitude and any given above threshold pump power which are equal in magnitude but opposite in phase when the OPI across the special diet basically chooses one of the two possible phases randomly, resulting in the generation of a single bit of information. If we consider to uncoupled, Opio has shown in the upper right diagram pumped it exactly the same power at all times. Then, as the pump power has increased through threshold, each Opio will independently choose the phase and thus to random bits are generated for any number of uncoupled. Oppose the threshold power per opio is unchanged from the single Opio case. Now, however, consider a scenario in which the two appeals air, coupled to each other by a mutual injection of their out coupled fields has shown in the diagram on the lower right. One can imagine that depending on the sign of the coupling parameter Alfa, when one Opio is lazing, it will inject a perturbation into the other that may interfere either constructively or destructively, with the feel that it is trying to generate by its own lazing process. As a result, when came easily showed that for Alfa positive, there's an effective ferro magnetic coupling between the two Opio fields and their collective oscillation threshold is lowered from that of the independent Opio case. But on Lee for the two collective oscillation modes in which the two Opio phases are the same for Alfa Negative, the collective oscillation threshold is lowered on Lee for the configurations in which the Opio phases air opposite. So then, looking at how Alfa is related to the J. I. J matrix of the easing spin coupling Hamiltonian, it follows that we could use this simplistic to a p o. C. I am to solve the ground state problem of a fair magnetic or anti ferro magnetic ankles to easing model simply by increasing the pump power from zero and observing what phase relation occurs as the two appeals first start delays. Clearly, we can imagine generalizing this story toe larger, and however the story doesn't stay is clean and simple for all larger problem instances. And to find a more complicated example, we only need to go to n equals four for some choices of J J for n equals, for the story remains simple. Like the n equals two case. The figure on the upper left of this slide shows the energy of various critical points for a non frustrated and equals, for instance, in which the first bifurcated critical point that is the one that I forget to the lowest pump value a. Uh, this first bifurcated critical point flows as symptomatically into the lowest energy easing solution and the figure on the upper right. However, the first bifurcated critical point flows to a very good but sub optimal minimum at large pump power. The global minimum is actually given by a distinct critical critical point that first appears at a higher pump power and is not automatically connected to the origin. The basic C am algorithm is thus not able to find this global minimum. Such non ideal behaviors needs to become more confident. Larger end for the n equals 20 instance, showing the lower plots where the lower right plot is just a zoom into a region of the lower left lot. It can be seen that the global minimum corresponds to a critical point that first appears out of pump parameter, a around 0.16 at some distance from the idiomatic trajectory of the origin. That's curious to note that in both of these small and examples, however, the critical point corresponding to the global minimum appears relatively close to the idiomatic projector of the origin as compared to the most of the other local minima that appear. We're currently working to characterize the face portrait topology between the global minimum in the antibiotic trajectory of the origin, taking clues as to how the basic C am algorithm could be generalized to search for non idiomatic trajectories that jump to the global minimum during the pump ramp. Of course, n equals 20 is still too small to be of interest for practical optimization applications. But the advantage of beginning with the study of small instances is that we're able reliably to determine their global minima and to see how they relate to the 80 about trajectory of the origin in the basic C am algorithm. In the smaller and limit, we can also analyze fully quantum mechanical models of Syrian dynamics. But that's a topic for future talks. Um, existing large scale prototypes are pushing into the range of in equals 10 to the 4 10 to 5 to six. So our ultimate objective in theoretical analysis really has to be to try to say something about CIA and dynamics and regime of much larger in our initial approach to characterizing CIA and behavior in the large in regime relies on the use of random matrix theory, and this connects to prior research on spin classes, SK models and the tap equations etcetera. At present, we're focusing on statistical characterization of the CIA ingredient descent landscape, including the evolution of critical points in their Eigen value spectra. As the pump power is gradually increased. We're investigating, for example, whether there could be some way to exploit differences in the relative stability of the global minimum versus other local minima. We're also working to understand the deleterious or potentially beneficial effects of non ideologies, such as a symmetry in the implemented these and couplings. Looking one step ahead, we plan to move next in the direction of considering more realistic classes of problem instances such as quadratic, binary optimization with constraints. Eso In closing, I should acknowledge people who did the hard work on these things that I've shown eso. My group, including graduate students Ed winning, Daniel Wennberg, Tatsuya Nagamoto and Atsushi Yamamura, have been working in close collaboration with Syria Ganguly, Marty Fair and Amir Safarini Nini, all of us within the Department of Applied Physics at Stanford University. On also in collaboration with the Oshima Moto over at NTT 55 research labs, Onda should acknowledge funding support from the NSF by the Coherent Easing Machines Expedition in computing, also from NTT five research labs, Army Research Office and Exxon Mobil. Uh, that's it. Thanks very much. >>Mhm e >>t research and the Oshie for putting together this program and also the opportunity to speak here. My name is Al Gore ism or Andy and I'm from Caltech, and today I'm going to tell you about the work that we have been doing on networks off optical parametric oscillators and how we have been using them for icing machines and how we're pushing them toward Cornum photonics to acknowledge my team at Caltech, which is now eight graduate students and five researcher and postdocs as well as collaborators from all over the world, including entity research and also the funding from different places, including entity. So this talk is primarily about networks of resonate er's, and these networks are everywhere from nature. For instance, the brain, which is a network of oscillators all the way to optics and photonics and some of the biggest examples or metal materials, which is an array of small resonate er's. And we're recently the field of technological photonics, which is trying thio implement a lot of the technological behaviors of models in the condensed matter, physics in photonics and if you want to extend it even further, some of the implementations off quantum computing are technically networks of quantum oscillators. So we started thinking about these things in the context of icing machines, which is based on the icing problem, which is based on the icing model, which is the simple summation over the spins and spins can be their upward down and the couplings is given by the JJ. And the icing problem is, if you know J I J. What is the spin configuration that gives you the ground state? And this problem is shown to be an MP high problem. So it's computational e important because it's a representative of the MP problems on NPR. Problems are important because first, their heart and standard computers if you use a brute force algorithm and they're everywhere on the application side. That's why there is this demand for making a machine that can target these problems, and hopefully it can provide some meaningful computational benefit compared to the standard digital computers. So I've been building these icing machines based on this building block, which is a degenerate optical parametric. Oscillator on what it is is resonator with non linearity in it, and we pump these resonate er's and we generate the signal at half the frequency of the pump. One vote on a pump splits into two identical photons of signal, and they have some very interesting phase of frequency locking behaviors. And if you look at the phase locking behavior, you realize that you can actually have two possible phase states as the escalation result of these Opio which are off by pie, and that's one of the important characteristics of them. So I want to emphasize a little more on that and I have this mechanical analogy which are basically two simple pendulum. But there are parametric oscillators because I'm going to modulate the parameter of them in this video, which is the length of the string on by that modulation, which is that will make a pump. I'm gonna make a muscular. That'll make a signal which is half the frequency of the pump. And I have two of them to show you that they can acquire these face states so they're still facing frequency lock to the pump. But it can also lead in either the zero pie face states on. The idea is to use this binary phase to represent the binary icing spin. So each opio is going to represent spin, which can be either is your pie or up or down. And to implement the network of these resonate er's, we use the time off blood scheme, and the idea is that we put impulses in the cavity. These pulses air separated by the repetition period that you put in or t r. And you can think about these pulses in one resonator, xaz and temporarily separated synthetic resonate Er's if you want a couple of these resonator is to each other, and now you can introduce these delays, each of which is a multiple of TR. If you look at the shortest delay it couples resonator wanted to 2 to 3 and so on. If you look at the second delay, which is two times a rotation period, the couple's 123 and so on. And if you have and minus one delay lines, then you can have any potential couplings among these synthetic resonate er's. And if I can introduce these modulators in those delay lines so that I can strength, I can control the strength and the phase of these couplings at the right time. Then I can have a program will all toe all connected network in this time off like scheme, and the whole physical size of the system scales linearly with the number of pulses. So the idea of opium based icing machine is didn't having these o pos, each of them can be either zero pie and I can arbitrarily connect them to each other. And then I start with programming this machine to a given icing problem by just setting the couplings and setting the controllers in each of those delight lines. So now I have a network which represents an icing problem. Then the icing problem maps to finding the face state that satisfy maximum number of coupling constraints. And the way it happens is that the icing Hamiltonian maps to the linear loss of the network. And if I start adding gain by just putting pump into the network, then the OPI ohs are expected to oscillate in the lowest, lowest lost state. And, uh and we have been doing these in the past, uh, six or seven years and I'm just going to quickly show you the transition, especially what happened in the first implementation, which was using a free space optical system and then the guided wave implementation in 2016 and the measurement feedback idea which led to increasing the size and doing actual computation with these machines. So I just want to make this distinction here that, um, the first implementation was an all optical interaction. We also had an unequal 16 implementation. And then we transition to this measurement feedback idea, which I'll tell you quickly what it iss on. There's still a lot of ongoing work, especially on the entity side, to make larger machines using the measurement feedback. But I'm gonna mostly focused on the all optical networks and how we're using all optical networks to go beyond simulation of icing Hamiltonian both in the linear and non linear side and also how we're working on miniaturization of these Opio networks. So the first experiment, which was the four opium machine, it was a free space implementation and this is the actual picture off the machine and we implemented a small and it calls for Mexico problem on the machine. So one problem for one experiment and we ran the machine 1000 times, we looked at the state and we always saw it oscillate in one of these, um, ground states of the icing laboratoria. So then the measurement feedback idea was to replace those couplings and the controller with the simulator. So we basically simulated all those coherent interactions on on FB g. A. And we replicated the coherent pulse with respect to all those measurements. And then we injected it back into the cavity and on the near to you still remain. So it still is a non. They're dynamical system, but the linear side is all simulated. So there are lots of questions about if this system is preserving important information or not, or if it's gonna behave better. Computational wars. And that's still ah, lot of ongoing studies. But nevertheless, the reason that this implementation was very interesting is that you don't need the end minus one delight lines so you can just use one. Then you can implement a large machine, and then you can run several thousands of problems in the machine, and then you can compare the performance from the computational perspective Looks so I'm gonna split this idea of opium based icing machine into two parts. One is the linear part, which is if you take out the non linearity out of the resonator and just think about the connections. You can think about this as a simple matrix multiplication scheme. And that's basically what gives you the icing Hambletonian modeling. So the optical laws of this network corresponds to the icing Hamiltonian. And if I just want to show you the example of the n equals for experiment on all those face states and the history Graham that we saw, you can actually calculate the laws of each of those states because all those interferences in the beam splitters and the delay lines are going to give you a different losses. And then you will see that the ground states corresponds to the lowest laws of the actual optical network. If you add the non linearity, the simple way of thinking about what the non linearity does is that it provides to gain, and then you start bringing up the gain so that it hits the loss. Then you go through the game saturation or the threshold which is going to give you this phase bifurcation. So you go either to zero the pie face state. And the expectation is that Theis, the network oscillates in the lowest possible state, the lowest possible loss state. There are some challenges associated with this intensity Durban face transition, which I'm going to briefly talk about. I'm also going to tell you about other types of non aerodynamics that we're looking at on the non air side of these networks. So if you just think about the linear network, we're actually interested in looking at some technological behaviors in these networks. And the difference between looking at the technological behaviors and the icing uh, machine is that now, First of all, we're looking at the type of Hamilton Ian's that are a little different than the icing Hamilton. And one of the biggest difference is is that most of these technological Hamilton Ian's that require breaking the time reversal symmetry, meaning that you go from one spin to in the one side to another side and you get one phase. And if you go back where you get a different phase, and the other thing is that we're not just interested in finding the ground state, we're actually now interesting and looking at all sorts of states and looking at the dynamics and the behaviors of all these states in the network. So we started with the simplest implementation, of course, which is a one d chain of thes resonate, er's, which corresponds to a so called ssh model. In the technological work, we get the similar energy to los mapping and now we can actually look at the band structure on. This is an actual measurement that we get with this associate model and you see how it reasonably how How? Well, it actually follows the prediction and the theory. One of the interesting things about the time multiplexing implementation is that now you have the flexibility of changing the network as you are running the machine. And that's something unique about this time multiplex implementation so that we can actually look at the dynamics. And one example that we have looked at is we can actually go through the transition off going from top A logical to the to the standard nontrivial. I'm sorry to the trivial behavior of the network. You can then look at the edge states and you can also see the trivial and states and the technological at states actually showing up in this network. We have just recently implement on a two D, uh, network with Harper Hofstadter model and when you don't have the results here. But we're one of the other important characteristic of time multiplexing is that you can go to higher and higher dimensions and keeping that flexibility and dynamics, and we can also think about adding non linearity both in a classical and quantum regimes, which is going to give us a lot of exotic, no classical and quantum, non innate behaviors in these networks. Yeah, So I told you about the linear side. Mostly let me just switch gears and talk about the nonlinear side of the network. And the biggest thing that I talked about so far in the icing machine is this face transition that threshold. So the low threshold we have squeezed state in these. Oh, pios, if you increase the pump, we go through this intensity driven phase transition and then we got the face stays above threshold. And this is basically the mechanism off the computation in these O pos, which is through this phase transition below to above threshold. So one of the characteristics of this phase transition is that below threshold, you expect to see quantum states above threshold. You expect to see more classical states or coherent states, and that's basically corresponding to the intensity off the driving pump. So it's really hard to imagine that it can go above threshold. Or you can have this friends transition happen in the all in the quantum regime. And there are also some challenges associated with the intensity homogeneity off the network, which, for example, is if one opioid starts oscillating and then its intensity goes really high. Then it's going to ruin this collective decision making off the network because of the intensity driven face transition nature. So So the question is, can we look at other phase transitions? Can we utilize them for both computing? And also can we bring them to the quantum regime on? I'm going to specifically talk about the face transition in the spectral domain, which is the transition from the so called degenerate regime, which is what I mostly talked about to the non degenerate regime, which happens by just tuning the phase of the cavity. And what is interesting is that this phase transition corresponds to a distinct phase noise behavior. So in the degenerate regime, which we call it the order state, you're gonna have the phase being locked to the phase of the pump. As I talked about non degenerate regime. However, the phase is the phase is mostly dominated by the quantum diffusion. Off the off the phase, which is limited by the so called shallow towns limit, and you can see that transition from the general to non degenerate, which also has distinct symmetry differences. And this transition corresponds to a symmetry breaking in the non degenerate case. The signal can acquire any of those phases on the circle, so it has a you one symmetry. Okay, and if you go to the degenerate case, then that symmetry is broken and you only have zero pie face days I will look at. So now the question is can utilize this phase transition, which is a face driven phase transition, and can we use it for similar computational scheme? So that's one of the questions that were also thinking about. And it's not just this face transition is not just important for computing. It's also interesting from the sensing potentials and this face transition, you can easily bring it below threshold and just operated in the quantum regime. Either Gaussian or non Gaussian. If you make a network of Opio is now, we can see all sorts off more complicated and more interesting phase transitions in the spectral domain. One of them is the first order phase transition, which you get by just coupling to Opio, and that's a very abrupt face transition and compared to the to the single Opio phase transition. And if you do the couplings right, you can actually get a lot of non her mission dynamics and exceptional points, which are actually very interesting to explore both in the classical and quantum regime. And I should also mention that you can think about the cup links to be also nonlinear couplings. And that's another behavior that you can see, especially in the nonlinear in the non degenerate regime. So with that, I basically told you about these Opio networks, how we can think about the linear scheme and the linear behaviors and how we can think about the rich, nonlinear dynamics and non linear behaviors both in the classical and quantum regime. I want to switch gear and tell you a little bit about the miniaturization of these Opio networks. And of course, the motivation is if you look at the electron ICS and what we had 60 or 70 years ago with vacuum tube and how we transition from relatively small scale computers in the order of thousands of nonlinear elements to billions of non elements where we are now with the optics is probably very similar to 70 years ago, which is a table talk implementation. And the question is, how can we utilize nano photonics? I'm gonna just briefly show you the two directions on that which we're working on. One is based on lithium Diabate, and the other is based on even a smaller resonate er's could you? So the work on Nana Photonic lithium naive. It was started in collaboration with Harvard Marko Loncar, and also might affair at Stanford. And, uh, we could show that you can do the periodic polling in the phenomenon of it and get all sorts of very highly nonlinear processes happening in this net. Photonic periodically polls if, um Diabate. And now we're working on building. Opio was based on that kind of photonic the film Diabate. And these air some some examples of the devices that we have been building in the past few months, which I'm not gonna tell you more about. But the O. P. O. S. And the Opio Networks are in the works. And that's not the only way of making large networks. Um, but also I want to point out that The reason that these Nana photonic goblins are actually exciting is not just because you can make a large networks and it can make him compact in a in a small footprint. They also provide some opportunities in terms of the operation regime. On one of them is about making cat states and Opio, which is, can we have the quantum superposition of the zero pie states that I talked about and the Net a photonic within? I've It provides some opportunities to actually get closer to that regime because of the spatial temporal confinement that you can get in these wave guides. So we're doing some theory on that. We're confident that the type of non linearity two losses that it can get with these platforms are actually much higher than what you can get with other platform their existing platforms and to go even smaller. We have been asking the question off. What is the smallest possible Opio that you can make? Then you can think about really wavelength scale type, resonate er's and adding the chi to non linearity and see how and when you can get the Opio to operate. And recently, in collaboration with us see, we have been actually USC and Creole. We have demonstrated that you can use nano lasers and get some spin Hamilton and implementations on those networks. So if you can build the a P. O s, we know that there is a path for implementing Opio Networks on on such a nano scale. So we have looked at these calculations and we try to estimate the threshold of a pos. Let's say for me resonator and it turns out that it can actually be even lower than the type of bulk Pip Llano Pos that we have been building in the past 50 years or so. So we're working on the experiments and we're hoping that we can actually make even larger and larger scale Opio networks. So let me summarize the talk I told you about the opium networks and our work that has been going on on icing machines and the measurement feedback. And I told you about the ongoing work on the all optical implementations both on the linear side and also on the nonlinear behaviors. And I also told you a little bit about the efforts on miniaturization and going to the to the Nano scale. So with that, I would like Thio >>three from the University of Tokyo. Before I thought that would like to thank you showing all the stuff of entity for the invitation and the organization of this online meeting and also would like to say that it has been very exciting to see the growth of this new film lab. And I'm happy to share with you today of some of the recent works that have been done either by me or by character of Hong Kong. Honest Group indicates the title of my talk is a neuro more fic in silica simulator for the communities in machine. And here is the outline I would like to make the case that the simulation in digital Tektronix of the CME can be useful for the better understanding or improving its function principles by new job introducing some ideas from neural networks. This is what I will discuss in the first part and then it will show some proof of concept of the game and performance that can be obtained using dissimulation in the second part and the protection of the performance that can be achieved using a very large chaos simulator in the third part and finally talk about future plans. So first, let me start by comparing recently proposed izing machines using this table there is elected from recent natural tronics paper from the village Park hard people, and this comparison shows that there's always a trade off between energy efficiency, speed and scalability that depends on the physical implementation. So in red, here are the limitation of each of the servers hardware on, interestingly, the F p G, a based systems such as a producer, digital, another uh Toshiba beautification machine or a recently proposed restricted Bozeman machine, FPD A by a group in Berkeley. They offer a good compromise between speed and scalability. And this is why, despite the unique advantage that some of these older hardware have trust as the currency proposition in Fox, CBS or the energy efficiency off memory Sisters uh P. J. O are still an attractive platform for building large organizing machines in the near future. The reason for the good performance of Refugee A is not so much that they operate at the high frequency. No, there are particular in use, efficient, but rather that the physical wiring off its elements can be reconfigured in a way that limits the funding human bottleneck, larger, funny and phenols and the long propagation video information within the system. In this respect, the LPGA is They are interesting from the perspective off the physics off complex systems, but then the physics of the actions on the photos. So to put the performance of these various hardware and perspective, we can look at the competition of bringing the brain the brain complete, using billions of neurons using only 20 watts of power and operates. It's a very theoretically slow, if we can see and so this impressive characteristic, they motivate us to try to investigate. What kind of new inspired principles be useful for designing better izing machines? The idea of this research project in the future collaboration it's to temporary alleviates the limitations that are intrinsic to the realization of an optical cortex in machine shown in the top panel here. By designing a large care simulator in silicone in the bottom here that can be used for digesting the better organization principles of the CIA and this talk, I will talk about three neuro inspired principles that are the symmetry of connections, neural dynamics orphan chaotic because of symmetry, is interconnectivity the infrastructure? No. Next talks are not composed of the reputation of always the same types of non environments of the neurons, but there is a local structure that is repeated. So here's the schematic of the micro column in the cortex. And lastly, the Iraqi co organization of connectivity connectivity is organizing a tree structure in the brain. So here you see a representation of the Iraqi and organization of the monkey cerebral cortex. So how can these principles we used to improve the performance of the icing machines? And it's in sequence stimulation. So, first about the two of principles of the estimate Trian Rico structure. We know that the classical approximation of the car testing machine, which is the ground toe, the rate based on your networks. So in the case of the icing machines, uh, the okay, Scott approximation can be obtained using the trump active in your position, for example, so the times of both of the system they are, they can be described by the following ordinary differential equations on in which, in case of see, I am the X, I represent the in phase component of one GOP Oh, Theo f represents the monitor optical parts, the district optical Parametric amplification and some of the good I JoJo extra represent the coupling, which is done in the case of the measure of feedback coupling cm using oh, more than detection and refugee A and then injection off the cooking time and eso this dynamics in both cases of CNN in your networks, they can be written as the grand set of a potential function V, and this written here, and this potential functionally includes the rising Maccagnan. So this is why it's natural to use this type of, uh, dynamics to solve the icing problem in which the Omega I J or the eyes in coping and the H is the extension of the icing and attorney in India and expect so. Not that this potential function can only be defined if the Omega I j. R. A. Symmetric. So the well known problem of this approach is that this potential function V that we obtain is very non convicts at low temperature, and also one strategy is to gradually deformed this landscape, using so many in process. But there is no theorem. Unfortunately, that granted conventions to the global minimum of There's even Tony and using this approach. And so this is why we propose, uh, to introduce a macro structures of the system where one analog spin or one D O. P. O is replaced by a pair off one another spin and one error, according viable. And the addition of this chemical structure introduces a symmetry in the system, which in terms induces chaotic dynamics, a chaotic search rather than a learning process for searching for the ground state of the icing. Every 20 within this massacre structure the role of the er variable eyes to control the amplitude off the analog spins toe force. The amplitude of the expense toe become equal to certain target amplitude a uh and, uh, and this is done by modulating the strength off the icing complaints or see the the error variable E I multiply the icing complaint here in the dynamics off air d o p. O. On then the dynamics. The whole dynamics described by this coupled equations because the e I do not necessarily take away the same value for the different. I thesis introduces a symmetry in the system, which in turn creates security dynamics, which I'm sure here for solving certain current size off, um, escape problem, Uh, in which the X I are shown here and the i r from here and the value of the icing energy showing the bottom plots. You see this Celtics search that visit various local minima of the as Newtonian and eventually finds the global minimum? Um, it can be shown that this modulation off the target opportunity can be used to destabilize all the local minima off the icing evertonians so that we're gonna do not get stuck in any of them. On more over the other types of attractors I can eventually appear, such as limits I contractors, Okot contractors. They can also be destabilized using the motivation of the target and Batuta. And so we have proposed in the past two different moderation of the target amateur. The first one is a modulation that ensure the uh 100 reproduction rate of the system to become positive on this forbids the creation off any nontrivial tractors. And but in this work, I will talk about another moderation or arrested moderation which is given here. That works, uh, as well as this first uh, moderation, but is easy to be implemented on refugee. So this couple of the question that represent becoming the stimulation of the cortex in machine with some error correction they can be implemented especially efficiently on an F B. G. And here I show the time that it takes to simulate three system and also in red. You see, at the time that it takes to simulate the X I term the EI term, the dot product and the rising Hamiltonian for a system with 500 spins and Iraq Spain's equivalent to 500 g. O. P. S. So >>in >>f b d a. The nonlinear dynamics which, according to the digital optical Parametric amplification that the Opa off the CME can be computed in only 13 clock cycles at 300 yards. So which corresponds to about 0.1 microseconds. And this is Toby, uh, compared to what can be achieved in the measurements back O C. M. In which, if we want to get 500 timer chip Xia Pios with the one she got repetition rate through the obstacle nine narrative. Uh, then way would require 0.5 microseconds toe do this so the submission in F B J can be at least as fast as ah one g repression. Uh, replicate pulsed laser CIA Um, then the DOT product that appears in this differential equation can be completed in 43 clock cycles. That's to say, one microseconds at 15 years. So I pieced for pouring sizes that are larger than 500 speeds. The dot product becomes clearly the bottleneck, and this can be seen by looking at the the skating off the time the numbers of clock cycles a text to compute either the non in your optical parts or the dog products, respect to the problem size. And And if we had infinite amount of resources and PGA to simulate the dynamics, then the non illogical post can could be done in the old one. On the mattress Vector product could be done in the low carrot off, located off scales as a look at it off and and while the guide off end. Because computing the dot product involves assuming all the terms in the product, which is done by a nephew, GE by another tree, which heights scarce logarithmic any with the size of the system. But This is in the case if we had an infinite amount of resources on the LPGA food, but for dealing for larger problems off more than 100 spins. Usually we need to decompose the metrics into ah, smaller blocks with the block side that are not you here. And then the scaling becomes funny, non inner parts linear in the end, over you and for the products in the end of EU square eso typically for low NF pdf cheap PGA you the block size off this matrix is typically about 100. So clearly way want to make you as large as possible in order to maintain this scanning in a log event for the numbers of clock cycles needed to compute the product rather than this and square that occurs if we decompose the metrics into smaller blocks. But the difficulty in, uh, having this larger blocks eyes that having another tree very large Haider tree introduces a large finding and finance and long distance start a path within the refugee. So the solution to get higher performance for a simulator of the contest in machine eyes to get rid of this bottleneck for the dot product by increasing the size of this at the tree. And this can be done by organizing your critique the electrical components within the LPGA in order which is shown here in this, uh, right panel here in order to minimize the finding finance of the system and to minimize the long distance that a path in the in the fpt So I'm not going to the details of how this is implemented LPGA. But just to give you a idea off why the Iraqi Yahiko organization off the system becomes the extremely important toe get good performance for similar organizing machine. So instead of instead of getting into the details of the mpg implementation, I would like to give some few benchmark results off this simulator, uh, off the that that was used as a proof of concept for this idea which is can be found in this archive paper here and here. I should results for solving escape problems. Free connected person, randomly person minus one spring last problems and we sure, as we use as a metric the numbers of the mattress Victor products since it's the bottleneck of the computation, uh, to get the optimal solution of this escape problem with the Nina successful BT against the problem size here and and in red here, this propose FDJ implementation and in ah blue is the numbers of retrospective product that are necessary for the C. I am without error correction to solve this escape programs and in green here for noisy means in an evening which is, uh, behavior with similar to the Cartesian mission. Uh, and so clearly you see that the scaring off the numbers of matrix vector product necessary to solve this problem scales with a better exponents than this other approaches. So So So that's interesting feature of the system and next we can see what is the real time to solution to solve this SK instances eso in the last six years, the time institution in seconds to find a grand state of risk. Instances remain answers probability for different state of the art hardware. So in red is the F B g. A presentation proposing this paper and then the other curve represent Ah, brick a local search in in orange and silver lining in purple, for example. And so you see that the scaring off this purpose simulator is is rather good, and that for larger plant sizes we can get orders of magnitude faster than the state of the art approaches. Moreover, the relatively good scanning off the time to search in respect to problem size uh, they indicate that the FPD implementation would be faster than risk. Other recently proposed izing machine, such as the hope you know, natural complimented on memories distance that is very fast for small problem size in blue here, which is very fast for small problem size. But which scanning is not good on the same thing for the restricted Bosman machine. Implementing a PGA proposed by some group in Broken Recently Again, which is very fast for small parliament sizes but which canning is bad so that a dis worse than the proposed approach so that we can expect that for programs size is larger than 1000 spins. The proposed, of course, would be the faster one. Let me jump toe this other slide and another confirmation that the scheme scales well that you can find the maximum cut values off benchmark sets. The G sets better candidates that have been previously found by any other algorithms, so they are the best known could values to best of our knowledge. And, um or so which is shown in this paper table here in particular, the instances, uh, 14 and 15 of this G set can be We can find better converse than previously known, and we can find this can vary is 100 times faster than the state of the art algorithm and CP to do this which is a very common Kasich. It s not that getting this a good result on the G sets, they do not require ah, particular hard tuning of the parameters. So the tuning issuing here is very simple. It it just depends on the degree off connectivity within each graph. And so this good results on the set indicate that the proposed approach would be a good not only at solving escape problems in this problems, but all the types off graph sizing problems on Mexican province in communities. So given that the performance off the design depends on the height of this other tree, we can try to maximize the height of this other tree on a large F p g a onda and carefully routing the components within the P G A and and we can draw some projections of what type of performance we can achieve in the near future based on the, uh, implementation that we are currently working. So here you see projection for the time to solution way, then next property for solving this escape programs respect to the prime assize. And here, compared to different with such publicizing machines, particularly the digital. And, you know, 42 is shown in the green here, the green line without that's and, uh and we should two different, uh, hypothesis for this productions either that the time to solution scales as exponential off n or that the time of social skills as expression of square root off. So it seems, according to the data, that time solution scares more as an expression of square root of and also we can be sure on this and this production show that we probably can solve prime escape problem of science 2000 spins, uh, to find the rial ground state of this problem with 99 success ability in about 10 seconds, which is much faster than all the other proposed approaches. So one of the future plans for this current is in machine simulator. So the first thing is that we would like to make dissimulation closer to the rial, uh, GOP oh, optical system in particular for a first step to get closer to the system of a measurement back. See, I am. And to do this what is, uh, simulate Herbal on the p a is this quantum, uh, condoms Goshen model that is proposed described in this paper and proposed by people in the in the Entity group. And so the idea of this model is that instead of having the very simple or these and have shown previously, it includes paired all these that take into account on me the mean off the awesome leverage off the, uh, European face component, but also their violence s so that we can take into account more quantum effects off the g o p. O, such as the squeezing. And then we plan toe, make the simulator open access for the members to run their instances on the system. There will be a first version in September that will be just based on the simple common line access for the simulator and in which will have just a classic or approximation of the system. We don't know Sturm, binary weights and museum in term, but then will propose a second version that would extend the current arising machine to Iraq off F p g. A, in which we will add the more refined models truncated, ignoring the bottom Goshen model they just talked about on the support in which he valued waits for the rising problems and support the cement. So we will announce later when this is available and and far right is working >>hard comes from Universal down today in physics department, and I'd like to thank the organizers for their kind invitation to participate in this very interesting and promising workshop. Also like to say that I look forward to collaborations with with a file lab and Yoshi and collaborators on the topics of this world. So today I'll briefly talk about our attempt to understand the fundamental limits off another continues time computing, at least from the point off you off bullion satisfy ability, problem solving, using ordinary differential equations. But I think the issues that we raise, um, during this occasion actually apply to other other approaches on a log approaches as well and into other problems as well. I think everyone here knows what Dorien satisfy ability. Problems are, um, you have boolean variables. You have em clauses. Each of disjunction of collaterals literally is a variable, or it's, uh, negation. And the goal is to find an assignment to the variable, such that order clauses are true. This is a decision type problem from the MP class, which means you can checking polynomial time for satisfy ability off any assignment. And the three set is empty, complete with K three a larger, which means an efficient trees. That's over, uh, implies an efficient source for all the problems in the empty class, because all the problems in the empty class can be reduced in Polian on real time to reset. As a matter of fact, you can reduce the NP complete problems into each other. You can go from three set to set backing or two maximum dependent set, which is a set packing in graph theoretic notions or terms toe the icing graphs. A problem decision version. This is useful, and you're comparing different approaches, working on different kinds of problems when not all the closest can be satisfied. You're looking at the accusation version offset, uh called Max Set. And the goal here is to find assignment that satisfies the maximum number of clauses. And this is from the NPR class. In terms of applications. If we had inefficient sets over or np complete problems over, it was literally, positively influenced. Thousands off problems and applications in industry and and science. I'm not going to read this, but this this, of course, gives a strong motivation toe work on this kind of problems. Now our approach to set solving involves embedding the problem in a continuous space, and you use all the east to do that. So instead of working zeros and ones, we work with minus one across once, and we allow the corresponding variables toe change continuously between the two bounds. We formulate the problem with the help of a close metrics. If if a if a close, uh, does not contain a variable or its negation. The corresponding matrix element is zero. If it contains the variable in positive, for which one contains the variable in a gated for Mitt's negative one, and then we use this to formulate this products caused quote, close violation functions one for every clause, Uh, which really, continuously between zero and one. And they're zero if and only if the clause itself is true. Uh, then we form the define in order to define a dynamic such dynamics in this and dimensional hyper cube where the search happens and if they exist, solutions. They're sitting in some of the corners of this hyper cube. So we define this, uh, energy potential or landscape function shown here in a way that this is zero if and only if all the clauses all the kmc zero or the clauses off satisfied keeping these auxiliary variables a EMS always positive. And therefore, what you do here is a dynamics that is a essentially ingredient descend on this potential energy landscape. If you were to keep all the M's constant that it would get stuck in some local minimum. However, what we do here is we couple it with the dynamics we cooperated the clothes violation functions as shown here. And if he didn't have this am here just just the chaos. For example, you have essentially what case you have positive feedback. You have increasing variable. Uh, but in that case, you still get stuck would still behave will still find. So she is better than the constant version but still would get stuck only when you put here this a m which makes the dynamics in in this variable exponential like uh, only then it keeps searching until he finds a solution on deer is a reason for that. I'm not going toe talk about here, but essentially boils down toe performing a Grady and descend on a globally time barren landscape. And this is what works. Now I'm gonna talk about good or bad and maybe the ugly. Uh, this is, uh, this is What's good is that it's a hyperbolic dynamical system, which means that if you take any domain in the search space that doesn't have a solution in it or any socially than the number of trajectories in it decays exponentially quickly. And the decay rate is a characteristic in variant characteristic off the dynamics itself. Dynamical systems called the escape right the inverse off that is the time scale in which you find solutions by this by this dynamical system, and you can see here some song trajectories that are Kelty because it's it's no linear, but it's transient, chaotic. Give their sources, of course, because eventually knowledge to the solution. Now, in terms of performance here, what you show for a bunch off, um, constraint densities defined by M overran the ratio between closes toe variables for random, said Problems is random. Chris had problems, and they as its function off n And we look at money toward the wartime, the wall clock time and it behaves quite value behaves Azat party nominally until you actually he to reach the set on set transition where the hardest problems are found. But what's more interesting is if you monitor the continuous time t the performance in terms off the A narrow, continuous Time t because that seems to be a polynomial. And the way we show that is, we consider, uh, random case that random three set for a fixed constraint density Onda. We hear what you show here. Is that the right of the trash hold that it's really hard and, uh, the money through the fraction of problems that we have not been able to solve it. We select thousands of problems at that constraint ratio and resolve them without algorithm, and we monitor the fractional problems that have not yet been solved by continuous 90. And this, as you see these decays exponentially different. Educate rates for different system sizes, and in this spot shows that is dedicated behaves polynomial, or actually as a power law. So if you combine these two, you find that the time needed to solve all problems except maybe appear traction off them scales foreign or merely with the problem size. So you have paranormal, continuous time complexity. And this is also true for other types of very hard constraints and sexual problems such as exact cover, because you can always transform them into three set as we discussed before, Ramsey coloring and and on these problems, even algorithms like survey propagation will will fail. But this doesn't mean that P equals NP because what you have first of all, if you were toe implement these equations in a device whose behavior is described by these, uh, the keys. Then, of course, T the continue style variable becomes a physical work off. Time on that will be polynomial is scaling, but you have another other variables. Oxidative variables, which structured in an exponential manner. So if they represent currents or voltages in your realization and it would be an exponential cost Al Qaeda. But this is some kind of trade between time and energy, while I know how toe generate energy or I don't know how to generate time. But I know how to generate energy so it could use for it. But there's other issues as well, especially if you're trying toe do this son and digital machine but also happens. Problems happen appear. Other problems appear on in physical devices as well as we discuss later. So if you implement this in GPU, you can. Then you can get in order off to magnitude. Speed up. And you can also modify this to solve Max sad problems. Uh, quite efficiently. You are competitive with the best heuristic solvers. This is a weather problems. In 2016 Max set competition eso so this this is this is definitely this seems like a good approach, but there's off course interesting limitations, I would say interesting, because it kind of makes you think about what it means and how you can exploit this thes observations in understanding better on a low continues time complexity. If you monitored the discrete number the number of discrete steps. Don't buy the room, Dakota integrator. When you solve this on a digital machine, you're using some kind of integrator. Um and you're using the same approach. But now you measure the number off problems you haven't sold by given number of this kid, uh, steps taken by the integrator. You find out you have exponential, discrete time, complexity and, of course, thistles. A problem. And if you look closely, what happens even though the analog mathematical trajectory, that's the record here. If you monitor what happens in discrete time, uh, the integrator frustrates very little. So this is like, you know, third or for the disposition, but fluctuates like crazy. So it really is like the intervention frees us out. And this is because of the phenomenon of stiffness that are I'll talk a little bit a more about little bit layer eso. >>You know, it might look >>like an integration issue on digital machines that you could improve and could definitely improve. But actually issues bigger than that. It's It's deeper than that, because on a digital machine there is no time energy conversion. So the outside variables are efficiently representing a digital machine. So there's no exponential fluctuating current of wattage in your computer when you do this. Eso If it is not equal NP then the exponential time, complexity or exponential costs complexity has to hit you somewhere. And this is how um, but, you know, one would be tempted to think maybe this wouldn't be an issue in a analog device, and to some extent is true on our devices can be ordered to maintain faster, but they also suffer from their own problems because he not gonna be affect. That classes soldiers as well. So, indeed, if you look at other systems like Mirandizing machine measurement feedback, probably talk on the grass or selected networks. They're all hinge on some kind off our ability to control your variables in arbitrary, high precision and a certain networks you want toe read out across frequencies in case off CM's. You required identical and program because which is hard to keep, and they kind of fluctuate away from one another, shift away from one another. And if you control that, of course that you can control the performance. So actually one can ask if whether or not this is a universal bottleneck and it seems so aside, I will argue next. Um, we can recall a fundamental result by by showing harder in reaction Target from 1978. Who says that it's a purely computer science proof that if you are able toe, compute the addition multiplication division off riel variables with infinite precision, then you could solve any complete problems in polynomial time. It doesn't actually proposals all where he just chose mathematically that this would be the case. Now, of course, in Real warned, you have also precision. So the next question is, how does that affect the competition about problems? This is what you're after. Lots of precision means information also, or entropy production. Eso what you're really looking at the relationship between hardness and cost of computing off a problem. Uh, and according to Sean Hagar, there's this left branch which in principle could be polynomial time. But the question whether or not this is achievable that is not achievable, but something more cheerful. That's on the right hand side. There's always going to be some information loss, so mental degeneration that could keep you away from possibly from point normal time. So this is what we like to understand, and this information laws the source off. This is not just always I will argue, uh, in any physical system, but it's also off algorithm nature, so that is a questionable area or approach. But China gets results. Security theoretical. No, actual solar is proposed. So we can ask, you know, just theoretically get out off. Curiosity would in principle be such soldiers because it is not proposing a soldier with such properties. In principle, if if you want to look mathematically precisely what the solar does would have the right properties on, I argue. Yes, I don't have a mathematical proof, but I have some arguments that that would be the case. And this is the case for actually our city there solver that if you could calculate its trajectory in a loss this way, then it would be, uh, would solve epic complete problems in polynomial continuous time. Now, as a matter of fact, this a bit more difficult question, because time in all these can be re scared however you want. So what? Burns says that you actually have to measure the length of the trajectory, which is a new variant off the dynamical system or property dynamical system, not off its parameters ization. And we did that. So Suba Corral, my student did that first, improving on the stiffness off the problem off the integrations, using implicit solvers and some smart tricks such that you actually are closer to the actual trajectory and using the same approach. You know what fraction off problems you can solve? We did not give the length of the trajectory. You find that it is putting on nearly scaling the problem sites we have putting on your skin complexity. That means that our solar is both Polly length and, as it is, defined it also poorly time analog solver. But if you look at as a discreet algorithm, if you measure the discrete steps on a digital machine, it is an exponential solver. And the reason is because off all these stiffness, every integrator has tow truck it digitizing truncate the equations, and what it has to do is to keep the integration between the so called stability region for for that scheme, and you have to keep this product within a grimace of Jacoby in and the step size read in this region. If you use explicit methods. You want to stay within this region? Uh, but what happens that some off the Eigen values grow fast for Steve problems, and then you're you're forced to reduce that t so the product stays in this bonded domain, which means that now you have to you're forced to take smaller and smaller times, So you're you're freezing out the integration and what I will show you. That's the case. Now you can move to increase its soldiers, which is which is a tree. In this case, you have to make domain is actually on the outside. But what happens in this case is some of the Eigen values of the Jacobean, also, for six systems, start to move to zero. As they're moving to zero, they're going to enter this instability region, so your soul is going to try to keep it out, so it's going to increase the data T. But if you increase that to increase the truncation hours, so you get randomized, uh, in the large search space, so it's it's really not, uh, not going to work out. Now, one can sort off introduce a theory or language to discuss computational and are computational complexity, using the language from dynamical systems theory. But basically I I don't have time to go into this, but you have for heart problems. Security object the chaotic satellite Ouch! In the middle of the search space somewhere, and that dictates how the dynamics happens and variant properties off the dynamics. Of course, off that saddle is what the targets performance and many things, so a new, important measure that we find that it's also helpful in describing thesis. Another complexity is the so called called Makarov, or metric entropy and basically what this does in an intuitive A eyes, uh, to describe the rate at which the uncertainty containing the insignificant digits off a trajectory in the back, the flow towards the significant ones as you lose information because off arrows being, uh grown or are developed in tow. Larger errors in an exponential at an exponential rate because you have positively up north spawning. But this is an in variant property. It's the property of the set of all. This is not how you compute them, and it's really the interesting create off accuracy philosopher dynamical system. A zay said that you have in such a high dimensional that I'm consistent were positive and negatively upon of exponents. Aziz Many The total is the dimension of space and user dimension, the number off unstable manifold dimensions and as Saddam was stable, manifold direction. And there's an interesting and I think, important passion, equality, equality called the passion, equality that connect the information theoretic aspect the rate off information loss with the geometric rate of which trajectory separate minus kappa, which is the escape rate that I already talked about. Now one can actually prove a simple theorems like back off the envelope calculation. The idea here is that you know the rate at which the largest rated, which closely started trajectory separate from one another. So now you can say that, uh, that is fine, as long as my trajectory finds the solution before the projective separate too quickly. In that case, I can have the hope that if I start from some region off the face base, several close early started trajectories, they kind of go into the same solution orphaned and and that's that's That's this upper bound of this limit, and it is really showing that it has to be. It's an exponentially small number. What? It depends on the end dependence off the exponents right here, which combines information loss rate and the social time performance. So these, if this exponents here or that has a large independence or river linear independence, then you then you really have to start, uh, trajectories exponentially closer to one another in orderto end up in the same order. So this is sort off like the direction that you're going in tow, and this formulation is applicable toe all dynamical systems, uh, deterministic dynamical systems. And I think we can We can expand this further because, uh, there is, ah, way off getting the expression for the escaped rate in terms off n the number of variables from cycle expansions that I don't have time to talk about. What? It's kind of like a program that you can try toe pursuit, and this is it. So the conclusions I think of self explanatory I think there is a lot of future in in, uh, in an allo. Continue start computing. Um, they can be efficient by orders of magnitude and digital ones in solving empty heart problems because, first of all, many of the systems you like the phone line and bottleneck. There's parallelism involved, and and you can also have a large spectrum or continues time, time dynamical algorithms than discrete ones. And you know. But we also have to be mindful off. What are the possibility of what are the limits? And 11 open question is very important. Open question is, you know, what are these limits? Is there some kind off no go theory? And that tells you that you can never perform better than this limit or that limit? And I think that's that's the exciting part toe to derive thes thes this levian 10.

>> from Seattle WASHINGTON. It's the Q covering AWS Imagine brought to you by Amazon Web service is >> Hey, welcome back already, Jeffrey. Here with the cue, we're in downtown Seattle at the AWS. Imagine, Edie, you event. It's a small conference. It's a second year, but it'll crow like a weed like everything else does the of us. And it's all about Amazon and a degree. As for education, and that's everything from K through 12 community college, higher education, retraining vets coming out of the service. It's a really big area. And we're really excited to have fresh off his keynote presentations where he changed his title on me from what it was >> this morning tow. It was the senator duties >> David Raymond, the director of what was the Virginia Cyber Range and now is the U. S. Cyber range. Virginia Tech. David, Great to see you. >> Yeah, Thank you. Thanks. So the Virginia cyber age actually will continue to exist in its current form. Okay, Well, it'll still serve faculty and students in the in the Commonwealth of Virginia, funded by the state of Virginia. Now the U. S. Cyber Angel fund will provide service to folks outside over, >> so we jumped ahead. So? So it's back up. A step ladder is the Virginia, >> So the Virginia Cyber Range provides courseware and infrastructure so students could do hands on cyber security, educational activities in Virginia, high schools and colleges so funded by the state of Virginia and, um provides this service at no charge to the schools >> and even in high school, >> even in high school. Yes, so now that there are now cybersecurity courses in the Virginia Department of Education course catalogue as of two years ago, and I mean they've grown like wildfire, >> I'm just so a ton of talk here about skills gap. And there's tremendous skills gap. Even the machine's gonna take everybody's job. There's a whole lot of jobs are filled, but what's interesting? I mean, it's the high school angle is really weird. I mean, how do you Most high school kids haven't even kind of clued in tow, privacy and security, opting in and opting out. It's gotta be a really interesting conversation when now you bring security into that a potential career into that and directly reflects on all those things that you do on your phone. >> Well, I would argue that that's exactly the problem. Students are not exposed to cyber security, you know. They don't want the curia potentials are they really don't understand what it is we talked about. We talked about teenagers being digital natives. Really? They know how to use smartphones. They know how to use computers, but they don't understand how they work. And they don't understand the security aspects that go along with using all this technology. And I would argue that by the time a student gets into college they have a plan, right? So I have a student in college. He's he's gonna be a doctor. He knows what a doctor is. He heard of that his whole life. And in high school, he was able to get certified as a nursing assistant. We need cyber security in that same realm, right? If we start students in high school and we and we expose them to cybersecurity courses, they're all elective courses. Some of the students will latch onto it, and I'll say, Hey, this is what I want to be when I grew up. And in Virginia, we have we have this dearth of cyber security expertise and this is true across the country. In Virginia, right now, we have over 30,000 cyber security jobs that are unfilled. That's about 1/3 of the cyber security jobs in this state. And I mean, that's a serious problem, not only in Virginia but nationwide. And one of the ways to fix that is to get high school students exposed to cybersecurity classes, give them some real hands on opportunities. So they're really doing it, not just learning the words and passing the test, and I mean really again in Virginia, this is this is grown like wildfire and really thinks revolutionized cybersecurity education in the state. >> And what are some of the topics that say, a high school level, where you know you're kind of getting versed on the vocabulary and the terminology vs when they go into into college and start to take those types, of course, is >> yeah, so in Virginia, there's actually cybersecurity courses across the C T E career pathways. And so SETI is the career and technical education curricula. And so there are courses like cyber security and health care, where students learn about personal health data and how to secure that specific specific kinds of data, they learn about the regulations behind that data. There's healthcare in manufacturing, where students learn about industrial control systems and you know how those things need to be secured and how they're different from a laptop or a phone. And the way those air secured and what feeds into all of those courses is an introductory course. Cyber security fundamentals, where students learn some of the very basics they learn the terminology. They learn things like the C I. A. Triad right, confidentiality, integrity and availability of the three basic components of security that you try to maintain for any system. So they start out learning the basics. But still they're doing that hands on. So they're so they're in a network environment where they see that you know that later on in the course during Capstone exercises, they might see someone trying to attack a computer that they're that they're tasked to defend and a defender of what does that look like? What are the things that I'm going to do? That computer? You know, I might install anti virus. I might have a firewall on the computer. And how do I set that up and etcetera etcetera. So high school start with the basics. As as students progressed through their high school years, there are opportunities to take further more advanced classes in the high schools. And then when they get to college, some of those students are gonna have latched onto cyber security as a potential career field. Now, now we've got him right way, get him into the right into the right majors and into the right courses. And our hope is that that's gonna sort of kick start this pipeline of students in Virginia colleges, >> right? And then I wonder if you could >> talk a little bit about the support at the state level. And it's pretty interesting that you had him from the state level we heard earlier today about supported the state level. And it was Louisiana for for another big initiative. So you know that the fact that the governor and the Legislature are basically branding this at the state level, not the individual school district level, is a pretty strong statement of the prioritization that they're putting on this >> that has been critical to our success. If we didn't have state level support, significant state level support, there's no way we could be where we are. So the previous governor of Virginia, Terry McAuliffe, he latched on to cyber security education as one of his signature initiatives. In fact, he was the president of the State Governors Association, and in that role he cybersecurity was one of his condition. So so he felt strongly about educating K 12 education college students feeding that cybersecurity pipeline Onda Cyberangels one of one of a handful of different initiatives. So they were veterans scholarships, and there were some community college scholarships and other other initiatives. Some of those are still ongoing so far are not. But but Cyber Range has been very successful. Funded by the state provides a service at no cost to high schools and colleges on Dad's Been >> critically, I can't help. We're at our say earlier this year, and I'm just thinking of all the CEOs that I was sitting with over the course of a couple of days that are probably looking for your phone number right now. Make introduction. But I'm curious. Are are the company's security companies. I mean, Arcee is a huge show. Amazon just had their first ever security conference means a lot of money being invested in this space. Are they behind it? Have you have you looked for in a kind of private company participation to help? Because they desperately need these employees? >> Definitely. So we've just started down that road, Really? I mean, our state funding has kept us strong to this point in our state funding is gonna continue into the foreseeable future. But you're right. There are definitely opportunities to work with industry. Certainly a DBS has been a very strong partner of our since the very beginning. They really I mean, without without the help of some, some of their cloud architects and other technical folks way could not have built what we built in the eight of us. Cloud. We've also been talking to Palo Alto about using some of their virtual appliances in our network environments. So yeah, so we're definitely going down the road of industry partners and that will continue to grow, I'm sure >> So then fast forward today to the keynote and your your announcement that now you taking it beyond just Virginia. So now it's the U. S. Cyber range. Have that come apart? Come about. What does that mean? >> Yes, So we've been We've been sharing the story of the Virginia cyber range for the last couple of years, and I goto national conferences and talk about it. And, um, just to just sort of inform other states, other other school systems what Virginia's doing. How could you? How could you potentially match what we're doing and what The question that I keep getting is I don't want to reinvent the wheel. How can I buy what you have? And that's been sort of a constant drumbeat over the last couple of years. So we decided fairly early on that we might want to try to expand beyond Virginia, and it just sort of the conditions were right about six months ago. So we set a mark on the wall, he said. In Summer of 2019 we're gonna make this available to folks outside of Virginia. And so, so again, the Virginia Cyberangels still exist. Funded by the Commonwealth of Virginia, the U. S cyber range is still part of Virginia Tech. So within Virginia Tech, but we will have to we will have to essentially recoup our costs so we'll have to spend money on cloud infrastructure and We'll have to spend salary money on folks who support this effort. And so we'll recoup costs from folks that are outside of Virginia using our service. But, um, we think the costs are gonna be very competitive compared to similar efforts. And we're looking forward to some successes here. >> And do you think you're you're kind of breakthrough will be at the high school level, the You know, that underground level, you know, where do you kind of see the opportunities? You've got the whole thing covered with state support in Virginia. How does that get started in California? How's that get started here? Yeah, that's a Washington state. >> That's a great question. So really, when we started this, I thought we were building a thing for higher ed. That's my experience. I've been teaching cyber security and higher ed for several years, and I knew I knew what I would want if I was using it, and I do use it. So I teach classes at Virginia Tech Graduate program. So I I used the Virginia side in my class, and, um, what has happened is that the high schools have latched onto this as I mentioned, and Most of our users are high schools. In Virginia, we have 180. Virginia High School is using the Virgin Cyber. That's almost >> 188 1 >> 180. That's almost half the high schools in the state using the Virginia cyber age. So we think. And if you think about, you know, higher. Ed has been teaching cybersecurity classes that the faculty members who have been teaching them a lot of them have set up their own network infrastructure. They have it set up the way they want it, and it ties into their existing courseware, and you know they're going to use that, At least for now. What we provide is is something that makes it so that a high school or a community college doesn't have to figure out how to fund or figure out how to actually put this network architecture together. They just come to us. They have the flexibility of the flexibility to use, just are very basic plug and play network environments, or they have flexibility to, um, make modifications depending on how sophisticated they themselves are with with, you know, manipulating systems and many playing the network so so Our expectation is that the biggest growth is going to be in the high school market, >> right? That's great, because when you say cyber range God, finally, Donna me use it like a target range. It's like a place to go practice >> where the name comes from, right? >> Absolutely. If I finally like okay, I get it. So because it's not only the curriculum and the course where and everything else but it's actually an environment, it depends on the stage things and do things exactly >> So students could d'oh offensive, offensive and defensive cybersecurity activities. And so early on, when we were teaching students howto hack essentially in colleges, you know, there were people who were concerned about that on the military case we make for that is you can't teach somebody how to defend unless they understand how they're gonna be attacked. The same is true in this case. So all of our all of our course, where has lots of ethics and no other legal and other other discussions embedded throughout. So students understand the implications of what their actions would be if they do it somewhere else. And, um, right, these are all isolated network environments their places where students can get hands on in a place where they can essentially do whatever they want without causing trouble on the school network or on the Internet. And it's very much akin to a rifle range, >> right? Like you said, you can have different scenarios. And I would imagine there's probably gonna be competitions of you think. Fact. You know what's going on in the robotics world for lots of all these things, right? Like white hat, black hat hacker. Well, very, very exciting. David, Congratulations. And it sounds like you're well on your way. Thanks. Great. Alright, >> He's David. I'm Jeff. You're watching The Cube were at Washington State Convention Centre just across the street at a W s. Imagine. Thanks for watching. We'll see you next time. >> Thanks.

>> Live, from Bellevue, Washington, it's theCUBE. Covering Smartsheet Engage 18. Brought to you by Smartsheet. >> Welcome back to theCUBE's continuing coverage of Smartsheet Engage 2018, I am Lisa Martin with Jeff Frick in Bellevue, Washington, our first time here. Second annual Smartsheet Engage and we're very please to be joined, welcoming back to theCUBE, Mark Mader, the CEO of Smartsheet. Mark, it's great to have you on the program. >> Thank you, good to be with you. >> Great job on the keynote. >> Thank you, appreciate it. >> So, you can see the buzz behind us, we just got out of the keynote, where, you guys kicked it up, there was a coupla things Jeff and I were talking about that were unique, that I haven't seen very much of at all, in all the keynotes that we go to. One, you started off with an explorer who had a very empowering, enlightening message, all about communication. And then, something that you did that I thought was really cool, that I don't think I've ever seen, is you actually, during your keynote, went into the audience, where you have about 2000 customers here, representing 1100 companies, across 20 countries, and just ad-libbed, hey guys, tell me about your company, how is Smartsheet empowering you, and as you said, that was all natural. >> I think part of it making it real for somebody, is giving you somebody that's relatable. So, we started off the conversation, as you said, with Ed Viesturs, arguably the most famous accomplished climber in the world, today, and he talked about the importance of communication and preparation, and teamwork, and clear decision making, in a context that was spectacularly visual, right, this mountain and those climbing shots, so, people relate to that, and then when you introduces those conducts in the business setting, it's like, oh, yeah, this applies to me, it applies to all of us. So, the notion of getting into the crowd, in a non-rehearsed way, is to really get people comfortable with, hey, I can share something, I can share an experience, and there's no one right answer, it's my experience. >> And that's why you're here, as you said in your keynote, and we know this as well, if companies aren't designing technology for the users, what's the point? >> Yeah, you're right and, one of the things I tried to highlight was, when you say for the user, it's not just for the user, the end user, like developed by a few people, spread to everybody, but it's empowering each and every person to say, hey I want to do something more transformational. I want to manage, automate, scale it, I don't want to be given that solution by someone, I want to do it. And there are hundreds of millions of people, who have the appetite and the interest, and the need for it. So, that's what we're trying to sell into. >> You know, Mark, we got to, so many shows, right, and everyone's chasing innovation. How do we get more innovative? Especially big companies, right? And you did show two really interesting messages, one, was your kind of core message, empowering everyone to improve, how they work, so, like you said, not just the top level decision makers, not down in the developer weave, but everybody up and down this stack. And then you shared a statement covey quote, really talking about how do people, keep 'em engaged and the way people are engaged is that they feel they're empowered to do something for their clients and their customers. So it's such an importannt piece and I think it's easy to talk about, harder to execute, but what is the answer to innovation? Giving more people the data, the tools and the power to take all that and do something for their customers, and thereby unlock all this tremendous value that you already have in your four doors. >> Absolutely, and I think the point of unlocking, so we have, you have 100% of your workforce. If you empower only 4.3% of them, for instance, the developers in your group, you're leaving so much opportunity on the table. And again, you don't get that unlock or that innovative spirit by just using something. You have to live with it, you have to work with it, you have to wrestle with it, And through that, innovation occurs. Ideas get generated. So, if you can get that ideation happening at the midpoint of your company, not the top 5%, huge opportunity. >> I think you were even quoted in the press release, maybe around the IPO that happened a few months ago, congratulations, >> Thank you. >> In saying that, maybe naysayers in the beginning, when you were a company of six, as you were talking about in your keynote, people thought, you're going to build this on a spreadsheet construct? And you said, but four hundred to five hundred million people know that construct. >> Right, right So you're going into an audience if knowledge workers, of which there's a massive percentage, designing something for lines of business, IT, finance, marketing, sales, who actually need to work with that, we're not talking about API's and developer and code speak, you're building this for a very large percentage of the population. >> We are, and I think when we talk about serving a large population, it's tempting to say, well, they can't handle much, let's go with the most common denominator. Let's give them something super, super simple. The problem is, with simple, you don't always get value. So how do you combine relevance and comfort and understanding, with capability. And the product's changed a lot since the early days, it's no longer just a grid, we have dashboards, we have forms, we have card view, we have all these elements that are now being brought forward, but one thing that we've always respected from the beginning is, don't throw away what somebody understands, and is comfortable with. That doesn't necessarily mean that it's the best, but they know it. And people are very nervous about just jettisoning the things they know, so like, embrace it. And then, what we had talked about earlier, was, how do you really listen to that customer's signal, and say okay, I'm comfortable, I like this, but I want more. And that ability to respond to that request, I think has really helped define who Smartsheet is today. You know, 12 years later. >> The other piece you talked on is kind of sideways off of that, is people have systems already in place, they have tools that they use every day. Right, there's this competition for the top layer of the desktop, but the reality is that we have many, many applications that we have to interact with every day. You guys are really taking a coopation approach with all these existing, >> Absolutely >> where it fits, where it's working, to your point, they're already using it and make it work. Integrate with. Don't try to rip and replace all these other systems that're in there. >> Yeah, and I think, you know you come across so many people in life, who want everything. I need total, complete, presence. And you're really discounting what people appreciate. And I think when you take the view of, I'm going to listen to my client, I'm going to listen to what they love and understand, and I'm going to let them articulate how they want it to work, we are in a very diverse, multi-app world today. If you actually march in somewhere and say, yeah all those decisions you made, those were the wrong decisions, you should trust me on everything, you'll be walked out of the building in about 4.2 seconds. So, we're really living that philosophy, and I think in great partnerships with Google, Microsoft and Slack, and Tableau, and others, we're actually able to demonstrate that. >> Yeah, and then to take it from the concept to reality, a great demo, I'm sure you didn't have this planned a couple of weeks ago, was, you talked about the state of North Carolina, and the preparation and the response to Hurricane Florence, and that they were very quickly able to build a super informative dashboard, to let everybody know who needed to know, what they needed to know. >> Correct. >> And how long did that take to put together? Amazing. >> That was under 24 hours. >> 24 hours? >> And the difference here is the difference between building or developing something, and configuring something. So, the difference there is when you actually build something from scratch, we have bare dirt, we need to put a foundation, we need to build a house, we need to shingle it, we need to insulate, that takes you a long time. So how about, we go to a house that exists, let's change the colors of the blinds, let's put in a certain sofa, let's furnish it. And the configuration element, versus construction, that gives people velocity. Now, what they also want is, they want to actually put their own texture to it, they want to make it their own, so the Department of Transportation dashboard that they produced for FEMA and the Coast Guard and the state governor's office, it didn't look like anybody else's dashboard. It was tailored, but it was so quick to build. And the great thing there was, so many people who accessed that site for information on on runway status and power and fuel, they could focus on the citizens as opposed to what the heck is going on, on the ground. >> Right. >> That provides a lot of purpose to our team, when we see our product used that way. >> You talked about speed just a minute ago, and speed, obviously, every enterprise of whatever size, needs to move and quite a bit quickly, to gain competitive advantage, to increase revenues, et cetera, you guys have some really very eye-catching statistics. That you're enabling customers to achieve. I read, enabling an average business leader to save 300 hours a year, 60,000 hours a year saved across on average organization. That's a big impact. How is speed a factor there? >> Yeah, I think speed I look at in a couple dimensions, One is, is it time saved, but there's also an element which is speed of experimentation So we go into an initiative, we say we have this amazing idea and we're going to have all these returns, we think. (chuckling) Well, not all the bets you place actually makes it. Or actually yields, so if you can empower a team to more quickly experiment, configure, try things, see what works and then double down behind those, if you can run five times as many plays as your competitor, you have five times as many chances to find that next winner. And so when we talk about speed, it's again, velocity of decision making, saving time, but also, organizationally, how can you unlock those possibilities? >> Part of that also is enabling cultural change. Which is not easy, it's essential for digital transformation, we talk about that at every event, and it's true, but how do you put that in action? You and I were chatting off camera about one of your customers that is an 125 year old oil and gas company. How do you enable them to kind of absorb and digest a culture of experimentation so that they can really move their business forward as quickly as they need to? >> Well, I think there's a great quote that one of my mentors early gave me. And it was, "All hat, no cattle." And the "All hat, no cattle" refers to the person who talks about how big their ranch is and how big their... Where's your herd? So you can talk a lot, but you have to demonstrate it. So when they go in, and there was another gentleman who talked about this idea of transforming their implementations across 300 project managers, and the quote was, we're going to get you up and running in two to three weeks, and he goes, "Never. No chance." Now, he ended up working with us, and we proved it to him and when you get a win like that, and you can demonstrate speed and impact, those things carry a lot of weight in organizations, but you have to show evidence. And when you talk about why we're landing and expanding in some of the world's largest brands, it's not because we're just talkin' a big game, it's because you're able to demonstrate those wins, and those lead to further growth. >> Right. And then you topped it off with a bit about the catalysts. But even more, I liked the concept of the point guard. Good point guards make everybody else on the team better. They do a little bit on their own, they hit a couple key shots, but they make everybody else better. And you're seeing that in terms of the expansion, and just in the way your go to market is, you don't come in usually as a big enterprise sale, I don't think, you come in small, you come in a group level, and then let the catalyst let those point guards, built successful in their own team, and then branch it out to a broader audience. >> Yeah, and I'm a big believer, and I don't think people can be classified into catalysts and non-catalysts. That's a very sort of blunt force approach. I view it as, you've catalysts, you've catalysts that haven't been unlocked, and then you have people that aren't catalysts. But very often that point guard, is going to activate the power forward, the center and holy smokes, where did that come from? And what we see is, when we see this growth happening in companies, those players around that point guard, get lit, get sparked, and once they're sparked, it's on. And then we see that growth happen for a long, long time. >> We saw some of that quotes, quotes >> We did. (all speaking at once) >> Queen of the world? >> Queen of the world. That's a big statement. >> That's empowerment, right there. >> It is empowerment. >> And the one where, I tweeted this, one of the quotes, I won't share this product name, but it can actually seem smart, she can help reduce work place anxiety. >> Anxiety! >> Which everybody needs. So, it's been six months since the IPO, you have doubled your attendance in your second year only, at Engage, up here in Bellevue, Washington, What are some of the exciting things that you anounced this morning, that have been fueled by the momentum of the IPO has as I imagine, ignited? >> Yeah, couple big things, is we, at every tech conference, you're going to hear about new capabilities. Here are the new bells and whistles and features and capabilities we have. But what we're hearing from customers, they also want us to frame those capabilites and things that are consumable. So, not everybody wants to configure or build as we talked about earlier today, they say I have a need, it's specific to this area, and do you have something for me. More turnkey, like that gentleman I said, two to three weeks to turn and sold him my implementation team. So those are being referred to as accelerators. So we announced a few new accelerators today in the sales realm, in terms of being able to better manage engagement plans with prospects and clients and on sophisticated deals it's a very common thing. And the other piece that I think is really important is, not just talking about business users, which is a huge focus for us, but also how do we better support IT and their needs to regulate, control, have visibility and to how Smartsheet is used. So, those were a couple of highlights, and then the ability to give people more controls over how they share their data. There've been some issues in the news recently, where people have shared too broadly, they've said that's the issue, so we're hearing from our customers, give us some more fine gated controls and confidence over how our corporate information is shared with others. Well, Mark Mader, I wish we had more time, but we thank you so much for stopping by theCUBE, and chatting with Jeff and me. >> Great to see you. >> Great momentum, we look forward to a number of your execs and customers and analysts on the program tonight. >> Great, thank you. >> Thank you, good to see you. >> Thanks, Mark, good to see you again. >> We just want to thank you for watching theCUBE, I'm Lisa Martin with Jeff Frick live from Smartsheet Engage 2018. Stick around, Jeff and I will be right back with our next guest. (techno music)

>> Announcer: Live, from Orlando, Florida, it's the Cube covering Grace Hopper's celebration of women in computing brought to you by Silicon Angle Media >> Welcome back to the Cube's coverage of the Grace Hopper conference here in Orlando, Florida I'm your host Rebecca Knight, along with my co host Jeff Frick. We're joined by Megan Smith. We're very excited to have you on the show. >> It's good to be here >> She is the third US CTO and also the CEO of a new company, Shift7.co, so thanks so much for joining us. >> Thanks for having me, it's great to be here. It's so fun to be at Hopper, >> Rebecca: It is, it is! >> It's cool, it's the Grace Hopper celebration, because we're trying to celebrate women in computing, and we're what, at 18 thousand people now, >> The biggest ever, >> Plus I think, 6 thousand people joining on the livestream, which is great. >> Before the cameras were rolling, we were talking about your role as the 3rd US CTO, and just talking about getting more technology into government to help leaders work together, and move faster. Tell us a little about this initiative. >> What's so great, is it's not partisan, fixing the government and making it work better, so all the work that we were doing continues. What we were able to put in place, during the Obama administration, and continues to Trump, were things like, the CT office. Having technical people, so I worked at Google, people work at Amazon, Facebook, Twitter, these companies who have that background, to join in on policy conversations, one, to join in on capacity building the government, so data sciences and tech and, let's have our services be as great as Amazon, or as Twitter, or Oracle, and not be sort of retro, really serve the American people. And then also, helping the American people in general, with capacity building, things like computer science for all. So that was an initiative that continues to get all of our children to have coding at school. That all children, you couldn't graduate from high school without having had some experience on learning of coding Coding is a 21st century fluency, it's a skill we all need, Like freshman biology. You want to know some biology, you want to know some coding, you want to know how to write, so making sure they have is tech-up, which was a program we started to help train Americans, there's six hundred thousand jobs open, in the United States, and they pay 50% more than the average American salary. The companies are starving. How do we rapidly get more Americans into these jobs? It turns out that people have, of course, created these fabulous code boot camps, you can train in three months for these jobs, some of them are paid, some times they pay you, all different kinds, some are online, some are offline, they're all over the country. So we're able to get more people to consider, a job like that, culturally they think, Well I don't, why would I, I don't know how to do that. Well you can, this is a fun and interesting and exciting career, you can do digital marketing, you can do user interface design. You can get involved in front end or back end coding, product management, all those things, sales. And so, how do you pull lots more Americans in, get our companies fueled so we have really the economic opportunity, and they're all over the country. Location wise, and topic wise. So we did tech hour now, and a tech jobs tour, which is not what we did in government, but we continue some of that work. >> This weird dichotomy, because on one end, people are worried about tech taking jobs, on the other hand, there's a ton of open tech jobs. And there's this transition period, that's difficult, obviously for people that didn't grow up, but one of the keynote speakers today, told a really heartening story, that she didn't get into it until the day she had to leave her abusive husband, and now she is a coder >> That's Doctor Sue Black, who was just given the Order of the British Empire, I mean, she is an incredible computer scientist. Yes, she escaped an abusive marriage with three small children, in her early 20s, I think. Ended up moving into public housing, and dealing with three children only being the school from 9 until 3, and eventually getting her PhD in computer science, and really, she started Techmoms now, she continues to do research in other things, but she's really trying to use her story, and her organizing capacity, to have more people realize this isn't hard like figuring out gravity waves that won the Nobel prize. This is hard like writing a hard essay, so we all can learn to write an essay. It takes some mastery work, you don't learn it in kindergarten but by the time you're in 7th, 8th, 9th, 10th, 12th grade, you can do it. >> It's not rocket science. >> Right, so coding is like that. >> The other piece you said that's very interesting, is the consumerization of IT. We've seen it at enterprise, a huge trend. But, now I expect everything that's on my phone, when I interact with Facebook or Amazon, or whatever, to be in all the applications, so, as you said, that's influencing government, and the way they have to deliver services, and I would imagine, too, with kind of the next wave of kids coming in, graduating, going into public service, they certainly have that expectation, right? They've been working on their phone forever of course it should be on the phone. >> And so we want everybody in our country fluent in computer science and coding at a basic level, like again, like freshman biology or takin' chemistry in high school, or taking writing. So that everyone could realize this is not rocket science we could have these kinds of capabilities as part of our services, from Housing and Urban Development, from the Department of Education. You know, a lot of us use our phones to get places, you know, on our maps, and so that's actually data coming from the US Geological survey, if you're looking at the weather, you're looking at NOAA's satellites, this is open government data. We were able to open over two hundred thousand data sets, from all over government, not private data, but public data, that you could make an entire app store, or Google play set of products on top of that. Government wouldn't have to pay for that, it just packages up the API as well. A really good example of that, is the US census team. There's nothing more big data than census, they have all of our information from a data perspective, and so they did opportunity.census.gov, and they said to various agencies, let us help you bridge these data sets into something that someone could build on top of, like we're seeing from the courses sector, we saw wonderful things like, Housing and Urban Development said, okay, our challenges are housing affordability, mobility, these are the challenges instead of having HUD make an app for Americans to come to, they just can explain what their problem is, what data sets, and then engage extraordinary companies, like airbnb, Redfins, Zillow, these fabulous tech companies, who can make instead a product for 100% of the Americans, rather than only wealthy or middle class Americans, and so they did things like, opportunity score, and airbnb helping you figuring out, if I rent a room in my house I can make my rent more affordable, very creative apps, that we can see, same thing for the Department of Ed or Department of Labor, and as the data gets out there, and as apps come, also the opportunity for data science and machine learning. What if, as much as we serve ads to ourselves, in these algorithms, what if we use the algorithms to help Americans find a job that they would love? You know, job matching, and these kinds of opportunities. of the problems in the world, and helping government get more fluent at that. And the way to do that is not so much, jam the government You have to do this, but find terrific talent like we see at Hopper, and have them cycle into the government, to be co-leaders just like a surgeon general would come. >> Are you facing recruitment challenges in that same way though? In the sense that technology is having a hard enough time recruiting and retaining women, but the government, too, is that seen as enough of an employer of choice for young talented, bright ambitious, young women? >> I'm not in government now, but when we were in there, we found a very interesting thing. Alex Mcgovern, who had been the general counsel of Twitter who was Stephanie's CTO with me and led a lot of our tech quals we called TQ like tech IQ in policy, together with economists and lawyers and others have if we're going to decide net neutrality, let's include everyone, including computer scientists, and we're going to sue bridge and open source, So we talked about that, and on the way going in Mcgovern, he said, wouldn't it be cool if, just like when you look at a lawyer's resume, you might see that they clerk and they served their county through clerking and through the judicial system, as well as being a private lawyer, they were a public defender, that's a pretty normal thing to see on a legal resume. If you looked at medical, you might see them going into NIH or doing some research, if you looked at a scientist, they might have gone to, done some NSF work or others. But for the tech crew, there is of course amazing technical people in NASA, NAH and the Department of Energy, and there's great IT teams, but there's not this thing that the Silicon Valley crew resume would say, oh, yeah, I served my country. So that's why, under President Obama, we were able to create these incredible programs. The Presidential Innovation Fellows, which was a one year term of service, The United States Digital Service, which is a three months to a two year term of service in the VA. What's more amazing if you build Amazon, than to go as a second act and help our veterans? It's an incredible honor, to the point of, will they come? Yes, that's what we were hoping, could we have that be a normal thing, and yes it's become a normal thing. And the Trump administration continues it. The 18F team is in the general services administration, they're on 18th and F so they have a code name. But that particular team is located around the country, not only in DC but in San Francisco, in Chicago, and others. So you see this tech sector flowing now into the government on a regular basis, and we welcome more peoples. The government is who shows up to help, so we need the tech sector to show up cause we've got a lot of money as a country, but if we're not effectively using it we're not serving the American people and foster children, veterans, elders, others need the services that they deserve and we have the money, so let's make it happen the way the tech sector is delivering Amazon packages or searches. >> What is your feeling, this is not your first Grace Hopper obviously, but what is your feeling about this conference, and advice that you would give to young women who are here, maybe for their first or second time, in terms of getting the most their time here? >> You know, I think the main thing is, it's a celebration, that's fun and you can walk up to anyone, so just talk to everyone. I've been talking to a million people on the floor, fabulous. Students are here, more senior technical leaders are here. We've been running speed mentoring, we're running a program called the Tech Jobs Tour, it's at Techjobstour.com, it's a #Americanshiring, and we've been going to 50 different cities and so we're running a version of that, and we do speed mentoring, so come to the speed mentoring sessions, it's a five minute pop, talk to someone about what you're tryin' to do. Lot's of programs like that, get into the sessions, come to the keynotes which are so inspiring, and Melinda Gates was amazing today, Dr. Fefe Lee was incredible, just across aboard, Dr Sue Black was here, I thought it was great today, actually, just to reflect on Melinda's keynote, I think this might have been the first time, I was talking to her, that she's really talked about her own technical experience >> That struck me, too! As a coder, starting in computer science. I didn't really understand that she had really started very early, with Apple 3 and the story of her dad >> And her love of her Apple 3, right! and really high school coding, which is so important for young people in high school and middle school, even younger. The Muscogee Creek Tribe, in Oklahoma, is teaching robotics in head start, so we can start in preschool. Just make it fun, and interesting. They're funny, they don't do battle bots, because you don't really want to teach 3 and 4 year olds to fight, so instead they have collaborative robots. >> Robots who work together Age appropriate. >> Well Megan Smith, this has been so fun talking to you, thanks so much for coming on our show. >> Thanks for having me. >> We will have more from the Grace Hopper Conference just after this, I'm Rebecca Knight for Jeff Frick (music)