(soft upbeat music) >> Hello and welcome to this Cube Conversation I'm Lisa Martin. This is going to be a great conversation about corporate social responsibility, and I'm very pleased to have two great guests here with me today. Karen McCloskey joins us the director of internal communications and corporate philanthropy at NETSCOUT and Professor Sally Eaves is here as well the CEO of Aspirational Futures. She's also a professor of emerging tech and a CTO by background. Ladies welcome to the program it's great to have you on today. >> Thank you. >> Absolute pleasure. Thank you, great to join you both. >> We're going to get some great perspectives here. As I mentioned corporate social responsibility we're seeing that emerge across every industry and every company is really focused on that. Karen I want to start with you where tech companies are concerned we see corporate social responsibility really aligning with STEM and STEAM. Why is that? >> There is probably a couple of reasons, I sort of wrap it up as it's what employees do, it's part of their jobs, so they get excited about it and they want to share what they do with the next generation. And the other aspect that helps it align with tech is it involves the educational aspect. So we're teaching and we need up and coming students and employees and entrepreneurs with those skills. And the other part about STEM is when you think of it, it's typically K to 12 and then it rolls into college and it's working with students and the next generation. So the education and the pipeline or the education and the students speak to the pipeline aspect and then you add in people getting excited about their job and what they do and that's the employee engagement aspect so it really brings the two pieces together. >> I want to dig into that employee engagement in a minute but Sally I would like to get your perspective. Tell us a little bit about Aspirational Futures and then let's talk about the alignment between that and STEM and STEAM. >> Absolutely, so yeah, Aspirational Futures, is a non-profit kind of working across tech education and social impact and really looking at kind of opening up opportunities to the industry to a diversity of experience and using tech and data as a force for good. We do projects locally and across the world I'm kind of breaking down those barriers. It's going to be all about democratization of opportunity I would say. And in terms of STEM to STEAM that's where I see the journey going at the moment and effectively with that STEAM focus you're bringing the arts to an equal stage to the tech skills as well. So for me that's really important because it comes down to curiosity, encouraging people to get into the sector, showing what you can do, building creative confidence, emotional intelligence, those types of skills alongside the tech skills to actually build it. So it's that combination There's complimentary factors that come together. So for me STEAM is a great way to get holistic learning for life With the rate of change we've got at the moment kind of gives you that tool set to work from to be empowered and confident for the future. >> That confidence is so critical. >> Its really. >> For anybody of any age, right? But one of the things that we've seen that is in the inaugural ESG report that NETSCOUT just published is this digital divide. We've seen it for quite a while now but we also saw it grow during the pandemic, Sally from your perspective, what is that and how can tech companies help to fill that gap? >> It's a great point. I think one other thing that the pandemic did was made it more visible as well. So I think particularly we're working in certain spaces we've seen it more, but I think for everybody it's affected our daily lives in education from home, for example for the first time it's made gaps more visible. So absolutely huge to focus on that. And I think we're seeing it from the organizational perspective as well. We're seeing gaps around certain types of roles. We're seeing higher churn because of a lack of data literacy skills. So it's becoming something that's becoming a CUBE Conversation, you know, in day-to-day family life but in organizations across the world as well. And also it's about challenging assumptions. And it just a few weeks ago there was some research that came out at the University of Reading in the UK in conglomerate with other universities as well. And it was kind of showing that actually you can't make the assumption, for example, that teenagers that kind of call digital natures all the time actually have full confidence in using data either, It was actually showing there were gaps there too. So we've got to challenge assumptions. So literacy in all its forms whether it's data, whether it's financial is absolutely key and we've got to start earlier. So what I'm seeing more of is better outreach from tech companies and other organizations in the primary schools through to universities as well kind of internships and placements, but also another really interesting area that we do with the nonprofit is looking at data waste as well. You know, 90% of data there's archive isn't touched again after kind of three months, you think about the amount of data we're producing at the moment how can we reuse that as a force for good as a training opportunity? So let's think creatively let's be pragmatic address some of these data literacy gaps, but we have to do it at all levels of the community and also for adult learners too. That's usually important. >> Right, there's no, it knows no age and you're right that the visibility on it I think it can be a very good thing shining that light finally going we've got to do something. Karen talked to us about what NETSCOUT is doing. The digital divide is there you guys are really focused on helping to mitigate that. >> That's right. That's right. So as guardians of the connected world, that's our job with our customers and our products, but also with our people in our communities is getting people connected and how can we do that? And in what ways are we able to do that? And recently we engaged with Tech Goes Home which is based in Boston and they provide those first three pillars that everybody needs access to a device, access to the internet and skills to use that. And they work with families and students and they say their programs go from nine to 90. So they've got everybody covered. And what's exciting for us is it kind of falls from a volunteer perspective right in our wheelhouse. So they had to transition from in-person to distance learning with the pandemic and suddenly their program materials needed to be online and they needed to get people up and running without the benefit of an in-person class. What NETSCOUT volunteers were able to do was create those tutorials and those programs that they needed and we also have people all over the world and then we translated them into a bunch of different languages and they were able to then move forward with their programs. So Tech Goes Home and programs like that are really that first step in bridging the digital divide. And then once you've got the basics, the toolkit and the skills what else can you do? And Sally mentioned visibility it's what are the opportunities? What can I do now? I didn't realize there was a career path here, I need these skills to build a business help me learn more. So then there's that whole other aspect of furthering what they can do now that they have those skills and the learning and something like a hackathon might be a fun way to engage kids in those skills and help them go a little further with the tools they have. >> And NETSCOUT has done a number of hackathon programs last year I know you had an All-Girls Hackathon virtually in 2020. Talk to me about some of that and then I want to get Sally I'm going to get your perspectives on what you're doing as well. So our hackathons and I'll try and keep this brief because we've done a lot. There were actually brought forward as an employee idea. So that also speaks to our culture. It's like, hey, we should do more of this. We have partner with Shooting Star Foundation and one of our employees is one of their, or is their board chair. And the hackathons what they do and these are beginners hackathons. So we're talking middle high school and the theme is civic. So something good for society. And what we do is over a course of 12 hours not to mention all the pre-planning. When we had the in-person ones they were in our office they got to see employees up close run around the building to the extent that they could and build their project. And Sally I think you had also mentioned that creativity in that confidence. I mean what those kids did in a day was amazing. You know, they came in and they're all kind of looking around and they don't really know what to do. And at the end of the they had made new friends, they were standing up in front of executive judges, presenting their idea, and they all felt really good about it and they had fun. So I think it can be a fun impactful way to both engage employees because it's a heck of a team building experience and sort of bring students in and give them that visibility to what's possible in their tech career. >> And that confidence. Sally talked to me about hackathons from your perspective and what you're involved in? >> Absolutely, funny now I've just come from one. So I'm a Cop 26 at the moment and I've been involved in one with a university again, using that talent and building that empowerment around STG challenges. So in this particular case around sustainability, so absolutely love that and really echo Karen's thoughts there about how this is a reciprocal relationship, it's also super rewarding for all the employees as well, we're all learning and learning from each other's, which I think is a fantastic thing. And also another point about visibility. Now seeing someone in a role that you might want to do in the future, I think is hugely important as well. So as part of the nonprofit I run a series called 365 and that's all about putting visibility on role models in tech every single day of the year. So not for example just like International Women's Day or Girls in ICT Day, but every single day and for a diversity of experience, because I think it's really important to interview people for C-suite level. But equally I just did an interview with a 14 year old. He did an amazing project in their community to support a local hospital using a 3D printer. It makes it relatable, you can see yourself in that particular role in the future, and you can also show how tech can be used for good business, but also for good for society at the same time. I think that can challenge assumptions and show there's lots of different roles, there's lots of different skills that make a difference in a tech career. So coding could be really important but so is empathy, and so is communication skills. So again, going back to that STEAM focus there's something for everyone. I think that's really important to kind of knock down those boundaries, challenge assumptions, and drop the the STEM drop off we say make it a little bit more STEAM focused I think that can help challenge those assumptions and get more people curious, creative, confident about tech. >> I couldn't agree more, curious, creative and confident. The three Cs that will help anyone and also to sell it to your point showing the breadth and diversity of roles within tech coding is one of them. >> Sally: Absolutely. >> As might be the one of the ones that's the most known but there's so many opportunities to allow these kids to be able to see what they can be is game-changing, especially in today's climate. Karen talk to me about you mentioned in the beginning of our interview Karen, the employee engagement, I know that that environmental social governance is core to NETSCOUT's DNA but we're talking over 2,400 employees in 35 countries. Your folks really want to be engaged and have a purpose. Talk to me about how you got the employees together, it sounds like it was maybe from within. >> That's absolutely right. We have a to support employees when they bring forth these good ideas and the hackathon was one example of that. And the cool thing about the hackathons is that it leads to all these other community connections and people bring forth other ideas. So we had an in-person hackathon at our Allen office in 2019. Some of the employees there met staff from Collin College who were said, "Hey, we'd like to bring this hackathon to us." So then the employees said, "Hey, can we do a hackathon with Collin College?" It's so really it's employee driven, employee organized, supported by the company with the resources and other employees love to be part of that. And the event at Collin College brought out all those skills from the students. It was on climate change so relatively hot topic. And they did a fantastic job while they were there, but that employee engagement as you said, it comes from within. So they have the idea we have a way and a path that they can find what is needed in their community and deliver on that. And it really becomes a sense of pride and accomplishment that it wasn't a top-down mandate that you must go volunteer or paint this wall. They identified the need in the community, propose the project, get the volunteers, get the corporate support and go forth and do it. And it's really amazing to see what people do in their community. >> Well, it's incredibly rewarding and fulfilling but also very symbiotic. There's one thing that's great about the students or those that are from nine to 90, like you said, having a mentor or mentors and sponsors but it's also another thing for employees to be even more productive and proud of themselves to be able to mentor and sponsor those folks in the next generations coming up. I can imagine that employee productivity would likely increase because the employees are able to fulfill have something fulfilling or rewarding with these programs. Karen talk to me a little bit about employee productivity as a kind of a side benefit of this. >> Well, I was going to say during the hackathons I don't know how productive we are 'cause there's a lot of planning and pre-work that goes into it. But I think what happens is it's an incredible team building experience across the company. So you reaching out to executives hey would you be a judge for this event? And you know you're explaining what it is and where it is. And you're roping your coworkers into spending 12 hours with you on a Sunday. And then you're finding somebody who has access to a speaker. So you're talking to people about it it's outside your day-to-day job. And then when it's over, you're like, "Oh yeah, hey, I know somebody in that group I worked with them on the hackathon or I can go up and talk to this executive because we hung out in the hackathon room for X many hours on a Saturday." So it's another way to build those relationships which in the end make you more or help you be more productive as a whole across the company. >> Absolutely relationship building, networking, those are all critical components to having a successful career. Whether we're talking about STEAM or not. I want to unpack something Sally that you said in our remaining few minutes you talked about challenging assumptions. And I guess suppose I'm one of those ones that always assumes if I see a gen Z or they're going to know way more about how to use my phone than I do but you bring up a really good point that there are these assumptions that we need to focus on, shine the light on, address them and crack them wide open to show these folks from nine to 90 that there are so many opportunities out there, there are limitless out there I would say. >> Absolutely, it's all about breaking down those barriers. And that research I mentioned something like 43% of teenagers about kind of 16 to 21 years of age we're saying they don't feel data literate. And that assumption is incorrect so gain so making sure we include everyone in this conversation. So going as young as possible in terms of introducing people to these opportunities but making sure we don't leave any particular age group behind it's that breadth of engagement with all ages is absolutely key. But again showing there are so many different routes into tech as a career. There isn't one linear path, you can come from a different area and those skills will be hugely valid in a tech career. So absolutely challenging assumptions, changing the narrative about what a tech career looks like, I think is absolutely hugely important hence why I do that series because you want to see someone that's relatable to you, at your next level potentially, it's something you need a three steps ahead. It just makes it so important. So for me democratization of opportunity, breaking down barriers, showing that you can go around different ways and it's absolutely fine. And you know what that would probably you can learn from that experience, you can learn from mistakes all those things make a difference so don't be put off and don't let anyone hold you back and reach out for mentor. You mentioned sponsorship earlier on as well I think that's another thing as well kind of using the sphere of influence we develop in our careers and maybe through social media and can helping people along the way not just through mentorship but through active sponsorship as well. There's so many things we can do together. I think organizations are really listening to this is better embedding around DEI initiatives now than ever before and as Karen has been describing fantastic outreach into communities through hackathons, through linking up with schools. So I think we're getting a real contagion of change that's positive here. And I think the pandemic has helped. It's helped us all to kind of pause and reflect, what we stand for as people, as organizations all the way through and I'm really excited that we can really harness this energy and take it forward and really make a difference here by coming together. >> And that is such a great silver lining all of those points Sally that you mentioned. Karen, I want to wrap with you. There's great momentum within NETSCOUT I mentioned, over 2,400 employees actively so many people in the employee community actively engaging in the hackathons and the opportunities to show from nine year olds to 90 year olds the opportunities that STEAM delivers. So what's next for NETSCOUT, what can we anticipate? >> More hackathons, more focused on the digital divide. I just want to, as Sally was speaking, something occurred to me when you said it's never a clear path on the tech journey. I would love to be listening to one of those conversations 10 years from now and have somebody say, oh, when you were asked that question that you're always asked what got you on your journey? What started? I'd love to hear someone say, "Oh, I went to this hackathon once "and it is something and ever since then I got interested in it." That would be a lot of fun. I would love to see that. And for NETSCOUT we're going to continue to do what we do best. We focus on where we can make a difference, we go in wholeheartedly, we engage with volunteers and we'll just keep doing what we're doing. >> Excellent ladies what a great conversation. I love the lights that you're shining on these very important topics there. You're right, I talked to a lot of people about their career paths and they're very, zig-zaggy. Its the exception to find one, you know, that we're studying computer science or engineering, but Karen I have no doubt with the focus that NETSCOUT's putting that Sally that your organization is putting on things like hackathons, getting people out there, educated becoming data literate that no doubt that the narrative will change in the next few years that I went to this hackathon that NETSCOUT did and here I am now. So great work, very important work. I think the pandemic has brought some silver linings there to what your organizations are both doing and look forward to seeing the next generation that you're inspiring. >> Thank you so much. >> Thank you. >> Real pleasure. >> Likewise. For Sally Eaves and Karen McCloskey, I'm Lisa Martin you're watching theCUBE Conversation. (soft upbeat music)

>>mm We're entering the fourth grade era of VM ware Executive management From its beginnings in the late 90s is a Silicon Valley startup. It's five founders quickly built the company and it ended up as one of the greatest acquisitions in the history of enterprise tech when EMC bought VM ware for $625 million as a public company. But still under EMC's governance, paul Maritz was appointed Ceo in 2000 and eight and set the company on a journey to build what he called at the time. The software mainframe meaning the company's platform would run any application at high performance with low overhead and world class recovery. Pat Gelsinger took over the Ceo reins in 2012 and through organic investments and clever m and a set of course for the software defined data center and after some early miscalculations in cloud, realigned the company strategy to successfully partner with hyper scale hours and position the company for the multi cloud future. The hallmarks of VM where over the course of its history have been great engineering that led to great products, loyal customers and a powerful ecosystem. The other telling attribute of VM where is it? CEOs have always had a deep understanding of technology and its latest Ceo is no different. It's our pleasure to welcome raghu Raghuram back to the cube the fourth Ceo of VM ware and yet another Silicon Valley Ceo graduated from the IIttie rgu, great to see you again and congratulations on your new role. >>Thanks. It's great to be here. >>Okay, five months in 1st 100 days what we have focused on that journey to become the Switzerland of multi cloud, tell us about your early experience as ceo >>it's been fantastic. Uh our customers, all our employees, all our partners have been very welcoming and of course I've given me great input. What we've been able to do in the last 100 days is to really crystallize the strategy and focus it around what I'm sure we're gonna be spending a lot of time talking about. And that's about the multi cloud era of computing that most enterprises are going to go through over the next decade. And so that's really what I've been up to and you'll see the results of that in next week's uh we involved and uh where we would be talking about the strategy and some product announcements that go along with the strategy and so it's a very exciting time to be at Vandenberg. >>Yeah, I mean, I referenced it in my intro, it's almost like the light bulb went off when VM ware realized, wow, this cloud build out is just an opportunity for us and that's really what you're doing with the multi cloud as you're building on top of all the infrastructure that the hyper cloud vendors are putting out there. Maybe you can talk about that, that opportunity and what customers are telling you. >>Yeah, it's uh here is how I describe what has happened in the industry. Right, and what will happen in the industry. So, if you look at the the past decade, as cloud became a mainstream thing, most customers pick the cloud, they built their first digital applications into it, the ones that serve their mobile users or end users with digital products and that worked great for them. Then they step back and say, okay, how many modernize everything that we're doing has become a digital company. And when you go from 10, of your portfolio, 100% of your application portfolio being modernized. What has to happen is you got to go from figuring out, okay, how am I gonna put everything in one cloud to what does the application need and how do I put it on the right place? I look at the same time, the industry has also evolved from being uh predominantly supplied by one cloud provider to multiple cloud providers. At the same time, the thanks to companies like IBM where the data center has been transformed into a private cloud. The edges growing up to be its own location for a cloud sovereign clouds are going. So truly what has happened is it's become a multi cloud world. And customers are saying in addition to just being cloud first, I want to be cloud smart. And so this distributed era of computing that we are entering is what we are seeing in the industry. And what the empire is trying to do is to say, look, let's provide customers with the fastest way of getting to this multi cloud era of computing so that they can go fast, they can spend less and most importantly, they can be free, in other words, choose the right application, right cloud for right applications and have control over how they deploy and use their applications and data. That really is a strategy that we are putting in place. This is something that we've been working towards in the last couple of years now. I'm accelerating that and making that the main piece of what we end, where is doing in order to do that, we have a great opportunity to take partner even better with all of our cloud provider partners and that's where the Switzerland of the industry comes in without impending spin, especially, we have great partnership with the cloud players, great partnerships with infrastructure players. We truly can be a neutral partner to the customers as they look at all these choices and make the right choices for their applications. >>So, I want to ask you about this multi cloud when when the early multi cloud narrative came out where I go, I was saying, look, multi cloud is really multi vendor, you you've got workloads and apps running on different, different clouds. And then increasingly, the promise and your promises, we're going to abstract the underlying complexity of those clouds and we're going to give you an experience whether it's on premise, hybrid into a cloud. Across clouds. Eventually out to the edge, it's gonna be a singular, substantially identical, if not identical experience and we're going to manage the whole kit and caboodle. And how where are we in that first of all? Is that the right way to think about it? Where are we in that sort of transition from plugging into any, you know, a cloud? I'm compatible with the cloud to it's a singular sort of VM ware cloud if you will. >>Yeah. So, um, so I wanna clarify something that he said because this tends to be very commonly confused by customers use the word abstraction. And usually when people think of abstraction, they think it hides capabilities of the uh, cloud providers. That's not what we are trying to. In fact, that's the last thing we're trying to do. What we're trying to do is to provide a consistent developer experience regardless of where you want to build your application so that you can use the cloud provider services if that's what you want to use. But the deficit cops toolchain, the runtime environment, which turns out to be Cuban aires and how you control the kubernetes environment. How do you manage and secure and connect all of these things. Those are the places where we are adding the value. Right. And so really the VM ware value proposition is you can build on the cloud of your choice but providing these consistent elements. Number one, you can make better use of us, your scarce developer or operator resources and expertise. Right. And number two, you can move faster and number three can spend less as a result of this. So that's really what we're trying to do, but not. So I just wanted to clarify the word abstraction in terms of their way, we're still, I would say in the early stages, so if you look at what customers are trying to do, they're trying to build these Greenfield applications and there is an entire ecosystem emerging around Campaneris. There is still kubernetes is not a developer platform. The developer experience on top of kubernetes is highly inconsistent. And so those are some of the areas where we are introducing new innovations with our towns, our application platform. And then if you take enterprise applications, what does it take to have enterprise applications running all the time, be entirely secure, etcetera, etcetera. That's where the we ever cloud assets that are traditionally this fear based come into play and we've got this now in all of the clouds but it's still in the early days from uh on Azure and google et cetera. How do you manage and secure those things again? We're in the early days. So that's where we are. I would say, >>yeah, thank you for that clarification, I want to sort of come back to that and just make sure we understand it. So for example, if I'm a developer and I want to take advantage of, let's say graviton uh and build an app on that, that so maybe it's some kind of data intensive app or whatever it is. I can do that. You won't restrict me from doing that at the same time. If I want to use the VM where management experience across all my clouds, I can do that as well. Is that the right way to think about it? >>Yeah, exactly. So the management experience by the way, and this is the other thing that gets missed in the remember dialogue because we've been so phenomenally successful with this fear. There's a misperception that everything we are doing atmosphere today works only on top. So everything we're doing at BM wear works only on top of the sphere. That's not the case. Take management, for example, our management portfolio is modular and independent of these, which means it can manage the Graviton application that you're building, right. It can manage a traditional, these fear based application, it can manage rage application, it can manage VM based applications, can manage computer based applications. Uh so it's truly uh, overall management layer. So that is really what we're trying to do. Same thing with our kubernetes example. Right, So our communities control plane allows you to control these kubernetes clusters. Whether the clusters are utilizing gravity and whether clusters are utilizing these fear based crew binaries environments. >>Okay, that's great. So it's kind of a set up question because my next question relates to project Monterey, Because, you know, I've always said when I write about about these things, when I saw Nitro, I saw Graviton, I saw project monitor, I said uh everybody needs a Nitro Nitro or a graviton because new workloads are coming. It's not just the X 86 can handle everything anymore asap whether it's sequel server, whatever we've got new workloads that are coming ai ml data intensive edge workloads, et cetera. Is that how we should think of? Project Monterrey. Where are you in Project Monterey? Why is it so important? Help people understand that? >>Yeah. Project mantra is super exciting for a couple of different reasons. One is uh in its first iteration and uh we announced project monitoring and last being well, we continue to build and we're making great progress along with the hardware partners that we are working with um in its first hydration it allows um um some of the functions that you would expect in the software defined data center to be offloaded into these montri processors. The smart nick processes. Right. So what that does, is it clears up the core CPU for other application functions. Right, so you get better scalability, more resource utilization, etcetera, etcetera. The second thing it does is because some of the software defined data center functions are done in the smart make um it gets accelerated as well. Because it takes advantage of the special accelerators that are there security functions, manageability functions, networking functions etcetera, etcetera. So that's that what you're alluding to is overall it's the v sphere, the sX Hyper Visor complimentary itself. That's moving into the specialized processors which allows the hyper Visor will be built into these smart mix, which means the main CPU can be an intel. CPU can be an M D C P. You can be an arm. CPU can be whatever it is you want in the future. So truly enables Monte CPU heterogeneous computing. So that's that's why this is exciting. And of course because it is the sphere, it can happen in the data centers, it can happen in Carlos. It can happen in Sovereign clouds. It can happen in the public clouds all over a period of time. And >>and potentially the Edge I would presume in the future. >>Sorry. Yeah, that's a great point. Thanks for pointing that out. In fact, the Edge is one of the most important places that will happen because we need these low latency applications such as in the telco case for example, right. Or we need these applications that have specialized processing the required. If you're setting up a cashier less store and you need to process and you need a lot of influence engines. So, Monterey helps with all of those things. >>I want to make sure our audience understands. It's because the software defined data center was awesome but but it also created waste in the sense that you have all these offload functions in storage and networking and security running on on x 86 processors which may not be the most efficient way. So emerging architectures around arm might be less expensive, maybe more cost effective, lower power. Uh maybe they do memory management differently. So there are these offload use cases. But as well you we talked about the edge there could be a lot of edge use cases that or whatever whether it's arm or in video etcetera. So now you're driving that optionality for customers so you can support more workloads of the future. >>Yeah, so this is exactly if you think about in europe when you talked about the embers evolution, the inverse core DNA has always been to master hydrogen. Itty right. And what we're seeing is this world of heterogeneous hardware coming alive. Right. You talked about Professor hydrogen Itty including GPU chips and so on. There is a memory architecture heterogeneous, their storage architecture heterogeneous. And so the idea is that regardless of what you use, how do you provide the best workload platform and a consistent way of managing all of these things and reducing the complexity while gaining the efficiency benefits and the other benefits that you talked about. >>So speaking of geniality that brings me to Tansu, you know early on people thought, oh wow containers, that's gonna kill VM where this is the opposite happened. You guys leaned in as as you have as a sign of great leadership these days. You don't get defensive, you just, you know, get the trend is your friend, as they say, give us the update on on Tan xue. Why is that so important to the future? >>Yeah. So if you look at any enterprises portfolio right, they are looking at it and saying look, there's a whole set of applications that I need to modernize. Now. The question becomes how do you modernize these applications in a way that it is essentially done with these microservices architectures and so on and so forth. In that context, how do I maximize the developer productivity and provide a great developer experience because there is not enough developers in the world to modernize every application that that's in every enterprise. Right. So, Tan xue is our answer to help enterprises modernize their applications and deliver in a way that the developed makes the developers very productive on the cloud of their choice. So that is really the strategic intent of Tancill and the core building block for Tan xue is of course kubernetes as you well know, Kubernetes has become the common infrastructure abstraction across clouds. So if you want portability for traditional VM based applications, he used this fear, if you want portability for traditional for containerized microservices applications, you assume kubernetes, that's how companies companies are thinking about it. And so that's the first thing that we did now. The second is you've got developers building applications all over the place. So now, just like you used to have physical server sprawl and now and then VM sprawl these days you have cluster sprawl, kubernetes, cluster sprawl and tons of mission control affects as a multi cloud, multi cluster kubernetes control plan works on the chaos and everything else that some of the Sun. The third point of Tanzania is the developer experience and we have introduced Andrew application platform, which is really focused on delivering a great developer experience on top of any Cuban Aires. So that's really how we're building out the towns of portfolio. And then of course we got Spring and uh as you well know a majority of enterprise applications today are java and if you want to modernize java, you use spring boot and so we had tremendous success with our uh spring boot technology and our startup, Springdale Ohio capabilities and so on and so forth. So that's the entirety of the towns of portfolio. It's multi cloud, it's kubernetes agnostic. Of course it runs great on this fear but it's really the approach making developers productive in the enterprise >>awesome. Thank you for that. I know we're tight on time but it's like speed dating with you raghu. So I'm gonna go on to another topic. Really important topic of security, you've made obviously some big acquisitions, there are things like carbon black, you've got a lot of stuff going on with, with, with endpoint, with end user computing, I'm first interested in sort of how you organize it looks like you're putting security and the networking piece together and then what's your swim lane? It seems like you're, you're focused obviously on your infrastructure. You're not trying to be all things to all people. Help us understand your strategy in that regard. >>Yeah, I mean security is a massive space, Right? And you covered very well. Hundreds and thousands of security problems that customers want to be solving. What we are focused on is how do we simplify the security problem for the customers? And we're doing it through three wells. The first one is we are baking security into the platforms that customers used ones. Right. But there are more obvious fear our workspace one, our container platform etcetera, etcetera. Right? Cloud platforms. So that's the first thing that we're doing. The second is we are putting um, bringing together, we're taking an end to end view of security, which is everything from an end user connecting from home to the corporate network or the sassy, sassy applications to the Windows devices they are using to the data center applications they're using to the club. Right? So we're taking a holistic view of security. So which means we want to combine our network security assets with our endpoint security assets with our workload security assets. That is why we bought all of those things together under one roof. And the third is we are instrumental in all of these and collecting signals from all of these and pulling it into the cloud and turning security into a machine learning and the data problem, right? And that is where the problem. Black cloud comes in and by doing that, we are able to provide a holistic view of where uh customer security posture, right? And these sensors can be on BMR platforms, on non BMR platforms etcetera. And so so that's really how we are approaching it. I mean there's the emerging industry term for a policy XDR. You might follow that. So that's really what we're trying to do. >>Outstanding. Last question and I know, I know we got to go. You mentioned the spin that's happening in november. That's an exciting time for a lot of reasons. I think the ecosystem, you know, emphasizing your independence but also gives you control of your balance sheet, regaining control of your balance sheet, tongue in cheek there. But it's important because all this, this cloud build out this multi cloud, exposing the primitives, leveraging the primitives and the A. P. I. S. Of these clouds making them identical across all these estates. That's not trivial and you're obviously gonna need resources to do that. So maybe you can talk about that and how you see the future playing out organic inorganic, maybe a little lemon A in there. What's your approach? How are you thinking about that? >>Yeah. So we are very excited with the impending spain, which like you said is on track to happen early november. Um and if you think about the spin, there are three aspects that we are excited about. The first aspect is uh we have a great relationship with Dell Tech, the company right. What we have done is we have codified that into a framework agreement that covers the gold market and technology collaboration and we are super excited by that and that baselines against what we do today and then as incentives on both sides to continue to grow that tremendously. So we're gonna continue being, doing that and that's going to continue being a great partner at the same time. From a partnership point of view, is truly going to be a Switzerland of the industry. So previously companies that were otherwise a little bit more competitive with dull now no longer have that reservation in partnering very deeply with us. I'm totally, like you said from a capital structure point of view, it gives us the flexibility to use to do em in a should we decide to do so in the future right? And use both equity and cash for them in a so so that's the capital structure, flexibility, the Switzerland positioning and the continuing great relationship with dull Those are the benefits of the spin >>love and the partner ecosystem has always been a source of, of innovation and it's a big part of the flywheel, the power of many versus the resources of one Ragu, Thanks so much for coming back in the queue. Best of luck. We're really excited for you and for the future of VM ware. >>Thank you and thanks for all the great work that you do and look forward to continuing to read your great research, >>appreciate that. And thank you for >>watching the cubes, continuous >>Coverage of VM World 2021. Keep it right there. >>Thank you. Mhm. Yeah.

(gentle upbeat music) >> People hate to be sold, but they love to buy. We become what we think about, think, and grow rich. If you want to gather honey, don't kick over the beehive. The world is replete with time-tested advice and motivational ideas for aspiring salespeople, Dale Carnegie, Napoleon Hill, Norman Vincent Peale, Earl Nightingale, and many others have all published classics with guidance that when followed closely, almost always leads to success. More modern personalities have emerged in the internet era, like Tony Robbins, and Gary Vaynerchuk, and Angela Duckworth. But for the most part, they've continued to rely on book publishing, seminars, and high value consulting to peddle their insights and inspire action. Welcome to this video exclusive on theCUBE. This is Dave Vellante, and I'm pleased to welcome back Professor Mark Roberge, who is one of the Managing Directors at Stage 2 Capital, and Paul Fifield, who's the CEO and Co-Founder of Sales Impact Academy. Gentlemen, welcome. Great to see you. >> You too Dave and thanks. >> All right, let's get right into it. Paul, you guys are announcing today a $4 million financing round. It comprises $3 million in a seed round led by Stage 2 and a million dollar in debt financing. So, first of all, congratulations. Paul, why did you start Sales Impact Academy? >> Cool, well, I think my background is sort of two times CRO, so I've built two reasonably successful companies. Built a hundred plus person teams. And so I've got kind of this firsthand experience of having to learn literally everything on the job whilst delivering these very kind of rapid, like achieving these very rapid growth targets. And so when I came out of those two journeys, I literally just started doing some voluntary teaching in and around London where I now live. I spend a bunch of time over in New York, and literally started this because I wanted to sort of kind of give back, but just really wanted to start helping people who were just really, really struggling in high pressure environments. And that's both leadership from sense of revenue leadership people, right down to sort of frontline SDRs. And I think as I started just doing this voluntary teaching, I kind of realized that actually the sort of global education system has done is a massive, massive disservice, right? I actually call it the greatest educational travesty of the last 50 years, where higher education has entirely overlooked sales as a profession. And the knock-on consequences of that have been absolutely disastrous for our profession. Partly that the profession is seen as a bit sort of embarrassing to be a part of. You kind of like go get a sales job if you can't get a degree. But more than that, the core fundamental within revenue teams and within sales people is now completely lacking 'cause there's no structured formal kind of like learning out there. So that's really the problem we're trying to solve on the kind of like the skill side. >> Great. Okay. And mark, always good to have you on, and I got to ask you. So even though, I know this is the wheelhouse for you and your partners, and of course, you've got a deep bench of LPs, but lay out the investment thesis here. What's the core problem that you saw and how are you looking at the market? >> Yeah, sure, Dave. So this one was a special one for me. We've spoken in the past. I mean, just personally I've always had a similar passion to Paul that it's amazing how important sales execution is to all companies, nevermind just the startup ecosystem. And I've always personally been motivated by anything that can help the startup ecosystem increase their success. Part of why I teach at Harvard and try to change some of the stuff that Paul's talking about, which is like, it's amazing how little education is done around sales. But in this particular one, not only personally was I excited about, but from a fun perspective, we've got to look at the economic outcomes. And we've been thinking a lot about the sales tech stack. It's evolved a ton in the last couple of decades. We've gone from the late '90s where every sales VP was just, they had a thing called the CRM that none of their reps even used, right? And we've come so far in 20 years, we've got all these amazing tools that help us cold call, that help us send emails efficiently and automatically and track everything, but nothing's really happened on the education side. And that's really the enormous gap that we've seen is, these organizations being much more proactive around adopting technology that can prove sales execution, but nothing on the education side. And the other piece that we saw is, it's almost like all these companies are reinventing the wheel of looking in the upcoming year, having a dozen sales people to hire, and trying to put together a sales enablement program within their organization to teach salespeople sales 101. Like how to find a champion, how to develop a budget, how to develop sense of urgency. And what Paul and team can do in the first phase of essay, is can sort of centralize that, so that all of these organizations can benefit from the best content and the best instructors for their team. >> So Paul, exactly, thank you, mark. Exactly what do you guys do? What do you sell? I'm curious, is this sort of, I'm thinking in my head, is this E-learning, is it really part of the sales stack? Maybe you could help us understand that better. >> Well, I think this problem of having to upscale teams has been around like forever. And kind of going back to the kind of education problem, it's what's wild is that we would never accept this of our lawyers, our accountants, or HR professionals. Imagine like someone in your finance team arriving on day one and they're searching YouTube to try and work out how to like put a balance sheet together. So it's a chronic, chronic problem. And so the way that we're addressing this, and I think the problem is well understood, but there's always been a terrible market, sort of product market fit for how the problem gets solved. So as mark was saying, typically it's in-house revenue leaders who themselves have got massive gaps in their knowledge, hack together some internal learning that is just pretty poor, 'cause it's not really their skillset. The other alternative is bringing in really expensive consultants, but they're consultants with a very single worldview and the complexity of a modern revenue organization is very, very high these days. And so one consultant is not going to really kind of like cover every topic you need. And then there's the kind of like fairly old fashioned sales training companies that just come in, one big hit, super expensive and then sort of leave again. So the sort of product market fit to solve, has always been a bit pretty bad. So what we've done is we've created a subscription model. We've essentially productized skills development. The way that we've done that is we teach live instruction. So one of the big challenges Andreessen Horowitz put a post out around this so quite recently, one of the big problems of online learning is that this kind of huge repository of online learning, which puts all the onus on the learner to have the discipline to go through these courses and consume them in an on-demand way is actually they're pretty ineffective. We see sort of completion rates of like 7 to 8%. So we've always gone from a live instruction model. So the sort of ingredients are the absolute very best people in the world in their very specific skill teaching live classes just two hours per week. So we're not overwhelming the learners who are already in work, and they have targets, and they've got a lot of pressure. And we have courses that last maybe four to like 12 hours over two to sort of six to seven weeks. So highly practical live instruction. We have 70, 80, sometimes even 90% completion rates of the sort of live class experience, and then teams then rapidly put that best practice into practice and see amazing results in things like top of funnel, or conversion, or retention. >> So live is compulsory and I presume on-demand? If you want to refresh you have an on demand option? >> Yeah, everything's recorded, so you can kind of catch up on a class if you've missed it, But that live instruction is powerful because it's kind of in your calendar, right? So you show up. But the really powerful thing, actually, is that entire teams within companies can actually learn at exactly the same pace. So we teach it eight o'clock Pacific, 11 o'clock Eastern, >> 4: 00 PM in the UK, and 5:00 PM Europe. So your entire European and North American teams can literally learn in the same class with a world-class expert, like a Mark, or like a Kevin Dorsey, or like Greg Holmes from Zoom. And you're learning from these incredible people. Class finishes, teams can come back together, talk about this incredible best practice they've just learned, and then immediately put it into practice. And that's where we're seeing these incredible, kind of almost instant impact on performance at real scale. >> So, Mark, in thinking about your investment, you must've been thinking about, okay, how do we scale this thing? You've got an instructor component, you've got this live piece. How are you thinking about that at scale? >> Yeah, there's a lot of different business model options there. And I actually think multiple of them are achievable in the longer term. That's something we've been working with Paul quite a bit, is like, they're all quite compelling. So just trying to think about which two to start with. But I think you've seen a lot of this in education models today. Is a mixture of on-demand with prerecorded. And so I think that will be the starting point. And I think from a scalability standpoint, we were also, we don't always try to do this with our investments, but clearly our LP base or limited partner base was going to be a key ingredient to at least the first cycle of this business. You know, our VC firm's backed by over 250 CRO CMOs heads of customer success, all of which are prospective instructors, prospective content developers, and prospective customers. So that was a little nicety around the scale and investment thesis for this one. >> And what's in it for them? I mean, they get paid. Obviously, you have a stake in the game, but what's in it for the instructors. They get paid on a sort of a per course basis? How does that model work? >> Yeah, we have a development fee for each kind of hour of teaching that gets created So we've mapped out a pretty significant curriculum. And we have about 250 hours of life teaching now already written. We actually think it's going to be about 3000 hours of learning before you get even close to a complete curriculum for every aspect of a revenue organization from revenue operations, to customer success, to marketing, to sales, to leadership, and management. But we have a development fee per class, and we have a teaching fee as well. >> Yeah, so, I mean, I think you guys, it's really an underserved market, and then when you think about it, most organizations, they just don't invest in training. And so, I mean, I would think you'd want to take it, I don't know what the right number is, 5, 10% of your sales budget and actually put it on this and the return would be enormous. How do you guys think about the market size? Like I said before, is it E-learning, is it part of the CRM stack? How do you size this market? >> Well, I think for us it's service to people. A highly skilled sales rep with an email address, a phone and a spreadsheet would do really well, okay? You don't need this world-class tech stack to do well in sales. You need the skills to be able to do the job. But the reverse, that's not true, right? An unskilled person with a world-class tech stack won't do well. And so fundamentally, the skill level of your team is the number one most important thing to get right to be successful in revenue. But as I said before, the product market for it to solve that problem, has been pretty terrible. So we see ourselves 100%. And so if you're looking at like a com, you look at Gong, who we've just signed as a customer, which is fantastic. Gong has a technology that helps salespeople do better through call recording. You have Outreach, who is also a customer. They have technologies that help SDRs be more efficient in outreach. And now you have Sales Impact Academy, and we help with skills development of your team, of the entirety of your revenue function. So we absolutely see ourselves as a key part of that stack. In terms of the TAM, 60 million people in sales are on, according to LinkedIn. You're probably talking 150 million people in go to market to include all of the different roles. 50% of the world's companies are B2B. The TAM is huge. But what blows my mind, and this kind of goes back to this why the global education system has overlooked this because essentially if half the world's companies are B2B, that's probably a proxy for the half of the world's GDP, Half of the world's economic growth is relying on the revenue function of half the world's companies, and they don't really know what they're doing, (laughs) which is absolutely staggering. And if we can solve that in a meaningfully meaningful way at massive scale, then the impact should be absolutely enormous. >> So, Mark, no lack of TAM. I know that you guys at Stage 2, you're also very much focused on the metrics. You have a fundamental philosophy that your product market fit and retention should come before hyper growth. So what were the metrics that enticed you to make this investment? >> Yeah, it's a good question, Dave, 'cause that's where we always look first, which I think is a little different than most early stage investors. There's a big, I guess, meme, triple, triple, double, double that's popular in Silicon Valley these days, which refers to triple your revenue in year one, triple your revenue in year two, double in year three, and four, and five. And that type of a hyper growth is critical, but it's often jumped too quickly in our opinion. That there's a premature victory called on product market fit, which kills a larger percentage of businesses than is necessary. And so with all our investments, we look very heavily first at user engagement, any early indicators of user retention. And the numbers were just off the charts for SIA in terms of the customers, in terms of the NPS scores that they were getting on their sessions, in terms of the completion rate on their courses, in terms of the customers that started with a couple of seats and expanded to more seats once they got a taste of the program. So that's where we look first as a strong foundation to build a scalable business, and it was off the charts positive for SIA. >> So how about the competition? If I Google sales training software, I'll get like dozens of companies. Lessonly, and MindTickle, or Brainshark will come up, that's not really a fit. So how do you think about the competition? How are you different? >> Yeah, well, one thing we try and avoid is any reference to sales training, 'cause that really sort of speaks to this very old kind of fashioned way of doing this. And I actually think that from a pure pedagogy perspective, so from a pure learning design perspective, the old fashioned way of doing sales training was pull a whole team off site, usually in a really terrible hotel with no windows for a day or two. And that's it, that's your learning experience. And that's not how human beings learn, right? So just even if the content was fantastic, the learning experience was so terrible, it was just very kind of ineffective. So we sort of avoid kind of like sales training, The likes of MindTickle, we're actually talking to them at the moment about a partnership there. They're a platform play, and we're certainly building a platform, but we're very much about the live instruction and creating the biggest curriculum and the broadest curriculum on the internet, in the world, basically, for revenue teams. So the competition is kind of interesting 'cause there is not really a direct subscription-based live like learning offering out there. There's some similar ish companies. I honestly think at the moment it's kind of status quo. We're genuinely creating a new category of in-work learning for revenue teams. And so we're in this kind of semi and sort of evangelical sort of phase. So really, status quo is one of the biggest sort of competitors. But if you think about some of those old, old fashioned sort of Miller Heimans, and then perhaps even like Sandlers, there's an analogy perhaps here, which is kind of interesting, which is a little bit like Siebel and Salesforce in the sort of late '90s, where in Siebel you have this kind of old way of doing things. It was a little bit ineffective. It was really expensive. Not accessible to a huge space of the market. And Salesforce came along and said, "Hey, we're going to create this cool thing. It's going to be through the browser, it's going to be accessible to everyone, and it's going to be really, really effective." And so there's some really kind of interesting parallels almost between like Siebel and Salesforce and what we're doing to completely kind of upend the sort of the old fashioned way of delivering sort of sales training, if you like. >> And your target customer profile is, you're selling to teams, right? B2B teams, right? It's not for individuals. Is that correct, Paul? >> Currently. Yeah, yeah. So currently we've got a big foothold in series A to series B. So broadly speaking out, our target market currently is really fast growth technology companies. That's the sector that we're really focusing on. We've got a very good strong foothold in series A series B companies. We've now won some much larger later stage companies. We've actually even won a couple of corporates, I can't say names yet, but names that are very, very, very familiar and we're incredibly excited by them, which could end up being thousand plus seat deals 'cause we do this on a per seat basis. But yeah, very much at the moment it's fast growth tech companies, and we're sort of moving up the chain towards enterprise. >> And how do you deal with the sort of maturity curve, if you will, of your students? You've got some that are brand new, just fresh out of school. You've got others that are more seasoned. What do you do, pop them into different points of the curriculum? How do you handle it? >> Yeah we have, I'll say we have about 30 courses right now. We have about another 15 in development where post this fundraise, we want to be able to get to around about 20 courses that we're developing every quarter and getting out to market. So we're literally, we've sort of identified about 20 to 25 key roles across everything within revenue. That's, let's say revenue ops, customer success, account management, sales, engineering, all these different kinds of roles. And we are literally plotting the sort of skills development for these individuals over multiple, multiple years. And I think what we've never ceases to amaze me is actually the breadth of learning in revenue is absolutely enormous. And what kind of just makes you laugh is, this is all of this knowledge that we're now creating it's what companies just hope that their teams somehow acquire through osmosis, through blogs, through events. And it's just kind of crazy that there is... It's absolutely insane that we don't already exist, basically. >> And if I understand it correctly, just from looking at your website, you've got the entry level package. I think it's up to 15 seats, and then you scale up from there, correct? Is it sort of as a seat-based license model? >> Yeah, it's a seat-based model, as Mark mentioned. In some cases we sell, let's say 20 or $30,000 deal out the gate and that's most of the team. That will be maybe a series A, series B deal, but then we've got these land and expand models that are working tremendously well. We have seven, eight customers in Q1 that have doubled their spend Q2. That's the impact that they're seeing. And our net revenue retention number for Q2 is looking like it's going to be 177% to think exceeds companies like Snowflakes. Well, our underlying retention metrics, because people are seeing this incredible impact on teams and performance, is really, really strong. >> That's a nice metric compare with Snowflake (Paul laughs) It's all right. (Dave and Paul laugh) >> So, Mark, this is a larger investment for Stage 2 You guys have been growing and sort of upping your game. And maybe talk about that a little bit. >> Yeah, we're in the middle of Fund II right now. So, Fund I was in 2018. We were doing smaller checks. It was our first time out of the gate. The mission has really taken of, our LP base has really taken off. And so this deal looks a lot like more like our second fund. We'll actually make an announcement in a few weeks now that we've closed that out. But it's a much larger fund and our first investments should be in that 2 to $3 million range. >> Hey, Paul, what are you going to do with the money? What are the use of funds? >> Put it on black, (chuckles) we're going to like- (Dave laughs) >> Saratoga is open. (laughs) (Mark laughs) >> We're going to, look, the curriculum development for us is absolutely everything, but we're also going to be investing in building our own technology platform as well. And there are some other really important aspects to the kind of overall offering. We're looking at building an assessment tool so we can actually kind of like start to assess skills across teams. We certify every course has an exam, so we want to get more robust around the certification as well, because we're hoping that our certification becomes the global standard in understanding for the first time in the industry what individual competencies and skills people have, which will be huge. So we have a broad range of things that we want to start initiating now. But I just wanted to quickly say Stage 2 has been nothing short of incredible in every kind of which way. Of course, this investment, the fit is kind of insane, but the LPs have been extraordinary in helping. We've got a huge number of them are now customers very quickly. Mark and the team are helping enormously on our own kind of like go to market and metrics. I've been doing this for 20 years. I've raised over 100 million myself in venture capital. I've never known a venture capital firm with such value add like ever, or even heard of other people getting the kind of value add that we're getting. So I just wanted to a quick shout out for Stage 2. >> Quite a testimony of you guys. Definitely Stage 2 punches above its weight. Guys, we'll leave it there. Thanks so much for coming on. Good luck and we'll be watching. Appreciate your time. >> Thanks, Dave. >> Thank you very much. >> All right, thank you everybody for watching this Cube conversation. This is Dave Vellante, and we'll see you next time.

from around the globe it's thecube with digital coverage of exascale day made possible by hewlett packard enterprise welcome everyone to the cube celebration of exascale day ben bennett is here he's an hpc strategist and evangelist at hewlett-packard enterprise ben welcome good to see you good to see you too son hey well let's evangelize exascale a little bit you know what's exciting you uh in regards to the coming of exoskilled computing um well there's a couple of things really uh for me historically i've worked in super computing for many years and i have seen the coming of several milestones from you know actually i'm old enough to remember gigaflops uh coming through and teraflops and petaflops exascale is has been harder than many of us anticipated many years ago the sheer amount of technology that has been required to deliver machines of this performance has been has been us utterly staggering but the exascale era brings with it real solutions it gives us opportunities to do things that we've not been able to do before if you look at some of the the most powerful computers around today they've they've really helped with um the pandemic kovid but we're still you know orders of magnitude away from being able to design drugs in situ test them in memory and release them to the public you know we still have lots and lots of lab work to do and exascale machines are going to help with that we are going to be able to to do more um which ultimately will will aid humanity and they used to be called the grand challenges and i still think of them as that i still think of these challenges for scientists that exascale class machines will be able to help but also i'm a realist is that in 10 20 30 years time you know i should be able to look back at this hopefully touch wood look back at it and look at much faster machines and say do you remember the days when we thought exascale was faster yeah well you mentioned the pandemic and you know the present united states was tweeting this morning that he was upset that you know the the fda in the u.s is not allowing the the vaccine to proceed as fast as you'd like it in fact it the fda is loosening some of its uh restrictions and i wonder if you know high performance computing in part is helping with the simulations and maybe predicting because a lot of this is about probabilities um and concerns is is is that work that is going on today or are you saying that that exascale actually you know would be what we need to accelerate that what's the role of hpc that you see today in regards to sort of solving for that vaccine and any other sort of pandemic related drugs so so first a disclaimer i am not a geneticist i am not a biochemist um my son is he tries to explain it to me and it tends to go in one ear and out the other um um i just merely build the machines he uses so we're sort of even on that front um if you read if you had read the press there was a lot of people offering up systems and computational resources for scientists a lot of the work that has been done understanding the mechanisms of covid19 um have been you know uncovered by the use of very very powerful computers would exascale have helped well clearly the faster the computers the more simulations we can do i think if you look back historically no vaccine has come to fruition as fast ever under modern rules okay admittedly the first vaccine was you know edward jenner sat quietly um you know smearing a few people and hoping it worked um i think we're slightly beyond that the fda has rules and regulations for a reason and we you don't have to go back far in our history to understand the nature of uh drugs that work for 99 of the population you know and i think exascale widely available exoscale and much faster computers are going to assist with that imagine having a genetic map of very large numbers of people on the earth and being able to test your drug against that breadth of person and you know that 99 of the time it works fine under fda rules you could never sell it you could never do that but if you're confident in your testing if you can demonstrate that you can keep the one percent away for whom that drug doesn't work bingo you now have a drug for the majority of the people and so many drugs that have so many benefits are not released and drugs are expensive because they fail at the last few moments you know the more testing you can do the more testing in memory the better it's going to be for everybody uh personally are we at a point where we still need human trials yes do we still need due diligence yes um we're not there yet exascale is you know it's coming it's not there yet yeah well to your point the faster the computer the more simulations and the higher the the chance that we're actually going to going to going to get it right and maybe compress that time to market but talk about some of the problems that you're working on uh and and the challenges for you know for example with the uk government and maybe maybe others that you can you can share with us help us understand kind of what you're hoping to accomplish so um within the united kingdom there was a report published um for the um for the uk research institute i think it's the uk research institute it might be epsrc however it's the body of people responsible for funding um science and there was a case a science case done for exascale i'm not a scientist um a lot of the work that was in this documentation said that a number of things that can be done today aren't good enough that we need to look further out we need to look at machines that will do much more there's been a program funded called asimov and this is a sort of a commercial problem that the uk government is working with rolls royce and they're trying to research how you build a full engine model and by full engine model i mean one that takes into account both the flow of gases through it and how those flow of gases and temperatures change the physical dynamics of the engine and of course as you change the physical dynamics of the engine you change the flow so you need a closely coupled model as air travel becomes more and more under the microscope we need to make sure that the air travel we do is as efficient as possible and currently there aren't supercomputers that have the performance one of the things i'm going to be doing as part of this sequence of conversations is i'm going to be having an in detailed uh sorry an in-depth but it will be very detailed an in-depth conversation with professor mark parsons from the edinburgh parallel computing center he's the director there and the dean of research at edinburgh university and i'm going to be talking to him about the azimoth program and and mark's experience as the person responsible for looking at exascale within the uk to try and determine what are the sort of science problems that we can solve as we move into the exoscale era and what that means for humanity what are the benefits for humans yeah and that's what i wanted to ask you about the the rolls-royce example that you gave it wasn't i if i understood it wasn't so much safety as it was you said efficiency and so that's that's what fuel consumption um it's it's partly fuel consumption it is of course safety there is a um there is a very specific test called an extreme event or the fan blade off what happens is they build an engine and they put it in a cowling and then they run the engine at full speed and then they literally explode uh they fire off a little explosive and they fire a fan belt uh a fan blade off to make sure that it doesn't go through the cowling and the reason they do that is there has been in the past uh a uh a failure of a fan blade and it came through the cowling and came into the aircraft depressurized the aircraft i think somebody was killed as a result of that and the aircraft went down i don't think it was a total loss one death being one too many but as a result you now have to build a jet engine instrument it balance the blades put an explosive in it and then blow the fan blade off now you only really want to do that once it's like car crash testing you want to build a model of the car you want to demonstrate with the dummy that it is safe you don't want to have to build lots of cars and keep going back to the drawing board so you do it in computers memory right we're okay with cars we have computational power to resolve to the level to determine whether or not the accident would hurt a human being still a long way to go to make them more efficient uh new materials how you can get away with lighter structures but we haven't got there with aircraft yet i mean we can build a simulation and we can do that and we can be pretty sure we're right um we still need to build an engine which costs in excess of 10 million dollars and blow the fan blade off it so okay so you're talking about some pretty complex simulations obviously what are some of the the barriers and and the breakthroughs that are kind of required you know to to do some of these things that you're talking about that exascale is going to enable i mean presumably there are obviously technical barriers but maybe you can shed some light on that well some of them are very prosaic so for example power exoscale machines consume a lot of power um so you have to be able to design systems that consume less power and that goes into making sure they're cooled efficiently if you use water can you reuse the water i mean the if you take a laptop and sit it on your lap and you type away for four hours you'll notice it gets quite warm um an exascale computer is going to generate a lot more heat several megawatts actually um and it sounds prosaic but it's actually very important to people you've got to make sure that the systems can be cooled and that we can power them yeah so there's that another issue is the software the software models how do you take a software model and distribute the data over many tens of thousands of nodes how do you do that efficiently if you look at you know gigaflop machines they had hundreds of nodes and each node had effectively a processor a core a thread of application we're looking at many many tens of thousands of nodes cores parallel threads running how do you make that efficient so is the software ready i think the majority of people will tell you that it's the software that's the problem not the hardware of course my friends in hardware would tell you ah software is easy it's the hardware that's the problem i think for the universities and the users the challenge is going to be the software i think um it's going to have to evolve you you're just you want to look at your machine and you just want to be able to dump work onto it easily we're not there yet not by a long stretch of the imagination yeah consequently you know we one of the things that we're doing is that we have a lot of centers of excellence is we will provide well i hate say the word provide we we sell super computers and once the machine has gone in we work very closely with the establishments create centers of excellence to get the best out of the machines to improve the software um and if a machine's expensive you want to get the most out of it that you can you don't just want to run a synthetic benchmark and say look i'm the fastest supercomputer on the planet you know your users who want access to it are the people that really decide how useful it is and the work they get out of it yeah the economics is definitely a factor in fact the fastest supercomputer in the planet but you can't if you can't afford to use it what good is it uh you mentioned power uh and then the flip side of that coin is of course cooling you can reduce the power consumption but but how challenging is it to cool these systems um it's an engineering problem yeah we we have you know uh data centers in iceland where it gets um you know it doesn't get too warm we have a big air cooled data center in in the united kingdom where it never gets above 30 degrees centigrade so if you put in water at 40 degrees centigrade and it comes out at 50 degrees centigrade you can cool it by just pumping it round the air you know just putting it outside the building because the building will you know never gets above 30 so it'll easily drop it back to 40 to enable you to put it back into the machine um right other ways to do it um you know is to take the heat and use it commercially there's a there's a lovely story of they take the hot water out of the supercomputer in the nordics um and then they pump it into a brewery to keep the mash tuns warm you know that's that's the sort of engineering i can get behind yeah indeed that's a great application talk a little bit more about your conversation with professor parsons maybe we could double click into that what are some of the things that you're going to you're going to probe there what are you hoping to learn so i think some of the things that that are going to be interesting to uncover is just the breadth of science that can be uh that could take advantage of exascale you know there are there are many things going on that uh that people hear about you know we people are interested in um you know the nobel prize they might have no idea what it means but the nobel prize for physics was awarded um to do with research into black holes you know fascinating and truly insightful physics um could it benefit from exascale i have no idea uh i i really don't um you know one of the most profound pieces of knowledge in in the last few hundred years has been the theory of relativity you know an austrian patent clerk wrote e equals m c squared on the back of an envelope and and voila i i don't believe any form of exascale computing would have helped him get there any faster right that's maybe flippant but i think the point is is that there are areas in terms of weather prediction climate prediction drug discovery um material knowledge engineering uh problems that are going to be unlocked with the use of exascale class systems we are going to be able to provide more tools more insight [Music] and that's the purpose of computing you know it's not that it's not the data that that comes out and it's the insight we get from it yeah i often say data is plentiful insights are not um ben you're a bit of an industry historian so i've got to ask you you mentioned you mentioned mentioned gigaflop gigaflops before which i think goes back to the early 1970s uh but the history actually the 80s is it the 80s okay well the history of computing goes back even before that you know yes i thought i thought seymour cray was you know kind of father of super computing but perhaps you have another point of view as to the origination of high performance computing [Music] oh yes this is um this is this is one for all my colleagues globally um you know arguably he says getting ready to be attacked from all sides arguably you know um computing uh the parallel work and the research done during the war by alan turing is the father of high performance computing i think one of the problems we have is that so much of that work was classified so much of that work was kept away from commercial people that commercial computing evolved without that knowledge i uh i have done in in in a previous life i have done some work for the british science museum and i have had the great pleasure in walking through the the british science museum archives um to look at how computing has evolved from things like the the pascaline from blaise pascal you know napier's bones the babbage's machines uh to to look all the way through the analog machines you know what conrad zeus was doing on a desktop um i think i think what's important is it doesn't matter where you are is that it is the problem that drives the technology and it's having the problems that requires the you know the human race to look at solutions and be these kicks started by you know the terrible problem that the us has with its nuclear stockpile stewardship now you've invented them how do you keep them safe originally done through the ascii program that's driven a lot of computational advances ultimately it's our quest for knowledge that drives these machines and i think as long as we are interested as long as we want to find things out there will always be advances in computing to meet that need yeah and you know it was a great conversation uh you're a brilliant guest i i love this this this talk and uh and of course as the saying goes success has many fathers so there's probably a few polish mathematicians that would stake a claim in the uh the original enigma project as well i think i think they drove the algorithm i think the problem is is that the work of tommy flowers is the person who took the algorithms and the work that um that was being done and actually had to build the poor machine he's the guy that actually had to sit there and go how do i turn this into a machine that does that and and so you know people always remember touring very few people remember tommy flowers who actually had to turn the great work um into a working machine yeah super computer team sport well ben it's great to have you on thanks so much for your perspectives best of luck with your conversation with professor parsons we'll be looking forward to that and uh and thanks so much for coming on thecube a complete pleasure thank you and thank you everybody for watching this is dave vellante we're celebrating exascale day you're watching the cube [Music]

>> From around the globe, it's theCUBE, with digital coverage of Exascale Day. Made possible by Hewlett Packard Enterprise. >> Hey, welcome back everybody Jeff Frick here with theCUBE. We are celebrating Exascale Day. 10, 18, I think it's the second year of celebrating Exascale Day, and we're really excited to have our next guest and talk about kind of what this type of compute scale enables, and really look a little bit further down the road at some big issues, big problems and big opportunities that this is going to open up. And I'm really excited to get in this conversation with our next guest. He is Bryan Pijanowski the Professor of Landscape and Soundscape Ecology at Purdue University. Bryan, great to meet you. >> Great to be here. >> So, in getting ready for this conversation, I just watched your TED Talk, and I just loved one of the quotes. I actually got one of quote from it that's basically saying you are exploring the world through sound. I just would love to get a little deeper perspective on that, because that's such a unique way to think about things and you really dig into it and explain why this is such an important way to enjoy the world, to absorb the world and think about the world. >> Yeah, that's right Jeff. So the way I see it, sound is kind of like a universal variable. It exists all around us. And you can't even find a place on earth where there's no sound, where it's completely silent. Sound is a signal of something that's happening. And we can use that information in ways to allow us to understand the earth. Just thinking about all the different kinds of sounds that exist around us on a daily basis. I hear the birds, I hear the insects, but there's just a lot more than that. It's mammals and some cases, a lot of reptiles. And then when you begin thinking outside the biological system, you begin to hear rain, wind, thunder. And then there's the sounds that we make, sounds of traffic, the sounds of church bells. All of this is information, some of it's symbolic, some of it's telling me something about change. As an ecologist that's what I'm interested in, how is the earth changing? >> That's great and then you guys set up at Purdue, the Purdue Center for Global Soundscapes. Tell us a little bit about the mission and some of the work that you guys do. >> Well, our mission is really to use sound as a lens to study the earth, but to capture it in ways that are meaningful and to bring that back to the public to tell them a story about how the earth kind of exists. There's an incredible awe of nature that we all experience when we go out and listen into to the wild spaces of the earth. I've gone to the Eastern Steppes of Mongolian, I've climbed towers in the Paleotropics of Borneo and listened at night. And ask the question, how are these sounds different? And what is a grassland really supposed to sound like, without humans around? So we use that information and bring it back and analyze it as a means to understand how the earth is changing and really what the biological community is all about, and how things like climate change are altering our spaces, our wild spaces. I'm also interested in the role that people play and producing sound and also using sound. So getting back to Mongolia, we have a new NSF funded project where we're going to be studying herders and the ways in which they use sonic practices. They use a lot of sounds as information sources about how the environment is changing, but also how they relate back to place and to heritage a special sounds that resonate, the sounds of a river, for example, are the resonance patterns that they tune their throat to that pay homage to their parents that were born at the side of that river. There's these special connections that people have with place through sound. And so that's another thing that we're trying to do. In really simple terms, I want to go out and, what I call it sounds rather simple, record the earth-- >> Right. >> What does that mean? I want to go to every major biome and conduct a research study there. I want to know what does a grassland sound like? What is a coral reef sound like? A kelp forest and the oceans, a desert, and then capture that as baseline and use that information-- >> Yeah. >> For scientific purposes >> Now, there's so much to unpack there Bryan. First off is just kind of the foundational role that sound plays in our lives that you've outlined in great detail and you talked about it's the first sense that's really activated as we get consciousness, even before we're born right? We hear the sounds of our mother's heartbeat and her voice. And even the last sense that goes at the end a lot of times, in this really intimate relationship, as you just said, that the sounds represent in terms of our history. We don't have to look any further than a favorite song that can instantly transport you, almost like a time machine to a particular place in time. Very, very cool. Now, it's really interesting that what you're doing now is taking advantage of new technology and just kind of a new angle to capture sound in a way that we haven't done before. I think you said you have sound listening devices oftentimes in a single location for a year. You're not only capturing sound, the right sound is changes in air pressure, so that you're getting changes in air pressure, you're getting vibration, which is kind of a whole different level of data. And then to be able to collect that for a whole year and then start to try to figure out a baseline which is pretty simple to understand, but you're talking about this chorus. I love your phrase, a chorus, because that sound is made up of a bunch of individual inputs. And now trying to kind of go under the covers to figure out what is that baseline actually composed of. And you talk about a bunch of really interesting particular animals and species that combine to create this chorus that now you know is a baseline. How did you use to do that before? I think it's funny one of your research papers, you reach out to the great bird followers and bird listeners, 'cause as you said, that's the easiest way or the most prolific way for people to identify birds. So please help us in a crowdsource way try to identify all the pieces that make this beautiful chorus, that is the soundscape for a particular area. >> Right, yeah, that's right. It really does take a team of scientists and engineers and even folks in the social sciences and the humanities to really begin to put all of these pieces together. Experts in many fields are extremely valuable. They've got great ears because that's the tools that they use to go out and identify birds or insects or amphibians. What we don't have are generalists that go out and can tell you what everything sounds like. And I'll tell you that will probably never ever happen. That's just way too much, we have millions of species that exist on this planet. And we just don't have a specific catalog of what everything sounds like, it's just not possible or doable. So I need to go out and discover and bring those discoveries back that help us to understand nature and understand how the earth is changing. I can't wait for us to eventually develop that catalog. So we're trying to develop techniques and tools and approaches that allow us to develop this electronic catalog. Like you're saying this chorus, and it doesn't necessarily have to be a species specific chorus, it can be a chorus of all these different kind of sounds that we think relate back to this kind of animal or that kind of animal based upon the animals instrument-- >> Right, great. >> And this is the sound. >> Now again, you know, keep it to the exascale theme, right? You're collecting a lot of data and you mentioned in one of the pieces I've dug up, that your longest study in a single location is 17 years. You've got over 4 million recordings. And I think you said over 230 years if you wanted to listen to them all back to back. I mean, this is a huge, a big data problem in terms of the massive amount of data that you have and need to run through an analysis. >> Yeah, that's right. We're collecting 48,000 data points per second. So that's 48 kilohertz. And then so you multiply everything and then you have a sense of how many data points you actually have to put them all together. When you're listening to a sound file over 10 minutes, you have hundreds of sounds that exist in them. Oftentimes you just don't know what they are, but you can more or less put some kind of measure on all of them and then begin to summarize them over space and time and try to understand it from a perspective of really science. >> Right, right. And then I just love to get your take as you progress down this kind of identification road, we're all very familiar with copyright infringement hits on YouTube or social media or whatever, when it picks up on some sound and the technology is actually really sophisticated to pick up some of those sound signatures. But to your point, it's a lot easier to compare against the known and to search for that known. Then when you've got this kind of undefined chorus that said we do know that there can be great analysis done that we've seen AI and ML applied, especially in the surveillance side on the video-- >> Right. >> With video that it can actually do a lot of computation and a lot of extracting signal from the noise, if you will. As you look down the road on the compute side for the algorithms that you guys are trying to build with the human input of people that know what you're listening to, what kind of opportunities do you see and where are we on that journey where you can get more leverage out of some of these technology tools? >> Well, I think what we're doing right now is developing the methodological needs, kind of describe what it is we need to move into that new space, which is going to require these computational, that computational infrastructure. So, for example, we have a study right now where we're trying to identify certain kinds of mosquitoes (chuckling) a vector-borne mosquitoes, and our estimates is that we need about maybe 900 to 1200 specific recordings per species to be able to put it into something like a convolutional neural network to be able to extract out the information, and look at the patterns and data, to be able to say indeed this is the species that we're interested in. So what we're going to need and in the future here is really a lot of information that allow us to kind of train these neural networks and help us identify what's in the sound files. As you can imagine the computational infrastructure needed to do that for data storage and CPU, GPU is going to be truly amazing. >> Right, right. So I want to get your take on another topic. And again the basis of your research is really all bound around the biodiversity crisis right? That's from the kind of-- >> Yeah. >> The thing that's started it and now you're using sound as a way to measure baseline and talk about loss of species, reduced abundancies and rampant expansion of invasive species as part of your report. But I'd love to get your take on cities. And how do you think cities fit the future? Clearly, it's an efficient way to get a lot of people together. There's a huge migration of people-- >> Right. >> To cities, but one of your themes in your Ted Talk is reconnecting with nature-- >> Yeah. >> Because we're in cities, but there's this paradox right? Because you don't want people living in nature can be a little bit disruptive. So is it better to kind of get them all in a tip of a peninsula in San Francisco or-- >> Yeah. >> But then do they lose that connection that's so important. >> Yeah. >> I just love to get your take on cities and the impacts that they're have on your core research. >> Yeah, I mean, it truly is a paradox as you just described it. We're living in a concrete jungle surrounded by not a lot of nature, really, honestly, occasional bird species that tend to be fairly limited, selected for limited environments. So many people just don't get out into the wild. But visiting national parks certainly is one of those kinds of experience that people oftentimes have. But I'll just say that it's getting out there and truly listening and feeling this emotional feeling, psychological feeling that wraps around you, it's a solitude. It's just you and nature and there's just no one around. >> Right. >> And that's when it really truly sinks in, that you're a part of this place, this marvelous place called earth. And so there are very few people that have had that experience. And so as I've gone to some of these places, I say to myself I need to bring this back. I need to tell the story, tell the story of the awe of nature, because it truly is an amazing place. Even if you just close your eyes and listen. >> Right, right. >> And it, the dawn chorus in the morning in every place tells me so much about that place. It tells me about all the animals that exist there. The nighttime tells me so much too. As a scientist that's spent most of his career kind of going out and working during the day, there's so much happening at night. Matter of fact-- >> Right. >> There's more sounds at night than there were during the day. So there is a need for us to experience nature and we don't do that. And we're not aware of these crises that are happening all over the planet. I do go to places and I listen, and I can tell you I'm listening for things that I think should be there. You can listen and you can hear the gaps, the gaps and that in that chorus, and you think what should be there-- >> Right. >> And then why isn't it there? And that's where I really want to be able to dig deep into my sound files and start to explore that more fully. >> It's great, it's great, I mean, I just love the whole concept of, and you identified it in the moment you're in the tent, the thunderstorm came by, it's really just kind of changing your lens. It's really twisting your lens, changing your focus, because that sound is there, right? It's been there all along, it's just, do you tune it in or do you tune it out? Do you pay attention? Do not pay attention is an active process or a passive process and like-- >> Right. >> I love that perspective. And I want to shift gears a little bit, 'cause another big environmental thing, and you mentioned it quite frequently is feeding the world's growing population and feeding it-- >> Yeah. >> In an efficient way. And anytime you see kind of factory farming applied to a lot of things you wonder is it sustainable, and then all the issues that come from kind of single output production whether that's pigs or coffee or whatever and the susceptibility to disease and this and that. So I wonder if you could share your thoughts on, based on your research, what needs to change to successfully and without too much destruction feed this ever increasing population? >> Yeah, I mean, that's one of the grand challenges. I mean, society is facing so many at the moment. In the next 20 years or so, 30 years, we're going to add another 2 billion people to the planet, and how do we feed all of them? How do we feed them well and equitably across the globe? I don't know how to do that. But I'll tell you that our crops and the ecosystem that supports the food production needs the animals and the trees and the microbes for the ecosystem to function. We have many of our crops that are pollinated by birds and insects and other animals, seeds need to be dispersed. And so we need the rest of life to exist and thrive for us to thrive too. It's not an either, it's not them or us, it has to be all of us together on this planet working together. We have to find solutions. And again, it's me going out to some of these places and bringing it back and saying, you have to listen, you have to listen to these places-- >> Right. >> They're truly a marvelous. >> So I know most of your listening devices are in remote areas and not necessarily in urban areas, but I'm curious, do you have any in urban areas? And if so, how has that signature changed since COVID? I just got to ask, (Bryan chuckling) because we went to this-- >> Yeah. >> Light switch moment in the middle of March, human activity slowed down-- >> Yeah. >> In a way that no one could have forecast ever on a single event, globally which is just fascinating. And you think of the amount of airplanes that were not flying and trains that we're not moving and people not moving. Did you have any any data or have you been able to collect data or see data as the impact of that? Not only directly in wherever the sensors are, but a kind of a second order impact because of the lack of pollution and the other kind of human activity that just went down. I mean, certainly a lot of memes (Bryan chuckling) on social media of all the animals-- >> Yeah. >> Come back into the city. But I'm just curious if you have any data in the observation? >> Yeah, we're part of actually a global study, there's couple of hundred of us that are contributing our data to what we call the Silent Cities project. It's being coordinated out of Europe right now. So we placed our sensors out in different areas, actually around West Lafayette area here in Indiana, near road crossings and that sort of thing to be able to kind of capture that information. We have had in this area here now, the 17 year study. So we do have studies that get into areas that tend to be fairly urban. So we do have a lot of information. I tell you, I don't need my sensors to tell me something that I already know and you suspect is true. Our cities were quiet, much quieter during the COVID situation. And it's continued to kind of get a little bit louder, as we've kind of released some of the policies that put us into our homes. And so yes, there is a major change. Now there have been a couple of studies that just come out that are pretty interesting. One, which was in San Francisco looking at the white-crowned sparrow. And they looked at historical data that went back something like 20 years. And they found that the birds in the cities were singing a much softer, 30% softer. >> Really? >> And they, yeah, and they would lower their frequencies. So the way sound works is that if you lower your frequencies that sound can travel farther. And so the males can now hear themselves twice as far just due to the fact that our cities are quieter. So it does have an impact on animals, truly it does. There was some studies back in 2001, during  the September, the 9/11 crisis as well, where people are going out and kind of looking at data, acoustic data, and discovering that things were much quieter. I'd be very interested to look at some of the data we have in our oceans, to what extent are oceans quieter. Our oceans sadly are the loudest part of this planet. It's really noisy, sound travels, five times farther. Generally the noise is lower frequencies, and we have lots of ships that are all over the planet and in our oceans. So I'd really be interested in those kinds of studies as well, to what extent is it impacting and helping our friends in the oceans. >> Right, right, well, I was just going to ask you that question because I think a lot of people clearly understand sound in the air that surrounds us, but you talk a lot about sound in ocean, and sound as an indicator of ocean health, and again, this concept of a chorus. And I think everybody's probably familiar with the sounds of the humpback whale right? He got very popular and we've all seen and heard that. But you're doing a lot of research, as you said, in oceans and in water. And I wonder if you can, again, kind of provide a little bit more color around that, because I don't think you people, maybe we're just not that tuned into it, think of the ocean or water as a rich sound environment especially to the degree as you're talking about where you can actually start to really understand what's going on. >> Yeah, I mean, some of us think that sound in the oceans is probably more important to animals than on land, on the terrestrial side. Sound helps animals to navigate through complex waterways and find food resources. You can only use site so far underwater especially when it gets to be kind of dark, once you get down to certain levels. So there many of us think that sound is probably going to be an important component to measuring the status of health in our oceans. >> It's great. Well, Bryan, I really enjoyed this conversation. I've really enjoyed your Ted Talk, and now I've got a bunch of research papers I want to dig into a little bit more as well. >> Okay.(chuckling) >> It's a fascinating topic, but I think the most important thing that you talked about extensively in your Ted Talk is really just taking a minute to take a step back from the individual perspective, appreciate what's around us, hear, that information and I think there's a real direct correlation to the power of exascale, to the power of hearing this data, processing this data, and putting intelligence on that data, understanding that data in a good way, in a positive way, in a delightful way, spiritual way, even that we couldn't do before, or we just weren't paying attention like with what you know is on your phone please-- >> Yeah, really. >> It's all around you. It's been there a whole time. >> Yeah. (both chuckling) >> Yeah, Jeff, I really encourage your viewers to count it, just go out and listen. As we say, go out and listen and join the mission. >> I love it, and you can get started by going to the Center for Global Soundscapes and you have a beautiful landscape. I had it going earlier this morning while I was digging through some of the research of Bryan. (Bryan chuckling) Thank you very much (Bryan murmurs) and really enjoyed the conversation best to you-- >> Okay. >> And your team and your continued success. >> Alright, thank you. >> Alright, thank you. All right, he's Bryan-- >> Goodbye. >> I'm Jeff, you're watching theCUBE. (Bryan chuckling) for continuing coverage of Exascale Day. Thanks for watching. We'll see you next time. (calm ambient music)

>>welcome. My name is Ben Bennett. I am the director of HPC Strategic programs here at Hewlett Packard Enterprise. It is my great pleasure and honor to be talking to Professor Mark Parsons from the Edinburgh Parallel Computing Center. And we're gonna talk a little about exa scale. What? It means we're gonna talk less about the technology on Maura about the science, the requirements on the need for exa scale. Uh, rather than a deep dive into the enabling technologies. Mark. Welcome. >>I then thanks very much for inviting me to tell me >>complete pleasure. Um, so I'd like to kick off with, I suppose. Quite an interesting look back. You and I are both of a certain age 25 plus, Onda. We've seen these milestones. Uh, I suppose that the S I milestones of high performance computing's come and go, you know, from a gig a flop back in 1987 teraflop in 97 a petaflop in 2000 and eight. But we seem to be taking longer in getting to an ex a flop. Um, so I'd like your thoughts. Why is why is an extra flop taking so long? >>So I think that's a very interesting question because I started my career in parallel computing in 1989. I'm gonna join in. IPCC was set up then. You know, we're 30 years old this year in 1990 on Do you know the fastest computer we have them is 800 mega flops just under a getting flogged. So in my career, we've gone already. When we reached the better scale, we'd already gone pretty much a million times faster on, you know, the step from a tariff block to a block scale system really didn't feel particularly difficult. Um, on yet the step from A from a petaflop PETA scale system. To an extent, block is a really, really big challenge. And I think it's really actually related to what's happened with computer processes over the last decade, where, individually, you know, approached the core, Like on your laptop. Whoever hasn't got much faster, we've just got more often So the perception of more speed, but actually just being delivered by more course. And as you go down that approach, you know what happens in the supercomputing world as well. We've gone, uh, in 2010 I think we had systems that were, you know, a few 1000 cores. Our main national service in the UK for the last eight years has had 118,000 cores. But looking at the X scale we're looking at, you know, four or five million cores on taming that level of parallelism is the real challenge. And that's why it's taking an enormous and time to, uh, deliver these systems. That is not just on the hardware front. You know, vendors like HP have to deliver world beating technology and it's hard, hard. But then there's also the challenge to the users. How do they get the codes to work in the face of that much parallelism? >>If you look at what the the complexity is delivering an annex a flop. Andi, you could have bought an extra flop three or four years ago. You couldn't have housed it. You couldn't have powered it. You couldn't have afforded it on, do you? Couldn't program it. But you still you could have You could have bought one. We should have been so lucky to be unable to supply it. Um, the software, um I think from our standpoint, is is looking like where we're doing mawr enabling with our customers. You sell them a machine on, then the the need then to do collaboration specifically seems mawr and Maura around the software. Um, so it's It's gonna be relatively easy to get one x a flop using limb pack, but but that's not extra scale. So what do you think? On exa scale machine versus an X? A flop machine means to the people like yourself to your users, the scientists and industry. What is an ex? A flop versus >>an exa scale? So I think, you know, supercomputing moves forward by setting itself challenges. And when you when you look at all of the excess scale programs worldwide that are trying to deliver systems that can do an X a lot form or it's actually very arbitrary challenge. You know, we set ourselves a PETA scale challenge delivering a petaflop somebody manage that, Andi. But you know, the world moves forward by setting itself challenges e think you know, we use quite arbitrary definition of what we mean is well by an exit block. So, you know, in your in my world, um, we either way, first of all, see ah flop is a computation, so multiply or it's an ad or whatever on we tend. Thio, look at that is using very high precision numbers or 64 bit numbers on Do you know, we then say, Well, you've got to do the next block. You've got to do a billion billion of those calculations every second. No, a some of the last arbitrary target Now you know today from HPD Aiken by my assistant and will do a billion billion calculations per second. And they will either do that as a theoretical peak, which would be almost unattainable, or using benchmarks that stressed the system on demonstrate a relaxing law. But again, those benchmarks themselves attuned Thio. Just do those calculations and deliver and explore been a steady I'll way if you like. So, you know, way kind of set ourselves this this this big challenge You know, the big fence on the race course, which were clambering over. But the challenge in itself actually should be. I'm much more interesting. The water we're going to use these devices for having built um, eso. Getting into the extra scale era is not so much about doing an extra block. It's a new generation off capability that allows us to do better scientific and industrial research. And that's the interesting bit in this whole story. >>I would tend to agree with you. I think the the focus around exa scale is to look at, you know, new technologies, new ways of doing things, new ways of looking at data and to get new results. So eventually you will get yourself a nexus scale machine. Um, one hopes, sooner rather >>than later. Well, I'm sure you don't tell me one, Ben. >>It's got nothing to do with may. I can't sell you anything, Mark. But there are people outside the door over there who would love to sell you one. Yes. However, if we if you look at your you know your your exa scale machine, Um, how do you believe the workloads are going to be different on an extra scale machine versus your current PETA scale machine? >>So I think there's always a slight conceit when you buy a new national supercomputer. On that conceit is that you're buying a capability that you know on. But many people will run on the whole system. Known truth. We do have people that run on the whole of our archer system. Today's A 118,000 cores, but I would say, and I'm looking at the system. People that run over say, half of that can be counted on Europe on a single hand in a year, and they're doing very specific things. It's very costly simulation they're running on. So, you know, if you look at these systems today, two things show no one is. It's very difficult to get time on them. The Baroque application procedures All of the requirements have to be assessed by your peers and your given quite limited amount of time that you have to eke out to do science. Andi people tend to run their applications in the sweet spot where their application delivers the best performance on You know, we try to push our users over time. Thio use reasonably sized jobs. I think our average job says about 20,000 course, she's not bad, but that does mean that as we move to the exits, kill two things have to happen. One is actually I think we've got to be more relaxed about giving people access to the system, So let's give more people access, let people play, let people try out ideas they've never tried out before. And I think that will lead to a lot more innovation and computational science. But at the same time, I think we also need to be less precious. You know, we to accept these systems will have a variety of sizes of job on them. You know, we're still gonna have people that want to run four million cores or two million cores. That's absolutely fine. Absolutely. Salute those people for trying really, really difficult. But then we're gonna have a huge spectrum of views all the way down to people that want to run on 500 cores or whatever. So I think we need Thio broaden the user base in Alexa Skill system. And I know this is what's happening, for example, in Japan with the new Japanese system. >>So, Mark, if you cast your mind back to almost exactly a year ago after the HPC user forum, you were interviewed for Premier Magazine on Do you alluded in that article to the needs off scientific industrial users requiring, you know, uh on X a flop or an exa scale machine it's clear in your in your previous answer regarding, you know, the workloads. Some would say that the majority of people would be happier with, say, 10 100 petaflop machines. You know, democratization. More people access. But can you provide us examples at the type of science? The needs of industrial users that actually do require those resources to be put >>together as an exa scale machine? So I think you know, it's a very interesting area. At the end of the day, these systems air bought because they are capability systems on. I absolutely take the argument. Why shouldn't we buy 10 100 pattern block systems? But there are a number of scientific areas even today that would benefit from a nexus school system and on these the sort of scientific areas that will use as much access onto a system as much time and as much scale of the system as they can, as you can give them eso on immediate example. People doing chroma dynamics calculations in particle physics, theoretical calculations, they would just use whatever you give them. But you know, I think one of the areas that is very interesting is actually the engineering space where, you know, many people worry the engineering applications over the last decade haven't really kept up with this sort of supercomputers that we have. I'm leading a project called Asimov, funded by M. P S O. C in the UK, which is jointly with Rolls Royce, jointly funded by Rolls Royce and also working with the University of Cambridge, Oxford, Bristol, Warrick. We're trying to do the whole engine gas turbine simulation for the first time. So that's looking at the structure of the gas turbine, the airplane engine, the structure of it, how it's all built it together, looking at the fluid dynamics off the air and the hot gasses, the flu threat, looking at the combustion of the engine looking how fuel is spread into the combustion chamber. Looking at the electrics around, looking at the way the engine two forms is, it heats up and cools down all of that. Now Rolls Royce wants to do that for 20 years. Andi, Uh, whenever they certify, a new engine has to go through a number of physical tests, and every time they do on those tests, it could cost them as much as 25 to $30 million. These are very expensive tests, particularly when they do what's called a blade off test, which would be, you know, blade failure. They could prove that the engine contains the fragments of the blade. Sort of think, continue face really important test and all engines and pass it. What we want to do is do is use an exa scale computer to properly model a blade off test for the first time, so that in future, some simulations can become virtual rather than having thio expend all of the money that Rolls Royce would normally spend on. You know, it's a fascinating project is a really hard project to do. One of the things that I do is I am deaf to share this year. Gordon Bell Price on bond I've really enjoyed to do. That's one of the major prizes in our area, you know, gets announced supercomputing every year. So I have the pleasure of reading all the submissions each year. I what's been really interesting thing? This is my third year doing being on the committee on what's really interesting is the way that big systems like Summit, for example, in the US have pushed the user communities to try and do simulations Nowhere. Nobody's done before, you know. And we've seen this as well, with papers coming after the first use of the for Goku system in Japan, for example, people you know, these are very, very broad. So, you know, earthquake simulation, a large Eddie simulations of boats. You know, a number of things around Genome Wide Association studies, for example. So the use of these computers spans of last area off computational science. I think the really really important thing about these systems is their challenging people that do calculations they've never done before. That's what's important. >>Okay, Thank you. You talked about challenges when I nearly said when you and I had lots of hair, but that's probably much more true of May. Um, we used to talk about grand challenges we talked about, especially around the teraflop era, the ski red program driving, you know, the grand challenges of science, possibly to hide the fact that it was a bomb designing computer eso they talked about the grand challenges. Um, we don't seem to talk about that much. We talk about excess girl. We talk about data. Um Where are the grand challenges that you see that an exa scale computer can you know it can help us. Okay, >>so I think grand challenges didn't go away. Just the phrase went out of fashion. Um, that's like my hair. I think it's interesting. The I do feel the science moves forward by setting itself grand challenges and always had has done, you know, my original backgrounds in particle physics. I was very lucky to spend four years at CERN working in the early stage of the left accelerator when it first came online on. Do you know the scientists there? I think they worked on left 15 years before I came in and did my little ph d on it. Andi, I think that way of organizing science hasn't changed. We just talked less about grand challenges. I think you know what I've seen over the last few years is a renaissance in computational science, looking at things that have previously, you know, people have said have been impossible. So a couple of years ago, for example, one of the key Gordon Bell price papers was on Genome Wide Association studies on some of it. If I may be one of the winner of its, if I remember right on. But that was really, really interesting because first of all, you know, the sort of the Genome Wide Association Studies had gone out of favor in the bioinformatics by a scientist community because people thought they weren't possible to compute. But that particular paper should Yes, you could do these really, really big Continental little problems in a reasonable amount of time if you had a big enough computer. And one thing I felt all the way through my career actually is we've probably discarded Mawr simulations because they were impossible at the time that we've actually decided to do. And I sometimes think we to challenge ourselves by looking at the things we've discovered in the past and say, Oh, look, you know, we could actually do that now, Andi, I think part of the the challenge of bringing an extra service toe life is to get people to think about what they would use it for. That's a key thing. Otherwise, I always say, a computer that is unused to just be turned off. There's no point in having underutilized supercomputer. Everybody loses from that. >>So Let's let's bring ourselves slightly more up to date. We're in the middle of a global pandemic. Uh, on board one of the things in our industry has bean that I've been particularly proud about is I've seen the vendors, all the vendors, you know, offering up machine's onboard, uh, making resources available for people to fight things current disease. Um, how do you see supercomputers now and in the future? Speeding up things like vaccine discovery on help when helping doctors generally. >>So I think you're quite right that, you know, the supercomputer community around the world actually did a really good job of responding to over 19. Inasmuch as you know, speaking for the UK, we put in place a rapid access program. So anybody wanted to do covert research on the various national services we have done to the to two services Could get really quick access. Um, on that, that has worked really well in the UK You know, we didn't have an archer is an old system, Aziz. You know, we didn't have the world's largest supercomputer, but it is happily bean running lots off covert 19 simulations largely for the biomedical community. Looking at Druk modeling and molecular modeling. Largely that's just been going the US They've been doing really large uh, combinatorial parameter search problems on on Summit, for example, looking to see whether or not old drugs could be reused to solve a new problem on DSO, I think, I think actually, in some respects Kobe, 19 is being the sounds wrong. But it's actually been good for supercomputing. Inasmuch is pointed out to governments that supercomputers are important parts off any scientific, the active countries research infrastructure. >>So, um, I'll finish up and tap into your inner geek. Um, there's a lot of technologies that are being banded around to currently enable, you know, the first exa scale machine, wherever that's going to be from whomever, what are the current technologies or emerging technologies that you are interested in excited about looking forward to getting your hands on. >>So in the business case I've written for the U. K's exa scale computer, I actually characterized this is a choice between the American model in the Japanese model. Okay, both of frozen, both of condoms. Eso in America, they're very much gone down the chorus plus GPU or GPU fruit. Um, so you might have, you know, an Intel Xeon or an M D process er center or unarmed process or, for that matter on you might have, you know, 24 g. P. U s. I think the most interesting thing that I've seen is definitely this move to a single address space. So the data that you have will be accessible, but the G p u on the CPU, I think you know, that's really bean. One of the key things that stopped the uptake of GPS today and that that that one single change is going Thio, I think, uh, make things very, very interesting. But I'm not entirely convinced that the CPU GPU model because I think that it's very difficult to get all the all the performance set of the GPU. You know, it will do well in H p l, for example, high performance impact benchmark we're discussing at the beginning of this interview. But in riel scientific workloads, you know, you still find it difficult to find all the performance that has promised. So, you know, the Japanese approach, which is the core, is only approach. E think it's very attractive, inasmuch as you know They're using very high bandwidth memory, very interesting process of which they are going to have to, you know, which they could develop together over 10 year period. And this is one thing that people don't realize the Japanese program and the American Mexico program has been working for 10 years on these systems. I think the Japanese process really interesting because, um, it when you look at the performance, it really does work for their scientific work clothes, and that's that does interest me a lot. This this combination of a A process are designed to do good science, high bandwidth memory and a real understanding of how data flows around the supercomputer. I think those are the things are exciting me at the moment. Obviously, you know, there's new networking technologies, I think, in the fullness of time, not necessarily for the first systems. You know, over the next decade we're going to see much, much more activity on silicon photonics. I think that's really, really fascinating all of these things. I think in some respects the last decade has just bean quite incremental improvements. But I think we're supercomputing is going in the moment. We're a very very disruptive moment again. That goes back to start this discussion. Why is extra skill been difficult to get? Thio? Actually, because the disruptive moment in technology. >>Professor Parsons, thank you very much for your time and your insights. Thank you. Pleasure and folks. Thank you for watching. I hope you've learned something, or at least enjoyed it. With that, I would ask you to stay safe and goodbye.

>> Hi everyone, this is Dave Vellante and I want to welcome you to our celebration of Exascale Day. A community event with support from Hewlett Packard Enterprise. Now, Exascale Day is October 18th, that's 10, 18 as in 10 to the power of 18. And on that day we celebrate the scientists, and researchers, who make breakthrough discoveries, with the assistance, of some of the most sophisticated supercomputers in the world. Ones that can run and Exascale. Now in this program, we're going to kick off the weekend and discuss the significance of Exascale computing, how we got here, why it's so challenging to get to the point where we're at now where we can perform almost, 10 to the 18th floating point operations per second. Or an exaFLOP. We should be there by 2021. And importantly, what innovations and possibilities Exascale computing will unlock. So today, we got a great program for you. We're not only going to dig into a bit of the history of supercomputing, we're going to talk with experts, folks like Dr. Ben Bennett, who's doing and some work with the UK government. And he's going to talk about some of the breakthroughs that we can expect with Exascale. You'll also hear from experts like, Professor Mark Parsons of the University of Edinburgh, who cut his teeth at CERN, in Geneva. And Dr. Brian Pigeon Nuskey of Purdue University, who's studying buyer diversity. We're going to also hear about supercomputers in space as we get as a great action going on with supercomputers up at the International Space Station. Let me think about that, powerful high performance water-cooled supercomputers, running on solar, and mounted overhead, that's right. Even though at the altitude at the International Space Station, there's 90% of the Earth's gravity. Objects, including humans they're essentially in a state of free fall. At 400 kilometers above earth, there no air. You're in a vacuum. Like have you ever been on the Tower of Terror at Disney? In that free fall ride, or a nosedive in an airplane, I have. And if you have binoculars around your neck, they would float. So the supercomputers can actually go into the ceiling, crazy right? And that's not all. We're going to hear from experts on what the exascale era. will usher in for not only space exploration, but things like weather forecasting, life sciences, complex modeling, and all types of scientific endeavors. So stay right there for all the great content. You can use the #ExascaleDay on Twitter, and, enjoy the program. Thanks everybody for watching.

>>from around the globe. It's the Cube covering >>space and cybersecurity. Symposium 2020 hosted by Cal Poly >>Over On Welcome to this Special virtual conference. The Space and Cybersecurity Symposium 2020 put on by Cal Poly with support from the Cube. I'm John for your host and master of ceremonies. Got a great topic today in this session. Really? The intersection of space and cybersecurity. This topic and this conversation is the cybersecurity workforce development through public and private partnerships. And we've got a great lineup. We have Jeff Armstrong's the president of California Polytechnic State University, also known as Cal Poly Jeffrey. Thanks for jumping on and Bang. Go ahead. The second director of C four s R Division. And he's joining us from the office of the Under Secretary of Defense for the acquisition Sustainment Department of Defense, D O D. And, of course, Steve Jake's executive director, founder, National Security Space Association and managing partner at Bello's. Gentlemen, thank you for joining me for this session. We got an hour conversation. Thanks for coming on. >>Thank you. >>So we got a virtual event here. We've got an hour, have a great conversation and love for you guys do? In opening statement on how you see the development through public and private partnerships around cybersecurity in space, Jeff will start with you. >>Well, thanks very much, John. It's great to be on with all of you. Uh, on behalf Cal Poly Welcome, everyone. Educating the workforce of tomorrow is our mission to Cal Poly. Whether that means traditional undergraduates, master students are increasingly mid career professionals looking toe up, skill or re skill. Our signature pedagogy is learn by doing, which means that our graduates arrive at employers ready Day one with practical skills and experience. We have long thought of ourselves is lucky to be on California's beautiful central Coast. But in recent years, as we have developed closer relationships with Vandenberg Air Force Base, hopefully the future permanent headquarters of the United States Space Command with Vandenberg and other regional partners, we have discovered that our location is even more advantages than we thought. We're just 50 miles away from Vandenberg, a little closer than u C. Santa Barbara, and the base represents the southern border of what we have come to think of as the central coast region. Cal Poly and Vandenberg Air force base have partner to support regional economic development to encourage the development of a commercial spaceport toe advocate for the space Command headquarters coming to Vandenberg and other ventures. These partnerships have been possible because because both parties stand to benefit Vandenberg by securing new streams of revenue, workforce and local supply chain and Cal Poly by helping to grow local jobs for graduates, internship opportunities for students, and research and entrepreneurship opportunities for faculty and staff. Crucially, what's good for Vandenberg Air Force Base and for Cal Poly is also good for the Central Coast and the US, creating new head of household jobs, infrastructure and opportunity. Our goal is that these new jobs bring more diversity and sustainability for the region. This regional economic development has taken on a life of its own, spawning a new nonprofit called Reach, which coordinates development efforts from Vandenberg Air Force Base in the South to camp to Camp Roberts in the North. Another factor that is facilitated our relationship with Vandenberg Air Force Base is that we have some of the same friends. For example, Northrop Grumman has has long been an important defense contractor, an important partner to Cal poly funding scholarships and facilities that have allowed us to stay current with technology in it to attract highly qualified students for whom Cal Poly's costs would otherwise be prohibitive. For almost 20 years north of grimness funded scholarships for Cal Poly students this year, their funding 64 scholarships, some directly in our College of Engineering and most through our Cal Poly Scholars program, Cal Poly Scholars, a support both incoming freshman is transfer students. These air especially important because it allows us to provide additional support and opportunities to a group of students who are mostly first generation, low income and underrepresented and who otherwise might not choose to attend Cal Poly. They also allow us to recruit from partner high schools with large populations of underrepresented minority students, including the Fortune High School in Elk Grove, which we developed a deep and lasting connection. We know that the best work is done by balanced teams that include multiple and diverse perspectives. These scholarships help us achieve that goal, and I'm sure you know Northrop Grumman was recently awarded a very large contract to modernized the U. S. I. C B M Armory with some of the work being done at Vandenberg Air Force Base, thus supporting the local economy and protecting protecting our efforts in space requires partnerships in the digital realm. How Polly is partnered with many private companies, such as AWS. Our partnerships with Amazon Web services has enabled us to train our students with next generation cloud engineering skills, in part through our jointly created digital transformation hub. Another partnership example is among Cal Poly's California Cybersecurity Institute, College of Engineering and the California National Guard. This partnership is focused on preparing a cyber ready workforce by providing faculty and students with a hands on research and learning environment, side by side with military, law enforcement professionals and cyber experts. We also have a long standing partnership with PG and E, most recently focused on workforce development and redevelopment. Many of our graduates do indeed go on to careers in aerospace and defense industry as a rough approximation. More than 4500 Cal Poly graduates list aerospace and defense as their employment sector on linked in, and it's not just our engineers and computer sciences. When I was speaking to our fellow Panelists not too long ago, >>are >>speaking to bang, we learned that Rachel sins, one of our liberal arts arts majors, is working in his office. So shout out to you, Rachel. And then finally, of course, some of our graduates sword extraordinary heights such as Commander Victor Glover, who will be heading to the International space station later this year as I close. All of which is to say that we're deeply committed the workforce, development and redevelopment that we understand the value of public private partnerships and that were eager to find new ways in which to benefit everyone from this further cooperation. So we're committed to the region, the state in the nation and our past efforts in space, cybersecurity and links to our partners at as I indicated, aerospace industry and governmental partners provides a unique position for us to move forward in the interface of space and cybersecurity. Thank you so much, John. >>President, I'm sure thank you very much for the comments and congratulations to Cal Poly for being on the forefront of innovation and really taking a unique progressive. You and wanna tip your hat to you guys over there. Thank you very much for those comments. Appreciate it. Bahng. Department of Defense. Exciting you gotta defend the nation spaces Global. Your opening statement. >>Yes, sir. Thanks, John. Appreciate that day. Thank you, everybody. I'm honored to be this panel along with President Armstrong, Cal Poly in my long longtime friend and colleague Steve Jakes of the National Security Space Association, to discuss a very important topic of cybersecurity workforce development, as President Armstrong alluded to, I'll tell you both of these organizations, Cal Poly and the N S. A have done and continue to do an exceptional job at finding talent, recruiting them in training current and future leaders and technical professionals that we vitally need for our nation's growing space programs. A swell Asare collective National security Earlier today, during Session three high, along with my colleague Chris Hansen discussed space, cyber Security and how the space domain is changing the landscape of future conflicts. I discussed the rapid emergence of commercial space with the proliferations of hundreds, if not thousands, of satellites providing a variety of services, including communications allowing for global Internet connectivity. S one example within the O. D. We continue to look at how we can leverage this opportunity. I'll tell you one of the enabling technologies eyes the use of small satellites, which are inherently cheaper and perhaps more flexible than the traditional bigger systems that we have historically used unemployed for the U. D. Certainly not lost on Me is the fact that Cal Poly Pioneer Cube SATs 2020 some years ago, and they set the standard for the use of these systems today. So they saw the valiant benefit gained way ahead of everybody else, it seems, and Cal Poly's focus on training and education is commendable. I especially impressed by the efforts of another of Steve's I colleague, current CEO Mr Bill Britain, with his high energy push to attract the next generation of innovators. Uh, earlier this year, I had planned on participating in this year's Cyber Innovation Challenge. In June works Cal Poly host California Mill and high school students and challenge them with situations to test their cyber knowledge. I tell you, I wish I had that kind of opportunity when I was a kid. Unfortunately, the pandemic change the plan. Why I truly look forward. Thio feature events such as these Thio participating. Now I want to recognize my good friend Steve Jakes, whom I've known for perhaps too long of a time here over two decades or so, who was in acknowledge space expert and personally, I truly applaud him for having the foresight of years back to form the National Security Space Association to help the entire space enterprise navigate through not only technology but Polly policy issues and challenges and paved the way for operational izing space. Space is our newest horrifying domain. That's not a secret anymore. Uh, and while it is a unique area, it shares a lot of common traits with the other domains such as land, air and sea, obviously all of strategically important to the defense of the United States. In conflict they will need to be. They will all be contested and therefore they all need to be defended. One domain alone will not win future conflicts in a joint operation. We must succeed. All to defending space is critical as critical is defending our other operational domains. Funny space is no longer the sanctuary available only to the government. Increasingly, as I discussed in the previous session, commercial space is taking the lead a lot of different areas, including R and D, A so called new space, so cyber security threat is even more demanding and even more challenging. Three US considers and federal access to and freedom to operate in space vital to advancing security, economic prosperity, prosperity and scientific knowledge of the country. That's making cyberspace an inseparable component. America's financial, social government and political life. We stood up US Space force ah, year ago or so as the newest military service is like the other services. Its mission is to organize, train and equip space forces in order to protect us and allied interest in space and to provide space capabilities to the joint force. Imagine combining that US space force with the U. S. Cyber Command to unify the direction of space and cyberspace operation strengthened U D capabilities and integrate and bolster d o d cyber experience. Now, of course, to enable all of this requires had trained and professional cadre of cyber security experts, combining a good mix of policy as well as high technical skill set much like we're seeing in stem, we need to attract more people to this growing field. Now the D. O. D. Is recognized the importance of the cybersecurity workforce, and we have implemented policies to encourage his growth Back in 2013 the deputy secretary of defense signed the D. O d cyberspace workforce strategy to create a comprehensive, well equipped cyber security team to respond to national security concerns. Now this strategy also created a program that encourages collaboration between the D. O. D and private sector employees. We call this the Cyber Information Technology Exchange program or site up. It's an exchange programs, which is very interesting, in which a private sector employees can naturally work for the D. O. D. In a cyber security position that spans across multiple mission critical areas are important to the d. O. D. A key responsibility of cybersecurity community is military leaders on the related threats and cyber security actions we need to have to defeat these threats. We talk about rapid that position, agile business processes and practices to speed up innovation. Likewise, cybersecurity must keep up with this challenge to cyber security. Needs to be right there with the challenges and changes, and this requires exceptional personnel. We need to attract talent investing the people now to grow a robust cybersecurity, workforce, streets, future. I look forward to the panel discussion, John. Thank you. >>Thank you so much bomb for those comments and you know, new challenges and new opportunities and new possibilities and free freedom Operating space. Critical. Thank you for those comments. Looking forward. Toa chatting further. Steve Jakes, executive director of N. S. S. A Europe opening statement. >>Thank you, John. And echoing bangs thanks to Cal Poly for pulling these this important event together and frankly, for allowing the National Security Space Association be a part of it. Likewise, we on behalf the association delighted and honored Thio be on this panel with President Armstrong along with my friend and colleague Bonneau Glue Mahad Something for you all to know about Bomb. He spent the 1st 20 years of his career in the Air Force doing space programs. He then went into industry for several years and then came back into government to serve. Very few people do that. So bang on behalf of the space community, we thank you for your long life long devotion to service to our nation. We really appreciate that and I also echo a bang shot out to that guy Bill Britain, who has been a long time co conspirator of ours for a long time and you're doing great work there in the cyber program at Cal Poly Bill, keep it up. But professor arms trying to keep a close eye on him. Uh, I would like to offer a little extra context to the great comments made by by President Armstrong and bahng. Uh, in our view, the timing of this conference really could not be any better. Um, we all recently reflected again on that tragic 9 11 surprise attack on our homeland. And it's an appropriate time, we think, to take pause while the percentage of you in the audience here weren't even born or babies then For the most of us, it still feels like yesterday. And moreover, a tragedy like 9 11 has taught us a lot to include to be more vigilant, always keep our collective eyes and ears open to include those quote eyes and ears from space, making sure nothing like this ever happens again. So this conference is a key aspect. Protecting our nation requires we work in a cybersecurity environment at all times. But, you know, the fascinating thing about space systems is we can't see him. No, sir, We see Space launches man there's nothing more invigorating than that. But after launch, they become invisible. So what are they really doing up there? What are they doing to enable our quality of life in the United States and in the world? Well, to illustrate, I'd like to paraphrase elements of an article in Forbes magazine by Bonds and my good friend Chuck Beans. Chuck. It's a space guy, actually had Bonds job a fuse in the Pentagon. He is now chairman and chief strategy officer at York Space Systems, and in his spare time he's chairman of the small satellites. Chuck speaks in words that everyone can understand. So I'd like to give you some of his words out of his article. Uh, they're afraid somewhat. So these are Chuck's words. Let's talk about average Joe and playing Jane. Before heading to the airport for a business trip to New York City, Joe checks the weather forecast informed by Noah's weather satellites to see what pack for the trip. He then calls an uber that space app. Everybody uses it matches riders with drivers via GPS to take into the airport, So Joe has lunch of the airport. Unbeknownst to him, his organic lunch is made with the help of precision farming made possible through optimized irrigation and fertilization, with remote spectral sensing coming from space and GPS on the plane, the pilot navigates around weather, aided by GPS and nose weather satellites. And Joe makes his meeting on time to join his New York colleagues in a video call with a key customer in Singapore made possible by telecommunication satellites. Around to his next meeting, Joe receives notice changing the location of the meeting to another to the other side of town. So he calmly tells Syria to adjust the destination, and his satellite guided Google maps redirects him to the new location. That evening, Joe watches the news broadcast via satellite. The report details a meeting among world leaders discussing the developing crisis in Syria. As it turns out, various forms of quote remotely sensed. Information collected from satellites indicate that yet another band, chemical weapon, may have been used on its own people. Before going to bed, Joe decides to call his parents and congratulate them for their wedding anniversary as they cruise across the Atlantic, made possible again by communications satellites and Joe's parents can enjoy the call without even wondering how it happened the next morning. Back home, Joe's wife, Jane, is involved in a car accident. Her vehicle skids off the road. She's knocked unconscious, but because of her satellite equipped on star system, the crash is detected immediately and first responders show up on the scene. In time, Joe receives the news books. An early trip home sends flowers to his wife as he orders another uber to the airport. Over that 24 hours, Joe and Jane used space system applications for nearly every part of their day. Imagine the consequences if at any point they were somehow denied these services, whether they be by natural causes or a foreign hostility. And each of these satellite applications used in this case were initially developed for military purposes and continue to be, but also have remarkable application on our way of life. Just many people just don't know that. So, ladies and gentlemen, now you know, thanks to chuck beans, well, the United States has a proud heritage being the world's leading space faring nation, dating back to the Eisenhower and Kennedy years. Today we have mature and robust systems operating from space, providing overhead reconnaissance to quote, wash and listen, provide missile warning, communications, positioning, navigation and timing from our GPS system. Much of what you heard in Lieutenant General J. T. Thompson earlier speech. These systems are not only integral to our national security, but also our also to our quality of life is Chuck told us. We simply no longer could live without these systems as a nation and for that matter, as a world. But over the years, adversary like adversaries like China, Russia and other countries have come to realize the value of space systems and are aggressively playing ketchup while also pursuing capabilities that will challenge our systems. As many of you know, in 2000 and seven, China demonstrated it's a set system by actually shooting down is one of its own satellites and has been aggressively developing counter space systems to disrupt hours. So in a heavily congested space environment, our systems are now being contested like never before and will continue to bay well as Bond mentioned, the United States has responded to these changing threats. In addition to adding ways to protect our system, the administration and in Congress recently created the United States Space Force and the operational you United States Space Command, the latter of which you heard President Armstrong and other Californians hope is going to be located. Vandenberg Air Force Base Combined with our intelligence community today, we have focused military and civilian leadership now in space. And that's a very, very good thing. Commence, really. On the industry side, we did create the National Security Space Association devoted solely to supporting the national security Space Enterprise. We're based here in the D C area, but we have arms and legs across the country, and we are loaded with extraordinary talent. In scores of Forman, former government executives, So S s a is joined at the hip with our government customers to serve and to support. We're busy with a multitude of activities underway ranging from a number of thought provoking policy. Papers are recurring space time Webcast supporting Congress's Space Power Caucus and other main serious efforts. Check us out at NSS. A space dot org's One of our strategic priorities in central to today's events is to actively promote and nurture the workforce development. Just like cow calling. We will work with our U. S. Government customers, industry leaders and academia to attract and recruit students to join the space world, whether in government or industry and two assistant mentoring and training as their careers. Progress on that point, we're delighted. Be delighted to be working with Cal Poly as we hopefully will undertake a new pilot program with him very soon. So students stay tuned something I can tell you Space is really cool. While our nation's satellite systems are technical and complex, our nation's government and industry work force is highly diverse, with a combination of engineers, physicists, method and mathematicians, but also with a large non technical expertise as well. Think about how government gets things thes systems designed, manufactured, launching into orbit and operating. They do this via contracts with our aerospace industry, requiring talents across the board from cost estimating cost analysis, budgeting, procurement, legal and many other support. Tasker Integral to the mission. Many thousands of people work in the space workforce tens of billions of dollars every year. This is really cool stuff, no matter what your education background, a great career to be part of. When summary as bang had mentioned Aziz, well, there is a great deal of exciting challenges ahead we will see a new renaissance in space in the years ahead, and in some cases it's already begun. Billionaires like Jeff Bezos, Elon Musk, Sir Richard Richard Branson are in the game, stimulating new ideas in business models, other private investors and start up companies. Space companies are now coming in from all angles. The exponential advancement of technology and microelectronics now allows the potential for a plethora of small SAT systems to possibly replace older satellites the size of a Greyhound bus. It's getting better by the day and central to this conference, cybersecurity is paramount to our nation's critical infrastructure in space. So once again, thanks very much, and I look forward to the further conversation. >>Steve, thank you very much. Space is cool. It's relevant. But it's important, as you pointed out, and you're awesome story about how it impacts our life every day. So I really appreciate that great story. I'm glad you took the time Thio share that you forgot the part about the drone coming over in the crime scene and, you know, mapping it out for you. But that would add that to the story later. Great stuff. My first question is let's get into the conversations because I think this is super important. President Armstrong like you to talk about some of the points that was teased out by Bang and Steve. One in particular is the comment around how military research was important in developing all these capabilities, which is impacting all of our lives. Through that story. It was the military research that has enabled a generation and generation of value for consumers. This is kind of this workforce conversation. There are opportunities now with with research and grants, and this is, ah, funding of innovation that it's highly accelerate. It's happening very quickly. Can you comment on how research and the partnerships to get that funding into the universities is critical? >>Yeah, I really appreciate that And appreciate the comments of my colleagues on it really boils down to me to partnerships, public private partnerships. You mentioned Northrop Grumman, but we have partnerships with Lockie Martin, Boeing, Raytheon Space six JPL, also member of organization called Business Higher Education Forum, which brings together university presidents and CEOs of companies. There's been focused on cybersecurity and data science, and I hope that we can spill into cybersecurity in space but those partnerships in the past have really brought a lot forward at Cal Poly Aziz mentioned we've been involved with Cube set. Uh, we've have some secure work and we want to plan to do more of that in the future. Uh, those partnerships are essential not only for getting the r and d done, but also the students, the faculty, whether masters or undergraduate, can be involved with that work. Uh, they get that real life experience, whether it's on campus or virtually now during Covic or at the location with the partner, whether it may be governmental or our industry. Uh, and then they're even better equipped, uh, to hit the ground running. And of course, we'd love to see even more of our students graduate with clearance so that they could do some of that a secure work as well. So these partnerships are absolutely critical, and it's also in the context of trying to bring the best and the brightest and all demographics of California and the US into this field, uh, to really be successful. So these partnerships are essential, and our goal is to grow them just like I know other colleagues and C. S u and the U C are planning to dio, >>you know, just as my age I've seen I grew up in the eighties, in college and during that systems generation and that the generation before me, they really kind of pioneered the space that spawned the computer revolution. I mean, you look at these key inflection points in our lives. They were really funded through these kinds of real deep research. Bond talk about that because, you know, we're living in an age of cloud. And Bezos was mentioned. Elon Musk. Sir Richard Branson. You got new ideas coming in from the outside. You have an accelerated clock now on terms of the innovation cycles, and so you got to react differently. You guys have programs to go outside >>of >>the Defense Department. How important is this? Because the workforce that air in schools and our folks re skilling are out there and you've been on both sides of the table. So share your thoughts. >>No, thanks, John. Thanks for the opportunity responded. And that's what you hit on the notes back in the eighties, R and D in space especially, was dominated by my government funding. Uh, contracts and so on. But things have changed. As Steve pointed out, A lot of these commercial entities funded by billionaires are coming out of the woodwork funding R and D. So they're taking the lead. So what we can do within the deal, the in government is truly take advantage of the work they've done on. Uh, since they're they're, you know, paving the way to new new approaches and new way of doing things. And I think we can We could certainly learn from that. And leverage off of that saves us money from an R and D standpoint while benefiting from from the product that they deliver, you know, within the O D Talking about workforce development Way have prioritized we have policies now to attract and retain talent. We need I I had the folks do some research and and looks like from a cybersecurity workforce standpoint. A recent study done, I think, last year in 2019 found that the cybersecurity workforce gap in the U. S. Is nearing half a million people, even though it is a growing industry. So the pipeline needs to be strengthened off getting people through, you know, starting young and through college, like assess a professor Armstrong indicated, because we're gonna need them to be in place. Uh, you know, in a period of about maybe a decade or so, Uh, on top of that, of course, is the continuing issue we have with the gap with with stamps students, we can't afford not to have expertise in place to support all the things we're doing within the with the not only deal with the but the commercial side as well. Thank you. >>How's the gap? Get? Get filled. I mean, this is the this is again. You got cybersecurity. I mean, with space. It's a whole another kind of surface area, if you will, in early surface area. But it is. It is an I o t. Device if you think about it. But it does have the same challenges. That's kind of current and and progressive with cybersecurity. Where's the gap Get filled, Steve Or President Armstrong? I mean, how do you solve the problem and address this gap in the workforce? What is some solutions and what approaches do we need to put in place? >>Steve, go ahead. I'll follow up. >>Okay. Thanks. I'll let you correct. May, uh, it's a really good question, and it's the way I would. The way I would approach it is to focus on it holistically and to acknowledge it up front. And it comes with our teaching, etcetera across the board and from from an industry perspective, I mean, we see it. We've gotta have secure systems with everything we do and promoting this and getting students at early ages and mentoring them and throwing internships at them. Eyes is so paramount to the whole the whole cycle, and and that's kind of and it really takes focused attention. And we continue to use the word focus from an NSS, a perspective. We know the challenges that are out there. There are such talented people in the workforce on the government side, but not nearly enough of them. And likewise on industry side. We could use Maura's well, but when you get down to it, you know we can connect dots. You know that the the aspect That's a Professor Armstrong talked about earlier toe where you continue to work partnerships as much as you possibly can. We hope to be a part of that. That network at that ecosystem the will of taking common objectives and working together to kind of make these things happen and to bring the power not just of one or two companies, but our our entire membership to help out >>President >>Trump. Yeah, I would. I would also add it again. It's back to partnerships that I talked about earlier. One of our partners is high schools and schools fortune Margaret Fortune, who worked in a couple of, uh, administrations in California across party lines and education. Their fifth graders all visit Cal Poly and visit our learned by doing lab and you, you've got to get students interested in stem at a early age. We also need the partnerships, the scholarships, the financial aid so the students can graduate with minimal to no debt to really hit the ground running. And that's exacerbated and really stress. Now, with this covert induced recession, California supports higher education at a higher rate than most states in the nation. But that is that has dropped this year or reasons. We all understand, uh, due to Kobe, and so our partnerships, our creativity on making sure that we help those that need the most help financially uh, that's really key, because the gaps air huge eyes. My colleagues indicated, you know, half of half a million jobs and you need to look at the the students that are in the pipeline. We've got to enhance that. Uh, it's the in the placement rates are amazing. Once the students get to a place like Cal Poly or some of our other amazing CSU and UC campuses, uh, placement rates are like 94%. >>Many of our >>engineers, they have jobs lined up a year before they graduate. So it's just gonna take key partnerships working together. Uh, and that continued partnership with government, local, of course, our state of CSU on partners like we have here today, both Stephen Bang So partnerships the thing >>e could add, you know, the collaboration with universities one that we, uh, put a lot of emphasis, and it may not be well known fact, but as an example of national security agencies, uh, National Centers of Academic Excellence in Cyber, the Fast works with over 270 colleges and universities across the United States to educate its 45 future cyber first responders as an example, so that Zatz vibrant and healthy and something that we ought Teoh Teik, banjo >>off. Well, I got the brain trust here on this topic. I want to get your thoughts on this one point. I'd like to define what is a public private partnership because the theme that's coming out of the symposium is the script has been flipped. It's a modern error. Things air accelerated get you got security. So you get all these things kind of happen is a modern approach and you're seeing a digital transformation play out all over the world in business. Andi in the public sector. So >>what is what >>is a modern public private partnership? What does it look like today? Because people are learning differently, Covert has pointed out, which was that we're seeing right now. How people the progressions of knowledge and learning truth. It's all changing. How do you guys view the modern version of public private partnership and some some examples and improve points? Can you can you guys share that? We'll start with the Professor Armstrong. >>Yeah. A zai indicated earlier. We've had on guy could give other examples, but Northup Grumman, uh, they helped us with cyber lab. Many years ago. That is maintained, uh, directly the software, the connection outside its its own unit so that students can learn the hack, they can learn to penetrate defenses, and I know that that has already had some considerations of space. But that's a benefit to both parties. So a good public private partnership has benefits to both entities. Uh, in the common factor for universities with a lot of these partnerships is the is the talent, the talent that is, that is needed, what we've been working on for years of the, you know, that undergraduate or master's or PhD programs. But now it's also spilling into Skilling and re Skilling. As you know, Jobs. Uh, you know, folks were in jobs today that didn't exist two years, three years, five years ago. But it also spills into other aspects that can expand even mawr. We're very fortunate. We have land, there's opportunities. We have one tech part project. We're expanding our tech park. I think we'll see opportunities for that, and it'll it'll be adjusted thio, due to the virtual world that we're all learning more and more about it, which we were in before Cove it. But I also think that that person to person is going to be important. Um, I wanna make sure that I'm driving across the bridge. Or or that that satellites being launched by the engineer that's had at least some in person training, uh, to do that and that experience, especially as a first time freshman coming on a campus, getting that experience expanding and as adult. And we're gonna need those public private partnerships in order to continue to fund those at a level that is at the excellence we need for these stem and engineering fields. >>It's interesting People in technology can work together in these partnerships in a new way. Bank Steve Reaction Thio the modern version of what a public, successful private partnership looks like. >>If I could jump in John, I think, you know, historically, Dodi's has have had, ah, high bar thio, uh, to overcome, if you will, in terms of getting rapid pulling in your company. This is the fault, if you will and not rely heavily in are the usual suspects of vendors and like and I think the deal is done a good job over the last couple of years off trying to reduce the burden on working with us. You know, the Air Force. I think they're pioneering this idea around pitch days where companies come in, do a two hour pitch and immediately notified of a wooden award without having to wait a long time. Thio get feedback on on the quality of the product and so on. So I think we're trying to do our best. Thio strengthen that partnership with companies outside the main group of people that we typically use. >>Steve, any reaction? Comment to add? >>Yeah, I would add a couple of these air. Very excellent thoughts. Uh, it zits about taking a little gamble by coming out of your comfort zone. You know, the world that Bond and Bond lives in and I used to live in in the past has been quite structured. It's really about we know what the threat is. We need to go fix it, will design it says we go make it happen, we'll fly it. Um, life is so much more complicated than that. And so it's it's really to me. I mean, you take you take an example of the pitch days of bond talks about I think I think taking a gamble by attempting to just do a lot of pilot programs, uh, work the trust factor between government folks and the industry folks in academia. Because we are all in this together in a lot of ways, for example. I mean, we just sent the paper to the White House of their requests about, you know, what would we do from a workforce development perspective? And we hope Thio embellish on this over time once the the initiative matures. But we have a piece of it, for example, is the thing we call clear for success getting back Thio Uh, President Armstrong's comments at the collegiate level. You know, high, high, high quality folks are in high demand. So why don't we put together a program they grabbed kids in their their underclass years identifies folks that are interested in doing something like this. Get them scholarships. Um, um, I have a job waiting for them that their contract ID for before they graduate, and when they graduate, they walk with S C I clearance. We believe that could be done so, and that's an example of ways in which the public private partnerships can happen to where you now have a talented kid ready to go on Day one. We think those kind of things can happen. It just gets back down to being focused on specific initiatives, give them giving them a chance and run as many pilot programs as you can like these days. >>That's a great point, E. President. >>I just want to jump in and echo both the bank and Steve's comments. But Steve, that you know your point of, you know, our graduates. We consider them ready Day one. Well, they need to be ready Day one and ready to go secure. We totally support that and and love to follow up offline with you on that. That's that's exciting, uh, and needed very much needed mawr of it. Some of it's happening, but way certainly have been thinking a lot about that and making some plans, >>and that's a great example of good Segway. My next question. This kind of reimagining sees work flows, eyes kind of breaking down the old the old way and bringing in kind of a new way accelerated all kind of new things. There are creative ways to address this workforce issue, and this is the next topic. How can we employ new creative solutions? Because, let's face it, you know, it's not the days of get your engineering degree and and go interview for a job and then get slotted in and get the intern. You know the programs you get you particularly through the system. This is this is multiple disciplines. Cybersecurity points at that. You could be smart and math and have, ah, degree in anthropology and even the best cyber talents on the planet. So this is a new new world. What are some creative approaches that >>you know, we're >>in the workforce >>is quite good, John. One of the things I think that za challenge to us is you know, we got somehow we got me working for with the government, sexy, right? The part of the challenge we have is attracting the right right level of skill sets and personnel. But, you know, we're competing oftentimes with the commercial side, the gaming industry as examples of a big deal. And those are the same talents. We need to support a lot of programs we have in the U. D. So somehow we have to do a better job to Steve's point off, making the work within the U. D within the government something that they would be interested early on. So I tracked him early. I kind of talked about Cal Poly's, uh, challenge program that they were gonna have in June inviting high school kid. We're excited about the whole idea of space and cyber security, and so on those air something. So I think we have to do it. Continue to do what were the course the next several years. >>Awesome. Any other creative approaches that you guys see working or might be on idea, or just a kind of stoked the ideation out their internship. So obviously internships are known, but like there's gotta be new ways. >>I think you can take what Steve was talking about earlier getting students in high school, uh, and aligning them sometimes. Uh, that intern first internship, not just between the freshman sophomore year, but before they inter cal poly per se. And they're they're involved s So I think that's, uh, absolutely key. Getting them involved many other ways. Um, we have an example of of up Skilling a redeveloped work redevelopment here in the Central Coast. PG and e Diablo nuclear plant as going to decommission in around 2020 24. And so we have a ongoing partnership toe work on reposition those employees for for the future. So that's, you know, engineering and beyond. Uh, but think about that just in the manner that you were talking about. So the up skilling and re Skilling uh, on I think that's where you know, we were talking about that Purdue University. Other California universities have been dealing with online programs before cove it and now with co vid uh, so many more faculty or were pushed into that area. There's going to be much more going and talk about workforce development and up Skilling and Re Skilling The amount of training and education of our faculty across the country, uh, in in virtual, uh, and delivery has been huge. So there's always a silver linings in the cloud. >>I want to get your guys thoughts on one final question as we in the in the segment. And we've seen on the commercial side with cloud computing on these highly accelerated environments where you know, SAS business model subscription. That's on the business side. But >>one of The >>things that's clear in this trend is technology, and people work together and technology augments the people components. So I'd love to get your thoughts as we look at the world now we're living in co vid um, Cal Poly. You guys have remote learning Right now. It's a infancy. It's a whole new disruption, if you will, but also an opportunity to enable new ways to collaborate, Right? So if you look at people and technology, can you guys share your view and vision on how communities can be developed? How these digital technologies and people can work together faster to get to the truth or make a discovery higher to build the workforce? These air opportunities? How do you guys view this new digital transformation? >>Well, I think there's there's a huge opportunities and just what we're doing with this symposium. We're filming this on one day, and it's going to stream live, and then the three of us, the four of us, can participate and chat with participants while it's going on. That's amazing. And I appreciate you, John, you bringing that to this this symposium, I think there's more and more that we can do from a Cal poly perspective with our pedagogy. So you know, linked to learn by doing in person will always be important to us. But we see virtual. We see partnerships like this can expand and enhance our ability and minimize the in person time, decrease the time to degree enhanced graduation rate, eliminate opportunity gaps or students that don't have the same advantages. S so I think the technological aspect of this is tremendous. Then on the up Skilling and Re Skilling, where employees air all over, they can be reached virtually then maybe they come to a location or really advanced technology allows them to get hands on virtually, or they come to that location and get it in a hybrid format. Eso I'm I'm very excited about the future and what we can do, and it's gonna be different with every university with every partnership. It's one. Size does not fit all. >>It's so many possibilities. Bond. I could almost imagine a social network that has a verified, you know, secure clearance. I can jump in, have a little cloak of secrecy and collaborate with the d o. D. Possibly in the future. But >>these are the >>kind of kind of crazy ideas that are needed. Are your thoughts on this whole digital transformation cross policy? >>I think technology is gonna be revolutionary here, John. You know, we're focusing lately on what we call digital engineering to quicken the pace off, delivering capability to warfighter. As an example, I think a I machine language all that's gonna have a major play and how we operate in the future. We're embracing five G technologies writing ability Thio zero latency or I o t More automation off the supply chain. That sort of thing, I think, uh, the future ahead of us is is very encouraging. Thing is gonna do a lot for for national defense on certainly the security of the country. >>Steve, your final thoughts. Space systems are systems, and they're connected to other systems that are connected to people. Your thoughts on this digital transformation opportunity >>Such a great question in such a fun, great challenge ahead of us. Um echoing are my colleague's sentiments. I would add to it. You know, a lot of this has I think we should do some focusing on campaigning so that people can feel comfortable to include the Congress to do things a little bit differently. Um, you know, we're not attuned to doing things fast. Uh, but the dramatic You know, the way technology is just going like crazy right now. I think it ties back Thio hoping Thio, convince some of our senior leaders on what I call both sides of the Potomac River that it's worth taking these gamble. We do need to take some of these things very way. And I'm very confident, confident and excited and comfortable. They're just gonna be a great time ahead and all for the better. >>You know, e talk about D. C. Because I'm not a lawyer, and I'm not a political person, but I always say less lawyers, more techies in Congress and Senate. So I was getting job when I say that. Sorry. Presidential. Go ahead. >>Yeah, I know. Just one other point. Uh, and and Steve's alluded to this in bonded as well. I mean, we've got to be less risk averse in these partnerships. That doesn't mean reckless, but we have to be less risk averse. And I would also I have a zoo. You talk about technology. I have to reflect on something that happened in, uh, you both talked a bit about Bill Britton and his impact on Cal Poly and what we're doing. But we were faced a few years ago of replacing a traditional data a data warehouse, data storage data center, and we partner with a W S. And thank goodness we had that in progress on it enhanced our bandwidth on our campus before Cove. It hit on with this partnership with the digital transformation hub. So there is a great example where, uh, we we had that going. That's not something we could have started. Oh, covitz hit. Let's flip that switch. And so we have to be proactive on. We also have thio not be risk averse and do some things differently. Eyes that that is really salvage the experience for for students. Right now, as things are flowing, well, we only have about 12% of our courses in person. Uh, those essential courses, uh, and just grateful for those partnerships that have talked about today. >>Yeah, and it's a shining example of how being agile, continuous operations, these air themes that expand into space and the next workforce needs to be built. Gentlemen, thank you. very much for sharing your insights. I know. Bang, You're gonna go into the defense side of space and your other sessions. Thank you, gentlemen, for your time for great session. Appreciate it. >>Thank you. Thank you. >>Thank you. >>Thank you. Thank you. Thank you all. >>I'm John Furry with the Cube here in Palo Alto, California Covering and hosting with Cal Poly The Space and Cybersecurity Symposium 2020. Thanks for watching.

>> So nice to meet you. And I'm Tetsuhiko Teshima from German branch of MEI Laboratories. I'm working at the Technische Universitat Munchen to conduct wet experiment like using chemical and biological samples. So it's great honor and pleasure for me to have a chance to share with you some topics about miniaturized biointerfaces that I have been working on over the last six or seven years, I guess. So before starting, please let me introduce myself and my background. So I started to work in this company since this March, but until the last year, I was working in NTT Basic Research Laboratories that is located in Kanagawa, Japan. And I have work on basic nanoscience research. But when going back to the further, I was originally a student studying biology especially infectious microbiology. And then I learned about the miniaturize fluidic system to manipulate single cells and MEMS technologies that is kind of a fabrication process for semiconductor devices. So, this background motivate me to start interdisciplinary work, especially about biomedical engineering at NTT Corporation. So in recent year, wearable electrodes have been developed to continuously monitor the vital data, including the heart rate, ECG, or EMG waveforms for rapid diagnosis and early stage treatment of disease. So conventionally, rigid metals or metal-plated fibers have been widely used as the electrodes but they lack flexibility and biocompatibilities, which results in the noise in obtaining data and the patient allergic reaction during the long time years. So at NTT, we are working on the research and development of the conductive composite materials. So, due to its high flexibility and hydrophilicity and biocompatibilities, so this electrodes can successfully record ECG without any rashes and itches to the skin. So now these wearable electrodes cores toy are commercially available and funds are applied for not only the medical care and rehabilitation for the patients, but also for example, remote monitoring system of the workers, integration with these sportswear and entertainment show. But this product is originated from the basic scientific findings especially on the conductive polymers, PEDOT:PSS and silk fibers. So there was some mainly conducted by two key scientists clinician doctor Tsukada, and chemist doctor, Nakashima. In order to realize this product, they try so many prototypes. And make so many effort to obtain the pharmaceutical probables for medical usage. So through this experience, we are going back to the original material science and research and making non-toxic interfaces with cells and tissues in order to seek new kind of development. So, as a next challenge, I have focused on the electrodes that work inside the bodies. So we have the tissues and organs with electrical signals like heart and brain. So if implanted electrodes can work on these tissues, this help us to increase the variety of the vital data like EEG. And also it can directly treat the targeted tissues as a surgical, too, like CRT pacing. So in this case, these biointerfaces should be populated in very humid environment and in non-toxic manner. They also should be transformed into soft, three dimensional structures, in order to fit the shape of cells and tissues because they have very complicated 3D structures. So I decided to develop the basic electrode component that meets all of these requirements that is biocompatible for example, like 3D film-electrodes. So what I tried at first is to create a non-toxic, very soft and flexible film-electrodes using the materials that are using the heatable electrodes that is silk bundles and PEDOT:PSS. So, firstly, I dissolve the silk bundle to extract a specific protein and process into a palette shape using MEMS technologies, one of my main skill. So by adding the conductive polymers, >> PEDOT: PSS little by little, the palettes will gradually become blue but maintain the high optical transparency. Through this experiment, I discover a very unique materials scientific aspect of silk fibroin. So when PEDOT:PSS got added, the molecular structure and the confirmation of silk protein dramatically change from alpha helix to the beta sheet, and I focused this structure change, leads to the increase in conductivity compared with the PEDOT:PSS pristine films. By using the lithographic fabrication process, the films can be process into very tiny shape, with same deviation as single cell Lego. So this electrode is made of the silk fibroin, the, are very cell friendly protein. So the suspender cells prefer to adhere to their surface. So after attaching the cells on a surface, I can manipulate the cells while maintain the adhesive properties and electrically simulate the cells for the cool, very weak electrical signals from the cells. So in this step we created a non-toxic, transparent, and very flexible films and film-based electrodes. But please note that the, they are 2D and they're not 3D. So in the next step, I try to investigated how to transform these same 2D film to 3D shape. So here, among two polymers I used, so I replace the PEDOT:PSS with different type of polymers, there is parylene, like this. So when the parylene is adhering to the silk fibroin layers so, the gradient of the mechanical stiffness is formed in the synchronous directions as shown here. And this gradient causes the driving force of same film folding, like this. So this is a, this is a movie of the self-folding bilayer films. And you can see these rectangular patterns spontaneously transform into the cylindrical shapes. So just before folding, I suspended the cells on top of the films that is derived from the heart muscles. So the folding films, so here can gently rub the cells inside the tubes and you can incubate them safely more than for two weeks in order to reconstitute the self-beating, fiber-shaped muscle tissues, as shown here. So also this reconstituted tissues can be manipulated like building blocks by picking up and dissolving using glass capillaries. So I believe this techniques has a potential to facilitate high-order self-assembly like artificial neural networks or tissue engineering. So I realized to transform the two different film to 3D shape. So I use this method to transform into 3D electrodes. So in the final step, instead of the silk fibroin, I focus on using extremely thin electrodes materials that is called graphene. So as I explained as extremely thin, so it consist of the only single layer of carbon atom. So since they has just a single atom thickness, it has very high optical transparency and flexibility. So when the graphene was transform to the parylene surface I found this bilayer was tightly bonded due to the strong molecular interactions and the graphene itself straight on the parylene surface and this cell film becomes three dimensional electrodes, like tubeless structures. So as you can see in this movie, like this. So just after releasing them from the service lead, I instantly undergoes a phase transition and collapse. So since, this hexagonal molecular structure of graphene is distorted due to the folding process, so electrical characteristics dramatically change from firstly metallic to the semiconductor like non-linear shape, shown here. Or interestingly, the curvature and direction of the cell folding can be well controls with number of graphene, this and it's crystalline directions. So when a merged layers graphene were transfer, the curvature radius become smaller and smaller. And when the crystal, crystal, sorry, single crystalline graphene was loaded on the surface of parylene, this bilayer was folded in one fixed same direction, especially along the arms here siding. So by simply transferring the single carbon atom layer to the parylene surface, so we achieved the self-assembly of 3D transparent electrodes. In order to demonstrate biocompatibility of this graphene electrodes, we apply for the interface with neurons. So as there was a self-folding of silk fibroin, so we suspended the neurons are encapsulated in the self-folded graphene tubes, like this. So I made it a very tiny holes on the films. So the encapsulated neurons can uptake the nutrition and oxygen through this pore. So I culture the neurons for, without any damage, to the cells, and they exhibit cell-cell contact for tissue-like structures and they elongate their nuclei and axon to the outside through this pore. Therefore, the embedded neurons properly exhibit cell-cell interaction and drive intrinsic morphologies and function, which shows achievement of biocompatibility of the graphene electrodes. So in summary, we have been working on producing tiny 3D electrodes, step-by-step, using only four materials. For example, by mixing conductive polymer, >> PEDOT: PSS with silk fibroin, I made transparent and flexible 2D electrodes. By making a bilayer with silk fibroin with parylene, I demonstrated the self-assembly from 2D film to 3D shape. Finally, by transferring the graphene to paralyene, we could assembly tiny 3D electrodes. So in the future, we will continue to work on making bioelectrodes from the material science and biological viewpoints. However, these two approaches are not sufficient for the research or the bioelectronics. And we especially needed the technology of electrochemical assessment of fabricated electrodes and the method to lead up of obtain vital data and manipulation and analysis of obtain data. Therefore, I belong to both of the TUM and NTT research, in order to achieve the four system. So when I look over the world R&D of the bioelectronics, especially implantable electronics are very active, regardless of the university and industry. So firstly, John Rogers' group in University of Illinois, in United States, started to advocate about the implantable, flexible bioelectronics, more than 10 years ago. So now the research on, about it, is rapidly growing all over the world, not only US, but the Asia and Europe. So, the industrial community also tend to participate in this field. So I really hope to contributed to the scientific achievement and the creation of industry from the German basis, by making the most of my experience and cooperation with Japan and American side. So finally, I like to introduce my colleagues in TUM. So they are loved members and he, he is supervisor, Professor Bernhard Wolfrum, especially of the electrochemistry and electrochemical engineering process for biomedical application. So I'm so happy to work with this wonderful team and also appreciated the daily support of the members in NTT research in United States. Finally, let me just conclude by acknowledging my supervisor, mentors, Professor Wolfrum, Director Tomoike, and Dr. Alexander. And also the member from NTT who always support me, especially Mr. Kikuchi, Dr. Nakashima, Tsukada fellow, Director Goto, Dr. Yamamoto, and Director Sogawa. Finally, let me thanks Professor Offenhausser from Julich, for his kind assistance and introduction to this wonderful collaboration schemes. So, that's all. And I hope this presentation was useful to you. Thank you very much.

>>thank you so much that sake for inviting me to the Entity Research Summit. And I'm really excited to talk to all of them today. So I will be talking about achieving indistinguishability obfuscation from well founded assumptions. And this is really the result of a wonderful two year collaboration with But now it's standing. Graduate student I use chain will be graduating soon on my outstanding co author, Rachel Lynde from the University of Washington. So let me jump right into it. We all know that constructing indistinguishable the obfuscation. Constructing Io has been perhaps the most consequential open problem in the foundations of photography. For several years now, they've seen over 100 papers written that show how to use Iot to achieve a number of remarkable cryptographic goals. Um, that really expand the scope of cryptography in addition to doing just remarkable, really interesting new things. Unfortunately, however, until this work, I told the work I'm about to tell you about all known constructions of Iove. All required new hardness, assumptions, heart assumptions that were designed specifically to prove that Iowa secure. And unfortunately, uh, this has a torture of history. And many of the assumptions were actually broken, which led to just a lot of doubt and uncertainty about the status of Iot, whether it really exists or doesn't exist. And the work I'm about to tell you about today changes that state of affairs in the continental way in that we show how to build io from the combination of four well established topographic assumptions. Okay, let me jump right into it and tell you how we do it. So before this work that I'm about to tell you about over the last two years with Rachel and Ayush, we actually constructed a whole sequence of works that have looked at this question. And what we showed was that if we could just build a certain special object, then that would be sufficient for constructing Io, assuming well established assumptions like L W E P R g s and M C zero and the 68 assumption of a violin. Your mouths. Okay, So what is this object? The object first starts with a P. R G and >>S zero. In other words, of trg with constant locality that stretches end bits of seed to M bits of output where am is ended one plus Epsilon for any constant Epsilon zero. Yes, but in addition to this prg, we also have these l w we like samples. So as usual, we have an elder Bluey Secret s which is random vector z b two k, where K is the dimension of the secret, which is much smaller than any way also have this public about vectors ai which are also going to be okay. And now what is given out is are the elderly samples where the error is this X I that is just brilliant value. Uh, where these excise air Also the input to our prg. Okay, unfortunately, we needed to assume that these two things together, this y and Z together is actually pseudo random. But if you think about it, there is some sort of kind of strange assumption that assumes some kind of special leakage resilience, property of elderly, we where elderly samples, even with this sort of bizarre leakage on the errors from all debris, is still surround or still have some surrounding properties. And unfortunately, we had no idea how to prove that. And we still don't have any idea how to prove this. Actually, So this is just a assumption and we didn't know it's a new assumption. So far, it hasn't been broken, but that's pretty much it. That's all we knew about it. Um and that was it. If we could. If this is true, then we could actually build. I'll now to actually use this object. We needed additional property. We needed a special property that the output of this prg here can actually be computed. Every single bit of the output could be computed by a polynomial over the public. Elder Louise samples Why? And an additional secret w with the property that this additional secret w is actually quite small. It's only excise em to the one minus delta or some constant delta gradients. Barroso polynomial smaller from the output of the prg. And crucially, the degree of this polynomial is on Lee to its violin e er can this secret double that's where the bottle in your mouth will come. Okay. And in fact, this part we did not approve. So in this previous work, using various clever transformations, we were able to show that in fact we are able to construct this in a way to this Parliament has existed only degree to be short secret values. Double mhm. So now I'm gonna show you how using our new ideas were actually gonna build. That's a special object just like this from standard assumptions. We're just gonna be sufficient for building io, and we're gonna have to modify it a little bit. Okay? One of the things that makes me so excited is that actually, our ideas are extremely simple. I want to try to get that across today. Thanks. So the first idea is let's take thes elder movie samples that we have here and change them up a little bit when it changed them up. Start before I get to that in this talk, I want you to think of K the dimension of the secret here as something very small. Something like end of the excellent. That's only for the stock, not for the previous work. Okay. All right. So we have these elderly samples right from the previous work, but I'm going to change it up instead of computing them this way, as shown in the biggest slide on this line. Let's add some sparse hair. So let's replace this error x i with the air e i plus x I where e is very sparse. Almost all of these IIs or zero. But when the I is not zero is just completely random in all of Z, pizza just completely destroys all information. Okay, so first I just want to point out that the previous work that I already mentioned applies also to this case. So if we only want to compute P R g of X plus E, then that can still be computer the polynomial. That's degree to in a short W that's previous work the jail on Guess work from 2019. I'm not going to recall that you don't have time to tell you how you do it. It's very simple. Okay, so why are we doing this? Why are we adding the sparse error? The key observation is that even though I have changed the input of the PRG to the X Plus E because he is so sparse, prg of explosive is actually the same as P. R. G of X. In almost every outlet location. It's only a tiny, tiny fraction of the outputs that are actually corrupted by the sparse Arab. Okay, so for a moment Let's just pretend that in fact, we knew how to compute PRGF X with a degree to polynomial over a short seeking. We'll come back to this, I promise. But suppose for a moment we actually knew how to compute care to your ex, Not just scared of explosive in that case were essentially already done. And the reason is there's the L. P n over zp assumption that has been around for many years, which says that if you look at these sort of elderly like samples ai from the A, I s but plus a sparse air e I where you guys most zero open when it's not serious, completely random then In fact, these samples look pseudo random. They're indistinguishable from a I r r. I just completely uniform over ZP, okay? And this is a long history which I won't go because I don't have time, but it's just really nice or something. Okay, so let's see how we can use it. So again, suppose for the moment that we were able to compute, not just appeared you've explosive but appeared to you that well, the first operation that since we're adding the sparse R E I This part the the L P N part here is actually completely random by the LP an assumption so by L P and G. P, we can actually replace this entire term with just all right. And now, no, there is no more information about X present in the samples, The only place where as is being used in the input to the prg and as a result, we could just apply to sit around this of the prg and say this whole thing is pseudo random and that's it. We've now proven that this object that I wanted to construct it is actually surrounded, which is the main thing that was so bothering us and all this previous work. Now we get it like that just for the snap of our fingers just immediately from people. Okay, so the only thing that's missing that I haven't told you yet is Wait, how do we actually compute prg attacks? Right? Because we can compute p r g of X plus e. But there's still gonna be a few outputs. They're gonna be wrong. So how can we correct those few corrupted output positions to recover PRGF s? So, for the purpose of this talks because I don't have enough time. I'm gonna make sort of a crazy simplifying assumption. Let's just assume that in fact, Onley one out the position of P r g of X plus e was correct. So it's almost exactly what PR gox. There's only one position in prg of Ecstasy which needs to be corrected to get us back to PR gox. Okay, so how can we do that? The idea is again really, really simple. Okay, so the output of the PRG is an M. Becker and so Dimension and Becker. But let's actually just rearrange that into a spirit of them by spirit of them matrix. And as I mentioned, there's only one position in this matrix that actually needs to be corrected. So let's make this correction matrix, which is almost everywhere. Zero just in position. I j it contains a single correction factor. Why, right? And if you can add this matrix to prg of explosive, then we'll get PR dribbles. Okay, so now the Onley thing I need to do is to compute this extremely sparse matrix. And here the observation was almost trivia. Just I could take a spirit of em by one maker That just has why in position I and I could take a one by spirit of them matrix. I just have one in position J zero everywhere else. If I just take the tensor product was music the matrix product of these two of these two off this column vector in a row vector. Then I will get exactly this correction matrix. Right? And note that these two vectors that's called them you and be actually really, really swamped their only spirit of n dimensional way smaller than them. Right? So if I want to correct PRGF Expo see, all I have to do is add you, Tenzer V and I can add the individual vectors u and V to my short secret w it's still short. That's not gonna make W's any sufficiently bigger. And you chancery is only a degree to computation. So in this way, using a degree to computation, we can quickly, uh, correct our our computation to recover prg events. And now, of course, this was oversimplifying situation, uh, in general gonna have many more areas. We're not just gonna have one error, like as I mentioned, but it turns out that that is also easy to deal with, essentially the same way. It's again, just a very simple additional idea. Very, very briefly. The idea is that instead of just having one giant square to them by sort of a matrix, you can split up this matrix with lots of little sub matrices and with suitable concentration bound simple balls and pins arguments we can show that we could never Leslie this idea this you Tenzer v idea to correct all of the remaining yet. Okay, that's it. Just, you see, he's like, three simple >>ah ha moments. What kind of all that it took, um, that allowed >>us to achieve this result to get idol from standard assumptions. And, um, of course I'm presenting to you them to you in this very simple way. We just these three little ideas of which I told you to. Um, but of course, there were only made possible because of years of struggling with >>all the way that didn't work, that all that struggling and mapping out all the ways didn't work >>was what allowed us toe have these ideas. Um, and again, it yields the first I'll construction from well established cryptographic assumptions, namely Theo Elgon, assumption over zp learning with errors, assumption, existence of PR GS and then zero that is PR juice with constant death circuits and the SX th assumption over by linear notes, all of which have been used many years for a number of other applications, including such things as publicly inversion, something simple public inversion that's the That's the context in which the assumptions have been used so very far from the previous state of affairs where we had assumptions that were introduced on Lee Professor constructing my own. And with that I will conclude, uh and, uh, thank you for your attention. Thanks so much.

>>Hi, Maya. Mr England. And I am an associate professor of electrical engineering and computer science at M I T. It's been fantastic to be part of this team that Professor Yamamoto put together, uh, for the entity Fire program. It's a great pleasure to report to you are update from the first year I will talk to you today about our recent work in photonic accelerators for machine intelligence. You can already get a flavor of the kind of work that I'll be presenting from the photonic integrated circuit that services a platonic matrix processor that we are developing to try toe break some of the bottle next that we encounter in inference, machine learning tasks in particular tasks like vision, games control or language processing. This work is jointly led with Dr Ryan heavily, uh, scientists at NTT Research, and he will have a poster that you should check out. Uh, in this conference should also say that there are postdoc positions available. Um, just take a look at announcements on Q P lab at m i t dot eu. So if you look at these machine learning applications, look under the hood. You see that a common feature is that they used these artificial neural networks or a and ends where you have an input layer of, let's say, and neurons and values that is connected to the first layer of, let's Say, also and neurons and connecting the first to the second layer would, if you represented it biomatrix requiring and biomatrix that has of order and squared free parameters. >>Okay, now, in traditional machine learning inference, you would have to grab these n squared values from memory. And every time you do that, it costs quite a lot of energy. Maybe you can match, but it's still quite costly in energy, and moreover, each of the input values >>has to be multiplied by that matrix. And if you multiply an end by one vector by an end square matrix, you have to do a border and squared multiplication. Okay, now, on a digital computer, you therefore have to do a voter in secret operations and memory access, which could be quite costly. But the proposition is that on a photonic integrated circuits, perhaps we could do that matrix vector multiplication directly on the P. I C itself by encoding optical fields on sending them through a programmed program into parameter and the output them would be a product of the matrix multiplied by the input vector. And that is actually the experiment. We did, uh, demonstrating that That this is, you know, in principle, possible back in 2017 and a collaboration with Professor Marine Soldier Judge. Now, if we look a little bit more closely at the device is shown here, this consists of a silicon layer that is pattern into wave guides. We do this with foundry. This was fabricated with the opposite foundry, and many thanks to our collaborators who helped make that possible. And and this thing guides light, uh, on about of these wave guides to make these two by two transformations Maxine and the kilometers, as they called >>input to input wave guides coming in to input to output wave guides going out. And by having to phase settings here data and five, we can control any arbitrary, uh, s U two rotation. Now, if I wanna have any modes coming in and modes coming out that could be represented by an S u N unitary transformation, and that's what this kind of trip allows you to dio and That's the key ingredient that really launched us in in my group. I should at this point, acknowledge the people who have made this possible and in particular point out Leon Bernstein and Alex lots as well as, uh, Ryan heavily once more. Also, these other collaborators problems important immigrant soldier dish and, of course, to a funding in particular now three entity research funding. So why optics optics has failed many times before in building computers. But why is this different? And I think the difference is that we now you know, we're not trying to build an entirely new computer out of optics were selective in how we apply optics. We should use optics for what it's good at. And that's probably not so much from non linearity, unnecessarily I mean, not memory, um, communication and fan out great in optics. And as we just said, linear algebra, you can do in optics. Fantastic. Okay, so you should make use of these things and then combine it judiciously with electronic processing to see if you can get an advantage in the entire system out of it, okay. And eso before I move on. Actually, based on the 2017 paper, uh, to startups were created, like intelligence and like, matter and the two students from my group, Nick Harris. And they responded, uh, co started this this this jointly founded by matter. And just after, you know, after, like, about two years, they've been able to create their first, uh, device >>the first metrics. Large scale processor. This is this device has called Mars has 64 input mode. 64 Promodes and the full program ability under the hood. Okay. So because they're integrating wave guides directly with Seamus Electron ICS, they were able to get all the wiring complexity, dealt with all the feedback and so forth. And this device is now able to just process a 64 or 64 unitary majors on the sly. Okay, parameters are three wants total power consumption. Um, it has ah, late and see how long it takes for a matrix to be multiplied by a factor of less than a nanosecond. And because this device works well over a pretty large 20 gigahertz, you could put many channels that are individually at one big hurts, so you can have tens of S U two s u 65 or 64 rotations simultaneously that you could do the sort of back in the envelope. Physics gives you that per multiply accumulate. You have just tens of Tempted jewels. Attn. A moment. So that's very, very competitive. That's that's awesome. Okay, so you see, plan and potentially the breakthroughs that are enabled by photonics here And actually, more recently, they actually one thing that made it possible is very cool Eyes thes My face shifters actually have no hold power, whereas our face shifters studios double modulation. These use, uh, nano scale mechanical modulators that have no hold power. So once you program a unitary, you could just hold it there. No energy consumption added over >>time. So photonics really is on the rise in computing on demand. But once again, you have to be. You have to be careful in how you compare against a chance to find where is the game to be had. So what I've talked so far about is wait stationary photonic processing. Okay, up until here. Now what tronics has that also, but it doesn't have the benefits of the coherence of optical fields transitioning through this, uh, to this to this matrix nor the bandwidth. Okay, Eso So that's Ah, that is, I think a really exciting direction. And these companies are off and they're they're building these trips and we'll see the next couple of months how well this works. Uh, on the A different direction is to have an output stationary matrix vector multiplication. And for this I want to point to this paper we wrote with Ryan, Emily and the other team members that projects the activation functions together with the weight terms onto a detector array and by the interference of the activation function and the weight term by Hamad and >>Affection. It's possible if you think about Hamad and affection that it actually automatically produces the multiplication interference turn between two optical fields gives you the multiplication between them. And so that's what that is making use of. I wanna talk a little bit more about that approach. So we actually did a careful analysis in the P R X paper that was cited in the last >>page and that analysis of the energy consumption show that this device and principal, uh, can compute at at an energy poor multiply accumulate that is below what you could theoretically dio at room temperature using irreversible computer like like our digital computers that we use in everyday life. Um, so I want to illustrate that you can see that from this plot here, but this is showing. It's the number of neurons that you have per layer. And on the vertical axis is the energy per multiply accumulate in terms of jewels. And when we make use of the massive fan out together with this photo electric multiplication by career detection, we estimate that >>we're on this curve here. So the more right. So since our energy consumption scales us and whereas for a for a digital computer it skills and squared, we, um we gain mawr as you go to a larger matrices. So for largest matrices like matrices of >>scale 1,005,000, even with present day technology, we estimate that we would hit and energy per multiply accumulate of about a center draw. Okay, But if we look at if we imagine a photonic device that >>uses a photonic system that uses devices that have already been demonstrated individually but not packaged in large system, you know, individually in research papers, we would be on this curve here where you would very quickly dip underneath the lander, a limit which corresponds to the thermodynamic limit for doing as many bit operations that you would have to do to do the same depth of neural network as we do here. And I should say that all of these numbers were computed for this simulated >>optical neural network, um, for having the equivalent, our rate that a fully digital computer that a digital computer would have and eso equivalent in the error rate. So it's limited in the error by the model itself rather than the imperfections of the devices. Okay. And we benchmark that on the amnesty data set. So that was a theoretical work that looked at the scaling limits and show that there's great, great hope to to really gain tremendously in the energy per bit, but also in the overall latency and throughput. But you shouldn't celebrate too early. You have to really do a careful system level study comparing, uh, electronic approaches, which oftentimes happened analogous approach to the optical approaches. And we did that in the first major step in this digital optical neural network. Uh, study here, which was done together with the PNG who is an electron ICS designer who actually works on, uh, tronics based on c'mon specifically made for machine on an acceleration. And Professor Joel, member of M I t. Who is also a fellow at video And what we studied there in particular, is what if we just replaced on Lee the communication part with optics, Okay. And we looked at, you know, getting the same equivalent error rates that you would have with electronic computer. And that showed that that way should have a benefit for large neural networks, because large neural networks will require lots of communication that eventually do not fit on a single Elektronik trip anymore. At that point, you have to go longer distances, and that's where the optical connections start to win out. So for details, I would like to point to that system level study. But we're now applying more sophisticated studies like this, uh, like that simulate full system simulation to our other optical networks to really see where the benefits that we might have, where we can exploit thes now. Lastly, I want to just say What if we had known nominee Garrity's that >>were actually reversible. There were quantum coherent, in fact, and we looked at that. So supposed to have the same architectural layout. But rather than having like a sexual absorption absorption or photo detection and the electronic non linearity, which is what we've done so far, you have all optical non linearity, okay? Based, for example, on a curve medium. So suppose that we had, like, a strong enough current medium so that the output from one of these transformations can pass through it, get an intensity dependent face shift and then passes into the next layer. Okay, What we did in this case is we said okay. Suppose that you have this. You have multiple layers of these, Uh um accent of the parameter measures. Okay. These air, just like the ones that we had before. >>Um, and you want to train this to do something? So suppose that training is, for example, quantum optical state compression. Okay, you have an optical quantum optical state you'd like to see How much can I compress that to have the same quantum information in it? Okay. And we trained that to discover a efficient algorithm for that. We also trained it for reinforcement, learning for black box, quantum simulation and what? You know what is particularly interesting? Perhaps in new term for one way corner repeaters. So we said if we have a communication network that has these quantum optical neural networks stationed some distance away, you come in with an optical encoded pulse that encodes an optical cubit into many individual photons. How do I repair that multi foot on state to send them the corrected optical state out the other side? This is a one way error correcting scheme. We didn't know how to build it, but we put it as a challenge to the neural network. And we trained in, you know, in simulation we trained the neural network. How toe apply the >>weights in the Matrix transformations to perform that Andi answering actually a challenge in the field of optical quantum networks. So that gives us motivation to try to build these kinds of nonlinear narratives. And we've done a fair amount of work. Uh, in this you can see references five through seven. Here I've talked about thes programmable photonics already for the the benchmark analysis and some of the other related work. Please see Ryan's poster we have? Where? As I mentioned we where we have ongoing work in benchmarking >>optical computing assed part of the NTT program with our collaborators. Um And I think that's the main thing that I want to stay here, you know, at the end is that the exciting thing, really is that the physics tells us that there are many orders of magnitude of efficiency gains, uh, that are to be had, Uh, if we you know, if we can develop the technology to realize it. I was being conservative here with three orders of magnitude. This could be six >>orders of magnitude for larger neural networks that we may have to use and that we may want to use in the future. So the physics tells us there are there is, like, a tremendous amount of gap between where we are and where we could be and that, I think, makes this tremendously exciting >>and makes the NTT five projects so very timely. So with that, you know, thank you for your attention and I'll be happy. Thio talk about any of these topics

>> Announcer: From around the globe, it's The Cube, covering Space and Cybersecurity Symposium 2020, hosted by Cal Poly. >> Everyone, welcome to this special virtual conference, the Space and Cybersecurity Symposium 2020 put on by Cal Poly with support from The Cube. I'm John Furey, your host and master of ceremony's got a great topic today, and this session is really the intersection of space and cybersecurity. This topic, and this conversation is a cybersecurity workforce development through public and private partnerships. And we've got a great lineup, we've Jeff Armstrong is the president of California Polytechnic State University, also known as Cal Poly. Jeffrey, thanks for jumping on and Bong Gumahad. The second, Director of C4ISR Division, and he's joining us from the Office of the Under Secretary of Defense for the acquisition and sustainment of Department of Defense, DOD, and of course Steve Jacques is Executive Director, founder National Security Space Association, and managing partner at Velos. Gentlemen, thank you for joining me for this session, we've got an hour of conversation, thanks for coming on. >> Thank you. >> So we've got a virtual event here, we've got an hour to have a great conversation, I'd love for you guys to do an opening statement on how you see the development through public and private partnerships around cybersecurity and space, Jeff, we'll start with you. >> Well, thanks very much, John, it's great to be on with all of you. On behalf of Cal Poly, welcome everyone. Educating the workforce of tomorrow is our mission at Cal Poly, whether that means traditional undergraduates, masters students, or increasingly, mid-career professionals looking to upskill or re-skill. Our signature pedagogy is learn by doing, which means that our graduates arrive at employers, ready day one with practical skills and experience. We have long thought of ourselves as lucky to be on California's beautiful central coast, but in recent years, as we've developed closer relationships with Vandenberg Air Force Base, hopefully the future permanent headquarters of the United States Space Command with Vandenberg and other regional partners, We have discovered that our location is even more advantageous than we thought. We're just 50 miles away from Vandenberg, a little closer than UC Santa Barbara and the base represents the Southern border of what we have come to think of as the central coast region. Cal Poly and Vandenberg Air Force Base have partnered to support regional economic development, to encourage the development of a commercial space port, to advocate for the space command headquarters coming to Vandenberg and other ventures. These partnerships have been possible because both parties stand to benefit. Vandenberg, by securing new streams of revenue, workforce, and local supply chain and Cal Poly by helping to grow local jobs for graduates, internship opportunities for students and research and entrepreneurship opportunities for faculty and staff. Crucially, what's good for Vandenberg Air Force Base and for Cal Poly is also good for the central coast and the U.S., creating new head of household jobs, infrastructure, and opportunity. Our goal is that these new jobs bring more diversity and sustainability for the region. This regional economic development has taken on a life of its own, spawning a new nonprofit called REACH which coordinates development efforts from Vandenberg Air Force Base in the South to Camp Roberts in the North. Another factor that has facilitated our relationship with Vandenberg Air Force Base is that we have some of the same friends. For example, Northrop Grumman has as long been an important defense contractor and an important partner to Cal Poly, funding scholarships in facilities that have allowed us to stay current with technology in it to attract highly qualified students for whom Cal Poly's costs would otherwise be prohibitive. For almost 20 years, Northrop Grumman has funded scholarships for Cal Poly students. This year, they're funding 64 scholarships, some directly in our College of Engineering and most through our Cal Poly Scholars Program. Cal Poly scholars support both incoming freshmen and transfer students. These are especially important, 'cause it allows us to provide additional support and opportunities to a group of students who are mostly first generation, low income and underrepresented, and who otherwise might not choose to attend Cal Poly. They also allow us to recruit from partner high schools with large populations of underrepresented minority students, including the Fortune High School in Elk Grove, which we developed a deep and lasting connection. We know that the best work is done by balanced teams that include multiple and diverse perspectives. These scholarships help us achieve that goal and I'm sure you know Northrop Grumman was recently awarded a very large contract to modernize the U.S. ICBM armory with some of the work being done at Vandenberg Air Force Base, thus supporting the local economy and protecting... Protecting our efforts in space requires partnerships in the digital realm. Cal Poly has partnered with many private companies such as AWS. Our partnerships with Amazon Web Services has enabled us to train our students with next generation cloud engineering skills, in part, through our jointly created digital transformation hub. Another partnership example is among Cal Poly's California Cyber Security Institute College of Engineering and the California National Guard. This partnership is focused on preparing a cyber-ready workforce, by providing faculty and students with a hands on research and learning environment side by side with military law enforcement professionals and cyber experts. We also have a long standing partnership with PG&E most recently focused on workforce development and redevelopment. Many of our graduates do indeed go on to careers in aerospace and defense industry. As a rough approximation, more than 4,500 Cal Poly graduates list aerospace or defense as their employment sector on LinkedIn. And it's not just our engineers in computer sciences. When I was speaking to our fellow panelists not too long ago, speaking to Bong, we learned that Rachel Sims, one of our liberal arts majors is working in his office, so shout out to you, Rachel. And then finally, of course, some of our graduates soar to extraordinary heights, such as Commander Victor Glover, who will be heading to the International Space Station later this year. As I close, all of which is to say that we're deeply committed to workforce development and redevelopment, that we understand the value of public-private partnerships, and that we're eager to find new ways in which to benefit everyone from this further cooperation. So we're committed to the region, the state and the nation, in our past efforts in space, cyber security and links to our partners at, as I indicated, aerospace industry and governmental partners provides a unique position for us to move forward in the interface of space and cyber security. Thank you so much, John. >> President Armstrong, thank you very much for the comments and congratulations to Cal Poly for being on the forefront of innovation and really taking a unique, progressive view and want to tip a hat to you guys over there, thank you very much for those comments, appreciate it. Bong, Department of Defense. Exciting, you've got to defend the nation, space is global, your opening statement. >> Yes, sir, thanks John, appreciate that. Thank you everybody, I'm honored to be in this panel along with Preston Armstrong of Cal Poly and my longtime friend and colleague Steve Jacques of the National Security Space Association to discuss a very important topic of a cybersecurity workforce development as President Armstrong alluded to. I'll tell you, both of these organizations, Cal Poly and the NSSA have done and continue to do an exceptional job at finding talent, recruiting them and training current and future leaders and technical professionals that we vitally need for our nation's growing space programs, as well as our collective national security. Earlier today, during session three, I, along with my colleague, Chris Samson discussed space cyber security and how the space domain is changing the landscape of future conflicts. I discussed the rapid emergence of commercial space with the proliferation of hundreds, if not thousands of satellites, providing a variety of services including communications, allowing for global internet connectivity, as one example. Within DOD, we continued to look at how we can leverage this opportunity. I'll tell you, one of the enabling technologies, is the use of small satellites, which are inherently cheaper and perhaps more flexible than the traditional bigger systems that we have historically used and employed for DOD. Certainly not lost on me is the fact that Cal Poly pioneered CubeSats 28, 27 years ago, and they set a standard for the use of these systems today. So they saw the value and benefit gained way ahead of everybody else it seems. And Cal Poly's focus on training and education is commendable. I'm especially impressed by the efforts of another of Steven's colleague, the current CIO, Mr. Bill Britton, with his high energy push to attract the next generation of innovators. Earlier this year, I had planned on participating in this year's cyber innovation challenge in June, Oops, Cal Poly hosts California middle, and high school students, and challenge them with situations to test their cyber knowledge. I tell you, I wish I had that kind of opportunity when I was a kid, unfortunately, the pandemic changed the plan, but I truly look forward to future events such as these, to participate in. Now, I want to recognize my good friend, Steve Jacques, whom I've known for perhaps too long of a time here, over two decades or so, who was an acknowledged space expert and personally I've truly applaud him for having the foresight a few years back to form the National Security Space Association to help the entire space enterprise navigate through not only technology, but policy issues and challenges and paved the way for operationalizing space. Space, it certainly was fortifying domain, it's not a secret anymore, and while it is a unique area, it shares a lot of common traits with the other domains, such as land, air, and sea, obviously all are strategically important to the defense of the United States. In conflict, they will all be contested and therefore they all need to be defended. One domain alone will not win future conflicts, and in a joint operation, we must succeed in all. So defending space is critical, as critical as to defending our other operational domains. Funny, space is the only sanctuary available only to the government. Increasingly as I discussed in a previous session, commercial space is taking the lead in a lot of different areas, including R&D, the so-called new space. So cybersecurity threat is even more demanding and even more challenging. The U.S. considers and futhered access to and freedom to operate in space, vital to advancing security, economic prosperity and scientific knowledge of the country, thus making cyberspace an inseparable component of America's financial, social government and political life. We stood up US Space Force a year ago or so as the newest military service. Like the other services, its mission is to organize, train and equip space forces in order to protect U.S. and allied interest in space and to provide spacecape builders who joined force. Imagine combining that U.S. Space Force with the U.S. Cyber Command to unify the direction of the space and cyberspace operation, strengthen DOD capabilities and integrate and bolster a DOD cyber experience. Now, of course, to enable all of this requires a trained and professional cadre of cyber security experts, combining a good mix of policy, as well as a high technical skill set. Much like we're seeing in STEM, we need to attract more people to this growing field. Now, the DOD has recognized the importance to the cybersecurity workforce, and we have implemented policies to encourage its growth. Back in 2013, the Deputy Secretary of Defense signed a DOD Cyberspace Workforce Strategy, to create a comprehensive, well-equipped cyber security team to respond to national security concerns. Now, this strategy also created a program that encourages collaboration between the DOD and private sector employees. We call this the Cyber Information Technology Exchange program, or CITE that it's an exchange program, which is very interesting in which a private sector employee can naturally work for the DOD in a cyber security position that spans across multiple mission critical areas, important to the DOD. A key responsibility of the cyber security community is military leaders, unrelated threats, and the cyber security actions we need to have to defeat these threats. We talked about rapid acquisition, agile business processes and practices to speed up innovation, likewise, cyber security must keep up with this challenge. So cyber security needs to be right there with the challenges and changes, and this requires exceptional personnel. We need to attract talent, invest in the people now to grow a robust cybersecurity workforce for the future. I look forward to the panel discussion, John, thank you. >> Thank you so much, Bob for those comments and, you know, new challenges or new opportunities and new possibilities and freedom to operate in space is critical, thank you for those comments, looking forward to chatting further. Steve Jacques, Executive Director of NSSA, you're up, opening statement. >> Thank you, John and echoing Bongs, thanks to Cal Poly for pulling this important event together and frankly, for allowing the National Security Space Association be a part of it. Likewise, on behalf of the association, I'm delighted and honored to be on this panel of President Armstrong, along with my friend and colleague, Bong Gumahad. Something for you all to know about Bong, he spent the first 20 years of his career in the Air Force doing space programs. He then went into industry for several years and then came back into government to serve, very few people do that. So Bong, on behalf of the space community, we thank you for your lifelong devotion to service to our nation, we really appreciate that. And I also echo a Bong shout out to that guy, Bill Britton. who's been a long time co-conspirator of ours for a long time, and you're doing great work there in the cyber program at Cal Poly, Bill, keep it up. But Professor Armstrong, keep a close eye on him. (laughter) I would like to offer a little extra context to the great comments made by President Armstrong and Bong. And in our view, the timing of this conference really could not be any better. We all recently reflected again on that tragic 9/11 surprise attack on our homeland and it's an appropriate time we think to take pause. While a percentage of you in the audience here weren't even born or were babies then, for the most of us, it still feels like yesterday. And moreover, a tragedy like 9/11 has taught us a lot to include, to be more vigilant, always keep our collective eyes and ears open, to include those "eyes and ears from space," making sure nothing like this ever happens again. So this conference is a key aspect, protecting our nation requires we work in a cyber secure environment at all times. But you know, the fascinating thing about space systems is we can't see 'em. Now sure, we see space launches, man, there's nothing more invigorating than that. But after launch they become invisible, so what are they really doing up there? What are they doing to enable our quality of life in the United States and in the world? Well to illustrate, I'd like to paraphrase elements of an article in Forbes magazine, by Bongs and my good friend, Chuck Beames, Chuck is a space guy, actually had Bongs job a few years in the Pentagon. He's now Chairman and Chief Strategy Officer at York Space Systems and in his spare time, he's Chairman of the Small Satellites. Chuck speaks in words that everyone can understand, so I'd like to give you some of his words out of his article, paraphrase somewhat, so these are Chuck's words. "Let's talk about average Joe and plain Jane. "Before heading to the airport for a business trip "to New York city, Joe checks the weather forecast, "informed by NOAA's weather satellites, "to see what to pack for the trip. "He then calls an Uber, that space app everybody uses, "it matches riders with drivers via GPS, "to take him to the airport. "So Joe has launched in the airport, "unbeknownst to him, his organic lunch is made "with the help of precision farming "made possible to optimize the irrigation and fertilization "with remote spectral sensing coming from space and GPS. "On the plane, the pilot navigates around weather, "aided by GPS and NOAA's weather satellites "and Joe makes his meeting on time "to join his New York colleagues in a video call "with a key customer in Singapore, "made possible by telecommunication satellites. "En route to his next meeting, "Joe receives notice changing the location of the meeting "to the other side of town. "So he calmly tells Siri to adjust the destination "and his satellite-guided Google maps redirect him "to the new location. "That evening, Joe watches the news broadcast via satellite, "report details of meeting among world leaders, "discussing the developing crisis in Syria. "As it turns out various forms of "'remotely sensed information' collected from satellites "indicate that yet another banned chemical weapon "may have been used on its own people. "Before going to bed, Joe decides to call his parents "and congratulate them for their wedding anniversary "as they cruise across the Atlantic, "made possible again by communication satellites "and Joe's parents can enjoy the call "without even wondering how it happened. "The next morning back home, "Joe's wife, Jane is involved in a car accident. "Her vehicle skids off the road, she's knocked unconscious, "but because of her satellite equipped OnStar system, "the crash is detected immediately, "and first responders show up on the scene in time. "Joe receives the news, books an early trip home, "sends flowers to his wife "as he orders another Uber to the airport. "Over that 24 hours, "Joe and Jane used space system applications "for nearly every part of their day. "Imagine the consequences if at any point "they were somehow denied these services, "whether they be by natural causes or a foreign hostility. "In each of these satellite applications used in this case, "were initially developed for military purposes "and continued to be, but also have remarkable application "on our way of life, just many people just don't know that." So ladies and gentlemen, now you know, thanks to Chuck Beames. Well, the United States has a proud heritage of being the world's leading space-faring nation. Dating back to the Eisenhower and Kennedy years, today, we have mature and robust systems operating from space, providing overhead reconnaissance to "watch and listen," provide missile warning, communications, positioning, navigation, and timing from our GPS system, much of which you heard in Lieutenant General JT Thomson's earlier speech. These systems are not only integral to our national security, but also to our quality of life. As Chuck told us, we simply no longer can live without these systems as a nation and for that matter, as a world. But over the years, adversaries like China, Russia and other countries have come to realize the value of space systems and are aggressively playing catch up while also pursuing capabilities that will challenge our systems. As many of you know, in 2007, China demonstrated its ASAT system by actually shooting down one of its own satellites and has been aggressively developing counterspace systems to disrupt ours. So in a heavily congested space environment, our systems are now being contested like never before and will continue to be. Well, as a Bong mentioned, the United States have responded to these changing threats. In addition to adding ways to protect our system, the administration and the Congress recently created the United States Space Force and the operational United States Space Command, the latter of which you heard President Armstrong and other Californians hope is going to be located at Vandenberg Air Force Base. Combined with our intelligence community, today we have focused military and civilian leadership now in space, and that's a very, very good thing. Commensurately on the industry side, we did create the National Security Space Association, devoted solely to supporting the National Security Space Enterprise. We're based here in the DC area, but we have arms and legs across the country and we are loaded with extraordinary talent in scores of former government executives. So NSSA is joined at the hip with our government customers to serve and to support. We're busy with a multitude of activities underway, ranging from a number of thought-provoking policy papers, our recurring spacetime webcasts, supporting Congress's space power caucus, and other main serious efforts. Check us out at nssaspace.org. One of our strategic priorities and central to today's events is to actively promote and nurture the workforce development, just like Cal-Poly. We will work with our U.S. government customers, industry leaders, and academia to attract and recruit students to join the space world, whether in government or industry, and to assist in mentoring and training as their careers progress. On that point, we're delighted to be working with Cal Poly as we hopefully will undertake a new pilot program with them very soon. So students stay tuned, something I can tell you, space is really cool. While our nation's satellite systems are technical and complex, our nation's government and industry workforce is highly diverse, with a combination of engineers, physicists and mathematicians, but also with a large non-technical expertise as well. Think about how government gets these systems designed, manufactured, launching into orbit and operating. They do this via contracts with our aerospace industry, requiring talents across the board, from cost estimating, cost analysis, budgeting, procurement, legal, and many other support tasks that are integral to the mission. Many thousands of people work in the space workforce, tens of billions of dollars every year. This is really cool stuff and no matter what your education background, a great career to be part of. In summary, as Bong had mentioned as well, there's a great deal of exciting challenges ahead. We will see a new renaissance in space in the years ahead and in some cases it's already begun. Billionaires like Jeff Bezos, Elon Musk, Sir Richard Branson, are in the game, stimulating new ideas and business models. Other private investors and startup companies, space companies are now coming in from all angles. The exponential advancement of technology and micro electronics now allows a potential for a plethora of small sat systems to possibly replace older satellites, the size of a Greyhound bus. It's getting better by the day and central to this conference, cybersecurity is paramount to our nation's critical infrastructure in space. So once again, thanks very much and I look forward to the further conversation. >> Steve, thank you very much. Space is cool, it's relevant, but it's important as you pointed out in your awesome story about how it impacts our life every day so I really appreciate that great story I'm glad you took the time to share that. You forgot the part about the drone coming over in the crime scene and, you know, mapping it out for you, but we'll add that to the story later, great stuff. My first question is, let's get into the conversations, because I think this is super important. President Armstrong, I'd like you to talk about some of the points that was teased out by Bong and Steve. One in particular is the comment around how military research was important in developing all these capabilities, which is impacting all of our lives through that story. It was the military research that has enabled a generation and generation of value for consumers. This is kind of this workforce conversation, there are opportunities now with research and grants, and this is a funding of innovation that is highly accelerated, it's happening very quickly. Can you comment on how research and the partnerships to get that funding into the universities is critical? >> Yeah, I really appreciate that and appreciate the comments of my colleagues. And it really boils down to me to partnerships, public-private partnerships, you have mentioned Northrop Grumman, but we have partnerships with Lockheed Martin, Boeing, Raytheon, Space X, JPL, also member of an organization called Business Higher Education Forum, which brings together university presidents and CEOs of companies. There's been focused on cybersecurity and data science and I hope that we can spill into cybersecurity and space. But those partnerships in the past have really brought a lot forward. At Cal Poly, as mentioned, we've been involved with CubeSat, we've have some secure work, and we want to plan to do more of that in the future. Those partnerships are essential, not only for getting the R&D done, but also the students, the faculty, whether they're master's or undergraduate can be involved with that work, they get that real life experience, whether it's on campus or virtually now during COVID or at the location with the partner, whether it may be governmental or industry, and then they're even better equipped to hit the ground running. And of course we'd love to see more of our students graduate with clearance so that they could do some of that secure work as well. So these partnerships are absolutely critical and it's also in the context of trying to bring the best and the brightest in all demographics of California and the U.S. into this field, to really be successful. So these partnerships are essential and our goal is to grow them just like I know our other colleagues in the CSU and the UC are planning to do. >> You know, just as my age I've seen, I grew up in the eighties and in college and they're in that system's generation and the generation before me, they really kind of pioneered the space that spawned the computer revolution. I mean, you look at these key inflection points in our lives, they were really funded through these kinds of real deep research. Bong, talk about that because, you know, we're living in an age of cloud and Bezos was mentioned, Elon Musk, Sir Richard Branson, you got new ideas coming in from the outside, you have an accelerated clock now in terms of the innovation cycles and so you got to react differently, you guys have programs to go outside of the defense department, how important is this because the workforce that are in schools and/or folks re-skilling are out there and you've been on both sides of the table, so share your thoughts. >> No, thanks Johnny, thanks for the opportunity to respond to, and that's what, you know, you hit on the nose back in the 80's, R&D and space especially was dominated by government funding, contracts and so on, but things have changed as Steve pointed out, allow these commercial entities funded by billionaires are coming out of the woodwork, funding R&D so they're taking the lead, so what we can do within the DOD in government is truly take advantage of the work they've done. And since they're, you know, paving the way to new approaches and new way of doing things and I think we can certainly learn from that and leverage off of that, saves us money from an R&D standpoint, while benefiting from the product that they deliver. You know, within DOD, talking about workforce development, you know, we have prioritized and we have policies now to attract and retain the talent we need. I had the folks do some research and it looks like from a cybersecurity or workforce standpoint, a recent study done, I think last year in 2019, found that the cyber security workforce gap in U.S. is nearing half a million people, even though it is a growing industry. So the pipeline needs to be strengthened, getting people through, you know, starting young and through college, like Professor Armstrong indicated because we're going to need them to be in place, you know, in a period of about maybe a decade or so. On top of that, of course, is the continuing issue we have with the gap with STEM students. We can't afford not have expertise in place to support all the things we're doing within DoD, not only DoD but the commercial side as well, thank you. >> How's the gap get filled, I mean, this is, again, you've got cybersecurity, I mean, with space it's a whole other kind of surface area if you will, it's not really surface area, but it is an IOT device if you think about it, but it does have the same challenges, that's kind of current and progressive with cybersecurity. Where's the gap get filled, Steve or President Armstrong, I mean, how do you solve the problem and address this gap in the workforce? What are some solutions and what approaches do we need to put in place? >> Steve, go ahead., I'll follow up. >> Okay, thanks, I'll let you correct me. (laughter) It's a really good question, and the way I would approach it is to focus on it holistically and to acknowledge it upfront and it comes with our teaching, et cetera, across the board. And from an industry perspective, I mean, we see it, we've got to have secure systems in everything we do, and promoting this and getting students at early ages and mentoring them and throwing internships at them is so paramount to the whole cycle. And that's kind of, it really takes a focused attention and we continue to use the word focus from an NSSA perspective. We know the challenges that are out there. There are such talented people in the workforce, on the government side, but not nearly enough of them and likewise on the industry side, we could use more as well, but when you get down to it, you know, we can connect dots, you know, the aspects that Professor Armstrong talked about earlier to where you continue to work partnerships as much as you possibly can. We hope to be a part of that network, that ecosystem if you will, of taking common objectives and working together to kind of make these things happen and to bring the power, not just of one or two companies, but of our entire membership thereabout. >> President Armstrong-- >> Yeah, I would also add it again, it's back to the partnerships that I talked about earlier, one of our partners is high schools and schools Fortune, Margaret Fortune, who worked in a couple of administrations in California across party lines and education, their fifth graders all visit Cal Poly, and visit our learned-by-doing lab. And you've got to get students interested in STEM at an early age. We also need the partnerships, the scholarships, the financial aid, so the students can graduate with minimal to no debt to really hit the ground running and that's exacerbated and really stress now with this COVID induced recession. California supports higher education at a higher rate than most states in the nation, but that has brought this year for reasons all understand due to COVID. And so our partnerships, our creativity, and making sure that we help those that need the most help financially, that's really key because the gaps are huge. As my colleagues indicated, you know, half a million jobs and I need you to look at the students that are in the pipeline, we've got to enhance that. And the placement rates are amazing once the students get to a place like Cal Poly or some of our other amazing CSU and UC campuses, placement rates are like 94%. Many of our engineers, they have jobs lined up a year before they graduate. So it's just going to take a key partnerships working together and that continued partnership with government local, of course, our state, the CSU, and partners like we have here today, both Steve and Bong so partnerships is the thing. >> You know, that's a great point-- >> I could add, >> Okay go ahead. >> All right, you know, the collaboration with universities is one that we put on lot of emphasis here, and it may not be well known fact, but just an example of national security, the AUC is a national centers of academic excellence in cyber defense works with over 270 colleges and universities across the United States to educate and certify future cyber first responders as an example. So that's vibrant and healthy and something that we ought to take advantage of. >> Well, I got the brain trust here on this topic. I want to get your thoughts on this one point, 'cause I'd like to define, you know, what is a public-private partnership because the theme that's coming out of the symposium is the script has been flipped, it's a modern era, things are accelerated, you've got security, so you've got all of these things kind of happenning it's a modern approach and you're seeing a digital transformation play out all over the world in business and in the public sector. So what is a modern public-private partnership and what does it look like today because people are learning differently. COVID has pointed out, which is that we're seeing right now, how people, the progressions of knowledge and learning, truth, it's all changing. How do you guys view the modern version of public-private partnership and some examples and some proof points, can you guys share that? We'll start with you, Professor Armstrong. >> Yeah, as I indicated earlier, we've had, and I could give other examples, but Northrop Grumman, they helped us with a cyber lab many years ago that is maintained directly, the software, the connection outside it's its own unit so the students can learn to hack, they can learn to penetrate defenses and I know that that has already had some considerations of space, but that's a benefit to both parties. So a good public-private partnership has benefits to both entities and the common factor for universities with a lot of these partnerships is the talent. The talent that is needed, what we've been working on for years of, you know, the undergraduate or master's or PhD programs, but now it's also spilling into upskilling and reskilling, as jobs, you know, folks who are in jobs today that didn't exist two years, three years, five years ago, but it also spills into other aspects that can expand even more. We're very fortunate we have land, there's opportunities, we have ONE Tech project. We are expanding our tech park, I think we'll see opportunities for that and it'll be adjusted due to the virtual world that we're all learning more and more about it, which we were in before COVID. But I also think that that person to person is going to be important, I want to make sure that I'm driving across a bridge or that satellite's being launched by the engineer that's had at least some in person training to do that in that experience, especially as a first time freshman coming on campus, getting that experience, expanding it as an adult, and we're going to need those public-private partnerships in order to continue to fund those at a level that is at the excellence we need for these STEM and engineering fields. >> It's interesting people and technology can work together and these partnerships are the new way. Bongs too with reaction to the modern version of what a public successful private partnership looks like. >> If I could jump in John, I think, you know, historically DOD's had a high bar to overcome if you will, in terms of getting rapid... pulling in new companies, miss the fall if you will, and not rely heavily on the usual suspects, of vendors and the like, and I think the DOD has done a good job over the last couple of years of trying to reduce that burden and working with us, you know, the Air Force, I think they're pioneering this idea around pitch days, where companies come in, do a two-hour pitch and immediately notified of, you know, of an a award, without having to wait a long time to get feedback on the quality of the product and so on. So I think we're trying to do our best to strengthen that partnership with companies outside of the main group of people that we typically use. >> Steve, any reaction, any comment to add? >> Yeah, I would add a couple and these are very excellent thoughts. It's about taking a little gamble by coming out of your comfort zone, you know, the world that Bong and I, Bong lives in and I used to live in the past, has been quite structured. It's really about, we know what the threat is, we need to go fix it, we'll design as if as we go make it happen, we'll fly it. Life is so much more complicated than that and so it's really, to me, I mean, you take an example of the pitch days of Bong talks about, I think taking a gamble by attempting to just do a lot of pilot programs, work the trust factor between government folks and the industry folks and academia, because we are all in this together in a lot of ways. For example, I mean, we just sent a paper to the white house at their request about, you know, what would we do from a workforce development perspective and we hope to embellish on this over time once the initiative matures, but we have a piece of it for example, is a thing we call "clear for success," getting back to president Armstrong's comments so at a collegiate level, you know, high, high, high quality folks are in high demand. So why don't we put together a program that grabs kids in their underclass years, identifies folks that are interested in doing something like this, get them scholarships, have a job waiting for them that they're contracted for before they graduate, and when they graduate, they walk with an SCI clearance. We believe that can be done, so that's an example of ways in which public-private partnerships can happen to where you now have a talented kid ready to go on day one. We think those kinds of things can happen, it just gets back down to being focused on specific initiatives, giving them a chance and run as many pilot programs as you can, like pitch days. >> That's a great point, it's a good segue. Go ahead, President Armstrong. >> I just want to jump in and echo both the Bong and Steve's comments, but Steve that, you know, your point of, you know our graduates, we consider them ready day one, well they need to be ready day one and ready to go secure. We totally support that and love to follow up offline with you on that. That's exciting and needed, very much needed more of it, some of it's happening, but we certainly have been thinking a lot about that and making some plans. >> And that's a great example, a good segue. My next question is kind of re-imagining these workflows is kind of breaking down the old way and bringing in kind of the new way, accelerate all kinds of new things. There are creative ways to address this workforce issue and this is the next topic, how can we employ new creative solutions because let's face it, you know, it's not the days of get your engineering degree and go interview for a job and then get slotted in and get the intern, you know, the programs and you'd matriculate through the system. This is multiple disciplines, cybersecurity points at that. You could be smart in math and have a degree in anthropology and be one of the best cyber talents on the planet. So this is a new, new world, what are some creative approaches that's going to work for you? >> Alright, good job, one of the things, I think that's a challenge to us is, you know, somehow we got me working for, with the government, sexy right? You know, part of the challenge we have is attracting the right level of skill sets and personnel but, you know, we're competing, oftentimes, with the commercial side, the gaming industry as examples is a big deal. And those are the same talents we need to support a lot of the programs that we have in DOD. So somehow we have do a better job to Steve's point about making the work within DOD, within the government, something that they would be interested early on. So attract them early, you know, I could not talk about Cal Poly's challenge program that they were going to have in June inviting high school kids really excited about the whole idea of space and cyber security and so on. Those are some of the things that I think we have to do and continue to do over the course of the next several years. >> Awesome, any other creative approaches that you guys see working or might be an idea, or just to kind of stoke the ideation out there? Internships, obviously internships are known, but like, there's got to be new ways. >> Alright, I think you can take what Steve was talking about earlier, getting students in high school and aligning them sometimes at first internship, not just between the freshman and sophomore year, but before they enter Cal Poly per se and they're involved. So I think that's absolutely key, getting them involved in many other ways. We have an example of upskilling or work redevelopment here in the central coast, PG&E Diablo nuclear plant that is going to decommission in around 2024. And so we have a ongoing partnership to work and reposition those employees for the future. So that's, you know, engineering and beyond but think about that just in the manner that you were talking about. So the upskilling and reskilling, and I think that's where, you know, we were talking about that Purdue University, other California universities have been dealing with online programs before COVID, and now with COVID so many more Faculty were pushed into that area, there's going to be a much more going and talk about workforce development in upskilling and reskilling, the amount of training and education of our faculty across the country in virtual and delivery has been huge. So there's always a silver linings in the cloud. >> I want to get your guys' thoughts on one final question as we end the segment, and we've seen on the commercial side with cloud computing on these highly accelerated environments where, you know, SAS business model subscription, and that's on the business side, but one of the things that's clear in this trend is technology and people work together and technology augments the people components. So I'd love to get your thoughts as we look at a world now, we're living in COVID, and Cal Poly, you guys have remote learning right now, it's at the infancy, it's a whole new disruption, if you will, but also an opportunity enable new ways to encollaborate, So if you look at people and technology, can you guys share your view and vision on how communities can be developed, how these digital technologies and people can work together faster to get to the truth or make a discovery, hire, develop the workforce, these are opportunities, how do you guys view this new digital transformation? >> Well, I think there's huge opportunities and just what we're doing with this symposium, we're filming this on Monday and it's going to stream live and then the three of us, the four of us can participate and chat with participants while it's going on. That's amazing and I appreciate you, John, you bringing that to this symposium. I think there's more and more that we can do. From a Cal Poly perspective, with our pedagogy so, you know, linked to learn by doing in-person will always be important to us, but we see virtual, we see partnerships like this, can expand and enhance our ability and minimize the in-person time, decrease the time to degree, enhance graduation rate, eliminate opportunity gaps for students that don't have the same advantages. So I think the technological aspect of this is tremendous. Then on the upskilling and reskilling, where employees are all over, they can re be reached virtually, and then maybe they come to a location or really advanced technology allows them to get hands on virtually, or they come to that location and get it in a hybrid format. So I'm very excited about the future and what we can do, and it's going to be different with every university, with every partnership. It's one size does not fit all, There's so many possibilities, Bong, I can almost imagine that social network that has a verified, you know, secure clearance. I can jump in, and have a little cloak of secrecy and collaborate with the DOD possibly in the future. But these are the kind of crazy ideas that are needed, your thoughts on this whole digital transformation cross-pollination. >> I think technology is going to be revolutionary here, John, you know, we're focusing lately on what we call visual engineering to quicken the pace of the delivery capability to warfighter as an example, I think AI, Machine Language, all that's going to have a major play in how we operate in the future. We're embracing 5G technologies, and the ability for zero latency, more IOT, more automation of the supply chain, that sort of thing, I think the future ahead of us is very encouraging, I think it's going to do a lot for national defense, and certainly the security of the country. >> Steve, your final thoughts, space systems are systems, and they're connected to other systems that are connected to people, your thoughts on this digital transformation opportunity. >> Such a great question and such a fun, great challenge ahead of us. Echoing my colleagues sentiments, I would add to it, you know, a lot of this has, I think we should do some focusing on campaigning so that people can feel comfortable to include the Congress to do things a little bit differently. You know, we're not attuned to doing things fast, but the dramatic, you know, the way technology is just going like crazy right now, I think it ties back to, hoping to convince some of our senior leaders and what I call both sides of the Potomac river, that it's worth taking this gamble, we do need to take some of these things you know, in a very proactive way. And I'm very confident and excited and comfortable that this is going to be a great time ahead and all for the better. >> You know, I always think of myself when I talk about DC 'cause I'm not a lawyer and I'm not a political person, but I always say less lawyers, more techies than in Congress and Senate, so (laughter)I always get in trouble when I say that. Sorry, President Armstrong, go ahead. >> Yeah, no, just one other point and Steve's alluded to this and Bong did as well, I mean, we've got to be less risk averse in these partnerships, that doesn't mean reckless, but we have to be less risk averse. And also, as you talk about technology, I have to reflect on something that happened and you both talked a bit about Bill Britton and his impact on Cal Poly and what we're doing. But we were faced a few years ago of replacing traditional data, a data warehouse, data storage, data center and we partnered with AWS and thank goodness, we had that in progress and it enhanced our bandwidth on our campus before COVID hit, and with this partnership with the digital transformation hub, so there's a great example where we had that going. That's not something we could have started, "Oh COVID hit, let's flip that switch." And so we have to be proactive and we also have to not be risk-averse and do some things differently. That has really salvaged the experience for our students right now, as things are flowing well. We only have about 12% of our courses in person, those essential courses and I'm just grateful for those partnerships that I have talked about today. >> And it's a shining example of how being agile, continuous operations, these are themes that expand the space and the next workforce needs to be built. Gentlemen, thank you very much for sharing your insights, I know Bong, you're going to go into the defense side of space in your other sessions. Thank you gentlemen, for your time, for a great session, I appreciate it. >> Thank you. >> Thank you gentlemen. >> Thank you. >> Thank you. >> Thank you, thank you all. I'm John Furey with The Cube here in Palo Alto, California covering and hosting with Cal Poly, the Space and Cybersecurity Symposium 2020, thanks for watching. (bright atmospheric music)

>> Announcer: From theCUBE studios in Palo Alto, in Boston, connecting with thought leaders all around the world. This is a CUBE conversation. >> Hey, welcome back, you're ready. Jeff Frick here with theCUBE. We are still getting through the year of 2020. It's still the year of COVID and there's no end in sight I think until we get to a vaccine. That said, we're really excited to have one of our favorite guests. We haven't had him on for a while. I haven't talked to him for a long time. He used to I think have the record for the most CUBE appearances of probably any CUBE alumni. We're excited to have him joining us from his house in Palo Alto. Bill Schmarzo, you know him as the Dean of Big Data, he's got more titles. He's the chief innovation officer at Hitachi Vantara. He's also, we used to call him the Dean of Big Data, kind of for fun. Well, Bill goes out and writes a bunch of books. And now he teaches at the University of San Francisco, School of Management as an executive fellow. He's an honorary professor at NUI Galway. I think he's just, he likes to go that side of the pond and a many time author now, go check him out. His author profile on Amazon, the "Big Data MBA," "The Art of Thinking Like A Data Scientist" and another Big Data, kind of a workbook. Bill, great to see you. >> Thanks, Jeff, you know, I miss my time on theCUBE. These conversations have always been great. We've always kind of poked around the edges of things. A lot of our conversations have always been I thought, very leading edge and the title Dean of Big Data is courtesy of theCUBE. You guys were the first ones to give me that name out of one of the very first Strata Conferences where you dubbed me the Dean of Big Data, because I taught a class there called the Big Data MBA and look what's happened since then. >> I love it. >> It's all on you guys. >> I love it, and we've outlasted Strata, Strata doesn't exist as a conference anymore. So, you know, part of that I think is because Big Data is now everywhere, right? It's not the standalone thing. But there's a topic, and I'm holding in my hands a paper that you worked on with a colleague, Dr. Sidaoui, talking about what is the value of data? What is the economic value of data? And this is a topic that's been thrown around quite a bit. I think you list a total of 28 reference sources in this document. So it's a well researched piece of material, but it's a really challenging problem. So before we kind of get into the details, you know, from your position, having done this for a long time, and I don't know what you're doing today, you used to travel every single week to go out and visit customers and actually do implementations and really help people think these through. When you think about the value, the economic value, how did you start to kind of frame that to make sense and make it kind of a manageable problem to attack? >> So, Jeff, the research project was eyeopening for me. And one of the advantages of being a professor is, you have access to all these very smart, very motivated, very free research sources. And one of the problems that I've wrestled with as long as I've been in this industry is, how do you figure out what is data worth? And so what I did is I took these research students and I stick them on this problem. I said, "I want you to do some research. Let me understand what is the value of data?" I've seen all these different papers and analysts and consulting firms talk about it, but nobody's really got this thing clicked. And so we launched this research project at USF, professor Mouwafac Sidaoui and I together, and we were bumping along the same old path that everyone else got, which was inched on, how do we get data on our balance sheet? That was always the motivation, because as a company we're worth so much more because our data is so valuable, and how do I get it on the balance sheet? So we're headed down that path and trying to figure out how do you get it on the balance sheet? And then one of my research students, she comes up to me and she says, "Professor Schmarzo," she goes, "Data is kind of an unusual asset." I said, "Well, what do you mean?" She goes, "Well, you think about data as an asset. It never depletes, it never wears out. And the same dataset can be used across an unlimited number of use cases at a marginal cost equal to zero." And when she said that, it's like, "Holy crap." The light bulb went off. It's like, "Wait a second. I've been thinking about this entirely wrong for the last 30 some years of my life in this space. I've had the wrong frame. I keep thinking about this as an act, as an accounting conversation. An accounting determines valuation based on what somebody is willing to pay for." So if you go back to Adam Smith, 1776, "Wealth of Nations," he talks about valuation techniques. And one of the valuation techniques he talks about is valuation and exchange. That is the value of an asset is what someone's willing to pay you for it. So the value of this bottle of water is what someone's willing to pay you for it. So everybody fixates on this asset, valuation in exchange methodology. That's how you put it on balance sheet. That's how you run depreciation schedules, that dictates everything. But Adam Smith also talked about in that book, another valuation methodology, which is valuation in use, which is an economics conversation, not an accounting conversation. And when I realized that my frame was wrong, yeah, I had the right book. I had Adam Smith, I had "Wealth of Nations." I had all that good stuff, but I hadn't read the whole book. I had missed this whole concept about the economic value, where value is determined by not how much someone's willing to pay you for it, but the value you can drive by using it. So, Jeff, when that person made that comment, the entire research project, and I got to tell you, my entire life did a total 180, right? Just total of 180 degree change of how I was thinking about data as an asset. >> Right, well, Bill, it's funny though, that's kind of captured, I always think of kind of finance versus accounting, right? And then you're right on accounting. And we learn a lot of things in accounting. Basically we learn more that we don't know, but it's really hard to put it in an accounting framework, because as you said, it's not like a regular asset. You can use it a lot of times, you can use it across lots of use cases, it doesn't degradate over time. In fact, it used to be a liability. 'cause you had to buy all this hardware and software to maintain it. But if you look at the finance side, if you look at the pure play internet companies like Google, like Facebook, like Amazon, and you look at their valuation, right? We used to have this thing, we still have this thing called Goodwill, which was kind of this capture between what the market established the value of the company to be. But wasn't reflected when you summed up all the assets on the balance sheet and you had this leftover thing, you could just plug in goodwill. And I would hypothesize that for these big giant tech companies, the market has baked in the value of the data, has kind of put in that present value on that for a long period of time over multiple projects. And we see it captured probably in goodwill, versus being kind of called out as an individual balance sheet item. >> So I don't think it's, I don't know accounting. I'm not an accountant, thank God, right? And I know that goodwill is one of those things if I remember from my MBA program is something that when you buy a company and you look at the value you paid versus what it was worth, it stuck into this category called goodwill, because no one knew how to figure it out. So the company at book value was a billion dollars, but you paid five billion for it. Well, you're not an idiot, so that four billion extra you paid must be in goodwill and they'd stick it in goodwill. And I think there's actually a way that goodwill gets depreciated as well. So it could be that, but I'm totally away from the accounting framework. I think that's distracting, trying to work within the gap rules is more of an inhibitor. And we talk about the Googles of the world and the Facebooks of the world and the Netflix of the world and the Amazons and companies that are great at monetizing data. Well, they're great at monetizing it because they're not selling it, they're using it. Google is using their data to dominate search, right? Netflix is using it to be the leader in on-demand videos. And it's how they use all the data, how they use the insights about their customers, their products, and their operations to really drive new sources of value. So to me, it's this, when you start thinking about from an economics perspective, for example, why is the same car that I buy and an Uber driver buys, why is that car more valuable to an Uber driver than it is to me? Well, the bottom line is, Uber drivers are going to use that car to generate value, right? That $40,000, that car they bought is worth a lot more, because they're going to use that to generate value. For me it sits in the driveway and the birds poop on it. So, right, so it's this value in use concept. And when organizations can make that, by the way, most organizations really struggle with this. They struggle with this value in use concept. They want to, when you talk to them about data monetization and say, "Well, I'm thinking about the chief data officer, try not to trying to sell data, knocking on doors, shaking their tin cup, saying, 'Buy my data.'" No, no one wants your data. Your data is more valuable for how you use it to drive your operations then it's a sell to somebody else. >> Right, right. Well, on of the other things that's really important from an economics concept is scarcity, right? And a whole lot of economics is driven around scarcity. And how do you price for scarcity so that the market evens out and the price matches up to the supply? What's interesting about the data concept is, there is no scarcity anymore. And you know, you've outlined and everyone has giant numbers going up into the right, in terms of the quantity of the data and how much data there is and is going to be. But what you point out very eloquently in this paper is the scarcity is around the resources to actually do the work on the data to get the value out of the data. And I think there's just this interesting step function between just raw data, which has really no value in and of itself, right? Until you start to apply some concepts to it, you start to analyze it. And most importantly, that you have some context by which you're doing all this analysis to then drive that value. And I thought it was really an interesting part of this paper, which is get beyond the arguing that we're kind of discussing here and get into some specifics where you can measure value around a specific business objective. And not only that, but then now the investment of the resources on top of the data to be able to extract the value to then drive your business process for it. So it's a really different way to think about scarcity, not on the data per se, but on the ability to do something with it. >> You're spot on, Jeff, because organizations don't fail because of a lack of use cases. They fail because they have too many. So how do you prioritize? Now that scarcity is not an issue on the data side, but it is this issue on the people resources side, you don't have unlimited data scientists, right? So how do you prioritize and focus on those opportunities that are most important? I'll tell you, that's not a data science conversation, that's a business conversation, right? And figuring out how you align organizations to identify and focus on those use cases that are most important. Like in the paper we go through several different use cases using Chipotle as an example. The reason why I picked Chipotle is because, well, I like Chipotle. So I could go there and I could write it off as research. But there's a, think about the number of use cases where a company like Chipotle or any other company can leverage your data to drive their key business initiatives and their key operational use cases. It's almost unbounded, which by the way, is a huge challenge. In fact, I think part of the problem we see with a lot of organizations is because they do such a poor job of prioritizing and focusing, they try to solve the entire problem with one big fell swoop, right? It's slightly the old ERP big bang projects. Well, I'm just going to spend $20 million to buy this analytic capability from company X and I'm going to install it and then magic is going to happen. And then magic is going to happen, right? And then magic is going to happen, right? And magic never happens. We get crickets instead, because the biggest challenge isn't around how do I leverage the data, it's about where do I start? What problems do I go after? And how do I make sure the organization is bought in to basically use case by use case, build out your data and analytics architecture and capabilities. >> Yeah, and you start backwards from really specific business objectives in the use cases that you outline here, right? I want to increase my average ticket by X. I want to increase my frequency of visits by X. I want to increase the amount of items per order from X to 1.2 X, or 1.3 X. So from there you get a nice kind of big revenue hit that you can plan around and then work backwards into the amount of effort that it takes and then you can come up, "Is this a good investment or not?" So it's a really different way to get back to the value of the data. And more importantly, the analytics and the work to actually call out the information. >> The technologies, the data and analytic technologies available to us. The very composable nature of these allow us to take this use case by use case approach. I can build out my data lake one use case at a time. I don't need to stuff 25 data sources into my data lake and hope there's someone more valuable. I can use the first use case to say, "Oh, I need these three data sources to solve that use case. I'm going to put those three data sources in the data lake. I'm going to go through the entire curation process of making sure the data has been transformed and cleansed and aligned and enriched and met of, all the other governance, all that kind of stuff this goes on. But I'm going to do that use case by use case, 'cause a use case can tell me which data sources are most important for that given situation. And I can build up my data lake and I can build up my analytics then one use case at a time. And there is a huge impact then, huge impact when I build out use case by use case. That does not happen. Let me throw something that's not really covered in the paper, but it is very much covered in my new book that I'm working on, which is, in knowledge-based industries, the economies of learning are more powerful than the economies of scale. Now think about that for a second. >> Say that again, say that again. >> Yeah, the economies of learning are more powerful than the economies of scale. And what that means is what I learned on the first use case that I build out, I can apply that learning to the second use case, to the third use case, to the fourth use case. So when I put my data into my data lake for my first use case, and the paper covers this, well, once it's in my data lake, the cost of reusing that data in a second, third and fourth use cases is basically, you know marginal cost is zero. So I get this ability to learn about what data sets are most important and to reapply that across the organization. So this learning concept, I learn use case by use case, I don't have to do a big economies of scale approach and start with 25 datasets of which only three or four might be useful. But I'm incurring the overhead for all those other non-important data sets because I didn't take the time to go through and figure out what are my most important use cases and what data do I need to support those use cases. >> I mean, should people even think of the data per se or should they really readjust their thinking around the application of the data? Because the data in and of itself means nothing, right? 55, is that fast or slow? Is that old or young? Well, it depends on a whole lot of things. Am I walking or am I in a brand new Corvette? So it just, it's funny to me that the data in and of itself really doesn't have any value and doesn't really provide any direction into a decision or a higher order, predictive analytics until you start to manipulate the data. So is it even the wrong discussion? Is data the right discussion? Or should we really be talking about the capabilities to do stuff within and really get people focused on that? >> So Jeff, there's so many points to hit on there. So the application of data is what's the value, and the queue of you guys used to be famous for saying, "Separating noise from the signal." >> Signal from the noise. Signal from a noise, right. Well, how do you know in your dataset what's signal and what's noise? Well, the use case will tell you. If you don't know the use case and you have no way of figuring out what's important. One of the things I use, I still rail against, and it happens still. Somebody will walk up my data science team and say, "Here's some data, tell me what's interesting in it." Well, how do you separate signal from noise if I don't know the use case? So I think you're spot on, Jeff. The way to think about this is, don't become data-driven, become value-driven and value is driven from the use case or the application or the use of the data to solve that particular use case. So organizations that get fixated on being data-driven, I hate the term data-driven. It's like as if there's some sort of frigging magic from having data. No, data has no value. It's how you use it to derive customer product and operational insights that drive value,. >> Right, so there's an interesting step function, and we talk about it all the time. You're out in the weeds, working with Chipotle lately, and increase their average ticket by 1.2 X. We talk more here, kind of conceptually. And one of the great kind of conceptual holy grails within a data-driven economy is kind of working up this step function. And you've talked about it here. It's from descriptive, to diagnostic, to predictive. And then the Holy grail prescriptive, we're way ahead of the curve. This comes into tons of stuff around unscheduled maintenance. And you know, there's a lot of specific applications, but do you think we spend too much time kind of shooting for the fourth order of greatness impact, instead of kind of focusing on the small wins? >> Well, you certainly have to build your way there. I don't think you can get to prescriptive without doing predictive, and you can't do predictive without doing descriptive and such. But let me throw a really one at you, Jeff, I think there's even one beyond prescriptive. One we're talking more and more about, autonomous, a ton of analytics, right? And one of the things that paper talked about that didn't click with me at the time was this idea of orphaned analytics. You and I kind of talked about this before the call here. And one thing we noticed in the research was that a lot of these very mature organizations who had advanced from the retrospective analytics of BI to the descriptive, to the predicted, to the prescriptive, they were building one off analytics to solve a problem and getting value from it, but never reusing this analytics over and over again. They were done one off and then they were thrown away and these organizations were so good at data science and analytics, that it was easier for them to just build from scratch than to try to dig around and try to find something that was never actually ever built to be reused. And so I have this whole idea of orphaned analytics, right? It didn't really occur to me. It didn't make any sense into me until I read this quote from Elon Musk, and Elon Musk made this statement. He says, " I believe that when you buy a Tesla, you're buying an asset that appreciates in value, not depreciates through usage." I was thinking, "Wait a second, what does that mean?" He didn't actually say it, "Through usage." He said, "He believes you're buying an asset that appreciates not depreciates in value." And of course the first response I had was, "Oh, it's like a 1964 and a half Mustang. It's rare, so everybody is going to want these things. So buy one, stick it in your garage. And 20 years later, you're bringing it out and it's worth more money." No, no, there's 600,000 of these things roaming around the streets, they're not rare. What he meant is that he is building an autonomous asset. That the more that it's used, the more valuable it's getting, the more reliable, the more efficient, the more predictive, the more safe this asset's getting. So there is this level beyond prescriptive where we can think about, "How do we leverage artificial intelligence, reinforcement, learning, deep learning, to build these assets that the more that they are used, the smarter they get." That's beyond prescriptive. That's an environment where these things are learning. In many cases, they're learning with minimal or no human intervention. That's the real aha moment. That's what I miss with orphaned analytics and why it's important to build analytics that can be reused over and over again. Because every time you use these analytics in a different use case, they get smarter, they get more valuable, they get more predictive. To me that's the aha moment that blew my mind. I realized I had missed that in the paper entirely. And it took me basically two years later to realize, dough, I missed the most important part of the paper. >> Right, well, it's an interesting take really on why the valuation I would argue is reflected in Tesla, which is a function of the data. And there's a phenomenal video if you've never seen it, where they have autonomous vehicle day, it might be a year or so old. And he's got his number one engineer from, I think the Microprocessor Group, The Computer Vision Group, as well as the autonomous driving group. And there's a couple of really great concepts I want to follow up on what you said. One is that they have this thing called The Fleet. To your point, there's hundreds of thousands of these things, if they haven't hit a million, that are calling home reporting home every day as to exactly how everyone took the Northbound 101 on-ramp off of University Avenue. How fast did they go? What line did they take? What G-forces did they take? And every one of those cars feeds into the system, so that when they do the autonomous update, not only are they using all their regular things that they would use to map out that 101 Northbound entry, but they've got all the data from all the cars that have been doing it. And you know, when that other car, the autonomous car couple years ago hit the pedestrian, I think in Phoenix, which is not good, sad, killed a person, dark tough situation. But you know, we are doing an autonomous vehicle show and the guy who made a really interesting point, right? That when something like that happens, typically if I was in a car wreck or you're in a car wreck, hopefully not, I learned the person that we hit learns and maybe a couple of witnesses learn, maybe the inspector. >> But nobody else learns. >> But nobody else learns. But now with the autonomy, every single person can learn from every single experience with every vehicle contributing data within that fleet. To your point, it's just an order of magnitude, different way to think about things. >> Think about a 1% improvement compounded 365 times, equals I think 38 X improvement. The power of 1% improvements over these 600,000 plus cars that are learning. By the way, even when the autonomous FSD, the full self-driving mode module isn't turned on, even when it's not turned on, it runs in shadow mode. So it's learning from the human drivers, the human overlords, it's constantly learning. And by the way, not only they're collecting all this data, I did a little research, I pulled out some of their job search ads and they've built a giant simulator, right? And they're there basically every night, simulating billions and billions of more driven miles because of the simulator. They are building, he's going to have a simulator, not only for driving, but think about all the data he's capturing as these cars are riding down the road. By the way, they don't use Lidar, they use video, right? So he's driving by malls. He knows how many cars are in the mall. He's driving down roads, he knows how old the cars are and which ones should be replaced. I mean, he has this, he's sitting on this incredible wealth of data. If anybody could simulate what's going on in the world and figure out how to get out of this COVID problem, it's probably Elon Musk and the data he's captured, be courtesy of all those cars. >> Yeah, yeah, it's really interesting, and we're seeing it now. There's a new autonomous drone out, the Skydio, and they just announced their commercial product. And again, it completely changes the way you think about how you use that tool, because you've just eliminated the complexity of driving. I don't want to drive that, I want to tell it what to do. And so you're saying, this whole application of air force and companies around things like measuring piles of coal and measuring these huge assets that are volume metric measured, that these things can go and map out and farming, et cetera, et cetera. So the autonomy piece, that's really insightful. I want to shift gears a little bit, Bill, and talk about, you had some theories in here about thinking of data as an asset, data as a currency, data as monetization. I mean, how should people think of it? 'Cause I don't think currency is very good. It's really not kind of an exchange of value that we're doing this kind of classic asset. I think the data as oil is horrible, right? To your point, it doesn't get burned up once and can't be used again. It can be used over and over and over. It's basically like feedstock for all kinds of stuff, but the feedstock never goes away. So again, or is it that even the right way to think about, do we really need to shift our conversation and get past the idea of data and get much more into the idea of information and actionable information and useful information that, oh, by the way, happens to be powered by data under the covers? >> Yeah, good question, Jeff. Data is an asset in the same way that a human is an asset. But just having humans in your company doesn't drive value, it's how you use those humans. And so it's really again the application of the data around the use cases. So I still think data is an asset, but I don't want to, I'm not fixated on, put it on my balance sheet. That nice talk about put it on a balance sheet, I immediately put the blinders on. It inhibits what I can do. I want to think about this as an asset that I can use to drive value, value to my customers. So I'm trying to learn more about my customer's tendencies and propensities and interests and passions, and try to learn the same thing about my car's behaviors and tendencies and my operations have tendencies. And so I do think data is an asset, but it's a latent asset in the sense that it has potential value, but it actually has no value per se, inputting it into a balance sheet. So I think it's an asset. I worry about the accounting concept medially hijacking what we can do with it. To me the value of data becomes and how it interacts with, maybe with other assets. So maybe data itself is not so much an asset as it's fuel for driving the value of assets. So, you know, it fuels my use cases. It fuels my ability to retain and get more out of my customers. It fuels ability to predict what my products are going to break down and even have products who self-monitor, self-diagnosis and self-heal. So, data is an asset, but it's only a latent asset in the sense that it sits there and it doesn't have any value until you actually put something to it and shock it into action. >> So let's shift gears a little bit and start talking about the data and talk about the human factors. 'Cause you said, one of the challenges is people trying to bite off more than they can chew. And we have the role of chief data officer now. And to your point, maybe that mucks things up more than it helps. But in all the customer cases that you've worked on, is there a consistent kind of pattern of behavior, personality, types of projects that enables some people to grab those resources to apply to their data to have successful projects, because to your point there's too much data and there's too many projects and you talk a lot about prioritization. But there's a lot of assumptions in the prioritization model that you can, that you know a whole lot of things, especially if you're comparing project A over in group A with project B, with group B and the two may not really know the economics across that. But from an individual person who sees the potential, what advice do you give them? What kind of characteristics do you see, either in the type of the project, the type of the boss, the type of the individual that really lends itself to a higher probability of a successful outcome? >> So first off you need to find somebody who has a vision for how they want to use the data, and not just collect it. But how they're going to try to change the fortunes of the organization. So it always takes a visionary, may not be the CEO, might be somebody who's a head of marketing or the head of logistics, or it could be a CIO, it could be a chief data officer as well. But you've got to find somebody who says, "We have this latent asset we could be doing more with, and we have a series of organizational problem challenges against which I could apply this asset. And I need to be the matchmaker that brings these together." Now the tool that I think is the most powerful tool in marrying the latent capabilities of data with all the revenue generating opportunities in the application side, because there's a countless number, the most important tool that I found doing that is design thinking. Now, the reason why I think design thinking is so important, because one of the things that design thinking does a great job is it gives everybody a voice in the process of identifying, validating, valuing, and prioritizing use cases you're going to go after. Let me say that again. The challenge organizations have is identifying, validating, valuing, and prioritizing the use cases they want to go after. Design thinking is a marvelous tool for driving organizational alignment around where we're going to start and what's going to be next and why we're going to start there and how we're going to bring everybody together. Big data and data science projects don't die because of technology failure. Most of them die because of passive aggressive behaviors in the organization that you didn't bring everybody into the process. Everybody's voice didn't get a chance to be heard. And that one person who's voice didn't get a chance to get heard, they're going to get you. They may own a certain piece of data. They may own something, but they're just waiting and lay, they're just laying there waiting for their chance to come up and snag it. So what you got to do is you got to proactively bring these people together. We call this, this is part of our value engineering process. We have a value engineering process around envisioning where we bring all these people together. We help them to understand how data in itself is a latent asset, but how it can be used from an economics perspective, drive all those value. We get them all fired up on how these can solve any one of these use cases. But you got to start with one, and you've got to embrace this idea that I can build out my data and analytic capabilities, one use case at a time. And the first use case I go after and solve, makes my second one easier, makes my third one easier, right? It has this ability that when you start going use case by use case two really magical things happen. Number one, your marginal cost flatten. That is because you're building out your data lake one use case at a time, and you're bringing all the important data lake, that data lake one use case at a time. At some point in time, you've got most of the important data you need, and the ability that you don't need to add another data source. You got what you need, so your marginal costs start to flatten. And by the way, if you build your analytics as composable, reusable, continuous learning analytic assets, not as orphaned analytics, pretty soon you have all the analytics you need as well. So your marginal cost flatten, but effect number two is that you've, because you've have the data and the analytics, I can accelerate time to value, and I can de-risked projects as I go use case by use case. And so then the biggest challenge becomes not in the data and the analytics, it's getting the all the business stakeholders to agree on, here's a roadmap we're going to go after. This one's first, and this one is going first because it helps to drive the value of the second and third one. And then this one drives this, and you create a whole roadmap of rippling through of how the data and analytics are driving this value to across all these use cases at a marginal cost approaching zero. >> So should we have chief design thinking officers instead of chief data officers that really actually move the data process along? I mean, I first heard about design thinking years ago, actually interviewing Dan Gordon from Gordon Biersch, and they were, he had just hired a couple of Stanford grads, I think is where they pioneered it, and they were doing some work about introducing, I think it was a a new apple-based alcoholic beverage, apple cider, and they talked a lot about it. And it's pretty interesting, but I mean, are you seeing design thinking proliferate into the organizations that you work with? Either formally as design thinking or as some derivation of it that pulls some of those attributes that you highlighted that are so key to success? >> So I think we're seeing the birth of this new role that's marrying capabilities of design thinking with the capabilities of data and analytics. And they're calling this dude or dudette the chief innovation officer. Surprise. >> Title for someone we know. >> And I got to tell a little story. So I have a very experienced design thinker on my team. All of our data science projects have a design thinker on them. Every one of our data science projects has a design thinker, because the nature of how you build and successfully execute a data science project, models almost exactly how design thinking works. I've written several papers on it, and it's a marvelous way. Design thinking and data science are different sides of the same coin. But my respect for data science or for design thinking took a major shot in the arm, major boost when my design thinking person on my team, whose name is John Morley introduced me to a senior data scientist at Google. And I was bottom coffee. I said, "No," this is back in, before I even joined Hitachi Vantara, and I said, "So tell me the secret to Google's data science success? You guys are marvelous, you're doing things that no one else was even contemplating, and what's your key to success?" And he giggles and laughs and he goes, "Design thinking." I go, "What the hell is that? Design thinking, I've never even heard of the stupid thing before." He goes, "I'd make a deal with you, Friday afternoon let's pop over to Stanford's B school and I'll teach you about design thinking." So I went with him on a Friday to the d.school, Design School over at Stanford and I was blown away, not just in how design thinking was used to ideate and bring and to explore. But I was blown away about how powerful that concept is when you marry it with data science. What is data science in its simplest sense? Data science is about identifying the variables and metrics that might be better predictors of performance. It's that might phrase that's the real key. And who are the people who have the best insights into what values or metrics or KPIs you might want to test? It ain't the data scientists, it's the subject matter experts on the business side. And when you use design thinking to bring this subject matter experts with the data scientists together, all kinds of magic stuff happens. It's unbelievable how well it works. And all of our projects leverage design thinking. Our whole value engineering process is built around marrying design thinking with data science, around this prioritization, around these concepts of, all ideas are worthy of consideration and all voices need to be heard. And the idea how you embrace ambiguity and diversity of perspectives to drive innovation, it's marvelous. But I feel like I'm a lone voice out in the wilderness, crying out, "Yeah, Tesla gets it, Google gets it, Apple gets it, Facebook gets it." But you know, most other organizations in the world, they don't think like that. They think design thinking is this Wufoo thing. Oh yeah, you're going to bring people together and sing Kumbaya. It's like, "No, I'm not singing Kumbaya. I'm picking their brains because they're going to help make their data science team much more effective and knowing what problems we're going to go after and how I'm going to measure success and progress. >> Maybe that's the next Dean for the next 10 years, the Dean of design thinking instead of data science, and who knew they're one and the same? Well, Bill, that's a super insightful, I mean, it's so, is validated and supported by the trends that we see all over the place, just in terms of democratization, right? Democratization of the tools, more people having access to data, more opinions, more perspective, more people that have the ability to manipulate the data and basically experiment, does drive better business outcomes. And it's so consistent. >> If I could add one thing, Jeff, I think that what's really powerful about design thinking is when I think about what's happening with artificial intelligence or AI, there's all these conversations about, "Oh, AI is going to wipe out all these jobs. Is going to take all these jobs away." And what we're actually finding is that if we think about machine learning, driven by AI and human empowerment, driven by design thinking, we're seeing the opportunity to exploit these economies of learning at the front lines where every customer engagement, every operational execution is an opportunity to gather not only more data, but to gather more learnings, to empower the humans at the front lines of the organization to constantly be seeking, to try different things, to explore and to learn from each of these engagements. I think it's, AI to me is incredibly powerful. And I think about it as a source of driving more learning, a continuous learning and continuously adapting an organization where it's not just the machines that are doing this, but it's the humans who've been empowered to do that. And my chapter nine in my new book, Jeff, is all about team empowerment, because nothing you do with AI is going to matter of squat if you don't have empowered teams who know how to take and leverage that continuous learning opportunity at the front lines of customer and operational engagement. >> Bill, I couldn't set a better, I think we'll leave it there. That's a great close, when is the next book coming out? >> So today I do my second to last final review. Then it goes back to the editor and he does a review and we start looking at formatting. So I think we're probably four to six weeks out. >> Okay, well, thank you so much, congratulations on all the success. I just love how the Dean is really the Dean now, teaching all over the world, sharing the knowledge and attacking some of these big problems. And like all great economics problems, often the answer is not economics at all. It's completely really twist the lens and don't think of it in that, all that construct. >> Exactly. >> All right, Bill. Thanks again and have a great week. >> Thanks, Jeff. >> All right. He's Bill Schmarzo, I'm Jeff Frick. You're watching theCUBE. Thanks for watching, we'll see you next time. (gentle music)

>> Narrator: From around the globe it's theCUBE, covering HPE Discover Virtual Experience brought to you by HPE. >> Hi everybody. Welcome back. This is Dave Vellante for theCUBE, and this is our coverage of discover 2020, the virtual experience of HPE discover. We've done many, many discoveries, as usually we're on the show floor, theCUBE has been virtualized and we talk a lot at HPE discovers, a lot of storage and server and infrastructure and networking which is great. But the conversation we're going to have now is really, we're going to be talking about helping the world solve some big problems. And I'm very excited to welcome back to theCUBE Dr. Eng Lim Goh. He's a senior vice president of and CTO for AI, at HPE. Hello, Dr. Goh. Great to see you again. >> Hello. Thank you for having us, Dave. >> You're welcome. And then our next guest is Professor Joachim Schultze, who is the Professor for Genomics, and Immunoregulation at the university of Bonn amongst other things Professor, welcome. >> Thank you all. Welcome. >> And then Prasad Shastry, is the Chief Technologist for the India Advanced Development Center at HPE. Welcome, Prasad. Great to see you. >> Thank you. Thanks for having me. >> So guys, we have a CUBE first. I don't believe we've ever had of three guests in three separate times zones. I'm in a fourth time zone. (guests chuckling) So I'm in Boston. Dr. Goh, you're in Singapore, Professor Schultze, you're in Germany and Prasad, you're in India. So, we've got four different time zones. Plus our studio in Palo Alto. Who's running this program. So we've got actually got five times zones, a CUBE first. >> Amazing. >> Very good. (Prasad chuckles) >> Such as the world we live in. So we're going to talk about some of the big problems. I mean, here's the thing we're obviously in the middle of this pandemic, we're thinking about the post isolation economy, et cetera. People compare obviously no surprise to the Spanish flu early part of last century. They talk about the great depression, but the big difference this time is technology. Technology has completely changed the way in which we've approached this pandemic. And we're going to talk about that. Dr. Goh, I want to start with you. You've done a lot of work on this topic of swarm learning. If we could, (mumbles) my limited knowledge of this is we're kind of borrowing from nature. You think about, bees looking for a hive as sort of independent agents, but somehow they come together and communicate, but tell us what do we need to know about swarm learning and how it relates to artificial intelligence and we'll get into it. >> Oh, Dave, that's a great analogy using swarm of bees. That's exactly what we do at HPE. So let's use the of here. When deploying artificial intelligence, a hospital does machine learning of the outpatient data that could be biased, due to demographics and the types of cases they see more also. Sharing patient data across different hospitals to remove this bias is limited, given privacy or even sovereignty the restrictions, right? Like for example, across countries in the EU. HPE, so I'm learning fixers this by allowing each hospital, let's still continue learning locally, but at each cycle we collect the lumped weights of the neural networks, average them and sending it back down to older hospitals. And after a few cycles of doing this, all the hospitals would have learned from each other, removing biases without having to share any private patient data. That's the key. So, the ability to allow you to learn from everybody without having to share your private patients. That's swarm learning, >> And part of the key to that privacy is blockchain, correct? I mean, you you've been too involved in blockchain and invented some things in blockchain and that's part of the privacy angle, is it not? >> Yes, yes, absolutely. There are different ways of doing this kind of distributed learning, which swarm learning is over many of the other distributed learning methods. Require you to have some central control. Right? So, Prasad, and the team and us came up together. We have a method where you would, instead of central control, use blockchain to do this coordination. So, there is no more a central control or coordinator, especially important if you want to have a truly distributed swamp type learning system. >> Yeah, no need for so-called trusted third party or adjudicator. Okay. Professor Schultze, let's go to you. You're essentially the use case of this swarm learning application. Tell us a little bit more about what you do and how you're applying this concept. >> I'm actually by training a physician, although I haven't seen patients for a very long time. I'm interested in bringing new technologies to what we call precision medicine. So, new technologies both from the laboratories, but also from computational sciences, married them. And then I basically allow precision medicine, which is a medicine that is built on new measurements, many measurements of molecular phenotypes, how we call them. So, basically that process on different levels, for example, the genome or genes that are transcribed from the genome. We have thousands of such data and we have to make sense out of this. This can only be done by computation. And as we discussed already one of the hope for the future is that the new wave of developments in artificial intelligence and machine learning. We can make more sense out of this huge data that we generate right now in medicine. And that's what we're interesting in to find out how can we leverage these new technologies to build a new diagnostics, new therapy outcome predictors. So, to know the patient benefits from a disease, from a diagnostics or a therapy or not, and that's what we are doing for the last 10 years. The most exciting thing I have been  through in the last three, four, five years is really when HPE introduced us to swarm learning. >> Okay and Prasad, you've been helping Professor Schultze, actually implements swarm learning for specific use cases that we're going to talk about COVID, but maybe describe a little bit about what you've been or your participation in this whole equation. >> Yep, thank. As Dr Eng Lim Goh, mentioned. So, we have used blockchain as a backbone to implement the decentralized network. And through that we're enabling a privacy preserved these centralized network without having any control points, as Professor explained in terms of depression medicines. So, one of the use case we are looking at he's looking at the blood transcriptomes, think of it, different hospitals having a different set of transcriptome data, which they cannot share due to the privacy regulations. And now each of those hospitals, will clean the model depending upon their local data, which is available in that hospital. And shared the learnings coming out of that training with the other hospitals. And we played to over several cycles to merge all these learnings and then finally get into a global model. So, through that we are able to kind of get into a model which provides the performance is equal of collecting all the data into a central repository and trying to do it. And we could really think of when we are doing it, them, could be multiple kinds of challenges. So, it's good to do decentralized learning. But what about if you have a non ID type of data, what about if there is a dropout in the network connections? What about if there are some of the compute nodes we just practice or probably they're not seeing sufficient amount of data. So, that's something we tried to build into the swarm learning framework. You'll handle the scenarios of having non ID data. All in a simple word we could call it as seeing having the biases. An example, one of the hospital might see EPR trying to, look at, in terms of let's say the tumors, how many number of cases and whereas the other hospital might have very less number of cases. So, if you have kind of implemented some techniques in terms of doing the merging or providing the way that different kind of weights or the tuneable parameters to overcome these set of challenges in the swarm learning. >> And Professor Schultze, you you've applied this to really try to better understand and attack the COVID pandemic, can you describe in more detail your goals there and what you've actually done and accomplished? >> Yeah. So, we have actually really done it for COVID. The reason why we really were trying to do this already now is that we have to generate it to these transcriptomes from COVID-19 patients ourselves. And we realized that the scene of the disease is so strong and so unique compared to other infectious diseases, which we looked at in some detail that we felt that the blood transcriptome would be good starting point actually to identify patients. But maybe even more important to identify those with severe diseases. So, if you can identify them early enough that'd be basically could care for those more and find particular for those treatments and therapies. And the reason why we could do that is because we also had some other test cases done before. So, we used the time wisely with large data sets that we had collected beforehand. So, use cases learned how to apply swarm learning, and we are now basically ready to test directly with COVID-19. So, this is really a step wise process, although it was extremely fast, it was still a step wise probably we're guided by data where we had much more knowledge of which was with the black leukemia. So, we had worked on that for years. We had collected many data. So, we could really simulate a Swarm learning very nicely. And based on all the experience we get and gain together with Prasad, and his team, we could quickly then also apply that knowledge to the data that are coming now from COVID-19 patients. >> So, Dr. Goh, it really comes back to how we apply machine intelligence to the data, and this is such an interesting use case. I mean, the United States, we have 50 different States with 50 different policies, different counties. We certainly have differences around the world in terms of how people are approaching this pandemic. And so the data is very rich and varied. Let's talk about that dynamic. >> Yeah. If you, for the listeners who are or viewers who are new to this, right? The workflow could be a patient comes in, you take the blood, and you send it through an analysis? DNA is made up of genes and our genes express, right? They express in two steps the first they transcribe, then they translate. But what we are analyzing is the middle step, the transcription stage. And tens of thousands of these Transcripts that are produced after the analysis of the blood. The thing is, can we find in the tens of thousands of items, right? Or biomarkers a signature that tells us, this is COVID-19 and how serious it is for this patient, right? Now, the data is enormous, right? For every patient. And then you have a collection of patients in each hospitals that have a certain demographic. And then you have also a number of hospitals around. The point is how'd you get to share all that data in order to have good training of your machine? The ACO is of course a know privacy of data, right? And as such, how do you then share that information if privacy restricts you from sharing the data? So in this case, swarm learning only shares the learnings, not the private patient data. So we hope this approach would allow all the different hospitals to come together and unite sharing the learnings removing biases so that we have high accuracy in our prediction as well at the same time, maintaining privacy. >> It's really well explained. And I would like to add at least for the European union, that this is extremely important because the lawmakers have clearly stated, and the governments that even non of these crisis conditions, they will not minimize the rules of privacy laws, their compliance to privacy laws has to stay as high as outside of the pandemic. And I think there's good reasons for that, because if you lower the bond, now, why shouldn't you lower the bar in other times as well? And I think that was a wise decision, yes. If you would see in the medical field, how difficult it is to discuss, how do we share the data fast enough? I think swarm learning is really an amazing solution to that. Yeah, because this discussion is gone basically. Now we can discuss about how we do learning together. I'd rather than discussing what would be a lengthy procedure to go towards sharing. Which is very difficult under the current privacy laws. So, I think that's why I was so excited when I learned about it, the first place with faster, we can do things that otherwise are either not possible or would take forever. And for a crisis that's key. That's absolutely key. >> And is the byproduct. It's also the fact that all the data stay where they are at the different hospitals with no movement. >> Yeah. Yeah. >> Learn locally but only shared the learnings. >> Right. Very important in the EU of course, even in the United States, People are debating. What about contact tracing and using technology and cell phones, and smartphones to do that. Beside, I don't know what the situation is like in India, but nonetheless, that Dr. Goh's point about just sharing the learnings, bubbling it up, trickling just kind of metadata. If you will, back down, protects us. But at the same time, it allows us to iterate and improve the models. And so, that's a key part of this, the starting point and the conclusions that we draw from the models they're going to, and we've seen this with the pandemic, it changes daily, certainly weekly, but even daily. We continuously improve the conclusions and the models don't we. >> Absolutely, as Dr. Goh explained well. So, we could look at like they have the clinics or the testing centers, which are done in the remote places or wherever. So, we could collect those data at the time. And then if we could run it to the transcripting kind of a sequencing. And then as in, when we learn to these new samples and the new pieces all of them put kind of, how is that in the local data participate in the kind of use swarm learning, not just within the state or in a country could participate into an swarm learning globally to share all this data, which is coming up in a new way, and then also implement some kind of continuous learning to pick up the new signals or the new insight. It comes a bit new set of data and also help to immediately deploy it back into the inference or into the practice of identification. To do these, I think one of the key things which we have realized is to making it very simple. It's making it simple, to convert the machine learning models into the swarm learning, because we know that our subject matter experts who are going to develop these models on their choice of platforms and also making it simple to integrate into that complete machine learning workflow from the time of collecting a data pre processing and then doing the model training and then putting it onto inferencing and looking performance. So, we have kept that in the mind from the beginning while developing it. So, we kind of developed it as a plug able microservices kind of packed data with containers. So the whole library could be given it as a container with a kind of a decentralized management command controls, which would help to manage the whole swarm network and to start and initiate and children enrollment of new hospitals or the new nodes into the swarm network. At the same time, we also looked into the task of the data scientists and then try to make it very, very easy for them to take their existing models and convert that into the swarm learning frameworks so that they can convert or enabled they're models to participate in a decentralized learning. So, we have made it to a set callable rest APIs. And I could say that the example, which we are working with the Professor either in the case of leukemia or in the COVID kind of things. The noodle network model. So we're kind of using the 10 layer neural network things. We could convert that into the swarm model with less than 10 lines of code changes. So, that's kind of a simply three we are looking at so that it helps to make it quicker, faster and loaded the benefits. >> So, that's an exciting thing here Dr. Goh is, this is not an R and D project. This is something that you're actually, implementing in a real world, even though it's a narrow example, but there are so many other examples that I'd love to talk about, but please, you had a comment. >> Yes. The key thing here is that in addition to allowing privacy to be kept at each hospital, you also have the issue of different hospitals having day to day skewed differently. Right? For example, a demographics could be that this hospital is seeing a lot more younger patients, and other hospitals seeing a lot more older patients. Right? And then if you are doing machine learning in isolation then your machine might be better at recognizing the condition in the younger population, but not older and vice versa by using this approach of swarm learning, we then have the biases removed so that both hospitals can detect for younger and older population. All right. So, this is an important point, right? The ability to remove biases here. And you can see biases in the different hospitals because of the type of cases they see and the demographics. Now, the other point that's very important to reemphasize is what precise Professor Schultze mentioned, right? It's how we made it very easy to implement this.Right? This started out being so, for example, each hospital has their own neural network and they training their own. All you do is we come in, as Pasad mentioned, change a few lines of code in the original, machine learning model. And now you're part of the collective swarm. This is how we want to easy to implement so that we can get again, as I like to call, hospitals of the world to uniting. >> Yeah. >> Without sharing private patient data. So, let's double click on that Professor. So, tell us about sort of your team, how you're taking advantage of this Dr. Goh, just describe, sort of the simplicity, but what are the skills that you need to take advantage of this? What's your team look like? >> Yeah. So, we actually have a team that's comes from physicians to biologists, from medical experts up to computational scientists. So, we have early on invested in having these interdisciplinary research teams so that we can actually spend the whole spectrum. So, people know about the medicine they know about them the biological basics, but they also know how to implement such new technology. So, they are probably a little bit spearheading that, but this is the way to go in the future. And I see that with many institutions going this way many other groups are going into this direction because finally medicine understands that without computational sciences, without artificial intelligence and machine learning, we will not answer those questions with this large data that we're using. So, I'm here fine. But I also realize that when we entered this project, we had basically our model, we had our machine learning model from the leukemia's, and it really took almost no efforts to get this into the swarm. So, we were really ready to go in very short time, but I also would like to say, and then it goes towards the bias that is existing in medicine between different places. Dr. Goh said this very nicely. It's one aspect is the patient and so on, but also the techniques, how we do clinical essays, we're using different robots a bit. Using different automates to do the analysis. And we actually try to find out what the Swan learning is doing if we actually provide such a bias by prep itself. So, I did the following thing. We know that there's different ways of measuring these transcriptomes. And we actually simulated that two hospitals had an older technology and a third hospital had a much newer technology, which is good for understanding the biology and the diseases. But it is the new technology is prone for not being able anymore to generate data that can be used to learn and then predicting the old technology. So, there was basically, it's deteriorating, if you do take the new one and you'll make a classifier model and you try old data, it doesn't work anymore. So, that's a very hard challenge. We knew it didn't work anymore in the old way. So, we've pushed it into swarm learning and to swarm recognize that, and it didn't take care of it. It didn't care anymore because the results were even better by bringing everything together. I was astonished. I mean, it's absolutely amazing. That's although we knew about this limitations on that one hospital data, this form basically could deal with it. I think there's more to learn about these advantages. Yeah. And I'm very excited. It's not only a transcriptome that people do. I hope we can very soon do it with imaging or the DCNE has 10 sites in Germany connected to 10 university hospitals. There's a lot of imaging data, CT scans and MRIs, Rachel Grimes. And this is the next next domain in medicine that we would like to apply as well as running. Absolutely. >> Well, it's very exciting being able to bring this to the clinical world And make it in sort of an ongoing learnings. I mean, you think about, again, coming back to the pandemic, initially, we thought putting people on ventilators was the right thing to do. We learned, okay. Maybe, maybe not so much the efficacy of vaccines and other therapeutics. It's going to be really interesting to see how those play out. My understanding is that the vaccines coming out of China, or built to for speed, get to market fast, be interested in U.S. Maybe, try to build vaccines that are maybe more longterm effective. Let's see if that actually occurs some of those other biases and tests that we can do. That is a very exciting, continuous use case. Isn't it? >> Yeah, I think so. Go ahead. >> Yes. I, in fact, we have another project ongoing to use a transcriptome data and other data like metabolic and cytokines that data, all these biomarkers from the blood, right? Volunteers during a clinical trial. But the whole idea of looking at all those biomarkers, we talking tens of thousands of them, the same thing again, and then see if we can streamline it clinical trials by looking at it data and training with that data. So again, here you go. Right? We have very good that we have many vaccines on. In candidates out there right now, the next long pole in the tenth is the clinical trial. And we are working on that also by applying the same concept. Yeah. But for clinical trials. >> Right. And then Prasad, it seems to me that this is a good, an example of sort of an edge use case. Right? You've got a lot of distributed data. And I know you've spoken in the past about the edge generally, where data lives bringing moving data back to sort of the centralized model. But of course you don't want to move data if you don't have to real time AI inferencing at the edge. So, what are you thinking in terms of other other edge use cases that were there swarm learning can be applied. >> Yeah, that's a great point. We could kind of look at this both in the medical and also in the other fields, as we talked about Professor just mentioned about this radiographs and then probably, Using this with a medical image data, think of it as a scenario in the future. So, if we could have an edge note sitting next to these medical imaging systems, very close to that. And then as in when this the systems producers, the medical immediate speed could be an X-ray or a CT scan or MRI scan types of thing. The system next to that, sitting on the attached to that. From the modernity is already built with the swarm lending. It can do the inferencing. And also with the new setup data, if it looks some kind of an outlier sees the new or images are probably a new signals. It could use that new data to initiate another round up as form learning with all the involved or the other medical images across the globe. So, all this can happen without really sharing any of the raw data outside of the systems but just getting the inferencing and then trying to make all of these systems to come together and try to build a better model. >> So, the last question. Yeah. >> If I may, we got to wrap, but I mean, I first, I think we've heard about swarm learning, maybe read about it probably 30 years ago and then just ignored it and forgot about it. And now here we are today, blockchain of course, first heard about with Bitcoin and you're seeing all kinds of really interesting examples, but Dr. Goh, start with you. This is really an exciting area, and we're just getting started. Where do you see swarm learning, by let's say the end of the decade, what are the possibilities? >> Yeah. You could see this being applied in many other industries, right? So, we've spoken about life sciences, to the healthcare industry or you can't imagine the scenario of manufacturing where a decade from now you have intelligent robots that can learn from looking at across men building a product and then to replicate it, right? By just looking, listening, learning and imagine now you have multiple of these robots, all sharing their learnings across boundaries, right? Across state boundaries, across country boundaries provided you allow that without having to share what they are seeing. Right? They can share, what they have lunch learnt You see, that's the difference without having to need to share what they see and hear, they can share what they have learned across all the different robots around the world. Right? All in the community that you allow, you mentioned that time, right? That will even in manufacturing, you get intelligent robots learning from each other. >> Professor, I wonder if as a practitioner, if you could sort of lay out your vision for where you see something like this going in the future, >> I'll stay with the medical field at the moment being, although I agree, it will be in many other areas, medicine has two traditions for sure. One is learning from each other. So, that's an old tradition in medicine for thousands of years, but what's interesting and that's even more in the modern times, we have no traditional sharing data. It's just not really inherent to medicine. So, that's the mindset. So yes, learning from each other is fine, but sharing data is not so fine, but swarm learning deals with that, we can still learn from each other. We can, help each other by learning and this time by machine learning. We don't have to actually dealing with the data sharing anymore because that's that's us. So for me, it's a really perfect situation. Medicine could benefit dramatically from that because it goes along the traditions and that's very often very important to get adopted. And on top of that, what also is not seen very well in medicine is that there's a hierarchy in the sense of serious certain institutions rule others and swarm learning is exactly helping us there because it democratizes, onboarding everybody. And even if you're not sort of a small entity or a small institutional or small hospital, you could become remembering the swarm and you will become as a member important. And there is no no central institution that actually rules everything. But this democratization, I really laugh, I have to say, >> Pasad, we'll give you the final word. I mean, your job is very helping to apply these technologies to solve problems. what's your vision or for this. >> Yeah. I think Professor mentioned about one of the very key points to use saying that democratization of BI I'd like to just expand a little bit. So, it has a very profound application. So, Dr. Goh, mentioned about, the manufacturing. So, if you look at any field, it could be health science, manufacturing, autonomous vehicles and those to the democratization, and also using that a blockchain, we are kind of building a framework also to incentivize the people who own certain set of data and then bring the insight from the data into the table for doing and swarm learning. So, we could build some kind of alternative monetization framework or an incentivization framework on top of the existing fund learning stuff, which we are working on to enable the participants to bring their data or insight and then get rewarded accordingly kind of a thing. So, if you look at eventually, we could completely make dais a democratized AI, with having the complete monitorization incentivization system which is built into that. You may call the parties to seamlessly work together. >> So, I think this is just a fabulous example of we hear a lot in the media about, the tech backlash breaking up big tech but how tech has disrupted our lives. But this is a great example of tech for good and responsible tech for good. And if you think about this pandemic, if there's one thing that it's taught us is that disruptions outside of technology, pandemics or natural disasters or climate change, et cetera, are probably going to be the bigger disruptions then technology yet technology is going to help us solve those problems and address those disruptions. Gentlemen, I really appreciate you coming on theCUBE and sharing this great example and wish you best of luck in your endeavors. >> Thank you. >> Thank you. >> Thank you for having me. >> And thank you everybody for watching. This is theCUBE's coverage of HPE discover 2020, the virtual experience. We'll be right back right after this short break. (upbeat music)