>> Narrator: From around the globe, it's theCUBE with digital coverage of AWS Public Sector Online. Brought to you by Amazon Web Services. >> Welcome back to the coverage of AWS Public Sector Summit virtual. I'm John for host of theCUBE. We're here in theCUBE studios, quarantine crew here talking to all the guests remotely as part of our virtual coverage of AWS Public Sector. So I've got a great guest here talking about data science, weather predictions, accurate climate modeling, really digging into how cloud is helping science. Dr. Chelle Gentemann, who is a senior scientist at Farallon Institute is my guest. Chelle, thank you for joining me. >> Thank you. >> So tell us a little about your research. It's fascinating how, I've always joked in a lot of my interviews, 10, 15, 20 years ago, you need super computers to do all these calculations. But now with cloud computing, it opens up so much more on the research side and the impact is significant. You're at an awesome Institute, the Farallon Institute, doing a lot of stuff in the sea and the ocean and a lot of your things. What's your focus? >> I study the ocean from space, and about 71% was covered by ocean. 40% of our population in the globe actually lives within 100 kilometers of the coast. The ocean influences our weather, it influences climate, but it also provides fisheries and recreational opportunities for people. So it's a really important part of the earth system. And I've been focused on using satellites. So from space, trying to understand how the ocean influences weather and climate >> And how new is this in terms of just state of the art? Fairly new, been around for a while? What's some of the progress for the state of the art we're involved in. >> I started working on satellite data in the 90s during school, and I liked the satellite data cause it's the interface of sort of applied math, computer science and physics. The state of the art is that we've really had remote sensing around for about 20, 30 years. But things are changing because right now we're having more sensors and different types of instruments up there and trying to combine that data is really challenging. To use it, our brain is really good in two and three dimensions, but once you get past that, it's really difficult for the human brain to try and interpret the data. And that's what scientists do. Is they try and take all these multidimensional data sets and try to build some understanding of the physics of what's going on. And what's really interesting is how cloud computing is impacting that. >> It sounds so exciting. The confluence of multiple disciplines kind of all right there, kind of geek out big time. So I've got to ask you, in the past you had the public data set program. Are you involved in that? Do you take advantage that research? How is some of the things that AWS is doing help you and is that public data set part of it? >> It's a big part of it now. I've helped to deploy some of the ocean temperature data sets on the cloud. And the way that AWS public data sets as sort of has potential to transform science is the way that we've been doing science, the way that I was trained in science was that you would go and download the data. And most of these big institutions that do research, you start to create these dark repositories where the institutions or someone in your group has downloaded data sets. And then you're trying to do science with these data, but you're not sure if it's the most recent version. It makes it really hard to do reproducible science, because if you want to share your code, somebody also has to access that data and download it. And these are really big data sets. So downloading it could take quite a long time. It's not very transparent, it's not very open. So when you move to a public data set program like AWS, you just take all of that download out of the equation. And instantly when I share my code now, people can run the code and just build on it and go right from there, or they can add to it or suggest changes. That's a really big advantage for trying to do open science. >> I had a dinner with Teresa Carlson who is awesome. She runs the Public Sector Summit for AWS. And I remember this was years ago and we were dreaming about a future where we would have national parks in the cloud or this concept of a Yosemite-like beautiful treasure. Physical place you could go there. And we were kind of dreaming that, wouldn't it be great to have like these data sets or supercomputer public commons. It sounds like that's kind of the vibe here where it's shareable and it's almost like a digital national park or something. Is that it's a shared resource. Is that kind of happening? First of all, what do you react to that? And what's your thoughts around that dream? And does this kind of tie to that? >> Yeah, I think it ties directly to that. When I think about how science is still being done and has been done for the past sort of 20 years, we had a real change about 20 years ago when a lot of the government agencies started requiring their data to be public. And that was a big change. So then we got, we actually had public data sets to work with. So more people started getting involved in science. Now I see it as sort of this fortress of data that in some ways have prevented scientists from really moving rapidly forward. But with moving onto the cloud and bringing your ideas and your compute to the data set, it opens up this entire Pandora's box, this beautiful world of how you can do science. You're no longer restricted to what you have downloaded or what you're able to do because you have this unlimited compute. You don't have to be at a big institution with massive supercomputers. I've been running hundreds of workers analyzing in my realm. Over two or 300 gigabytes of data on a $36 Raspberry Pi that I was playing around with my kids. That's transformative. That allows anyone to access data. >> And if you think about what it would have to do to do that in the old days, stack and spike servers. Call, first of all, you'll get the cash, buy servers, rack them and stack them, connect to a download of nightmare. So I got to ask you now with all this capability, first of all, you're talking to someone who loves the cloud. So I'm pretty biased. What are you doing now with the cloud that you couldn't do before? So certainly the old way from a provisioning standpoint, check, done. Innovation, bars raised. Now you're creative, you're looking at solutions, you're building enabling device like a Raspberry Pi, almost like a switch or an initiation point. How has the creativity changed? What can you do now? What are some of the things that are possible that you're doing? >> I think that you can point to within some of the data sets that have already gone on the cloud are being used in these really new, different ways. Again, it points to this, when you don't have access to the data, just simply because you have to download it. So that downloading the data and figuring out how to use it and figuring out how to store it is a big barrier for people. But when things like the HF Radar data set went online. Within a couple of months, there was a paper where people were using it to monitor bird migration in ways that they'd never been able to do before, because they simply hadn't been able to get the data. There's other research being done, where they've put whale recordings on the cloud and they're using AI to actually identify different whales. It's using one data set, but it's also the ability to combine all these different data sets and have access to them at the same time and not be limited by your computer anymore. Which for a lot of science, we've been limited by our access to compute. And that when you take away that, it opens all these new doors into doing different types of research with new types of data, >> You could probably correlate the whale sounds with the temperature and probably say, hey, it's cold. >> Chelle: Exactly. >> I'm making that up. But that's the kind of thing that wouldn't be possible before because you'd have to get the data set, do some math. I mean, this is cool stuff with the ocean. I mean, can you just take a minute to share some, give people an insight in some of the cool projects that are being either thought up or dreamed up or initiated or done or in process or in flight, because actually there's so much data in the ocean. So much things to do, it's very dynamic. There's a lot of data obviously. Share, for the folks that might not have a knowledge of what goes on. What are you guys thinking about? >> A lot of what we're thinking about is how to have societal impact. So as a scientist, you want your work to be relevant. And one of the things that we found is that the ocean really impacts weather at scales that we simply can't measure right now. So we're really trying to push forward with space instrumentation so that we can monitor the ocean in new ways at new resolutions. And the reason that we want to do that is because the ocean impacts longterm predictability in the weather forecast. So a lot of weather forecasts now, if you look out, you can go on to Weather Underground or whatever weather site you want. And you'll see the forecast goes out 10 days and that's because there's not a lot of accuracy after that. So a lot of research is going into how do we extend into seasonal forecast? I'm from Santa Rosa, California. We've been massively impacted by wildfires. And being able to understand how to prepare for the coming season is incredibly important. And surprisingly, I think to a lot of people, the ocean plays a big role in that. The ocean can impact how much storm systems, how they grow, how they evolve, how much water they actually got. Moisture they pick up from the ocean and then transport over land. So if you want to talk about, it's really interesting to talk about how the ocean impacts our weather and our seasonal weather. So that's an area where people are doing a lot of research. And again, you're talking about different data sets and being able to work together in a collaborative environment on the cloud is really what's starting to transform how people are working together, how they're communicating and how they're sharing their science. >> I just hope it opens up someone's possibilities. I want to get your vision of what you think the breakthroughs might be possible with cloud for research and computing. Because you have kind of old school and new school. Amazon CEO, Andy Jassy calls it old guard, new guard. The new guard is really more looking for self provisioning, auto-scaling, all that. Super computer on demand, all that stuff at your fingertips. Great, love that. But is there any opportunity for institutional change within the scientific community? What's your vision around the impact? It's not just scientific. It also can go to government for societal impact. So you start to see this modernization trend. What's your vision on the impact of the scientific community with cloud? >> I think that the way the scientific community has been organized for a long time is that scientists that are at an institute. And a lot of the research has been siloed. And it's siloed in part because of the way the funding mechanism works. But that inhibits creativity and inhibits collaboration. And it inhibits the advancement of science. Because if you hold onto data, you hold on to code. You're not allowing other people to work on it and to build on what you do. The traditional way that scientists have moved forward is you make a discovery, you write up a paper, you describe it in a journal article, and then you publish that. Then if someone wants to build on your research, they get your journal article, they read it. Then they try to understand what you did. They maybe recode all of your analysis. So they're redoing the work that you did, which is simply not efficient. Then they have to download the data sets that you access. This slows down all of science. And it also inhibits bringing in new data sets again because you don't have access to them. So one of the things I'm really excited about with cloud computing is that by bringing our scientific ideas and our compute to the data, it allows us to break out of these silos and collaborate with people outside of our institution, outside of our country, and bring new ideas and new voices and elevate everyone's ideas to another level. >> It brings the talent and the ideas together. And now you have digital and virtual worlds, cause we've been virtualized with COVID-19. You can create content as a community building capability or your work can create a network effect with other peers. And is a flash mobbing effect of potential collaboration. So work, work forces, workplaces, work loads, work flows, kind of are interesting or kind of being changed in real time. You were just talking about speed, agility. These are technical concepts being applied to kind of real world scenarios. I mean your thoughts on that. >> I now work with people like right now, I'm working with students in Denmark, Oman, India, France, and the US. That just wasn't possible 10 years ago. And we're able to bring all these different voices together, which it really frees up science and it frees up who can participate in science, which is really fun. I mean, I'm a scientist. I do it because it's really, really fun. And I love working with other people. So this new ability that I've gained in the last couple of years by moving onto the cloud has really accelerated all the different types of collaborations I'm involved with. And hopefully accelerating science as a whole. >> I love this topic. It's one of my passion areas where it's an issue I've been scratching for over a decade too. Is that content and your work is an enabler for community engagement because you don't need to publish it to a journal. It's like waterfall mentality. It's like you do it. But if you can publish something or create something and show it, demo it or illustrate it, that's better than a paper. If you're on video, you can talk about it. It's going to attract other people, like-minded peers can come together. That's going to create more collaboration data. That's going to create more solidarity around topics and accelerate the breakthroughs. >> For our last paper, we actually published all the software with it. We got a digital firewall for the software, published the software and then containerized it so that when you read our paper, at the bottom of the paper, you get a link. You go to that link, you click on a button and you're instantly in our compute environment, you can reproduce all of our results. Do the error propagation analysis that we did. And then if you don't like something, go ahead and change it or add onto it or ask us some questions. That's just magical. >> Yeah, it really is. And Amazon has been a real investor and I got to give props to Teresa Carlson and her team and Andy Jassy, the CEO, because they've been investing in credits and collaborating with groups like Jet Propulsion Lab, you guys, everyone else. Just space has been a big part of that. I see Bezos love space. So they've been investing in that and bringing that resource to the table. So you've got to give Amazon some props for that. But great work that you're doing. I'm fascinated. I think it's one of those examples where it's a moonshot, but it's doable. It's like you can get there. >> Yeah, and it's just so exciting. I'm the lead on a proposal for a new science mission to NASA. And we are going all in with the cloud computing. So we're going to do all the processing on the cloud. We want to do the entire science team on the cloud and create a science data platform where we're all working together. That's just never happened before. And I think that by doing this, we multiply the benefits of all of our analysis. We make it faster and we make it better and we make it more collaborative. So everyone wins. >> Sure, you're an inspiration to many. I'm so excited to do this interview with you. I love what you said earlier at the beginning about your focus of being in computer science, physics, space. That confluence is multiple disciplines. Not everyone can have that. Some people just get a computer science degree. Some people get, I'm premed, or I'm going to do biology. I'm going to do this. This notion of multiple disciplines coming together is really what society needs now. Is we're converging or virtualizing or becoming a global society. And that brings up my final question. Is something I know that you're passionate about creating a more inclusive scientific community because you don't have to be the, just the computer science major. Now, if you have all three, it's a multi-tool when you're a multiple skill player. But you don't have to be something to get into this new world. Because if you have certain disciplines, whether it's math, maybe you don't have computer science but it's quick to learn. There's frameworks out there, no code, low code. So cloud computing supports this. What's your vision and what's your opinion of how more inclusivity can come into the scientific community? >> I think that, when you're at an institution or at a commercial company or a nonprofit, if you're at some sort of organized institution, you have access to things that not everyone has access to. And in a lot of the world, there's trouble with internet connectivity. There is trouble downloading data. They simply don't have the ability to download large data sets. So I'm passionate about inclusivity because I think that, until we include global voices in science, we're not going to see these global results that we need to. We need to be more interdisciplinary. And that means working with different scientists in different fields. And if we can all work together on the same platform that really helps explode interdisciplinary science and what can be done. A lot of science has been quite siloed because you work at an institution. So you talked to the people one door down, or two doors down or on the same floor. But when you start working in this international community and people don't have to be online all the time, they can write code and then just jump on and upload it. You don't need to have these big, powerful resources or institutions behind you. And that gives a platform for all types of scientists, that all types of levels to start working with everyone. >> This is why I love the idea of the content and the community being horizontally scalable. Because if you're stuck around a physical institution or space, you kind of like have group think, or maybe you have the same kind of ideas being talked about. But here, when you pull back the remote work with COVID-19, as an example, it highlights it. The remote scientist could be anywhere. So that's going to increase access. What can we do to accept those voices? Is there a way or an idea or formula you see that people could, assuming there's access, which I would say, yes. What do we do? What do you do? >> I think you have to be open and you have to listen. Because, if I ask a question into the room where my colleagues work, we're going to come up with an answer. But we're going to come up with an answer that's informed by how we were trained in science and what fields we know. So when you open up this box and you allow other voices to participate in science, you're going to get new and different answers. And as a scientist, you need to be open to allowing those voices to be heard and to acting on them and including them in your research results and thinking about how they may change what you think and bring you to new conclusions. >> Machine learning has been a part. I know your work in the past, obviously cloud you're a big fan, obviously can tell. Proponent of it. Machine learning and AI can be a big part of this too, both on not only sourcing new voices and identifying what's contextually relevant at any given time, but also on the science-side machine learning. Because if we can take a minute to give your thoughts on the and relevance of machine learning and AI, because you still got the humans and you got machines augmenting each other, that relationship is going to be a constant conversation point going forward. Is there data about the data and what's the machines doing? What's your thoughts on all of these? Machine learning and AI as an impact. >> It's funny you say impact. So I work with this NASA IMPACT project, which is this interdisciplinary team that tries to advance science, and it's really into machine learning and AI. One of the difficulties when you start to do science is you have an idea like, okay, I want to study tropical storms. And then you have to go and wade through all these different types of data to identify when events happened and then gather all the data from those different events and start to try and do some analysis. They're working and they've been really successful in using AI to actually do this sort of event identification. So what's interesting and how can we use AI and machine learning to identify those interesting events and gathering everything together for scientists to then try and bring for analysis? So AI is being used in a lot of different ways in science. It's being used to look at these multi-dimensional problems that are just a little bit too big for our brains to try and understand. But if we can use AI and machine learning to gather insights into certain aspects of them, it starts to lead to new conclusions and it starts to allow us to see new connections. AI and machine learning has this potential to transform how we do science. Cloud computing is part of that because we have access to so much more data now. >> It's a real enabling technology. And when you have enabling technology, the power is in the hands of the creative minds. And it's really what you can think up and what you can dream up and that's going to come from people. Phenomenal. Final question for you, to kind of end on a light note. Dr. Chelle Gentemann here, senior scientist at the Farallon Institute. You're doing a lot of work on the ocean, space, ocean interaction. What's the coolest thing you're working on right now? Or you you've worked on that you think would be worth sharing. >> There's a couple of things. I have to think about what's the most fun. Right now, I'm working on doing some analysis with data. We had a big, huge international field campaign this winter off of Barbados, there were research festival, rustles and aircraft. There were sail drones involved, which are these autonomous robotic vehicles that go along the ocean surface and measure air-sea interactions. Right now we're working on analyzing that data. So we have all of this ground truth data. We're bringing in all the satellite observations to see how we can better understand the earth system in that region with a specific focus on air-sea interactions over the ocean where when it rains, you get the salinity stratification. When there's strong solar, you get diurnal stratification. So you have upper ocean stratification and heat and salinity. And how those impact the fluxes and how the ocean impacts the heat and moisture transport into the atmosphere, which then affects weather. So again, this is this multidimensional data set with all these different types of both ground truth data, satellite data that we're trying to bring together and it's really exciting. >> It could shape policy, it could shape society. Maybe have a real input into global warming. Our behaviors in the world, sounds awesome. Plus, I love the ground truth and the observational data. It sounds like our media business algorithm, we got to get the observation, get the truth, report it. Sounds like there's something in there that we could learn from. (both giggling) >> Yeah, it's very interesting cause you often find what you see from a distance is not quite true up close. >> I can tell you that we as in media as we do a lot of investigative journalism. So we appreciate that. Dr. Chelle Gentemann, senior scientist at the Farallon Institute, here as part of AWS Public Sector Summit. Thank you so much for time. What a great story. We'll keep in touch. Love the sails drone. Great innovation. And continue the good work, I'm looking forward to checking in later. Thanks for joining. >> Thanks so much. It was nice talking to you. >> I'm John Furrier with theCUBE. We're here in our studios covering the Amazon Web Services Public Sector Summit virtual. This is theCUBE virtual bringing you all the coverage with Amazon and theCUBE. Thanks for watching. (upbeat music)

>> Live from Mountain View, California It's theCUBE covering DevNet Create 2019, brought to you by Cisco. >> Hi, welcome back to theCUBE's continuing coverage, day one at CISCO DEVNET CREATE 2019, I'm Lisa Martin with John Furrier, we are at The Computer History Museum at Mountain View. We're pleased to welcome to theCUBE Ash Seddeek, strategic business consultant and story telling coach. >> Yes. >> This is an interesting combination, story telling coach with a bunch of developers. So first of all, talk to us about what it is that you, who do you help learn how to tell stories and then what your work is with Cisco. >> Fantastic. So, primarily at Cisco, I work with a lot of leaders in a coaching environment where we are looking at what they are trying to achieve with the organization and how they can articulate that message in an energetic and inspiring way. And we find stories are the best way to engage the audience. I'm working with one leader, he keeps telling me, the last talk he gave, the one thing people remember is the story. So, everyone is sort of realizing that if I want to tell them something about how we're transitioning from one technology platform to another, if I can find a metaphor, an analogy, a story, I have much better luck connecting with them and giving them something that they can remember. >> Is this like a personal story that they need to share and kind of open up some some vulnerability? Or just some other metaphor that everybody would understand? >> Yes, we actually sometimes use one or the other. Like in one case, we're using the car racing metaphor to talk about how teams come together to create amazing results. So then in that case, it's not about just the driver of that car or the team at the pit changing the tires and how fast they do that, but how they collectively then, have that success at the end of the race. Or, maybe to your point, maybe it's a personal story that then shows them, hey, I went through a lot of challenges and I know as engineers, you're going through a lot of challenges, and I can see us getting past it. So we also try to tap into what they've been able to achieve in the past. So then he can actually call on their memory, we've been able to produce these products for Cisco. Now, the customer expectations are changing and we need to get them to the market sooner, therefore, we need to figure ways where we can build some high preforming teams and get these products to the market much sooner. >> You know Ash, since hearing about your story telling here on theCUBE, as we do a lot of story telling, is that in the tech world, designed thinking has been a big part of the discipline around building products. >> Yes. >> How has some of the things that you're bringing to this kind of design story telling, >> Yes. >> Kind of ethos and thinking, >> Yes. >> Into the story telling creation process, not just, like hey I created this thing, now let's go bolt a story onto it. Is there an integration point inside the construction of the creative process, >> Yes. >> That might feed that. Can you take us through your state of the art thinking around this? >> Absolutely. It's actually, it was very comforting to find that the very first step in designed thinking is empathize, which essentially means, you have a particular target audience that you're trying to serve with a particular solution. We actually use the word hero to think about that audience and then we basically say, if she's a mom walking into the hospital lobby with her baby, what is the experience for that mother? Can we really empathize? Can we find out what the story is? What's been happening at home? The way she's going into the hospital, now she's driving into the lobby, how is she being received in that lobby, in the service level. And then we basically describe the story again of where things are today, which we can call experience number one. And then we basically talk to them about how can we envision a beautiful, delightful, experience for that mom? That becomes experience number two, and we use these stories between one and two to really energize us, to really help people understand we need to come up with a much better solution. >> I want to get your thoughts on Steve Jobs always said story telling was critical. It was his mantra before he passed away. I had a chance to interview John Chambers at his house recently. >> Yes. >> He's got a new book coming out and he's always been about trends and being on the right wave, so between the two, you had one product leader with Steve Jobs, you have a trend seer with John Chambers. How much of the DNA of the person you are coaching, that their natural talent shapes how you engage with how they could be a better story teller? >> Yeah, what I'm finding a lot, especially also with technical leaders, a lot of the time they are very sort of reserved. They sort of walk in the building a all of a sudden have this sort of character where I am not as, you know, charging ahead as I should be and a lot of the time I basically say, hey, can we get this voice to have a little bit of character? Can we get some vocal variety in here? Can we actually tell a story? Can you actually get up, stand up, and open up and really you know, tell us something about who you are and why you want to do this project to lead this team forward. So to your point, I really help them find out that they're actually like any other average citizen, they have so much energy and power within them, they just come into the corporate office and think, oh, I need to have a corporate character, then I come back and say, you know what, I actually need you, I need John to be here, in person, with all the stories that you can tell. And I tell them, go back into your old child and let's figure out some of those stories so that when you're talking about those stories, you remember the excitement, you remember the people that were there. And then all of a sudden, there's a bit of life in them, you know, so that's sort of, what I help them discover is that actually they have these stories. And they are engaging, they are inspiring, if they actually let them come out. >> I imagine that's got to be easier with some guys and girls than others. Some of those who really, maybe don't like public speaking or having to explain something that can be quite (inaudible) to certain audiences. >> Yes. >> What are some of the things that you've learned about working with some of these technologists that have helped kind of refine your methodology for cracking that surface and unleashing this energy and this sort of, natural passion. >> Yes. >> That's hidden inside. >> Absolutely and you know what's happening here at Cisco, especially at Cisco, where you see technology being used to do a lot of communication, a lot of them are realizing, if I don't articulate my message, I'm not going to get the funding. I'm not going to get the best resources. So they realize that communication became part of how do I influence up and make sure that my stakeholders understand that we have a critical project, so there is part of it where they know that there is a lot on the line if they don't speak up. And then they come to someone like me and say, Ash, how can we do this? So we then talk through what are you trying to accomplish with this team? What's that vision and how can we build it, a case for change and that becomes the thing that energizes them first and then we energize their teams and we think about, how do you take this message to executives that can give you the funding that you're looking for. >> So you talked about, before we went live, this program at Cisco, this sort of shark tank-like program, >> Yes. >> Where you're working with very technical men and women. >> Exactly, yeah. >> Who might have a brilliant idea, but in terms of articulating that to be able to get, like you said for, get funding or sponsorship for programs, Can you give us, maybe, one of your favorite examples of a, when you started with experience one or phase one, where it took you about a half an hour to figure out, that's the goal. To getting to the ah, there's the story. >> Yes, that's a good question. >> Tell us something that really sticks with you. >> YGreat question, so the program is called Hack It IT and it's an incubator program, as I mentioned. And one example, a team in China actually, was working on the idea of how do we reduce the number of customers that could be thinking about walking away from Cisco? So the technical term for that is customer churn. So I got on the phone with them, and of course, there are some challenges when it comes to speaking English by a lot of our Chinese colleagues. But then I listened in and I paid attention and then I started asking them, what got you interested in this idea? But we started to really kind of break down the fact that they have figured out that there is a way to listen into the data within Cisco and figure out that once they actually identify certain signals, they can help the sales teams realize they need to go talk to John, because John, if he doesn't have someone talking to him very soon, he or she might actually shift and go to another company, and then I said, well, what percentage do you think that churn is right now? And we found out that maybe like about 7% and with the technology they are building, we could bring it down to three 3%. I was like ding, ding, ding, ding! Earnings per share, number of customers, dollars per quarter, it was just an amazing opportunity and once they came out and communicated clearly, it was the winning idea at the end of the day. >> So you're helping take these technical folks, start to understand the business impact, >> Absolutely, yeah. >> And communicate-- >> And how big it is. >> Right. >> And how big it is. >> That can be pretty transformative for I think anybody in any field, right? >> And I remember on the call, I said, guys did we take a look at the industry averages on the churn? You know, what's the situation at Juniper? What's the situation at HPE? How does Cisco compare? How can we make sure that Cisco is much better off? Phenomenal opportunity for Cisco to listen in and catch things before they happen. >> What would be your advice to folks watching around? How to be a better story teller? Because you can really reel people in, get their attention and then deliver the pay load, whether it's venture funding, >> Yes. >> Or getting a project funded inside a corporation. There's always people interested in how they could be better story tellers, what's your playbook? >> Absolutely. So, the reason I talk about what I do is, I help people become chief excitement officers, which means we need to find the excitement, once we find the excitement, it's like finding gold in a very, very tough mountain and once we find the gold, then we can extract it out and then we can showcase it, right? So I think a lot of the time we're having difficulty finding out where the gold is. And that's one of the things that I help them with, but if they sit with their teams and really brainstorm what opportunities do we have? What are the sizes? How can we get some of these ideas out? Then all of a sudden that idea, that gold starts to show up and they are much more equipped to talk about it. And I have on the executivegreatness.com/storytelling, there is a nice cheat sheet that people can download and use to start really crafting these stories by first using a template in the beginning and then once they do it once, twice, three times it gets easier and better and if they can build a culture around story telling, it makes life so much easier. >> So you've got the, I think you mentioned it, but I want to make sure our viewers heard it. The executive greatness institute is something that you've created. >> Yes. >> And that people could go to that and find that template that you were just mentioning. >> Exactly, so executivegreatness.com/storytelling and they can download that template, it should be a very easy fill-in process in the beginning and it's a fantastic experience to really get that visual story. >> Find that gold, make some fine jewelry make some bars. >> Yes. Its amazing, there's so much potential because-- >> So this must be for anybody, and sorry to interrupt, in any industry. >> Absolutely yes. >> Anybody who can learn to find a way to connect with whomever, whatever, but it sounds like a lot of, kind of, horizontal benefits for anybody. >> Absolutely. >> And any level of their career. >> Totally because what we're finding is the clarity of the message once people get it, then you can actually ask them to do things for you or with you, but until then, there's a huge divide. People sit in these, in all hands meetings, the executive speaks, he or she speaks, they're not really catching on, you know, it's not so clear. >> It's about connecting. >> It's about connecting and clarity is the passage and story becomes the fantastic bridge. >> Yeah. >> To really do that connection. >> And really making it about being part of the same story, >> Yes, exactly. >> That connection creates more retention, success, one proposal versus the other. >> Exactly. >> Could be a swing, the swing could be the story. >> Yes, exactly, 'cause what, when we're working with these teams, we found out that if they can't communicate it, we could be losing out on a multi billion dollar idea. >> You know one thing I want to hear your thoughts on while your here because, >> Sure. >> It's as if I feel like I'm in a counseling session 'cause all we have to try to do is figure out how to tell our story better and our customers who come on theCUBE, they have social media channels, they have more channels. >> Yes. >> The story is broken down into little highlights and small video clips, so companies are challenged, not just individuals, to have a brand. >> Exactly. >> In social media. >> Absolutely. >> How do you take the gold, that excitement, and break it up, >> Yes, into a branding story-- >> Share the story in all channels possible. >> Absolutely. >> Do you have any opinion on that, or? >> It's a lot of tough work, but to your point, we need to find what that brand story is and make sure that everybody's actually clear on it 'cause a lot of times to your point, when you bring them together, each one has a different story. >> Absolutely. >> You know, so I think part of it is to really come together and say, let's get the story, let's honor it, by then, spreading it across the organization, >> And in a consistent way. >> And then we use it on the website, we use it in our marketing and our sales conversations. And if you started with that story with customers, you have something that's a whole lot more engaging, >> Get that story out there in a digital footprint. >> Exactly, yeah, exactly. >> Awesome. >> And I wonder if even what you're talking about in terms of you're right, it's about connection, is even more important as the world gets more and more and more connective with devices, and we get so focused on talking to a device, we've got to kind of come back to your sort of, bringing people back to the basic communication. >> With the human connection, so yeah. >> Exactly, which is, thankfully still needed and to your point, I think, what you were able to show your customers is a tremendous business impact, >> Yes. >> That this connection, this basic human connection in story telling can make. >> Absolutely. And the fact that you are really talking about human beings at the end, those experiences at the very end are touching somebody and we need to get excited, we basically, one of the executives from GE basically said, we need people who can go to the future and then get so excited and then come back, kind of keep that excitement on their face and walk around the organization, keep telling them, you know, when we get to Yosemite, you're going to see these waterfalls, the fresh air is amazing, I've been there, I saw it. I can't wait to get you guys there. And that's what they do on a daily basis, they're just walking around with that bug inside of them, they can see what it's like, and they can't wait to get everybody there. >> This is also somebody that can really breed and foster cultural transformation within a GE, an organization that has been around and has so many moving parts. >> Yes. >> Cultural transformation is essential for any company to transform digitally and that's a hard thing to do. >> Yeah, exactly. >> But it sounds like if, you know, you can, I like-- >> It's a big part of it. >> I like chief excitement officer, I think my dog is my chief excitement officer. But being able to maintain that and sustain it from a cultural transformation perspective is huge. >> Absolutely, 'cause all the digital transformation efforts are about that vision of the future, whether it's healthcare, to your point, or automotive industry or any other industry. It's about what kind of experience, much better experience are we going to create? >> Ash, great talking with you, exciting topic. >> Yes. >> Thank you for giving some time to John and me today at DevNet. >> Absolutely, thank you so much. >> We appreciate it. >> Thank, John, thank you so much. For John Furrier, I am Lisa Martin, you're watching theCUBE live at Cisco DevNet Create 2019. Thanks for watching. (outro music)

>> Announcer: Live from Washington, D.C., it's theCUBE covering AWS Public Sector Summit 2017. Brought to you by Amazon Web Services and it's partner ecosystem. >> Welcome back, live here on theCUBE along with John Furrier, I'm John Walls. Welcome to AWS Public Sector Summit 2017. Again, live from Washington, D.C., your nation's capital, our nation's capital. With us now is our host for the week, puts on one heck of a show, I'm want to tell you, 10,000 strong here, jammed into the Washington Convention Center, Theresa Carlson from World Wide Public Sector. Nice to have you here, Theresa. >> Hi, good afternoon. >> Thanks for joining us. >> Love theCUBE and thank you for being here with us today. >> Absolutely. >> All week in fact. >> It's been great, it really has. Let's just talk about the show first off. Way back, six years ago, we could probably get everybody there jammed into our little area here, just about I think. >> Pretty much. >> Hard to do today. >> That's right. >> How do you feel about when you've seen this kind of growth not only of the show, but in your sector in general? >> I think at AWS we're humbled and excited and, on a personal level because I was sort of given the charge of go create this Public Sector business world-wide, I'm blown away, I pinch myself every time because you did hear my story. The first event, we had about 50 people in the basement of some hotel. And then, we're like, okay. And today, 10,000 people. Last year we had it at the Marriott Wardman Park and we shut down Connecticut Avenue so we knew we needed to make a change. (laughing) But it's great, this is really about our customers and partners. This is really for them. It's for them to make connections, share, and the whole theme of this is superheroes and they are our superheroes. >> One of the heroes you had on the stage today, John Edwards from the CIA, one of your poster-children if you will for great success and that kind of collaboration, said something to the effect of quote, "The best decision we ever made at the CIA "was engaging with AWS in that partnership." When you hear something like that from such a treasured partner, you got to feel pretty good. >> You just have to drop the microphone, boom, and you're sort of done. They are doing amazing work and their innovation levels are really leading, I would say, in the US Public Sector for sure and also, not just in US Public Sector but around the world. Their efforts of what they're doing and the scale and reach at which they're doing it so that's pretty cool. >> John, you've talked about the CIA moment, I'd like to hear the story, share with Theresa. >> Oh, you're going to steal my thunder here? >> No, I'm setting you up. That's what a good partner does. It's all yours. >> Well, John, we've talked multiple times already so I'll say it for the third time. The shot heard around the cloud was my definition of seminal moment, in big mega-trends there's always a moment. It was when Obama tweeted, Twitter grew, plane landing on the Hudson, there's always a seminal moment in major trends that make or break companies. For you guys, it was the CIA. Since then, it's just been a massive growth for you guys. That deal was interesting because it validated Shadow IT, validated the cloud, and it also unseated IBM, the behemoth sales organization that owned the account. In a way, a lot of things lined up. Take us through what's happened then, and since then to now. >> Well, you saw between yesterday at Werner Vogels' keynote and my keynote this morning, just the breadth and depth of the type of customers we have. Everything from the UK government, GCHQ, the Department of Justice with the IT in the UK, to the centers for Medicare for HHS, to amazing educational companies, Cal. Polytech., Australian Tax Office. That's just the breadth and depth of the type of customers we have and all of their stories were impactful, every story is impactful in their own way and across whatever sector they have. That really just tells you that the type of workloads that people are running has evolved because I remember in the early days, when you and I first talked, we talked about what are the kind of workloads and we were talking a little bit about website hosting. That's, of course, really evolved into things like machine learning, artificial intelligence, a massive scale of applications. >> Five or six years ago when we first chatted at re:Invent, it's interesting 'cause now this is the size of re:Invent what it was then so you're on a same trajectory from a show size. Again, validation to the growth in Public Sector. But I was complimenting you on our opening today, saying that you're tenacious because we've talked early days, it was a slog in the early days to get going in the cloud, you were knocking on a lot of doors, convincing people, hey, the future's going to look his way and I don't want to say they slammed the proverbial door in your face but it was more of, woah, they don't believe the cloud is ever going to happen for the government. Share some of those stories because now, looking back, obviously the world has changed. >> It has and, in fact, it's changed in many aspects of it, from policy makers, which I think would be great for you all to have on here sometime to get their perspective on cloud, but policy makers who are now thinking about, we just had a new modernization of IT mandate come out in the US Federal Government where they're going to give millions and millions of dollars toward the modernization of IT for US Government agencies which is going to be huge. That's the first time that's ever happened. To an executive order around cyber-security which is pretty much mandated to look at cloud and how you use it. You're seeing thing like that to even how grants are given where it used to be an old-school model of hardware only to now use cloud. Those ideas and aspects of how individuals are using IT but also just the procurements that are coming out. The buying vehicles that you're seeing come out of government, almost all of them have cloud now. >> John and I were talking about D.C. and the political climate. Obviously, we always talk about it on my show, comment on that. But, interesting, theCUBE, we could do damage here in D.C.. So much target-rich environment for content but more than ever, to me, is the tech scene here is really intrinsically different. For example, this is not a shiny new toy kind of trend, it is a fundamental transformation of the business model. What's interesting to me is, again, since the CIA shot heard around the cloud moment, you've seen a real shift in operating model. So the question I have for you, Theresa, if you can comment on this is: how has that changed? How has the procuring of technology changed? How has he human side of it changed? Because people want to do a good job, they're just on minicomputers and mainframes from the old days with small incremental improvement over the years in IT but now to a fundamental, agile, there's going to be more apps, more action. >> You said something really important just a moment ago, this is a different kind of group than you'll get in Silicon Valley and it is but it's very enterprise. Everybody you see here, every project they work on, we're talking DoD, the enterprise of enterprises. They have really challenging and tough problems to solve every day. How that's changed, in the old days here in government, they know how to write acquisitions for a missile or a tank or something really big in IT. What's changing is their ability to write acquisitions for agile IT, things like cloud utility based models, moving fast, flywheel approach to IT acquisitions. That's what's changing, that kind of acquisition model. Also, you're seeing the system integrator community here change. Where they were, what I call, body shops to do a lot of these projects, they're having to evolve their IT skills, they're getting much more certified in areas of AWS, at the system admin to certified solution architects at the highest level, to really roll these projects out. So training, education, the type of acquisition, and how they're doing it. >> What happened in terms of paradigm shift, mindset? Something had to happen 'cause you brought a vision to the table but somebody had to buy it. Usually, when we talk about legacy systems, it was a legacy mindset too, resistant, reluctant, cautious, all those things. >> Theresa: Well, everything gets thrown out. >> What happened? Where did it tip the other way? Where did it go? >> I think, over time, it's different parts of the government but culture is the hardest thing to, always, change. Other elements of any changes, you get there, but culture is fundamentally the hardest thing. You're seeing that. You've always heard us say, you can't fight gravity, and cloud is the new normal. That's for the whole culture. People are like, I cannot do my project anymore without the use of cloud computing. >> We also have a saying, you can't fight fashion either, and sometimes being in fashion is what the trends are going on. So I got to ask you, what is the fashion statement in cloud these days with your customers? Is it, you mentioned there, moving much down in the workload, is it multi-cloud? Is it analytics? Where's the fashionable, cool action right now? >> I think, here, right now, the cool thing that people really are talking about are artificial intelligence and machine learning, how they take advantage of that. You heard a lot about recognition yesterday, Poly and Lex, these new tools how they are so differentiating anything that they can possibly develop quickly. It's those kind of tools that really we're hearing and of course, IOT for state and local is a big deal. >> I got to ask you the hard question, I always ask Andy a hard question too, if he's watching, you're going to get this one probably at re:Invent. Amazon is a devops culture, you ship code fast and you make all these updates and it's moving very, very fast. One of the things that you guys have done well, but I still think you need some work to do in terms of critical analysis, is getting the releases out that are on public cloud into the GovCloud. You guys have shortened that down to less than a year on most things. You got the east region now rolled out so full disaster recovery but government has always been lagging behind most commercial. How are you guys shrinking that window? When do you see the day when push button commercial, GovCloud are all lockstep and pushing code to both clouds? >> We could do that today but there's a couple of big differentiators that are important for the GovCloud. That is it requires US citizenship, which as you know, we've talked about the challenges of technology and skills. That's just out there, right? At Amazon Web Services, we're a very diverse company, a group of individuals that do our coding and development, and not all of them are US citizens. So for these two clouds, you have to be a US citizen so that is an inhibitor. >> In terms of developers? In terms of building the product? >> Not building but the management aspect. Because of their design, we have multiple individuals managing multiple clouds, right? Now, with us, it's about getting that scale going, that flywheel for us. >> So now it's going to be managed in the USA versus made in the USA with everything as a service. >> Yeah, it is. For us, it's about making sure, number one, we can roll them out, but secondly, we do not want to roll services into those clouds unless they are critical. We are moving a lot faster, we rolled in a lot more services, and the other cool thing is we're starting to do some unique things for our GovCloud regions which, maybe the next time, we can talk a little bit more about those things. >> Final question for me, and let John jump in, the CIA has got this devops factory thing, I want you to talk about it because I think it points to the trend that's encouraging to me at least 'cause I'm skeptical on government, as you know. But this is a full transformation shift on how they do development. Talk about these 4000 developers that got rid of their development workstations, are now doing cloud, and the question is, who else is doing it? Is this a trend that you see happening across other agencies? >> The reason that's really important, I know you know, in the old-school model, you waited forever to provision anything, even just to do development, and you heard John talk about that. That's what he meant on this sort of workstation, this long period of time it took for them to do any kind of development. Now, what they do is they just use any move they have and they go and they provision the cloud like that. Then, they can also not just do that, they can create armies of cores or Amazon machine images so they have super-repeatable tools. Think about that. When you have these super-repeatable tools sitting in the cloud, that you can just pull down these machine images and begin to create both code and development and build off those building blocks, you move so much faster than you did in the past. So that's sort of a big trend, I would say they're definitely leading it. But other key groups are NASA, HHS, Department of Justice. Those are some of the key, big groups that we're seeing really do a lot changes in their dev. >> I got to ask you about the-- >> Oh, I have to say DHS, also DHS on customs and border patrols, they're doing the same, really innovators. >> One of the things that's happening which I'm intrigued by is the whole digital transformation in our culture, right, society. Certainly, the Federal Government wants to take care of the civil liberties of the citizens. So it's not a privacy question, it's more about where smart cities is going. We're starting to see, I call, the digital parks, if you will, where you're starting to see a digital park go into Yosemite and camping out and using pristine resources and enjoying them. There's a demand for citizens to democratize resources available to them, supercomputing or datasets, what's your philosophy on that? What is Amazon doing to facilitate and accelerate the citizen's value of technology so it can be in the hands of anyone? >> I love that question because I'll tell you, at the heart of our business is what we call citizen service, paving the way for disruptive innovation, making the world a better place. That's through citizen's services and they're access. For us, we have multiple things. Everything from our dataset program, where we fund multiple datasets that we put up on the cloud and let everybody take advantage of them, from the individual student to the researcher, for no fee. >> John F.: You pick up the cost on that? >> We do, we fund, we put those datasets in completely, we allow them to go and explore and use. The only time they would ever pay is if they go off and start creating their own systems. The most highly curated datasets up there right now are pretty much on AWS. You heard me talk about the earth, through AWS Earth that we have that shows the earth. We have weather datasets, cancer datasets, we're working with so many groups, genomic, phenotypes, genomes of rice, the rice genome that we've done. >> So this is something that you see that you're behind, >> Oh, completely. >> you're passionate about and will continue to do? >> Because you never know when that individual student or small community school is out there and they can access tools that they never could've accessed before. The training and education, that creativity of the mind, we need to open that up to everybody and we fundamentally believe that cloud is a huge opportunity for that. You heard me tell the 1000 genomes story in the past of where took that cancer dataset or that genome dataset from NIH, put it into AWS for the first time, the first week we put it up we had 3200 new researchers crowdsource on that dataset. That was the first time, that I know of, that anyone had put up a major dataset for researchers. >> And the scale, certainly, is a great resource. And smart cities is an interesting area. I want to get your thoughts on your relationship with Intel. They have 5G coming out, they have a full network transformation, you're going to have autonomous vehicles out there, you're going to have all kinds of digital. How are you guys planning on powering the cloud and what's the role that Intel will play with you guys in the relationship? >> Of course, serverless computing comes into play significantly in areas like that because you want to create efficiencies, even in the cloud, we're all about that. People have always said, oh, AWS won't do that 'cause that's disrupting themselves. We're okay with disrupting ourselves if it's the right thing. We also don't want to hog resourcing of these tools that aren't necessary. So when it comes to devices like that and IOT, you need very efficient computing and you need tools that allow that efficient computing to both scale but not over-resource things. You'll see us continue to have models like that around IOT, or lambda, or serverless computing and how we access and make sure that those resources are used appropriately. >> We're almost out of time so I'd like to shift over if we can. Really impressed with the NGO work, the non-profit work as well and your work in the education space. Just talk about the nuance, differences between working with those particular constituents in the customer base, what you've learned and the kind of work you're providing in those silos right now. >> They are amazing, they are so frugal with their resources and it makes you hungry to really want to go out and help their mission because what you will find when you go meet with a lot of these not-for-profits, they are doing some of the most amazing work that even many people have really not heard of and they're being so frugal with how they resource and drive IT. There's a program called Feed the World and I met the developer of this and it's like two people. They've fed millions of people around the world with like three developers and creating an app and doing great work. To everything from like the American Heart Association that has a mission, literally, of stopping heart disease which is our number one killer around the world. When you meet them and you see the things they're doing and how they are using cloud computing to change and forward their mission. You heard us talk about human trafficking, it's a horrible, misunderstood environment out there that more of us need to be informed on and help with but computing can be a complete differentiator for them, cloud computing. We give millions of dollars of grants away, not just give away, we help them. We help them with the technical resourcing, how they're efficient, and we work really hard to try to help forward their mission and get the word out. It's humbling and it's really nice to feel that you're not only doing things for big governments but you also can help that individual not-for-profit that has a mission that's really important to not only them but groups in the world. >> It's a different level of citizen service, right? I mean, ocean conservancy this morning, talking about that and tidal change. >> What's the biggest thing that, in your mind, personal question, obviously you've been through from the beginning to now, a lot more growth ahead of you. I'm speculating that AWS Public Sector, although you won't disclose the numbers, I'll find a number out there. It's big, you guys could run the table and take a big share, similar to what you've done with startup and now enterprise market. Do you have a pinch-me moment where you go, where are we? Where are you on that spectrum of self-awareness of what's actually happening to you and this world and your team? In Public Sector, we operate just like all of AWS and all of Amazon. We really have treated this business like a startup and I create new teams just like everybody else does. I make them frugal and small and I say go do this. I will tell you, I don't even think about it because we are just scratching the surface, we are just getting going, and today we have customers in 155 countries and I have employees in about 25 countries now. Seven years ago, that was not the case. When you're moving that fast, you know that you're just getting going and that you have so much more that you can do to help your customers and create a partner ecosystem. It's a mission for us, it really is a mission and my team and myself are really excited, out there every day working to support our customers, to really grow and get them moving faster. We sort of keep pushing them to go faster. We have a long way to go and maybe ask me five years from now, we'll see. >> How about next year? We'll come back, we'll ask you again next year. >> Yeah, maybe I'll know more next year. >> John W.: Theresa, thank you for the time, very generous with your time. I know you have a big schedule over the course of this week so thank you for being here with us once again on theCUBE. >> Thank you. >> Many time CUBE alum, Theresa Carlson from AWS. Back with more here from the AWS Public Sector Summit 2017, Washington, D.C. right after this. (electronic music)

>> Announcer: Live, from Las Vegas, it's the Cube, covering HPE Discover 2017. Brought to you by Hewlett Packard Enterprise. >> Hey, welcome back everyone. We are here live in Las Vegas for day two of three days of exclusive coverage from the Cube here at HPE Discover 2017. Our two next guests is Bill Mannel, VP and General Manager of HPC and AI for HPE. Bill, great to see you. And Dr. Nick Nystrom, senior of research at Pittsburgh's Supercomputer Center. Welcome to The Cube, thanks for coming on, appreciate it. >> My pleasure >> Thanks for having us. >> As we wrap up day two, first of all before we get started, love the AI, love the high performance computing. We're seeing great applications for compute. Everyone now sees that a lot of compute actually is good. That's awesome. What is the Pittsburgh Supercomputer Center? Give a quick update and describe what that is. >> Sure. The quick update is we're operating a system called Bridges. Bridges is operating for the National Science Foundation. It democratizes HPC. It brings people who have never used high performance computing before to be able to use HPC seamlessly, almost as a cloud. It unifies HPC big data and artificial intelligence. >> So who are some of the users that are getting access that they didn't have before? Could you just kind of talk about some of the use cases of the organizations or people that you guys are opening this up to? >> Sure. I think one of the newest communities that's very significant is deep learning. So we have collaborations between the University of Pittsburgh life sciences and the medical center with Carnegie Mellon, the machine learning researchers. We're looking to apply AI machine learning to problems in breast and lung cancer. >> Yeah, we're seeing the data. Talk about some of the innovations that HPE's bringing with you guys in the partnership, because we're seeing, people are seeing the results of using big data and deep learning and breakthroughs that weren't possible before. So not only do you have the democratization cool element happening, you have a tsunami of awesome open source code coming in from big places. You see Google donating a bunch of machine learning libraries. Everyone's donating code. It's like open bar and open source, as I say, and the young kids that are new are the innovators as well, so not just us systems guys, but a lot of young developers are coming in. What's the innovation? Why is this happening? What's the ah-ha moment? Is it just cloud, is it a combination of things, talk about it. >> It's a combination of all the big data coming in, and then new techniques that allow us to analyze and get value from it and from that standpoint. So the traditional HPC world, typically we built equations which then generated data. Now we're actually kind of doing the reverse, which is we take the data and then build equations to understand the data. So it's a different paradigm. And so there's more and more energy understanding those two different techniques of kind of getting two of the same answers, but in a different way. >> So Bill, you and I talked in London last year. >> Yes. With Dr. Gho. And we talked a lot about SGI and what that acquisition meant to you guys. So I wonder if you could give us a quick update on the business? I mean it's doing very well, Meg talked about it on the conference call this last quarter. Really high point and growing. What's driving the growth, and give us an update on the business. >> Sure. And I think the thing that's driving the growth is all this data and the fact that customers want to get value from it. So we're seeing a lot of growth in industries like financial services, like in manufacturing, where folks are moving to digitization, which means that in the past they might have done a lot of their work through experimentation. Now they're moving it to a digital format, and they're simulating everything. So that's driven a lot more HPC over time. As far as the SGI, integration is concern. We've integrated about halfway, so we're at about the halfway point. And now we've got the engineering teams together and we're driving a road map and a new set of products that are coming out. Our Gen 10-based products are on target, and they're going to be releasing here over the next few months. >> So Nick, from your standpoint, when you look at, there's been an ebb and flow in the supercomputer landscape for decades. All the way back to the 70s and the 80s. So from a customer perspective, what do you see now? Obviously China's much more prominent in the game. There's sort of an arms race, if you will, in computing power. From a customer's perspective, what are you seeing, what are you looking for in a supplier? >> Well, so I agree with you, there is this arms race for exaflops. Where we are really focused right now is enabling data-intensive applications, looking at big data service, HPC is a service, really making things available to users to be able to draw on the large data sets you mentioned, to be able to put the capability class computing, which will go to exascale, together with AI, and data and Linux under one platform, under one integrated fabric. That's what we did with HPE for Bridges. And looking to build on that in the future, to be able to do the exascale applications that you're referring to, but also to couple on data, and to be able to use AI with classic simulation to make those simulations better. >> So it's always good to have a true practitioner on The Cube. But when you talk about AI and machine learning and deep learning, John and I sometimes joke, is it same wine, new bottle, or is there really some fundamental shift going on that just sort of happened to emerge in the last six to nine months? >> I think there is a fundamental shift. And the shift is due to what Bill mentioned. It's the availability of data. So we have that. We have more and more communities who are building on that. You mentioned the open source frameworks. So yes, they're building on the TensorFlows, on the Cafes, and we have people who have not been programmers. They're using these frameworks though, and using that to drive insights from data they did not have access to. >> These are flipped upside down, I mean this is your point, I mean, Bill pointed it out, it's like the models are upside down. This is the new world. I mean, it's crazy, I don't believe it. >> So if that's the case, and I believe it, it feels like we're entering this new wave of innovation which for decades we talked about how we march to the cadence of Moore's Law. That's been the innovation. You think back, you know, your five megabyte disk drive, then it went to 10, then 20, 30, now it's four terabytes. Okay, wow. Compared to what we're about to see, I mean it pales in comparison. So help us envision what the world is going to look like in 10 or 20 years. And I know it's hard to do that, but can you help us get our minds around the potential that this industry is going to tap? >> So I think, first of all, I think the potential of AI is very hard to predict. We see that. What we demonstrated in Pittsburgh with the victory of Libratus, the poker-playing bot, over the world's best humans, is the ability of an AI to beat humans in a situation where they have incomplete information, where you have an antagonist, an adversary who is bluffing, who is reacting to you, and who you have to deal with. And I think that's a real breakthrough. We're going to see that move into other aspects of life. It will be buried in apps. It will be transparent to a lot of us, but those sorts of AI's are going to influence a lot. That's going to take a lot of IT on the back end for the infrastructure, because these will continue to be compute-hungry. >> So I always use the example of Kasperov and he got beaten by the machine, and then he started a competition to team up with a supercomputer and beat the machine. Yeah, humans and machines beat machines. Do you expect that's going to continue? Maybe both your opinions. I mean, we're just sort of spitballing here. But will that augmentation continue for an indefinite period of time, or are we going to see the day that it doesn't happen? >> I think over time you'll continue to see progress, and you'll continue to see more and more regular type of symmetric type workloads being done by machines, and that allows us to do the really complicated things that the human brain is able to better process than perhaps a machine brain, if you will. So I think it's exciting from the standpoint of being able to take some of those other roles and so forth, and be able to get those done in perhaps a more efficient manner than we're able to do. >> Bill, talk about, I want to get your reaction to the concept of data. As data evolves, you brought up the model, I like the way you're going with that, because things are being flipped around. In the old days, I want to monetize my data. I have data sets, people are looking at their data. I'm going to make money from my data. So people would talk about how we monetizing the data. >> Dave: Old days, like two years ago. >> Well and people actually try to solve and monetize their data, and this could be use case for one piece of it. Other people are saying no, I'm going to open, make people own their own data, make it shareable, make it more of an enabling opportunity, or creating opportunities to monetize differently. In a different shift. That really comes down to the insights question. What's your, what trends do you guys see emerging where data is much more of a fabric, it's less of a discreet, monetizable asset, but more of an enabling asset. What's your vision on the role of data? As developers start weaving in some of these insights. You mentioned the AI, I think that's right on. What's your reaction to the role of data, the value of the data? >> Well, I think one thing that we're seeing in some of our, especially our big industrial customers is the fact that they really want to be able to share that data together and collect it in one place, and then have that regularly updated. So if you look at a big aircraft manufacturer, for example, they actually are putting sensors all over their aircraft, and in realtime, bringing data down and putting it into a place where now as they're doing new designs, they can access that data, and use that data as a way of making design trade-offs and design decision. So a lot of customers that I talk to in the industrial area are really trying to capitalize on all the data possible to allow them to bring new insights in, to predict things like future failures, to figure out how they need to maintain whatever they have in the field and those sorts of things at all. So it's just kind of keeping it within the enterprise itself. I mean, that's a challenge, a really big challenge, just to get data collected in one place and be able to efficiently use it just within an enterprise. We're not even talking about sort of pan-enterprise, but just within the enterprise. That is a significant change that we're seeing. Actually an effort to do that and see the value in that. >> And the high performance computing really highlights some of these nuggets that are coming out. If you just throw compute at something, if you set it up and wrangle it, you're going to get these insights. I mean, new opportunities. >> Bill: Yeah, absolutely. >> What's your vision, Nick? How do you see the data, how do you talk to your peers and people who are generally curious on how to approach it? How to architect data modeling and how to think about it? >> I think one of the clearest examples on managing that sort of data comes from the life sciences. So we're working with researchers at University of Pittsburgh Medical Center, and the Institute for Precision Medicine at Pitt Cancer Center. And there it's bringing together the large data as Bill alluded to. But there it's very disparate data. It is genomic data. It is individual tumor data from individual patients across their lifetime. It is imaging data. It's the electronic health records. And trying to be able to do this sort of AI on that to be able to deliver true precision medicine, to be able to say that for a given tumor type, we can look into that and give you the right therapy, or even more interestingly, how can we prevent some of these issues proactively? >> Dr. Nystrom, it's expensive doing what you do. Is there a commercial opportunity at the end of the rainbow here for you or is that taboo, I mean, is that a good thing? >> No, thank you, it's both. So as a national supercomputing center, our resources are absolutely free for open research. That's a good use of our taxpayer dollars. They've funded these, we've worked with HP, we've designed the system that's great for everybody. We also can make this available to industry at an extremely low rate because it is a federal resource. We do not make a profit on that. But looking forward, we are working with local industry to let them test things, to try out ideas, especially in AI. A lot of people want to do AI, they don't know what to do. And so we can help them. We can help them architect solutions, put things on hardware, and when they determine what works, then they can scale that up, either locally on prem, or with us. >> This is a great digital resource. You talk about federally funded. I mean, you can look at Yosemite, it's a state park, you know, Yellowstone, these are natural resources, but now when you start thinking about the goodness that's being funded. You want to talk about democratization, medicine is just the tip of the iceberg. This is an interesting model as we move forward. We see what's going on in government, and see how things are instrumented, some things not, delivery of drugs and medical care, all these things are coalescing. How do you see this digital age extending? Because if this continues, we should be doing more of these, right? >> We should be. We need to be. >> It makes sense. So is there, I mean I just not up to speed on what's going on with federally funded-- >> Yeah, I think one thing that Pittsburgh has done with the Bridges machine, is really try to bring in data and compute and all the different types of disciplines in there, and provide a place where a lot of people can learn, they can build applications and things like that. That's really unusual in HPC. A lot of times HPC is around big iron. People want to have the biggest iron basically on the top 500 list. This is where the focus hasn't been on that. This is where the focus has been on really creating value through the data, and getting people to utilize it, and then build more applications. >> You know, I'll make an observation. When we first started doing The Cube, we observed that, we talked about big data, and we said that the practitioners of big data, are where the guys are going to make all the money. And so far that's proven true. You look at the public big data companies, none of them are making any money. And maybe this was sort of true with ERP, but not like it is with big data. It feels like AI is going to be similar, that the consumers of AI, those people that can find insights from that data are really where the big money is going to be made here. I don't know, it just feels like-- >> You mean a long tail of value creation? >> Yeah, in other words, you used to see in the computing industry, it was Microsoft and Intel became, you know, trillion dollar value companies, and maybe there's a couple of others. But it really seems to be the folks that are absorbing those technologies, applying them, solving problems, whether it's health care, or logistics, transportation, etc., looks to where the huge economic opportunities may be. I don't know if you guys have thought about that. >> Well I think that's happened a little bit in big data. So if you look at what the financial services market has done, they've probably benefited far more than the companies that make the solutions, because now they understand what their consumers want, they can better predict their life insurance, how they should-- >> Dave: You could make that argument for Facebook, for sure. >> Absolutely, from that perspective. So I expect it to get to your point around AI as well, so the folks that really use it, use it well, will probably be the ones that benefit it. >> Because the tooling is very important. You've got to make the application. That's the end state in all this That's the rubber meets the road. >> Bill: Exactly. >> Nick: Absolutely. >> All right, so final question. What're you guys showing here at Discover? What's the big HPC? What's the story for you guys? >> So we're actually showing our Gen 10 product. So this is with the latest microprocessors in all of our Apollo lines. So these are specifically optimized platforms for HPC and now also artificial intelligence. We have a platform called the Apollo 6500, which is used by a lot of companies to do AI work, so it's a very dense GPU platform, and does a lot of processing and things in terms of video, audio, these types of things that are used a lot in some of the workflows around AI. >> Nick, anything spectacular for you here that you're interested in? >> So we did show here. We had video in Meg's opening session. And that was showing the poker result, and I think that was really significant, because it was actually a great amount of computing. It was 19 million core hours. So was an HPC AI application, and I think that was a really interesting success. >> The unperfect information really, we picked up this earlier in our last segment with your colleagues. It really amplifies the unstructured data world, right? People trying to solve the streaming problem. With all this velocity, you can't get everything, so you need to use machines, too. Otherwise you have a haystack of needles. Instead of trying to find the needles in the haystack, as they was saying. Okay, final question, just curious on this natural, not natural, federal resource. Natural resource, feels like it. Is there like a line to get in? Like I go to the park, like this camp waiting list, I got to get in there early. How do you guys handle the flow for access to the supercomputer center? Is it, my uncle works there, I know a friend of a friend? Is it a reservation system? I mean, who gets access to this awesomeness? >> So there's a peer reviewed system, it's fair. People apply for large allocations four times a year. This goes to a national committee. They met this past Sunday and Monday for the most recent. They evaluate the proposals based on merit, and they make awards accordingly. We make 90% of the system available through that means. We have 10% discretionary that we can make available to the corporate sector and to others who are doing proprietary research in data-intensive computing. >> Is there a duration, when you go through the application process, minimums and kind of like commitments that they get involved, for the folks who might be interested in hitting you up? >> For academic research, the normal award is one year. These are renewable, people can extend these and they do. What we see now of course is for large data resources. People keep those going. The AI knowledge base is 2.6 petabytes. That's a lot. For industrial engagements, those could be any length. >> John: Any startup action coming in, or more bigger, more-- >> Absolutely. A coworker of mine has been very active in life sciences startups in Pittsburgh, and engaging many of these. We have meetings every week with them now, it seems. And with other sectors, because that is such a great opportunity. >> Well congratulations. It's fantastic work, and we're happy to promote it and get the word out. Good to see HP involved as well. Thanks for sharing and congratulations. >> Absolutely. >> Good to see your work, guys. Okay, great way to end the day here. Democratizing supercomputing, bringing high performance computing. That's what the cloud's all about. That's what great software's out there with AI. I'm John Furrier, Dave Vellante bringing you all the data here from HPE Discover 2017. Stay tuned for more live action after this short break.