>> Live from Las Vegas, it's theCUBE covering VMworld 2018. Brought to you by VMware and its ecosystem partners. >> Welcome back to theCUBE's coverage of VMworld 2018. Two sets, wall-to-wall coverage. We had Michael Dell on this morning. We had Pat Gelsinger on this afternoon. And happy to welcome to the program, first time guest, Keith Moran, who's the vice president with Nutanix. Keith, I've talked to you lots about theCUBE, you've watched theCUBE, first time on theCUBE. Thanks so much for joining us. Yeah, thanks for having me. It's a great show. >> Alright, so let's set the stage here. We're here in Vegas. It's my ninth year doing VMworld. How many of these have you done? >> So this is my fourth. >> Yeah? How's the energy of the show? The expo hall's hopping. You guys have a nice booth. What are you hearing from the customers here? >> I think that we're seeing just a lot of discussion around where the market's going with hybrid cloud. I think that it's a massive opportunity. I think people are trying to connect the dots on where it's going in the next five years. The vibe's extremely strong right now. >> I've met you at some of the Nutanix shows in the past and seen you at some of these, but tell us a little bit about your role, how long you've been there, where you came from before. >> I run the Central US for Nutanix, and I spent a long time in the converged, whether it was that app at EMC, through a few start-ups, and then I've been at Nutanix for four years. It's been a great ride, seeing how the market's adopting to hyperconverged. The core problem and vision that Dheeraj saw nine years ago is playing out. He's five chess moves ahead of everyone. I think there's, again, a massive opportunity as we move forward. >> Keith, I love your to share. I love people in the field. You're talking to customers every day. You hear their mindset. I think back over the last 15 years in my career, and when Blade Server first came out, or when we started building converged solutions. It was like, "Oh, wait." Getting the organization together, sorting out the budgets. There were so many hurdles because this was the way we did things, and this is the way we're organized, and this is the way the budgets go. I think we've worked through a number of those, but I'd love to hear from you where we are with most customers, how many of them are on board, and doing more things, modernizing, and making changes, and being more flexible. >> Yeah, so I think you're spot on in the sense that the silos was the enemy in the sense that people were doing business as usual and that there was process, and they didn't want to take risks. But I think that the wave of disruption has been so strong and that we're in this period of mass extinction where customers, They don't have a choice anymore. That they have to protect against the competitive threat or exploit opportunity, and I think that the speed and the agility with hyperconverged is, And what the market disruption is forcing them to make those changes and forcing them to innovate. At the end of the day, that's their core revenue stream is how they experiment, how they innovate. Again, you're seeing the disruptions coming so fast that people are changing to survive. >> Yeah, we have some interesting paradoxes in the industry. We're talking about things like hyperconverged, yet really what we're trying to do is build distributed architectures. >> Correct. >> We're talking about, "Oh, well I want simplicity, and I want to get rid of the silos, but now I've got multicloud environment where I've got lots of different SaaS pieces, I've got multiple public clouds, I often have multiple vendors in my public cloud, and I've like recreated silos and certifications and expertise." How do customers deal with that? How do you help, and your team help to educate and get them up so that hopefully the new modern era is a little bit better than what they were dealing with? >> Yeah, and I think that's part of where the opportunity is. I think that the private cloud people don't do public well, and I don't think that the public cloud vendors do private well. So that's why the opportunity's so big. And I think for us, we're going to continue to harden the IaaS stack of what we built, and then our vision is how do we build a control plane for the next generation. If you look at our acquisition strategy, and where we're putting in it, how do you have a single operating system that spans the user experience from the public to private, making an exact replica. Again, I think customers are struggling with this problem and that as apps scale up, and scale down, and the demand for them, that they want this ability to course correct and be able to move VMs and containers in a very seamless fashion from one app to the next and adjust for the business market conditions. >> Yeah, I had a comment actually by one of my guests this week. We now have pervasive multicloud. We spent a few years sorting out who are the public clouds going to be. And there's still moves and changes, but we know there's a handful of the real big guys, then there's the next tier of all of the server providers, and the software players, like Nutanix. Look, you're not trying to become a competitor at Amazon or Google. You're partners. I see Nutanix at those shows. So maybe explain what's the long-term strategy. How does Nutanix, as you've been talking about enterprise cloud for a number of years, but what's that long-term vision as to how Nutanix plays in this ecosystem? >> Yeah. So for us I think part of it is our own cloud, which is Xi, and it's living in this multicloud world where our customer can do DRs of service with that single operating system, moving it from a Nutanix on-prem solution, moving it to a Nutanix cloud, moving it to Azure, moving it up to TCP, or moving it to AWS. And they have to do with it with thought because clearly there are so many interdependencies with these apps. There's governance, there's laws of the land, there's physics. There's so many things that are going to make this a complex equation for customers. But again, they're demanding, and that's forcing the issue where customers have to make these decisions. >> Keith, I want to hear, when you talk to your customers, where are they with their cloud strategy? I heard a one conference, 85% of customers have a cloud strategy, and I kind of put tongue in cheek. I said, "Well 15% of the people got to figure something out, and the other 85, when you talk to them next quarter, the strategy probably has changed quite a bit." Because things are changing fast, and you need to be agile and be able to change and adjust with what's going on. So where do your customers, I'm sure it's a big spectrum but? >> It is. The interesting thing for me for cloud is on average, we're seeing that the utilization rate, specifically in AWS, is somewhere in the 25% rate for reserved entrance, which was very surprising to me because the whole point of cloud is to test it, to deploy it, and to scale up, and if you're running in an environment where the utilization rate that the economics aren't working. So I think that people are starting to look at, alright, what are the economics behind the app? Does it make sense in the cloud? Does it make sense on-prem? Again, what are the interdependencies of it? The classic problems they're having are still around. They're spending 80% of their time just managing firmware and drivers and spending thousands of hours per quarter just troubleshooting and not impacting the business. So I think, fundamentally, that's what the customers are trying to solve is how do we get out of this business of spending all our time keeping the lights on and how do we drive innovation. And that ratio has been historically for 20 years. And I think, again, Nutanix helps drive that in the sense that we're helping customers shift that ratio and that pain. I always say, "Put your smartest people on your hardest problems," and when you've got these high-end SAN administrators spending a lot of time, they should be working on automation, orchestration, repeatable process that gives scale and again, impacts the business. >> Yeah. A line that I used at your most recent Nutanix show is talking to customers. Step one was modernize the platform, and step two, they could modernize the application. >> Absolutely. >> Speak a little bit to that because in this environment, we know the journey we went through to virtualize a lot of applications. I talked to a Nutanix customer this morning and talked about deploying Oracle, and I said, "Tell me how that was," because how many years did we spend fighting as customers? "You want to virtualize Oracle?" And Oracle would be like, "No, no, no. You have to use OVM. You have to use Oracle this. You have to use Oracle that." We've gone through that. And is it certified on Nutanix? It's good to go. It's ready to go. He's like, "It was pretty easy." And I'm like, it's so refreshing to see that. But when you talk about new modern applications and customers have this whole journey to embrace things like Agile, LMC ICD, and the like. Where does Nutanix play in this, and how are you helping? >> Yeah, so I think on the first. When you look at the classic database, so things like Sequel were automating so that you can extract it in a very simple manner. You look at the mode 2 apps like Kubernetes, we're taking a 37 page deployment guide and automating it down into three clicks because customers want the speed, they want the deployment cycles, they want the automation associated with that. And it's having a big impact in the sense that these customers are trying to figure out, "Where am I going here in the next three years?" For us, we're seeing massive workloads, whether it's Oracle, Sequel, people deploying on it. And again, there's so much pressure for people to change and constantly disrupt themselves, and that's what we're seeing. And layer that all on top of a lot of legacy apps. So we've got oil and gas customers, and big retailers, and when they show us the dependency maps of their applications, it's incredible. How complex these are, and they want simplicity and speed, and how do they get out of that business of the tangled mess. >> Yeah. Keith, I wonder if you have an example, and you might not be able to use an exact customer, but you mentioned some industries, so here's something I hear at a show like this. Alright, I understand my virtualized environment. I've deployed HCI. I really need to start extending and using public cloud. What are some first steps that you've seen customers as to how they're making that successful? What are some of those important patterns, what works, and where's good places for them to start? >> I look at it almost, when I see some of the automation deployment cycles they have of how they get a VM through the full lifecycle, and behind the scenes they have such massive complexities that it's hindering their ability to create automations. So the first layer is how do you simplify the infrastructure underneath, and it goes back to that dependency map. So again, oil and gas, that's big retailers. When they show us what their infrastructure is, they want to simplify that layer first, and then from there they can build incredible automation that gives them a multiple in the return that is much greater than what they're seeing in today's infrastructure. >> Keith, what's exciting you in the marketplace today? You get to meet with a lot of customers. Just kind of an open-ended. >> So for me, it's I've worked in a lot of big legacy companies, and I've never seen customers that have the passion towards Nutanix. And I think that it's the problems that we're solving for them, the impacts we're having on the business is driving that loyal following. But again, how fast people are either trying to exploit a competitive advantage or protect against a threat, that it's interesting to be right in this, in the epicenter of this big shift that's happening, right? Tectonic plates are shifting in that you've got a massive cloud provider like AWS. You've got a big player like VMware. What's the next generation going to look like? For me it's fascinating to see how these businesses are competing. I look at a customer. I've got a Fortune 500, The CTO's comment to me was, "I'm one app away from disruption." So they're a massive commercial real estate organization, and he's terrified of what could happen next, and he's got to stay way ahead of the curve, and I think that the innovation rate that we're bringing, the support, the infrastructure. I think it's a great place because of how we're serving what we call the underserved customer and having a big impact. >> Yeah. It's interesting. We always poke at the how much are customers just dreading that potential disruption and how much are they excited about what they can do different. You talk about working with traditional vendors in IT for the last decade or so, it's like IT and the business were kind of fighting over it. There's a line one of our hosts here, Alan Cohen, used to use. Actually, the first time I heard it was at the Nutanix show in Miami when we had it on. And he said there's this triangle, and where you want to get people is away from the no and the slow, and get them to go. Do you feel more people are fearful, or more people are excited. Is it a mix of-- >> It is. >> Those for your customers? >> And again, I think that the marketforce is really helping because people there they have to shift to stay competitive, and they're pushing every day to the level of change and how people are embracing change is much faster than it was. Because again, these disruption cycles are much faster and they're coming at customers in a totally different way that they weren't prepared for. >> Alright, Keith, final word from you is how many of theCUBE interviews have you watched in the last bunch of years? >> The content, I mean, it's off the charts. Hundreds and hundreds of hours, I would say. >> Well, hey. Really appreciate you joining us. Keith Moran, not only a long-time watcher, but now a CUBE alumni with the thousands that we've done. So pleasure to talk with ya on-camera, as well as always off-camera. >> Yeah, great stuff, Stu. >> We'll be back with lots more coverage here from VMworld 2018. I'm Stu Miniman, and thanks for watching theCUBE. (upbeat music)

>> [Narrator] Live from New York, it's theCube, covering Machine Learning Everywhere. Build your ladder to AI, brought to you by IBM. >> Welcome back here to New York City as we continue at IBM's Machine Learning Everywhere, build your ladder to AI, along with Dave Vellante, I'm John Walls. It is now a great honor of ours to have I think probably and arguably the greatest chess player of all time, Garry Kasparov now joins us. He's currently the chairman of the Human Rights Foundation, political activist in Russia as well some time ago. Thank you for joining us, we really appreciate the time, sir. >> Thank you for inviting me. >> We've been looking forward to this. Let's just, if you would, set the stage for us. Artificial Intelligence obviously quite a hot topic. The maybe not conflict, the complementary nature of human intelligence. There are people on both sides of the camp. But you see them as being very complementary to one another. >> I think that's natural development in this industry that will bring together humans and machines. Because this collaboration will produce the best results. Our abilities are complementary. The humans will bring creativity and intuition and other typical human qualities like human judgment and strategic vision while machines will add calculation, memory, and many other abilities that they have been acquiring quickly. >> So there's room for both, right? >> Yes, I think it's inevitable because no machine will ever reach 100% perfection. Machines will be coming closer and closer, 90%, 92, 94, 95. But there's still room for humans because at the end of the day even with this massive power you have guide it. You have to evaluate the results and at the end of the day the machine will never understand when it reaches the territory of diminishing returns. It's very important for humans actually to identify. So what is the task? I think it's a mistake that is made by many pundits that they automatically transfer the machine's expertise for the closed systems into the open-ended systems. Because in every closed system, whether it's the game of chess, the game of gall, video games like daughter, or anything else where humans already define the parameters of the problem, machines will perform phenomenally. But if it's an open-ended system then machine will never identify what is the sort of the right question to be asked. >> Don't hate me for this question, but it's been reported, now I don't know if it's true or not, that at one point you said that you would never lose to a machine. My question is how capable can we make machines? First of all, is that true? Did you maybe underestimate the power of computers? How capable to you think we can actually make machines? >> Look, in the 80s when the question was asked I was much more optimistic because we saw very little at that time from machines that could make me, world champion at the time, worry about machines' capability of defeating me in the real chess game. I underestimated the pace it was developing. I could see something was happening, was cooking, but I thought it would take longer for machines to catch up. As I said in my talk here is that we should simply recognize the fact that everything we do while knowing how we do that, machines will do better. Any particular task that human perform, machine will eventually surpass us. >> What I love about your story, I was telling you off-camera about when we had Erik Brynjolfsson and Andrew McAfee on, you're the opposite of Samuel P. Langley to me. You know who Samuel P. Langley is? >> No, please. >> Samuel P. Langley, do you know who Samuel P. Langley is? He was the gentleman that, you guys will love this, that the government paid. I think it was $50,000 at the time, to create a flying machine. But the Wright Brothers beat him to it, so what did Samuel P. Langley do after the Wright Brothers succeeded? He quit. But after you lost to the machine you said you know what? I can beat the machine with other humans, and created what is now the best chess player in the world, is my understanding. It's not a machine, but it's a combination of machines and humans. Is that accurate? >> Yes, in chess actually, we could demonstrate how the collaboration can work. Now in many areas people rely on the lessons that have been revealed, learned from what I call advanced chess. That in this team, human plus machine, the most important element of success is not the strengths of the human expert. It's not the speed of the machine, but it's a process. It's an interface, so how you actually make them work together. In the future I think that will be the key of success because we have very powerful machine, those AIs, intelligent algorithms. All of them will require very special treatment. That's why also I use this analogy with the right fuel for Ferrari. We will have expert operators, I call them the shepherds, that will have to know exactly what are the requirements of this machine or that machine, or that group of algorithms to guarantee that we'll be able by our human input to compensate for their deficiencies. Not the other way around. >> What let you to that response? Was it your competitiveness? Was it your vision of machines and humans working together? >> I thought I could last longer as the undefeated world champion. Ironically, 1997 when you just look at the game and the quality of the game and try to evaluate the Deep Blue real strengths, I think I was objective, I was stronger. Because today you can analyze these games with much more powerful computers. I mean any chess app on your laptop. I mean you cannot really compare with Deep Blue. That's natural progress. But as I said, it's not about solving the game, it's not about objective strengths. It's about your ability to actually perform at the board. I just realized while we could compete with machines for few more years, and that's great, it did take place. I played two more matches in 2003 with German program. Not as publicized as IBM match. Both ended as a tie and I think they were probably stronger than Deep Blue, but I knew it would just be over, maybe a decade. How can we make chess relevant? For me it was very natural. I could see this immense power of calculations, brute force. On the other side I could see us having qualities that machines will never acquire. How about bringing together and using chess as a laboratory to find the most productive ways for human-machine collaboration? >> What was the difference in, I guess, processing power basically, or processing capabilities? You played the match, this is 1997. You played the match on standard time controls which allow you or a player a certain amount of time. How much time did Deep Blue, did the machine take? Or did it take its full time to make considerations as opposed to what you exercised? >> Well it's the standard time control. I think you should explain to your audience at that time it was seven hours game. It's what we call classical chess. We have rapid chess that is under one hour. Then you have blitz chess which is five to ten minutes. That was a normal time control. It's worth mentioning that other computers they were beating human players, myself included, in blitz chess. In the very fast chess. We still thought that more time was more time we could have sort of a bigger comfort zone just to contemplate the machine's plans and actually to create real problems that machine would not be able to solve. Again, more time helps humans but at the end of the day it's still about your ability not to crack under pressure because there's so many things that could take you off your balance, and machine doesn't care about it. At the end of the day machine has a steady hand, and steady hand wins. >> Emotion doesn't come into play. >> It's not about apps and strength, but it's about guaranteeing that it will play at a certain level for the entire game. While human game maybe at one point it could go a bit higher. But at the end of the day when you look at average it's still lower. I played many world championship matches and I analyze the games, games played at the highest level. I can tell you that even the best games played by humans at the highest level, they include not necessarily big mistakes, but inaccuracies that are irrelevant when humans facing humans because I make a mistake, tiny mistake, then I can expect you to return the favor. Against the machine it's just that's it. Humans cannot play at the same level throughout the whole game. The concentration, the vigilance are now required when humans face humans. Psychologically when you have a strong machine, machine's good enough to play with a steady hand, the game's over. >> I want to point out too, just so we get the record straight for people who might not be intimately familiar with your record, you were ranked number one in the world from 1986 to 2005 for all but three months. Three months, that's three decades. >> Two decades. >> Well 80s, 90s, and naughts, I'll give you that. (laughing) That's unheard of, that's phenomenal. >> Just going back to your previous question about why I just look for some new form of chess. It's one of the key lessons I learned from my childhood thanks to my mother who spent her live just helping me to become who I am, who I was after my father died when I was seven. It's about always trying to make the difference. It's not just about winning, it's about making a difference. It led me to kind of a new motto in my professional life. That is it's all about my own quality of the game. As long as I'm challenging my own excellence I will never be short of opponents. For me the defeat was just a kick, a push. So let's come up with something new. Let's find a new challenge. Let's find a way to turn this defeat, the lessons from this defeat into something more practical. >> Love it, I mean I think in your book I think, was it John Henry, the famous example. (all men speaking at once) >> He won, but he lost. >> Motivation wasn't competition, it was advancing society and creativity, so I love it. Another thing I just want, a quick aside, you mentioned performing under pressure. I think it was in the 1980s, it might have been in the opening of your book. You talked about playing multiple computers. >> [Garry] Yeah, in 1985. >> In 1985 and you were winning all of them. There was one close match, but the computer's name was Kasparov and you said I've got to beat this one because people will think that it's rigged or I'm getting paid to do this. So well done. >> It's I always mention this exhibition I played in 1985 against 32 chess-playing computers because it's not the importance of this event was not just I won all the games, but nobody was surprised. I have to admit that the fact that I could win all the games against these 32 chess-playing computers they're only chess-playing machine so they did nothing else. Probably boosted my confidence that I would never be defeated even by more powerful machines. >> Well I love it, that's why I asked the question how far can we take machines? We don't know, like you said. >> Why should we bother? I see so many new challenges that we will be able to take and challenges that we abandoned like space exploration or deep ocean exploration because they were too risky. We couldn't actually calculate all the odds. Great, now we have AI. It's all about increasing our risk because we could actually measure against this phenomenal power of AI that will help us to find the right pass. >> I want to follow up on some other commentary. Brynjolfsson and McAfee basically put forth the premise, look machines have always replaced humans. But this is the first time in history that they have replaced humans in the terms of cognitive tasks. They also posited look, there's no question that it's affecting jobs. But they put forth the prescription which I think as an optimist you would agree with, that it's about finding new opportunities. It's about bringing creativity in, complementing the machines and creating new value. As an optimist, I presume you would agree with that. >> Absolutely, I'm always saying jobs do not disappear, they evolve. It's an inevitable part of the technological progress. We come up with new ideas and every disruptive technology destroys some industries but creates new jobs. So basically we see jobs shifting from one industry to another. Like from agriculture, manufacture, from manufacture to other sectors, cognitive tasks. But now there will be something else. I think the market will change, the job market will change quite dramatically. Again I believe that we will have to look for riskier jobs. We will have to start doing things that we abandoned 30, 40 years ago because we thought they were too risky. >> Back to the book you were talking about, deep thinking or machine learning, or machine intelligence ends and human intelligence begins, you talked about courage. We need fail safes in place, but you also need that human element of courage like you said, to accept risk and take risk. >> Now it probably will be easier, but also as I said the machine's wheel will force a lot of talent actually to move into other areas that were not as attractive because there were other opportunities. There's so many what I call raw cognitive tasks that are still financially attractive. I hope and I will close many loops. We'll see talent moving into areas where we just have to open new horizons. I think it's very important just to remember it's the technological progress especially when you're talking about disruptive technology. It's more about unintended consequences. The fly to the moon was just psychologically it's important, the Space Race, the Cold War. But it was about also GPS, about so many side effects that in the 60s were not yet appreciated but eventually created the world we have now. I don't know what the consequences of us flying to Mars. Maybe something will happen, one of the asteroids will just find sort of a new substance that will replace fossil fuel. What I know, it will happen because when you look at the human history there's all this great exploration. They ended up with unintended consequences as the main result. Not what was originally planned as the number one goal. >> We've been talking about where innovation comes from today. It's a combination of a by-product out there. A combination of data plus being able to apply artificial intelligence. And of course there's cloud economics as well. Essentially, well is that reasonable? I think about something you said, I believe, in the past that you didn't have the advantage of seeing Deep Blue's moves, but it had the advantage of studying your moves. You didn't have all the data, it had the data. How does data fit into the future? >> Data is vital, data is fuel. That's why I think we need to find some of the most effective ways of collaboration between humans and machines. Machines can mine the data. For instance, it's a breakthrough in instantly mining data and human language. Now we could see even more effective tools to help us to mine the data. But at the end of the day it's why are we doing that? What's the purpose? What does matter to us, so why do we want to mine this data? Why do we want to do here and not there? It seems at first sight that the human responsibilities are shrinking. I think it's the opposite. We don't have to move too much but by the tiny shift, just you know percentage of a degree of an angle could actually make huge difference when this bullet reaches the target. The same with AI. More power actually offers opportunities to start just making tiny adjustments that could have massive consequences. >> Open up a big, that's why you like augmented intelligence. >> I think artificial is sci-fi. >> What's artificial about it, I don't understand. >> Artificial, it's an easy sell because it's sci-fi. But augmented is what it is because our intelligent machines are making us smarter. Same way as the technology in the past made us stronger and faster. >> It's not artificial horsepower. >> It's created from something. >> Exactly, it's created from something. Even if the machines can adjust their own code, fine. It still will be confined within the parameters of the tasks. They cannot go beyond that because again they can only answer questions. They can only give you answers. We provide the questions so it's very important to recognize that it is we will be in the leading role. That's why I use the term shepherds. >> How do you spend your time these days? You're obviously writing, you're speaking. >> Writing, speaking, traveling around the world because I have to show up at many conferences. The AI now is a very hot topic. Also as you mentioned I'm the Chairman of Human Rights Foundation. My responsibilities to help people who are just dissidents around the world who are fighting for their principles and for freedom. Our organization runs the largest dissident gathering in the world. It's called the Freedom Forum. We have the tenth anniversary, tenth event this May. >> It has been a pleasure. Garry Kasparov, live on theCube. Back with more from New York City right after this. (lively instrumental music)

>> Voiceover: Live from Las Vegas, it's theCUBE covering VMworld 2017, brought to you by VMware and it's ecosystem partners. (electronic music) >> Hello everyone, welcome back to live coverage. This is theCUBE at VMworld 2017, our eighth year covering VMworld, going back to 2010. I'm John Furrier, co-host of theCUBE, and my co-host this segment, Justin Warren, industry analyst, and our guest, Alan Cohen, Chief Commercial Officer, COO for Illumio. Great to see you, CUBE alumni. Special guest appearance, guest analyst appearance, but also Chief Commercial Officer, Illumio is a security start-up, growing. Thanks for coming on. >> It's not even a startup anymore. >> Justin: It's technically a startup. >> John: After five years, it's not a startup. >> It's not a startup right, you raise $270 million, it's not exactly a startup. >> (laughs) That's true. Well, welcome back. >> Alan: Thank you. >> Welcome back from vacation. Justin and I were talking before you came on, look at, let's go get you on and get some commentary going. >> Alan: Okay. >> You're an industry vet, again, in security, some perspective, but industry perspective, you've seen this VMware cycle many times. What's your analysis right now, obviously stock's 107, they don't to a cloud, no big catback, so it's good. You've made a decision. What's your take on this? >> I've been coming to VMworld for a long time, as you guys have as well, and from my perspective, this was probably the biggest or most significant transition in the history of the company. If you think about the level of dialogue, obviously there's a lot about NSX, which came from the Nicira, I'm always happy about. But, if you hear about, talking about cloud, and kind of talking about a post-infrastructure world, about capabilities, about control, about security, about being able to manage your compute in multiple environments, this is, I think, the beginning of a fundamentally different era. I always think about VMware, this is the company that defined virtualization. No one will argue with that point, so when they come out and they start talking about how are your computes going to operate in multiple environments? And how you're going to put that together, this is not cloud-washing, this is a fairly, all right they have fully acknowledged that the cloud is not a fad, the cloud is not for third tier workloads, this is mainstream computing. I think this is the third wave of computing and VMware is starting to put its markers down for the type of role that it intends to play in this transition. >> Yeah, I agree. >> We have to argue if you don't agree (laughs). >> I'll mostly agree with you, how about that? >> All right that's good. >> At this show, VMware has stopped apologizing for existing. I think, previously, they've been trying to say, "No, no we're a cloud too, "in fact, we're better than cloud "and you shouldn't be using it." It forced customers to choose between two of their children, really, like which one do you love more? And customers don't like that. Whereas at this show, I think it's finally being recognized that customers want to be able to use cloud, as well as use VMware, so that they're taking a more partnership approach to that and it's more about the ecosystem. And, agree, they're not about the infrastructure so much, they're not about the Hypervisor, they're about what you run on top of that. But, I still think there's a lot of infrastructure in that because VMware is fundamentally an infrastructure. >> Alan: Well, you got to get paid, right? >> That's right, (Alan laughs) and there's a lot of stuff out there that's already on VMware. What do you think about the approach? Like with cloud, they have a lot of people doing things in new ways and you mentioned this is the third wave of computing that we're doing it a new way. A lot of VMware stuff is really the whole reason it was popular is that we have people doing things a particular way on physical hardware and then they kept doing more or less the same thing, only on virtual hardware. What do you say about people who are still essentially going to be doing virtual hardware, they're just running it on cloud now? That's not really changing much. >> The way I think about it is: Are you going to be the Chevy Volt or are you going to be a Tesla? What I mean by that, and by the way now GM has the Bolt, which is their move toward Tesla, which is that if you look at the auto industry, they talk about hybrid and you talk about it, and you talk to Elon Musk and he goes, "Hybrids are bullshit." Either you're burning gas, or you're using electricity. To me, this cloud movement is about electricity, which is: I'm going to use cloud-native controls, I'm going to use cloud-native services, I'm going to be using Python and Ruby, and I'm going to have scripting, and I'm going to act like DevOps. And so, cloud is not just a physical place where I rent cycles from Amazon or Azure, it is a way of computing that's got a distributed, dynamic, heterogeneous, and hybrid. When you're in your virtualization on top of cloud, you're still in your Chevy Volt moment, but when you say, "I'm going to actually be native "across all of these environments," then you're really moving into the Tesla movement. >> Hold on. Let me smoke a little bit, I'll pass it over to you because that's complete fantasy. Right now the reality is, is that-- >> It's legal here in LA, in Las Vegas. >> (laughs) I don't think so yet, is it? >> Only outside. >> You can go to Walgreens across the street. >> Whatever you're smoking is good stuff. No, I agree, cloud obviously as a future scenario, there's no debate, but the reality is, like the Volt, Tesla is a one-trick pony. So, greenfield-- >> But, once again, I'm not disagreeing with you, John, but my point is that VMware and most of the IT industry is not there. Most companies don't have DevOps people, you run up and down, you go to all of these shows, ask these guys how many of these guys does Ruby, Python, real scripting, they don't do that. They still have Lu-Wise and management consults and they have the old IT, but this is the beginning movement-- >> They've got legacy bag, I mean we call it legacy baggage in the business, we know what that is. >> Heritage systems. (all laugh) >> Well, Gelsinger was here, I had him in at one o'clock and I kind of, sometimes VMware, they make the technical mistake in PR, they don't really get sometimes where to position things, but the Google announcement was very strategic intent, but they kind of made it a land grab and they tried to overplay their hand, in my opinion, on that one thing, it's strategic intent. This audience, they're not DevOps ready, they're Ops trying to do Dev, so they're not truly ready. So, it's okay to say, "Here's Amazon. "Great, that's today, if you want to do that, "let's get going, checking the boxes, "we're hitting the milestones." And then to dump a headroom deal announcement, that's more headroom, which is cool, but not push it on the Ops guys. >> Here's the opportunity and here's the risk: If Amazon is a $16 billion a year business, it's a rounding error in IT spend. When you take the hype away, nothing against it, and I love that prices are cheaper at Amazon and you can buy a Dot in the fruit aisle, that would totally-- >> John: I think the margins are like 60% (laughs). >> On your cloud. >> My wife took a picture of a rib steak and it said $18, now $13.99, I said, "Fantastic, thank you, Jeff Bezos. "We're eating well, "and we're going to have a little extra money." What I think this transition is not about infrastructure, it's about how IT people do their job. >> John: That's a main point. >> Justin: That's a big, big change. >> Yeah. >> Okay, in this show today doing your job, Justin I want you to comment on this because you were talking with Stu about it. I'm a VMware customer, what do I care about right now in my world? Just today. >> Well, in my world I've got conflicting things, I need to get my job done now. There's nothing different about the IT job, really, which is a shame because some of it needs to change, but there is a gradual realization that it's not about IT, it's not about building infrastructure for the sake of, "Because I like shiny infrastructure." It's, "I'm being paid by my business "to do IT things in service of the business." I have customers who are buying Apples, or using Apple docs, you're laundering. >> In IT you're paid for an outcome. You don't create the outcome. The way IT works is business creates the outcome, IT helps fulfill the outcome, unless you work-- >> John: Is IT a department today? >> Yeah, it's still a department. >> It's still a department? >> Yeah, it is, but it's a department in the same way that, well finance is important, but it's actually the business. Sales is part, they're all integrated. In a really well-run business, they're all integrated. >> How do you know what a real business is? You go to a building, you go to the main offices, you visit the marketing floor, you visit the IT floor. Tell me what the decor is like. They'll tell you what they care about in a business. (John laughs) I've been in a lot of IT shops, not the beautiful shiny glass windows because it's perceived as a back office cost center. >> Digital transformation is always about taking costs, that's table stakes, but now some of the tech vendors need to understand that as you get more business focused, you got to start thinking about driving top line. >> You're also thinking about being in the product. For example, my company, we have three of the four top SAS vendors, as Illumio customers, we do the micro-segmentation for them. We're not their micro-segmentation, we're a component in the software they sell you guys. >> Justin: You're an input. >> Yeah, you are a commodity in the mix of what somebody's building and I think that's going to be one of the changes. The move to cloud, it's not rent or buy, it's not per hour per server, or call Michael Dell and send me a bunch of Q-series, or whatever the heck it's called, it's increasingly saying, "We have these outcomes, we have these dates, "we have these deliverables, "what am I doing to support that and be part of that?" >> Justin: That's it, it's a support function. It's a very important support function, but there's very few businesses, like digital transformation, I don't like that as a term-- >> What the heck does that mean? >> It means something to do with fingers. >> Alan: You use it a lot, what does it really mean, digital transformation? >> To me, first of all, I'm not a big hype person, I like the buzz word in the sense that it does have a relevance now in terms of doing business digitally means you're completely 100% technology-enabled in your business. That means IT is a power function, not a cost center, it's completely native, like electricity in the company-- >> Unless, let's say I have two customers, I have the Yellow Cab company of Las Vegas and I have Uber or Lyft as a customer. My role, as a technologist, or technology provider, is dramatically different in either one of those-- >> Digital transformation to me is a mindset of things like, "I'm going to do a blockchain, "I'm going to start changing the game, "I'm going to use technology "to change the value equation for my customer." It's not IT conversation in the sense of, let's buy more servers to make something happen for the guy who had a request in that saying, "Let's use technology digitally to change the outcomes." >> But, given that, if we assume that that's true, then there's two ways of doing that. Either we have the IT people need to learn more about business, or the business people need to learn more about IT. >> That's right. >> Which one do you think should happen? Traditionally-- >> I think they're on a collision course. >> I don't think you can survive as a senior executive in most businesses anymore by saying, "Oh, I'll get my CIO in here." >> I would like to believe that that's true, but when people say that it should be a strategic resource and so on, and yet we spend decades outsourcing IT to someone else. If it's really truly important to your business, why aren't you doing it yourself? >> Justin, it's a great question and here's my observations, just thinking out loud here. One, just from a Silicon Valley perspective, looking at entrepreneurial as a canary in the coalmine, you've seen over the past 10-15 years, recently past 10, entrepreneurs have become developer entrepreneurs, product entrepreneurs, have become very savvy on the business side. That's the programmer. When we see Travis with Uber, no VC, they got smart because they could educate themselves. AngelList, Venture Hacks, there's a lot of data out there, so I see some signs of developers specifically building apps because user design is really important, they are leading into, what I call, the street MBA. They're not actually getting an MBA, they don't read the Wall Street Journal, but they're learning about some business concepts that they have to understand to program. IT I think is still getting there, but not as much as the developers. >> Here's a great question that I've learned over the years, and look, I'm coming out of the IT side, as we all are. When I visit a customer and I try to sell them my product, my first question is, "If I didn't exist, what would you do? "And if you don't buy my product, what happens in your business?" And if they're saying, "I have this other alternative." Or it's like, "Ah, we'll do it next year." I mean, maybe I can sell them some product, but what they're really telling me is, "I don't matter." >> All right, let's change the conversation a little bit, just move to another direction I want to get your thoughts on. And I should have, on the intro, given you more prompts, Alan. You were also involved in Nicira, the startup that VMware had bought-- >> Alan: Before all this NSX stuff, I was early. >> Hold on, let me finish the intro. We've interviewed Martin Casado. Stu talks to us all the time, I'm sure Chess has been hearing on the other set, "Oh, hey Martin Casado." It was a great interview, of course they're on theCUBE directory. But, you were there when it was just developing and then boom, software-defined networking, it's going to save the world. NSX has become very important to VMware, what's your thoughts on that? What does the alumni from Nicira and that folks that are still here and outside of VMware think about what's it's turned into? Is it relevant? And where is it going? >> Look, I could have not predicted five years ago when Nicira was acquired by VMware, it would be the heart of everything that their CEO and their team is talking about, if you want to know if that's important, go to the directory of sessions and one out of every three are about NSX. But, I think what it really means is there's a recognition that the network component, which is what really NSX represents, is the part that's going to allow them to transcend the traditional software-defined data center. I have two connections, so Steve Herrod is my investor, Steve is the inventor of the software-defined data center. That was the old Kool-Aid, not the new Kool-Aid. We've left the software-defined data center, we've moved into this cloud era and for them NSX is their driving force on being able to extend the VMware control plane into environments they used to never play in before. That's imminently clear. >> John: Justin, what's your take on NSX? >> NSX is the compatibility mechanism for being able do VMware in multiple places, so I think it's very, very important for VMware as a company. I don't think it's the only solution to that particular problem of being able to have networks that move around, it's possible to do it in other ways. For example, cloud-native type things, will do the networking thing in a different way. But, the network hasn't really undergone the same kind of change that happened in server or it did in storage, it's been pretty much the same for a long, long time. >> You've had an industry structurally dominated by one company, things don't change when-- >> Justin: And it still is, yeah. >> John: Security, security, because we've got a little bit of time I want to get to security. You guys are in the security space. >> Thanks for noticing. >> (laughs) I still don't know what you did, I'm only kidding. Steve Harrod is your investor, former CEO of VMware, very relevant for folks watching. Guys, security Pat Gelsinger said years ago it should be a duo, we've got to fix this. Nothing has really happened. What is the state of the union, if you will, of security? Where the frig is it going? What the hell's going on with security? >> There's two issues with that. If we put our industry analyst hat on, security is the largest segment of IT where nobody owns 5% market share, so there's not gorilla force that can drive that. VMware was the gorilla force driving virtualization, Cisco drove networking, EMC, in the early days, drove storage, but when you get to security you have this kind of-- >> John: Diluted. >> It's like the Balkans, it's like feudal states. >> Justin: It's a ghastly nightmare. >> What I think what Pat was talking about, which we also subscribe to, there are some movements in security, which micro-segmentation is one of them, which are kind of reinstalling a form of forensic hygiene into saying, "Your practices, if they occur, "they will reduce the risk profile." But, I think 50% of the security solutions and categories-- >> So, if I've lost my teeth, I don't get cavities. That kind of thing going on. >> If you're a doctor and you're making rounds in the hospital, you wash your hands or you put on gloves. >> And that's where we are. That is the stage we are at with security is we're at the stage where surgeons didn't believe they should wash their hands because they knew better and they'd say, "No, this couldn't possibly be making patients sick." People have finally realized that people get sick and the germ theory is real and we should wash our hands. >> Your network makes you sick. Your network is the carrier. Everything that's happening in network is effectively the Typhoid Mary of security. (John laughs) We're building flat, fast, unsegmented Layer 3 networks, which allow viruses to move at the speed of light across your environment. So, movements like, what's that called App Defense? >> Justin: App Defense, yep. >> App Defense or micro-segmentation from Illumio and Vmware, are the kind of new hygiene and new practices that are going to reduce the wide-spread disease growing. >> From an evolution theory, then the genetics of networks are effed up. This is what you're saying, we need to fix-- >> No, the networks are getting back to what they were supposed to do. Networks move packets from point A to point D. >> The dumb network? >> Alan: Yes, the dumber the better. >> Okay. You agree? >> Alan: Dumb them down. >> Dumb networks, smart end points. Smart networks doesn't scale as well as smart end points, and we're seeing that with edge computing, for example. Distributed networking is a hard problem and there is so much compute going out there, everything has a computer in it, they're just getting tinier and tinier. If we rely on the network to secure all of that, we're doomed. >> Better off at the end point. And this fuels the whole IoT edge thing, straight up one of the key wave slides out there. >> What you're going to have is a lot of telemetry points and you're going to have a lot of enforcement points. Our architecture is compatible with this, VMware is moving in this direction, other people are, but the people that are clinging to the gum up my network with all kinds of crap, because actually people want it to go the other way. If you think about it, the Internet was built to move packets from point A to point B in case of a nuclear war and, other than routing, there wasn't a whole lot-- >> We still might have that problem (laughs) >> Yeah, well there's always that (laughs). >> Fingers crossed. >> Guys, we got to break, next segment. Al, I'll give you the last word, just give a quick plug for Illumio. Thanks for coming on and being a special guest analyst, as usual, great stuff. Little slow from vacation, you're usually a little snappier. >> Alan: Little slow off the vacation mark. >> Yeah, come on. Back in Italy-- >> Too much Brunello di Motalcino, yeah. >> John: (laughs) Quick plug for Illumio, do a quick plug. >> We're really great to be here. John, you and I talked recently, Illumio is growing very rapidly, clearly we are probably emerging as one of the leaders in this micro-segmentation movement. >> John: A wannabe gorilla. >> What's that? >> You're a wannabe gorilla, go big or go home. >> We are, well, gorillas have to start as little gorillas first, we're not a wannabe gorilla, we're just gorillas growing really rapidly. It takes a lot more food at the zoo to keep us going. About 200 people growing rapidly, just moved into Asia, Pat, we got a guy in your part of the world we work with. >> First of all, it's not a zoo, it's still a jungle. The zoo is not yet established. >> That's true. We're going to establish the zoo. Things are great at Illumio. We have amazing things on the floor here today of, basically the system will actually write its own security policy for you. It's a lot of movement into machine learning, a lot of good stuff. >> All right. Guys, thanks so much. Alan Cohen with Illumio, >> Alan: Thank you. >> Chief Commercial Officer. And Justin Warren, analyst, I'm John Furrier. More live coverage from VMworld after this short break. (electronic music)

>> Announcer: Live from Las Vegas, it's theCUBE covering HPE Discover 2017, brought to you by Hewlett Packard Enterprise. >> Okay, welcome back everyone. We're live here in Las Vegas for SiliconANGLE's CUBE coverage of HPE Discover 2017. This is our seventh year of covering HPE Discover Now. HPE Discover in its second year. I'm John Furrier, my co-host Dave Vellante. We've got two great guests, two doctors, PhD's in the house here. So Eng Lim Goh, VP and SGI CTO, PhD, and Tuomas Sandholm, Professor at Carnegie Mellon University of Computer Science and also runs the marketplace lab over there, welcome to theCube guys, doctors. >> Thank you. >> Thank you. >> So the patient is on the table, it's called machine learning, AI, cloud computing. We're living in a really amazing place. I call it open bar and open source. There's so many new things being contributed to open source, so much new hardware coming on with HPE that there's a lot of innovation happening. So want to get your thoughts first on how you guys are looking at this big trend where all this new software is coming in and these new capabilities, what's the vibe, how do you look at this. You must be, Carnegie Mellon, oh this is an amazing time, thoughts. >> Yeah, it is an amazing time and I'm seeing it both on the academic side and the startup side that you know, you don't have to invest into your own custom hardware. We are using HPE with the Pittsburgh Supercomputing Center in academia, using cloud in the startups. So it really makes entry both for academic research and startups easier, and also the high end on the academic research, you don't have to worry about maintaining and staying up to speed with all of the latest hardware and networking and all that. You know it kind of. >> Focus on your research. >> Focus on the research, focus on the algorithms, focus on the AI, and the rest is taken care of. >> John: Eng talk about the supercomputer world that's now there, if you look at the abundant computer intelligent edge we're seeing genome sequencing done in minutes, the prices are dropping. I mean high performance computing used to be this magical, special thing, that you had to get a lot of money to pay for or access to. Democratization is pretty amazing can I just hear your thoughts on what you see happening. >> Yes, Yes democratization in the traditional HPC approach the goal is to prediction and forecasts. Whether the engine will stay productive, or financial forecasts, whether you should buy or sell or hold, let's use the weather as an example. In traditional HPC for the last 30 years what we do to predict tomorrows weather, what we do first is to write all the equations that models the weather. Measure today's weather and feed that in and then we apply supercomputing power in the hopes that it will predict tomorrows weather faster than tomorrow is coming. So that has been the traditional approach, but things have changed. Two big things changed in the last few years. We got these scientists that think perhaps there is a new way of doing it. Instead of calculating your prediction can you not use data intensive method to do an educated guess at your prediction and this is what you do. Instead of feeding today's weather information into the machine learning system they feed 30 years everyday, 10 thousand days. Everyday they feed the data in, the machine learning system guess at whether it will rain tomorrow. If it gets it wrong, it's okay, it just goes back to the weights that control the inputs and adjust them. Then you take the next day and feed it in again after 10 thousand tries, what started out as a wild guess becomes an educated guess, and this is how the new way of doing data intensive computing is starting to emerge using machine learning. >> Democratization is a theme I threw that out because I think it truly is happening. But let's get specific now, I mean a lot of science has been, well is climate change real, I mean this is something that is in the news. We see that in today's news cycle around climate change things of that as you mentioned weather. So there's other things, there's other financial models there's other in healthcare, in disease and there's new ways to get at things that were kind of hocus pocus maybe some science, some modeling, forecasting. What are you seeing that's right low hanging fruit right now that's going to impact lives? What key things will HPC impact besides weather? Is healthcare there, where is everyone getting excited? >> I think health and safety immediately right. Health and safety, you mentioned gene sequencing, drug designs, and you also mentioned in gene sequencing and drug design there is also safety in designing of automobiles and aircrafts. These methods have been traditionally using simulation, but more and more now they are thinking while these engines for example, are flying can you collect more data so you can predict when this engine will fail. And also predict say, when will the aircraft lands what sort of maintenance you should be applying on the engine without having to spend some time on the ground, which is unproductive time, that time on the ground diagnosing the problems. You start to see application of data intensive methods increased in order to improve safety and health. >> I think that's good and I agree with that. You could also kind of look at some of the technology perspective as to what kind of AI is going to be next and if you look back over the last five to seven years, deep learning has become a very hot part of machine learning and machine learning is part of AI. So that's really lifted that up. But what's next there is not just classification or prediction, but decision making on top of that. So we'll see AI move up the chain to actual decision making on top of just the basic machine learning. So optimization, things like that. Another category is what we call strategic reasoning. Traditionally in games like chess, or checkers and now Go, people have fallen to AI and now we did this in January in poker as well, after 14 years of research. So now we can actually take real strategic reasoning under imperfect information settings and apply it to various settings like business strategy optimization, automated negotiation, certain areas of finance, cyber security, and so forth. >> Go ahead. >> I'd like to interject, so we are very on it and impressed right. If we look back years ago IBM beat the worlds top chess player right. And that was an expert system and more recently Google Alpha Go beat even a more complex game, Go, and beat humans in that. But what the Professor has done recently is develop an even more complex game in a sense that it is incomplete information, it is poker. You don't know the other party's cards, unlike in the board game you would know right. This is very much real life in business negotiation in auctions, you don't quite know what the other party' thinking. So I believe now you are looking at ways I hope right, that poker playing AI software that can handle incomplete information, not knowing the other parties but still able to play expertly and apply that in business. >> I want to double down on that, I know Dave's got a question but I want to just follow this thread through. So the AI, in this case augmented intelligence, not so much artificial, because you're augmenting without the perfect information. It's interesting because one of the debates in the big data world has been, well the streaming of all this data is so high-velocity and so high-volume that we don't know what we're missing. Everyone's been trying to get at the perfect information in the streaming of the data. And this is where the machine learning if I get your point here, can do this meta reasoning or this reasoning on top of it to try to use that and say, hey let's not try to solve the worlds problems and boil the ocean over and understand it all, let's use that as a variable for AI. Did I get that right? >> Kind of, kind of I would say, in that it's not just a technical barrier to getting the big data, it's also kind of a strategic barrier. Companies, even if I could tell you all of my strategic information, I wouldn't want to. So you have to worry not just about not having all the information but are there other guys explicitly hiding information, misrepresenting and vice versa, you doing strategic action as well. Unlike in games like Go or chess, where it's perfect information, you need totally different kinds of algorithms to deal with these imperfect information games, like negotiation or strategic pricing where you have to think about the opponents responses. >> It's your hairy window. >> In advance. >> John: Knowing what you don't know. >> To your point about huge amounts of data we are talking about looking for a needle in a haystack. But when the data gets so big and the needles get so many you end up with a haystack of needles. So you need some augmentation to help you to deal with it. Because the humans would be inundated with the needles themselves. >> So is HPE sort of enabling AI or is AI driving HPC. >> I think it's both. >> Both, yeah. >> Eng: Yeah, that's right, both together. In fact AI is driving HPC because it is a new way of using that supercomputing power. Not just doing computer intensive calculation, but also doing it data intensive AI, machine learning. Then we are also driving AI because our customers are now asking the same questions, how do I transition from a computer intensive approach to a data intensive one also. This is where we come in. >> What are your thoughts on how this affects society, individuals, particularly students coming in. You mentioned Gary Kasparov losing to the IBM supercomputer. But he didn't stop there, he said I'm going to beat the supercomputer, and he got supercomputers and humans together and now holds a contest every year. So everybody talks about the impact of machines replacing humans and that's always happened. But what do you guys see, where's the future of work, of creativity for young people and the future of the economy. What does this all mean? >> You want to go first or second? >> You go ahead first. (Eng and Tuomas laughing) >> They love the fighting. >> This is a fun topic, yeah. There's a lot of worry about AI of course. But I think of AI as a tool, much like a hammer or a saw So It's going to make human lives better and it's already making human lives better. A lot of people don't even understand all the things that already have AI that are helping them out. There's this worry that there's going to be a super species that's AI that's going to take over humans. I don't think so, I don't think there's any demand for a super species of AI. Like a hammer and a saw, a hammer and a saw is better than a hammersaw, so I actually think of AI as better being separate tools for separate applications and that is very important for mankind and also nations and the world in the future. One example is our work on kidney exchange. We run the nationwide kidney exchange for the United Network for Organ Sharing, which saves hundreds of lives. This is an example not only that saves lives and makes better decisions than humans can. >> In terms of kidney candidates, timing, is all of that. >> That's a long story, but basically, when you have willing but incompatible live donors, incompatible with the patient they can swap their donors. Pair A gives to pair B gives to pair C gives to pair A for example. And we also co-invented this idea of chains where an altruist donor creates a while chain through our network and then the question of which combination of cycles and chains is the best solution. >> John: And no manual involvement, your machines take over the heavy lifting? >> It's hard because when the number of possible solutions is bigger than the number of atoms in the universe. So you have to have optimization AI actually make the decisions. So now our AI makes twice a week, these decisions for the country or 66% of the transplant centers in the country, twice a week. >> Dr. Goh would you would you add anything to the societal impact of AI? >> Yes, absolutely on the cross point on the saw and hammer. That's why these AI systems today are very specific. That's why some call them artificial specific intelligence, not general intelligence. Now whether a hundred years from now you take a hundred of these specific intelligence and combine them, whether you get an emergent property of general intelligence, that's something else. But for now, what they do is to help the analyst, the human, the decision maker and more and more you will see that as you train these models it's hard to make a lot of correct decisions. But ultimately there's a difference between a correct decision and, I believe, a right decision. Therefore, there always needs to be a human supervisor there to ultimately make the right decision. Of course, he will listen to the machine learning algorithm suggesting the correct answer, but ultimately the human values have to be applied to decide whether society accepts this decision. >> All models are wrong, some are useful. >> So on this thing there's a two benefits of AI. One is a this saves time, saves effort, which is a labor savings, automation. The other is better decision making. We're seeing the better decision making now become more of an important part instead of just labor savings or what have you. We're seeing that in the kidney exchange and now with strategic reasoning, now for the first time we can do better strategic reasoning than the best humans in imperfect information settings. Now it becomes almost a competitive need. You have to have, what I call, strategic augmentation as a business to be competitive. >> I want to get your final thoughts before we end the segment, this is more of a sharing component. A lot of young folks are coming in to computer science and or related sciences and they don't need to be a computer science major per se, but they have all the benefits of this goodness we're talking about here. Your advice, if both of you could share you opinion and thoughts in reaction to the trend where, the question we get all the time is what should young people be thinking about if they're going to be modeling and simulating a lot of new data scientists are coming in some are more practitioner oriented, some are more hard core. As this evolution of simulations and modeling that we're talking about have scale here changes, what should they know, what should be the best practice be for learning, applying, thoughts. >> For me you know the key thing is be comfortable about using tools. And for that I think the young chaps of the world as they come out of school they are very comfortable with that. So I think I'm actually less worried. It will be a new set of tools these intelligent tools, leverage them. If you look at the entire world as a single system what we need to do is to move our leveraging of tools up to a level where we become an even more productive society rather than worrying, of course we must be worried and then adapt to it, about jobs going to AI. Rather we should move ourselves up to leverage AI to be an even more productive world and then hopefully they will distribute that wealth to the entire human race, becomes more comfortable given the AI. >> Tuomas your thoughts? >> I think that people should be ready to actually for the unknown so you've got to be flexible in your education get the basics right because those basics don't change. You know, math, science, get that stuff solid and then be ready to, instead of thinking about I'm going to be this in my career, you should think about I'm going to be this first and then maybe something else I don't know even. >> John: Don't memorize the test you don't know you're going to take yet, be more adaptive. >> Yes, creativity is very important and adaptability and people should start thinking about that at a young age. >> Doctor thank you so much for sharing your input. What a great world we live in right now. A lot of opportunities a lot of challenges that are opportunities to solve with high performance computing, AI and whatnot. Thanks so much for sharing. This is theCUBE bringing you all the best coverage from HPE Discover. I'm John Furrier with Dave Vellante, we'll be back with more live coverage after this short break. Three days of wall to wall live coverage. We'll be right back. >> Thanks for having us.