>> Announcer: From Denver, Colorado, it's theCUBE, covering Super Computing 17. Brought to you by Intel. (soft electronic music) Hey, welcome back, everybody. Jeff Frick here with theCUBE. We're gettin' towards the end of the day here at Super Computing 2017 in Denver, Colorado. 12,000 people talkin' really about the outer limits of what you can do with compute power and lookin' out into the universe and black holes and all kinds of exciting stuff. We're kind of bringin' it back, right? We're all about democratization of technology for people to solve real problems. We're really excited to have our last guest of the day, bringin' the energy, Armughan Ahmad. He's SVP and GM, Hybrid Cloud and Ready Solutions for Dell EMC, and a many-time CUBE alumni. Armughan, great to see you. >> Yeah, good to see you, Jeff. So, first off, just impressions of the show. 12,000 people, we had no idea. We've never been to this show before. This is great. >> This is a show that has been around. If you know the history of the show, this was an IEEE engineering show, that actually turned into high-performance computing around research-based analytics and other things that came out of it. But, it's just grown. We're seeing now, yesterday the super computing top petaflops were released here. So, it's fascinating. You have some of the brightest minds in the world that actually come to this event. 12,000 of them. >> Yeah, and Dell EMC is here in force, so a lot of announcements, a lot of excitement. What are you guys excited about participating in this type of show? >> Yeah, Jeff, so when we come to an event like this, HBC-- We know that HBC is also evolved from your traditional HBC, which was around modeling and simulation, and how it started from engineering to then clusters. It's now evolving more towards machine learning, deep learning, and artificial intelligence. So, what we announced here-- Yesterday, our press release went out. It was really related to how our strategy of advancing HBC, but also democratizing HBC's working. So, on the advancing, on the HBC side, the top 500 super computing list came out. We're powering some of the top 500 of those. One big one is TAC, which is Texas Institute out of UT, University of Texas. They now have, I believe, the number 12 spot in the top 500 super computers in the world, running an 8.2 petaflops off computing. >> So, a lot of zeros. I have no idea what a petaflop is. >> It's very, very big. It's very big. It's available for machine learning, but also eventually going to be available for deep learning. But, more importantly, we're also moving towards democratizing HBC because we feel that democratizing is also very important, where HBC should not only be for the research and the academia, but it should also be focused towards the manufacturing customers, the financial customers, our commercial customers, so that they can actually take the complexity of HBC out, and that's where our-- We call it our HBC 2.0 strategy, off learning from the advancements that we continue to drive, to then also democratizing it for our customers. >> It's interesting, I think, back to the old days of Intel microprocessors getting better and better and better, and you had Spark and you had Silicon Graphics, and these things that were way better. This huge differentiation. But, the Intel I32 just kept pluggin' along and it really begs the question, where is the distinction now? You have huge clusters of computers you can put together with virtualization. Where is the difference between just a really big cluster and HBC and super computing? >> So, I think, if you look at HBC, HBC is also evolving, so let's look at the customer view, right? So, the other part of our announcement here was artificial intelligence, which is really, what is artificial intelligence? It's, if you look at a customer retailer, a retailer has-- They start with data, for example. You buy beer and chips at J's Retailer, for example. You come in and do that, you usually used to run a SEQUEL database or you used to run a RDBMS database, and then that would basically tell you, these are the people who can purchase from me. You know their purchase history. But, then you evolved into BI, and then if that data got really, very large, you then had an HBC cluster, would which basically analyze a lot of that data for you, and show you trends and things. That would then tell you, you know what, these are my customers, this is how many times they are frequent. But, now it's moving more towards machine learning and deep learning as well. So, as the data gets larger and larger, we're seeing datas becoming larger, not just by social media, but your traditional computational frameworks, your traditional applications and others. We're finding that data is also growing at the edge, so by 2020, about 20 billion devices are going to wake up at the edge and start generating data. So, now, Internet data is going to look very small over the next three, four years, as the edge data comes up. So, you actually need to now start thinking of machine learning and deep learning a lot more. So, you asked the question, how do you see that evolving? So, you see an RDBMS traditional SQL evolving to BI. BI then evolves into either an HBC or hadoop. Then, from HBC and hadoop, what do you do next? What you do next is you start to now feed predictive analytics into machine learning kind of solutions, and then once those predictive analytics are there, then you really, truly start thinking about the full deep learning frameworks. >> Right, well and clearly like the data in motion. I think it's funny, we used to make decisions on a sample of data in the past. Now, we have the opportunity to take all the data in real time and make those decisions with Kafka and Spark and Flink and all these crazy systems that are comin' to play. Makes Hadoop look ancient, tired, and yesterday, right? But, it's still valid, right? >> A lot of customers are still paying. Customers are using it, and that's where we feel we need to simplify the complex for our customers. That's why we announced our Machine Learning Ready Bundle and our Deep Learning Ready Bundle. We announced it with Intel and Nvidia together, because we feel like our customers either go to the GPU route, which is your accelerator's route. We announced-- You were talking to Ravi, from our server team, earlier, where he talked about the C4140, which has the quad GPU power, and it's perfect for deep learning. But, with Intel, we've also worked on the same, where we worked on the AI software with Intel. Why are we doing all of this? We're saying that if you thought that RDBMS was difficult, and if you thought that building a hadoop cluster or HBC was a little challenging and time consuming, as the customers move to machine learning and deep learning, you now have to think about the whole stack. So, let me explain the stack to you. You think of a compute storage and network stack, then you think of-- The whole eternity. Yeah, that's right, the whole eternity of our data center. Then you talk about our-- These frameworks, like Theano, Caffe, TensorFlow, right? These are new frameworks. They are machine learning and deep learning frameworks. They're open source and others. Then you go to libraries. Then you go to accelerators, which accelerators you choose, then you go to your operating systems. Now, you haven't even talked about your use case. Retail use case or genomic sequencing use case. All you're trying to do is now figure out TensorFlow works with this accelerator or does not work with this accelerator. Or, does Caffe and Theano work with this operating system or not? And, that is a complexity that is way more complex. So, that's where we felt that we really needed to launch these new solutions, and we prelaunched them here at Super Computing, because we feel the evolution of HBC towards AI is happening. We're going to start shipping these Ready Bundles for machine learning and deep learning in first half of 2018. >> So, that's what the Ready Solutions are? You're basically putting the solution together for the client, then they can start-- You work together to build the application to fix whatever it is they're trying to do. >> That's exactly it. But, not just fix it. It's an outcome. So, I'm going to go back to the retailer. So, if you are the CEO of the biggest retailer and you are saying, hey, I just don't want to know who buys from me, I want to now do predictive analytics, which is who buys chips and beer, but who can I sell more things to, right? So, you now start thinking about demographic data. You start thinking about payroll data and other datas that surround-- You start feeding that data into it, so your machine now starts to learn a lot more of those frameworks, and then can actually give you predictive analytics. But, imagine a day where you actually-- The machine or the deep learning AI actually tells you that it's not just who you want to sell chips and beer to, it's who's going to buy the 4k TV? You're makin' a lot of presumptions. Well, there you go, and the 4k-- But, I'm glad you're doin' the 4k TV. So, that's important, right? That is where our customers need to understand how predictive analytics are going to move towards cognitive analytics. So, this is complex but we're trying to make that complex simple with these Ready Solutions from machine learning and deep learning. >> So, I want to just get your take on-- You've kind of talked about these three things a couple times, how you delineate between AI, machine learning, and deep learning. >> So, as I said, there is an evolution. I don't think a customer can achieve artificial intelligence unless they go through the whole crawl walk around space. There's no shortcuts there, right? What do you do? So, if you think about, Mastercard is a great customer of ours. They do an incredible amount of transactions per day, (laughs) as you can think, right? In millions. They want to do facial recognitions at kiosks, or they're looking at different policies based on your buying behavior-- That, hey, Jeff doesn't buy $20,000 Rolexes every year. Maybe once every week, you know, (laughs) it just depends how your mood is. I was in the Emirates. Exactly, you were in Dubai (laughs). Then, you think about his credit card is being used where? And, based on your behaviors that's important. Now, think about, even for Mastercard, they have traditional RDBMS databases. They went to BI. They have high-performance computing clusters. Then, they developed the hadoop cluster. So, what we did with them, we said okay. All that is good. That data that has been generated for you through customers and through internal IT organizations, those things are all very important. But, at the same time, now you need to start going through this data and start analyzing this data for predictive analytics. So, they had 1.2 million policies, for example, that they had to crunch. Now, think about 1.2 million policies that they had to say-- In which they had to take decisions on. That they had to take decisions on. One of the policies could be, hey, does Jeff go to Dubai to buy a Rolex or not? Or, does Jeff do these other patterns, or is Armughan taking his card and having a field day with it? So, those are policies that they feed into machine learning frameworks, and then machine learning actually gives you patterns that they can now see what your behavior is. Then, based on that, eventually deep learning is when they move to next. Deep learning now not only you actually talk about your behavior patterns on the credit card, but your entire other life data starts to-- Starts to also come into that. Then, now, you're actually talking about something before, that's for catching a fraud, you can actually be a lot more predictive about it and cognitive about it. So, that's where we feel that our Ready Solutions around machine learning and deep learning are really geared towards, so taking HBC to then democratizing it, advancing it, and then now helping our customers move towards machine learning and deep learning, 'cause these buzzwords of AIs are out there. If you're a financial institution and you're trying to figure out, who is that customer who's going to buy the next mortgage from you? Or, who are you going to lend to next? You want the machine and others to tell you this, not to take over your life, but to actually help you make these decisions so that your bottom line can go up along with your top line. Revenue and margins are important to every customer. >> It's amazing on the credit card example, because people get so pissed if there's a false positive. With the amount of effort that they've put into keep you from making fraudulent transactions, and if your credit card ever gets denied, people go bananas, right? The behavior just is amazing. But, I want to ask you-- We're comin' to the end of 2017, which is hard to believe. Things are rolling at Dell EMC. Michael Dell, ever since he took that thing private, you could see the sparkle in his eye. We got him on a CUBE interview a few years back. A year from now, 2018. What are we going to talk about? What are your top priorities for 2018? >> So, number one, Michael continues to talk about that our vision is advancing human progress through technology, right? That's our vision. We want to get there. But, at the same time we know that we have to drive IT transformation, we have to drive workforce transformation, we have to drive digital transformation, and we have to drive security transformation. All those things are important because lots of customers-- I mean, Jeff, do you know like 75% of the S&P 500 companies will not exist by 2027 because they're either not going to be able to make that shift from Blockbuster to Netflix, or Uber taxi-- It's happened to our friends at GE over the last little while. >> You can think about any customer-- That's what Michael did. Michael actually disrupted Dell with Dell technologies and the acquisition of EMC and Pivotal and VMWare. In a year from now, our strategy is really about edge to core to the cloud. We think the world is going to be all three, because the rise of 20 billion devices at the edge is going to require new computational frameworks. But, at the same time, people are going to bring them into the core, and then cloud will still exist. But, a lot of times-- Let me ask you, if you were driving an autonomous vehicle, do you want that data-- I'm an Edge guy. I know where you're going with this. It's not going to go, right? You want it at the edge, because data gravity is important. That's where we're going, so it's going to be huge. We feel data gravity is going to be big. We think core is going to be big. We think cloud's going to be big. And we really want to play in all three of those areas. >> That's when the speed of light is just too damn slow, in the car example. You don't want to send it to the data center and back. You don't want to send it to the data center, you want those decisions to be made at the edge. Your manufacturing floor needs to make the decision at the edge as well. You don't want a lot of that data going back to the cloud. All right, Armughan, thanks for bringing the energy to wrap up our day, and it's great to see you as always. Always good to see you guys, thank you. >> All right, this is Armughan, I'm Jeff Frick. You're watching theCUBE from Super Computing Summit 2017. Thanks for watching. We'll see you next time. (soft electronic music)