>>from around the globe. It's the Cube with digital coverage of AWS reinvent 2020 sponsored by Intel, AWS and our community partners. Welcome back to the Cubes Coverage of 80 Bus Reinvent 2020. This is the Cube virtual. I'm your host, John Ferrier normally were there in person, a lot of great face to face, but not this year with the pandemic. We're doing a lot of remote, and he's got a great great content guest here. Rebecca Weekly, who's the senior director and senior principal engineer at for Intel's hyper scale strategy and execution. Rebecca. Thanks for coming on. A lot of great news going on around Intel on AWS. Thanks for coming on. >>Thanks for having me done. >>So Tell us first, what's your role in Intel? Because obviously compute being reimagined. It's going to the next level, and we're seeing the sea change that with Cove in 19, it's putting a lot of pressure on faster, smaller, cheaper. This is the cadence of Moore's law. This is kind of what we need. More horsepower. This is big theme of the event. What's what's your role in intel? >>Oh, well, my team looks after a joint development for product and service offerings with Intel and A W s. So we've been working with AWS for more than 14 years. Um, various projects collaborations that deliver a steady beat of infrastructure service offerings for cloud applications. So Data Analytics, ai ml high performance computing, Internet of things, you name it. We've had a project or partnership, several in those the main faces on thanks to that relationship. You know, today, customers Committee choose from over 220 different instance types on AWS global footprint. So those feature Intel processors S, P. J s ai accelerators and more, and it's been incredibly rewarding an incredibly rewarding partnership. >>You know, we've been covering Intel since silicon angle in the Cube was formed 10 years ago, and this is what we've been to every reinvent since the first one was kind of a smaller one. Intel's always had a big presence. You've always been a big partner, and we really appreciate the contribution of the industry. Um, you've been there with with Amazon. From the beginning, you've seen it grow. You've seen Amazon Web services become, ah, big important player in the enterprise. What's different this year from your perspective. >>Well, 2020 has been a challenging here for sure. I was deeply moved by the kinds of partnership that we were able to join forces on within telling a W s, uh, to really help those communities across the globe and to address all the different crisis is because it it hasn't just been one. This has been, ah, year of of multiple. Um, sometimes it feels like rolling crisis is So When the pandemic broke out in India in March of this year, there were schools that were forced to close, obviously to slow the spread of the disease. And with very little warning, a bunch of students had to find themselves in remote school out of school. Uh, so the Department of Education in India engaged career launcher, which is a partner program that we also sponsor and partner with, and it really they had to come up with a distance learning solutions very quickly, uh, that, you know, really would provide Children access to quality education while they were remote. For a long as they needed to be so Korean launcher turned to intel and to a W s. We helped design infrastructure solution to meet this challenge and really, you know, within the first, the first week, more than 100 teachers were instructing classes using that online portal, and today it serves more than 165,000 students, and it's going to accommodate more than a million over the fear. Um, to me, that's just a perfect example of how Cove it comes together with technology, Thio rapidly address a major shift in how we're approaching education in the times of the pandemic. Um, we also, you know, saw kind of a climate change set of challenges with the wildfires that occurred this year in 2020. So we worked with a partner, Roman, as well as a partner who is a partner with AWS end until and used the EEC Thio C five instances that have the second Gen Beyond available processors. And we use them to be able to help the Australian researchers who were dealing with that wildfire increase over 60 fold the number of parallel wildfire simulations that they could perform so they could do better forecasting of who needed to leave their homes how they could manage those scenarios. Um, and we also were able toe work with them on a project to actually thwart the extinction of the Tasmanian Devils. Uh, in also in Australia. So again, that was, you know, an HPC application. And basically, by moving that to the AWS cloud and leveraging those e c two instances, we were able to take their analysis time from 10 days to six hours. And that's the kind of thing that makes the cloud amazing, right? We work on technology. We hope that we get thio, empower people through that technology. But when you can deploy that technology a cloud scale and watch the world's solve problems faster, that has made, I would say 2020 unique in the positivity, right? >>Yeah. You don't wanna wish this on anyone, but that's a real upside for societal change. I mean, I love your passion on that. I think this is a super important worth calling out that the cloud and the cloud scale With that kind of compute power and differentiation, you gets faster speed to value not just horsepower, but speed to value. This is really important. And it saved lives that changes lives. You know, this is classic change. The world kind of stuff, and it really is on center stage on full display with Cove. I really appreciate, uh, you making that point? It's awesome. Now with that, I gotta ask you, as the strategist for hyper scale intel, um, this is your wheelhouse. You get the fashion for the cloud. What kind of investments are you making at Intel To make more advancements in the clock? You take a minute, Thio, share your vision and what intel is working on? >>Sure. I mean, obviously were known more for our semiconductor set of investments. But there's so much that we actually do kind of across the cloud innovation landscape, both in standards, open standards and bodies to enable people to work together across solutions across the world. But really, I mean, even with what we do with Intel Capital, right, we're investing. We've invested in a bunch of born in the cloud start up, many of whom are on top of AWS infrastructure. Uh, and I have found that to be a great source of insights, partnerships, you know, again how we can move the needle together, Thio go forward. So, in the space of autonomous learning and adopt is one of the start ups we invested in. And they've really worked to use methodologies to improve European Health Co network monitoring. So they were actually getting a ton of false positive running in their previous infrastructure, and they were able to take it down from 50 k False positive the day to 50 using again a I on top of AWS in the public cloud. Um, using obviously and a dog, you know, technology in the space of a I, um we've also seen Capsule eight, which is an amazing company that's enabling enterprisers enterprises to modernize and migrate their workloads without compromising security again, Fully born in the cloud able to run on AWS and help those customers migrate to the public cloud with security, we have found them to be an incredible partner. Um, using simple voice commands on your on your smartphone hypersonic is another one of the companies that we've invested in that lets business decision makers quickly visualized insects insight from their disparate data sources. So really large unstructured data, which is the vast majority of data stored in the world that is exploding. Being able to quickly discern what should we do with this. How should we change something about our company using the power of the public cloud? I'm one of the last ones that I absolutely love to cover kind of the wide scope of the waves. That cloud is changing the innovation landscape, Um, Model mine, which is basically a company that allows people thio take decades of insights out of the mainframe data and do something with it. They actually use Amazon's cloud Service, the cloud storage service. So they were able Teoh Teik again. Mainframe data used that and be able to use Amazon's capabilities. Thio actually create, you know, meaningful insights for business users. So all of those again are really exciting. There's a bunch of information on the Intel sponsor channel with demos and videos with those customer stories and many, many, many more. Using Amazon instances built on Intel technology, >>you know that Amazon has always been in about startup born in the cloud. You mentioned that Intel has always been investing with Intel Capital, um, generations of great investments. Great call out there. Can you tell us more about what, uh, Amazon technology about the new offerings and Amazon has that's built on Intel because, as you mentioned at the top of the interview, there's been a long, long standing partnership since inception, and it continues. Can you take a minute to explain some of the offerings built on the Intel technology that Amazon's offering? >>Well, I've always happened to talk about Amazon offerings on Intel products. That's my day job. You know, really, we've spent a lot of time this year listening to our customer feedback and working with Amazon to make sure that we are delivering instances that are optimized for fastest compute, uh, better virtual memory, greater storage access, and that's really being driven by a couple of very specific workloads. So one of the first that we are introducing here it reinvents is the n five the n instant, and that's really ah, high frequency, high speed, low Leighton see network variants of what was, you know, the traditional Amazon E. C two and five. Um, it's powered by a second Gen Intel scalable processors, The Cascade late processors and really these have the highest all court turbo CPU performance from the on scalable processors in the club, with a frequency up to 4.5 gigahertz. That is really exciting for HPC work clothes, uh, for gaining for financial applications. Simulation modeling applications thes are ones where you know, automation, Um, in the automotive space in the aerospace industries, energy, Telkom, all of them can really benefit from that super low late and see high frequency. So that's really what the M five man is all about, um, on the br to others that we've introduced here today and that they are five beats and that is that can utilize up thio 60 gigabits per second of Amazon elastic block storage and really again that bandwidth and the 260 I ops that it can deliver is great for large relational databases. So the database file systems kind of workload. This is really where we are super excited. And again, this is built on Cascade Lake. The 2nd 10. Yeah, and it takes It takes advantage of many different aspects of how we're optimizing in that processor. So we were excited to partner with customers again using E. B s as well as various other solutions to ensure that data ingestion times for applications are reduced and they can see the delivery to what you were mentioning before right time to results. It's all about time to results on the last one is t three e. N. 33 e n is really the new D three instant. It's again on the Alexa Cascade Lake. We offer those for high density with high density local hard drive storage so very cost optimized but really allowing you to have significantly higher network speed and disk throughput. So very cost optimized for storage applications that seven x more storage capacity, 80% lower costs given terabytes of storage compared to the previous B two instances. So we will really find that that would be ideal for workloads in distributed and clustered file system, Big data and analytics. Of course, you need a lot of capacity on high capacity data lakes. You know, normally you want to optimize a day late for performance, but if you need tons of capacity, you need to walk that line. And I think the three and really will help you do that. And and of course, I would be absolutely remiss to not mention that last month we announced the Amazon Web Services Partnership with us on an Intel select solution, which is the first, you know, cloud Service provider to really launching until select solution there. Um, and it's an HPC space, So this is really about in high performance computing. Developers can spend weeks for months researching, you know, to manage compute storage network software configuration options. It's not a field that has gone fully cloud native by default, and those recipes air still coming together. So this is where the AWS parallel cluster solution using. It's an Intel Select solution for simulation and modeling on top of AWS. We're really excited about how it's going to make it easier for scientists and researchers like the ones I mentioned before, but also I t administrators to deploy and manage and just automatically scale those high performance computing clusters in Aws Cloud. >>Wow, that's a lot. A lot of purpose built e mean, no, you guys were really nailing. I mean, low late and see you got stories, you got density. I mean, these air use cases where there's riel workloads that require that kind of specialty and or e means beyond general purpose. Now, you're kind of the general purpose of the of the use case. This is what cloud does this is amazing. Um, final comments this year. I want to get your thoughts because you mentioned Cloud Service provider. You meant to the select program, which is an elite thing, right? Okay, we're anticipating Mawr Cloud service providers. We're expecting Mawr innovation around chips and silicon and software. This is just getting going. It feels like to me, it's just the pulse is different this year. It's faster. The cadence has changed. As a strategist, What's your final comments? Where is this all going? Because this is pretty different. Its's not what it was pre code, but I feel like this is going to continue transforming and being faster. What's your thoughts? >>Absolutely. I mean, the cloud has been one of the biggest winners in a time of, you know, incredible crisis for our world. I don't think anybody has come out of this time without understanding remote work, you know, uh, remote retail, and certainly a business transformation is inevitable and required thio deliver in a disaster recovery kind of business continuity environment. So the cloud will absolutely continue on continue to grow as we enable more and more people to come to it. Um, I personally, I couldn't be more excited than to be able Thio leverage a long term partnership, incredible strength of that insulin AWS partnership and these partnerships with key customers across the ecosystem. We do so much with SVS Os Vives s eyes MSP, you know, name your favorite flavor of acronym, uh, to help end users experience that digital transformation effectively, whatever it might be. And as we learn, we try and take those learnings into any environment. We don't care where workloads run. We care that they run best on our architecture. Er and that's really what we're designing. Thio. And when we partner between the software, the algorithm on the hardware, that's really where we enable the best and user demand and the end use their time to incite and use your time to market >>best. >>Um, so that's really what I'm most excited about. That's obviously what my team does every day. So that's of course, what I'm gonna be most excited about. Um, but that's certainly that's that's the future that you see. And I think it is a bright and rosy one. Um, you know, I I won't say things I'm not supposed to say, but certainly do be sure to tune into the Cube interview with It's on. And you know, also Chatan, who's the CEO of Havana and obviously shaken, is here at A W s, a Z. They talk about some exciting new projects in the AI face because I think that is when we talk about the software, the algorithms and the hardware coming together, the specialization of compute where it needs to go to help us move forward. But also, the complexity of managing that heterogeneity at scale on what that will take and how much more we need to do is an industry and as partners to make that happen. Um, that is the next five years of managing. You know how we are exploding and specialized hardware. I'm excited about that, >>Rebecca. Thank you for your great insight there and thanks for mentioning the Cube interviews. And we've got some great news coming. We'll be breaking that as it gets announced. The chips in the Havana labs will be great stuff. I wouldn't be remiss if I didn't call out the intel. Um, work hard, play hard philosophy. Amazon has a similar approach. You guys do sponsor the party every year replay party, which is not gonna be this year. So we're gonna miss that. I think they gonna have some goodies, as Andy Jassy says, Plan. But, um, you guys have done a great job with the chips and the performance in the cloud. And and I know you guys have a great partner. Concerts provide a customer in Amazon. It's great showcase. Congratulations. >>Thank you so much. I hope you all enjoy olive reinvents even as you adapt to New time. >>Rebecca Weekly here, senior director and senior principal engineer. Intel's hyper scale strategy and execution here in the queue breaking down the Intel partnership with a W s. Ah, lot of good stuff happening under the covers and compute. I'm John for your host of the Cube. We are the Cube. Virtual Thanks for watching

>>from around the globe. It's the Cube with digital coverage of AWS reinvent 2020 sponsored by Intel, AWS and our community partners. Everyone welcome back to the cubes. Coverage of AWS Reinvent Amazon Web services Annual conference theme. Cuba's normally there in person. This year we can't be. It's a virtual event. This is the Cube virtual. I'm your host for the Cube. John Ferrier Tresh Damn Kroger, VP of G M and G m of the high performance computing team at Intel is here in the Cube until a big part of the cube every year. Trish, thank you for coming on Were remote. We can't be in person. Um, good to see you. >>Good to see you. >>I'm really impressed with Reinvent Has grown from kind of small show eight years ago to now kind of a bellwether. And and every year it's the same story. More scale, more performance, lower prices. This is kind of the intel cadence that we've seen of Intel over the years. But high performance computing, which >>has been >>around for a while, has gotten much more mainstream thinking because it's applying now to scale. So I want to get your thoughts and and just set the table real quick. What is high performance computing mean these days from Intel? And has that relate to what people are experiencing >>e high performance computing? Um, yes, it's been traditionally known as something that's, you know, in the in the labs and the government, you know, not used widely. But high performance computing is truly just changing the world is what you can dio Cove. It is a great example of where they've taken high performance computing to speed up the discovery of drugs and vaccines for or cova 19. They use it every day. You know, whether it's making Pampers or Clorox boxes. So they are those bottles so that they, when you drop them, they don't break, um, to designing airplanes and designing, um, Caterpillar tractors. So it is pervasive throughout. And, um, sometimes people don't realize that high performance computing infrastructure is kind of that basics that you use whenever you need to do something with dense compute. >>So what some examples of workloads can you just share? I mean, obviously Xeon processor. We've covered that many times, but I mean from a workload standpoint, what kind of workloads are high performance computing kind of related or unable or ideal for that's out there, >>right? Z on scalable processors are the foundation for high performance computing. If you look at what most people run high performance computing on its see on, and I think that it's so broad. So if you look at seismic processing or molecular dynamics for the drug discovery type work or if you think about, um, open foam for fluid dynamics or, um, you know, different financial trade service, you know, frequency, fats, frequency trading or low. I can't even think of that word. But anyway, trading is very common using high performance computing. I mean, it's just used pervasively throughout. >>Yeah, and you're seeing you're seeing the cloud of clarification of that. I want to get your thoughts. The next question is, you know it's not just Intel hardware. You mentioned Zeon, but HBC in AWS were here. It reinvent. Can you share how that plays out? What's your what's your What's your take on that? Because it's not just hard work and you just take them into explain relationship, >>right? So we definitely have seen the growth of high performance computing in the cloud over the last couple of years. We've talked about this for, you know, probably a decade, and we've definitely seen that shift. And with AWS, we have this wonderful partnership where Intel is not only bringing the hardware like you say, the Z on scalable processors, but we're also having accelerators and then on that whole software ecosystem where we work closely with our I s V and O S v partners. And when we bring, um, not only compilers but also analyzers in our full to tool suite so people can move between an on Prem situation Thio Public cloud like aws. Um, seamlessly. >>So talk about the developer impact. As I say, it's that learning show reinvent. There's a lot of developers here. I'll see mainstream you're seeing, you know, obviously the born in the cloud. But now you're seeing large scale enterprises and big businesses. You mentioned financial services from high frequency trading to oil and gas. Every vertical has a need for cloud and and what, you should be traditionally on premises compute. So you have. You're kind of connecting those dots here with AWS. Um, what is some of the developer angle here? Because they're in the cloud to they want to develop. How does how does the developer, um, engage with you guys on HPC in Amazon, >>Right? Well, there's there's a couple ways. I mean, so we do work with some of our partners eso that they could help move those workloads to the cloud. So an example is 69 which recently helped a customer successfully port a customized version of the in car models for prediction across scales. So they chose the C 59 18 x large instance type because this is what really deliver the highest performance and the lowest price for compute ratio. Another great example is P. K. I, which is a partner out of the UK, has worked with our customers to implement AI in retail and other segments running on Intel Instances of the EEC too. So I think these air just so you could have people help you migrate your workloads into the cloud. But then also, one of the great things I would like to talk about is, um a ws has come out with the parallel cluster, which is an Intel select solution, which really helps, um, ease that transition from on Prem to cloud. >>That's awesome. Um, let's get into that parallel cluster and you mentioned Intel Select Solution program. There's been some buzz on that. Can you take a minute to explain what that is? I >>mean, the HBC has, AH reputation of being hard, and the whole philosophy between behind the Intel Select solution is to make it easier for our customers to run HBC workloads in the cloud or on Prem and with E Intel Select Solution. It's also about scaling your job across a large number of notes, so we've made it a significant investment into the full stack. So this is from the silicon level all the way up to the application level so that we ensure that your application runs best on Intel and we bring together all the everything that you need into. Basically, it's a reference design. So it's a recipe where we jointly created it with our I, C, P and O S V partners and our open source environment for all the different relevant workloads. And so Amazon Web Services is the first cloud service provider to actually verify a service such as Intel Select Solution and this is this is amazing because this truly means that somebody can say it works today on Prem, and I know it will work exactly the same in AWS Cloud. >>That's huge. And I wanna just call that out because I think it's worth noting. You guys just don't throw this around like in the industry like doing these kind of partnerships. Intel's been pretty hard core on the quality, and so having a cloud service provider kind of go through the thing, it's really notable you mentioned parallel cluster um, deal. What is Can you just tie that together? Because if I get this right, the Intel, uh, select solution with the cloud service provider Amazon is a reference designed for how to go on premise or edge or revenue. It is to cloud in and out of cloud. How does this parallel cluster project fit into all this? Can you just unpack that a little bit? >>Right. So the parallel cluster basically, um, it's a parallel cluster until select solution. And there's three instances that we're featuring with the Intel Xeon Scalable processor, which gives you a variety of compute characteristics. So the select solution gives you the compute, the storage, the memory the networking that you need. You know, it says the specifications for what you need to run a non optimal way. And then a WS has allowed us to take some of the C five or some of the instances, and we are on. Three different instances were on the C five, in instance. But that's for your compute optimize work clothes. We're on the in five instance and that's really for a balanced between higher memory per core ratio. And then you have your are five and instance at a W s that's really targeted for that memory intensive workloads. And so all of these are accessible within the single A. W s parallel cholesterol environment on bits at scale. And it's really you're choosing of what you want to take and do. And then on top of that, the they're enabled with the next generation AWS Nitro system, which delivers 100 gigabits of networking for the HBC workloads. So that is huge for HPC. >>I was gonna get to the Nitro is my one of my top questions. Thanks >>for >>thanks for clarifying that. You know, I'm old enough to remember the old days when you have the intel inside the PC a shell of, ah box and create all that great productivity value. But with cloud, it's almost like we're seeing that again. You just hit on some key points you have. Yeah, this is HPC is like memory storage. You've got networking a compute. All these things kind of all kind of working together. If I get that right, you just kind of laid that out there. And it's not an intel Has to be intel. Everything. Your intel inside the cloud now and on premise, which is the There is no on premise anymore. It's cloud operations. If I get this right because you're essentially bridging the two worlds with the chips, you bring on premise which could be edge a big edge or small legend in cloud. Is that right? I mean, this is kind of where this is >>going. Yeah, so I mean, what I think about so a lot of them. The usages for HBC in the cloud is burst capacity. Most HBC centers are 100% not 100% because they have to do maintenance, but 95% utilized, so there is no more space. And so when you have a need to do a larger run or you need thio, you know, have something done quickly you burst to the cloud. That's just what you need to do now. I mean, or you want to try out different instances. So you want to see whether maybe that memory intensive workload would work better? Maybe in kind of that are five in instance, and that gives you that opportunity to see and also, you know, maybe what you want to purchase. So truly, we're entering this hybrid cloud bottle where you can't, um the demand for high performance computing is so large that you've got to be able to burst to the cloud. >>I think you guys got it right. I'm really impressed. And I like what I'm seeing. And I think you talked about earlier the top of the interview, government labs and whatnot. I think those are the early adopters because when they need more power and they usually don't have a lot of big budgets, a little max out and then go to the cloud Whether it's, you know, computing, you know what's going on in the ocean and climate change are all these things that they work on that need massive compute and power. That's a a pretext to enterprise. So if you can't connect the dots, you're kind of right in line with what we're seeing. So super impressive. Thanks for sharing that. Final thoughts on this is that performance. So Okay, the next question is, OK, all great. You're looking good off the tee or looking down the road. Clear path to success in the future. How does the performance compare in the cloud versus on premise? >>It could be well, and that's one of the great things about the Intel select solution because we have optimized that reference designed so that you can get the performance you're used to on Prem in the AWS Cloud. And so that is what's so cool honestly, about this opportunity So we can help you know, that small and medium business that doesn't maybe have this resource is or even those industries that do. And they know they're already a reference using that modeling SIM reference design, and they can now just burst to the cloud and it will work. But the performance they expect >>Trish, great to have you on great insight. Thanks for sharing all the great goodness from Intel and the A W s final thoughts on the on the partnership. We're not in person. And by the way, Intel usually has a huge presence. The booth is usually right behind the cube stage, which you guys sponsor. Thank you very much greater. Always partner with you. Great party. You sponsor the replay, which is always great, and it's always great party and great partnership. Good content. We're not there this year. What's the relationship like? And you take a minute to explain your final thoughts on a Amazon Web services and intel. >>Yeah, I know we have, Ah, Long term partnership 14 plus year partnership with AWS. And I mean, I think it's with the your, um taking Intel Select solution. It's going to be even a richer partnership we're gonna have in the future. So I'm thrilled that I have the opportunity to talk about it and really talk about how excited I am to be able Thio bring Mawr HBC into the world. It's all about the democratization of HBC because HBC changes the world >>well. Tricia, congratulations on the select program with AWS and the first cloud service provider really is a nice directional indicator of what's gonna happen. Futures laid out. Of course. Intel's in front. Thank you for coming. I appreciate it. >>Oh, thank you, John. >>Okay, that's the cubes. Virtual coverage Cube. Virtual. We're not in person. Aws reinvent 2020 is virtual. Three weeks were over the next three weeks, we're gonna bring you coverage. Of course. Cube Live in studio in Palo Alto will be covering a lot of the news. Stay with us from or coverage after this short break. Thank you.

>>long from Las Vegas. It's the Q covering a ws re invent 2019. Brought to you by Amazon Web service is and in along with its ecosystem partners. >>Welcome back to the Cube. We are in Las Vegas, Lisa Martin with John Wall's. I'm very excited that we're kind of color coordinated >>way. Didn't compare notes to begin with, but certainly the pink thing. It's worth it if >>you like. You complete me. >>Oh, thank you. Really, Joe, I don't hear that very often. My wife says that >>you tell that we're at the end of day one of the coverage of A W s three in bed. Good day, though. Yes, it has been very excited. We have a couple of guests joining us for our final segment on this. Please welcome. We have Bill Gerard CTO of Digital Transformation and Scale solutions at Intel Bill, welcome to our show. >>Thank you very much. Happy to be here >>And one of our friends. That's no stranger to the Cube. One of our former host, Bobby Allyn, the CEO of Cloud Generate. Bobby. >>Thank you. Thank you for having us. >>Guys, here we are. This there has not been a lull in the background noise all day. Not reinvent day one. But Bobby want to start with you. Talk to her audience about cloud genera. Who are you guys? What do you do? And what's different about what you're delivering? >>One of the first things is different about Claude Generous where we're located. So we're in Charlotte, which I call Silicon South. So we're kind of representing the East Coast, and we're a company that focuses, focuses on helping with workload, placement and transformation. So where you don't know whether something should go on from off grim. If you put it in Amazon, which service's should have consumed licensing models? Pricing models way help you make data driven decisions, right? So you're not just going based on opinion, you're going based on fact. >>And that's challenging because, you know, in the as, as John Ferrier would say, No Cloud Wanda Otto, which was compute network storage, it was the easy I shouldn't say easy, but the lift and shit applications that enterprises do are these workloads should go to the cloud. Now we have you know what's left over, and that's challenging for organization. Some of the legacy once can't move. How do you help from a Consul Tatum's down point that customers evaluate workloads? What data are they running? What the value that data has and if they are able to move some of those more challenging applications. >>So part of the framework for us, Lisa, is we want to make sure we understand what people are willing and able to change right, because sometimes it's not just about lower costs. Sometimes it's about agility, flexibility, deploying a different region. So what we often start with his wit is better look like you would assist us with life for your organization. And so then, based on that, we analyze the applications with an objective, data driven framework and then make sure the apse land where they're supposed to go. We're not selling any skewer product. We're selling advice to give you inside about what you should do, >>Bobby, I think. And maybe Bill to you could chime in here on this. If you give people a choice, What does this look like? What you know, What do you want? I don't want to do anything right. I want to stay put, right? But that obviously that's not an option, But you I'm sure you do get pushed back quite a bit from these almost the legacy mindset. And we've talked a lot about this whole transformation versus transition. Some people don't want to go, period. So how do you cajole them? Persuade them bring them along on this journey? Because it's gonna be a long trip. Yeah, I think you gotta pack a lunch. >>It's a good point. I think what we've seen, most of them have data experience that this is a tried and elements didn't get the results that they expected. This is where you know, the partnership that we have with call General. Really? You know that data driven, intelligent, based planning is super important, right? We want to really fundamentally health organizations move the right workloads, make sure they get the right results and not have to redo it. Right? And so part of that, you know, move when you're either past scars or not used to what you're doing. Give him the data and the information to be able to do that intelligently and make that as fast as they can. And you know, at the right, you know, experience in performance from a capability perspective. >>So so many businesses these days, if they're not legacy if they're not looking in the rear view mirror, what is the side mirror site? Objects are closer than they appear, even for Amazon. Right? For all of these companies, there are smaller organizations that might be born in a cloud compared to the legacy two words. And if they're not looking at, we have to transform from the top down digitally, truly transform. Their business may not be here in a year or two, so the choice and I think they need to pack a lunch and a hip flask for this because it's quite the journey. But I'm curious with the opportunity that cloud provides. When you have these consultation conversations, what are This? Could be so transformative not just to a business, but to a do an entire industry. Bill talked to us from your perspective about some of the things that you've seen and how this next generation of cloud with a I machine learning, for example, can can really transfer like what's the next industry that you think is prime to be really flipped upside down? >>Well, the good news is I think most of the industries in the segment that we talked to have realized they need to some level of transformation. So doing the business as usual really isn't an option to really grow and drive in the future. But I do think the next evolution really does center on what's happening in a I and analytics. Whether it's, you know, moving manufacturing from video based defect detection, supply chain integrity. You know what's happening from a retail was really the first in that evolution, but we see it in health care in Federal Data Center modernization, and it's really moving at a faster pace and adopting those cloud technologies wherever they needed, both in their data center in the public, cloud out of the edge. And we'll start to see a real shift from really consolidation in tow. Large hyper converts, data centers to distributed computing where everything again. And that's where we're excited about the work we're doing with the Amazon, the work we're doing with Eyes V partners to be at the capability where they need it, but I think it will be really the next. Evolution of service is everywhere. >>Never talk us through an example or use case of a customer that you're working with, a cloud genera with intel and and a W S. What does that trifecta look like for, say, a retailer or financial service is organization >>so that that looks like this? ELISA. When we when we talk about workload placement, we think that most companies look at that as a single question. It's at least a five fold question. Right there is the venue. There's the service. There's the configuration, the licensing model and the pricing model. You need to look at all five of those things. So even if you decided on a DBS is your strategic partner, we're not done yet. So we have a very large financialservices customer that I can't name publicly. But we've collaborated with them to analyze tens of thousands of workloads, some that go best off from some that go best on for him. And they need guidance and coaching on things like, Are you paying for redhead twice your pay for licensing on him? Are you also paying for that in the cloud? There are things that maybe should be running an RT s database as a service. Here's your opportunity to cut down on labor and shift some of the relationships tohave, toe re index and databases is not glamorous or differential to value for your business. Let's take advantage of what a TBS does well and make this better for your company. One of the things that I want to kind of introduce to piggyback on your question. We lean on people process technology as kind of the three, the three legged horse in the Enterprise. I want to change that people process product or people process problem. We're falling in love with the tech and getting lazy. Technology should be almost ubiquitous or under the covers to make a product better or to solve a problem for the customer. >>Well, maybe on that, I mean automation concern to come in and make a big play here because we're taking all these new tasks if you could automate them that you free your people, your developers to do their thing right. So you raise an interesting point on that about being lazy and relying on things. But yet you do want off put our offload some of these nasty not to free up that creativity and free up the people to do what they're supposed to be doing. It's a delicate balance, though, isn't it? It is. It is. This >>is where I think the data driven, you know, informed decisions important. We did a lot of research with Cloud Jenner and our customers, and there's really four key technical characteristics when evaluating workload. The 1st 1 of course, is the size of the data. Where is the created words They use Words that consumed the 2nd 1? Is the performance right? Either performance not only to other systems around it or the end user, but the performance of the infrastructure. What do you need out of the capability? The level of integration with other systems? And then, of course, security. We hear that time and again, right? Regulatory needs. What are we having from top secret data to company sensitive data? Really Getting that type of information to drive those workload placement decision becomes at the forefront of that on getting, you know, using cloud gender to help understand the number of interfaces in and out the sides of the data. The performance utilization of the system's really helps customers understand how to move the right workload. What's involved and then how to put that in the right eight of us instance, and use the right ideas capabilities, >>and you and you both have hit on something here because the complexity of this decision, because it's multi dimensional, you talked about the five points a little bit ago. Now you talked about four other factors. Sue, this is not a static environment, No, and to me that as you're making a decision, that point is what's very difficult for, I would assume for the people that you're interfacing with on the company level. Yes, because it's a moving target for them, right? They just it's it's dynamic and changing your data flows exponentially. Increasing capabilities are changing. How do you keep them from just breaking down? >>I don't want to jump in on that, because again, I'm going to repeat this again. That my thesis is often technology is the easy part. We need to have conversations about what we want to do. And so I had a conversation earlier today. Think of Amazon like a chef. They could make anything I want, but I need to decide what I want to eat. If I'm a vegan and he wants steak. That's not Amazons fault. If they can't cook something, that's a mismatch of a bad conversation. We need to communicate. So what I'm finding is a lot of executives are worried about this. There were Then you're going to give me the right the wrong answer to the right question. The reality is you may have the wrong question. First of all right, the question is usually further upstream, so the worry that you're gonna give me the wrong answer to the right question. But often you need to worry that you're getting your starting with the wrong question. You're gonna get the right answer asked the right question first. And then you got a chance to get to the final destination. But >>and then he in this multi cloud world that many organizations live in, mostly not My strategy could be by Emma A could be bi developer preference for different solutions. A lot of Serios air telling us we've inherited a lot of this multi cloud and technical debt. Exactly. So does not just compound the problem because to your point, I mean you think of one way we hear so many different stats about the number of clouds that on average enterprises using is like 5 to 9. That whole world. That's a reality for organizations. So in terms of how the business can be transformed by what you guys are doing together, it seems like there's a tremendous opportunity there. But to your point, Bobby, where do you start? How do you help them understand what? That right first question is at the executive level so that those four technical points that Bill talked about Tek thee you know, the executive staff is all on board with Yes, this is the question we're asking then will understand it. The technology is right. Sold >>it. It's got to start with, Really? What? The company's business imperatives, right? It can't start with an I t objective. It's it's Are we moving into new markets? Do we need thio deploy capabilities faster? Are we doing a digital customer experience? Transformation? Are we deploying new factories, new products into new regions, and so really the first areas? What's the core company strategy, imperatives of the business objectives? And >>then how >>does I t really help them achieve that? In some cases, it may be we have to shift and reduce our data center footprints way have to move capabilities to where we have a new region. Deployments, right? We've got to get him over to Europe. We don't have capabilities in Europe. We're going to Asia. I've got a mobile sales force now where I need to get that customer, meet the customer where they're doing, you know, in the retail store, and >>that >>really then leads quite simply, too. What are the capabilities that we have in house that we're using? >>How are >>they being utilized? And he's using them, and then how do we get them to where they need to be? Some cases accost, imperative. Some cases and agility, Time to market and another's and we're seeing this more often is really what are the new sets of technologies? A. I service is training in forgetting that we're not experience to do and set up, and we don't want to spend the time to go train our infrastructure teams on the technology. So we'll put our data scientists in there figuring out the right set of workloads, the right set of technology, that we can then transform and move our applications to utilize it really starts, I think with the business conversation, or what's the key inflection point that they're experiencing? >>And have you seen that change in the last few years that now it's where you know, cloud not cloud. What goes on Cloud was an I t conversation to your point, Bill. And then the CEO got involved in a little bit later. But now we're we're seeing and hearing the CEO has got to be involved from a business imperative perspective. >>Share some data, right? Uh, so, you know, a couple of years ago, everybody was pursuing cloud largely for cost. Agility started to become primary, and that's still very important. A lot of the internal enterprise data modernizations were essentially stalled a bit because they were trying to figure how much do we move to the the public cloud, right. We want to take advantage of those modern service is at that time, we did a lot of research with our partners. He was roughly 56% of enterprise workload for in their own data center. You know, the rest of them Republic Cloud. And then we saw really the work, the intelligent workload discussion that says we've had some false starts. Organizations now really consistently realize they need both, you know, their own infrastructure and public cloud, and we've actually seen on increase of infrastructure modernization. While they're moving more and more stuff to the cloud, they're actually growing there on centre. It's now roughly 59% on Prem today for that same business, and that's largely because they're using more. Cloud service is that they're also even using Maur on premise, and they're realizing it's a balance and not stalling one or starving one and then committing to the other the committing to both and really just growing the business where it needs to go. >>Strategic reasons. All right? >>Yes, well, there should be four strategic reasons. There aren't always back to your question about which question asked. One of the questions I often ask is, What do you think the benefits will be if you go to cloud? And part of what happens is is not a cloud capability? Problem is an expectation problem. You're not gonna put your GOP system in the cloud and dropped 30% costs in a month, and so that's where we need to have a conversation on, You know, let's iterating on what this is actually gonna look like. Let's evolve the organization. Let's change our thinking. And then the other part of this and this were clouded or an intel come in. Let's model with simulation looks like. So we're gonna take those legacy work clothes unless model containers. Let's model Micro Service is so before you have to invest in transformation to may not make sense. Let's see what the outcome's look like through simulation through a through M l and understand. Where does it make sense to apply? The resource is, you know, to double click on that solution that will help the business. >>I was gonna finish my last question, Bobby, with you saying, Why, Cloud General? But I think you just answered that. So last question for you, though, from from an expectation perspective, give me one of your favorite examples of customer whatever kind of industry there and that you've come in and helped them really level, set their expectations and kick that door wide open. >>That's tough, many >>to choose from. >>Yeah, let me let me try to tackle that one quickly. Store's computer databases. Those are all things that people look at I think what people are struggling with the most in terms of kind of expectations is what they're willing and able to change. So this is kind of what I leave on. Bill and I talked about this earlier today. A product is good, a plan is better. A partnership is best. Because with the enterprises of saying is, we're overwhelmed. Either fix it for me or get in there with me and do it right. Be in this together. So what we've learned is it's not about were close applications. It's all kind of the same. We need help. We're overwhelmed. I want a partner in telling Claude Juncker the get in this thing with me. Help me figure this out because I told you this cloud is at best a teenager. They just learned how to drive is very capable, but it needs some guard rails. >>I love that. Thanks you guys So much for explaining with Johnny what you guys are doing together and how you're really flipping the model for what customers need to be evaluated and what they need to be asking. We appreciate your time. >>Thank you for having us >>our pleasure. Thank you. for John Wall's I'm Lisa Martin. You've been watching the Cube at Reinvent 19 from Vegas. Wants to go tomorrow.

>> Announcer: Live from Las Vegas, it's theCUBE! Covering AWS re:Invent 2019. Brought to you by Amazon Web Services and Intel, along with its ecosystem partners. >> Welcome back to the Sands Convention Center in Las Vegas everybody, you're watching theCUBE, the leader in live tech coverage. My name is Dave Vellante, I'm here with my cohost Justin Warren, this is day one of our coverage of AWS re:Invent 2019, Naveen Rao here, he's the corporate vice president and general manager of artificial intelligence, AI products group at Intel, good to see you again, thanks for coming to theCUBE. >> Thanks for having me. >> Dave: You're very welcome, so what's going on with Intel and AI, give us the big picture. >> Yeah, I mean actually the very big picture is I think the world of computing is really shifting. The purpose of what a computer is made for is actually shifting, and I think from its very conception, from Alan Turing, the machine was really meant to be something that recapitulated intelligence, and we took sort of a divergent path where we built applications for productivity, but now we're actually coming back to that original intent, and I think that hits everything that Intel does, because we're a computing company, we supply computing to the world, so everything we do is actually impacted by AI, and will be in service of building better AI platforms, for intelligence at the edge, intelligence in the cloud, and everything in between. >> It's really come full circle, I mean, when I first started this industry, AI was the big hot topic, and really, Intel's ascendancy was around personal productivity, but now we're seeing machines replacing cognitive functions for humans, that has implications for society. But there's a whole new set of workloads that are emerging, and that's driving, presumably, different requirements, so what do you see as the sort of infrastructure requirements for those new workloads, what's Intel's point of view on that? >> Well, so maybe let's focus that on the cloud first. Any kind of machine learning algorithm typically has two phases to it, one is called training or learning, where we're really iterating over large data sets to fit model parameters. And once that's been done to a satisfaction of whatever performance metrics that are relevant to your application, it's rolled out and deployed, that phase is called inference. So these two are actually quite different in their requirements in that inference is all about the best performance per watt, how much processing can I shove into a particular time and power budget? On the training side, it's much more about what kind of flexibility do I have for exploring different types of models, and training them very very fast, because when this field kind of started taking off in 2014, 2013, typically training a model back then would take a month or so, those models now take minutes to train, and the models have grown substantially in size, so we've still kind of gone back to a couple of weeks of training time, so anything we can do to reduce that is very important. >> And why the compression, is that because of just so much data? >> It's data, the sheer amount of data, the complexity of data, and the complexity of the models. So, very broad or a rough categorization of the complexity can be the number of parameters in a model. So, back in 2013, there were, call it 10 million, 20 million parameters, which was very large for a machine learning model. Now they're in the billions, one or two billion is sort of the state of the art. To give you bearings on that, the human brain is about a three to 500 trillion model, so we're still pretty far away from that. So we got a long way to go. >> Yeah, so one of the things about these models is that once you've trained them, that then they do things, but understanding how they work, these are incredibly complex mathematical models, so are we at a point where we just don't understand how these machines actually work, or do we have a pretty good idea of, "No no no, when this model's trained to do this thing, "this is how it behaves"? >> Well, it really depends on what you mean by how much understanding we have, so I'll say at one extreme, we trust humans to do certain things, and we don't really understand what's happening in their brain. We trust that there's a process in place that has tested them enough. A neurosurgeon's cutting into your head, you say you know what, there's a system where that neurosurgeon probably had to go through a ton of training, be tested over and over again, and now we trust that he or she is doing the right thing. I think the same thing is happening in AI, some aspects we can bound and say, I have analytical methods on how I can measure performance. In other ways, other places, it's actually not so easy to measure the performance analytically, we have to actually do it empirically, which means we have data sets that we say, "Does it stand up to all the different tests?" One area we're seeing that in is autonomous driving. Autonomous driving, it's a bit of a black box, and the amount of situations one can incur on the road are almost limitless, so what we say is, for a 16 year old, we say "Go out and drive," and eventually you sort of learn it. Same thing is happening now for autonomous systems, we have these training data sets where we say, "Do you do the right thing in these scenarios?" And we say "Okay, we trust that you'll probably "do the right thing in the real world." >> But we know that Intel has partnered with AWS, I ran autonomous driving with their DeepRacer project, and I believe it's on Thursday is the grand final, it's been running for, I think it was announced on theCUBE last year, and there's been a whole bunch of competitions running all year, basically training models that run on this Intel chip inside a little model car that drives around a race track, so speaking of empirical testing of whether or not it works, lap times gives you a pretty good idea, so what have you learned from that experience, of having all of these people go out and learn how to use these ALM models on a real live race car and race around a track? >> I think there's several things, I mean one thing is, when you turn loose a number of developers on a competitive thing, you get really interesting results, where people find creative ways to use the tools to try to win, so I always love that process, I think competition is how you push technology forward. On the tool side, it's actually more interesting to me, is that we had to come up with something that was adequately simple, so that a large number of people could get going on it quickly. You can't have somebody who spends a year just getting the basic infrastructure to work, so we had to put that in place. And really, I think that's still an iterative process, we're still learning what we can expose as knobs, what kind of areas of innovation we allow the user to explore, and where we sort of walk it down to make it easy to use. So I think that's the biggest learning we get from this, is how I can deploy AI in the real world, and what's really needed from a tool chain standpoint. >> Can you talk more specifically about what you guys each bring to the table with your collaboration with AWS? >> Yeah, AWS has been a great partner. Obviously AWS has a huge ecosystem of developers, all kinds of different developers, I mean web developers are one sort of developer, database developers are another, AI developers are yet another, and we're kind of partnering together to empower that AI base. What we bring from a technological standpoint are of course the hardware, our CPUs, our AI ready now with a lot of software that we've been putting out in the open source. And then other tools like OpenVINO, which make it very easy to start using AI models on our hardware, and so we tie that in to the infrastructure that AWS is building for something like DeepRacer, and then help build a community around it, an ecosystem around it of developers. >> I want to go back to the point you were making about the black box, AI, people are concerned about that, they're concerned about explainability. Do you feel like that's a function of just the newness that we'll eventually get over, and I mean I can think of so many examples in my life where I can't really explain how I know something, but I know it, and I trust it. Do you feel like it's sort of a tempest in a teapot? >> Yeah, I think it depends on what you're talking about, if you're talking about the traceability of a financial transaction, we kind of need that maybe for legal reasons, so even for humans we do that. You got to write down everything you did, why did you do this, why'd you do that, so we actually want traceability for humans, even. In other places, I think it is really about the newness. Do I really trust this thing, I don't know what it's doing. Trust comes with use, after a while it becomes pretty straightforward, I mean I think that's probably true for a cell phone, I remember the first smartphones coming out in the early 2000s, I didn't trust how they worked, I would never do a credit card transaction on 'em, these kind of things, now it's taken for granted. I've done it a million times, and I never had any problems, right? >> It's the opposite in social media, most people. >> Maybe that's the opposite, let's not go down that path. >> I quite like Dr. Kate Darling's analogy from MIT lab, which is we already we have AI, and we're quite used to them, they're called dogs. We don't fully understand how a dog makes a decision, and yet we use 'em every day. In a collaboration with humans, so a dog, sort of replace a particular job, but then again they don't, I don't particularly want to go and sniff things all day long. So having AI systems that can actually replace some of those jobs, actually, that's kind of great. >> Exactly, and think about it like this, if we can build systems that are tireless, and we can basically give 'em more power and they keep going, that's a big win for us. And actually, the dog analogy is great, because I think, at least my eventual goal as an AI researcher is to make the interface for intelligent agents to be like a dog, to train it like a dog, reinforce it for the behaviors you want and keep pushing it in new directions that way, as opposed to having to write code that's kind of esoteric. >> Can you talk about GANs, what is GANs, what's it stand for, what does it mean? >> Generative Adversarial Networks. What this means is that, you can kind of think of it as, two competing sides of solving a problem. So if I'm trying to make a fake picture of you, that makes it look like you have no hair, like me, you can see a Photoshop job, and you can kind of tell, that's not so great. So, one side is trying to make the picture, and the other side is trying to guess whether it's fake or not. We have two neural networks that are kind of working against each other, one's generating stuff, and the other one's saying, is it fake or not, and then eventually you keep improving each other, this one tells that one "No, I can tell," this one goes and tries something else, this one says "No, I can still tell." The one that's trying with a discerning network, once it can't tell anymore, you've kind of built something that's really good, that's sort of the general principle here. So we basically have two things kind of fighting each other to get better and better at a particular task. >> Like deepfakes. >> I use that because it is relevant in this case, and that's kind of where it came from, is from GANs. >> All right, okay, and so wow, obviously relevant with 2020 coming up. I'm going to ask you, how far do you think we can take AI, two part question, how far can we take AI in the near to mid term, let's talk in our lifetimes, and how far should we take it? Maybe you can address some of those thoughts. >> So how far can we take it, well, I think we often have the sci-fi narrative out there of building killer machines and this and that, I don't know that that's actually going to happen anytime soon, for several reasons, one is, we build machines for a purpose, they don't come from an embattled evolutionary past like we do, so their motivations are a little bit different, say. So that's one piece, they're really purpose-driven. Also, building something that's as general as a human or a dog is very hard, and we're not anywhere close to that. When I talked about the trillions of parameters that a human brain has, we might be able to get close to that from a engineering standpoint, but we're not really close to making those trillions of parameters work together in such a coherent way that a human brain does, and efficient, human brain does that in 20 watts, to do it today would be multiple megawatts, so it's not really something that's easily found, just laying around. Now how far should we take it, I look at AI as a way to push humanity to the next level. Let me explain what that means a little bit. Simple equation I always sort of write down, is people are like "Radiologists aren't going to have a job." No no no, what it means is one radiologist plus AI equals 100 radiologists. I can take that person's capabilities and scale it almost freely to millions of other people. It basically increases the accessibility of expertise, we can scale expertise, that's a good thing. It makes, solves problems like we have in healthcare today. All right, that's where we should be going with this. >> Well a good example would be, when, and probably part of the answer's today, when will machines make better diagnoses than doctors? I mean in some cases it probably exists today, but not broadly, but that's a good example, right? >> It is, it's a tool, though, so I look at it as more, giving a human doctor more data to make a better decision on. So, what AI really does for us is it doesn't limit the amount of data on which we can make decisions, as a human, all I can do is read so much, or hear so much, or touch so much, that's my limit of input. If I have an AI system out there listening to billions of observations, and actually presenting data in a form that I can make better decisions on, that's a win. It allows us to actually move science forward, to move accessibility of technologies forward. >> So keeping the context of that timeframe I said, someday in our lifetimes, however you want to define that, when do you think that, or do you think that driving your own car will become obsolete? >> I don't know that it'll ever be obsolete, and I'm a little bit biased on this, so I actually race cars. >> Me too, and I drive a stick, so. >> I kind of race them semi-professionally, so I don't want that to go away, but it's the same thing, we don't need to ride horses anymore, but we still do for fun, so I don't think it'll completely go away. Now, what I think will happen is that commutes will be changed, we will now use autonomous systems for that, and I think five, seven years from now, we will be using autonomy much more on prescribed routes. It won't be that it completely replaces a human driver, even in that timeframe, because it's a very hard problem to solve, in a completely general sense. So, it's going to be a kind of gentle evolution over the next 20 to 30 years. >> Do you think that AI will change the manufacturing pendulum, and perhaps some of that would swing back to, in this country, anyway, on-shore manufacturing? >> Yeah, perhaps, I was in Taiwan a couple of months ago, and we're actually seeing that already, you're seeing things that maybe were much more labor-intensive before, because of economic constraints are becoming more mechanized using AI. AI as inspection, did this machine install this thing right, so you have an inspector tool and you have an AI machine building it, it's a little bit like a GAN, you can think of, right? So this is happening already, and I think that's one of the good parts of AI, is that it takes away those harsh conditions that humans had to be in before to build devices. >> Do you think AI will eventually make large retail stores go away? >> Well, I think as long as there are humans who want immediate satisfaction, I don't know that it'll completely go away. >> Some humans enjoy shopping. >> Naveen: Some people like browsing, yeah. >> Depends how fast you need to get it. And then, my last AI question, do you think banks, traditional banks will lose control of the payment systems as a result of things like machine intelligence? >> Yeah, I do think there are going to be some significant shifts there, we're already seeing many payment companies out there automate several aspects of this, and reducing the friction of moving money. Moving money between people, moving money between different types of assets, like stocks and Bitcoins and things like that, and I think AI, it's a critical component that people don't see, because it actually allows you to make sure that first you're doing a transaction that makes sense, when I move from this currency to that one, I have some sense of what's a real number. It's much harder to defraud, and that's a critical element to making these technologies work. So you need AI to actually make that happen. >> All right, we'll give you the last word, just maybe you want to talk a little bit about what we can expect, AI futures, or anything else you'd like to share. >> I think it's, we're at a really critical inflection point where we have something that works, basically, and we're going to scale it, scale it, scale it to bring on new capabilities. It's going to be really expensive for the next few years, but we're going to then throw more engineering at it and start bringing it down, so I start seeing this look a lot more like a brain, something where we can start having intelligence everywhere, at various levels, very low power, ubiquitous compute, and then very high power compute in the cloud, but bringing these intelligent capabilities everywhere. >> Naveen, great guest, thanks so much for coming on theCUBE. >> Thank you, thanks for having me. >> You're really welcome, all right, keep it right there everybody, we'll be back with our next guest, Dave Vellante for Justin Warren, you're watching theCUBE live from AWS re:Invent 2019. We'll be right back. (techno music)

>> Live from Las Vegas, it's theCUBE, covering AWS re:Invent 2018. Brought to you by Amazon Web Services, Intel, and their Ecosystem partners. >> Oh welcome back, to theCUBE. Continuing coverage here from AWS re:Invent, as we start to wind down our coverage here on the second day. We'll be here tomorrow as well, live on theCUBE, bringing you interviews from Hall D at the Sands Expo. Along with Justin Warren, I'm John Walls, and we're joined by Jonathan Ballon, who's the Vice President of the internet of things at Intel. Jonathan, thank you for being with us today. Good to see you, >> Thanks for having me guys. >> All right, interesting announcement today, and last year it was all about DeepLens. This year it's about DeepRacer. Tell us about that. >> What we're really trying to do is make AI accessible to developers and democratize various AI tools. Last year it was about computer vision. The DeepLens camera was a way for developers to very inexpensively get a hold of a camera, the first camera that was a deep-learning enabled, cloud connected camera, so that they could start experimenting and see what they could do with that type of device. This year we took the camera and we put it in a car, and we thought what could they do if we add mobility to the equation, and specifically, wanted to introduce a relatively obscure form of AI called reinforcement learning. Historically this has been an area of AI that hasn't really been accessible to most developers, because they haven't had the compute resources at their disposal, or the scale to do it. And so now, what we've done is we've built a car, and a set of tools that help the car run. >> And it's a little miniature car, right? I mean it's a scale. >> It's 1/118th scale, it's an RC car. It's four-wheel drive, four-wheel steering. It's got GPS, it's got two batteries. One that runs the car itself, one that runs the compute platform and the camera. It's got expansion capabilities. We've got plans for next year of how we can turbo-charge the car. >> I love it. >> Right now it's baby steps, so to speak, and basically giving the developer the chance to write a reinforcement learning model, an algorithm that helps them to determine what is the optimum way that this car can move around a track, but you're not telling the car what the optimum way is, you're letting the car figure it out on their own. And that's really the key to reinforcement learning is you don't need a large dataset to begin with, it's pre-trained. You're actually letting, in this case, a device figure it out for themselves, and this becomes very powerful as a tool, when you think about it being applied to various industries, or various use-cases, where we don't know the answer today, but we can allow vast amounts of computing resources to run a reinforcement model over and over, perhaps millions of times, until they find the optimum solution. >> So how do you, I mean that's a lot of input right? That's a lot, that's a crazy number of variables. So, how do you do that? So, how do you, like in this case, provide a car with all the multiple variables that will come into play. How fast it goes, and which direction it goes, and all that, and on different axes and all those things, to make these own determinations, and how will that then translate to a real specific case in the workplace? >> Well, I mean the obvious parallel is of course autonomous driving. AWS had Formula One on stage today during Andy Jassy's keynote, that's also an Intel customer, and what Formula One does is they have the fastest cars in the world, and they have over 120 sensors on that car that are bringing in over a million pieces of data per second. Being able to process that vast amount of data that quickly, which includes a variety of data, like it's not just, it's also audio data, it's visual data, and being able to use that to inform decisions in close to real time, requires very powerful compute resources, and those resources exist both in the cloud as well as close to the source of the data itself at the edge, in the physical environment. >> So, tell us a bit about the software that's involved here, 'cause people think of Intel, you know that some people don't know about the software heritage that Intel has. It's not just about, the Intel inside isn't just the hardware chips that's there, there's a lot of software that goes into this. So, what's the Intel angle here on the software that powers this kind of distributed learning. >> Absolutely, software is a very important part of any AI architecture, and for us we've a tremendous amount of investment. It's almost perhaps, equal investment in software as we do in hardware. In the case of what we announced today with DeepRacer and AWS, there's some toolkits that allow developers to better harness the compute resources on the car itself. Two things specifically, one is we have a tool called, RL Coach or Reinforcement Learning Coach, that is integrated into SageMaker, AWS' machine learning toolkit, that allows them to access better performance in the cloud of that data that's coming into the, off their model and into their cloud. And then we also have a toolkit called OpenVINO. It's not about drinking wine. >> Oh darn. >> Alright. >> Open means it's an opensource contribution that we made to the industry. Vino, V-I-N-O is Visual Inference and Neural Network Optimization, and this is a powerful tool, because so much of AI is about harnessing compute resources efficiently, and as more and more of the data that we bring into our compute environments is actually taking place in the physical world, it's really important to be able to do that in a cost-effective and power-efficient way. OpenVINO allows developers to actually isolate individual cores or an integrated GPU on a CPU without knowing anything about hardware architecture, and it allows them then to apply different applications, or different algorithms, or inference workloads very efficiently onto that compute architecture, but it's abstracted away from any knowledge of that. So, it's really designed for an application developer, who maybe is working with a data scientist that's built a neural network in a framework like TensorFlow, or Onyx, or Pytorch, any tool that they're already comfortable with, abstract away from the silicon and optimize their model onto this hardware platform, so it performs at orders of magnitude better performance then what you would get from a more traditional GPU approach. >> Yeah, and that kind of decision making about understanding chip architectures to be able to optimize how that works, that's some deep magic really. The amount of understanding that you would need to have to do that as a human is enormous, but as a developer, I don't know anything about chip architectures, so it sounds like the, and it's a thing that we've been hearing over the last couple of days, is these tools allow developers to have essentially superpowers, so you become an augmented intelligence yourself. Rather than just giving everything to an artificial intelligence, these tools actually augment the human intelligence and allow you to do things that you wouldn't otherwise be able to do. >> And that's I think the key to getting mass market adoption of some of these AI implementations. So, for the last four or five years since ImageNet solved the image recognition problem, and now we have greater accuracy from computer models then we do from our own human eyes, really AI was limited to academia, or large IT tech companies, or proof-of-concepts. It didn't really scale into these production environments, but what we've seen over the couple of years is really a democratization of AI by companies like AWS and Intel that are making tools available to developers, so they don't need to know how to code in Python to optimize a compute module, or they don't need to, in many cases, understand the fundamental underlying architectures. They can focus on whatever business problem they're tryin' to solve, or whatever AI use-case it is that they're working on. >> I know you talked about DeepLens last year, and now we've got DeepRacer this year, and you've got the contest going on throughout this coming year with DeepRacer, and we're going to have a big race at the AWS re:Invent 2019. So what's next? I mean, or what are you thinking about conceptually to, I guess build on what you've already started there? >> Well, I can't reveal what next years, >> Well that I understand >> Project will be. >> But generally speaking. >> But what I can tell you, what I can tell you is what's available today in these DeepRacer cars is a level playing field. Everyone's getting the same car and they have essentially the same tool sets, but I've got a couple of pro-tips for your viewers if they want to win some of these AWS Summits that are going to be around the world in 2019. Two pro-tips, one is they can leverage the OpenVINO toolkit to get much higher inference performance from what's already on that car. So, I encourage them to work with OpenVINO. It's integrated into SageMaker, so that they have easy access to it if they're an AWS developer, but also we're going to allow an expansion of, almost an accelerator of the car itself, by being able to plug in an Intel Neural Compute Stick. We just released the second version of this stick. It's a USB form factor. It's got a Movidius Myriad X Vision processing unit inside. This years version is eight times more powerful than last years version, and when they plug it into the car, all of that inference workload, all of those images, and information that's coming off those sensors will be put onto the VPU, allowing all the CPU, and GPU resources to be used for other activities. It's going to allow that car to go at turbo speed. >> To really cook. >> Yeah. (laughing) >> Alright, so now you know, you have no excuse, right? I mean Jonathan has shared the secret sauce, although I still think when you said OpenVINO you got Justin really excited. >> It is vino time. >> It is five o'clock actually. >> Alright, thank you for being with us. >> Thanks for having me guys. >> And good luck with DeepRacer for the coming year. >> Thank you. >> It looks like a really, really fun project. We're back with more, here at AWS re:Invent on theCUBE, live in Las Vegas. (rhythmic digital music)

>> Live, from Las Vegas, it's theCUBE. Covering AWS re:Invent, 2018. Brought to you by Amazon Web Services, Intel, and, their ecosystem partners. >> Welcome back everyone, live here in Las Vegas, for AWS, Amazon Web Services, re:Invent, 2018. I'm John Furrier with Dave Vellante. Dave, our sixth year covering this event. We've been to all the re:Invents, except for the original one, watched the progress of cloud computing. And it's a lot of new things happening, more compute, more power. We're here with our special guest, RaeJeanne Skillern, who's also known as RJ inside Intel. Vice-President of Data Center Group and the General Manager of the Cloud Service Provider Platform Group at Intel. Good to see you again. >> Nice to see you again. >> The headline on silkenangle dot com right now, "In a blockbuster move, "Amazon jumps into data center with both feet". Which really validates kind of some of the commentary we've been seeing in the queue for many, many years. And our analysts and you guys are involved in the Data Center. Data Center's still going to be a big part of computing. It's not going away. That's your business. >> Yes. >> And the cloud service provider, which is also growing. So, take a minute. >> It is growing, I've been personally covering the public cloud at Intel for a decade. And, when I started I'm not sure I had any concept how big this was going to be. And the one thing that I'm positive about, is we're just still at the beginning. Because every use case you see, all the development, all the IOT, all the business transformation, we're just starting, right. This is a good place to be, but there's more coming. And, if I look at just 2018, I'm a little competitive at work, but we were to proud to announce earlier this year, the end of the summer, that the cloud is now 43% of the Data Center Group's revenue. So, coming from when I started, 10% or something like that little, now to be the number one contributor. And, we, in the first half of the year, had a 43% a year revenue growth. This industry is booming. And I wish I could say it was my hard doing, but I mean, if you come to an event like this, you know why it's growing. >> And the cham is increasing in the total market availability with the cloud, is requiring more and more horsepower. >> Yes. >> You've got IOT Edge, you've got the Data Center, you got the cloud, and software is being written, specifically to take advantage of something. So, huge market opportunity, still. >> Yes. >> What are some of the innovations? Take us through a little bit of your mindset on how you guys are attacking this growth, surface area of the market, starting to see specialized things, general purpose, compute is not going away, storage, networking, still very important. You've got FTGAs out there. I mean, amazing amount of opportunities, with innovations. >> You know, you hit so much of it, and I really agree with some of the comments you made. It started off for us, with silicon technology. But, what a lot of people don't know is, we have core computes, network, storage, FPGAs, purpose built accelerators, and we can create custom aesics for any one of our customers. We also have a unique ability to not only just customize, uniquely, but you talked about the many different use cases from Edge to Data Center, it's because every workload demands a different set of technology capability. If you want true optimization at the TCO, per TCO level. And so that's why it's so important for us to work with customers like Amazon, not just to customize one SKU, but many SKUs. We are, and I was surprised at this number, out of our latest Xeon processor, the Intel Xeon scalable processors, there's actually 54 instances, on just that one CPU generation alone, and 51 of those, are from a custom CPU, that were tailored for unique workloads and instance types. So, that's part of it, but you also talked about the software. And, that's another thing, I think people think Intel's the hardware company. OK, we make hardware, we're a huge software company, thousands of engineers. And, what I love about my job, is I built a team and call them the Cloud Ninjas, but they're software and hardware engineers, that go onsite with customers. They, whether it's performance tuning and optimization, or we are co-creating cloud services. New cloud services, with our customers, that innovation, up and down the stack, that's where real innovation can happen. Two heads together, not just one. (laughs) >> So the cloud is now the number one consumer of your technologies. >> Yes. >> There are a lot of misconceptions early on about the cloud. Everybody thought, okay, the cloud is going to be just one big cloud. It's actually quite diverse. It's global in scale, it's a services business, which has always been sort of fragmented and global, despite Amazon's dominance in infrastructure service. The Data Center itself, the players are kind of consolidating, which is kind of interesting. So, how has cloud affected the way in which you guys look at the market, go to market, everybody else thought everything was going to be standard off the shelf components, in the early days of cloud. >> No. >> Now you're driving towards customization. >> No. >> So what's happening there, what are the big ideas. >> I think we've learned a lot along the way, you're right. One of the things, I mean, these cloud service providers are pushing me off the road map. They want more than we can deliver, so that's where we bring so much at hand to do about it. But, I'd say while a lot of big players are getting bigger, the market is still really in a healthy way diversified. The Super Seven, as we call them, the world's largest, they're growing fast, about 35% around the world. The next wave around the world are growing almost as fast, about 25, 27%. Consumer SAS, has been, Twitters, and Facebooks, and Ubers, right, has been a large part of the cloud. It's now 50/50 with business. And they're both growing at the same categories going forwards, so you're going to see the diversity. Not just big players, but also small. Not just consumers, but business services. And then that's spanning a lot of global growth, and a lot of, if you see the wall of logos in any Amazon presentation, it's because they have partners all over the world. >> RaeJeanne, I want to get your perspective. I talked about this a couple years ago on theCUBE, about the power law of distribution of cloud providers, the top of the head is the big guys, then kind of narrows down. But then I was predicting a cloud service provider market was going to expand and I want to get your thoughts cause that's kind of happening now, you're kind of saying. But I want to get specific on this. You got core cloud, Amazon's of the world. Then you've got hybrid cloud, kind of Data Center. Then you've got the business cloud, business SAS. >> Business SAS. >> Sales force, Twitter, you mentioned those guys. >> Uh huh. >> They run clouds. Enterprises are now going to be cloudified, with commonality. >> Multi-cloud road. >> This is changing the nature of the business. Do you see it that way, talk about this business cloud, it's not competing with core cloud, it's just an expansionary. >> It's so interesting because there is certainly some competition or cannibalization within the cloud. But what I tend to see is, whichever part of this, because you'll hear a Business SAS company, some of it's running in the cloud, some of it's running on their own premises. They're doing that for a reason and both are growing. And then you talk about infrastructure service, but what really happens, especially we another rise moves their business into the cloud, there is just some part of it, just moved A to B, but what we're finding is about 30% of it is TAM expansive, because there are things when you move to an Amazon, or you move to another cloud service provider, take a mature SAS provider, they're just things that they can do that you never would have been able to do in your own IT shop. So, that's driving TAM expansion on top of it. That's also creating a lot of new market entry points, for new businesses to come in and innovate around. Security offerings, verticalized offerings, geo-based specialized offerings. So, yes, there's some friendly competition, but even when I ask somebody who would say, they might be the little challenger to a big infrastructure service player, they say but you know what, we actually get so much business by working with them too, it's hard for me to say, are they competition or a partner, right. That's the industry we live in. >> Co-creation, you mentioned that earlier, a big part of it. >> And the other big TAM expander is you've got the data, you've got AI, machine learning. >> Yes. >> You've got the cloud for scale and then you've got Edge. >> Yeah. >> These are not, it's not a zero sum game, where you're moving stuff from the Data Center into the cloud, these are all incremental. >> New. All new. >> So what are you seeing there? >> Yeah, I'm really excited about the Edge. For me, it kind of feels like that next, uncharted frontier, everybody's investing, everybody's doing amazing things. We're getting the 5G out, we're getting better technologies, we're learning how to store data, and move it faster, quicker, and cheaper. We're getting set up, but the use cases are just yet to be really fully defined. And I'll be honest, when I look at my market modeling, over the next five to 10 years, I always put a little disclaimer, this does not comprehend what's going to happen when billions of devices come online, when we activate. So I think when people say, it's a cloud, it's been going so fast it's going to just slow down. Why? Because innovation's not stopping. >> I think you hit the nail on a point we try to clarify on theCUBE here, is that a lot of people are misunderstanding what a cloud is, and about cloud service providers. As it grows, it's a rising tide floats all boats, so everyone tries to squint through, they're winning and a market share there. It's a different game changing, so that's a great point. I want to, as we get ready to end this segment here, give you a chance to talk about the relationship with Intel. You guys, again, cloud service provider is growing. Big part of your business. But you guys have been working with Amazon, for a long time, talk about the relationship between Intel and AWS. >> Yeah it is, it's a privilege, to be able to. The folks at a company like Amazon, and specifically the ones at Amazon I work with, they have the ability, obviously, to track some of the most amazing talent in the industry. And these people move fast. And, they have a lot of choice. You can either be there with them, ahead of them, and do the customization and differentiate them, and give them what they need. Or, they're going to leave you in the dust. So, I'd like to say we have a great partnership, because they've given us the honor over 12 years. We have so many, from the Data Center, to the Edge, the car, the racer car, deep lines. So many things we're doing together, Stage Maker, Machine and Deep Learning. But it's a, if we slow down, even for a bit, we're going to get left behind. So my job is to just keep running and trying to get ahead of them. And every time I think I get there, they come and poof. But, we're working together. It's a great, challenging partnership. But one that I can guarantee there's better innovation, from Intel, coming out of it, because of getting the opportunity to work with Amazon. >> And you guys are contributing to them too. It's a good win, win scenario. >> I believe so. They've said some really nice things about us, so, about our processing technologies. Our products, seven generations of our products, we're in every availability zone, every instance frame. We've got a great position. >> Well, congratulations on the business performance, I love the Cloud Service Provider expansion, love the Data Center focus, that's really relevant. And acknowledging by Amazon, that's good news to see. And, just stuff. Thank you guys for your partnership with theCUBE. >> Yeah, thank you. >> Here at theCUBE, Intel Cube. Intel is a big sponsor of theCUBE, we really appreciate that. I'm John with Dave Vallante. Stay with us for more AWS coverage re:Invent, our sixth year in a row, covering all the action. All the value being created. Stay tuned for more after this short break. (techno music)

>> Announcer: Live, from Las Vegas, it's the Cube, covering AWS Re:invent 2017 presented by AWS, Intel, and our ecosystem of partners. >> Welcome back everyone, we're here live in Las Vegas for 45,000 tech industry folks and customers with Amazon re:Invent 2017. This is the Cube's exclusive coverage, I'm John Furrier, with my co-host Justin Warren this segment. Our next guest, Bill Magro, is the Chief Technologist for Intel covering HPC high performance computing. Bill, welcome to the Cube. >> Thank you. >> John: Thanks for coming on. You guys, your booth's behind us, I don't if they can see it in the wide shot, but Intel is really taking advantage of the I don't want to say Intel inside the Cloud 'cause that's really what you guys are doing, but you got so much compute, this is your wheelhouse. Compute is what Intel is. >> Bill: Right. >> Andy Jassy at AWS, talking with their customers, they want more compute, edge of the network, so HPC, high performance computing's been around for awhile. What's the state of the art and how should people think about HPC versus the Cloud, are they the same, what's the relationship? >> Intel actually thinks of HPC or high performance computing more in terms of the activity and the workloads than the infrastructure that it runs on. So very early in the days of Cloud computing, there were a lot of people who said that the Cloud was kind of the opposite of HPC and therefore, they could never go together. But we think of Cloud as a delivery vehicle, a way to get access to compute storage networking and HPC is what you're doing. And so then, if you think about HPC as kind of a range of workloads, you can start to think about which ones are a good fit for the Cloud, and which ones aren't. So we talk a little bit about the high performance computing and tailored infrastructure for the most extreme cases of HPC. That's where you see the biggest differences with Cloud, 'cause they're at opposite ends of the spectrum. >> But you see holistically the Cloud is interplaying with HPC. >> Yeah. >> They're not mutually exclusive. >> Absolutely, we see Cloud as a way to deliver HPC capabilities. So if you think of the most demanding HPC problems, the ones that are used in national security, that are used to design commercial airplanes, and so on, those are some of the hardest problems. Predicting the climate change, predicting the weather, paths of hurricanes, those are what we call grand challenge problems. Those are not running in the Cloud. Those are running on dedicated, tailored, infrastructure built for high performance computing at that extreme. And those systems have a lot of characteristics such as very high performance networks, different from ethernet, custom topologies and are designed with software to really minimize variation because it's one large problem that has to move forward. The Cloud is kinda the opposite in a sense. It started as taking a large amount of resources and making it possible to carve them up, right. It's the opposite of aggregating resources. And so that's where a lot of the early thoughts of Cloud and HPC being at odds with each other. >> It seems to be a dream scenario because I mean, in the old days, in 80s and 90s when I was breaking into the business. If you were a database guy or a compute guy, you were a specialist, it was high end kind of computing. Moore's Law, certainly Intel, you guys took advantage of it. But now, you see so much, it's cool to do more compute. So like, it's been democratized. Databases and compute, certainly in all the conversations, for everybody, not just, the technologists. >> Right, where that's where Cloud fits in for HPC. So if you think of HPC in terms of the characteristics of the workload, it's something that's really demanding computationally. The product of the computation is like an intellectual insight. You can design a better airplane wing, a safer car, you can figure out where that hurricane is going and tell which people to evacuate. There's an intellectual product to the compute. And then the last characteristic is when you apply more compute power appropriately, you get a more valuable result. So it could be better prediction of that hurricane path, it could be a safer car because you have more time, you have more capability and were able to build a better design ahead of that deadline to get that model year of the car out. And so, if you think about that, there's a lot, there's a broad spectrum and I talked about some of those most extreme problems, but even in something like designing an airplane, there might be 16, 20, a hundred different small design variations you want to explore. Well those can actually be great for The Cloud, 'cause they're small calculations and you run many of them at the same time. And the elastic capability of The Cloud augments the supercomputer that you might be using to run your hardest problems. >> So the aperture of problem-solving is huge now. >> Bill: That's right. >> You can do more. I mean we had Thorn on yesterday. Thorn was a company that partners with Intel to do, you know, find missing and exploited children. AI for good, so everything's possible. >> Yeah even AI we think of as an example of a high performance computing workload because what does it do? It gives you insights that you didn't have otherwise, it's compute intensive, and it does better when you apply more resources. So that fits our definition. So AI is definitely under the umbrella of high performance computing. >> One of the things, one of the great benefits of Cloud is the elasticity which you mentioned before. It's like, and some of the, we know that Amazon's just brought out the C5 Instances which is a specific instance site, which would be quite useful for HPC. But what is it about the bursting workloads or that elasticity that specifically works well for HPC do you think? >> Well, there's a couple use cases that we think are particularly relevant. One of them is an existing company. Just imagine some Fortune 50 manufacturer. They have a lot of stuff that they really need to build their own supercomputer for, their own high performance computing system, but their usage, even though they keep that system busy all the time, there is some variability and they have opportunity costs of an engineer sitting while their job is in the queue, 'cause you're paying that engineer but you're not giving them insights, right. And so the Cloud can augment that, but we have a lot of examples of large Fortune 500, Fortune 50 companies augmenting their on-premise with Cloud as a way to push those workloads that can run on the Cloud there, to free up those on-prem resources which are much more tailored, much more expensive and get more value out of them. >> Okay, and what's Intel doing to help customers figure out which of those workloads is best suited for Cloud and which ones are better suited for something which is running on site? >> Well, it's mostly through our influencer sales force who engages with many, many major companies and provides consulting, because Intel doesn't sell computers directly to anyone, so it's more of a knowledge, our knowledge and sharing that with people. And what we're trying to help enterprises understand is what workloads need to stay on premise, which ones can go to the Cloud and how this, the elasticity of the Cloud can augment those on-premise resources and thus, you know, go back and forth. >> It's the classic mission for Intel, make the apps go faster, faster, smaller, cheaper, right. >> And get 'em land in the right place. So really, the two biggest considerations we find in deciding whether a workload goes into the Cloud or stays on-premise in high performance computing are the following, one, is really the sensitivity of the IP. There's a lot of workloads that could run in the Cloud and people simply want to keep it on-premise 'cause they're more comfortable knowing that their IP is sitting inside their own firewall. Though the reality is, more and more companies are getting comfortable with Cloud security as they see data breaches. And realize that some of the big Cloud providers, like Amazon, maybe have better access to the security talent than they do. >> I think Goldman-Sachs just announced they're going all in. That's Goldman-Sachs, they never do a testimonial. >> So the privacy and the sensitivity of the data is king, you know, you have to be willing to put it in the Cloud. Then the second question is, is it a technical fit? And that's where this spectrum of workloads comes in. The bigger a workload goes and the more you want to speed it up but keep the workload the same size, that's what we call strong scaling and that starts to stress the network, and stress the system. And that's where these tailored systems come in. And so, you have to look at where things fall on the spectrum. A good example of workloads that would fit is these design space explorations, anything we would call pleasingly parallel or embarrassingly parallel in the industry where the communication does happen, but it's not the limiter of the calculation. So screening for a drug candidates, for personalized medicine, lot of life sciences applications, financial services is a good fit, in manufacturing a design space exploration maybe for different designs and materials for a dashboard or a component of a car. >> Bill, when you were at your Thanksgiving dinner and your family or wherever, you're moving around in your personal life, you're a technologist. How do explain the phenomenon of Amazon Web Services and the Cloud action right now? Because, you know, you're in it every day. You're close to all the action. But I get asked all the time, what's the hub-bub about AWS and it's hard to explain the phenomenon. How would describe the, I mean you're talking about tailored systems, elasticity, I mean it's a tech dream. I mean, how do you explain it to like a normal person? >> The conversation's usually pretty short because my family involves a historian, an English major, an accountant and people who really couldn't have, a musician, a singer, people who really don't have the slightest interest in technology. >> It's hard to talk about lambda, when you're. >> So I'm really the only technologist in my family so I just avoid it, but the question does come up with my parents. You know, parents like to brag on their kids so they like to know what you do, and every year my mom asks me what I do and I try to explain high performance computing to her and she says, oh, I don't get it. But when you explain it in terms of things like climate modeling and being able to support the nuclear test ban that's worldwide, that's done with high performance computing. Safing cars, finding missing children, better quality of life through all the AI that we're now experiencing. >> John: Analytics is a great use case. >> Then people say, oh, you know, they can understand the use cases. The elasticity of the Cloud, really is not something that I discuss with family, but even coworkers, I think, that's what the conversation focuses on. Recognizing that high performance computing is a range of workloads. >> Okay, so I'll rephrase it differently. What's your perspective on, what observations that you get excited about that are enabled now by these new use cases? 'Cause there's new things now that are possible. The number of computations, you got analytics, you mentioned a few of them. What jumps out at you, wow, that's really awesome, we can do that now? >> You know, this is gonna sound a little odd, and maybe not what you expected, but I'm not actually a technology enthusiast, believe it or not, despite. I think technology's cool, I like what it does, but I don't get super excited about technology. One of the things that I'm excited about with the Cloud is probably at the opposite extreme of what you would expect which is, back to, how does the elasticity of the Cloud fit? There's so many companies in this world who could benefit from high performance computing and don't today. A recent study showed that 95 percent of U.S. small medium manufacturers which is over 300,000 are not using HPC today. And so, as they're part of this supply chain, whether it's into a Boeing or and Airbus or a Lockheed Martin or a Honda or a Toyota, there's this whole supply chain. HPC's being used at the top, it's not being used at the bottom, so I think the Cloud is actually really, really exciting because it allows somebody to get over those initial hurdles, the cap-ex, getting access to pay as you go, prove the value proposition, because a small medium business actually has to take a risk to use HPC. They have to divert capital and divert resources. And they could lose a contract. >> So do you see a lot more companies starting to take advantage of some of this high performance computing capability just because it's now, you can rent it by the hour and try it out, give it a bit of a whirl, and then see, actually this is going to be really valuable for us, and then deploy a lot more of it. >> Exactly and that's one of the key things we're promoting is 'cause we want to bring more people into the world of high performance computing. So, AWS provides all the building blocks. Compute, elastic storage and so on. But high performance computing applications really expect a specific type of platform that they can run on, and that platform aggregates the resources so there's a number of companies Rescale is one, Psycho-Computing, and others who are actually providing that platform layer. And then once you've got the platform layer, all the, I'll call it the, geeky stuff that they do, AWS has abstracted away. Now the applications can run and that's that's what's bringing new users in. >> Bill, final question for you. AWS launched its C5 Instances. What's that about, what's it mean for customers? Can you explain a little bit more on that one piece? >> Sure, we're delighted to see Amazon deploying the C5 Instances. It's based on our latest technology in the Xeon product family. We call that the Intel Xeon scalable processor family. It includes, it's based on what we call Skylake technology or code name Skylake. There's a lot of innovations in that processor and that platform that are specifically driven by the needs of high performance computing. There's something called AVX 512, which is a doubling of the vector width. Means that every core can actually do 32 floating point, double-precision floating point operations per clock. That's tremendous, tremendous compute capability, in a 2X over the previous generation. On the memory bandwidth side, which is another huge factor for high performance computing applications, like 66 percent increase in memory bandwidth. So it's a balanced platform, and we're seeing improvements in high performance computing apps of anywhere from 1.7x sometimes almost up to 5x improvement in going from the C4 to C5 Instances on a per note basis. >> This is really going to enable a lot of action. IOT, tons of great stuff. >> Absolutely and as I talked about that range of HPC and you know, what fits and what doesn't fit in the Cloud, every generation of technology, what fits in the Cloud is growing, and C5 is another important step in that direction. >> Bill, thanks for coming on the Cube. Bill Magro, Chief Technologist at Intel, HPC, high performance computing. The Cloud is one big high performance machine in the sky, wherever you want to look at it, really great opportunity at enabling all new use cases, doing things for society benefits, and customers. Great stuff here, Cloud impact is significant. IOT to the Cloud. This is the Cube, doing our share here at AWS in Las Vegas. We'll be right back with more coverage after this short break. (electronic music)

>> Narrator: Live from Las Vegas, it's theCUBE covering AWS re:Invent 2017. Presented by AWS, Intel, and our ecosystem of partners. >> Hey, welcome back everyone. We are here live at AWS re:Invent in Las Vegas. This is 45,000 people are here inside the Sands Convention Center at the Venetian, the Palazzo, and theCUBE is here >> Offscreen: I don't have an earpiece, by the way. >> for the fifth straight year, and we're excited to be here, and I wanna say it's our fifth year, we've got two sets, and I wanna thank Intel for their sponsorship, and of course our next guest is from Intel. Scott Macepole, director of the CTO's office at Intel PSG. Welcome to theCUBE. >> Thank you. >> Thanks for coming on. So, had a lot of Intel guests on, lot of great guests from customers of Amazon, Amazon executives, Amy Jessup coming on tomorrow. The big story is all this acceleration. of software development. >> Scott: Right. >> You guys at the FPGA within intel are doing acceleration at a whole nother level. 'Cause these clouds have data centers, they have to power the machines even though it's going serverless. What's going on with FPGAs, and how does that relate to the cloud world? >> Well, FPGAs I think have a unique place in the cloud. They're used in a number of different areas, and I think the great thing about them is they're inherently parallel. So you know, they're programmable hardware, so instead of something like a GPU or a purpose-built accelerator, you can make them do a whole bunch of different things, so they can do computer acceleration, they can do network acceleration, and they can do those at the same time. They can also do things like machine learning, and there's structures built inside of them that really help them achieve all of those tasks. >> Why is it gonna pick up lately? Because what are they doing differently now with FPGAs than they were before? Because there's more talk of that now more than ever. >> You know, I mean, I think it's just finally come to a confluence where the programmability is finally really needed. It's very difficult to actually create customized chips for specific markets, and it takes a long time to actually go do that. So by the time you actually create this chip, you may have not had the right solution. FPGAs are unique in that they're programmable, and you can actually create the solution on the fly, and if the solution's not correct you can go and you can actually change that, and they're actually pretty performant now. So the performance has continued to increase generation to generation, and I think that's really what sets them apart. >> So what's the relationship with Amazon? Because now I'm kinda connecting the dots in my head. Amazon's running full speed ahead. >> Scott: Yeah. And they're moving fast, I mean thousands of services. Does FPGAs give you guys faster time to market when they do joint designs with Intel? And how does your relationship with Amazon connect on all this? >> Absolutely, we have a number of relationships with Amazon, clearly the Xeon processors being one of them. The FPGAs are something that we continue to try to work with them on, but we're also in a number of their other applications, such as Alexa, so and there's actually technologies within Alexa that we could take and implement either in Xeon CPUs or actually in FPGAs to further accelerate those, so a lot of the speech processing, a lot of the AI that's behind that, and that's something that, it's not very prevalent now, but I think it'll be in the future. >> So, all that speech stuff matters for you guys, right? That helps you guys, the speech, all the voice stuff that's happening, and the Alexa news, machine learning. >> Right. >> That's good for you, right? I mean, that, I mean... >> It's very good, and it's actually, it's really in the FPGA sweet spot. There's a lot of structures within the FPGAs that make them a lot better for AI than a GPU. So for instance, they have a lot of memory on the inside of the device, and you can actually do the compute and the memory right next to where it needs to be, and that's actually very important, because you want the latency to be very low so that you can process these things very quickly. And there's just a phenomenal amount of bandwidth inside of an FPGA today. There's over 60 terabytes a second of bandwidth in our mid-range Stratix 10 device. And when you couple that together with the unique math capabilities, you can really build exactly what you want. So when you look at GPUs, they're kinda limited to double precision floating pointers, single precision, or integer. The FPGAs can do all of those and more, and you can actually custom build your mathematical path to what you need, save power, be more efficient, and lower the latency. So... >> So Andy Jessup talked about this is a builder's conference. The developers, giving the tools to the developers they need to create amazing things. One of the big announcements was the bare metal servers from AWS. >> Scott: Yeah. How do you see something like an FPGA playing in a service like that? >> Well, the FPGAs could use to help provide security for that. They could obviously be used to help do some of the network processing as well. In addition, they could be used in a lot of classical modes that they could be used in, whether it's like an attached solution for pure acceleration. So just because it's bare metal doesn't mean it can't be bare metal with FPGA to do acceleration. >> And then, let's talk about some of the... You guys, FPGAs is pretty big in the networking space. >> Scott: Yeah. >> Let's talk about some of the surrounding Intel technologies around FPGAs. How are you guys enabling your partners, network partners, to take advantage of X86, Xeon, FPGAs, and accelerating networking services inside of a solution like Amazon. >> We have a number of solutions that we're developing, both with partners and ourselves, to attach to our nix, and other folks' nix, to help accelerate those. We've also released what's called the acceleration stack, and what that's about is really just kinda lowering the barrier of entry for FPGAs, and it has actually a driver solution that goes with it as well, it's called OPAE, and what that driver solution does, it actually creates kind of a containerized environment with an open source software driver so that it just really helps remove the barrier of, you know, you have this FPGA next to a CPU. How do I talk to it? How can we connect to it with our software? And so we're trying to make all of this a lot simpler, and then we're making it all open so that everybody can contribute and that the market can grow faster. >> Yeah, and let's talk about ecosystem around data, the telemetry data coming off of systems. A lot of developers want as much telemetry data, even from AWS, as possible. >> Scott: Yeah. >> Are you guys looking to expose any of that to developers? >> It's always something under consideration, and one of the things that FPGAs are really good at is that you can kinda put them towards the edge so that they can actually process the data so that you don't have to dump the full stream of data that gets generated down off to some other processing vehicle, right? So you can actually do a ton of the processing and then send limited packets off of that. >> So we looked at the camera today, super small device doing some really amazing things, how does FPGAs playing a role in that, the IOT? >> They do a lot of, FPGAs are great for image processing. They can do that actually much quicker than most other things. When you start listening, or reading a little bit about AI, you'll see that a lot of times when you're processing images, you'll have to take a whole batch of them for GPUs to be efficient. FPGAs can operate down at a batch size of one, so they can respond very quickly. They can work on individual images, and again, they can actually do it not just efficiently in terms of the, kinda the amount of hardware that you implement, but efficiently in the power that's required to go do that. >> So when we look at advanced IOT use cases, what are some of the things that end-user customers will be able to do potentially with FPGAs out to the edge, of course less data, less power needed to go back to the cloud, but practically, what are some of the business outcomes from using FPGAs out at the edge? >> You know, there's a number of different applications, you know, for the edge. If you go back to the Alexa, there's a lot of processing smarts that actually go on there. This is an example where the FPGA could actually be used right next to the Xeons to further accelerate some of the speech, and that's stuff that we're looking at now. >> What's the number one use case you're seeing that people, what's the number one use case that you're seeing that people could relate to? Is it Alexa? Is it the video-- >> For the edge, or? >> Host: For FPGAs, the value of accelerating. >> For FPGAs, I mean, while there's usage well beyond data center, you know. There's a classic what we would call wire line where it's used in everything today. You know, if you're making a cellphone call, it likely goes through an FPGA at some point. In terms of data center, I think where it's really being used today, there's been a couple of very public announcements. Obviously in network processing in some of the top cloud providers, as well as AI. So, you know, and I think a lot of people were surprised by some of those announcements, but as people look into them a little further, I think they'll see that there's a lot of merit to that. >> The devices get smaller and faster and just the deep lens device has got a graphics engine that would've been on a mainframe a few years ago. I mean, it's huge software power. >> Yeah. >> You guys accelerate that, right? I mean I'm looking, is that a direction? What is the future direction for you guys? What's the future look like for FPGAs? >> It's fully programmable, so, you know, it's really limited by what our customers and us really wanna go invest in. You know, one of the other things that we're trying to do to make FPGAs more usable is remove the kind of barrier where people traditionally do RTL, if you're familiar with that, they actually do the design, and really make it a lot more friendly for software developers, so that they can write things in C or openCL, and that application will actually end up on the inside of the FPGA using some of these other frameworks that I talked about, the acceleration stack. So they don't have to really go and build all the guts of the FPGA, they just focus on their application, you have the FPGA here whether it's attached to the network, coherently attached to a processor, or next to a processor on a, on PCI Express, all of those can be supported, and there's a nice software model to help you do all that development. >> So you wanna make it easy for developers. >> Scott: We wanna make it very easy. >> What specifically do you have for them right now? >> We have the, they call it the DLA framework, the deep learning framework that we released. As I said before, we have the acceleration stack, we have the OPEA which is the driver stack that goes along with that, as well of all our, what we call our high-level synthesis tools, HLS, and that supports C and openCL. So it basically will take your classic software and convert it into gates, and help you get that on the FPGA. >> Will bots be programming this soon? Soon AI's going to be programming the FPGAs? Software, programming software? >> That might be a little bit of a stretch right now, but you know, in the coming years perhaps. >> Host: Scott, thanks for coming onto theCUBE, really appreciate it. >> Thanks for having me. >> Scott Macepole who is with Intel, he's the director of the CTO's office at Intel PSG, they make FPGAs, really instrumental device in software to help accelerate the chips, make it better for developers, power your phone, Alexa, all the things pretty much in our life. Thanks for coming on the Cube, appreciate it. >> Thank you. >> We'll be back with more live coverage. 45,000 people here in Las Vegas, it's crazy. It's Amazon Web Services re:Invent, we'll be right back. (soft electronic music)

>> Narrator: Live from Las Vegas, it's theCUBE. Covering AWS re:Invent 2017 presented by AWS, Intel and our ecosystem of partners. >> Hello and welcome back. Live here is theCUBE's exclusive coverage here in Las Vegas. 45,000 people attending Amazon Web Services' AWS re:Invent 2017. I'm John Furrier with Lisa Martin. Our next guest is Miles Kingston, he is the General Manager of the Smart Home Group at Intel Corporation. Miles, it's great to have you. >> Thank you so much for having me here, I'm really happy to be here. >> Welcome to theCUBE Alumni Club. First time on. All the benefits you get as being an Alumni is to come back again. >> Can't wait, I'll be here next year, for sure. >> Certainly, you are running a new business for Intel, I'd like to get some details on that, because smart homes. We were at the Samsung Developer Conference, we saw smart fridge, smart living room. So we're starting to see this become a reality, for the CES, every 10 years, that's smart living room. So finally, with cloud and all of the computing power, it's arrived or has it? >> I believe we're almost there. I think the technology has finally advanced enough and there is so much data available now that you have this combination of this technology that can analyze all of this data and truly start doing some of the artificial intelligence that will help you make your home smarter. >> And we've certainly seen the growth of Siri with Apple, Alexa for the home with Amazon, just really go crazy. In fact, during the Industry Day, yesterday, you saw the repeat session most attended by developers, was Alexa. So Alexa's got the minds and has captured the imagination of the developers. Where does it go from here and what is the difference between a smart home and a connected home? Can you just take a minute to explain and set the table on that? >> Yeah and I agree, the voice capability in the home, it's absolutely foundational. I think I saw a recent statistic that by 2022, 55% of US households are expected to have a smart speaker type device in their home. So that's a massive percentage. So I think, if you look in the industry, connected home and smart home, they're often use synonymously. We personally look at it as an evolution. And so what I mean by that is, today, we think the home is extremely connected. If I talk about my house, and I'm a total geek about this stuff, I've got 60 devices connected to an access point, I've got another 60 devices connected to an IOT hub. My home does not feel very smart. It's crazy connected, I can turn on lights on and off, sprinklers on and off, it's not yet smart. What we're really focused on at Intel, is accelerating that transition for your home to truly become a smart home and not just a connected home. >> And software is a key part of it, and I've seen developers attack this area very nicely. At the same time, the surface area with these Smart Homes for security issues, hackers. Cause WiFi is, you can run a process on, these are computers. So how does security fit into all of this? >> Yeah, security is huge and so at Intel we're focused on four technology pillars, which we'll get through during this discussion. One of the first ones is connectivity, and we actually have technology that goes into a WiFi access point, the actual silicon. It's optimized for many clients to be in the home, and also, we've partnered with companies, like McAfee, on security software that will sit on top of that. That will actually manage all of the connected devices in your home, as that extra layer of security. So we fundamentally agree that the security is paramount. >> One of the things that I saw on the website that says, Intel is taking a radically different approach based on proactive research into ways to increase smart home adoption. What makes Intel's approach radically different? >> Yeah, so I'm glad that you asked that. We've spent years going into thousands of consumers' homes in North America, Western Europe, China, etc. To truly understand some of the pain points they were experiencing. From that, we basically, gave all this information to our architects and we really synthesized it into what areas we need to advance technology to enable some of these richer use cases. So we're really working on those foundational building blocks and so those four ones I mentioned earlier, connectivity, that one is paramount. You know, if you want to add 35 to 100 devices in your home, you better make sure they're all connected, all the time and that you've got good bandwidth between them. The second technology was voice, and it's not just voice in one place in your home, it's voice throughout your home. You don't want to have to run to the kitchen to turn your bedroom lights on. And then, vision. You know, making sure your home has the ability to see more. It could be cameras, could be motion sensors, it could be vision sensors. And then this last one is this local intelligence. This artificial intelligence. So the unique approach that Intel is taking is across all of our assets. In the data center, in our artificial intelligence organization, in our new technology organization, our IOT organization, in our client computing group. We're taking all of these assets and investing them in those four pillars and kind of really delivering unique solutions, and there's actually a couple of them that have been on display this week so far. >> How about DeepLens? That certainly was an awesome keynote point, and the device that Andy introduced is essentially a wireless device, that is basically that machine learning an AI in it. And that is awesome, because it's also an IOT device, it's got so much versatility to it. What's behind that? Can you give some color to DeepLens? What does it mean for people? >> So, we're really excited about that one. We partnered with Amazon at AWS on that for quite some time. So, just a reminder to everybody, that is the first Deep Learning enabled wireless camera. And what we're helped do in that, is it's got an Intel Atom processor inside that actually runs the vision processing workload. We also contributed a Deep Learning toolkit, kind of a software middleware layer, and we've also got the Intel Compute Library for deep neural networks. So basically, a lot of preconfigured algorithms that developers can use. The bigger thing, though, is when I talked about those four technology pillars; the vision pillar, as well as the artificial intelligence pillar, this is a proof point of exactly that. Running an instance of the AWS service on a local device in the home to do this computer vision. >> When will that device be available? And what's the price point? Can we get our hands on one? And how are people going to be getting this? >> Yeah, so what was announced during the keynote today is that there are actually some Deep Learning workshops today, here at re:Invent where they're actually being given away, and then actually as soon as the announcement was made during the keynote today, they're actually available for pre-order on Amazon.com right now. I'm not actually sure on the shipping date on Amazon, but anybody can go and check. >> Jeff Frick, go get one of those quickly. Order it, put my credit card down. >> Miles: Yes, please do. >> Well, that's super exciting and now, where's the impact in that? Because it seems like it could be a great IOT device. It seems like it would be a fun consumer device. Where do you guys see the use cases for these developing? >> So the reason I'm excited about this one, is I fundamentally believe that vision is going to enable some richer use cases. The only way we're going to get those though, is if you get these brilliant developers getting their hands on the hardware, with someone like Amazon, who's made all of the machine learning, and the cloud and all of the pieces easier. It's now going to make it very easy for thousands, ideally, hundreds of thousands of developers to start working on this, so they can enable these new use cases. >> The pace of innovation that AWS has set, it's palpable here, we hear it, we feel it. This is a relatively new business unit for Intel. You announced this, about a year ago at re:Invent 2016? Are you trying to match the accelerated pace of innovation that AWS has? And what do you see going on in the next 12 months? Where do you think we'll be 12 months from now? >> Yeah, so I think we're definitely trying to be a fantastic technology partner for Amazon. One of the things we have since last re:Invent is we announced we were going to do some reference designs and developer kits to help get Alexa everywhere. So during this trade show, actually, we are holding, I can't remember the exact number, but many workshops, where we are providing the participants with a Speech Enabling Developer toolkit. And basically, what this is, is it's got an Intel platform, with Intel's dual DSP on it, a microarray, and it's paired with Raspberry Pi. So basically, this will allow anybody who already makes a product, it will allow them to easily integrate Alexa into that product with Intel inside. Which is perfect for us. >> So obviously, we're super excited, we love the cloud. I'm kind of a fanboy of the cloud, being a developer in my old days, but the resources that you get out of the cloud are amazing. But now when you start looking at these devices like DeepLens, the possibilities are limitless. So it's really interesting. The question I have for you is, you know, we had Tom Siebel on earlier, pioneer, invented the CRM category. He's now the CEO of C3 IOT, and I asked him, why are you doing a startup, you're a billionaire. You're rich, you don't need to do it. He goes, "I'm a computer guy, I love doing this." He's an entrepreneur at heart. But he said something interesting, he said that the two waves that he surfs, they call him a big time surfer, he's hanging 10 on the waves, is IOT and AI. This is an opportunity for you guys to reimagine the smart home. How important is the IOT trend and the AI trend for really doing it right with smart home, and whatever we're calling it. There's an opportunity there. How are you guys viewing that vision? What progress points have you identified at Intel, to kind of, check? >> Completely agree. For me, AI really is the key turning point here. 'Cause even just talking about connected versus smart, the thing that makes it smart is the ability to learn and think for itself. And the reason we have focused on those technology pillars, is we believe that by adding voice everywhere in the home, and the listening capability, as well as adding the vision capability, you're going to enable all of this rich new data, which you have to have some of these AI tools to make any sense of, and when you get to video, you absolutely have to have some amount of it locally. So, that either for bandwidth reasons, for latency reasons, for privacy reasons, like some of the examples that were given in the keynote today, you just want to keep that stuff locally. >> And having policy and running on it, you know, access points are interesting, it gives you connectivity, but these are computers, so if someone gets malware on the home, they can run a full threaded process on these machines. Sometimes you might not want that. You want to be able to control that. >> Yes, absolutely. We would really believe that the wireless access point in the home is one of the greatest areas where you can add additional security in the home and protect all of the devices. >> So you mentioned, I think 120 different devices in your home that are connected. How far away do you think your home is from being, from going from connected to smart? What's that timeline like? >> You know what I think, honestly, I think a lot of the hardware is already there. And the examples I will give is, and I'm not just saying this because I'm here, but I actually do have 15 Echos in my house because I do want to be able to control all of the infrastructure everywhere in the home. I do believe in the future, those devices will be listening for anomalies, like glass breaking, a dog barking, a baby crying, and I believe the hardware we have today is very capable of doing that. Similarly, I think that a lot of the cameras today are trained to, whenever they see motion, to do certain things and to start recording. I think that use case is going to evolve over time as well, so I truly believe that we are probably two years away from really seeing, with some of the existing infrastructure, truly being able to enable some smarter home use cases. >> The renaissance going on, the creativity is going to be amazing. I'm looking at a tweet that Bert Latimar, from our team made, on our last interview with the Washington County Sheriff, customer of Amazon, pays $6 a month for getting all the mugshots. He goes, "I'm gonna use DeepLens for things like "recognizing scars and tattoos." Because now they have to take pictures when someone comes in as a criminal, but now with DeepLens, they can program it to look for tattoos. >> Yeah, absolutely. And if you see things like the Ring Doorbell today, they have that neighborhood application of it so you can actually share within your local neighborhood if somebody had a package stolen, they can post a picture of that person. And even just security cameras, my house, it feels like Fort Knox sometimes, I've got so many security cameras. It used to be, every time there was a windstorm, I got 25 alerts on my phone, because a branch was blowing. Now I have security cameras that actually can do facial recognition and say, your son is home, your daughter is home, your wife is home. >> So are all the houses going to have a little sign that says,"Protected by Alexa and Intel and DeepLens" >> Don't you dare, exactly. (laughs) >> Lisa: And no sneaking out for the kids. >> Yes, exactly. >> Alright, so real quick to end the segment, quickly summarize and share, what is the Intel relationship with Amazon Web Services? Talk about the partnership. >> It's a great relationship. We've been partnering with Amazon for over a decade, starting with AWS. Over the last couple of years, we've started working closely with them on their first party products. So, many of you have seen the Echo Show and the Echo Look, that has Intel inside. It also has a RealSense Camera in the Look. We've now enabled the Speech Enabling Developer Kit, which is meant to help get Alexa everywhere, running on Intel. We've now done DeepLens, which is a great example of local artificial intelligence. Partnered with all the work we've done with them in the cloud, so it really is, I would say the partnership expands all the way from the very edge device in the home, all the way to the cloud. >> Miles, thanks for coming, Miles Kingston with Intel, General Manager of the Smart Home Group, new business unit at Intel, really reimagining the future for people's lives. I think in this great case where technology can actually help people, rather than making it any more complicated. Which we all know if we have access points and kids gaming, it can be a problem. It's theCUBE, live here in Las Vegas. 45,000 people here at Amazon re:Invent. Five years ago, our first show, only 7,000. Now what amazing growth. Thanks so much for coming out, Lisa Martin and John Furrier here, reporting from theCUBE. More coverage after this short break. (light music)

>> Announcer: Live, from Las Vegas, it's theCUBE, covering AWS re:Invent 2017. Presented by AWS, Intel, and our ecosystem of partners. >> Welcome back everyone, live here in Las Vegas it's AWS re:Invent, it's theCUBE exclusive coverage of re:Invent 2017, our 5th year covering Amazon, watching the explosive growth. I'm John Furrier, the co-founder of SiliconANGLE Media, with Justin Warren my co-host here for this segment. Our next guest is Raejeanne Skillern, Vice President of the Data Center Group and also General Manager of the Cloud Service Provider, part of Intel. Welcome to theCUBE. >> Thank you very much, it's nice to be here again. >> So, Intel inside the Cloud is big growth for you, what's the numbers look like, your earnings look pretty good, what's the business update? >> You know, it is growing, and it's growing in so many different angles, it's coming from multiple fronts. Part of it is just these killer workloads that are driving the need for change. Artificial Intelligence, immersive media, network transformation to handle all the data. It is causing this real spur in growth, from both the very largest of Cloud service providers across the ecosystem, and one of the things when I look at the growth, I try to say, now where is it really coming from? Yes, Amazon is just insanely growing fast and the big guys are doing well, their global expansion, But, also, the next wave of Cloud service providers, and regions, and different countries, are doing really well too, they're actually growing faster, so the whole ecosystem is growing with demand. >> Yeah, it's certainly not slowing down. I can't see any way in which that it would slow down at all, if anything it's just gonna get faster. >> I think so, and when I track trends like CapEx spending and analyst predictions on the future, consumer demand, everything indicates we're gonna see another strong 5-10 years. >> Justin: Yep. >> So how do the Cloud service providers shake out because if you look at this creativity of software renaissance, the interactivity, all the amazing use cases, from Thorn, finding missing exploited children, to, you know, high end HPC scientists, genome sequencing stuff. Massive use cases. >> Raejeanne: Right. >> So that's going to be a tsunami of software developers, Changing. How does that shape the Cloud providers, does it segment them into tiers, how are you guys looking at that business? >> Well I think it breeds a lot of new opportunity. So, yes, you see more and more new services and capabilities to enable developers to more easily develop code, to more quickly, to get better utilization out of their code. But I also think just the concept of a mocking performance and ease, means new insights, new services, new capabilities, things that we just couldn't, a developer, we could not have done before. So it's that, just like electricity, when you make it cheaper and easier to consume, we use more of it. I think Cloud computing is the same way. So we're seeing kind of a natural tam expansion, market expansion, around these things you just couldn't do without the Cloud. >> And new workloads, too. >> And new workloads. >> And all of those workloads, Intel is basically completely repositioning itself from just being a chip company, it's like, you're a data company now. So, what are some of the things that Intel's doing to help people understand how they can make use of these new software techniques, and these new tools, and the capabilities of the Intel chips they're dependent on. So what are some of the things that Intel's doing there? >> So obviously it starts at the bottom, with the best silicon, not just compute, but compute, network, and storage, and accelerators, for all different workloads. We move up to the platforms, we do a lot of hardware engineering with the ecosystem, with our top CSPs, actually many top CSPs, we do direct engineering work to get better systems of architecture. We have a host of libraries we're creating that ease of use, and mocking performance out of it. Reference architectures, co-partners, and solutions. So for us when we talk about being a data company, we can't do it from just even being a chip, we have to be a solutions partner, with Cloud service providers, enterprise, IOT edge solutions, we try to be there. >> You guys always enabled some very cool demos in the day, even back on the PC you always had interactivity, you pioneered multimedia. You always had that eye on the applications. Okay, on stage today all this greatness is out there: NFL demos, all this cool stuff. That's really powering your business, so talk about your relationship with Amazon web services, what's it like, how do you guys engage with them, what's the relationship, 'cause you guys are power engine for AWS. >> Yeah, well we try to be. We wanna be the best performance into their data center. We've had a many, many, many year deep relationship with Amazon. It started from simple co-development and engineering, and is extended much more pervasively across their environment and it poured sometimes into the services. We just, we want to one, make sure that what we're delivering today has been already optimized specifically for their unique environment, and that means we have to start a year or two before, if not earlier, to really understand where they're going, and get their feedback, so that when we either optimize a product or technology across compute network or storage, or create something potentially custom for them, right, it takes a number of years of work, so our partnership has a lot of in-depth engineering, it has a lot of future and near-term enabling, and then we hope to see an expansion. What we really want to do is use our technology to differentiate Amazon. We want their services better because of the technologies or capabilities we put in, so wherever they wanna align in terms of strategic investment and growth markets, we wanna make sure the silicon can enable it. >> Intel always marched to the cadence of Moore's law, and you guys have always been a rapid machine, execution. Amazon looks good, I mean, they're executing. >> Raejeanne: They're fast. >> I mean, what's it like? Share some story, I mean, they're years ahead, what's it like working with Amazon? >> Well I think, I mean, they are fast. It's amazing how quickly they can move and innovate, how rapidly those innovations roll out to the market. I will be honest, there are times where we miss windows because we are slow, and they just look at us like, "Well we told you "this four weeks ago, here it is, right?" (laughing) >> Can't design a chip in four weeks. We don't have kubernetes, no containers for chips. >> I mean they are just, they have it down. So what we're trying to do is part of this transition from being a client company, to a Cloud and data center company, and IOT company, means everything we have to do is faster. So we have to design our chips more quickly, we have to put in more modularity for faster derivatives, and we have to move at Cloud speed, not classic Intel speed. >> Right, yeah. So what are some of the lessons that other companies can take from Intel, I mean it's a hardware company, or it was originally a hardware company, and now you've transitioned to being a Cloud company, and you're being pushed by Amazon to move faster and faster. So what are some of the lessons that you can share with other companies who are trying to start moving at Cloud speed? >> You know, I think, I love Jeff Bezos' approach, customer-obsessed. If you aren't understanding how the end customers, starting with Amazon's customers, but also then my customer, Amazon, how they're using and consuming technology, we can't really create good technology. I would say a lot of companies create a great thing and then try to go sell it at markets, >> Yep. >> Versus starting with the market, and creating the specific thing. The other thing we've learned, I mean, Intel is a very data driven company, both in our decision making, as well as our company growth, this is, and we talk about it from a developer envirogroup, but it's the same iterate fast. Fail quickly and move on. You don't need perfect. This is one of our learnings, right? Don't wait six months for perfect, move fast, get 70-80% of the way there, I've heard governments say they get 40-50 way percent of there, make decisions, because they have to move that quickly. For military or other exercises. So what we're trying to do is match that type of speed as well. >> It's a world of compute now, I mean, I was at Alibaba Conference, they had their Cloud coachings hearing it here, the same message: more compute. They're not saying I need more little chips, they're saying I want more horsepower. >> Raejeanne: Right. >> And you guys just announced the C5 instance, recently, a couple weeks. >> And the budget. >> What is that gonna do? I mean, it's fairly new. >> Raejeanne: Yes. >> What does that mean? Is that gonna be an IOT edge opportunity? Is it all workloads? That's gonna be like, a pretty big deal. What's your take on it? >> It is, their C instant line has always been for high performances growing workloads. We're seeing like, for HPC workloads, anywhere from two to four-and-a-half X the performance moving from C4 to C5, right? This is an instance that can handle the most demanding workloads from high performance compute to artificial Intelligence to others. So, you know, we have our latest and greatest Intel ZN scalable process in there, a very high performant one, that we customized specifically for their environment, but then they do all this amazing software work and efficiency work around it, to really unlock. I was really glad to see when they talked about those C5 instances launching, 25% on workload's price performance up to 50% price performance improvements on some others. So, I mean, once again, when you can take more compute and make it more cost effective, it's just a lot more things people can do in the industry, so we're very excited about that instance. >> What's the biggest thing in the past five years that jumped out at you in the massive change in the industry? Application, startups, business growth? What amazes you? >> Yeah, there's so much, I would kind of combine it under what the industry calls Digital Transformation. You know, when I look at it, one of the points that always sticks in my mind, is CEOs, 70% of them have digital transformation at the heart because the data suggests that by the end of 2018, the top 40% of the top 20, 40% of the top 20 industries are gonna be disrupted. So that to me, that amount of disruption happening, and the company's trying to disrupt themselves whether it's healthcare, retail, manufacturing, oil and glass, the use and pervasiveness of where technology and Cloud can fit has really kind of astonished me and I love, once again, I love that they're making what they do today better. But the new things that they're doing, I mean, in healthcare, right? It's just amazing. >> I mean, we used to use the word back in the day when I broke into the biz in the '80s, Data Processing Department. I mean, the Cloud is one big data processor. >> It is. It is a compute power. We call it the brain, right? If Cloud is now ubiquitous, right? It is, from public Cloud to private to Edge, and everything in between, it's that brain, right? >> John: It's the brain. >> That's enabling the compute, so we have to-- >> You people have always been on the inside of everything, so congratulations on your success, on the Cloud growth. General manager of the Cloud service provider and Vice President of Data Center Group, Raejeanne Skillern, here inside theCUBE with Intel, I'm Jeff Furrier with Justin Warren, back with more live coverage, here in Las Vegas, for Amazon web services 2017 re:Invent. More after this short break. (techno music)

>> Narrator: Live from Las Vegas, it's theCUBE, covering AWS re:Invent 2017, presented by AWS, Intel, and our ecosystem of partners. >> Hello everyone, welcome to a special CUBE presentation here, live in Las Vegas for Amazon Web Service's AWS re:Invent 2017. This is theCUBE's fifth year here. We've been watching the progression. I'm John Furrier with Justin here as my co-host. Our two next guests are Bob Rogers, the chief data scientist at Intel, and Julie Cardoa, who's the CEO of Thorn. Great guests, showing some AI for good. Intel, obviously, good citizen and great technology partner. Welcome to theCUBE. >> Thank you, thanks for having us! >> So, I saw your talk you gave at the Public Sector Breakfast this morning here at re:Invent. Packed house, fire marshal was kicking people out. Really inspirational story. Intel, we've talked at South by Southwest. You guys are really doing a lot of AI for good. That's the theme here. You guys are doing incredible work. >> Julie: Thank you. >> Tell your story real quick. >> Yeah, so Thorn is a nonprofit, we started about five years ago, and we are just specifically dedicated to build new technologies to defend children form sexual abuse. We were seeing that, as, you know, new technologies emerge, there's new innovation out there, how child sexual abuse was presenting itself was changing dramatically. So, everything from child sex trafficking online, to the spread of child sexual abuse material, livestreaming abuse, and there wasn't a concentrated effort to put the best and brightest minds and technology together to be a part of the solution, and so that's what we do. We build products to stop child abuse. >> John: So you're a nonprofit? >> Julie: Yep! >> And you're in that public sector, but you guys have made a great progress. What's the story behind it? How did you get to do so effective work in such a short period of time as a nonprofit? >> Well, I think there's a couple things to that. One is, well, we learned a lot really quickly, so what we're doing today is not what we thought we would do five years ago. We thought we were gonna talk to big companies, and push them to do more, and then we realized that we actually needed to be a hub. We needed to build our own engineering teams, we needed to build product, and then bring in these companies to help us, and to add to that, but there had to be some there there, and so we actually have evolved. We're a nonprofit, but we are a product company. We have two products used in 23 countries around the world, stopping abuse every day. And I think the other thing we learned is that we really have to break down silos. So, we didn't, in a lot of our development, we didn't go the normal route of saying, okay, well this is a law enforcement job, so we're gonna go bid for a big government RFE. We just went and built a tool and gave it to a bunch of police officers and they said, "Wow, this works really well, "we're gonna keep using it." And it kinda spread like wildfire. >> And it's making a difference. It's really been a great inspirational story. Check out Thorn, amazing work, real use case, in my mind, a testimonial for how fast you can accelerate. Congratulations. Bob, I wanna get your take on this because it's a data problem that, actually, the technology's applying to a problem that people have been trying to crack the code on for a long time. >> Yeah, well, it's interesting, 'cause the context is that we're really in this era of AI explosion, and AI is really computer systems that can do things that only humans could do 10 years ago. That's kind of my basic way of thinking about it, so the problem of being able to recognize when you're looking at two images of the same child, which is the piece that we solved for Thorn, actually, you know, is a great example of using the current AI capabilities. You start with the problem of, if I show an algorithm two different images of the same child, can it recognize that they're the same? And you basically customize your training to create a very specific capability. Not a basic image recognition or facial recognition, but a very specific capability that's been trained with specific examples. I was gonna say something about what Julie was describing about their model. Their model to create that there there has been incredible because it allows them to really focus our energy into the right problems. We have lots of technology, we have lots of different ways of doing AI and machine learning, but when we get a focus on this is the data, this is the exact problem we need to solve, and this is the way it needs to work for law enforcement, for National Center for Missing and Exploited Children. It has really just turned the knob up to 11, so to speak. >> I mean, this is an example where, I mean, we always talk about how tech transformation can make things go faster. It's such an obvious problem. I mean, it's almost everyone kinda looks away because it's too hard. So, I wanna ask you, how do people make this happen for other areas for good? So, for instance, you know, what was the bottlenecks before? What solved the problem, because, I mean, you could really make a difference here. You guys are. >> Well, I think there's a couple things. I think you hit on one, which is this is a problem people turn away from. It's really hard to look at. And the other thing is is there's not a lot of money to be made in using advanced technology to find missing and exploited children, right? So, it did require the development of a nonprofit that said, "We're gonna do this, "and we're gonna fundraise to get it done." But it also required us to look at it from a technology angle, right? I think a lot of times people look at social issues from the impact angle, which we do, but we said, "What if we looked at it "from a different perspective? "How can technology disrupt in this area?" And then we made that the core of what we do, and we partnered with all the other amazing organizations that are doing the other work. And I think, then, what Bob said was that we created a hub where other experts could plug into, and I think, in any other issue area that you're working on, you can't just talk about it and convene people. You actually have to build, and when you build, you create a platform that others can add to, and I think that is one of the core reasons why we have seen so much progress, is we started out convening and really realized that wasn't gonna last very long, and then we built, and once we started building, we scaled. >> So, you got in the market quickly with something. >> Yeah. >> So, one of the issues with any sort of criminal enterprise is it tends to end up in a bit of an arms race, so you've built this great technology but then you've gotta keep one step ahead of the bad guys. So, how are you actually doing that? How are you continuing to invest in this and develop it to make sure that you're always one step ahead? >> So, I can address that on a couple of levels. One is, you know, working with Thorn, and I lead a program at Intel called the Safer Children Program, where we work with Thorn and also the National Center for Missing and Exploited Children. Those conversations bring in all of the tech giants, and there's a little bit of sibling rivalry. We're all trying to throw in our best tech. So, I think we all wanna do as well as we can for these partnerships. The other thing is, just in very tactical terms, working with Thorn, we've actually, Thorn and with Microsoft, we've created a capability to crowdsource more data to help improve the accuracy of these deep learning algorithms. So, by getting critical mass around this problem, we've actually now created enough visibility that we're getting more and more data. And as you said earlier, it's a data problem, so if you have enough data, you can actually create the models with the accuracy and the capability that you need. So, it starts to feed on itself. >> Julie talked about the business logic, how she attacked that. That's really, 'cause I think one thing notable, good use case, but from a tech perspective, how does the cloud fit in with Intel specifically? Because it really, the cloud is an enabler too. >> Bob: Yeah, absolutely. >> How's that all working with Intel? And you go on about whole new territory you guys are forging in here, it's awesome, but the cloud. >> Right, so, for us, the cloud is an incredible way for us to make our compute capability available to anyone who needs to do computing, especially in this data-driven algorithm era where more and more machine learning, more and more AI, more and more data-driven problems are coming to the fore, doing that work on the cloud and being able to scale your work according to how much data is coming in at any time, it makes the cloud a really natural place for us. And of course, Intel's hardware is a core component of pretty much all the cloud that you could connect to. >> And the compute that you guys provide, and Amazon adds to it, their cloud is impressive. Now, I'd like to know what you guys are gonna be talking about in your session. You have a session here at re:Invent. What's the title of the session, what's the agenda, is it the same stuff here, what's gonna be talked about? >> So, we're talking about life-changing AI applications, and in specific we're gonna talk about, at the end Julie will talk about what Thorn has done with the child-finder and the AI that we and Microsoft built for them. We'll also, I'll start out by talking about Intel's role broadly in the computing and AI space. Intel really looks to take all of its different hardware, and networking, and memory assets, and make it possible for anybody to do the kinds of artificial intelligence or machine learning they need to do. And then in the middle, there's a really cool deployment on AWS sandwich that (something) will talk about how they've taken the models and really dialed them up in terms of how fast you can go through this data, so that we can go through millions and millions of images in our searches, and come back with results really, really fast. So, it's a great sort of three piece story about the conception of AI, the deployment at scale and with high performance, and then how Thorn is really taking that and creating a human impact around it. >> So, Bob, I asked you the Intel question because no one calls up Intel and says, "Hey, give me some AI for good." I mean, I wish that would be the case. >> Well, they do now. >> If they do, well, share your strategy, because cloud makes sense. I could see how you could provision easily, get in there, really empowering people to do stuff that's passionable and relevant. But how do you guys play in all of this? 'Cause I know you supply stuff to the cloud guys. Is this a formal program you're doing at Intel? Is this a one-off? >> Yeah, so Safer Children is a formal program. It started with two other folks, Lisa Davis and Lisa Theinai, going to the VP of the entire data center group and saying, "There is an opportunity to make a big impact "with Intel technology, and we'd like to do this." And it started literally because Intel does actually want to do good work for humankind, and frankly, the fact that these people are using our technology and other technology to hurt children, it steams our dumplings, frankly. So, it started with that. >> You've been a team player with Amazon and everyone else. >> Exactly, so then, once we've been able to show that we can actually create technology and provide infrastructure to solve these problems, it starts to become a self-fulfilling prophecy where people are saying, "Hey, we've got this "interesting adjacent problem that "this kind of technology could solve. "Is there an opportunity to work together and solve that?" And that fits into our bigger, you know, people ask me all the time, "Why does Intel have a chief data scientist?" We're a hardware company, right? The answer is-- >> That processes a lot of data! >> Yes, that processes a lot of data. Literally, we need to help people know how to get value from their data. So, if people are successful with their analytics and their AI, guess what, they're gonna invest in their infrastructure, and it sort of lifts Intel's boat across the board. >> You guys have always been a great citizen, and great technology provider, and hats off to Intel. Julie, tell a story about an example people can get a feel for some of the impact, because I saw you on stage this morning with Theresa Carlson, and we've been tracking her efforts in the public sector have been amazing, and Intel's been part of that too, congratulations. But you were kind of emotional, and you got a lot of applause. What's some of the impact? Tell a story of how important this really is, and your work at Thorn. >> Yeah, well, I mean, one of the areas we work in is trying to identify children who are being sold online in the US. A lot of people, first of all, think that's happening somewhere else. No, that's here in this country. A lot of these kids are coming out of foster care, or are runaways, and they get convinced by a pimp or a trafficker to be sold into prostitution, basically. So, we have 150,000 escort ads posted every single day in this country, and somewhere in there are children, and it's really difficult to look through that with your eye, and determine what's a child. So, we built a tool called Spotlight that basically reads and analyzes every ad as it comes in, and we layer on smart algorithms to say to an officer, "Hey, this is an ad you need to pay attention to. "It looks like this could be a child." And we've had over 6,000 children identified over the last year. >> John: That's amazing. >> You know, it happens in a situation where, you know, you have online it says, you know, this girl's 18, and it's actually a 15-year-old girl who met a man who said he was 17, he was actually 30, had already been convicted of sex trafficking, and within 48 hours of meeting this girl, he had her up online for sale. So, that sounds like a unique incident. It is not unique, it happens every single day in almost every city and town across this country. And the work we're doing is to find those kids faster, and stop that trauma. >> Well, I just wanna say congratulations. That's great work. We had a CUBE alumni, founder of CloudAir, Jeff Hammerbacher, good friend of theCUBE. He had a famous quote that he said on theCUBE, then said on the Charlie Rose Show, "The best minds of our generations "are thinking about how to make people click ads. "That sucks." This is an example where you can really put the best minds on some of the real important things. >> Yeah, we love Jeff. I read that quote all the time. >> It's really a most important quote. Well, thanks so much. Congratulations, great inspiration, great story. Bob, thanks for coming on, appreciate it. CUBE live coverage here at AWS re:Invent 2017, kicking off day one of three days of wall-to-wall coverage here, live in Las Vegas. We'll be right back with more after this short break.

>> 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. >> And welcome to our nation's capitol. Here we are in Washington, D.C. TheCUBE coming live from the Walter Washington Convention Center, here for AWS Private Sector Summit. It's our maiden voyage with the Public Sector so looking forward to this. John Walsh and John Furrier, glad to have you along for the ride, John, this is going to be a good week. >> Hey, it'll be fun. >> A good couple of days. John Galvin joins us. He is the Vice President and General Manager of the Public Sector Intel. John, thank you for being with us here on theCUBE; glad to have you. >> It's a pleasure to be here, thank you. >> Tell us a little bit first off, about your portfolio. >> Sure. >> I understand you cover not only United States, but you have a global footprint as well. Touch base a little bit with our audience with what you're up to. >> Yeah, absolutely. So first of all, I have to put it in perspective for everyone. People know us as a micro-processor company. They don't always attribute us to going out and calling on government, or education decision makers. So we really act as a trusted advisor. We don't sell directly to government or to education entities, and I have sales people, or account exec's, around the world who are going in and meeting with ministers of education or ministers of ICT. Sometimes it's a school superintendent or a district superintendent, but, overall, what we're talking about is digital transformation and how technology can be used to advance government or advance education. And sometimes at a national level, could be at a state level, could be at a district level. >> Well, John and I were talking in our open segment just a little bit ago, about maybe a glacial pace isn't fair to say about how government had that reputation, obviously, for many years about being, maybe, reluctant. >> Right. >> To embrace change. What do you see now in that space? Is there this shift going on, that there's more of an embracing of technology? And of more entrepreneurial kind of spirit within the operation of government? >> Yeah, absolutely. It is happening so quickly. The categorization of government moving slowly is absolutely true. Education the same. But now wherever I go around the world, everyone is talking about transformation and they're starting to launch projects that might be a pilot or a proof of concept, but they're getting started. The challenge is when you talk about digital transformation it is so big so it becomes difficult for governments to really get their hands around it, and what are they going to do to improve citizens quality of life. Is that going to be a healthcare initiative? Is it going to be a transportation initiative? Sometimes it's an education initiative; and we're seeing them all. I think what is causing it to advance now is they see proof-points that it works. That by making those investments it really is changing the quality of life for people. And in emerging markets they don't have existing infrastructure that they have to tear out and replace. And some of the mature markets, it's how do you actually breakdown those silos. >> Well, John, I'm really glad you came on. Intel, in my opinion, I've been following Intel for many years, recently, has been pretty amazing. But you guys have always been a bellwether for trends, I'd say, five to 10 years out. I mean, look at everything that Intel's done with technology You have that five to 10 year stair instantly in what you're proposing. We've been seeing a lot of the AI commercials with Intel, what is the Public Sector trends that intersect with the vision of Intel? >> Well, you're absolutely right. If you look at what Intel does we're similar to the auto industry. It takes us five to six years to produce our next processor, and so we have to be looking that far out of what are the use cases, and really, what are those technological boundaries that we're going to either cross or break? And AI is absolutely the conversation today. It's sort of around artificial intelligence and it's no longer science fiction. We're not talking about it in the future; we're now talking about how can we use it today? Machine learning big, big topic, and not just the role that Intel plays, but companies like AWS; big players, in terms of how that actually comes to life in your home. It's not just how it's going to come to life in a big government institution or a big enterprise. >> And the Public Sector landscape, for the folks that are watching some know the Public Sector, what is the Public Sector? Because it's not the government. There's education, there's health, so what's the layout. How do you categorically look at it? How should people think about Public Sector? Not just GovCloud because there's a GovCloud, but is there a Public Sector cloud? I mean, how should people think about it? >> Yeah, great question. I work as part of a group at Intel that are all verticals. There's a healthcare team, there's a transportation team, there's an energy team. Public Sector is completely different because we're all of those things. We're working on transportation projects, we're working big healthcare projects, and so Public Sector you have to look at in the biggest sense where it's not just a federal presence but it is a state presence, it's a city presence or a county presence. And so our opportunity is to be able to connect all of those things, and that is what I think is so exciting about the transformation that is taking place right now is for that vision to be realized those silos really need to be broken. You know, you're going to hear comments over the next couple of days about forming a data lake. Which is bringing in all of those data streams into a single spot so that you can apply analytics and be able to get to insights that we've never been able to get to before. >> So how do you do that if you talk about municipality levels, state levels, federal levels, different operating systems, different processes, different procedures? And all great resources, how do you pull all that together and make that an asset instead of a morass? >> Well, in that question you just captured how big this opportunity is, and the way that we do it is we work with our ecosystem partners. The strength that Intel has when we enter into those conversations is we work with everyone. We work with the big cloud providers, we work with all the different operating system providers. We're not only with the computer companies that are our partners and our customers, but we're working now with internet and think companies, and so we have the ability to now work across that ecosystem to start pulling all of those pieces together. The heart of your question though is that those are all different systems that have been built over time. And if you look at what's been happening in enterprise over the past 10 years is CIO's and CTO's at the enterprise levels have been breaking down those silos and moving more to single systems and big data streams. And now that's what's happening with in the Public Sector is that data has to come together. >> John, talk about the collaboration between Intel and AWS and what is going on with you guys, how you guys are working together, and what's the impact in serving Public Sector customers? >> Well, we have had a great partnership with AWS from the beginning. (audio cuts out) (audio cuts out) That's going to take on this bigger vision is going to have a cloud discussion. There will still be things that they're going to be doing on premise, but it's most likely going to be a hybrid environment. And so with AWS we really have the opportunity to have a bigger discussion, where they can really have that cloud discussion and even some of the analytics layer. They're also doing more at an IOT perspective; we're able to join that conversation in terms of how our technology really plays into it. But I think the other thing we're able to do with AWS is really look for innovators. We're able to identify either those small companies, or even some of the cities are doing some really great things. And then because of their global footprint and our global footprint we can share that pretty broadly. >> And ecosystem's critical. You guys, Intel's always been ecosystem friendly company. With that in mind I got to ask you the question that everyone's talking about, and certainly, we're covering Mobile World Congress this year in Barcelona. And you couldn't go anywhere without hearing 5G and these new phones that are coming out. And then under the hood network transformation, you're hearing about software to find networking, machine learning, AI a lot of things that you guys are talking about. So the question for you is Smart Cities. It is a really, really hot opportunity just to even think about the concept of what a Smart City entails. I mean, here in D.C., like other cities, they have bicycles people can take out and ride around. That's a smart city, that's a cool service. But now you bring digital all to it. Imagine, Air B&B, you've got Uber, you've got Lift you've got all kinds of digital services, digital experiences. This is a government, this is a Public Sector issue. This is an interesting one. How is Intel's view on Smart Cities, how do you see that rolling out? >> First of all, we're very excited about what's happening within Smart Cities, and to the beginning of your question we think 5G is going to be an accelerant. It's going to cause it to happen even faster than it's happening now. What's interesting about Smart City is that it really does take a lot of different formats. And so we see cities who are really focused on public security and safety. We have examples whether it's Singapore, London of how they're now capturing new data with the cameras that they put up, and can do real-time analytics on it using AI and machine learning. So it's not that they just have all of these data streams, but they're doing real-time analysis of the data stream to be able to identify potential threats. But we also have examples where we're seeing cities invest in new technology to, essentially, replace what are the old ways of us being able to communicate and engage with the government. And that could be as simple as there's new information that's available to us. Or as they're collecting all these data streams they're making that data public and available for innovation, and so entrepreneurs now have the ability to also build solutions on those data streams. It's an incredibly exciting time. >> I mean, it's mind boggling to just think about how we live our lives in our cities. I can call the police department, the fire department, call for services in the analog world. Imagine video chat. Is that going to go to the certain departments? So how people engage, which side of the street do the cars drive on, who decides all that? And this is kind of how big this is. It's mind blowing. >> Well, it is big, and I'm going to answer that in two ways. Yes, the way that we did things before is changing and it's changing rapidly. To your 911 reference, I don't know, does it have to be a video engagement? Or through video are we actually capturing real-time that there's an incident that the fire department or an ambulance or police need to be dispatched. Where no phone call actually needs to be made. >> Real-time analytics. >> Yeah. >> Predicted, prescriptive analytics could come to the table. >> Yeah, absolutely. And so we're already seeing examples of that, where that's happening today. What we're not seeing happening at scale, but I think we will see it happening at scale, all of those early adopters they had to figure it out on their own. But now we have blueprints, we have frameworks that we can share with other cities where they will be able to do it much more quickly. >> All right, what project really stands out for you, in all the things you're looking at, in the Public Sector because there's so much going on that you guys are doing I mean, props to Intel love what they're doing. The AI mission really puts a vision in place but also it's reality now with machine learning. What projects stand out for you that you see as real innovative, collaborative between Intel and AWS? >> Yeah, so we did a project with AWS where we, essentially, created a competition for new ideas to be able to come forward, and out of that what we've seen is some cities really doing some innovative things, just taking those first steps. What that does for us is it gives us a broader view than we would be able to get on our own. But some of that's basic. Say exciting stuff, we have exciting examples, the kiosks on the street corners in New York are an exciting example. What we see some of the universities doing, I think, is really exciting. Universities around the world have an issue with student retention. Where they just experience high drop out rates at the end of the freshman year and the end of the sophomore year. The challenge is how do you identify a student at risk? Well, automate attendance and you can now see are students actually attending classes. Or are they skipping class? Start using sensors and beacons on the campus and you can actually detect what those student patterns are and you just might need to have a counselor step in or a professor step in and really sit down with them and walk them through it. >> Use the IOT example, humans are things too, right? I mean, wearables, they got all kinds of sensors that could be even on-person device too. Absolutely, we have been working with the University of Texas Arlington, exactly, on that project. Through a sensor you can actually capture the emotional state of student. Are they highly stressed? And should that be, again, an environment where-- >> Explain how. How does that work? >> Through body temperature and -- >> So biometrics being measured. >> Yeah. >> Body temperature, respiration rates, all those kinds of things. >> Mental health is a huge issue in colleges and universities around the pressure. >> You can see that idea from a health perspective, strep throat, right? >> Sure. >> It's like the freshman plague. Every freshman gets strep throat. But if you could identify anxiety as it's being formulated before it manifests itself in academic performance, you could treat that. >> Sure, and now you combine that with capturing data from the student cafeteria or dorms of what are they're eating patterns, what are they're sleeping patterns? Are they actually getting enough sleep? So you get a much more holistic view of the student. And we have to be careful here, right, because-- >> There's privacy concerns. >> Right, there's absolutely privacy and security concerns. And anyone who engages in these projects, heightened awareness of that. So it really is about quality of life and how do you create a better educational experience, not create anything that's threatening, but it becomes a much more personalized learning experience. >> The convergence and the conflicts between IOT and cloud and processing power and software, it's interesting, I was looking on prior to the show coming in I saw on your website at Intel Farmers in America. And then on Amazon's site there's a City on the Cloud. Can you take a minute and explain those projects. I think they're both Intel and AWS collaborations. Can you just take a minute to explain the City on the Cloud and the Farmers of America, what's the big aha there? >> So it's a three year project that we've been working on in collaboration with AWS, and the whole idea was for us to be able to identify some innovative ideas within the space because it is still a new area. How do we, essentially, give some of these entrepreneurs and innovative people a chance to be able to bring their idea into fruition? And so agriculture and Farmers America's a great example because that data is being collected in terms of weather patterns and how they can now, essentially, access that data to be able to plan differently what they're doing as well as better enable them to share with others what they're finding as they're making changes too. >> The farm tech has been hot on the D.C. community, certainly, in the Silicon Valley seeing people doing farm tech. Farm tech is one of those things, agriculture's a huge area that health implications too. People are interested in automating a lot of things and bringing tech there. And then also healthcare is a factor too. One of the areas is education but healthcare is another one that you guys are, what's the new thing in there that you guys are doing in healthcare? >> Yeah, we're doing quite a bit in healthcare around the world, and if you really think about it the challenge with healthcare is that your records are typically with your doctor or with your hospital. They're not always shared and they don't move with you when you travel. And so the first opportunity is how does that data actually become standardized so that it can actually be shared. But the other opportunity in healthcare is for those CTO's and CIO's to start using data very differently, to understand the patterns of what's happening within their hospitals. And you're earlier reference, John, to strep throat within a campus, how do you, essentially, start tracking that there's a trend and that there's something that you could potentially deal with much more quickly once you have the insights to it. >> All right, so take a minute as end this segment here, I want to get your thoughts on, give us a taste and showcase some of the Intel speeds and feeds, some of the tech, what's under the hood, what's coming out of Intel that's powering all of this because remember we're all driving the self-driving digital tooling out there. It's all powered by the Zeons, all kinds of cool stuff. What's the latest state-of-the-art that you got from Intel that you guys are bringing to the market in the Public Sector? >> Yeah, well, thank you for that question. I don't normally get it. >> John loves it. He's a speed and feed guy. >> To get too much feedback. Too geeky. >> Well, your earlier question was around AI and machine learning and for us that's the zonify. And if you look at the power of zonify it's, essentially, three times the teraflops of the largest super computer that existed 20 years ago, in a single processor. And so for us it's an opportunity to then really be able to advance and accelerate what's happening with artificial intelligence as well as machine learning. >> Well, it's an exciting new world. Obviously with a realm that goes healthcare to ag, to education, to government with Intel very much at the center of that. John, thanks for being with us. >> It was great to be here. >> We appreciate the time on theCUBE. We look forward to having you back. We'll continue our coverage live here from the AWS Public Sector Summit here on theCUBE. Back with more in just a bit. (upbeat music)

>> Narrator: Live from Washington D.C. it's the CUBE covering the AWS Public Sector Summit 2017. Brought to you by the Amazon web services and its partner Ecosystem. >> Melvin Greer is with us now he's the director of Data Science and Analytics at Intel. Now Melvin, thank you for being here with us on the CUBE. Good to see you here this morning. >> Thank you John and John I appreciate getting a chance to talk with you it's great to be here at the AWS Public Sector Summit. >> Yeah we make it easy for you. >> I never forget the names. >> John and John. Let's talk just about data science in general and analytics I mean tell us about, give us the broad definition of that. You know the elevator speech about what's being done and then we'll drill down a little bit deeper about Intel and what you're doing with in terms of government work and healthcare work. >> Sure well data science and analytics covers a number of key areas and it's really important to consider the granularity of each of these key areas. Primarily because there's so much confusion about what people think of as artificial intelligence. It's certainly got a number of facets associated with it. So we have core analytics like descriptive, diagnostic, predictive and prescriptive. This describes what happened, what's going to happen next, why is it happening and what should I do about it. So those are core analytics. >> And (mumbles) oh go ahead. >> And a different tech we have machine learning cognitive computing. These things are different than core analytics in that they are recognizing patterns and relying on the concepts of training algorithms and then inference. The use of these trained algorithms to infer new knowledge. And then we have things like deep learning and convolutional neuro networks which use convolutional layers to drive better and better granularity and understanding of data. They often typically don't rely on training and have a large focus area around deep learning and deep cognitive skills. And then all of those actually line up in this discussion around narrow artificial intelligence and you've seen a lot of that already haven't you john? You've seen where we teach a machine how to play poker or we teach a machine how to play Jeopardy or Go. These are narrow AI applications. When we think about general AI however, this is much different. This is when we're actually outsourcing human cognition to a thinking machine at internet speed. >> This is amazing I love this conversation cause couple things, in that thread you just brought up is poker which is great cause it's not just Jeopardy it's poker is unknown conditions. You don't know the personality of the other guy. You don't know their cards their dealing with so it's a lot like unstructured data and you have to think about that so but it really highlights the (mumbles) between super computing paradigm and data and that really kind of changes the game on data science cause the old data warehouse model storing information, pulling it back, latency, and so we're seeing machine learning in these new aps really disrupting old data analytics models. So, I want to get your thoughts on this because and what is Intel doing because you guys have restructured things a bit differently. The AI messages out there as this new revolution takes place with data, how are you guys handling that? >> So Intel formed in late 2016 its artificial intelligence product group and the formation of this group is extremely consistent with our pivot to becoming a data company. So we're certainly not going to be abandoning any of that great performance and strong capabilities that we have in silicon architectures but as a data company it means that now we're going to be using all of these assets in artificial intelligence, machine learning cognitive computing and Intel in fact by using this is really in a unique position to focus on what we have termed and what you'll hear our CEO talk about as the virtuous cycle of growth. This cycle of growth includes cloud computing, data center, and IOT. And our ability to harness the power of artificial intelligence in data science and analytics means that Intel is really capable of driving this discussion around cloud computing and powering the cloud and also driving the work that's required to make a smart and a connected world a reality. Our artificial intelligence product group expands our portfolio and it means that we're bringing all these capabilities that I talked to you that make up data science and analytics. Cognitive, machine learning, artificial intelligence, deep learning, convolutional neuro networks, to bare to solve some of the nation's most significant and important problems and it means that Intel with its partners are really focused on the utilization of our core capabilities to drive government missions. >> Well give us an example then in terms of federal government NAI. How you're applying that to the operation of what's going on in this giant bureaucracy of a town that we have. >> So one of the things that I'm most excited about it that there's really no agency almost every federal agency in the U.S. is doing an investigation of artificial intelligence. It started off with this discussion around business intelligence and as you said data warehousing and other things but clearly the government has come to realize that turning data into a strategic asset is important, very very important. And so there are a number of key domain spaces in the federal government where Intel has made a significant impact. One is in health and life sciences so when you think about health and life sciences and biometrics, genomics, using advanced analytics for phenotype and genotype analysis this is where Intel's strengths are in performance in the ability to deliver. We created a collaborative cancer cloud that allows researches to use Intel hardware and software to accelerate the learnings from all of these health and life sciences advances that they want. Sharing data without compromising that data. We're focused significantly on cyber intelligence where we're applying threat and vulnerability analytics to understanding how to identify real cyber problems and big cyber vulnerabilities. We are now able to use Intel products to encrypt from the bios all the way up through the application stack and what it means is, is that our government clients who typically are hyper sensitive around security, get a chance to have data follow their respective process and meet their mission in a safe and secure way. >> If I can drill down on that for a second cause this is kind of a really sweet area for innovation. Data is now the new development environment the new development >> You said Bacon is the Oil is the new bacon (laughing) >> Versus the gold nuggets so I was talking with >> You hear what he said? >> No. >> It's the new bacon. >> The new bacon (laughs) love that. >> Data's the new bacon. >> Everyone loves bacon, everyone loves data. There's a thirst for the data and this also applies is that I ask you the role of the CDO, the chief data officer is emerging in companies and so we're seeing that also at the federal level. I want to get your thoughts on that but to quote the professor from Carnegie Mellon who I interviewed last week said the problem with a lot of data problems its like looking for a needle in the haystack with there's so much data now you have a haystack of needles so his premise is you can't find everything you got to use machine learning and AI to help with that so this is also going to be an issue for this chief data officer a new role. So is there a chief data officer role is there a need for that is there a CCO? Who handles the data? (laughing) >> Yeah so this is >> it's a tough one cause there's a lot a tech involved but also there's policies. >> Yeah so the federal government has actually mandated that each agency assign a federal chief data officer at the agency level and this person is working very closely with the chief information officer and the agency leaders to insure that they have the ability to take advantage of this large set of data that they collect. Intel's been working with most of the folks in the federal data cabinet who are the CDO's who are working to solve this problem around data and analysis of data. We're excited about the fact that we have chief data officers as an entry point to help discuss this hyper convergence that you described in technology. Where we have large data sets, we have faster hardware, of course Intel's helping to provide much of that and then better mathematics and algorithms. When we converge these three things together it's the soup that makes it possible for us to continue to drive artificial intelligence but that not withstanding federal data officers have a really hard job and we've been engaging them at many levels. We just had our artificial intelligence day in government where we had folks from many federal agencies that are on that cabinet and they shared with us directly how important it is to get Intel's on both hardware, hardware performance but also on software. When we think about artificial intelligence and the chief data officer or the data scientist this is likely a different individual than the person that is buying our silicon architectures. This is a person who is focused primarily on an agency mission and is looking for Intel to provide hardware and software capabilities that drive that mission. >> I got to ask you from an Intel perspective you guys are doing a lot of innovative things you have a great R and D group but also silicon you mentioned is important and you know software is eating the world but data's eating software so what's next what's eating data? We believe it's memory and silica and so one of the trends in big data is real time analytics is moving closer and closer to memory and then and now silicon who have some of those security paradigms with data involved seeing silicon implementations, root security, malware, firmware, kind of innovations. This is an interesting trend cause if software gets on to the silicon to the level that is better security you have fingerprinting all kinds of technologies. How is that going to impact the analytics world? So if you believe that they want faster lower latency data it's going to end up in the silicon. >> John you described exactly why Intel is focused on the virtuous cycle of growth. Because as more cloud enabled data moves itself from the cloud through our 5g networks and out to the edge in IOT devices whether they be autonomous vehicles or drones this is exactly why we have this continuum that allows data to move seamlessly between these three areas and operationalizes the core missions of government as well as provides a unique experience that most people can't even imagine. You likely saw the NBA finals you talked about Kevin Durant and you saw there the Intel 360 demonstration >> Love that! >> Where you're able to see how through different camera angles the entire play is unfolding. That is a prime example of how we use back end cloud hyper connected hardware with networks and edge devices where we're pushing analytics closer and closer to the edge >> by the way that's a real life media example of an IOT situation where it's at the edge of the network AKA stadium. I mean we geek out on that as well as Amazon has the MLB thing Andy (mumbles) knows I love that because it's like we're both baseball fans. >> We're excited about it too we think that along with autonomous vehicles, we think that this whole concept of experiences rather than capabilities and technologies >> but most people don't know that that example of basketball takes massive amounts of compute I mean to make that work at that level. >> In real time. >> This is the CG environment we're seeing with gaming culture the people are expecting an interface that looks more like Call of Duty (laughing) or Minecraft than they are Windows desktop machines what we're used to. We think that's great. >> That's why we say we're building the future John. (men laughing) >> You touched on something you said a little bit ago. A data officer of the federal government has got a tough job, a big job. >> Yes. >> What's the difference between private and public sector somebody who is handling the same kinds of responsibilities but has different compliance pressures different enforcement pressures and those kinds of things so somebody in the public space, what are they facing that somebody on the other side of the fence is not? >> All data officers have a tough job whether it's about cleansing data, being able to ingest it. What we talk about, and you described this, a haystack of needles is the need and ability to create a hyper relevancy to data because hyper relevancy is what makes it possible for personalized medicine and precision medicine. That's what makes it possible for us to do hyper scale personalized retail. This is what makes it possible to drive new innovation is this hyper relevancy and so whether you're working in a highly regulated environment like energy or financial services or whether you're working in the federal government with the department of defense and intelligence agencies or deep space exploration like at NASA you're still solving many data problems that are in common. Of course there are some differences right when you work for the federal government you're a steward of citizen's data that adds a different level of responsibility. There's a legal framework that guides how that data's handled as opposed to just a regulatory and legal one but when it comes to artificial intelligence all of us as practitioners are really focusing on the legal, ethical, and societal implications associate with the implementation of these advanced technologies. >> Quick question end this segment I know we're a little running over time but I wanted to get this last point in and this is something that we've talked on the CUBE a lot me and Dave have been debating because data is very organic innovation. You don't know what your going to do until you get into it, alchemy if you will, but trust and security and policy is a top down slow down mentality so often in the past it's been restricting growth so the balance here that you're getting at is how do you provide the speed and agility of real time experiences while maintaining all the trust and secure requirements that have slowed things down. >> You mention a topic there John and in my last book, 21st Century Leadership I actually described this concept as ambidextrous leadership. This concept of being able to do operational excellence extremely well and focus on delivery of core mission and at the same time be in a position to drive innovation and look forward enough to think about how, not where you are today but where you will be going in the future. This ambidexterity is really a critical factor when we talk about all leadership today, not just leaders in government or people who just work mostly on artificial intelligence. >> It's multidimensional, multi disciplined too right I mean. >> That's right, that's right. >> That's the dev opps ethos, that's the cloud. Move fast, I mean Mark Zuckerberg had the best quote with Facebook, "move fast and break stuff" up until that time he had about a billion users and then changed to move fast and be secure and reliable. (laughing) >> Yeah and don't break anything >> Well he understood you can't just break stuff at some point you got to move fast and be reliable. >> One of five books I want to mention by the way. >> That's right I'm working on my sixth and seventh now but yeah. >> And also the managing of the Greer Institute of Leadership and Management so you've written now almost seven books, you're running this leadership, you're working with Intel what do you do in your spare time Melvin? >> My wife is the chef and >> He eats a lot. (laughing) >> And so I get a chance to chance to enjoy all of the great food she cooks and I have two young sons and they keep me very very busy believe me. >> I think you're busy enough (laughing). Thanks for being on the CUBE. >> I very much appreciate it. >> It's good to have you >> Thank you. >> With us here at the AWS Public Sector Summit back with more coverage live with here on the Cube, Washington D.C. right after this.

>> 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. >> And welcome to our nation's capitol. Here we are in Washington, D.C. TheCUBE coming live from the Walter Washington Convention Center, here for AWS Private Sector Summit. It's our maiden voyage with the Public Sector so looking forward to this. John Walsh and John Furrier, glad to have you along for the ride, John, this is going to be a good week. >> Hey, it'll be fun. >> A good couple of days. John Galvin joins us. He is the Vice President and General Manager of the Public Sector Intel. John, thank you for being with us here on theCUBE; glad to have you. >> It's a pleasure to be here, thank you. >> Tell us a little bit first off, about your portfolio. >> J.Galvin: Sure. >> I understand you cover not only United States, but you have a global footprint as well. Touch base a little bit with our audience with what you're up to. >> Yeah, absolutely. So first of all, I have to put it in perspective for everyone. People know us as a micro-processor company. They don't always attribute us to going out and calling on government, or education decision makers. So we really act as a trusted advisor. We don't sell directly to government or to education entities, and I have sales people, or account exec's, around the world who are going in and meeting with ministers of education or ministers of ICT. Sometimes it's a school superintendent or a district superintendent, but, overall, what we're talking about is digital transformation and how technology can be used to advance government or advance education. And sometimes at a national level, could be at a state level, could be at a district level. >> Well, John and I were talking in our open segment just a little bit ago, about maybe a glacial pace isn't fair to say about how government had that reputation, obviously, for many years about being, maybe, reluctant. >> Right. >> To embrace change. What do you see now in that space? Is there this shift going on, that there's more of an embracing of technology? And of more entrepreneurial kind of spirit within the operation of government? >> Yeah, absolutely. It is happening so quickly. The categorization of government moving slowly is absolutely true. Education the same. But now wherever I go around the world, everyone is talking about transformation and they're starting to launch projects that might be a pilot or a proof of concept, but they're getting started. The challenge is when you talk about digital transformation it is so big so it becomes difficult for governments to really get their hands around it, and what are they going to do to improve citizens quality of life. Is that going to be a healthcare initiative? Is it going to be a transportation initiative? Sometimes it's an education initiative; and we're seeing them all. I think what is causing it to advance now is they see proof-points that it works. That by making those investments it really is changing the quality of life for people. And in emerging markets they don't have existing infrastructure that they have to tear out and replace. And some of the mature markets, it's how do you actually breakdown those silos. >> Well, John, I'm really glad you came on. Intel, in my opinion, I've been following Intel for many years, recently, has been pretty amazing. But you guys have always been a bellwether for trends, I'd say, five to 10 years out. I mean, look at everything that Intel's done with technology You have that five to 10 year stair instantly in what you're proposing. We've been seeing a lot of the AI commercials with Intel, what is the Public Sector trends that intersect with the vision of Intel? >> Well, you're absolutely right. If you look at what Intel does we're similar to the auto industry. It takes us five to six years to produce our next processor, and so we have to be looking that far out of what are the use cases, and really, what are those technological boundaries that we're going to either cross or break? And AI is absolutely the conversation today. It's sort of around artificial intelligence and it's no longer science fiction. We're not talking about it in the future; we're now talking about how can we use it today? Machine learning big, big topic, and not just the role that Intel plays, but companies like AWS; big players, in terms of how that actually comes to life in your home. It's not just how it's going to come to life in a big government institution or a big enterprise. >> And the Public Sector landscape, for the folks that are watching some know the Public Sector, what is the Public Sector? Because it's not the government. There's education, there's health, so what's the layout. How do you categorically look at it? How should people think about Public Sector? Not just GovCloud because there's a GovCloud, but is there a Public Sector cloud? I mean, how should people think about it? >> Yeah, great question. I work as part of a group at Intel that are all verticals. There's a healthcare team, there's a transportation team, there's an energy team. Public Sector is completely different because we're all of those things. We're working on transportation projects, we're working big healthcare projects, and so Public Sector you have to look at in the biggest sense where it's not just a federal presence but it is a state presence, it's a city presence or a county presence. And so our opportunity is to be able to connect all of those things, and that is what I think is so exciting about the transformation that is taking place right now is for that vision to be realized those silos really need to be broken. You know, you're going to hear comments over the next couple of days about forming a data lake. Which is bringing in all of those data streams into a single spot so that you can apply analytics and be able to get to insights that we've never been able to get to before. >> So how do you do that if you talk about municipality levels, state levels, federal levels, different operating systems, different processes, different procedures? And all great resources, how do you pull all that together and make that an asset instead of a morass? >> Well, in that question you just captured how big this opportunity is, and the way that we do it is we work with our ecosystem partners. The strength that Intel has when we enter into those conversations is we work with everyone. We work with the big cloud providers, we work with all the different operating system providers. We're not only with the computer companies that are our partners and our customers, but we're working now with internet and think companies, and so we have the ability to now work across that ecosystem to start pulling all of those pieces together. The heart of your question though is that those are all different systems that have been built over time. And if you look at what's been happening in enterprise over the past 10 years is CIO's and CTO's at the enterprise levels have been breaking down those silos and moving more to single systems and big data streams. And now that's what's happening with in the Public Sector is that data has to come together. >> John, talk about the collaboration between Intel and AWS and what is going on with you guys, how you guys are working together, and what's the impact in serving Public Sector customers? >> Well, we have had a great partnership with AWS from the beginning. (audio cuts out) (audio cuts out) That's going to take on this bigger vision is going to have a cloud discussion. There will still be things that they're going to be doing on premise, but it's most likely going to be a hybrid environment. And so with AWS we really have the opportunity to have a bigger discussion, where they can really have that cloud discussion and even some of the analytics layer. They're also doing more at an IOT perspective; we're able to join that conversation in terms of how our technology really plays into it. But I think the other thing we're able to do with AWS is really look for innovators. We're able to identify either those small companies, or even some of the cities are doing some really great things. And then because of their global footprint and our global footprint we can share that pretty broadly. >> And ecosystem's critical. You guys, Intel's always been ecosystem friendly company. With that in mind I got to ask you the question that everyone's talking about, and certainly, we're covering Mobile World Congress this year in Barcelona. And you couldn't go anywhere without hearing 5G and these new phones that are coming out. And then under the hood network transformation, you're hearing about software to find networking, machine learning, AI a lot of things that you guys are talking about. So the question for you is Smart Cities. It is a really, really hot opportunity just to even think about the concept of what a Smart City entails. I mean, here in D.C., like other cities, they have bicycles people can take out and ride around. That's a smart city, that's a cool service. But now you bring digital all to it. Imagine, Air B&B, you've got Uber, you've got Lift you've got all kinds of digital services, digital experiences. This is a government, this is a Public Sector issue. This is an interesting one. How is Intel's view on Smart Cities, how do you see that rolling out? >> First of all, we're very excited about what's happening within Smart Cities, and to the beginning of your question we think 5G is going to be an accelerant. It's going to cause it to happen even faster than it's happening now. What's interesting about Smart City is that it really does take a lot of different formats. And so we see cities who are really focused on public security and safety. We have examples whether it's Singapore, London of how they're now capturing new data with the cameras that they put up, and can do real-time analytics on it using AI and machine learning. So it's not that they just have all of these data streams, but they're doing real-time analysis of the data stream to be able to identify potential threats. But we also have examples where we're seeing cities invest in new technology to, essentially, replace what are the old ways of us being able to communicate and engage with the government. And that could be as simple as there's new information that's available to us. Or as they're collecting all these data streams they're making that data public and available for innovation, and so entrepreneurs now have the ability to also build solutions on those data streams. It's an incredibly exciting time. >> I mean, it's mind boggling to just think about how we live our lives in our cities. I can call the police department, the fire department, call for services in the analog world. Imagine video chat. Is that going to go to the certain departments? So how people engage, which side of the street do the cars drive on, who decides all that? And this is kind of how big this is. It's mind blowing. >> Well, it is big, and I'm going to answer that in two ways. Yes, the way that we did things before is changing and it's changing rapidly. To your 911 reference, I don't know, does it have to be a video engagement? Or through video are we actually capturing real-time that there's an incident that the fire department or an ambulance or police need to be dispatched. Where no phone call actually needs to be made. >> J.Furrier: Real-time analytics. >> Yeah. >> Predicted, prescriptive analytics could come to the table. >> Yeah, absolutely. And so we're already seeing examples of that, where that's happening today. What we're not seeing happening at scale, but I think we will see it happening at scale, all of those early adopters they had to figure it out on their own. But now we have blueprints, we have frameworks that we can share with other cities where they will be able to do it much more quickly. >> All right, what project really stands out for you, in all the things you're looking at, in the Public Sector because there's so much going on that you guys are doing I mean, props to Intel love what they're doing. The AI mission really puts a vision in place but also it's reality now with machine learning. What projects stand out for you that you see as real innovative, collaborative between Intel and AWS? >> Yeah, so we did a project with AWS where we, essentially, created a competition for new ideas to be able to come forward, and out of that what we've seen is some cities really doing some innovative things, just taking those first steps. What that does for us is it gives us a broader view than we would be able to get on our own. But some of that's basic. Say exciting stuff, we have exciting examples, the kiosks on the street corners in New York are an exciting example. What we see some of the universities doing, I think, is really exciting. Universities around the world have an issue with student retention. Where they just experience high drop out rates at the end of the freshman year and the end of the sophomore year. The challenge is how do you identify a student at risk? Well, automate attendance and you can now see are students actually attending classes. Or are they skipping class? Start using sensors and beacons on the campus and you can actually detect what those student patterns are and you just might need to have a counselor step in or a professor step in and really sit down with them and walk them through it. >> Use the IOT example, humans are things too, right? I mean, wearables, they got all kinds of sensors that could be even on-person device too. Absolutely, we have been working with the University of Texas Arlington, exactly, on that project. Through a sensor you can actually capture the emotional state of student. Are they highly stressed? And should that be, again, an environment where-- >> Explain how. How does that work? >> Through body temperature and -- >> So biometrics being measured. >> Yeah. >> Body temperature, respiration rates, all those kinds of things. >> Mental health is a huge issue in colleges and universities around the pressure. >> You can see that idea from a health perspective, strep throat, right? >> Sure. >> It's like the freshman plague. Every freshman gets strep throat. But if you could identify anxiety as it's being formulated before it manifests itself in academic performance, you could treat that. >> Sure, and now you combine that with capturing data from the student cafeteria or dorms of what are they're eating patterns, what are they're sleeping patterns? Are they actually getting enough sleep? So you get a much more holistic view of the student. And we have to be careful here, right, because-- >> Host: There's privacy concerns. >> Right, there's absolutely privacy and security concerns. And anyone who engages in these projects, heightened awareness of that. So it really is about quality of life and how do you create a better educational experience, not create anything that's threatening, but it becomes a much more personalized learning experience. >> The convergence and the conflicts between IOT and cloud and processing power and software, it's interesting, I was looking on prior to the show coming in I saw on your website at Intel Farmers in America. And then on Amazon's site there's a City on the Cloud. Can you take a minute and explain those projects. I think they're both Intel and AWS collaborations. Can you just take a minute to explain the City on the Cloud and the Farmers of America, what's the big aha there? >> So it's a three year project that we've been working on in collaboration with AWS, and the whole idea was for us to be able to identify some innovative ideas within the space because it is still a new area. How do we, essentially, give some of these entrepreneurs and innovative people a chance to be able to bring their idea into fruition? And so agriculture and Farmers America's a great example because that data is being collected in terms of weather patterns and how they can now, essentially, access that data to be able to plan differently what they're doing as well as better enable them to share with others what they're finding as they're making changes too. >> The farm tech has been hot on the D.C. community, certainly, in the Silicon Valley seeing people doing farm tech. Farm tech is one of those things, agriculture's a huge area that health implications too. People are interested in automating a lot of things and bringing tech there. And then also healthcare is a factor too. One of the areas is education but healthcare is another one that you guys are, what's the new thing in there that you guys are doing in healthcare? >> Yeah, we're doing quite a bit in healthcare around the world, and if you really think about it the challenge with healthcare is that your records are typically with your doctor or with your hospital. They're not always shared and they don't move with you when you travel. And so the first opportunity is how does that data actually become standardized so that it can actually be shared. But the other opportunity in healthcare is for those CTO's and CIO's to start using data very differently, to understand the patterns of what's happening within their hospitals. And you're earlier reference, John, to strep throat within a campus, how do you, essentially, start tracking that there's a trend and that there's something that you could potentially deal with much more quickly once you have the insights to it. >> All right, so take a minute as end this segment here, I want to get your thoughts on, give us a taste and showcase some of the Intel speeds and feeds, some of the tech, what's under the hood, what's coming out of Intel that's powering all of this because remember we're all driving the self-driving digital tooling out there. It's all powered by the Zeons, all kinds of cool stuff. What's the latest state-of-the-art that you got from Intel that you guys are bringing to the market in the Public Sector? >> Yeah, well, thank you for that question. I don't normally get it. >> Host: John loves it. He's a speed and feed guy. >> To get too much feedback. Too geeky. >> Well, your earlier question was around AI and machine learning and for us that's the zonify. And if you look at the power of zonify it's, essentially, three times the teraflops of the largest super computer that existed 20 years ago, in a single processor. And so for us it's an opportunity to then really be able to advance and accelerate what's happening with artificial intelligence as well as machine learning. >> Well, it's an exciting new world. Obviously with a realm that goes healthcare to ag, to education, to government with Intel very much at the center of that. John, thanks for being with us. >> It was great to be here. >> We appreciate the time on theCUBE. We look forward to having you back. We'll continue our coverage live here from the AWS Public Sector Summit here on theCUBE. Back with more in just a bit. (upbeat music)