>> Presenter: From around the globe, it's the cube, with digital coverage of pager duty summit 2020. Brought to you by pager duty. >> Welcome to this cube conversation. I'm Lisa Martin, today talking with Tammy Bryant is a cube alumna, the principal Site reliability engineer at Gremlin and the co-founder and CTO of the Girl Geek Academy. Tammy, it's great to have you on the program again. >> Hi Lisa, thanks so much for having me again. It's great to be here. >> So one of the things I saw in your background 10 plus years of technical expertise, and SRE, and chaos engineering, and I thought chaos engineering, I feel like I'm living in chaos right now. What is chaos engineering and why do you break things on purpose? >> Yep. So the idea of chaos engineering is that we're, breaking systems but in a thoughtful controlled way, to identify weaknesses in systems. So that's really what it's all about. The idea there is, you know, When you're doing really complicated work with technical systems, so like, for example, distributed systems and say, for example, you're working at a bank, it's tough to be able to pinpoint the exact failure mode that could cause a really large outage for your customers. And that's what chaos engineering is all about. you inject the failure proactively, to identify the issues and then you fix them before they actually cause really big problems for customers and you do it during the middle of the day, you know, when you're feeling great, instead of being paged in the middle of the night for an incident, that's actually like causing your customers pain, and making you lose a lot of money. So that's what chaos engineering really is. >> Are you seeing in the last six months since the world is so different, are you seeing an increase in customers? Now with, the for example, Brick and Mortars shut down and everything having to convert to digital if it wasn't already? Is there an increase in demand for chaos engineering services? >> Yeah, definitely. So a lot of people are asking what is chaos engineering, how can I use ,it will it help me reduce my incidents? and definitely because there are a lot of new services that have been rolled out recently, say, for example, curbside pickup. That's a whole new thing that had to be created really recently to be able to handle a large amount of load. And you know, people show up, they want to get their product really fast, 'cause they want to be able to just get back home quickly. And that's something that we've been working on with our customers is to make sure that curbside pickup experience is really great. The other interesting thing that we've been working on because of the pandemic is making sure that banks are really reliable, and that customers are able to get access to their money when they need it. And able to see that information too. And you can imagine that not as when you're in lockdown, and you only can leave your house for maybe an hour a day, you need to be able to quickly get access to your money to buy food, and we've seen some big incidents recently, where that hasn't been the case. Yeah. >> And I can imagine I mean, just thinking of what happened with, everything six months ago and how people were, we are just, demanding, right, consumers were demanding, we expect to get whatever we want, whether it's something we buy on Amazon, something that we stream on Netflix, or whatnot, we have this expectation that we can almost get it in real time. But there was a there was, you know what, there was a delay a few months ago, and there still is to some degree. But companies like Amazon and Netflix, I can imagine, really must have a big focus on chaos engineering, to test these things regularly. And now have proved, I would imagine to some degree that with chaos engineering that they have built, they're built to withstand that. >> Yes, exactly. So our founders at Gremlin came from Netflix and Amazon, our CEO had worked at both where he done chaos engineering, and that's actually why he decided to create Gremlin. It's the first company in the world to offer chaos engineering as a service. And you know, obviously, when you're working somewhere like Netflix, you know the whole product, you have to be able to get access to that movie, that TV show, right in that moment, and also customers expect to be able to see that on for example. There PlayStation in their living room and it should work and there paying for a subscription, So, to be able to keep them on that subscription, you need to offer a great service. Same thing with Amazon, you know, Amazon.com, they've done a lot of chaos engineering work over many years now to be able to make sure that everything is available. And it's not just that, the entire amazon.com is up and running. It's also for example, that when you go and look at a page that the recommendation service works toO and they're able to show you, hey, here's some other things that you might like to get to buy at this time. And I like as as a consumer, I love that 'cause it helps me save time and effort and even money as well 'cause it's giving you some good advice. So that's the type of statement we do. >> Exactly, So. when you're working with customers, I'd love to understand just a little bit from the, like the conversational standpoint is this now, is chaos engineering now, at kind of the sea level or is it still sort of in within the engineering folks 'cause looking at this as a make or break, knowing that for example, Netflix, there's Hulu, there's Disney Plus, there's Apple TV. Plus, if we don't get something that we're looking for right away, there's prime, we're going to go to another streaming service. So are you starting to see like an increase in demand from companies that no, we have competition right behind us, we've got to be able to set up the infrastructure and ensure that it is reliable. Now more than ever. >> Yeah, exactly. That's really, really important. I'm seeing a lot of executives. I mean, I've seen that since the beginning, really, since I first started working at Gremlin. I would often be invited by executives to come and give talks actually, within their company, to help the teams learn about chaos engineering, and I love doing that, It's really great. So I'd be invited by C levels, or VPs, from different departments. And I often get people adding me on LinkedIn from all over the world who are in leadership roles, because really, like, you know, they're responsible for making sure that their companies can hit those critical metrics and make sure that they're able to achieve their really, you know, demanding business goals, and then they're trying to help their teams be able to achieve that, too. So I've actually been so pleased to see that as well. Like it is really cool to have an executive reach out and say, hey, I'm thinking of helping my team, I'd like to get them introduced to you can you come and just teach them about this topic? And I love being able to do that it's really positive. And it's the right way to improve. >> It is, and I think nowadays, with reliability being more important than ever, you know, we talked to leaders from industry, from every industry. And there are certain things right now that are going to be shaping the winners and the losers of tomorrow. And it sounds to me like chaos engineering is one of those things that's going to be fundamental to any type of business to not just survive these times, but to thrive going forward. >> Yes, I definitely think so. I mean, obviously, people can easily just go to a different URL and try and use a different service. And you know, we're seeing now failure across so many different industries. We didn't see that before. But for example, you know, I'm sure you've seen in the news or heard from friends and family about schools, now being completely online. And then kids can't actually access, their calls their resources, what they need to learn every day. So that really just shows you how much it's impacting us as a society, we really know that the internet is critical. It's amazing that we have the internet, like how lucky we are to have this, but it needs to work for us to actually be able to get value out of it. And that's what chaos engineering is all about. You know, were able to make sure that everything is reliable, so it's up and running. And we do that by looking at things like redundancy. So we'll do failover work where we completely shut down an application or service and make sure it gracefully fails over. We also do a lot of dependency failure work, where you're actually looking to say, this is the critical path of this service. And a lot of people don't think about this, but the critical path really starts at sign in. So you need to make sure that login and sign in works really well. It's not just about like the experience once you've signed in, that has to work well all the way through. So actually if you have a good understanding of user experience, it helps you create a much better pathway and understand those critical pieces that the customer needs to be able to do to have a great experience. And I care a lot about that. Like whenever I go and work somewhere, I always read customer tickets, I always try and understand what are the customer pain points. And I love listening to customers and then just solving their problems. The last thing I want them to do is, you know, be complaining or be really annoyed on Twitter because something just isn't working when they need it to be working. And it is really critical these days. It's a the internet is a really serious part of our day to day life. >> Oh, it's a lifeline. I mean, that's, some folks. It's the only way that they're connecting with the outside world, is through the internet. So when things aren't, I had a friend whose son first day of college couple weeks ago, freshman year, first class couldn't get into zoom. And that's a stressful situation. But I imagine too, though, that and I know you're going to be speaking at the pager duty summit that more folks need to understand what this is. And I can tell the you have a real authentic passion for it. Talk to us about what you're going to be talking about at the pager duty summit. >> Sure thing, I'm really excited to be speaking at Pager Duty Summit very soon. My talk is called building, and scaling SRE teams, so site reliability engineering teams. And this is something that I've done previously. I've built out the SRE teams at Dropbox for both databases as well as storage. So block storage, and then I also lead the code workflows team. And that's for, you know, over 500 million users, people accessing the critical data that they store on Dropbox all the time. You know the way that folks use Dropbox is in so many different ways. Maybe it's like really famous music musicians who are trying to create an amazing new album that happens or maybe it's a lawyer preparing for a court case, and they need to be able to access their documents. So those are a lot of customer stories that would come up over time. And prior to that, I worked at the National Australia Bank as well leading teams too and obviously like people care about their money if they can't access their money. If there incorrect transactions, if there are missing transactions, you know, duplicate transactions, maybe people don't mind so much about it you get like a double deposit, but it's still not good from the bank's perspective. So there's all types of different chaos that can happen. And I found it to be really interesting to be able to dive into that and make sure that you can make improvements. And I love that it makes customers happier. And also, it helps you improve your company as a whole. So it's a really good thing to be able to do, And with my talk, I'm going to talk to folks about, you know, not only why it's important to build out a reliability practice at your organization, you know, back in the day, people used to go, why would you need a security team? You know, why would we need that? now everybody has a security team, everyone has a chief security officer as well. But why don't we focus on reliability, like we know that we see incidents out in the news all the time, but for some reason, we don't have the chief reliability officer. I think that's definitely going to be something that will appear in the future just like the chief security officer roll up. But that's what I'm going to talk about there. How you can find site reliability engineers, I'll share a few of my secrets. I won't give any spoilers out. But there's actually quite a few places that you can find amazing people. There's even a school that you can hire them from, which I've done in the past. And then I'll talk to you about how you can interview them to make sure that you get the best people on your team. There are a number of things that I think are very important to interview for. And then once you've got those folks on your team, I'll talk to you about how you can make sure that they're successful. How to set them up for success and make sure that they're aligned to not only your business goals, but also your core values as a company, which is really important too. >> Yeah, that's fantastic. It's very well rounded, I'm curious, what are some of the the characteristics that you think are really critical for someone to become a successful SRE? >> Yeah, so there's a few key things that I look for. One thing is that, somebody who is really good at troubleshooting, so they need to be able to be comfortable with complexity, ambiguity and open ended challenges and problems and also thrive in those types of environments. Because often you're seeing something that you've never seen happen before. And also you're working with really complicated systems. So you just need to be able to feel good in that moment. And you can test for that during an interview question on troubleshooting and debugging. So that's something that I'll go into in more detail. But that's definitely the first characteristic. The other thing, of course, is you want to have someone who is good at being able to build solutions. So they can code, they understand automation, they can figure out how can I take this pain point, this problem? And how can I automate it and then scale this out and make it available for everyone across my organization? So someone who has that mindset of building tools for others, and often they are internal tools, because maybe you're building a tool that helps everybody know, who's on call every single critical service at the company and also non critical service and they can identify that in a minute or less like maybe even just in a few seconds, and then they can quickly get that person involved, if anything need to escalate to them. Via for example, a tool like pager duty, that's really what you want. You want them to be able to think, how can I just make this efficient? How can I make sure that we can get really great results? And yeah, I think they also just need to be really personable too and work well in a really complicated organizational structure. Because usually they have to work with the engineering team, the finance team to understand the revenue impact. They need to be able to work with the PR team and the social media team, if they're incidents, and then they need to provide information about when this incident is going to be resolved, and how they can update VIP customers. They need to talk to the sales team, because what happens if you're giving a demonstration, and then somehow there's an issue, or failure that happens, an incident and then in the middle of your very important sales demo, you're not able to actually deliver it that can happen a lot too. So there are a lot of very important key skills. >> Sounds like it's a really cross functional role, pivotal to an organization, that needs to understand how these different functions not only operate, but also operate together, is that somebody that you think has certain types of previous work experience? Is this something that you talked to the Girl Geek Academy girls about? How did they get into? I'm curious, like what the career path is? >> Yeah, it's interesting, like I find a lot of SRE's often come from either a few different backgrounds. One is they came through the world of Linux and understanding systems, and just being really interested in that. Like deep diving into the kernel, understanding how to improve performance of systems. The other side is maybe they came from coding background where they were actually building applications and features. I started off actually on that side, but I also had a passion for Linux. And then I sort of spread over into the other side and was able to learn both. And then often you know, someone who's comfortable with being on call and handling incidents, but it is a lot of skills, like that's actually something that I often talk to folks about, and they asked me how can I become a great SRE? There's so many things I need to learn. And I just say, you know, take it slow, try and gradually increase your number of skills. People often say that there is like there's some curve for SRE's, where you have the operations side, on one side, and then the coding side on the other. And often like the best person sits right in the middle where they have both ops and engineering skills. But it's really hard to find those people. It's okay if you have someone that's like, really deep, has amazing knowledge of Linux and scaling systems and internet management, and then you can pair them up with a really amazing programmer who's great at software engineering and software architecture, that's okay, too. >> We've been hearing for a long time about this sort of negative unemployment with respect to cyber security professionals. Is that, are you guys falling into that same category as well with SRE? Or is it somehow different or you just know this is exactly what we're looking for? We want to go out there, and even in the Girl, Greek Academy, maybe help girls learn how to be able to find what I imagine are a lot of opportunities. >> Yeah, there are so many opportunities for this. So it's definitely an opportunity because what I see is there's not enough SRE's. So tons of companies all over the world will actually ping me and say, hey, Tommy, how do I hire SRE's, that's why I decided to give this talk because I wanted to package that up and just share that information as to how you can do it. And also, maybe you can't find the SRE's because they don't exist. But you can help retrain your team. So you can have an engineer learn the skills that are required to be an SRE, that's totally possible too, maybe move them over to become an SRE. With girl geek Academy, one of the things that I've done is run hackathons and workshops and just online training sessions to help girls learn these new skills. So that's exactly what our mission is, is to teach 1 million girls technical skills by 2025. And I love to do mentoring at scale, which is why it's been really cool to be able to do it online and through these like workshops and remote hackathons. And I definitely love to do something where else work with some of our customers actually, and run an event. I did one a while back, it was really cool, we were able to have all of the girls come in and be at the customer's office and actually learn skills with the customer, which was really fun. And it helps them actually think, hey, I could work one day that would be really amazing. And I'm going to do that again in November. And it's kind of fun too. We can do things like have like, you know, dad and mom and then daughter day, where you actually bring your daughter to work and help her learn technical skills. That's really fun because they get to see what you do and they understand it more and see how cool chaos engineering really is. Then they think oh, wow, you're so awesome, this is great. >> I love it, that's fantastic. Well it sounds like, like I said before your passion for it is really there. What, I think is really interesting is how you're talking about chaos engineering and just the word in and of itself chaos. But you painted in such a positive lights critical business critical, but also the all the opportunities there that businesses have to learn and fine tune so such an interesting conversation. Yeah, Tammy. We have you back on the program. But I thank you so much for joining me today. And for those folks that lucky enough that are attending the pager duty summit, they're going to get to learn a lot from you. Thank you. >> Thanks so much for having me, Lisa. >> For Tammy Bryant, I'm Lisa Martin. You're watching this cube conversation. (upbeat music)

>>Hi lisa martin here with the cubes coverage of demon 2021. I've got to alumni joining me. Please welcome back to the cube Danny. Alan beam's ceo Danny. It's great to see you. >>I am delighted to be here lisa. >>Excellent brian Schwartz is here as well. Google director outbound product management brian welcome back to the program. Uh >>thanks for having me again. Excited to be >>here. Excited to be here. Yes, definitely. We're gonna be talking all about what Demon google are doing today. But let's go ahead and start Danny with you. Seems vision is to be the number one trusted provider of backup and recovery solutions for the, for for modern data protection. Unpack that for me, trust is absolutely critical. But when you're talking about modern data protection to your customers, what does that mean? >>Yeah. So I always, I always tell our customers there's three things in there that are really important. Trust is obviously number one and google knows this. You've been the most trusted search provider uh, forever. And, and so we have 400,000 customers. We need to make sure that our products work. We need to make sure they do data protection, but we need to do it in a modern way. And so it's not just back up and recovery, that's clearly important. It's also all of the automation and orchestration to move workloads across infrastructures, move it from on premises to the google cloud, for example, it also includes things like governance and compliance because we're faced with ransomware, malware and security threats. And so modern data protection is far more than just back up. It's the automation, it's the monitoring, it's a governance and compliance. It's the ability to move workloads. Um, but everything that we look at within our platform, we focus on all of those different characteristics and to make sure that it works for our customers. >>One of the things that we've seen in the last year, Danny big optic in ransom were obviously the one that everyone is the most familiar with right now. The colonial pipeline. Talk to me about some of the things that the team has seen, what your 400,000 customers have seen in the last 12 months of such a dynamic market, a massive shift to work from home and to supporting SAS for clothes and things like that. What have you seen? >>Well, certainly the employees working from home, there's a massive increase in the attack surface for organizations because now, instead of having three offices, they have, you know, hundreds of locations for their end users. And so it's all about protecting their data at the same time as well. There's been this explosion in malware and ransomware attacks. So we really see customers focusing on three different areas. The first is making sure that when they take a copy of their data, that it is actually secure and we can get into, you know, a mutability and keeping things offline. But really taking the data, making sure it's secure. The second thing that we see customers doing is monitoring their environment. So this is both inspection of the compute environment and of the data itself. Because when ransomware hits, for example, you'll see change rates on data explode. So secure your data monitor the environment. And then lastly make sure that you can recover intelligently is let us say because the last thing that you want to do if you're hit by ransomware is to bring the ransomware back online from a backup. So we call this security cover re secure, restore. We really see customers focusing on those three areas >>And that restoration is critical there because as we know these days, it's not if we get hit with ransomware, it's really a matter of when. Let's go ahead now and go into the google partnership, jenny talked to me about it from your perspective, the history of the strength of the partnership, all that good stuff. >>Yeah. So we have a very deep and long and lengthy relationship with google um, on a number of different areas. So for example, we have 400,000 customers. Where do they send their backups? Most customers don't want to continue to invest in storage solutions on their premises. And so they'll send their data from on premises and tear it into google cloud storage. So that's one integration point. The second is when the running workloads within the clouds. So this is now cloud native. If you're running on top of the google cloud platform, we are inside the google America place and we can protect those workloads. A third area is around the google vm ware engine, there's customers that have a hybrid model where they have some capacity on premises and some in google using the VM ware infrastructure and we support that as well. That's a third area and then 1/4 and perhaps the longest running um, google is synonymous with containers and especially kubernetes, they were very instrumental in the foundations of kubernetes and so r K 10 product which does data protection for kubernetes is also in the google America place. So a very long and deep relationship with them and it's to the benefit of our customers. >>Absolutely. And I think I just saw the other day that google celebrated the search engine. It's 15th birthday. I thought what, what did we do 16 years ago when we couldn't just find anything we wanted brian talked to me about it from Google's perspective of being partnership. >>Yeah, so as Danny mentioned, it's really multifaceted, um it really starts with a hybrid scenario, you know, there's still a lot of customers that are on their journey into the cloud and protecting those on premises workloads and in some senses, even using beams capabilities to move data to help migrate into the cloud is I'd say a great color of the relationship. Um but as Danny mentioned increasingly, more and more primary applications are running in the cloud and you know, the ability to protect those and have, you know, the great features and capabilities, uh you know, that being provides, whether it be for GCB er VM where you know, capability and google cloud or things like G k e R kubernetes offering, which has mentioned, you know, we've been deep and wide in kubernetes, we really birthed it many, many years ago um and have a huge successful business in, in the managing and hosting containers, that having the capabilities to add to those. It really adds to our ecosystem. So we're super excited about the partnership, we're happy to have this great foundation to build together with them into the future. >>And Danny Wien launched, just been in february a couple of months ago, being backup for google cloud platform. Talk to us about that technology and what you're announcing at them on this year. >>Yeah, sure. So back in february we released the first version of the VM backup for G C p product in the marketplace and that's really intended to protect of course, i as infrastructure as a service workloads running on top of G C p and it's been very, very successful. It has integration with the core platform and what I mean by that is if you do a backup in G C P, you can do you can copy that back up on premises and vice versa. So it has a light integration at the data level. What we're about to release later on this summer is version two of that product that has a deep integration with the VM platform via what we call the uh team service platform, a PS themselves. And that allows a rich bidirectional uh interaction between the two products that you can do not just day one operations, but also day to operations. So you can update the software, you can harmonize schedules between on premises and in the cloud. It really allows customers to be more successful in a hybrid model where they're moving from on premises to the cloud. >>And that seems to be really critically important. As we talk about hybrid club all the time, customers are in hybrid. They're living in the hybrid cloud for many reasons, whether it's acquisition or you know, just the nature of lines of business leveraging their cloud vendor of choice. So being able to support the hybrid cloud environment for customers and ensure that that data is recoverable is table stakes these days. Does that give them an advantage over your competition Danny? >>It does. Absolutely. So customers want the hybrid cloud experience. What we find over time is they do trend towards the cloud. There's no question. So if you have the hybrid experience, if they're sending their data there, for example, a step one, step two, of course, is just to move the workload into the cloud and then step three, they really start to be able to unleash their data. If you think about what google is known for, they have incredible capabilities around machine learning and artificial intelligence and they've been doing that for a very long time. So you can imagine customers after they start putting their data there, they start putting their workloads here, they want to unlock it into leverage the insights from the data that they're storing and that's really exciting about where we're going. It's, they were early days for most customers. They're still kind of moving and transitioning into the cloud. But if you think of the capabilities that are unlocked with that massive platform in google, it just opens up the ability to address big challenges of today, like climate change and sustainability and you know, all the health care challenges that we're faced with it. It really is an exciting time to be partnered with Google >>Ryan. Let's dig into the infrastructure in the architecture from your perspective, help us unpack that and what customers are coming to you for help with. >>Yeah. So Danny mentioned, you know the prowess that google has with data and analytics and, and a, I I think we're pretty well known for that. Uh, there's a tremendous opportunity for people in the future. Um, the thing that people get just right out of the box is the access to the technology that we built to build google cloud itself. Just the scale and, and technology, it's, you know, it's, it's a, you know, just incredible. You know, it's a fact that we have eight products here at google that have a billion users and when you have, you know, most people know the search and maps and gmail and all these things. When you have that kind of infrastructure, you build a platform like google cloud platform and you know, the network as a perfect example, the network endpoints, they're actually close to your house. There's a reason our technology is so fast because you get onto the google private network, someplace really close to where you actually live. We have thousands and thousands of points of presence spread around the world and from that point forward you're riding on our internal network, you get better quality of service. Uh the other thing I like to mention is, you know, the google cloud storage, that team is built on our object storage. It's uh it's the same technology that underpins Youtube and other things that most people are familiar with and you just think about that for a minute, you can find the most obscure Youtube video and it's gonna load really fast. You know, you're not going to sit there waiting for like two minutes waiting for something to load and that same under underlying technology underpins GCS So when you're going to go and you know, go back to an old restore, you know, to do a restore, it's gonna load fast even if you're on one of the more inexpensive storage classes. So it's a really nice experience for data protection. It has this global network properties you can restore to a different region if there was ever a disaster, there's just the scale of our foundation of infrastructure and also, you know, Danny mentioned if we're super proud about the investments that google has made for sustainability, You know, our cloud runs on 100% renewable energy at the cloud at our scale. That's a lot of, that's a lot of green energy. We're happy to be one of the largest consumers of green energy out there and make continued investments in sustainability. So, you know, we think we have some of the greenest data centers in the world and it's just one more benefit that people have when they come to run on Google Cloud. >>I don't know what any of us would do without google google cloud platform or google cloud storage. I mean you just mentioned all of the enterprise things as well as the at home. I've got to find this really crazy, obscure youtube video but as demanding customers as we are, we want things asAP not the same thing. If you know, an employee can't find a file or calendar has been deleted or whatnot. Let's go in to finish our time here with some joint customer use case examples. Let's talk about backing up on prem workloads to google cloud storage using existing VM licensing Danny. Tell us about that. >>Yeah. So one of the things that we've introduced at beam is this beam, universal licensing and it's completely portable license, you can be running your workloads on premises now and on a physical system and then you can, you know, make that portable to go to a virtual system and then if you want to go to the cloud, you can send that data up to the work load up to the cloud. One of the neat things about this transition for customers from a storage perspective, we don't charge for that. If you're backing up a physical system and sending your your back up on premises, you know, we don't charge for that. If you want to move to the cloud, we don't charge for that. And so as they go through this, there's a predictability and and customers want that predictability so much um that it's a big differentiating factor for us. They don't want to be surprised by a bill. And so we just make it simple and seamless. They have a single licensing model and its future proof as they move forward on the cloud journey. They don't have to change anything. >>Tell me what you mean by future proof as a marketer. I know that term very well, but it doesn't mean different things to different people. So for means customers in the context of the expansion of partnership with google the opportunities, the choices that you're giving customers to your customers, what does future proof actually delivered to them? >>It means that they're not locked into where they are today. If you think about a customer right now that's running a workload on premises maybe because they have to um they need to be close to the data that's being generated or feeding into that application system. Maybe they're locked into that on premises model. Now they have one of two choices when their hardware gets to the end of life. They can either buy more hardware which locks them into where they are today for the next three years in the next four years Or they can say, you know what, I don't want to lock into that. I want to model the license that is portable that maybe 12 months from now, 18 months from now, I can move to the cloud and so it future proof some, it doesn't give them another reason to stay on premises. It allows them the flexibility that licensing is taken off the table because it moves with you that there's zero thought or consideration and that locks you into where you are today. And that's exciting because it unlocks the capabilities of the cloud without being handicapped if you will by what you have on premises. >>Excellent. Let's go to the second uh use case lift and shift in that portability brian. Talk to us about it from your perspective. >>Yeah, so we obviously constantly in discussions with our customers about moving more applications to the cloud and there's really two different kind of approach is the lift and shift and modernization. You know, do you want to change and run on kubernetes when you come to the cloud as you move it in? In some cases people want to do that or they're gonna obviously build a new application in the cloud. But increasingly we see a lot of customers wanting to do lift and shift, they want to move into the cloud relatively quickly. As Danny said, there's like compelling events on like refreshes and in many cases we've had a number of customers come to us and say look we're going to exit our data centers. We did a big announcement Nokia, they're gonna exit 50 data centers in the coming years around the world and just move that into the cloud. In many cases you want to lift and shift that application to do the migration with his little change as possible. And that's one of the reasons we've really invested in a lot of enterprise, more classic enterprise support type technologies. And also we're super excited to have a really wide set of partners and ecosystem like the folks here at Wien. So the customers can really preserve those technologies, preserve that operational experience that they're already familiar with on prem and use that in the cloud. It just makes it easier for them to move to the cloud faster without having to rebuild as much stuff on the way in. >>And that's critical. Let's talk about one more use case and that is native protection of workloads that run on g c p Danny. What are you enabling customers to do there? >>Well? So we actually merged the capabilities of two different things. One is we leverage the native Api is of G C p to take a snapshot and we merge that with our ability to put it in a portable data format. Now. Why is that important? Because you want to use the native capabilities of G CPU want to leverage those native snapshots. The fastest way to recover a file or the fastest way to recover of'em is from the G C p snapshot. However, if you want to take a copy of that and move it into another locale or you want to pull it back on premises for compliance reasons or put it in a long term storage format, you probably want to put it in GCS or in our portable storage format. And so we merge those two capabilities, the snapshot and back up into a single product. And in addition to that, one of the things that we do, again, I talked about predictability. We tell customers what that policy is going to cost them because if for example a customer said, well I like the idea of doing my backups in the cloud, but I want to store it on premises. We'll tell them, well if you're copying that data continually, you know what the network charges look like, What the CPU and compute charges look like, What do the storage costs looks like. So we give them the forecast of what the cost model looks like even before they do a single backup. >>That forecasting has got to be key, as you said with so much unpredicted things that we can't predict going on in this world the last year has taught us that with a massive shift, the acceleration of digital business and digital transformation, it's really critical that customers have an idea of what their costs are going to be so that they can make adjustments and be agile as they need the technology to be. Last question Bryant is for you, give us a view uh, and all the V mon attendees, what can we expect from the partnership in the next 12 >>months? You know, we're excited about the foundation of the partnership across hybrid and in cloud for both VMS and containers. I think this is the real beginning of a long standing relationship. Um, and it's really about a marriage of technology. You think about all the great data protection and orchestration, all the things that Danny mentioned married with the cloud foundation that we have at scale this tremendous network. You know, we just signed a deal with SpaceX in the last couple of days to hook their satellite network up to the google cloud network, you know, chosen again because we just have this foundational capability to push large amounts of data around the world. And that's you know, for Youtube. We signed a deal with Univision, same type of thing, just massive media uh, you know, being pushed around the world. And if you think about it that that same foundation is used for data protection. Data protection. There's a lot of data and moving large sets of data is hard. You know, we have just this incredible prowess and we're excited about the future of how our technology and beans. Technology is going to evolve over time >>theme and google a marriage of technology Guys, thank you so much for joining me, sharing what's new? The opportunities that demand google are joined me delivering to your joint customers. Lots of great step. We appreciate your time. >>Thanks lisa >>For Danielle in and Brian Schwartz. I'm Lisa Martin. You're watching the cubes coverage of Lehman 2021.

>>from around the globe. It's the Q. With digital coverage >>of exa scale day made possible by Hewlett Packard Enterprise. We're back at the celebration of Exa Scale Day. This is Dave Volant, and I'm pleased to welcome to great guests Brian Dance Berries Here. Here's what The ISS Program Science office at the Johnson Space Center. And Dr Mark Fernandez is back. He's the Americas HPC technology officer at Hewlett Packard Enterprise. Gentlemen, welcome. >>Thank you. Yeah, >>well, thanks for coming on. And, Mark, Good to see you again. And, Brian, I wonder if we could start with you and talk a little bit about your role. A T. I s s program Science office as a scientist. What's happening these days? What are you working on? >>Well, it's been my privilege the last few years to be working in the, uh, research integration area of of the space station office. And that's where we're looking at all of the different sponsors NASA, the other international partners, all the sponsors within NASA, and, uh, prioritizing what research gets to go up to station. What research gets conducted in that regard. And to give you a feel for the magnitude of the task, but we're coming up now on November 2nd for the 20th anniversary of continuous human presence on station. So we've been a space faring society now for coming up on 20 years, and I would like to point out because, you know, as an old guy myself, it impresses me. That's, you know, that's 25% of the US population. Everybody under the age of 20 has never had a moment when they were alive and we didn't have people living and working in space. So Okay, I got off on a tangent there. We'll move on in that 20 years we've done 3000 experiments on station and the station has really made ah, miraculously sort of evolution from, ah, basic platform, what is now really fully functioning national lab up there with, um, commercially run research facilities all the time. I think you can think of it as the world's largest satellite bus. We have, you know, four or five instruments looking down, measuring all kinds of things in the atmosphere during Earth observation data, looking out, doing astrophysics, research, measuring cosmic rays, X ray observatory, all kinds of things, plus inside the station you've got racks and racks of experiments going on typically scores, you know, if not more than 50 experiments going on at any one time. So, you know, the topic of this event is really important. Doesn't NASA, you know, data transmission Up and down, all of the cameras going on on on station the experiments. Um, you know, one of one of those astrophysics observatory's you know, it has collected over 15 billion um uh, impact data of cosmic rays. And so the massive amounts of data that that needs to be collected and transferred for all of these experiments to go on really hits to the core. And I'm glad I'm able toe be here and and speak with you today on this. This topic. >>Well, thank you for that, Bryan. A baby boomer, right? Grew up with the national pride of the moon landing. And of course, we've we've seen we saw the space shuttle. We've seen international collaboration, and it's just always been something, you know, part of our lives. So thank you for the great work that you guys were doing their mark. You and I had a great discussion about exa scale and kind of what it means for society and some of the innovations that we could maybe expect over the coming years. Now I wonder if you could talk about some of the collaboration between what you guys were doing and Brian's team. >>Uh, yeah, so yes, indeed. Thank you for having me early. Appreciate it. That was a great introduction. Brian, Uh, I'm the principal investigator on Space Born computer, too. And as the two implies, where there was one before it. And so we worked with Bryant and his team extensively over the past few years again high performance computing on board the International Space Station. Brian mentioned the thousands of experiments that have been done to date and that there are currently 50 orm or going on at any one time. And those experiments collect data. And up until recently, you've had to transmit that data down to Earth for processing. And that's a significant amount of bandwidth. Yeah, so with baseball and computer to we're inviting hello developers and others to take advantage of that onboard computational capability you mentioned exa scale. We plan to get the extra scale next year. We're currently in the era that's called PETA scale on. We've been in the past scale era since 2000 and seven, so it's taken us a while to make it that next lead. Well, 10 years after Earth had a PETA scale system in 2017 were able to put ah teraflop system on the International space station to prove that we could do a trillion calculations a second in space. That's where the data is originating. That's where it might be best to process it. So we want to be able to take those capabilities with us. And with H. P. E. Acting as a wonderful partner with Brian and NASA and the space station, we think we're able to do that for many of these experiments. >>It's mind boggling you were talking about. I was talking about the moon landing earlier and the limited power of computing power. Now we've got, you know, water, cool supercomputers in space. I'm interested. I'd love to explore this notion of private industry developing space capable computers. I think it's an interesting model where you have computer companies can repurpose technology that they're selling obviously greater scale for space exploration and apply that supercomputing technology instead of having government fund, proprietary purpose built systems that air. Essentially, you use case, if you will. So, Brian, what are the benefits of that model? The perhaps you wouldn't achieve with governments or maybe contractors, you know, kind of building these proprietary systems. >>Well, first of all, you know, any any tool, your using any, any new technology that has, you know, multiple users is going to mature quicker. You're gonna have, you know, greater features, greater capabilities, you know, not even talking about computers. Anything you're doing. So moving from, you know, governor government is a single, um, you know, user to off the shelf type products gives you that opportunity to have things that have been proven, have the technology is fully matured. Now, what had to happen is we had to mature the space station so that we had a platform where we could test these things and make sure they're gonna work in the high radiation environments, you know, And they're gonna be reliable, because first, you've got to make sure that that safety and reliability or taken care of so that that's that's why in the space program you're gonna you're gonna be behind the times in terms of the computing power of the equipment up there because, first of all and foremost, you needed to make sure that it was reliable and say, Now, my undergraduate degree was in aerospace engineering and what we care about is aerospace engineers is how heavy is it, how big and bulky is it because you know it z expensive? You know, every pound I once visited Gulfstream Aerospace, and they would pay their employees $1000 that they could come up with a way saving £1 in building that aircraft. That means you have more capacity for flying. It's on the orders of magnitude. More important to do that when you're taking payloads to space. So you know, particularly with space born computer, the opportunity there to use software and and check the reliability that way, Uh, without having to make the computer, you know, radiation resistance, if you will, with heavy, you know, bulky, um, packaging to protect it from that radiation is a really important thing, and it's gonna be a huge advantage moving forward as we go to the moon and on to Mars. >>Yeah, that's interesting. I mean, your point about cots commercial off the shelf technology. I mean, that's something that obviously governments have wanted to leverage for a long, long time for many, many decades. But but But Mark the issue was always the is. Brian was just saying the very stringent and difficult requirements of space. Well, you're obviously with space Born one. You got to the point where you had visibility of the economics made sense. It made commercial sense for companies like Hewlett Packard Enterprise. And now we've sort of closed that gap to the point where you're sort of now on that innovation curve. What if you could talk about that a little bit? >>Yeah, absolutely. Brian has some excellent points, you know, he said, anything we do today and requires computers, and that's absolutely correct. So I tell people that when you go to the moon and when you go to the Mars, you probably want to go with the iPhone 10 or 11 and not a flip phone. So before space born was sent up, you went with 2000 early two thousands computing technology there which, like you said many of the people born today weren't even around when the space station began and has been occupied so they don't even know how to program or use that type of computing. Power was based on one. We sent the exact same products that we were shipping to customers today, so they are current state of the art, and we had a mandate. Don't touch the hardware, have all the protection that you can via software. So that's what we've done. We've got several philosophical ways to do that. We've implemented those in software. They've been successful improving in the space for one, and now it's space born to. We're going to begin the experiments so that the rest of the community so that the rest of the community can figure out that it is economically viable, and it will accelerate their research and progress in space. I'm most excited about that. Every venture into space as Brian mentioned will require some computational capability, and HP has figured out that the economics air there we need to bring the customers through space ball into in order for them to learn that we are reliable but current state of the art, and that we could benefit them and all of humanity. >>Guys, I wanna ask you kind of a two part question. And, Brian, I'll start with you and it z somewhat philosophical. Uh, I mean, my understanding was and I want to say this was probably around the time of the Bush administration w two on and maybe certainly before that, but as technology progress, there was a debate about all right, Should we put our resource is on moon because of the proximity to Earth? Or should we, you know, go where no man has gone before and or woman and get to Mars? Where What's the thinking today, Brian? On that? That balance between Moon and Mars? >>Well, you know, our plans today are are to get back to the moon by 2024. That's the Artemus program. Uh, it's exciting. It makes sense from, you know, an engineering standpoint. You take, you know, you take baby steps as you continue to move forward. And so you have that opportunity, um, to to learn while you're still, you know, relatively close to home. You can get there in days, not months. If you're going to Mars, for example, toe have everything line up properly. You're looking at a multi year mission you know, it may take you nine months to get there. Then you have to wait for the Earth and Mars to get back in the right position to come back on that same kind of trajectory. So you have toe be there for more than a year before you can turn around and come back. So, you know, he was talking about the computing power. You know, right now that the beautiful thing about the space station is, it's right there. It's it's orbiting above us. It's only 250 miles away. Uh, so you can test out all of these technologies. You can rely on the ground to keep track of systems. There's not that much of a delay in terms of telemetry coming back. But as you get to the moon and then definitely is, you get get out to Mars. You know, there are enough minutes delay out there that you've got to take the computing power with you. You've got to take everything you need to be able to make those decisions you need to make because there's not time to, um, you know, get that information back on the ground, get back get it back to Earth, have people analyze the situation and then tell you what the next step is to do. That may be too late. So you've got to think the computing power with you. >>So extra scale bring some new possibilities. Both both for, you know, the moon and Mars. I know Space Born one did some simulations relative. Tomorrow we'll talk about that. But But, Brian, what are the things that you hope to get out of excess scale computing that maybe you couldn't do with previous generations? >>Well, you know, you know, market on a key point. You know, bandwidth up and down is, of course, always a limitation. In the more computing data analysis you can do on site, the more efficient you could be with parsing out that that bandwidth and to give you ah, feel for just that kind of think about those those observatory's earth observing and an astronomical I was talking about collecting data. Think about the hours of video that are being recorded daily as the astronauts work on various things to document what they're doing. They many of the biological experiments, one of the key key pieces of data that's coming back. Is that video of the the microbes growing or the plants growing or whatever fluid physics experiments going on? We do a lot of colloids research, which is suspended particles inside ah liquid. And that, of course, high speed video. Is he Thio doing that kind of research? Right now? We've got something called the I s s experience going on in there, which is basically recording and will eventually put out a syriza of basically a movie on virtual reality recording. That kind of data is so huge when you have a 360 degree camera up there recording all of that data, great virtual reality, they There's still a lot of times bringing that back on higher hard drives when the space six vehicles come back to the Earth. That's a lot of data going on. We recorded videos all the time, tremendous amount of bandwidth going on. And as you get to the moon and as you get further out, you can a man imagine how much more limiting that bandwidth it. >>Yeah, We used to joke in the old mainframe days that the fastest way to get data from point a to Point B was called C Tam, the Chevy truck access method. Just load >>up a >>truck, whatever it was, tapes or hard drive. So eso and mark, of course space born to was coming on. Spaceport one really was a pilot, but it proved that the commercial computers could actually work for long durations in space, and the economics were feasible. Thinking about, you know, future missions and space born to What are you hoping to accomplish? >>I'm hoping to bring. I'm hoping to bring that success from space born one to the rest of the community with space born to so that they can realize they can do. They're processing at the edge. The purpose of exploration is insight, not data collection. So all of these experiments begin with data collection. Whether that's videos or samples are mold growing, etcetera, collecting that data, we must process it to turn it into information and insight. And the faster we can do that, the faster we get. Our results and the better things are. I often talk Thio College in high school and sometimes grammar school students about this need to process at the edge and how the communication issues can prevent you from doing that. For example, many of us remember the communications with the moon. The moon is about 250,000 miles away, if I remember correctly, and the speed of light is 186,000 miles a second. So even if the speed of light it takes more than a second for the communications to get to the moon and back. So I can remember being stressed out when Houston will to make a statement. And we were wondering if the astronauts could answer Well, they answered as soon as possible. But that 1 to 2 second delay that was natural was what drove us crazy, which made us nervous. We were worried about them in the success of the mission. So Mars is millions of miles away. So flip it around. If you're a Mars explorer and you look out the window and there's a big red cloud coming at you that looks like a tornado and you might want to do some Mars dust storm modeling right then and there to figure out what's the safest thing to do. You don't have the time literally get that back to earth have been processing and get you the answer back. You've got to take those computational capabilities with you. And we're hoping that of these 52 thousands of experiments that are on board, the SS can show that in order to better accomplish their missions on the moon. And Omar, >>I'm so glad you brought that up because I was gonna ask you guys in the commercial world everybody talks about real time. Of course, we talk about the real time edge and AI influencing and and the time value of data I was gonna ask, you know, the real time, Nous, How do you handle that? I think Mark, you just answered that. But at the same time, people will say, you know, the commercial would like, for instance, in advertising. You know, the joke the best. It's not kind of a joke, but the best minds of our generation tryingto get people to click on ads. And it's somewhat true, unfortunately, but at any rate, the value of data diminishes over time. I would imagine in space exploration where where you're dealing and things like light years, that actually there's quite a bit of value in the historical data. But, Mark, you just You just gave a great example of where you need real time, compute capabilities on the ground. But but But, Brian, I wonder if I could ask you the value of this historic historical data, as you just described collecting so much data. Are you? Do you see that the value of that data actually persists over time, you could go back with better modeling and better a i and computing and actually learn from all that data. What are your thoughts on that, Brian? >>Definitely. I think the answer is yes to that. And, you know, as part of the evolution from from basically a platform to a station, we're also learning to make use of the experiments in the data that we have there. NASA has set up. Um, you know, unopened data access sites for some of our physical science experiments that taking place there and and gene lab for looking at some of the biological genomic experiments that have gone on. And I've seen papers already beginning to be generated not from the original experimenters and principal investigators, but from that data set that has been collected. And, you know, when you're sending something up to space and it to the space station and volume for cargo is so limited, you want to get the most you can out of that. So you you want to be is efficient as possible. And one of the ways you do that is you collect. You take these earth observing, uh, instruments. Then you take that data. And, sure, the principal investigators air using it for the key thing that they designed it for. But if that data is available, others will come along and make use of it in different ways. >>Yeah, So I wanna remind the audience and these these these air supercomputers, the space born computers, they're they're solar powered, obviously, and and they're mounted overhead, right? Is that is that correct? >>Yeah. Yes. Space borne computer was mounted in the overhead. I jokingly say that as soon as someone could figure out how to get a data center in orbit, they will have a 50 per cent denser data station that we could have down here instead of two robes side by side. You can also have one overhead on. The power is free. If you can drive it off a solar, and the cooling is free because it's pretty cold out there in space, so it's gonna be very efficient. Uh, space borne computer is the most energy efficient computer in existence. Uh, free electricity and free cooling. And now we're offering free cycles through all the experimenters on goal >>Eso Space born one exceeded its mission timeframe. You were able to run as it was mentioned before some simulations for future Mars missions. And, um and you talked a little bit about what you want to get out of, uh, space born to. I mean, are there other, like, wish list items, bucket bucket list items that people are talking about? >>Yeah, two of them. And these air kind of hypothetical. And Brian kind of alluded to them. Uh, one is having the data on board. So an example that halo developers talk to us about is Hey, I'm on Mars and I see this mold growing on my potatoes. That's not good. So let me let me sample that mold, do a gene sequencing, and then I've got stored all the historical data on space borne computer of all the bad molds out there and let me do a comparison right then and there before I have dinner with my fried potato. So that's that's one. That's very interesting. A second one closely related to it is we have offered up the storage on space borne computer to for all of your raw data that we process. So, Mr Scientist, if if you need the raw data and you need it now, of course, you can have it sent down. But if you don't let us just hold it there as long as they have space. And when we returned to Earth like you mentioned, Patrick will ship that solid state disk back to them so they could have a new person, but again, reserving that network bandwidth, uh, keeping all that raw data available for the entire duration of the mission so that it may have value later on. >>Great. Thank you for that. I want to end on just sort of talking about come back to the collaboration between I S s National Labs and Hewlett Packard Enterprise, and you've got your inviting project ideas using space Bourne to during the upcoming mission. Maybe you could talk about what that's about, and we have A We have a graphic we're gonna put up on DSM information that you can you can access. But please, mark share with us what you're planning there. >>So again, the collaboration has been outstanding. There. There's been a mention off How much savings is, uh, if you can reduce the weight by a pound. Well, our partners ice s national lab and NASA have taken on that cost of delivering baseball in computer to the international space station as part of their collaboration and powering and cooling us and giving us the technical support in return on our side, we're offering up space borne computer to for all the onboard experiments and all those that think they might be wanting doing experiments on space born on the S s in the future to take advantage of that. So we're very, very excited about that. >>Yeah, and you could go toe just email space born at hp dot com on just float some ideas. I'm sure at some point there'll be a website so you can email them or you can email me david dot volonte at at silicon angle dot com and I'll shoot you that that email one or that website once we get it. But, Brian, I wanna end with you. You've been so gracious with your time. Uh, yeah. Give us your final thoughts on on exa scale. Maybe how you're celebrating exa scale day? I was joking with Mark. Maybe we got a special exa scale drink for 10. 18 but, uh, what's your final thoughts, Brian? >>Uh, I'm going to digress just a little bit. I think I think I have a unique perspective to celebrate eggs a scale day because as an undergraduate student, I was interning at Langley Research Center in the wind tunnels and the wind tunnel. I was then, um, they they were very excited that they had a new state of the art giant room size computer to take that data we way worked on unsteady, um, aerodynamic forces. So you need a lot of computation, and you need to be ableto take data at a high bandwidth. To be able to do that, they'd always, you know, run their their wind tunnel for four or five hours. Almost the whole shift. Like that data and maybe a week later, been ableto look at the data to decide if they got what they were looking for? Well, at the time in the in the early eighties, this is definitely the before times that I got there. They had they had that computer in place. Yes, it was a punchcard computer. It was the one time in my life I got to put my hands on the punch cards and was told not to drop them there. Any trouble if I did that. But I was able thio immediately after, uh, actually, during their run, take that data, reduce it down, grabbed my colored pencils and graph paper and graph out coefficient lift coefficient of drag. Other things that they were measuring. Take it back to them. And they were so excited to have data two hours after they had taken it analyzed and looked at it just pickled them. Think that they could make decisions now on what they wanted to do for their next run. Well, we've come a long way since then. You know, extra scale day really, really emphasizes that point, you know? So it really brings it home to me. Yeah. >>Please, no, please carry on. >>Well, I was just gonna say, you know, you talked about the opportunities that that space borne computer provides and and Mark mentioned our colleagues at the I S s national lab. You know, um, the space station has been declared a national laboratory, and so about half of the, uh, capabilities we have for doing research is a portion to the national lab so that commercial entities so that HP can can do these sorts of projects and universities can access station and and other government agencies. And then NASA can focus in on those things we want to do purely to push our exploration programs. So the opportunities to take advantage of that are there marks opening up the door for a lot of opportunities. But others can just Google S s national laboratory and find some information on how to get in the way. Mark did originally using s national lab to maybe get a good experiment up there. >>Well, it's just astounding to see the progress that this industry is made when you go back and look, you know, the early days of supercomputing to imagine that they actually can be space born is just tremendous. Not only the impacts that it can have on Space six exploration, but also society in general. Mark Wayne talked about that. Guys, thanks so much for coming on the Cube and celebrating Exa scale day and helping expand the community. Great work. And, uh, thank you very much for all that you guys dio >>Thank you very much for having me on and everybody out there. Let's get the XO scale as quick as we can. Appreciate everything you all are >>doing. Let's do it. >>I've got a I've got a similar story. Humanity saw the first trillion calculations per second. Like I said in 1997. And it was over 100 racks of computer equipment. Well, space borne one is less than fourth of Iraq in only 20 years. So I'm gonna be celebrating exa scale day in anticipation off exa scale computers on earth and soon following within the national lab that exists in 20 plus years And being on Mars. >>That's awesome. That mark. Thank you for that. And and thank you for watching everybody. We're celebrating Exa scale day with the community. The supercomputing community on the Cube Right back

>> No, that makes total sense. You've got me thinking a little bit. You see some of this right now going on general fitness and these thirty six minute classes will fit thirty six is awesome there. Big group No. One, their trainers. And they do a really good job of it. But the onset of maybe not such, um, high intensity aspects that you're doing. But you're promoting motor patterns, right? So it's not like, Okay, let's train for thirty six minutes. Generally was trained for forty five minutes. Let's train for an hour. But let's have a specific program that we're picking on to develop an athlete and push him in direction. So I mean by that is, I kind of see this in this is my attempt to digest cores. Mind not break it down and bring her with me. I thought you'd like to roost e a seven day period. And then you said in this period, I want to accomplish, you know, thiss five sets off total or five sets of ten reps and back squat and then your micro dose in mind like you, you slice it up, and so all of a sudden it doesn't become a five by ten because fifty total wrapped trying to get you won't take that ten reps here and twenty wraps here and maybe five reps here, and you put it in different ways. So if you look at it holistically, it's this very on the certainly first. See, it looks almost just organized, but looks like a lot happening at once. When you take us back, you look at a full truck, the full pies there, and so people they come and see me one of your workout So they see on Instagram that, oh, it's just Korea Doing, you know, appears to be basic patterns that kind of seem random. But really, you said, Okay, this is my goal. This is what I want from these guys and you're taking a step back. You applied it in a very strategic way. So it's not just people say, Oh, it's a fitness class. No, First off, Micro does seem just That's if I like, you know, a thirty minute workout. It's a thirty minute directed work out with the candle quantifiable goal over Baghdad, a period of time. Is that a fair assessment? I dove into the brain of Cory. No, my deal >> looked like this. Lookit. Let's look at another population. We look at prisoners when they go to the yard. How much time do they have a day? All right, >> You know what, >> Right. That's what I'm saying. Like, it's not a lot like they're locked up in a cell for the whole day. So when they go to the yard, they go ham on whatever's available, it ain't like they got this nice little hole like, Okay, we're going to do from squads. And they were gonna go to bench and they were going to Arlo, and we're going to do no. They pick something that is available and they go ham on it for an hour, and they're on really terrible food and really terrible environments, but tend to get really strong. Okay, well, that makes sense. So and you know what? They do it again the next day and the next day and the next day. So I'm not saying we're trained like prisoners, But what I'm saying is there's a reason why if I was to tell any elite level lifter, OK? All you can do today for thirty minutes is squad. What do you think's gonna happen? They're going to go heavy often. And they're going to be able to be fresh the next day to do the same thing. I mean, no one leaves a power lifting meet the next day saying, Oh, time to go train again. No, their body is trashed, right? Because of all the intensity that they didn't through multiple movements. Same idea, right? All I'm doing is isolating it. So, for instance, I'm looking for a specific response. If I want to train relative string, I want to find a movement that they can move a lot of way, obviously not through a high speed. And that's the movement we're going to do. If I want a absolute velocity, for instance, Woodchuck and Tendo terms, I want them to be very elastic. Reactive owned him to move very, very fast. Then I'm gonna pick a movement, say, like a barbell squad job. Maybe it's a credible swing. Maybe it's throws and then they're going to go ham on that. But if you just take that one isolated lift, I don't care. If you do tend doubles at it, you're not going to be that sword, especially if you've been doing this for over a year. First start the preseason. We gotta look at stress holistically. The biggest stress they have is basketball. So the last thing I'm going to do is beat them down. And here I'm just going to make sure that we'Ll stay on the cart. So you look at our total volume. It looks something like four sets of four. But by the time we're at the end of the season January, February, March, we're hitting our P R's and reason why we're hit Rp. Ours is because we've made this huge reservoir of stress that they're able tto handle. So now practises cut in half. So I have more reserves in the weight room. So that force that's afore we were hitting for those compound movements in preseason. Well, now they look like ten sets of doubles or twelve sets of singles because they have that reservoir. So now we're expressing in a controlled environment faster weights have your weights at the time of year that we're looking for those adaptations so that now we're quote unquote stronger and faster. We're trying to win the championship, not tryingto win it and the summer, which you generally see like thereby sent PR is before they go home and summer. Well, that's great. And then they go into their maintenance program for the season, which last six months. Can you maintain anything for longer than six? No, you can't, like, maybe your oil, but you've not wantto patients, you know? I'm saying so. You know, that's that's where it really came down to is I'm trying to find the best means to produce performance, >> so I'm on times Lower standard. Yeah. Please do not mind around it. So I get it correct. Nowhere earthly it's looking at How do we given work out at that? Fits? The current state needed the athlete, so Okay, there begin the year, right? Their capacity only so localize outside stressors to fit in the workout around the other twenty three hours. Right? And then you're applying a stressor that's heavy enough, but not too light. And you do it. I'm not not overly fatigued them, but at least stimulate them. So you working guide rails? Not a written in stone. A type of thing, >> right? Yeah. So yeah. Yeah. How Basically how I how I keep the best part of the best way to put it is what I've done this year that I haven't done in the past is abuse Tendo Units, I'm just That's my way of just monitoring. How about speed? Okay, Cool, because load is one thing. But once again, how do you move that load now? We're not We're not dicing up like, Oh, it's point seven. You're supposed to hit point five like up. You know, add thirty kilos or vice versa, right? Like you're not exact. But if you're within a range, it gives me a whole lot of details, all right? And then you're basically all we do from that point is record the wait, not the speed. I just keep them in a certain zone. Stay within this. You, for instance, our strength speed or a relative strength and strength. Speed movements can't go anything more than triples our speed, strength and are absolute velocity. You can't go anything over five reps. If you hit quote unquote those triples or those fives, then the next time you come in, guess what we get to upload if you're not above that was going to stick with the same load. And if you prove it within your early work sex, then we'LL have a little bit alert. But that's our way of day to day, keeping them on the road, if you will. >> No, that makes sense. Do I couldn't agree more. I see it carrying over so well. Universally way you looked at the origins of strength training and we're like Oh, came from Russia and even your ever pashanski for those people aren't nerds like myself. Russian sports science even started like appeared ization. It's kind of a made up thing, right? So one hundred percent made up haven't made up and it kind of came from the four years cycle of Russia itself. America takes that andan. What happens is you get the the non athlete world's intelligent public world. Everything is monetized, right? So it's like, Okay, we know that training really heavy every days and probably a good. So we're going to make these things called, you know, in small little workouts that might last twenty five minutes are our six minutes, you know, have a shrink it as Lois and possibly can. But no, let's make it not necessarily difficult, but challenging. Um and we make money office. We labeled something different and you see different fitness fads come off when I come and go. But a lot of because I got the capitalistic market monetization. People try to make money off of things. But that really does him from, like the athletic side. If you're thinking about Hey, I'm Cory. I'm dealing with Alex. I don't know how they're going to walk into my door today. I don't know if they're going to be high lower, you know, just normal. How can I then give myself the opportunity to provide environment where they can work successfully and and what you do, which is really cool, And I find it really inspiring kind of cheesy word. But you give a lot of ownership to all your athletes when it comes to selection of exercises and movements. And I find that to be something that we don't say. We as in the general world of anything sports, science and fitness don't always like to do. Um, and you say Okay, you know, credit. I'm wrong, Corey to I don't want take worth mountains, him incorrectly. Just so you know, here's a pattern and maybe select one of these three exercises that you feel like gets you ready. And what's so great about that? It removes the constraints of this exercise is the best. You know, this is the golden exercise and really, I mean you and I know it, but we want to feel good. We would always have a bench press when I came in town, but absolutely, it's like, Okay, let's let's really understand that it's not really a difference between Aback Squad versus upfront squad versus may be something of a trap, our poll, especially if you're using it to get the athlete ready. So talk. If you could talk a little bit about how you decide some of that and what led you down that path and giving those athletes that kind of ownership and understanding of you know, I want to do this versus I have to >> do this right? I mean, to me, autonomy is everything, because what you generally see and it's to me, it's almost criminal is everyone gets the piece of paper. They fill it out with me you get, then you do the same thing, right? You get that piece of paper the next day, fill it out. Get that piece of paper. Next thing, fill it out. And then four years later you go. Well, I'm leaving now. Where's my piece of paper For the rest of my life. Oh, so you didn't really learn how to train, did you? You didn't really learn what worked for you. You didn't really In the really issue is like I deal with crazy, different levers. I mean, I got guys that are five eight all the way to seven foot. So you can't tell me there's a golden exercise that it doesn't exist in my world. >> I >> like knowing you're on. I would love to have everybody do the exact same thing. They love doing it. And they all do it very, very well so that I can have my little lab and I can have my control and I can show. Hey, guys, look how much better we got this year because of my implementation. Bax Wass What? What does that say? That says that I care more about what I'm doing more than what's best for that athlete and what they're doing if you really the real reason why I got to this autonomy stage is when I realized what I do is such a small percentage of their overall success and the reason why I say that I'm not necessarily saying I agree with hit or disagree with Hit, but you could have a hit program. You could have an Olympic based program. You could have your holistic based program, whatever you want to say, and I see the hit program Win a national championship and I'm like, what happened? Like I don't agree with that program, but they won well, it's all about it's all about the dude's. So if I can give quote unquote my dudes the best training environment that works for them. So what I mean by that is Look, here's a squad. You hate doing back squats because the bar on your back, it's jerking the hell out of your shoulders because you don't like to be an external rotation will. Then maybe I'm just going to hate. How about this Bar safety squad bar that feel better? Cool court. My knees are super tender away. It's basketball. Everybody's needs at some point this season, every a super tender last thing I'm going to do is put them in an environment. Teo, flame up those tendons so that they can't perform at a higher level on the basketball court. So what are we going to do? Well, let's Hinch, how about we just do some already? L stay. How about we do some kettle bell swings? Maybe some tribe are dead. Lift. It doesn't necessarily have to be this golden exercise that everybody fits in. And I think really what it stands from is that strength coaches got approved to their sport coaches that we'll look at, our numbers go up and they have to have a control to do that. And the exact opposite. It's a sport. Coaches coming down saying one of our guys bench. Well, if our sport coaches cares so much about bench press, well, then what do you think I got to do? Well, I gotta bench my guys so we could get those numbers so I could look like, you know, I'm validated my job. Well, how about we take something that's oh, universally accepted. So how about a counter movement? Jump out force plate. Now, I'm not saying everybody has forced plates, but you could just use jump height. Friend sits. Who cares how you got there? As long as you are trending right, that's all that matters. Why should we be fixated to a certain methodology or a certain pattern or not? Pattern but exercise. Just give them a pattern, let him choose. And to be honest with you, if it feels right, it's going to fly, right? If it feels good to do attract bar squat, opposed to doing a front squat well, they're probably gonna put more load and they put more load that I'm going to get the stress response adaptation. If I don't like the front squat because it's choking me the hell out. Well, then I'm probably not going to put his much load on it. Now, I have a negative connotation now have all these internal stress is going on, and then I'm gonna have a weird as look atyou, saying I don't like what we're doing in here. So now you think the quote unquote Byeon is going to be there. So now we're not getting any stresses that are going to give me that positive adaptation I'm looking for. So at the end of the day, if I can give them the education tto, learn how to do these movements and how to choose for themselves, well, then now it's not just what they did here for four years. I just gave them skills for the rest of their life. And if they're good enough to play pros now, they can take that and they can articulate it to the next coaching stuff so they could do a better >> job. No, that's that's awesome, man like this. A lot of things I want. I head into their I'LL keep it all Diamond all nine hundred promised. But I couldn't agree more and one of things that you say, you know, let's have a king P I They said jump high, for example, a point of reference. Then let's not care what we d'Oh, to the extent I mean not care. But let's not constrain ourselves of what we dio in order to improve that k p I. So the way I think about it, it's kind of like you ever use waze before that? Yes, that we got right. It knows to things and knows where you are. It knows where you were. If you're driving, it knows where you're going. Road. And then as okay, all I care about getting to point B So it will take you on detours left and right. Little Granny is driving slow in front of you for the pothole. If whatever is going to find the best way to get there, it doesn't care how it gets there, right, Right. And so work that it's say, OK, let's get the sevens environment where we can learn. And we know we need to get to be for me. And I'm not gonna say to go in a straight line because you might go through building and crashing hit pedestrians. We're gonna find a way to get to be. We're going to find a way that makes sense for the athlete and yourself. So my teaching them, you know, let's have you like and learn to do some of these movements then don't know taking a left at this next stop light to get to point B will be quicker than you saying go straight because they're the one in the driver's seat, right? And if that educational environment where you start to look at this a really complex system, her planting a really simple abie model and apply it to something as complex as the human body so that we can learn. And the example I give. It's like, you know, the ways part like, that's the more complex and assumptions we make more room for aeri half All right, we'Ll screw this. We assume that the sumo gets here. Well, if we assume in order to get to A to B, we got a one a two a three a four, a five. But any point on the line that, you know, assumption breaks, we don't get to be all right, you guys, you stuck at a whatever and doing. You know, we have to follow this waterfall method. It's very much a living method where things come in, things come out, things make you change. But you know what? You want to go? I >> mean, it's we work in team sports. Like the only objective we are the only objective that matters is wins and losses, period. Right? So if I wasn't a stopwatch sport, maybe my mind would change a little bit, right? Maybe I got okay. We need to drift towards this because literally it's did you get faster? Did you not get faster? Right? Swimming whatever you're doing, maybe these are the things we need to do more often to make that happen. But I'm dealing with incompetent. I mean great human beings, but just physically incompetent. There's still learning about their bodies were still growing into their bodies. I think it's the most arrogance thing that a strength coach could do is to say, Here's a program that's gonna get you better for six weeks. What? What is that? Even here's a block that's going to get youto point me. How do you know Like, till you know Saddamist like, can you honestly tell me that following this six week plan is doing that? Hey, they got sport practice. They got exams, they got pick up your tell me none of those factors could potentially there off your little plan or that your little plan can go up. They're KP eyes, if you will, or their Their goal is just a play basketball. So that to me, that's where as this thing, it's like the most arrogant thing in our field and it just drives me up the wall. But the other day, like I got a sport coach who has all the faith in the world of me gives me the keys to the castle. He just tells me, Do what you think is best. I I report the numbers that he doesn't even know he needs. That's what's awesome about he's like Chord. I just trust you like these were things that I want to see my guys do. We want a quote unquote play fast. Well, okay, here's some standards that we can set And these Airways that we know we got quote unquote faster. Now, from the technical tactical aspect, that's where you guys come in and you guys got it. Apply what you think is best to make that happen, right? But I gave you the physical requirements. I told you exactly what you need to get done and how we got there. Now you guys apply the technical tactical aspect. And then there we go. Now we have a happy marriage is long as I can supply valuable information. It doesn't matter what the information ISS, and that's where everybody gets stuck on these controlled environment numbers like like looking, swatting inventions like Who cares? Like Who cares about written load? Load gets you to here right after that, it's all about It's all about speed. It's all about rhythm coordination, your vestibular system that there's so many things that go into making. You better not just, uh, put three fifteen on the back squat suite. No, >> that's you know. Yes, yes, I agree. I'm not going to deviate too far. My ma, you know how I work or my mind races and I don't go in straight lines. I apologized immediately. Good. I was thinking about your friend mentioned earlier. It was everything that this lately, too. People who've been the private sector's I work in personal training, and I worked in exercise clinic for two and a half years. Iowa State, where don't older adults randall off cool testing on them. But ultimately they showed up because they enjoy it. And one things that I think we I don't mean We have everybody some people forget is that it needs to be enjoyable back. And when you're in a private sector and you're literally your food is the ability for something to come back to you. Hey, it's really different and you start. You said Okay, you know what exercise and movement do you like, and then you manipulate How do I make that exercise the most effective exercise for that person? And that's what you kind of mentioned with the educational process for your athletes. You're taking this approach. Where? How did you get them to win? Firstly, they gotta want to be here, but they don't want to be who I try hard. And secondly, no Adam, take ownership of these movements. I really like that concept because it's really melting in the world of Hey, you're here. You have to get better. But everyone knows when you want to get better. Vs have to get better, right? The be out a little different and unusual marks Lefton excited to move. I just keep thinking about that from like the private side. That's really where, like the general public, and you could deal with great Alan to deal with a lot of athletes who really want to be there. But unfortunately, majority the world doesn't want to work out like they're they're not interested, and I hate to make an assumption, but it's hard not to think that it's either them not knowing or them intimidated that have to do something in there, right? Right. I'm like that mindset a beam to apply. Okay, let's have an ownership model that drives it, because if you talk to people, her successful personal trainers, they have a way to make sure people come back. Oh, for should join a box in a way that a strength coach you're no environment might not even have to be exposed to just because it's the nature of >> well, for me, like the off season. I mean, when I get a freshman, that's a great thing about basketball. But I get a freshman. I mean, maybe they picked up some weights like a B. There's still just such a greenhorn in the weight room. They don't know what's good and what's bad, right? So, essentially the off season is a little bit of dictatorship like Sorry, I'm to tell you what to do because you don't know shit, right? But the goal is to earn that autonomy as well. So, you know, my guys that are kind of like slaps like for the whole offseason. Well, their leashes a lot tighter like Nah, bro, you're going to do this because I know you need to do this. You have earned the right to have that a top. So I want to make sure that that's, like pretty clear, too, because if you just give autonomy all day and there's going to run over you. But the one aspect that I think that is so important with our autonomy is it's my biggest performance enhancer, and I actually had dated Approve it. Like if I just look at my C M J members from our force plates once again. Yes, there are some maybe eight sets of doubles or six sets of triples or whatever, right? But once again, that is Tendo based, like to a certain agree with most of our movement. So you know, it could be a triple. It could be a double. It could be a single. It depends on where they fall in on along those lines, but essentially the flexibility of the sets and wraps, the unbelievable latitude of the movement pattern that they're doing. But yet counter movement jumps in February. They are p r ng, not season. P R's. I'm talking life top ers Guys that have been here for three years are hidden from nineteen point one to twenty six point four. I can't say names the twenty six point four in February. So what does that say? It says that my biggest performance enhancer is the kids saying I want to do that. Cool. That's what we're going to do. >> No, I love it that zik perfect. If you want to be there, you're intense. Going to be high. You're going to try harder. You're going toe actually care about what you d'oh and that mindset really house dr an aspect of performance that otherwise we can't because all internal right korea we really started wrapping up towards the end you buy a couple questions for you before you go yourself thank you i appreciate it it's always good to have you next way clich a weekly cycle korea >> will make a >> record you know fire i slowly thanks for having you guys we wanted to come with because you're a scientist I mean, if you had to share a bitter fight and this is to anybody and this isn't their coach, Jenny, where nobody is looking to enhance their fitness, their performance, um, their overall well being You that with activity, right? How is what would you advise someone to get into and regards Tio training our house to someone Initiate That's on top of the micro dose in a kind of giving that much of credit here, obviously some e How does someone injured? I heard it put that way and I'll get straight to the point that one look into into exercise probably should do some form of micro dose in to see if you even like it everyone to overdose. How do they start that process if they're not athletes per se how they decide where they began? >> Well, essentially is what do you want to end up like, What's the what's the point beyond ways, right? Do you just want to look aesthetically better? How aesthetically do you want to look? Do you wanna look like a big body voter? Do you want to look like a swimmer? What do you want to look like? And I think that the vein than fan ity. And I mean, that's what drives my basketball players there in tank tops here around. Of course, they want nice arms. Right? So there's certain things that you gotta know. Like, I want to look like this. Now, some of the performance guys, Maybe I wantto sprint faster or jump higher. Like that's a whole another aspect. But we're talking about general population number one. What do you wanna look like? Okay, so if I'm three hundred pounds and I want to lose some body fat for my own general health and I want to, you know, be more presentable, if you will. And smaller clothing. Well, then maybe just walking ten minutes every day, and then you start adding layers to it, So Okay, You know what I mean? Killing these walks. How about we go Stairmaster? Okay, that's a little tougher. Okay, how about we introduce maybe some med ball exercises because that's not necessarily too complex to do that. I can do it through different ranges. It's easy to manipulate. Okay, Now, let's take a dumb bill or kettle bill. Then we work our way to a bar bill and now. Oh, man, what do you know? I just dropped one hundred pounds and in them. Oh, before all of that eating. But like, we're just talking about the physical aspects, but as far as that, where do you want to be? Okay, I want to look like Brad Pitt. OK, for one, get plastic surgery. But if you want to look cool air at Brad Pitt and Fight Club Okay, well, these are the things that I need to do. So let's reverse into near the process, okay? He cut his little jack, so that means he's got muscular strength. OK, cool. So that means weights are going to get involved at some point we'll he got really lean for this too. So my general fitness sucks. Maybe I just need to start with walking. Maybe a jump rope, maybe just medicine Ball toss is something that's super easy. The number one. What's going to make me more consistent? What consistency is goingto win? It's not. They'll work out you do that's going to make you go from a counter movement jumped a nineteen point one to twenty six point for It's the consistency that got you there. All right. That was a two year process for that kid. Just to get to that point, right? If you try to hijack the system, if you try to go, I want to get from point A to point Z like that. Well, you're going to run into multiple things. One possibly injury and two. What's the real reason why you're Russian? The real reason why you Russians, Because I don't want to be there in first place. Now you've just ruined the whole concept. Now you've just ruined the journey. To me, that is much more important. Like when I used to be a fake body motor, if you will, that when I try to get ready for shows. I don't remember the show at all. The only thing I remembered was those nights where I was damn hungry those mornings where I had to get up, do my quote unquote fasted cardio meal prep backs without remember only big. How I was on stage for forty five seconds like that was twelve weeks for forty five seconds. Right? So that's where you gotta understand like it's the beauty or what is it that Jake whole line of the beauty is in the is in the cash. Basically what? The thing that you want to fall in love with the most is the adversity that they were going to fall in love with the most is the stressful points. That's what's going to create the beauty, if you will remember that Jake Colon. But essentially, that Google >> search really quick pressure that the Brad Pitt Fight Club I >> mean, that dude was solid, Man, that was a solid right. May like Brad Pitt. He was a pretty boy until fight club. And I was like, Yo, that is some white trash. I would not mess with him. He can go. >> Uh, great. I love it. Lastly, Yeah. Course lesson. Where do we find you? On social media and other venues? Assault media were coming here more than beauty and wonder himself. >> Yeah. So Instagram is probably what you can find me on the most slash strength as C h L E s strength. You could find me there pretty active on it. You want to see so naked cats? So to sphinx, with my beautiful wife and ah, multiple podcast. I'm on a lot of different podcast that you just Google. I, too, are goingto iTunes type in my name. You'LL find many other platforms where I go into a lot more depth about how we train on And then, of course, speaking engagements. I do multiple speaking, engage with the nationally and internationally. And so there's opportunities to meet me in person there. >> There's beauty in the struggle. >> There is beauty in the struggle. This beauty >> I got my end. >> Yes, there is beauty in the struggle. That's when they >> get here in Britain, right? Right there. Where >> you Brooks. But there's beauty in the struggle >> A lasting well, Korea appreciate you have coming on here. I mean, I hope something useful. I >> was one hundred percent. My pleasure, Max. I love working with you, man. >> Now you do. And anybody curious about Corey? I mean, I really encourage checking out his social media. Yeah, I know. It's a lot of crazy stuff on Instagram that is really thought provoking. Put it that way and I can't believe it. Oh, my goodness. I can't let you escape Korea quite yet. >> Well, what you got? >> Uh, whole off the exit. Give me five minutes on it. I was going to ask his social media is going to ask. Yeah, way rehab itself. Yeah, to spring loaded monster man who means you want to share a little bit on this because I know you have been doing this yourself. Yeah, this is it in chorus singer based Achilles program. I love some of the actors. I love thee, not the unloaded foot contact under your hand motion who was seen Alice into this isn't the course in a chair, and he's for lack of better words. Words. MacInnis foot on the floor like a pogo stick and doing extremely extremely unloaded movements early on that site, too early on but in the rehab process itself to introduce low level plyometrics, He's doing band assisted jumps. He's doing isometrics. He's doing heavy squads. He's doing some bar bell curls. All things important for the curies. >> Sure are. Absolutely yeah beyond you. My understandings of the lower leg complex is off the charts because of my injury. So for the viewer's eye, tor macula or a ruptured my Achilles tendon with a full rupture but right at the insertion, which is the very atypical tear because I've been dealing teno sis for over a year before I tore it. So they had it cut me up top to bring me down low, if you will. So usually Achilles ruptures that all they do is bring it together and then tie it. There are. So it through the mind was at the very bottom. So essentially, they had to cut me up top toh length and me and then, uh, suitors through. So is very atypical, which sucks only that that part sucks. Spike. Um, it's not that I am Well, maybe a little bit arrogant, but I honestly want to take full control of my physical therapy because I think that intuitively I understand the process not just of rehab, but of how to increase performance. So all I did was watered down as much of that is possible and truly started as soon as I got to the pain free. And so, yeah, with all the unloaded stuff, it just made sense to me like that's something you just don't see in physical therapy to It's kind of blows. My mind is what's the first thing to go like when you get older? What happens? Will you lose your ability to do very forceful things or to lose power or the ability to generate power. So that's the first thing that came in my mind when I rupture. Or when a Torme Achilles was okay. I need to go back and not be old because essentially, I'm staying still. So if I'm staying still, it's like use it or lose it protocol. So from that perspective, I told myself, I need to move fast at some point. So I started with all my available limbs at the time, just moving fast. Then I progress toe when my suitors seal or excuse me with my I want my wound healed. I got into the pool, so that's the most is about is unloaded. You should get, and all it did was just frail. My leg and there a cz muchas I could through different planes and of course, he has fold up. But of course, it's going to like your adding a stress. And so I just did it Mohr or Mohr. And so I just Kim. Training fast, even though, is the most unloaded way you can do it. And then, like Max was talking about, I got to a seated position and I just started doing be most unloaded pogo jumps you've ever seen or ankle pops or whatever you want to call it. So then I transition to standing on it isometrics, then putting more force into the forefoot isometrics. And then I started using the bands I mean super heavy bands and then just started like Pogo's and then start lighting the bands I went to arm went the body weight. To me, it's like super common sense, but I don't know, maybe the physical world. It doesn't really look at it that way. They look at it and isolation opposed to global. So to me, I knew if I could quickly get back to global patterns that I will be able to promote healing faster. And so, like Chase talked about, his last one ought to be a far protocols. Luckily, I had him as a resource to help me with my healing process, but right now, on that four and a half months, almost five months, and I'm doing some pretty cool things if just to give you a point of reference. Dez Bryant, wide receiver. He tore his a week after mine, and essentially, you guys Essentially, he's What's a similar athletes level athlete? You know, very someone. Uh, actually, he's going to be up until eight to nine months. John Wall tour has a few months after mine. He's going to be an entire year for his process. Boog, Golden State warriors took him a whole year to get back on my goal. If I can get it back and lesson seven months, that means I did something, right? >> No, I love it. Well, that's tough stuff. Get to see if you check out his instagram page. So me, please, dear, do yourself a service. Go check out the man. He's a good dude, Tio. So sometimes no kid. Don't >> you know you're right there, e >> I don't want call corps on a bad day. >> You >> know, it's all good now. I really appreciate it, man. Thanks for being on here. And, uh, again we follow sometime in near future. I feel I'm expecting that shirt. By the way, where is my core bighead T shirt? >> You know, I want to find one of my earlier body building picks, and I'm gonna put it on a T shirts and, Tio, >> I love it. How I rocked the hell out of it. Man, >> you're beard in a most >> and be right here. Yes, right behind. Maybe my postal records slash proposing bronze and gold. You're welcome. You're welcome. An absolutely huge in that >> purple banana hammock to >> Wouldn't ask for another way. What? The full real deal. Korean stage. Ready, you know. Awesome. Well armed man up that thing. You guys, Listen, I appreciate it. Great South Korea on. If we're curious about finding more, check him out on instagram and look for Teo. No doing more. These in near future. >> Awesome. Thanks, Max.

(upbeat music) >> Welcome to this special Cube Conversation. We're here in Palo Alto, California with a special guest. Dialing in remotely Paul Martino, the founder of Bullpen Capital and also the producer of an upcoming film called The Inside Game. It's a story about a true story about an NBA betting scandal. It's really, it's got everything you want to know. It's got sports, it's got gambling, it's got fixing of games. Paul Martino, known for being a serial entrepreneur and then an investor, investing in some great growth companies, and now running his own firm called Bullpen Capital, which bets on high-growth companies and takes them to the next level. Paul, great to see you. Thanks for spending the time. Good to see you again. >> John, always good to see you. Thanks for having me on the show. >> So, you're a unique individual. You're a computer science whiz, investor, entrepreneur, now film producer. This story kind of crosses over your interests. Obviously in Philly, you're kind of like me, kind of a blue collar kind of guy. You know hot starters when you see it. You also were an investor in a lot of the sports, gambling, betting, kind of online games, we've talked about in the past. But now you're crossing over into filming movies. Which is, seems like very cool and obviously we're living in a date of digital media where code is software, code is content, obviously we believe that. What's this movie all about? All the buzz is out there, Inside Game. You get it on sports radio all the time. Give us the scoop. Why Inside Game? What's it about? Give us the 411. >> Yeah, so John, I mean, this is a story that picked me. My producing partner in this is a guy named Michael Pierce who made a bunch of great movies, including The Cooler, one of the best gambling movies, with William H Macy. And he says sometimes the movie picks you and sometimes you pick the movie. And I wasn't sitting around one day going wow I want to be a movie producer, it was just much more that my cousin is the principal in the story. My cousin was the go-between between the gambler and the referee. The three of them were friends ever since they were kids. And when they all got out of jail Tommy called me, Tommy Martino. He said hey Paulie, you're about the only legitimate business guy I know. Could you help me with my life rights? And that's how this started almost six years ago. >> And what progressed next? You sat down, had a couple cocktails, beers, said okay here's how we're going to structure it. Was it more brainstorming and then it kind of went from there? Take us through that progression. >> It was a pure intellectual property exercise, and this is where being a startup guy was helpful. I was like, Tommy, I'll buy your life rights. Maybe we'll get a script written, we'll put it on the shelf, so that if anybody ever wants to make this story they have to go through us. Almost like a blocking patent or a copyright. And he's like okay cool. And so I said I have no delusions of ever making this movie. I actually don't know that, I don't know anybody to make a movie. This is not my skill set. But if anybody ever wants to make the movie, they're going to have to come deal with us. And then the lucky break happens, like anything in a startup. I have this random meeting with a guy named Michael Pierce, who was at a firm called WPS Challenger out of London. And we're down in Hillstone in Santa Monica, and I say to him, I say I've got this script written about this NBA betting scandal, would you do me a favor? He literally laughs in my face. He goes a venture guy from Silicon Valley is going to hand me a script. What a bad, anyway, I was like look dude, I'm a good guy to have owe you a favor so just read this dang thing. About 8 hours later my phone rings, he says who the hell is Andy Callahan? This is the best script I've ever read in my entire life. Let's go make a movie. Andy Callahan was a friend of a friend from high school who wrote the script. He actually once beat Kobe Bryant when he was a center at Haverford when Kobe Bryant played at Lower Merion here in the Philly suburbs. So, it's kind of this local Philly story. I'm a local Philly blue collar guy, we put the pieces together, and I'll be danged and now six years later the film is in the can and you're probably going to see it during the NBA finals this year in June. >> All right, so there's some news out there it's on the cover on ESPN Magazine, the site is now launched. I've been hearing buzz all morning on this in the sports radio world. A lot of buzz, a lot of organic virality around it. Reminds of the Crazy, Rich Asians, which kind of started organically, similar kind of community behind it. This has really got some legs to it. Give us some taste of what's some of the latest organic growth here around the buzz. >> Yeah so, think about this. This happened in, primarily '06 and '07. They were sentenced in 2010 and were in jail in 2011. It is 2019 and the front page story on ESPN is What Tim, Tommy, and Jimmy Battista Did. Those were the three guys, the gambler, the ref, and the go-between. And this is a front page story on ESPN all these years later. So we know this story has tremendous legs. We know this movie has a tremendous built-in audience. And so now it's just our job to leverage all those marketing channels, places we pioneered, like Zynga and FanDuel to get people who care about the story into the theaters. And we're hoping we can really show people how to do a modern way to market a film using those channels we've pioneered at places like FanDuel and Zynga. >> You and I have had many conversations privately and here on the Cube in the past around startups disruption, and it's the same pattern right? No one thinks it's a great idea, you get the rights to it, and you kind of got to find that inflection point, that magical moment which comes through networking and just hard work and hustle. And then you've got everything comes together. And then it comes together. And then it grows. As the world changes, you're seeing digital completely change the game on Hollywood. For instance, Netflix, you've got Prime, you've got Hulu. This is, essentially, a democratization, I'm not saying, well first of all you've made some money so you had some dough to put into it, but here's a script from a friend. You guys put it together. This is now the new startup model going to Hollywood. Talk about that dynamic, what's your vision there? Because this, I think, is an important signal in how digital content, whether it's guys in the Cube doing stuff or Cube Studios, which we'll, we have a vision for. This is something that's real. Talk about the dynamic. How do you see the entrepeneurial vision around how movies are made, how content's made, and then, ultimately, how they're merchandised in the future. >> Right, there's a whole, there's a whole bunch of buckets. There's the intellectual property bucket of the story, the script, etc. Then there's the bucket of getting the movie made. You know, that's the on the set and that's the director and that's post-production, and then there's the marketing. And what was really interesting is even though I'd never made a movie, two of those three buckets I knew a tremendous amount about from my experience as a startup investor. The marketing and the IP side I understood almost completely, even though I'd never made a film. And so all of the disruptive technologies that we learn for doing disruptive things like marketing a new thing called Daily Fantasy Sports, we were able to bring to bear to this film. Now, I had fun on the set and meeting all the actors, etc. But I had no delusion that I knew about the making of the movie part. So I plead ignorance there, but of the three buckets that you need to go make something in the media space 66% of what I knew as a startup guy overlapped and I think this is what the future of the media is. Because guys like me and you, John, we actually know a lot about this because we're startup people as opposed to we have to learn about it in terms of how to market and how to get an audience. I mean, my last company Aggregate Knowledge designs custom audiences for ad targeting. So we know how to find gamblers to go see this movie. That's literally the company I started. And so that's a thing that I'm very, very comfortable with and it's exciting to then work with the producer who did the creative and the director and I say hey guys, I've got this marketing thing under control, I know how to do it, oh by the way, the old Head of Marketing from FanDuel, he's a consultant to the project. Right, so, we got that. >> You got that, and the movie's being made. That's also again, back to entrepreneurship, risk. You got to take risks, right? This is all about risk management at the end of the day and you know, navigating as the lead entrepreneur, getting it done, there's heavy lifting and costs involved in making the movie, >> Right >> How did you, that's like production, right? You got to build a product. That is ultimately the product when it has to get to market. How did that go, what's your thoughts on your first time running a movie like this, from a production standpoint, learnings, observations? >> I learned a tremendous amount. I must admit, I was along for the ride on that piece of the puddle, puzzle. The product development piece of this was all new to me. But then again, I mean think about it, John, I started four companies, a social network, an ad targeting company, a game company, and a security company. I didn't know anything about those four companies when I started them either in terms of what the product needed to do. So learning a new product called make a movie was kind of par for the course, even though I didn't really know anything about it. You know, if you're going to be a startup person you got to have no fear. That's the real attribute you need to have in these kinds of situations. >> So I got to >> And so, witnessed that first-hand and, you know what, now, if I ever make a movie again I kind of know how to make that product. >> Yeah, well looking forward. You've got great instincts as an entrepreneur. I love hanging out with you. I got to ask you a question. I talk to a lot of young people, my son and his friends and I see people coming out of business school, all this stuff. You know, every college has an entrepreneurial program. Music, film, you know, whatever, they all have kind of bolted on entrepreneurship. You're essentially breaking down that kind of dogma of that you have to have a discipline. Anyone can do this, right? So talk about the folks that are out there, trying to be entrepreneurial, whether you're a musician. This is direct to consumer. If you have skills as an entrepreneur it translates. Talk about what it takes to be an entrepreneur, if you're a musician or someone who has, say, content rights or has content story. What do they do? What's your advice? >> We have lived through, perhaps the most awesome period of the last five to 10 years, where it got cheap to do a startup. You know, when we're doing our first startups 20 years ago, it cost 5 million bucks to go get a license from Oracle and go hire a DBA and do all that stuff. You know what, for 5 grand you can get your website up, you can build, you can use your iPhone, you can film your movie. That's all happened in the last five to 10 years. And what it's done is exactly the word you used. It's democratized who can become an entrepreneur. Now people who never thought entrepreneurship was for them, are able to do it. One of our great examples of this is Ipsy, our cosmetics company. You know, Michelle Phan was a cocktail waitress working in Florida, but she had this YouTube following around watching her videos of her putting her makeup on. And you know when we met her, we're like you know what? You're the next generation of what entrepreneurs look like. Because no, she didn't go to Stanford. She didn't have a PhD in computer science, but she knew what this next generation of content marketing was going to look like. She knew what it was to be a celebrity influencer. You know, that company Ipsy makes hundreds of millions of dollars every year now, and I don't think most people on Sand Hill would've necessarily given Michelle the chance because she didn't look like what the traditional entrepreneur looked like. So it's so cool we live in a time where you don't need to look like what you think an entrepreneur needs to look like or went to the school you had to think you'd go to to become an entrepreneur. It's open to everybody now. >> And the key to success, you know, again, we've talked about those privately all the time when we meet, but I want to get your comment on the record here. But I mean, there's some basic blocking and tackling that's independent of where you went to school that's being creative, networking, networking, networking, you know, and being, good hustle. And being, obviously good judgment and being smart. Do your thoughts on the keys to success for as those folks saying hey you know I didn't have to go to these big, fancy schools. I want to go out there. I want to test my idea. I want to go push the envelope. I want to go for it. What's the tried and true formula from your perspective? >> So when you're in the early stage of hustling and you want to figure out if you're good at being an entrepreneur, I tell entrepreneurs this all the time. Every meeting is a job interview. Now, you might not think it's a job interview, but you want to think about every meeting, this might be the next person I start my company with. This might be the person I end up hiring to go run something at my company. This might be the person I end up getting money for, from to start my company. And so show up, have some skills, have some passion, have a vision, and impress the person on the other side of the table. Every once in a while I get invited to a college and they're like well Paul, life's easy for you, you started a company with Mark Pinkus and you're friend with Reid Hoffman and this... Well how the hell do you think I met those people? I did the same thing I'm telling you to do. When I was nobody coming out of school, I went and did stuff for these guys. I helped them with a business plan. I wrote the code of Tribe, and then now all of the sudden we've got a whole network of people you can go to. Well, that didn't happen by accident. You had to show up and have some skills, talent, and passion and then impress the person on the other side of the table. >> Yeah >> And guess what? If you do that enough times in a row, you're going to end up having your own network. And then you're going to have kids come in and say, wow, how can I impress you? >> Be authentic, be genuine, hustle, do networking, do the job interview, great stuff. All right, back to final point I want to get your thoughts on because I think this is your success and getting this movie out of the gate. Everyone, first, everyone should go see Inside Game. Insidegamemovie.com is the URL. The site just went up. This should be a great movie. I'm looking forward to it, and knowing the work that went in, I followed your journey on this. It should be great. I'm looking forward to seeing it. Uh, digital media, um, your thoughts because we're seeing a direct to consumer model. You've got the big companies, YouTube, Amazon, others. There's kind of a, a huge distribution of those guys. The classic Web 2.0 search kind of paradigm and portal. But now you've got a whole 'nother set of distribution or network effects. Your thoughts, because you were involved in, again, social networking before it became the monster that it is now. How is digital media changing? What's your vision of how that's happening and how does someone jump on that wave and be successful? >> Yeah, we're in the midst of disruption. I mean, I'm in the discussions and final negotiations right now on how we're going to end up ultimately doing the film distribution. And I am very disappointed with the quality of the thinking of the people on the other side of the table. Because they come from very traditional backgrounds. And I'm talking to them about, I want to do a site takeover across Zynga. I want to do a digital download on FanDuel of a 20 minute clip of the film. And they're like what's FanDuel? Who's Zynga? And I'm sitting there, I'm like guys, this is the new media. Oh, by the way, there's a sports app called Wave and Wave is where the local influencers in the markets who want to write the stories are, and we want to do a deal with those guys. And oh, by the way, the CEO of that company is a buddy of mine I met years ago, right? One of those kids I gave advice to, and now I'm going to ask him for a favor from, right, that's how it works. But, it's amazing when you have these conversations with traditional old line media companies. They don't understand any of the words coming out of your mouth. They're like Paul, here's how much I'll give you for your film. Thank you, we'll go market it. I'm like, really? Seriously? I got the former CMO of FanDuel going to help out on this. You don't want to talk to him? >> Yeah >> And so this is where the industry is really ripe for disruption. Because the people from the startup world have already disrupted the apple cart and now we've just got to demonstrate that this model is going to continue to work for the future and be ready when the next new kind of digital transmedia thing comes along and embrace that, as opposed to be scared to death of it or not even know how to talk the language of the people on it. >> Well, you're doing some amazing venturing in your, kind of, unique venture capital model on Bullpen Capital. Certainly isn't your classic venture capital thing, so I'm sure people are going to be talking to you about oh, Paul, are all VCs going to be doing movies? I'm sure that's a narrative that's out there. But you're not just a normal venture capital. You certainly invest. So, venture capitals have reputation issues right now. People talk about, well, you know, they're group think. You know, they only invest in who they see themselves. You mentioned that comment there. The world's changing in venture. Your thoughts on that, how you guys started your firm, and your evolution of venture capital. And is this a sign that you'll see venture capitalists go into movies? >> Well, I don't know about that part. There have been a couple venture people who have done movies. But the part I will talk about is the you got to know somebody, it's an inside game, ha ha, we'll play double entendre on Inside Game here. You know, 20% of the deal we've done at Bullpen, we've done over 100. 20% of them were cold emails on something like LinkedIn or business plans at bullpen.com. 20%, now there's this old trope in venture if you don't get a warm intro I won't even talk to you. Well 20% of our deals came in and we had no idea who the person on the other side was. That's how we run the firm. And so if you're out there going I'm one of those entrepreneurs in the Midwest and no one, I don't know anyone. I'm not in a network, send me a plan. I'm someone who's going to look at it. It doesn't mean I'm going to be an investor, but you know what I'm going to do? I'm going to give you a shot. And I don't care where you're from or what school you went to or what social clique you're in or what your political persuasion is. Matter of fact, I literally don't care. I'm going to give you a shot. Come into my office and that, I think, is what was missing in a lot of firms, where it's a we only do security and we only look at companies that spun out of Berkeley and Stanford. And yeah, there can be an old boys network in that. But you know what, we like to talk to everybody. And the more blue collar the CEO is, the more we love them at Bullpen. >> That's awesome. Talk about the movie real quick on terms of how Hollywood's handling it. Um, expectations, in terms of reaction, was it positive, is it positive, what's the vibe going on in Hollywood, is this going to be a grassroots kind of thing around the FanDuels and your channels? What's your plan for that and what's the reaction of Hollywood? >> So it's going to be a lot of all of the above. But PR is going to be a huge component, I mean, part of the reason we're on today is there's a huge front page story on ESPN about Tim Donaghy and the NBA betting scandal of 2007. And so the earned media is going to be a huge component of this. And I think this is where the Hollywood people do understand the language we're speaking. We're like, look, we have a huge built-in audience that we know how to market to. We have a story. Actually, in the early days, you asked about risk? Back when I was thinking about if I would do this project I would do the following little market research. I'd walk into a sports bar, it didn't matter what town I was in. I could be in Dallas, I could be in Houston, I could be in Boston. I would literally walk up to the bar and say, hey, uh, six of you at the bar, ever hear of Tim Donaghy? It'd be amazing. About seven out of 10 people would go yeah he was the referee, crooked referee in the NBA. I'm like, this is amazing. Seven out of 10 people I meet in a bar know about the story I want to go tell. That sounds like a good chance to make a movie, as opposed to a movie that has no built-in audience. And so, a built-in audience with PR channels that we know work, I think we can really show Hollywood how to do this in a different way if this all works. >> And this comes back to my point around built-in audiences. You know, YouTube has got a million subscribers. That's kind of an old metric. That means they, like an RSS feed kind of model. That's a million people that are, could be, amplifying their network connections. It is a massive built-in audience. The iteration, the DevOps kind of mindset, we talk about cloud computing, can be applied to movies. It's agile movie making. That's what you're talking about. >> Yeah, and by the way, so we have a social network of all the actors and people in the film. So when it's ready, let's go activate our network of all the actors that are in the film. Each of them have a couple million followers. So let's go be smart. Let's, two weeks before the movie, let's send some screenshots. A week before the movie let's show some exclusive videos. Two days before the film, go see it, it's now out in the theaters. You know what, that's pretty, that's 101. We've got actors. We've got producers. Like, let's go use the influencer network we built that actually got the movie made. Let's go on Sports Talk, talk about the movie. Let's go on places like this and talk about how a venture guy made a movie. This is the confluence of all of the pieces all coming together at once. And I just don't think enough people in the film business or in the media business think big enough about going after these audiences. It's oh, we're going to take ads out on TV and I'm going to see my trailer and we're going to do this and that's how we do it. There's so many better ways to get your audience now. >> And this is going to change, just while I've got you here, it's just awesome, awesome conversation. Bringing it back to kind of the CMO in big companies, whether it's consumer or B to B or whatever, movies, the old model of here's our channels. There's certainly this earned media kind of formula and it's not your classic we've got a website, we're going to do all this instrumentation, it's a whole 'nother mechanism. So talk about, in your opinion, the importance of earned media, vis a vis the old other buckets. Owned media, paid media, well-defined Web 1.0, Web 2.0 tactics, earned media is not just how good is our PR? It's actually infrastructure channels, it's networks, a new kind of way to do things. How relevant and how important will this be going forward? Because there's no more website. It's a, you're basically building a media company for this movie. >> That is exactly right. We're building an ad hoc media business. I think this is what the next generation of digital agencies are going to look like. And there are some agencies that we've talked to that really understand all of what you've just said. They are few and far between, unfortunately. >> Yeah, well, Paul, this was theCube. We love talking to people, making it happen. Again, our model's the same as yours. We're open to anyone who's got signal, and you certainly are doing a great job and great to know you and follow your entrepreneur journey, your investment journey, and now your film making journey. Paul Martino, General Pen on Bullpen Capital, with the hot film Inside Game. I'm definitely going to see it. It should be really strong and it's going to be one of those movies like Crazy, Rich Asians, where not looking, not really well produced, I mean not predicted to be great and then goes game buster so I think this is going to be one of those examples. Paul, thanks for coming on. >> Love it, thank you! >> This Cube Conversation, I'm John Furrier here in Palo Alto, California, bringing ya all the action. Venture capitalist turned film maker Paul Martino with the movie Inside Game. I'm John Furrier, thanks for watching. (triumphant music)

(upbeat electronic music) >> Narrator: Live from Nassau in the Bahamas, it's theCUBE! Covering Polygon '18, brought to you by Polyman. >> Welcome back everyone, we're live here at theCUBE in the Bahamas, this is the live coverage in the Bahamas for Polycon '18, I'm John Furrier, this is a wrap up of our day two. We're going to do show wrap up, brought in special analyst guest, Dave Vellante, they had to jump on a plane, head back to Boston, get out before the snow storm, to head to California. Al Burgio and I are going to wrap it up. Al, serial entrepreneur, founder of FuseChain, and CEO of FuseChain and DigitalBits, an open source project, had you on yesterday, we also were out scouring last night and getting all the data. You were the only Cube alumni at this event, now we add in another 20, good success, good to add more, thought leaders into the family, with Polycon, but big story here is the security token. I mean, I was talking to the founder of Polymath, and Genevieve with Grit Capital, and just my take is, looking at the ecosystem, it's been a sigh of relief on one hand, oh my god, finally, documents we understand accredited investors, no scams, a feel for a good, solid foundation to get funding, no rush to do a utility token, because although utility is super important, people were using utility tokens to get funding, using that money and running as fast as they can to build a product, sub-optimized kind of role there, so again, big news there. >> No, absolutely, it's been, it's the natural evolution and companies like Polymath and Secure Ties and others are helping with this natural progression and birth of the security token. There's clearly a lot of people here interested in that, lot of action, lot of new announcements at the event as well. >> John: What jumped out at you for news announcements? >> The news, I guess. >> John: Ecosystem news is big. >> If we go with the latest today, announcement with Barbados Stock Exchange, folks at Polymath, it's interesting. These emerging markets embracing new technology, it's the next wave and a lot of capital is going to be raised this way. >> What did you learn last night, I mean, first of all this event just for the folks watching, was a real interesting event, it was a 400 plus attendees, really an industry conference about, what the thought was, you had whales, billion dollars of whales here, called whales, which they have a net worth in billions and millions, hundreds of millions, then you have investors, variety of investor types and then entrepreneurs, all coming together. I heard a lot of different things last night, what did you hear? >> You know, it's interesting, I mean a lot of people were sharing their perspectives. Some are presenting different perspectives of the future, (laughing) >> Come on, spit it out! >> Others are, you know, really, in some cases, stating the obvious. But there's definitely a strong ecosystem that's coming together here, strong alignment on a number of things, irrespective of where everybody's sort of come from or the industry that they're in. A lot of people want to see this new ASA class, come and grow and be very successful. So, you had YouTuber influencers here, you had CEOs of well-established organizations, and up-and-coming CEOs of a lot of these blockchain emerging companies. There's definitely tremendous synergy amongst some of them as well, in terms of how they're sharing perspective, and how they're, in some cases, working together. >> Liquidity has been a big option, I heard people talk about liquidity. What's your take on that? What's your observation of how that's evolving? >> Well, I think there's a huge opportunity with areas where traditionally, they've lacked liquidity. Or there's been minimal liquidity, tremendous friction and challenges in terms of being able to leverage what one possesses. Blockchain really presents a huge opportunity to change the game there, as it relates to DigitalBits and what we're focused on, we see a huge opportunity in all things loyalty rewards. There's in a lot of cases, these centralized organizations, you can kind of think of them like a central bank, and people have had these difficulties in earning points, if it's a pair of golf clubs you want, you maybe have to earn points for maybe three years and you get tired after a year. >> That's your venture. >> Yeah. >> I mean FuseChain and DigitalBits specifically is solving a big problem. >> Big problem, there's tremendous lack of liquidity in all things loyalty rewards. >> What's your angle of attack there? Obviously disrupting the pre-existing and somewhat fragmented loyalty programs. I mean, I'm in so many, I don't even use the airlines things anymore. I get so many points, I never use them, I try to use the good ones that I use a lot, like Southwest or whatever, as an example, I use because my kids need to fly to an event or soccer or whatever. But other ones, I've lost all my points. I don't even know the number. I mean, where the hell is it? >> Well it's. >> What email address did I use? >> It's about perceived value, right, maybe you started off with some degree of enthusiasm and had a higher perceived value, but then towards the end it goes to nil. 'Cause it's really. >> John: But I can't get (mumbles) with my points. This is the problem I want to ask you. >> Traditionally, what you see now, a few weeks ago we saw announcement by Singapore Airlines, announcing by August their existing loyalty programs and we place them into a blockchain. We're seeing examples of this almost every week now, companies are embracing blockchain technology and what this allows for now is a more frictionless transfer of points. So, for those companies that are embracing blockchain technology, if you have points, and yeah you could potentially, after you have X number of points, go and redeem them for something you like, but in the meantime, you get discouraged, maybe you love Southwest, but maybe some of these other programs, you could trade them and hand them over to someone that actually could take advantage of it and get an alternative asset that you have a higher perceived value for. >> Digital currencies and gaming has been around for a while. We've seen the young guns get that, that's like a fish to water. Obviously loyalty has different assets than old school techniques, old stacks, technology, if that. So anyway, I ask you the question, how is blockchain disrupting the loyalty program that is the massive billions of dollars being spent and earned in that market? >> A third of points never get redeemed. There's a huge problem with many corporations, they have, as they're issuing points, it's a liability on their balance sheet. More points get issued, it's a hemorrhaging issue. It could potentially create solvency issues for companies. There's actually been professors from some reputable organizations that have really done a tremendous research in this area, it really evolves nicely into what blockchain can do. >> Like, give me an example, I mean what is the disruptive nature of it? Is it storing of the value? Is it trading on that value? Is it, I mean what is the real one thing that blockchain does to the loyalty program? >> The fact that it allows for a more frictionless transfer of points, so for the programs that are tokenizing their points on a block chain, it empowers the user to be able to directly transfer those points. >> So you guys of FuseChain and DigitalBits, you're tokenizing loyalty. >> We're supporting organizations, our big mission is to support organizations that have either existing loyalty programs or wishing to create new loyalty programs to be able to tokenize those on chain, and the ability to then allow the consumers, the users of these points programs, to, in addition to the traditional uses, redeeming them perhaps in a rewards store or what have you, the ability to transfer them for other assets that they like. >> John: So if I understand this correctly. >> Other points that they like. >> The trend that you like, or would like to see continue or happen, is retailers or loyalty programs would tokenize themselves. So, there'd be, literally, thousands and thousands of loyalty tokens and you would be the platform to support that? >> That's correct, absolutely. So, I've used the sort of red hat analogy, we have FuseChain as well that's really focused on helping support enterprises that maybe are struggling to spell blockchain. But they see all the value. >> That's everybody. >> Well from a technology perspective. Similar to Linux being born, enterprises needed to go to companies like a red hat, to support them with the integration, maintenance, so on and so forth of such technology. We're focused on having an evolving ecosystem of other organizations that can support enterprises that have loyalty programs, consume blockchain technology. >> You're a tech entrepreneur, I'm a tech entrepreneur. I have a media business, you're building another business, you sold your last business, you're very successful. You and I always talk about this, but I want to ask you here live on theCUBE, as a tech entreprenur, what is the opportunity that this ecosystem of tokenizing your business, using blockchain, how do you look at it and how would a solid tech entrepreneur look at this opportunity to integrate it, a new enabling technology, what's the orientation, what's your view on how tech entrepreneurs should look at it, and how do you look at it? >> Well, so, if we just, as it relates to the liquidity issue, this is a very powerful thing. Right now, perceived value for many points programs is very low. So, if the perceived value, you solve the liquidity issue or you create technology that can help solve the liquidity issue, the opportunity for the perceived value to be perceived in a more optimal light, everybody kind of wins. The merchant, the business that is issuing these points, they now have a more desirable asset that they're issuing, and as a result of that, consumers have an ever-growing desire to want to be part of these programs and earn points. So this is, it's fascinating when you start to think of it, in terms of. >> Technology is applying, 'cause it's the application of societal impact, whether it's a retailer or a non-profit, tokenization is happening. >> Absolutely, and it's happening obviously, not just in loyalty rewards, we've seen it happen, starting to happen now in other spaces, and with different. >> John: Your big takeaway, obviously. >> ASA classes. >> You've done a lot of work, and I know you can't talk about it 'cause you're in start-up mode and you're doing some financing right now, but just generally speaking, and I'm totally, the landscape of this ecosystem, health-wise, feels like the security token has been a good thing, utility token is still evolving, under observation, obviously SEC and other regulatory challenges, good, bad, ugly, I mean still scams out there? We're hearing the community loud and clear, we're going to stamp out the scams and flush that through the system, as fast as possible. Your take on this ecosystem? >> I think those that are taking their time to build great technology and doing it at the right pace will build great products and ideally do it at such a rate and in such an order that they'll stay out of trouble. (laughs) We're seeing a lot of great entrepreneurs come together, surround themselves with their own ecosystems and building great platforms. I think where we see others that are moving a little too quickly, they might trip on their shoelaces. >> Yeah and people don't, I mean the general consensus is "You're going to move fast, but you don't want to be in jail." Literally, I heard that quote here on theCUBE. (laughs) Investors we've been meeting, we've had on theCUBE but also we've chatted, I know I've seen you chatting, sidebars, I've had a lot of sidebars, Dave has as well, conversation among investors, not necessarily with you, I know you can't talk about it, 'cause that's, it's a hot deal, but I mean, in general, generally speaking, what's the conversations in the investor landscape that you're seeing and hearing here? >> Its interesting, everyone is trying to find their own point of view or speculating in terms of what's going to happen next. I've heard comments in terms of arbitrage as a result of income tax, people realizing that transferring between alt coins is actually likely taxable, and accountants making new investors in the space aware of these things, and having to potentially sell to be able to pay that bill. Then there's others where a lot of us are seeing this as an emerging technology, the actual use of certain, let's say, utility coins, it has not yet been demonstrated. That doesn't necessarily suggest that a particular project is bad, things do take time, I mean, we saw in the 90's with the internet, I mean, remember starting in that space, I call it the dial-up modem era, (laughs) You know, but we had these big visions of video, and theCUBE could not be possible at that time. But the vision of a Cube could be, you know, a wonderful thing, people could've bought into that. You kind of ride the trend, evolve your technology, and then you disrupt and you help change the game. >> Final question, obviously your business is, you're doing some things here, how did the show go for you here? You feel good about it? >> Absolutely. Obviously this is not like an Amazon, some of the other events we've been at but. >> It's more intimate. >> But. >> John: But there's money here, there's billionaires here. >> Absolutely, and look at any of those type of events, I mean they start with thousands, and tens of thousands, and the next year it's twenty thousand, we're going to see that kind of growth in this space as well. It's great to be involved in it early, but there's definitely quality, high-profiled individuals here, high net worth individuals, and they're investing their money in this space and they're going to help drive it forward. >> I remember the first show we did with Amazon and meeting Andy Jassy for the first time, first of all, really like him a lot, sports fan like me, but he's also really smart, a great operator, he made a comment that some of the best companies are ones that are misunderstood in the beginning, obviously we run a different kind of media business, people don't really understand us, cryptocurrency and blockchain is funny because everyone understands it, but doesn't understand it. (laughing) They understand how big it's going to be, and there's money involved, so that's the key learning that I had this week, was, yeah, we see the big opportunity, we can see money being made, but people still don't truly understand what it is. If you talk to all the smartest people, whether it's Jeremy, that came on at 26 years old, to Bill Tie, they say, "We're learning, everyday." The women in tech, the CryptoChicks came on and said, "This is learning environment, "this is still not understood." >> Absolutely. >> "And this is the big opportunity." >> It is a huge opportunity. In the early 90's, people didn't understand the internet, and there's a classic program episode of The Today Show, and I think it was Bryant Gumbel trying to understand what is the internet, you know, and so forth. Fast forward, here we are. Fascinating things, there's smart individuals that can see and embrace the vision right away, others were scratching their head but eventually, we'll all get there. (laughs) >> Al, great to see you and great to see a Cube alumni here too, I'm glad you were here, 'cause I get to know at least one person that I know intimately of Cube alumni. We added 20 more new Cube alumnis, the sun is setting here in theCUBE, day two of wall-to-wall coverage, I'm John Furrier, really excited to have been part of this event, it begins, kicks off our 2018 cryptocurrency tokenizing the world, blockchain, top events, theCUBE will be there, theCUBE is there, it's relevant, we're going to be tracking all the signal, and extracting it from the noise and sharing it with you. It's a wrap up of the cryptocurrency token economics decentralized internet at Polycon 18, here in the Bahamas, thanks for watching. I want to thank all the crew here, great job, and you guys watching. More to come! Stay tuned, check out siliconangle.com, thecube.net, and wikibon.com, of course, CubeCoin coming soon, stay tuned for what we're doing love to tokenize that business, everyone's doing it, it's really relevant and thanks for watching. (upbeat electronic music)

(techno music) >> Announcer: It's the Cube, covering Sapphire Now 2017, brought to you by SAP Cloud Platform, and HANA Enterprise Cloud. >> Hey, welcome back to our exclusive SAP coverage here in our studio in Palo Alto, our 4,500 square foot studio. I'm John Furrier. Our three days, we're on third day, of Sapphire Now 2017 coverage. I'm on the phone with RJ Bibby, who's the SAP Global Alliance Manager for SAP. Handles the relationship. RJ, great to have you on the phone and thanks for calling in from Orlando, really appreciate it. >> RJ: You bet, John. Love the Cube. Love SiliconANGLE. We're great partners. It's been a great week and looking forward to talking to you about it. >> Tell us what's going on on the ground. First, give us the updates on day three. So, pretty much everyone's coming-- And always a great activities at night as well. So, SAP, a lot of business done during the day. They work hard. They play hard. But, day three, what's it like? What's settling in as the storylines for Sapphire 2017? >> RJ: Yeah, absolutely. So, you're starting to feel-- You've gone through about-- We're in our third tour. For the partner's community, we're in day four, cause we had the partner day. Last night was the big partner night. We actually NetApped with our partners with Cisco and KPIT did a private event at Universal Studios at the Jimmy Fallon Theme Park that was highly successful. What was great about today, was in the morning, we kicked off will Bill McDermott on stage with Kobe Bryant and Derek Jeter. And it was all about leadership and mentorship and experience in being in the business, whatever industry that you're in for so long and how you just stay creative, hungry, and passionate. And it was packed. One of the comments was they couldn't believe, on the day after the big party night of all the partners that you still have a lot of energy on the floor. Ultimately, it's still about data, which is great for our business that we can get into at NetApp. There's a lot of buzzword bingo going on here, John, all week, whether it's machine to machine, blocked chain, Cloud-- And at the end of it, it's still our customers who we've talked to a lot this week, and wow. What are we going to do with out data? How do we analyze it? And how do we improve that user experience based on all this data that we have? And I think that's one of the things that I see on the floor that's almost overwhelming with the amount of people, 30,000, all the partners. Just a lot of information. And lastly, I'll say, the good news with that is everybody is hungry for content. Whether it's a mini-theater, whether it's at one of the booths, interactions one-on-one, it's people are hungry for what is happening in the industry. And I think that's exciting for all of us. >> Well, we do our part and try and get as much coverage as possible, even if we are going to do it from Palo Alto. Question for you on NetApp. I mean, you guys have been-- The scuttlebutt in Silicon Valley is that NetApp is doing very well with the Hyperscale (mumbles). I know for a fact. I've interviewed the former CEO and others within NetApp. They were really on early with AWS. And obviously, AWS a big part of the announcement at Sapphire. So, you guys are kind of like getting these relationships with these key players. It's changed a little bit of the business model, or culture within NetApp. What's different about NetApp right now? With resect to some of the big players that you've had relationships with. It's not this new relationship with SAP. You guys have a deep relationship. What's changing as the CloudWave hits, as the DataWave hits? Those are the biggest waves hitting the world right now. How are you guys playing in that world? And share some insight there. >> RJ: Absolutely. Great question. 'Cause the world is going through digital transformation and so is NetApp. So, we are actually celebrating our 25th year as a company right now and we've been a traditional, global technology and data management company. And, the digital shift to Hybrid Cloud is where we're moving. So, specifically with partners like AWS, Microsoft and Azure, the Hyperscalers like CenturyLink, it's how we can help our customers really collect, transport, analyze, protect data, in whatever environment they want to hold their data. Whether it's On-Premises, if your in a Cloud, you can choose whatever Hyperscaler you want. You still have to deal with the data. And then, how do we manage it? How do we consume it? Where is dead data that needs to be taken out? So, data's the currency and with our data fabric methodology and tools from software, hardware, we're really able to help manage that complete life cycle, whether it's SAP, or any other type of environment we hold. So, the exciting thing for us, and the stock prices is showing that at an all time high, is what Bill McDermott said on Monday, in the keynote, or excuse me, Tuesday, "Data is the currency. "Our new mission statement is we're trying "to empower our customers to change the world with data." So, back to the buzzWord bingo comment I made earlier, we're still dealing with fact that we have all these great technologies: all these censors, machine-to-machine, On-Print to Cloud. At the heart of everything is the data and what you do with it. And I think that one of the things that NetApp does and the best in the world of, is we continually evolve digital transformations with the tools on how we deal with data. So, that's high level. >> How about the data dynamic? >> Data is the fundamental story, in my opinion. Cloud has been around, the Clouderati. We were part of that from the beginning. Now, Cloud is mainstream. Amazon stock prices looking like a hockey stick now, it's going straight up. But, that took years of development, right? I mean, you saw the Cloud formation coming, really, in the mid-2000s and then, really at 2008, -09, -10 was the foundational years and then the rest is history. Data's now going through the same thing. As people get over themselves and say, "Okay, big data's not a dupe. It's everything." IOT is certainly highlighting a lot of that. SAP has recognized that legacy systems have to move to a MultiCloud and certainly multi-vendor world in a whole new way. But, at the end of the day, you still got to store this stuff. So, that's your business. How are you keeping up with the moving train of data as is architecturally shifts in the marketplace? >> RJ: Great question. I think that we have some of the best minds in Silicon Valley. Again, been there 25 years. I think with the deep relationships we have with companies like SAP. On the front end, I think the one thing that we bring as a value to SAP is the consumption model, life exists. Through owning the data and the user experience, we're able to enable and accelerate the license consumption to the edge. Right from application in to the system. From an architectural standpoint, it still comes down to the thing that we are creating and blabs and launching around, like the data fabric, the tool system, really software. The software that can help from an analytical perspective affect the user experience. Everybody wants it live. And the other part is the data protection and the DR aspect of it. And I think that's another core competency that we're continuing to develop as a service for the customer. So, I hop I've answered your question. >> Yup. >> RJ: But if-- >> (mumbles) a bottom line then, why NetApps? Say I'm a customer. Okay, I get the SAP. Why should I go with you guys over new the Delium see powerhouse over there, or the White-Box Storage? >> RJ: At the end of the day, we are best at capitalizing the value of data in the Hybrid Cloud. Nobody can help collect, analyze, test, and do life-cycle management live like NetApp can. And that's the reason that we are going more upstream, selling like we say at EPC, always selling to the CXO. I think we're changing the landscape from a true storage company on the infrastructure side to a full end-to-end Hybrid Cloud data management portfolio company. And it's been proven by the acquisition of Salazar from bringing Slash in to the portfolio, our cloning, and snapshot capabilities. So, anywhere in the stack at any time during the day when you're looking live at your operations or your data that you can take live snapshots. Just so if there's a glitch from a data protection side, or there's some type of spike from a request on the ticketing side or demand side of your system. So, I think that's some of the things that we're differentiating. And that's the reason that the AWSs and the Azures and the SAPs are so excited about co-innovating together to again, improving the customer experience with their data. >> RJ, final question. What's the net-net? What's the bumper sticker for you this year at Sapphire 2017? What's the walk-away revelation? >> RJ: Well, I think from the SAP side, it's the revelation on the push of Leonardo. I think that SAP-- I'd like to see them continue to hone out the 'what' and the 'if' from partners with Leonardo from blotching in machine-to-machine and IOT. For us, it is the beautiful fact that now at the center of everything that SAP and the ecosystem is trying to do is around the data side of it and it's the actual currency. And the fact that we have kind of the leading-edge tools to enhance the customer experience with our platform for customers' and partners' data is really, really exciting for us. And we're excited. We're all psyched to be partnered with the Cube. And everything we do is in the Cloud. So, I'm here to help. >> Alright. >> RJ, thanks so much for takin' the time callin' in from Orlando. RJ Bibby, SAP Global Alliance Executive with NetApp. He runs the the relationship with NetApp. And again, it's been a long-term relationship. I remember takin' photos on my phone, way back in the day, years ago. So, not a new relationship and continued momentum. Congratulations and thanks for sharing the insight from Orlando. 'Preciate it. >> RJ: You bet. Thanks for the partnership. Have a great day. >> 'Kay, more coverage from the Cube in Palo Alto on SAP, Sapphire 2017 after this short break. Stay with us. (techno music)

>> Welcome to the Intel AI Lounge. Today, we're very excited to share with you the Precision Medicine panel discussion. I'll be moderating the session. My name is Kay Erin. I'm the general manager of Health and Life Sciences at Intel. And I'm excited to share with you these three panelists that we have here. First is John Madison. He is a chief information medical officer and he is part of Kaiser Permanente. We're very excited to have you here. Thank you, John. >> Thank you. >> We also have Naveen Rao. He is the VP and general manager for the Artificial Intelligence Solutions at Intel. He's also the former CEO of Nervana, which was acquired by Intel. And we also have Bob Rogers, who's the chief data scientist at our AI solutions group. So, why don't we get started with our questions. I'm going to ask each of the panelists to talk, introduce themselves, as well as talk about how they got started with AI. So why don't we start with John? >> Sure, so can you hear me okay in the back? Can you hear? Okay, cool. So, I am a recovering evolutionary biologist and a recovering physician and a recovering geek. And I implemented the health record system for the first and largest region of Kaiser Permanente. And it's pretty obvious that most of the useful data in a health record, in lies in free text. So I started up a natural language processing team to be able to mine free text about a dozen years ago. So we can do things with that that you can't otherwise get out of health information. I'll give you an example. I read an article online from the New England Journal of Medicine about four years ago that said over half of all people who have had their spleen taken out were not properly vaccinated for a common form of pneumonia, and when your spleen's missing, you must have that vaccine or you die a very sudden death with sepsis. In fact, our medical director in Northern California's father died of that exact same scenario. So, when I read the article, I went to my structured data analytics team and to my natural language processing team and said please show me everybody who has had their spleen taken out and hasn't been appropriately vaccinated and we ran through about 20 million records in about three hours with the NLP team, and it took about three weeks with a structured data analytics team. That sounds counterintuitive but it actually happened that way. And it's not a competition for time only. It's a competition for quality and sensitivity and specificity. So we were able to indentify all of our members who had their spleen taken out, who should've had a pneumococcal vaccine. We vaccinated them and there are a number of people alive today who otherwise would've died absent that capability. So people don't really commonly associate natural language processing with machine learning, but in fact, natural language processing relies heavily and is the first really, highly successful example of machine learning. So we've done dozens of similar projects, mining free text data in millions of records very efficiently, very effectively. But it really helped advance the quality of care and reduce the cost of care. It's a natural step forward to go into the world of personalized medicine with the arrival of a 100-dollar genome, which is actually what it costs today to do a full genome sequence. Microbiomics, that is the ecosystem of bacteria that are in every organ of the body actually. And we know now that there is a profound influence of what's in our gut and how we metabolize drugs, what diseases we get. You can tell in a five year old, whether or not they were born by a vaginal delivery or a C-section delivery by virtue of the bacteria in the gut five years later. So if you look at the complexity of the data that exists in the genome, in the microbiome, in the health record with free text and you look at all the other sources of data like this streaming data from my wearable monitor that I'm part of a research study on Precision Medicine out of Stanford, there is a vast amount of disparate data, not to mention all the imaging, that really can collectively produce much more useful information to advance our understanding of science, and to advance our understanding of every individual. And then we can do the mash up of a much broader range of science in health care with a much deeper sense of data from an individual and to do that with structured questions and structured data is very yesterday. The only way we're going to be able to disambiguate those data and be able to operate on those data in concert and generate real useful answers from the broad array of data types and the massive quantity of data, is to let loose machine learning on all of those data substrates. So my team is moving down that pathway and we're very excited about the future prospects for doing that. >> Yeah, great. I think that's actually some of the things I'm very excited about in the future with some of the technologies we're developing. My background, I started actually being fascinated with computation in biological forms when I was nine. Reading and watching sci-fi, I was kind of a big dork which I pretty much still am. I haven't really changed a whole lot. Just basically seeing that machines really aren't all that different from biological entities, right? We are biological machines and kind of understanding how a computer works and how we engineer those things and trying to pull together concepts that learn from biology into that has always been a fascination of mine. As an undergrad, I was in the EE, CS world. Even then, I did some research projects around that. I worked in the industry for about 10 years designing chips, microprocessors, various kinds of ASICs, and then actually went back to school, quit my job, got a Ph.D. in neuroscience, computational neuroscience, to specifically understand what's the state of the art. What do we really understand about the brain? And are there concepts that we can take and bring back? Inspiration's always been we want to... We watch birds fly around. We want to figure out how to make something that flies. We extract those principles, and then build a plane. Don't necessarily want to build a bird. And so Nervana's really was the combination of all those experiences, bringing it together. Trying to push computation in a new a direction. Now, as part of Intel, we can really add a lot of fuel to that fire. I'm super excited to be part of Intel in that the technologies that we were developing can really proliferate and be applied to health care, can be applied to Internet, can be applied to every facet of our lives. And some of the examples that John mentioned are extremely exciting right now and these are things we can do today. And the generality of these solutions are just really going to hit every part of health care. I mean from a personal viewpoint, my whole family are MDs. I'm sort of the black sheep of the family. I don't have an MD. And it's always been kind of funny to me that knowledge is concentrated in a few individuals. Like you have a rare tumor or something like that, you need the guy who knows how to read this MRI. Why? Why is it like that? Can't we encapsulate that knowledge into a computer or into an algorithm, and democratize it. And the reason we couldn't do it is we just didn't know how. And now we're really getting to a point where we know how to do that. And so I want that capability to go to everybody. It'll bring the cost of healthcare down. It'll make all of us healthier. That affects everything about our society. So that's really what's exciting about it to me. >> That's great. So, as you heard, I'm Bob Rogers. I'm chief data scientist for analytics and artificial intelligence solutions at Intel. My mission is to put powerful analytics in the hands of every decision maker and when I think about Precision Medicine, decision makers are not just doctors and surgeons and nurses, but they're also case managers and care coordinators and probably most of all, patients. So the mission is really to put powerful analytics and AI capabilities in the hands of everyone in health care. It's a very complex world and we need tools to help us navigate it. So my background, I started with a Ph.D. in physics and I was computer modeling stuff, falling into super massive black holes. And there's a lot of applications for that in the real world. No, I'm kidding. (laughter) >> John: There will be, I'm sure. Yeah, one of these days. Soon as we have time travel. Okay so, I actually, about 1991, I was working on my post doctoral research, and I heard about neural networks, these things that could compute the way the brain computes. And so, I started doing some research on that. I wrote some papers and actually, it was an interesting story. The problem that we solved that got me really excited about neural networks, which have become deep learning, my office mate would come in. He was this young guy who was about to go off to grad school. He'd come in every morning. "I hate my project." Finally, after two weeks, what's your project? What's the problem? It turns out he had to circle these little fuzzy spots on these images from a telescope. So they were looking for the interesting things in a sky survey, and he had to circle them and write down their coordinates all summer. Anyone want to volunteer to do that? No? Yeah, he was very unhappy. So we took the first two weeks of data that he created doing his work by hand, and we trained an artificial neural network to do his summer project and finished it in about eight hours of computing. (crowd laughs) And so he was like yeah, this is amazing. I'm so happy. And we wrote a paper. I was the first author of course, because I was the senior guy at age 24. And he was second author. His first paper ever. He was very, very excited. So we have to fast forward about 20 years. His name popped up on the Internet. And so it caught my attention. He had just won the Nobel Prize in physics. (laughter) So that's where artificial intelligence will get you. (laughter) So thanks Naveen. Fast forwarding, I also developed some time series forecasting capabilities that allowed me to create a hedge fund that I ran for 12 years. After that, I got into health care, which really is the center of my passion. Applying health care to figuring out how to get all the data from all those siloed sources, put it into the cloud in a secure way, and analyze it so you can actually understand those cases that John was just talking about. How do you know that that person had had a splenectomy and that they needed to get that pneumovax? You need to be able to search all the data, so we used AI, natural language processing, machine learning, to do that and then two years ago, I was lucky enough to join Intel and, in the intervening time, people like Naveen actually thawed the AI winter and we're really in a spring of amazing opportunities with AI, not just in health care but everywhere, but of course, the health care applications are incredibly life saving and empowering so, excited to be here on this stage with you guys. >> I just want to cue off of your comment about the role of physics in AI and health care. So the field of microbiomics that I referred to earlier, bacteria in our gut. There's more bacteria in our gut than there are cells in our body. There's 100 times more DNA in that bacteria than there is in the human genome. And we're now discovering a couple hundred species of bacteria a year that have never been identified under a microscope just by their DNA. So it turns out the person who really catapulted the study and the science of microbiomics forward was an astrophysicist who did his Ph.D. in Steven Hawking's lab on the collision of black holes and then subsequently, put the other team in a virtual reality, and he developed the first super computing center and so how did he get an interest in microbiomics? He has the capacity to do high performance computing and the kind of advanced analytics that are required to look at a 100 times the volume of 3.2 billion base pairs of the human genome that are represented in the bacteria in our gut, and that has unleashed the whole science of microbiomics, which is going to really turn a lot of our assumptions of health and health care upside down. >> That's great, I mean, that's really transformational. So a lot of data. So I just wanted to let the audience know that we want to make this an interactive session, so I'll be asking for questions in a little bit, but I will start off with one question so that you can think about it. So I wanted to ask you, it looks like you've been thinking a lot about AI over the years. And I wanted to understand, even though AI's just really starting in health care, what are some of the new trends or the changes that you've seen in the last few years that'll impact how AI's being used going forward? >> So I'll start off. There was a paper published by a guy by the name of Tegmark at Harvard last summer that, for the first time, explained why neural networks are efficient beyond any mathematical model we predict. And the title of the paper's fun. It's called Deep Learning Versus Cheap Learning. So there were two sort of punchlines of the paper. One is is that the reason that mathematics doesn't explain the efficiency of neural networks is because there's a higher order of mathematics called physics. And the physics of the underlying data structures determined how efficient you could mine those data using machine learning tools. Much more so than any mathematical modeling. And so the second thing that was a reel from that paper is that the substrate of the data that you're operating on and the natural physics of those data have inherent levels of complexity that determine whether or not a 12th layer of neural net will get you where you want to go really fast, because when you do the modeling, for those math geeks in the audience, a factorial. So if there's 12 layers, there's 12 factorial permutations of different ways you could sequence the learning through those data. When you have 140 layers of a neural net, it's a much, much, much bigger number of permutations and so you end up being hardware-bound. And so, what Max Tegmark basically said is you can determine whether to do deep learning or cheap learning based upon the underlying physics of the data substrates you're operating on and have a good insight into how to optimize your hardware and software approach to that problem. >> So another way to put that is that neural networks represent the world in the way the world is sort of built. >> Exactly. >> It's kind of hierarchical. It's funny because, sort of in retrospect, like oh yeah, that kind of makes sense. But when you're thinking about it mathematically, we're like well, anything... The way a neural can represent any mathematical function, therfore, it's fully general. And that's the way we used to look at it, right? So now we're saying, well actually decomposing the world into different types of features that are layered upon each other is actually a much more efficient, compact representation of the world, right? I think this is actually, precisely the point of kind of what you're getting at. What's really exciting now is that what we were doing before was sort of building these bespoke solutions for different kinds of data. NLP, natural language processing. There's a whole field, 25 plus years of people devoted to figuring out features, figuring out what structures make sense in this particular context. Those didn't carry over at all to computer vision. Didn't carry over at all to time series analysis. Now, with neural networks, we've seen it at Nervana, and now part of Intel, solving customers' problems. We apply a very similar set of techniques across all these different types of data domains and solve them. All data in the real world seems to be hierarchical. You can decompose it into this hierarchy. And it works really well. Our brains are actually general structures. As a neuroscientist, you can look at different parts of your brain and there are differences. Something that takes in visual information, versus auditory information is slightly different but they're much more similar than they are different. So there is something invariant, something very common between all of these different modalities and we're starting to learn that. And this is extremely exciting to me trying to understand the biological machine that is a computer, right? We're figurig it out, right? >> One of the really fun things that Ray Chrisfall likes to talk about is, and it falls in the genre of biomimmicry, and how we actually replicate biologic evolution in our technical solutions so if you look at, and we're beginning to understand more and more how real neural nets work in our cerebral cortex. And it's sort of a pyramid structure so that the first pass of a broad base of analytics, it gets constrained to the next pass, gets constrained to the next pass, which is how information is processed in the brain. So we're discovering increasingly that what we've been evolving towards, in term of architectures of neural nets, is approximating the architecture of the human cortex and the more we understand the human cortex, the more insight we get to how to optimize neural nets, so when you think about it, with millions of years of evolution of how the cortex is structured, it shouldn't be a surprise that the optimization protocols, if you will, in our genetic code are profoundly efficient in how they operate. So there's a real role for looking at biologic evolutionary solutions, vis a vis technical solutions, and there's a friend of mine who worked with who worked with George Church at Harvard and actually published a book on biomimmicry and they wrote the book completely in DNA so if all of you have your home DNA decoder, you can actually read the book on your DNA reader, just kidding. >> There's actually a start up I just saw in the-- >> Read-Write DNA, yeah. >> Actually it's a... He writes something. What was it? (response from crowd member) Yeah, they're basically encoding information in DNA as a storage medium. (laughter) The company, right? >> Yeah, that same friend of mine who coauthored that biomimmicry book in DNA also did the estimate of the density of information storage. So a cubic centimeter of DNA can store an hexabyte of data. I mean that's mind blowing. >> Naveen: Highly done soon. >> Yeah that's amazing. Also you hit upon a really important point there, that one of the things that's changed is... Well, there are two major things that have changed in my perception from let's say five to 10 years ago, when we were using machine learning. You could use data to train models and make predictions to understand complex phenomena. But they had limited utility and the challenge was that if I'm trying to build on these things, I had to do a lot of work up front. It was called feature engineering. I had to do a lot of work to figure out what are the key attributes of that data? What are the 10 or 20 or 100 pieces of information that I should pull out of the data to feed to the model, and then the model can turn it into a predictive machine. And so, what's really exciting about the new generation of machine learning technology, and particularly deep learning, is that it can actually learn from example data those features without you having to do any preprogramming. That's why Naveen is saying you can take the same sort of overall approach and apply it to a bunch of different problems. Because you're not having to fine tune those features. So at the end of the day, the two things that have changed to really enable this evolution is access to more data, and I'd be curious to hear from you where you're seeing data come from, what are the strategies around that. So access to data, and I'm talking millions of examples. So 10,000 examples most times isn't going to cut it. But millions of examples will do it. And then, the other piece is the computing capability to actually take millions of examples and optimize this algorithm in a single lifetime. I mean, back in '91, when I started, we literally would have thousands of examples and it would take overnight to run the thing. So now in the world of millions, and you're putting together all of these combinations, the computing has changed a lot. I know you've made some revolutionary advances in that. But I'm curious about the data. Where are you seeing interesting sources of data for analytics? >> So I do some work in the genomics space and there are more viable permutations of the human genome than there are people who have ever walked the face of the earth. And the polygenic determination of a phenotypic expression translation, what are genome does to us in our physical experience in health and disease is determined by many, many genes and the interaction of many, many genes and how they are up and down regulated. And the complexity of disambiguating which 27 genes are affecting your diabetes and how are they up and down regulated by different interventions is going to be different than his. It's going to be different than his. And we already know that there's four or five distinct genetic subtypes of type II diabetes. So physicians still think there's one disease called type II diabetes. There's actually at least four or five genetic variants that have been identified. And so, when you start thinking about disambiguating, particularly when we don't know what 95 percent of DNA does still, what actually is the underlining cause, it will require this massive capability of developing these feature vectors, sometimes intuiting it, if you will, from the data itself. And other times, taking what's known knowledge to develop some of those feature vectors, and be able to really understand the interaction of the genome and the microbiome and the phenotypic data. So the complexity is high and because the variation complexity is high, you do need these massive members. Now I'm going to make a very personal pitch here. So forgive me, but if any of you have any role in policy at all, let me tell you what's happening right now. The Genomic Information Nondiscrimination Act, so called GINA, written by a friend of mine, passed a number of years ago, says that no one can be discriminated against for health insurance based upon their genomic information. That's cool. That should allow all of you to feel comfortable donating your DNA to science right? Wrong. You are 100% unprotected from discrimination for life insurance, long term care and disability. And it's being practiced legally today and there's legislation in the House, in mark up right now to completely undermine the existing GINA legislation and say that whenever there's another applicable statute like HIPAA, that the GINA is irrelevant, that none of the fines and penalties are applicable at all. So we need a ton of data to be able to operate on. We will not be getting a ton of data to operate on until we have the kind of protection we need to tell people, you can trust us. You can give us your data, you will not be subject to discrimination. And that is not the case today. And it's being further undermined. So I want to make a plea to any of you that have any policy influence to go after that because we need this data to help the understanding of human health and disease and we're not going to get it when people look behind the curtain and see that discrimination is occurring today based upon genetic information. >> Well, I don't like the idea of being discriminated against based on my DNA. Especially given how little we actually know. There's so much complexity in how these things unfold in our own bodies, that I think anything that's being done is probably childishly immature and oversimplifying. So it's pretty rough. >> I guess the translation here is that we're all unique. It's not just a Disney movie. (laughter) We really are. And I think one of the strengths that I'm seeing, kind of going back to the original point, of these new techniques is it's going across different data types. It will actually allow us to learn more about the uniqueness of the individual. It's not going to be just from one data source. They were collecting data from many different modalities. We're collecting behavioral data from wearables. We're collecting things from scans, from blood tests, from genome, from many different sources. The ability to integrate those into a unified picture, that's the important thing that we're getting toward now. That's what I think is going to be super exciting here. Think about it, right. I can tell you to visual a coin, right? You can visualize a coin. Not only do you visualize it. You also know what it feels like. You know how heavy it is. You have a mental model of that from many different perspectives. And if I take away one of those senses, you can still identify the coin, right? If I tell you to put your hand in your pocket, and pick out a coin, you probably can do that with 100% reliability. And that's because we have this generalized capability to build a model of something in the world. And that's what we need to do for individuals is actually take all these different data sources and come up with a model for an individual and you can actually then say what drug works best on this. What treatment works best on this? It's going to get better with time. It's not going to be perfect, because this is what a doctor does, right? A doctor who's very experienced, you're a practicing physician right? Back me up here. That's what you're doing. You basically have some categories. You're taking information from the patient when you talk with them, and you're building a mental model. And you apply what you know can work on that patient, right? >> I don't have clinic hours anymore, but I do take care of many friends and family. (laughter) >> You used to, you used to. >> I practiced for many years before I became a full-time geek. >> I thought you were a recovering geek. >> I am. (laughter) I do more policy now. >> He's off the wagon. >> I just want to take a moment and see if there's anyone from the audience who would like to ask, oh. Go ahead. >> We've got a mic here, hang on one second. >> I have tons and tons of questions. (crosstalk) Yes, so first of all, the microbiome and the genome are really complex. You already hit about that. Yet most of the studies we do are small scale and we have difficulty repeating them from study to study. How are we going to reconcile all that and what are some of the technical hurdles to get to the vision that you want? >> So primarily, it's been the cost of sequencing. Up until a year ago, it's $1000, true cost. Now it's $100, true cost. And so that barrier is going to enable fairly pervasive testing. It's not a real competitive market becaue there's one sequencer that is way ahead of everybody else. So the price is not $100 yet. The cost is below $100. So as soon as there's competition to drive the cost down, and hopefully, as soon as we all have the protection we need against discrimination, as I mentioned earlier, then we will have large enough sample sizes. And so, it is our expectation that we will be able to pool data from local sources. I chair the e-health work group at the Global Alliance for Genomics and Health which is working on this very issue. And rather than pooling all the data into a single, common repository, the strategy, and we're developing our five-year plan in a month in London, but the goal is to have a federation of essentially credentialed data enclaves. That's a formal method. HHS already does that so you can get credentialed to search all the data that Medicare has on people that's been deidentified according to HIPPA. So we want to provide the same kind of service with appropriate consent, at an international scale. And there's a lot of nations that are talking very much about data nationality so that you can't export data. So this approach of a federated model to get at data from all the countries is important. The other thing is a block-chain technology is going to be very profoundly useful in this context. So David Haussler of UC Santa Cruz is right now working on a protocol using an open block-chain, public ledger, where you can put out. So for any typical cancer, you may have a half dozen, what are called sematic variance. Cancer is a genetic disease so what has mutated to cause it to behave like a cancer? And if we look at those biologically active sematic variants, publish them on a block chain that's public, so there's not enough data there to reidentify the patient. But if I'm a physician treating a woman with breast cancer, rather than say what's the protocol for treating a 50-year-old woman with this cell type of cancer, I can say show me all the people in the world who have had this cancer at the age of 50, wit these exact six sematic variants. Find the 200 people worldwide with that. Ask them for consent through a secondary mechanism to donate everything about their medical record, pool that information of the core of 200 that exactly resembles the one sitting in front of me, and find out, of the 200 ways they were treated, what got the best results. And so, that's the kind of future where a distributed, federated architecture will allow us to query and obtain a very, very relevant cohort, so we can basically be treating patients like mine, sitting right in front of me. Same thing applies for establishing research cohorts. There's some very exciting stuff at the convergence of big data analytics, machine learning, and block chaining. >> And this is an area that I'm really excited about and I think we're excited about generally at Intel. They actually have something called the Collaborative Cancer Cloud, which is this kind of federated model. We have three different academic research centers. Each of them has a very sizable and valuable collection of genomic data with phenotypic annotations. So you know, pancreatic cancer, colon cancer, et cetera, and we've actually built a secure computing architecture that can allow a person who's given the right permissions by those organizations to ask a specific question of specific data without ever sharing the data. So the idea is my data's really important to me. It's valuable. I want us to be able to do a study that gets the number from the 20 pancreatic cancer patients in my cohort, up to the 80 that we have in the whole group. But I can't do that if I'm going to just spill my data all over the world. And there are HIPAA and compliance reasons for that. There are business reasons for that. So what we've built at Intel is this platform that allows you to do different kinds of queries on this genetic data. And reach out to these different sources without sharing it. And then, the work that I'm really involved in right now and that I'm extremely excited about... This also touches on something that both of you said is it's not sufficient to just get the genome sequences. You also have to have the phenotypic data. You have to know what cancer they've had. You have to know that they've been treated with this drug and they've survived for three months or that they had this side effect. That clinical data also needs to be put together. It's owned by other organizations, right? Other hospitals. So the broader generalization of the Collaborative Cancer Cloud is something we call the data exchange. And it's a misnomer in a sense that we're not actually exchanging data. We're doing analytics on aggregated data sets without sharing it. But it really opens up a world where we can have huge populations and big enough amounts of data to actually train these models and draw the thread in. Of course, that really then hits home for the techniques that Nervana is bringing to the table, and of course-- >> Stanford's one of your academic medical centers? >> Not for that Collaborative Cancer Cloud. >> The reason I mentioned Standford is because the reason I'm wearing this FitBit is because I'm a research subject at Mike Snyder's, the chair of genetics at Stanford, IPOP, intrapersonal omics profile. So I was fully sequenced five years ago and I get four full microbiomes. My gut, my mouth, my nose, my ears. Every three months and I've done that for four years now. And about a pint of blood. And so, to your question of the density of data, so a lot of the problem with applying these techniques to health care data is that it's basically a sparse matrix and there's a lot of discontinuities in what you can find and operate on. So what Mike is doing with the IPOP study is much the same as you described. Creating a highly dense longitudinal set of data that will help us mitigate the sparse matrix problem. (low volume response from audience member) Pardon me. >> What's that? (low volume response) (laughter) >> Right, okay. >> John: Lost the school sample. That's got to be a new one I've heard now. >> Okay, well, thank you so much. That was a great question. So I'm going to repeat this and ask if there's another question. You want to go ahead? >> Hi, thanks. So I'm a journalist and I report a lot on these neural networks, a system that's beter at reading mammograms than your human radiologists. Or a system that's better at predicting which patients in the ICU will get sepsis. These sort of fascinating academic studies that I don't really see being translated very quickly into actual hospitals or clinical practice. Seems like a lot of the problems are regulatory, or liability, or human factors, but how do you get past that and really make this stuff practical? >> I think there's a few things that we can do there and I think the proof points of the technology are really important to start with in this specific space. In other places, sometimes, you can start with other things. But here, there's a real confidence problem when it comes to health care, and for good reason. We have doctors trained for many, many years. School and then residencies and other kinds of training. Because we are really, really conservative with health care. So we need to make sure that technology's well beyond just the paper, right? These papers are proof points. They get people interested. They even fuel entire grant cycles sometimes. And that's what we need to happen. It's just an inherent problem, its' going to take a while. To get those things to a point where it's like well, I really do trust what this is saying. And I really think it's okay to now start integrating that into our standard of care. I think that's where you're seeing it. It's frustrating for all of us, believe me. I mean, like I said, I think personally one of the biggest things, I want to have an impact. Like when I go to my grave, is that we used machine learning to improve health care. We really do feel that way. But it's just not something we can do very quickly and as a business person, I don't actually look at those use cases right away because I know the cycle is just going to be longer. >> So to your point, the FDA, for about four years now, has understood that the process that has been given to them by their board of directors, otherwise known as Congress, is broken. And so they've been very actively seeking new models of regulation and what's really forcing their hand is regulation of devices and software because, in many cases, there are black box aspects of that and there's a black box aspect to machine learning. Historically, Intel and others are making inroads into providing some sort of traceability and transparency into what happens in that black box rather than say, overall we get better results but once in a while we kill somebody. Right? So there is progress being made on that front. And there's a concept that I like to use. Everyone knows Ray Kurzweil's book The Singularity Is Near? Well, I like to think that diadarity is near. And the diadarity is where you have human transparency into what goes on in the black box and so maybe Bob, you want to speak a little bit about... You mentioned that, in a prior discussion, that there's some work going on at Intel there. >> Yeah, absolutely. So we're working with a number of groups to really build tools that allow us... In fact Naveen probably can talk in even more detail than I can, but there are tools that allow us to actually interrogate machine learning and deep learning systems to understand, not only how they respond to a wide variety of situations but also where are there biases? I mean, one of the things that's shocking is that if you look at the clinical studies that our drug safety rules are based on, 50 year old white guys are the peak of that distribution, which I don't see any problem with that, but some of you out there might not like that if you're taking a drug. So yeah, we want to understand what are the biases in the data, right? And so, there's some new technologies. There's actually some very interesting data-generative technologies. And this is something I'm also curious what Naveen has to say about, that you can generate from small sets of observed data, much broader sets of varied data that help probe and fill in your training for some of these systems that are very data dependent. So that takes us to a place where we're going to start to see deep learning systems generating data to train other deep learning systems. And they start to sort of go back and forth and you start to have some very nice ways to, at least, expose the weakness of these underlying technologies. >> And that feeds back to your question about regulatory oversight of this. And there's the fascinating, but little known origin of why very few women are in clinical studies. Thalidomide causes birth defects. So rather than say pregnant women can't be enrolled in drug trials, they said any woman who is at risk of getting pregnant cannot be enrolled. So there was actually a scientific meritorious argument back in the day when they really didn't know what was going to happen post-thalidomide. So it turns out that the adverse, unintended consequence of that decision was we don't have data on women and we know in certain drugs, like Xanax, that the metabolism is so much slower, that the typical dosing of Xanax is women should be less than half of that for men. And a lot of women have had very serious adverse effects by virtue of the fact that they weren't studied. So the point I want to illustrate with that is that regulatory cycles... So people have known for a long time that was like a bad way of doing regulations. It should be changed. It's only recently getting changed in any meaningful way. So regulatory cycles and legislative cycles are incredibly slow. The rate of exponential growth in technology is exponential. And so there's impedance mismatch between the cycle time for regulation cycle time for innovation. And what we need to do... I'm working with the FDA. I've done four workshops with them on this very issue. Is that they recognize that they need to completely revitalize their process. They're very interested in doing it. They're not resisting it. People think, oh, they're bad, the FDA, they're resisting. Trust me, there's nobody on the planet who wants to revise these review processes more than the FDA itself. And so they're looking at models and what I recommended is global cloud sourcing and the FDA could shift from a regulatory role to one of doing two things, assuring the people who do their reviews are competent, and assuring that their conflicts of interest are managed, because if you don't have a conflict of interest in this very interconnected space, you probably don't know enough to be a reviewer. So there has to be a way to manage the conflict of interest and I think those are some of the keypoints that the FDA is wrestling with because there's type one and type two errors. If you underregulate, you end up with another thalidomide and people born without fingers. If you overregulate, you prevent life saving drugs from coming to market. So striking that balance across all these different technologies is extraordinarily difficult. If it were easy, the FDA would've done it four years ago. It's very complicated. >> Jumping on that question, so all three of you are in some ways entrepreneurs, right? Within your organization or started companies. And I think it would be good to talk a little bit about the business opportunity here, where there's a huge ecosystem in health care, different segments, biotech, pharma, insurance payers, etc. Where do you see is the ripe opportunity or industry, ready to really take this on and to make AI the competitive advantage. >> Well, the last question also included why aren't you using the result of the sepsis detection? We do. There were six or seven published ways of doing it. We did our own data, looked at it, we found a way that was superior to all the published methods and we apply that today, so we are actually using that technology to change clinical outcomes. As far as where the opportunities are... So it's interesting. Because if you look at what's going to be here in three years, we're not going to be using those big data analytics models for sepsis that we are deploying today, because we're just going to be getting a tiny aliquot of blood, looking for the DNA or RNA of any potential infection and we won't have to infer that there's a bacterial infection from all these other ancillary, secondary phenomenon. We'll see if the DNA's in the blood. So things are changing so fast that the opportunities that people need to look for are what are generalizable and sustainable kind of wins that are going to lead to a revenue cycle that are justified, a venture capital world investing. So there's a lot of interesting opportunities in the space. But I think some of the biggest opportunities relate to what Bob has talked about in bringing many different disparate data sources together and really looking for things that are not comprehensible in the human brain or in traditional analytic models. >> I think we also got to look a little bit beyond direct care. We're talking about policy and how we set up standards, these kinds of things. That's one area. That's going to drive innovation forward. I completely agree with that. Direct care is one piece. How do we scale out many of the knowledge kinds of things that are embedded into one person's head and get them out to the world, democratize that. Then there's also development. The underlying technology's of medicine, right? Pharmaceuticals. The traditional way that pharmaceuticals is developed is actually kind of funny, right? A lot of it was started just by chance. Penicillin, a very famous story right? It's not that different today unfortunately, right? It's conceptually very similar. Now we've got more science behind it. We talk about domains and interactions, these kinds of things but fundamentally, the problem is what we in computer science called NP hard, it's too difficult to model. You can't solve it analytically. And this is true for all these kinds of natural sorts of problems by the way. And so there's a whole field around this, molecular dynamics and modeling these sorts of things, that are actually being driven forward by these AI techniques. Because it turns out, our brain doesn't do magic. It actually doesn't solve these problems. It approximates them very well. And experience allows you to approximate them better and better. Actually, it goes a little bit to what you were saying before. It's like simulations and forming your own networks and training off each other. There are these emerging dynamics. You can simulate steps of physics. And you come up with a system that's much too complicated to ever solve. Three pool balls on a table is one such system. It seems pretty simple. You know how to model that, but it actual turns out you can't predict where a balls going to be once you inject some energy into that table. So something that simple is already too complex. So neural network techniques actually allow us to start making those tractable. These NP hard problems. And things like molecular dynamics and actually understanding how different medications and genetics will interact with each other is something we're seeing today. And so I think there's a huge opportunity there. We've actually worked with customers in this space. And I'm seeing it. Like Rosch is acquiring a few different companies in space. They really want to drive it forward, using big data to drive drug development. It's kind of counterintuitive. I never would've thought it had I not seen it myself. >> And there's a big related challenge. Because in personalized medicine, there's smaller and smaller cohorts of people who will benefit from a drug that still takes two billion dollars on average to develop. That is unsustainable. So there's an economic imperative of overcoming the cost and the cycle time for drug development. >> I want to take a go at this question a little bit differently, thinking about not so much where are the industry segments that can benefit from AI, but what are the kinds of applications that I think are most impactful. So if this is what a skilled surgeon needs to know at a particular time to care properly for a patient, this is where most, this area here, is where most surgeons are. They are close to the maximum knowledge and ability to assimilate as they can be. So it's possible to build complex AI that can pick up on that one little thing and move them up to here. But it's not a gigantic accelerator, amplifier of their capability. But think about other actors in health care. I mentioned a couple of them earlier. Who do you think the least trained actor in health care is? >> John: Patients. >> Yes, the patients. The patients are really very poorly trained, including me. I'm abysmal at figuring out who to call and where to go. >> Naveen: You know as much the doctor right? (laughing) >> Yeah, that's right. >> My doctor friends always hate that. Know your diagnosis, right? >> Yeah, Dr. Google knows. So the opportunities that I see that are really, really exciting are when you take an AI agent, like sometimes I like to call it contextually intelligent agent, or a CIA, and apply it to a problem where a patient has a complex future ahead of them that they need help navigating. And you use the AI to help them work through. Post operative. You've got PT. You've got drugs. You've got to be looking for side effects. An agent can actually help you navigate. It's like your own personal GPS for health care. So it's giving you the inforamation that you need about you for your care. That's my definition of Precision Medicine. And it can include genomics, of course. But it's much bigger. It's that broader picture and I think that a sort of agent way of thinking about things and filling in the gaps where there's less training and more opportunity, is very exciting. >> Great start up idea right there by the way. >> Oh yes, right. We'll meet you all out back for the next start up. >> I had a conversation with the head of the American Association of Medical Specialties just a couple of days ago. And what she was saying, and I'm aware of this phenomenon, but all of the medical specialists are saying, you're killing us with these stupid board recertification trivia tests that you're giving us. So if you're a cardiologist, you have to remember something that happens in one in 10 million people, right? And they're saying that irrelevant anymore, because we've got advanced decision support coming. We have these kinds of analytics coming. Precisely what you're saying. So it's human augmentation of decision support that is coming at blazing speed towards health care. So in that context, it's much more important that you have a basic foundation, you know how to think, you know how to learn, and you know where to look. So we're going to be human-augmented learning systems much more so than in the past. And so the whole recertification process is being revised right now. (inaudible audience member speaking) Speak up, yeah. (person speaking) >> What makes it fathomable is that you can-- (audience member interjects inaudibly) >> Sure. She was saying that our brain is really complex and large and even our brains don't know how our brains work, so... are there ways to-- >> What hope do we have kind of thing? (laughter) >> It's a metaphysical question. >> It circles all the way down, exactly. It's a great quote. I mean basically, you can decompose every system. Every complicated system can be decomposed into simpler, emergent properties. You lose something perhaps with each of those, but you get enough to actually understand most of the behavior. And that's really how we understand the world. And that's what we've learned in the last few years what neural network techniques can allow us to do. And that's why our brain can understand our brain. (laughing) >> Yeah, I'd recommend reading Chris Farley's last book because he addresses that issue in there very elegantly. >> Yeah we're seeing some really interesting technologies emerging right now where neural network systems are actually connecting other neural network systems in networks. You can see some very compelling behavior because one of the things I like to distinguish AI versus traditional analytics is we used to have question-answering systems. I used to query a database and create a report to find out how many widgets I sold. Then I started using regression or machine learning to classify complex situations from this is one of these and that's one of those. And then as we've moved more recently, we've got these AI-like capabilities like being able to recognize that there's a kitty in the photograph. But if you think about it, if I were to show you a photograph that happened to have a cat in it, and I said, what's the answer, you'd look at me like, what are you talking about? I have to know the question. So where we're cresting with these connected sets of neural systems, and with AI in general, is that the systems are starting to be able to, from the context, understand what the question is. Why would I be asking about this picture? I'm a marketing guy, and I'm curious about what Legos are in the thing or what kind of cat it is. So it's being able to ask a question, and then take these question-answering systems, and actually apply them so that's this ability to understand context and ask questions that we're starting to see emerge from these more complex hierarchical neural systems. >> There's a person dying to ask a question. >> Sorry. You have hit on several different topics that all coalesce together. You mentioned personalized models. You mentioned AI agents that could help you as you're going through a transitionary period. You mentioned data sources, especially across long time periods. Who today has access to enough data to make meaningful progress on that, not just when you're dealing with an issue, but day-to-day improvement of your life and your health? >> Go ahead, great question. >> That was a great question. And I don't think we have a good answer to it. (laughter) I'm sure John does. Well, I think every large healthcare organization and various healthcare consortiums are working very hard to achieve that goal. The problem remains in creating semantic interoperatability. So I spent a lot of my career working on semantic interoperatability. And the problem is that if you don't have well-defined, or self-defined data, and if you don't have well-defined and documented metadata, and you start operating on it, it's real easy to reach false conclusions and I can give you a classic example. It's well known, with hundreds of studies looking at when you give an antibiotic before surgery and how effective it is in preventing a post-op infection. Simple question, right? So most of the literature done prosectively was done in institutions where they had small sample sizes. So if you pool that, you get a little bit more noise, but you get a more confirming answer. What was done at a very large, not my own, but a very large institution... I won't name them for obvious reasons, but they pooled lots of data from lots of different hospitals, where the data definitions and the metadata were different. Two examples. When did they indicate the antibiotic was given? Was it when it was ordered, dispensed from the pharmacy, delivered to the floor, brought to the bedside, put in the IV, or the IV starts flowing? Different hospitals used a different metric of when it started. When did surgery occur? When they were wheeled into the OR, when they were prepped and drapped, when the first incision occurred? All different. And they concluded quite dramatically that it didn't matter when you gave the pre-op antibiotic and whether or not you get a post-op infection. And everybody who was intimate with the prior studies just completely ignored and discounted that study. It was wrong. And it was wrong because of the lack of commonality and the normalization of data definitions and metadata definitions. So because of that, this problem is much more challenging than you would think. If it were so easy as to put all these data together and operate on it, normalize and operate on it, we would've done that a long time ago. It's... Semantic interoperatability remains a big problem and we have a lot of heavy lifting ahead of us. I'm working with the Global Alliance, for example, of Genomics and Health. There's like 30 different major ontologies for how you represent genetic information. And different institutions are using different ones in different ways in different versions over different periods of time. That's a mess. >> Our all those issues applicable when you're talking about a personalized data set versus a population? >> Well, so N of 1 studies and single-subject research is an emerging field of statistics. So there's some really interesting new models like step wedge analytics for doing that on small sample sizes, recruiting people asynchronously. There's single-subject research statistics. You compare yourself with yourself at a different point in time, in a different context. So there are emerging statistics to do that and as long as you use the same sensor, you won't have a problem. But people are changing their remote sensors and you're getting different data. It's measured in different ways with different sensors at different normalization and different calibration. So yes. It even persists in the N of 1 environment. >> Yeah, you have to get started with a large N that you can apply to the N of 1. I'm actually going to attack your question from a different perspective. So who has the data? The millions of examples to train a deep learning system from scratch. It's a very limited set right now. Technology such as the Collaborative Cancer Cloud and The Data Exchange are definitely impacting that and creating larger and larger sets of critical mass. And again, not withstanding the very challenging semantic interoperability questions. But there's another opportunity Kay asked about what's changed recently. One of the things that's changed in deep learning is that we now have modules that have been trained on massive data sets that are actually very smart as certain kinds of problems. So, for instance, you can go online and find deep learning systems that actually can recognize, better than humans, whether there's a cat, dog, motorcycle, house, in a photograph. >> From Intel, open source. >> Yes, from Intel, open source. So here's what happens next. Because most of that deep learning system is very expressive. That combinatorial mixture of features that Naveen was talking about, when you have all these layers, there's a lot of features there. They're actually very general to images, not just finding cats, dogs, trees. So what happens is you can do something called transfer learning, where you take a small or modest data set and actually reoptimize it for your specific problem very, very quickly. And so we're starting to see a place where you can... On one end of the spectrum, we're getting access to the computing capabilities and the data to build these incredibly expressive deep learning systems. And over here on the right, we're able to start using those deep learning systems to solve custom versions of problems. Just last weekend or two weekends ago, in 20 minutes, I was able to take one of those general systems and create one that could recognize all different kinds of flowers. Very subtle distinctions, that I would never be able to know on my own. But I happen to be able to get the data set and literally, it took 20 minutes and I have this vision system that I could now use for a specific problem. I think that's incredibly profound and I think we're going to see this spectrum of wherever you are in your ability to get data and to define problems and to put hardware in place to see really neat customizations and a proliferation of applications of this kind of technology. >> So one other trend I think, I'm very hopeful about it... So this is a hard problem clearly, right? I mean, getting data together, formatting it from many different sources, it's one of these things that's probably never going to happen perfectly. But one trend I think that is extremely hopeful to me is the fact that the cost of gathering data has precipitously dropped. Building that thing is almost free these days. I can write software and put it on 100 million cell phones in an instance. You couldn't do that five years ago even right? And so, the amount of information we can gain from a cell phone today has gone up. We have more sensors. We're bringing online more sensors. People have Apple Watches and they're sending blood data back to the phone, so once we can actually start gathering more data and do it cheaper and cheaper, it actually doesn't matter where the data is. I can write my own app. I can gather that data and I can start driving the correct inferences or useful inferences back to you. So that is a positive trend I think here and personally, I think that's how we're going to solve it, is by gathering from that many different sources cheaply. >> Hi, my name is Pete. I've very much enjoyed the conversation so far but I was hoping perhaps to bring a little bit more focus into Precision Medicine and ask two questions. Number one, how have you applied the AI technologies as you're emerging so rapidly to your natural language processing? I'm particularly interested in, if you look at things like Amazon Echo or Siri, or the other voice recognition systems that are based on AI, they've just become incredibly accurate and I'm interested in specifics about how I might use technology like that in medicine. So where would I find a medical nomenclature and perhaps some reference to a back end that works that way? And the second thing is, what specifically is Intel doing, or making available? You mentioned some open source stuff on cats and dogs and stuff but I'm the doc, so I'm looking at the medical side of that. What are you guys providing that would allow us who are kind of geeks on the software side, as well as being docs, to experiment a little bit more thoroughly with AI technology? Google has a free AI toolkit. Several other people have come out with free AI toolkits in order to accelerate that. There's special hardware now with graphics, and different processors, hitting amazing speeds. And so I was wondering, where do I go in Intel to find some of those tools and perhaps learn a bit about the fantastic work that you guys are already doing at Kaiser? >> Let me take that first part and then we'll be able to talk about the MD part. So in terms of technology, this is what's extremely exciting now about what Intel is focusing on. We're providing those pieces. So you can actually assemble and build the application. How you build that application specific for MDs and the use cases is up to you or the one who's filling out the application. But we're going to power that technology for multiple perspectives. So Intel is already the main force behind The Data Center, right? Cloud computing, all this is already Intel. We're making that extremely amenable to AI and setting the standard for AI in the future, so we can do that from a number of different mechanisms. For somebody who wants to develop an application quickly, we have hosted solutions. Intel Nervana is kind of the brand for these kinds of things. Hosted solutions will get you going very quickly. Once you get to a certain level of scale, where costs start making more sense, things can be bought on premise. We're supplying that. We're also supplying software that makes that transition essentially free. Then taking those solutions that you develop in the cloud, or develop in The Data Center, and actually deploying them on device. You want to write something on your smartphone or PC or whatever. We're actually providing those hooks as well, so we want to make it very easy for developers to take these pieces and actually build solutions out of them quickly so you probably don't even care what hardware it's running on. You're like here's my data set, this is what I want to do. Train it, make it work. Go fast. Make my developers efficient. That's all you care about, right? And that's what we're doing. We're taking it from that point at how do we best do that? We're going to provide those technologies. In the next couple of years, there's going to be a lot of new stuff coming from Intel. >> Do you want to talk about AI Academy as well? >> Yeah, that's a great segway there. In addition to this, we have an entire set of tutorials and other online resources and things we're going to be bringing into the academic world for people to get going quickly. So that's not just enabling them on our tools, but also just general concepts. What is a neural network? How does it work? How does it train? All of these things are available now and we've made a nice, digestible class format that you can actually go and play with. >> Let me give a couple of quick answers in addition to the great answers already. So you're asking why can't we use medical terminology and do what Alexa does? Well, no, you may not be aware of this, but Andrew Ian, who was the AI guy at Google, who was recruited by Google, they have a medical chat bot in China today. I don't speak Chinese. I haven't been able to use it yet. There are two similar initiatives in this country that I know of. There's probably a dozen more in stealth mode. But Lumiata and Health Cap are doing chat bots for health care today, using medical terminology. You have the compound problem of semantic normalization within language, compounded by a cross language. I've done a lot of work with an international organization called Snowmed, which translates medical terminology. So you're aware of that. We can talk offline if you want, because I'm pretty deep into the semantic space. >> Go google Intel Nervana and you'll see all the websites there. It's intel.com/ai or nervanasys.com. >> Okay, great. Well this has been fantastic. I want to, first of all, thank all the people here for coming and asking great questions. I also want to thank our fantastic panelists today. (applause) >> Thanks, everyone. >> Thank you. >> And lastly, I just want to share one bit of information. We will have more discussions on AI next Tuesday at 9:30 AM. Diane Bryant, who is our general manager of Data Centers Group will be here to do a keynote. So I hope you all get to join that. Thanks for coming. (applause) (light electronic music)

>> Hey welcome back everybody. Jeff Frick here with theCUBE, we are on the ground in downtown San Francisco at the Google Next 17 Conference. It's this crazy conference week, and arguably this is the center of all the action. Cloud is big, Google Cloud Platform is really coming out with a major enterprise shift and focus, which they've always had, but now they're really getting behind it. And I think this conference is over 14,000 people, has grown quite a bit from a few years back, and we're really excited to have one of the powerhouse partners with Google, who's driving to the enterprise, and that's Intel, and I'm really excited to be joined by Raejeanne Skillern, she's the VP and GM of the Cloud Platform Group, Raejeanne, great to see you. >> Thank you, thanks for having me. >> Yeah absolutely. So when we got this scheduled, I was thinking, wow, last time I saw you was at the Open Compute Project 2015, and we were just down there yesterday. >> Yesterday. And we missed each other yesterday, but here we are today. >> So it's interesting, there's kind of the guts of the cloud, because cloud is somebody else's computer that they're running, but there is actually a computer back there. Here, it's really kind of the front end and the business delivery to people to have the elastic capability of the cloud, the dynamic flexibility of cloud, and you guys are a big part of this. So first off, give us a quick update, I'm sure you had some good announcements here at the show, what's going on with Intel and Google Cloud Platform? >> We did, and we love it all, from the silicon ingredients up to the services and solutions, this is where we invest, so it's great to be a part of yesterday and today. I was on stage earlier today with Urs Holzle talking about the Google and Intel Strategic Alliance, we actually announced this alliance last November, between Diane Green and Diane Bryant of Intel. And we had a history, a decade plus long of collaborating on CPU level optimization and technology optimization for Google's infrastructure. We've actually expanded that collaboration to cover hybrid cloud orchestration, security, IOT edge to cloud, and of course, artificial intelligence, machine learning, and deep learning. So we still do a lot of custom work with Google, making sure our technologies run their infrastructure the best, and we're working beyond the infrastructure to the software and solutions with them to make sure that those software and solutions run best on our architecture. >> Right cause it's a very interesting play, with Google and Facebook and a lot of the big cloud providers, they custom built their solutions based on their application needs and so I would presume that the microprocessor needs are very specific versus say, a typical PC microprocessor, which has a more kind of generic across the board type of demand. So what are some of the special demands that cloud demands from the microprocessor specifically? >> So what we've seen, right now, about half the volume we ship in the public cloud segment is customized in some way. And really the driving force is always performance per dollar TCO improvement. How to get the best performance and the lowest cost to pay for that performance. And what we've found is that by working with the top, not just the Super Seven, we call them, but the Top 100, closely, understanding their infrastructure at scale, is that they benefit from more powerful servers, with performance efficiency, more capability, more richly configured platforms. So a lot of what we've done, these cloud service providers have actually in some cases pushed us off of our roadmap in terms of what we can provide in terms of performance and scalability and agility in their infrastructure. So we do a lot of tweaks around that. And then of course, as I mentioned, it's not just the CPU ingredients, we have to optimize in the software level, so we do a lot of co-engineering work to make sure that every ounce of performance and efficiency is seen in their infrastructure. And that's how they, their data center is their cost to sales, they can't afford to have anything inefficient. So we really try to partner to make sure that it is completely tailor-optimized for that environment. >> Right, and the hyperscale, like you said, the infrastructure there is so different than kind of classic enterprise infrastructure, and then you have other things like energy consumption, which, again, at scale, itty bitty little improvements >> It's expensive. >> Make a huge impact. And then application far beyond the cloud service providers, so many of the applications that we interact with now today on a day to day basis are cloud-based applications, whether it is the G Suite for documents or this or that, or whether it's Salesforce, or whether we just put in Asana for task tracking, and Slack, and so many of these things are now cloud-based applications, which is really the way we work more and more and more on our desktops. >> Absolutely. And one of the things we look at is, applications really have kind of a gravity. Some applications are going to have a high affinity to public cloud. You see Tustin Dove, you see email and office collaboration already moving into the public cloud. There are some legacy applications, complex, some of the heavier modeling and simulation type apps, or big huge super computers that might stay on premise, and then you have this middle ground of applications, that, for various reasons, performance, security, data governance, data gravity, business need or IP, could go between the public cloud or stay on premise. And that's why we think it's so important that the world recognizes that this really is about a hybrid cloud. And it's really nice to partner with Google because they see that hybrid cloud as the end state, or they call it the Multi Cloud. And their Kubernetes Orchestration Platform is really designed to help that, to seamlessly move those apps from on a customer's premise into the Google environment and have that flow. So it's a very dynamic environment, we expect to see a lot of workloads kind of continue to be invested and move into the public cloud, and people really optimizing end-to-end. >> So you've been in the data center space, we talked a little bit before we went live, you've been in the data center space for a long, long time. >> Long time. >> We won't tell you how long. (laughing) >> Both: Long time. >> So it must be really exciting for you to see this shift in computing. There's still a lot of computing power at the edge, and there's still a lot of computing power now in our mobile devices and our PCs, but so much more of the heavy lift in the application infrastructure itself is now contained in the data center, so much more than just your typical old-school corporate data centers that we used to see. Really fun evolution of the industry, for you. >> Absolutely, and the public cloud is now one of the fastest growing segments in the enterprise space, in the data center space, I should say. We still have a very strong enterprise business. But what I love is it's not just about the fact that the public cloud is growing, this hybrid really connects our two segments, so I'm really learning a lot. It's also, I've been at Intel 23 years, most of it in the data center, and last year, we reorganized our company, we completely restructured Intel to be a cloud and IoT company. And from a company that for multiple decades was a PC or consumer-based client device company, it is just amazing to have data center be so front and center and so core to the type of infrastructure and capability expansion that we're going to see across the industry. We were talking about, there isn't going to be an industry left untouched by technology. Whether it's agriculture, or industrial, or healthcare, or retail, or logistics. Technology is going to transform them, and it all comes back to a data center and a cloud-based infrastructure that can handle the data and the scale and the processing. >> So one of the new themes that's really coming on board, next week will it be a Big Data SV, which has grown out of Hadoop and the old big data conversation. But it's really now morphing into the next stage of that, which is machine learning, deep learning, artificial intelligence, augmented reality, virtual reality, so this whole 'nother round that's going to eat up a whole bunch of CPU capacity. But those are really good cloud-based applications that are now delivering a completely new level of value and application sophistication that's driven by power back at the data center. >> Right. We see, artificial intelligence has been a topic since the 50s. But the reality is, the technology is there today to both capture and create the data, and compute on the data. And that's really unlocking this capabilities. And from us as a company, we see it as really something that is going to not just transform us as a business but transform the many use cases and industries we talked about. Today, you or I generate about a gig and a half of data, through our devices and our PC and tablet. A smart factory or smart plane or smart car, autonomous car, is going to generate terabytes of data. Right, and that is going to need to be stored. Today it's estimated only about 5% of the data captured is used for business insight. The rest just sits. We need to capture the data, store the data efficiently, use the data for insights, and then drive that back into the continuous learning. And that's why these technologies are so amazing, what they're going to be able to do, because we have the technology and the opportunity in the business space, whether it's AI for play or for good or for business, AI is going to transform the industry. >> It's interesting, Moore's Law comes up all the time. People, is Moore's Law done, is Moore's Law done? And you know, Moore's Law is so much more than the physics of what he was describing when he first said that in the first place, about number of transistors on a chip. It's really about an attitude, about this unbelievable drive to continue to innovate and iterate and get these order of magnitude of increase. We talked to David Floyer at OCP yesterday, and he's talking about it's not only the microprocessors and the compute power, but it's the IO, it's the networking, it's storage, it's flash storage, it's the interconnect, it's the cabling, it's all these things. And he was really excited that we're getting to this massive tipping point, of course in five years we'll look back and think it's archaic, of these things really coming together to deliver low latency almost magical capabilities because of this combination of factors across all those different, kind of the three horseman of computing, if you will, to deliver these really magical, new applications, like autonomous vehicles. >> Absolutely. And we, you'll hear Intel talk about Jevons Paradox, which is really about, if you take something and make it cheaper and easier to consume, people will consume more of it. We saw that with virtualization. People predicted oh everything's going to slow down cause you're going to get higher utilization rates. Actually it just unlocked new capabilities and the market grew because of it. We see the same thing with data. Our CEO will talk about, data is the new oil. It is going to transform, it's going to unlock business opportunity, revenue growth, cost savings in environment, and that will cause people to create more services, build new businesses, reach more people in the industry, transform traditional brick and mortar businesses to the digital economy. So we think we're just on the cusp of this transformation, and the next five to 10 years is going to be amazing. >> So before we let you go, again, you've been doing this for 20 plus years, I wasn't going to say anything, she said it, I didn't say it, and I worked at Intel the same time, so that's good. As you look forward, what are some of your priorities for 2017, what are some of the things that you're working on, that if we get together, hopefully not in a couple years at OCP, but next year, that you'll be able to report back that this is what we worked on and these are some of the new accomplishments that are important to me? >> So I'm really, there's a number of things we're doing. You heard me mention artificial intelligence many, many times. In 2016, Intel made a number of significant acquisitions and investments to really ensure we have the right technology road map for artificial intelligence. Machine learning, deep learning, training and inference. And we've really shored up that product portfolio, and you're going to see these products come to market and you're going to see user adoption, not just in my segment, but transforming multiple segments. So I'm really excited about those capabilities. And a lot of what we'll do, too, will be very vertical-based. So you're going to see the power of the technology, solving the health care problem, solving the retail problem, solving manufacturing, logistics, industrial problems. So I like that, I like to see tangible results from our technology. The other thing is the cloud is just growing. Everybody predicted, can it continue to grow? It does. Companies like Google and our other partners, they keep growing and we grow with them, and I love to help figure out where they're going to be two or three years from now, and get our products ready for that challenge. >> Alright, well I look forward to our next visit. Raejeanne, thanks for taking a few minutes out of your time and speaking to us. >> It was nice to see you again. >> You too. Alright, she's Raejeanne Skillern and I'm Jeff Frick, you're watching theCUBE, we're at the Google Cloud Next Show 2017, thanks for watching. (electronic sounds)

>> [Narrator] Live from Boston Massachusetts. This is the Cube, covering Sparks Summit East 2017. Brought to you by Databricks. Now here are your hosts, Dave Alante and George Gilbert. >> Back to you Boston everybody. This is the Cube and we're here live at Spark Summit East, #SparkSummit. Ziya Ma is here. She's the Vice President of Big Data at Intel. Ziya, thanks for coming to the Cube. >> Thanks for having me. >> You're welcome. So software is our topic. Software at Intel. You know people don't necessarily associate Intel with always with software but what's the story there? >> So actually there are many things that we do for software. Since I manage the Big Data engineering organization so I'll just say a little bit more about what we do for Big Data. >> [Dave] Great. >> So you know Intel do all the processors, all the hardware. But when our customers are using the hardware, they like to get the best performance out of Intel hardware. So this is for the Big Data space. We optimize the Big Data solution stack, including Spark and Hadoop on top of Intel hardware. And make sure that we leverage the latest instructions set so that the customers get the most performance out of the newest released Intel hardware. And also we collaborated very extensively with the open source community for Big Data ecosystem advancement. For example we're a leading contributor to Apache Spark ecosystem. We're also a top contributor to Apache Hadoop ecosystem. And lately we're getting into the machine learning and deep learning and the AI space, especially integrating those capabilities into the Big Data eTcosystem. >> So I have to ask you a question to just sort of strategically, if we go back several years, you look at during the Unix days, you had a number of players developing hardware, microprocessors, there were risk-based systems, remember MIPS and of course IBM had one and Sun, et cetera, et cetera. Some of those live on but very, very small portion of the market. So Intel has dominated the general purpose market. So as Big Data became more mainstream, was there a discussion okay, we have to develop specialized processors, which I know Intel can do as well, or did you say, okay, we can actually optimize through software. Was that how you got here? Or am I understanding that? >> We believe definitely software optimization, optimizing through software is one thing that we do. That's why Intel actually have, you may not know this, Intel has one of the largest software divisions that focus on enabling and optimizing the solutions in Intel hardware. And of course we also have very aggressive product roadmap for advancing continuously our hardware products. And actually, you mentioned a general purpose computing. CPU today, in the Big Data market, still has more than 95% of the market. So that's still the biggest portion of the Big Data market. And will continue our advancement in that area. And obviously as the Ai and machine learning, deep learning use cases getting added into the Big Data domain and we are expanding our product portfolio into some other Silicon products. >> And of course that was kind of the big bet of, we want to bet on Intel. And I guess, I guess-- >> You should still do. >> And still do. And I guess, at the time, Seagate or other disk mounts. Now flash comes in. And of course now Spark with memory, it's really changing the game, isn't it? What does that mean for you and the software group? >> Right, so what do we... Actually, still we focus on the optimi-- Obviously at the hardware level, like Intel now, is not just offering the computing capability. We also offer very powerful network capability. We offer very good memory solutions, memory hardware. Like we keep talking about this non-volatile memory technologies. So for Big Data, we're trying to leverage all those newest hardware. And we're already working with many of our customers to help them, to improve their Big Data memory solution, the e-memory, analytics type of capability on Intel hardware, give them the most optimum performance and most secure result using Intel hardware. So that's definitely one thing that we continue to do. That's going to be our still our top priority. But we don't just limit our work to optimization. Because giving user the best experience, giving user the complete experience on Intel platform is our ultimate goal. So we work with our customers from financial services company. We work with folks from manufacturing. From transportation. And from other IOT internet of things segment. And to make sure that we give them the easiest Big Data analytics experience on Intel hardware. So when they are running those solutions they don't have to worry too much about how to make their application work with Intel hardware, and how to make it more performant with Intel hardware. Because that's the Intel software solution that's going to bridge the gap. We do that part of the job. And so that it will make our customers experience easier and more complete. >> You serve as the accelerant to the marketplace. Go ahead George. >> [Ziya] That's right. >> So Intel's big ML as the news product, as of the last month of so, open source solution. Tell us how there are other deep learning frameworks that aren't as fully integrated with Spark yet and where BigML fits in since we're at a Spark conference. How it backfills some functionality and how it really takes advantage of Intel hardware. >> George, just like you said, BigDL, we just open sourced a month ago. It's a deep learning framework that we organically built onto of Apache Spark. And it has quite some differences from the other mainstream deep learning frameworks like Caffe, Tensorflow, Torch and Tianu are you name it. The reason that we decide to work on this project was again, through our experience, working with our analytics, especially Big Data analytic customers, as they build their AI solutions or AI modules within their analytics application, it's funny, it's getting more and more difficult to build and integrate AI capability into their existing Big Data analytics ecosystem. They had to set up a different cluster and build a different set of AI capabilities using, let's say, one of the deep learning frameworks. And later they have to overcome a lot of challenges, for example, moving the model and data between the two different clusters and then make sure that AI result is getting integrated into the existing analytics platform or analytics application. So that was the primary driver. How do we make our customers experience easier? Do they have to leave their existing infrastructure and build a separate AI module? And can we do something organic on top of the existing Big Data platform, let's say Apache Spark? Can we just do something like that? So that the user can just leverage the existing infrastructure and make it a naturally integral part of the overall analytics ecosystem that they already have. So this was the primary driver. And also the other benefit that we see by integrating this BigDL framework naturally was the Big Data platform, is that it enables efficient scale-out and fault tolerance and elasticity and dynamic resource management. And those are the benefits that's on naturally brought by Big Data platform. And today, actually, just with this short period of time, we have already tested that BigDL can scale easily to tens or hundreds of nodes. So the scalability is also quite good. And another benefit with solution like BigDL, especially because it eliminates the need of setting a separate cluster and moving the model between different hardware clusters, you save your total cost of ownership. You can just leverage your existing infrastructure. There is no need to buy additional set of hardware and build another environment just for training the model. So that's another benefit that we see. And performance-wise, again we also tested BigDL with Caffe, Torch and TensorFlow. So the performance of BigDL on single node Xeon is orders of magnitude faster than out of box at open source Caffe, TensorFlow or Torch. So it definitely it's going to be very promising. >> Without the heavy lifting. >> And useful solution, yeah. >> Okay, can you talk about some of the use cases that you expect to see from your partners and your customers. >> Actually very good question. You know we already started a few engagement with some of the interested customers. The first customer is from Stuart Industry. Where improving the accuracy for steel-surface defect recognition is very important to it's quality control. So we worked with this customer in the last few months and built end-to-end image recognition pipeline using BigDL and Spark. And the customer just through phase one work, already improved it's defect recognition accuracy to 90%. And they're seeing a very yield improvement with steel production. >> And it used to by human? >> It used to be done by human, yes. >> And you said, what was the degree of improvement? >> 90, nine, zero. So now the accuracy is up to 90%. And another use case and financial services actually, is another use case, especially for fraud detection. So this customer, again I'm not at the customer's request, they're very sensitive the financial industry, they're very sensitive with releasing their name. So the customer, we're seeing is fraud risks were increasing tremendously. With it's wide range of products, services and customer interaction channels. So the implemented end-to-end deep learning solution using BigDL and Spark. And again, through phase one work, they are seeing the fraud detection rate improved 40 times, four, zero times. Through phase one work. We think there were more improvement that we can do because this is just a collaboration in the last few month. And we'll continue this collaboration with this customer. And we expect more use cases from other business segments. But that are the two that's already have BigDL running in production today. >> Well so the first, that's amazing. Essentially replacing the human, have to interact and be much more accurate. The fraud detection, is interesting because fraud detection has come a long way in the last 10 years as you know. Used to take six months, if they found fraud. And now it's minutes, seconds but there's a lot of false positives still. So do you see this technology helping address that problem? >> Yeah, we actually that's continuously improving the prediction accuracy is one of the goals. This is another reason why we need to bring AI and Big Data together. Because you need to train your model. You need to train your AI capabilities with more and more training data. So that you get much more improved training accuracy. Actually this is the biggest way of improving your training accuracy. So you need a huge infrastructure, a big data platform so that you can host and well manage your training data sets. And so that it can feed into your deep learning solution or module for continuously improving your training accuracy. So yes. >> This is a really key point it seems like. I would like to unpack that a little bit. So when we talk to customers and application vendors, it's that training feedback loop that gets the models smarter and smarter. So if you had one cluster for training that was with another framework, and then Spark was your... Rest of your analytics. How would training with feedback data work when you had two separate environments? >> You know that's one of the drivers why we're creating BigDL. Because, we tried to port, we did not come to BigDL at the very beginning. We tried to port the existing deep learning frameworks like Caffe and Tensorflow onto Spark. And you also probably saw some research papers folks. There's other teams that out there that's also trying to port Caffe, Tensorflow and other deep learning framework that's out there onto Spark. Because you have that need. You need to bring the two capabilities together. But the problem is that those systems were developed in a very traditional way. With Big Data, not yet in consideration, when those frameworks were created, were innovated. But now the need for converging the two becomes more and more clear, and more necessary. And that's we way, when we port it over, we said gosh, this is so difficult. First it's very challenging to integrate the two. And secondly the experience, after you've moved it over, is awkward. You're literally using Spark as a dispatcher. The integration is not coherent. It's like they're superficially integrated. So this is where we said, we got to do something different. We can not just superficially integrate two systems together. Can we do something organic on top of the Big Data platform, on top of Apache Spark? So that the integration between the training system, between the feature engineering, between data management can &be more consistent, can be more integrated. So that's exactly the driver for this work. >> That's huge. Seamless integration is one of the most overused phrases in the technology business. Superficial integration is maybe a better description for a lot of those so-called seamless integrations. You're claiming here that it's seamless integration. We're out of time but last word Intel and Spark Summit. What do you guys got going here? What's the vibe like? >> So actually tomorrow I have a keynote. I'm going to talk a little bit more about what we're doing with BigDL. Actually this is one of the big things that we're doing. And of course, in order for BigDL, system like BigDL or even other deep learning frameworks, to get optimum performance on Intel hardware, there's another item that we're highlighting at MKL, Intel optimized Math Kernel Library. It has a lot of common math routines. That's optimized for Intel processor using the latest instruction set. And that's already, today, integrated into the BigDL ecosystem.z6 So that's another thing that we're highlighting. And another thing is that those are just software. And at hardware level, during November, Intel's AI day, our executives from BK, Diane Bryant and Doug Fisher. They also highlighted the Nirvana product portfolio that's coming out. That will give you different hardware choices for AI. You can look at FPGA, Xeon Fi, Xeon and our new Nirvana based Silicon like Crestlake. And those are some good silicon products that you can expect in the future. Intel, taking us to Nirvana, touching every part of the ecosystem. Like you said, 95% share and in all parts of the business. Yeah, thanks very much for coming the Cube. >> Thank you, thank you for having me. >> You're welcome. Alright keep it right there. George and I will be back with our next guest. This is Spark Summit, #SparkSummit. We're the Cube. We'll be right back.

>> Narrator: Congratulations, Reggie Jackson. >> Certainly in the moment, is about what are youth is and who we are today as a country, as a universe. You are CUBE alumni. Live from San Francisco, it's theCUBE covering Oracle OpenWorld 2016. Brought to you by Oracle now here's your host John Furrier and Peter Burris. >> Hey welcome back everyone we're live here in San Francisco at Oracle OpenWorld. This is SiliconANGLE Media's flagship program, theCUBE, where we go out to the events and extract the signal from noise. I'm John Furrier, co-CEO of SiliconANGLE Media, with Peter Burris, general manager at Wikibon research, and Head of Research at SiliconANGLE Media. Our next guest is CUBE alumni, Chuck Hollis, Senior Vice-President of Infrastructure at cloud and storage. Welcome back to theCUBE. >> It's always a pleasure. I always have a good time when I'm here. >> So the best part of having you on is you've seen the movie before, you've lived it on other teams, you're now at Oracle, what, two and a half years? >> Chuck: One year at Oracle. >> Almost two years, so -- >> Chuck: I'm not dead yet. >> I don't think you -- >> What's that mean? Let's explore that. When will you be dead? >> You're looking good right now. You actually look like you been working out. >> A little tan, like you, like you, you know? >> So is it the country club here at Oracle? >> No, no, no. >> Chairs spinning at five o' clock? >> I'm up early and to bed late and weekends included, right? >> Well, certainly, Dave Donatelli's here, and a team of people really ramping up, essentially engineered systems, AKA hardware engineered in with the software. >> Both, in the cloud, and on premises, right? >> In the cloud and on premises. Clear, end-to-end oracle solution, which will, one, be optimized to run on Oracle, or -- >> Among other things, yes. >> So give us the update; what's the new announcements today? >> So Larry from onstage was very proud to talk about our new gen-two infrastructures of service, and our belief is there's a gap in the market. We have people doing public cloud, right, which, basically, is Startover, Azure, AWS. No chance of an on-prem solution. We have the private cloud guys, basically a Vmware shop, infrastructure only, no pass no nothing, and certainly not a lot of choices if you want to go to public cloud. We think that Oracle's doing a good job of creating that third option. Here's a combined, integrated strategy, on-premises and in the cloud, same technology, same set of capabilities aimed at enterprise applications that basically works the way enterprise IT needs it to work. So this next-gen two infrastructures of service is kind of the first peak of this massive investment we'd be making making entirely new infrastructure cloud that meets the needs of enterprise IT. >> So is this a reboot, or is this an extension of where you guys were? Some were, analysts were saying, not us, but -- >> Chuck: Ah, you'd never say that. >> Well, they said, I was using their words. Holger at Constellation said it's a reboot of their other infrastructure service, so he didn't want to say it failed, implied a transition -- >> Well, I wouldn't say it failed, it's more like a leapfrog. >> John: Explain. >> Oracle got into this business software as service, rather than standalone Sass packages, they worked on integrating everything tightly together, unifying the company. That was followed by platform as a service, aimed at 9,000,000 Java developers around the planet and everything they do. Infrastructure as a service was just made separately about a year ago. We got into the market, we learned a lot of things, but we also realized that we could actually start over again. We look at the engineering team, it's up to about 400 people who are building this next-gen IS, are all ex-Amazon, all ex-Azure. This is not their first infrastructure cloud, and because they were handed a blank piece of paper and said, "you can start over again," it actually is pretty exciting what they've done architecturally. >> So there's got to be something Oracle's doing that's distinct, so just for any number of reasons. Oracle has a lot of existing customers that're running heavy-duty enterprise applications. >> Chuck: Yeah, the tough stuff. >> The tough stuff, so talk to us about how the tough stuff is going to end up in the cloud. >> I think you bring up a good point. One way of looking at it now is that the easy stuff is gone. Desktop has gone to Office 365, and those kids from college are playing with AWS, and maybe I've got some generic workload consolidation sitting in the back room with a private cloud. What about those hairy applications, the demanding databases, in-memory analytics, the big to-do workloads? Where are they going to go? Well, what you see with out infrastructure-to-service is that we're actually providing two capabilities. We can run all of those through our cloud using those exact same technologies that we're running on-premises. You're probably familiar with products like Exadata. Well, you can buy an Exadata. You can use the Exadata in the Oracle public cloud, or you can consume it as a cloud machine, something we call "cloud-to-customer" on premises. And I think that's an important differentiation. A lot of this market is focused on consolidating generic workloads. That's more moderately interesting to us. To your point, what we're really interested are the big, hairy ones. As I joke, these are the ones that have vice-presidents attached to them, right? Yeah, the ones that people really care about. >> Peter: And typically eight figures. >> Depends on the size of the company. Like, Mark was interviewing a lot of people, a lot of customers this morning, and some of them were not large shops. >> But even those partners that're serving those customers often have eight figures associated with their investment in Oracle as well, so it cascades out through the entire industry. But it's also, I want to ask you this, Chuck. It's also not always the applications that have to be brought forward, but we were talking about ageism and it's always better if it's new, but there's a lot of skills in the industry. It's not a question of we want to bring them along. That's still where a lot of the value's being created, so talk about how this third way is going to make not only existing customers and existing apps, but also existing skill sets more rapidly develop inside and experience the expertise with these new technologies. >> I think that's a very good important because any IT organization's only as good as their skill set portfolio. I think anybody who's worked with IT understands that. By the same token, look at the portfolio. Walk into an average IT shop. Here's the stuff that was built decades ago. Here's the stuff that's kind of modern client-server three-tier. Here's the new stuffs that were using containers and microservices. If you're going to be an enterprise cloud provider to that IT shop, you got to support the old stuff, you got to support the kind of current stuff, and you definitely got to give a little pathway to the new stuff, and give me the ability to evolve that portfolio, and peoples' skills forward at the same time. This is what my big arguments that most public cloud providers is public cloud is easy. Just blow everything up and start over in our cloud. Well, as attractive as that might sound, that may not just be a financial reality for the majority of IT organizations. >> Yeah, operationally, too, they can't run their business. So so much for the container stuff. Ravello was the new container cloud server. >> Two things. So we have Ravello and we have a new container cloud service. So we'll put that on Ravello. So we all know hypervisors virtualize hardware. Ravello virtualizes hypervisors. What it does is it comes in to a VC or KVM environment, lifts it up, strips off the hypervisor, encapsulates the network to storage and the compute, then you can actually choose your cloud. You want to run it on AWS, you want to run it on Google, or do you want to run on the Oracle cloud? And it'll show you the prices for each, and you can shop there, so the reason we think that's interesting is nobody really wants to get locked into anybody's cloud, and if we can give people workload portability through VMs, that's great. Well, that's for stuff that we wrapped with virtualization. What about the new containerization? Well, trick with containers is container management, and today, if you want to do container management, you got to graft some open-source stuff and basically build your own. What Oracle has done is created and end-to-end container management service that says, alright, if you really would like to build your own, have at it, but in the meantime, here's something that kind of works. We can do that on-premises, on our cloud machines. We can do this in public Oracle clouds. We have this fast-burning desire to do this on other people's clouds just as soon as we get our own stuff sorted out. But it's the same thing. If I'm developing an application, Oracle has to go compete for that infrastructure business. It can't just say, well, you're an Oracle customer, you have go on all our stuff. And it would be the rare IT leader that would accept lock-in at the cloud level. >> There's no reason to do it today. There's absolutely no reason to do that. >> They may choose to go with us. >> But even if they choose to go with you, they want to do so in a way that doesn't force the lock-in. >> We all flew here, did you pay attention to the flight attendant when she showed where the exit rows are and everything? You may not plan on using that, but it's nice to know they're there. >> And it's nice for you to know where they are, too. Because you guys have learned that to stay at the vanguard of the industry, you have to be always aware of who's about to eat your lunch. >> And I think the Oracle database did a good job back in the day, and still to this day of being affordable. You can invest in the database, it can go wherever you want. And we're trying to do the same thing for that application ecosystem. And we're trying to involve three categories. The old, legacy stuff, the somewhat contemporary stuff, and the emerging containers, microservices-based stuff. >> So talk about your partners, because I know that something that we've been talking about on theCUBE a fair amount is -- >> Partners, we got lots of them. Infrastructure partners in particular? >> John: Well, Centure has an announcement. >> There's a disco party going on behind us here. >> There sure is, unfortunately theCUBE sign's in the way. Otherwise I could participate in it. >> I can see. >> But come back to this notion of a lot of the value that has always been created in the Oracle ecosystems has been created in partners. I have this theory, we have this theory at Wikibon that ultimately there will be more examples of college suppliers being created by your customers and your partners than by individual like AWS and Oracle and Microsoft. >> So Oracle's always had a very rich partner ecosystem. Applications, development, to infrastructure. And the exciting thing that I'm seeing with out partners is like they're seeing opportunity. So let's say that you have this cool vertical application. Five years ago your were selling on-prem hardware with all that entailed. Now you can run the in the Oracle cloud and simply sell a subscription service to your customers. You've evolved your business model forward. Folks that we partner with do application development. They have a platform now for application integration where they have vastly more capablites as opposed to the old school, got to go build it, got to go assemble it, etc, etc. The people who're feeling a little threatened by all of this not surprisingly, are the box-shifters, right? They're guys who just move hardware from A to B. And we're working with them, it's like there's still opportunity there. You just have to look up the stack a little bit. Their skills are still valid, they're just not assembling hardware. >> And you got a Centure announced that the business groups taking the infrastructure-to-service products out, that press release went out today. We covered that. >> I didn't know if that went out yet, but thanks for confirming. >> Oh, maybe that was embargoed, oops. >> Roll back, roll back, roll back. >> Put that back in the model, live TV. >> Centure, all these guys, they want to provide more value to their clients, and 10 years ago, that was stitching together hardware. Now it's about teaching them how to intelligently consume cloud. And I think what these partners like about the Oracle offering is designed to work the way enterprise IT works. It's not this, hey, here's our model, take it or leave it. >> One more thought on this, that there's a difference between the traditional, as you said, three-tier infrastructure, client-server innovation center, and some of the new analytic stuff that's on the horizon. Talk about how you guys are specifically focusing on some of the new analytics applications that are on the horizon coming into the cloud and how you intend to make the two worlds work better together. >> So I think that's great. Old-school analytics we used to call data warehousing, and business intelligence. That hasn't gone away. If you look back five years, it was all about big data, and mining values. Now we're moving to a phase of real-time decision making. Welcome to in-memory analytics things as fast as they can be. And once you figure out how to monetize data, it's addictive, you just want to do it faster and faster and faster and faster. Also, we're talking about relatively exotic infrastructure, right? Multi-terabyte memory spaces, shared Numa architectures. Pretty hard to go down to Best Buy and find the hardware for that and go build that, so as people start pushing the envelope, they're looking more for on-prem engineered solutions or more often, what can you do for me in the cloud. Interestingly enough, we talked about this gen-2 infrastructure service. One of the things it's very good at is having enormous memory spaces and very fast to compute, this kind of bare-metal compute we're seeing in real-time analytics. I think the other factor on this is internet of things, forgive me for playing buzzword bingo, the easy part is gathering the data. The real-time decisioning and actioning on it, that's heavy computing. >> Peter: And delivery with control. >> Yeah, delivering with control. You've got 10 million gas meters. Okay, how do I reason over that in real time, right? That kind of thing. >> So I had to ask you, we've been hearing about this spark-based exadata, what it's all about. What's that all about, is it a new product? >> Another member in the family. So you guys probably know the headlines on the spark chip has a couple of unique talents. It's got 32 encryption processors, so it can encrypt in real time, no delay. Has this ability to take queries and run them in silicon. It also has the ability to compress and decompress memory for in-memory analytics. So the exadata is basically a purpose-built, engineered system for database, so by taking our processor technology and putting it in this purpose-built machine, it gets a whole bunch of new talents for no more money because again, that's part of our differentiation. Things I've learned since I've been a year at Oracle is it's nice to have your own chips. Sometimes they come in very very handy as you build differentiated solutions, so I think exadata customers will have a new option, and I'm sure in the fullness of time it'll be available in our public cloud, it'll be available as a cloud -- >> But this brings up a good point, though. Intel was on stage yesterday, gave the same old corporate pitch, didn't really learn anything new there. >> Chuck: They had nice slides, though. >> That Ian Bryant's awesome. But the thing is, and Larry said that I find compelling is now that I can get your thoughts on it because it kind of comes back to the hyperconversion trend, which is he said, "we are going to provide it faster and cheaper." So he's clearly looking at infrastructures, bring this thing down, cost down to zero if possible, while performance he wants to bring up to a whole other level. How are you guys going to do that, what's the strategy? >> I think Larry and Oracle have the ability to invest like crazy. Don't forget, we build our own hardware. We build our own servers. We build our own data center fabrics. We don't have to buy this stuff from anybody. We build it, so Larry and the team, a couple years ago set this team up with a mission to go compete. Now if you've looked at Amazon, AWS margins, you know there's a lot of fat there. They're also running on really old stuff, the basic architecture was designed 10, 11 years ago. I don't want to throw aspersions around, but you could call it legacy cloud, right? >> John: What do you call it? >> Legacy cloud, anything 10 years or older, it's got to be legacy. So there's a clear opportunity to go build something new. That being said, this is a big boy's game. This is not let's round up a couple million dollars of VC and build a new cloud. So to look at the aggregate spend Oracle's putting behind this infrastructure -- >> Well, you just said the big boys are public, like Rackspace, they couldn't make it, right? So you're starting to see, they were a little, kind of a big boy, I mean... >> They're reasonable out there. But look at it this way, Oracle's got a national software franchise. Much like Microsoft does bring people on. We build our own hardware. We build our own data centers. We actually can become a vertical supplier in this and the argument is efficiency is result. >> So we're going to see Dave Donatelli on Wednesday after his keynote. I know he's prepping up for that. How's it going with Dave, what's going on with Dave? >> Dave's having a good time. I mean, we all came to Oracle on the same premise, is that the industry was rotating, and I think we've seen that in some of the analyst numbers, less and less on-premise spend, more and more spent in the cloud. >> A lot of new hires coming in from an industry that we know on Oracle, pre-existing players. >> And if you asked 'em five years ago if they ever would end up working for Oracle, they might have not said so. >> John: You're being polite. They'd say, "no friggin' way." >> Go through your mind and think what are the traditional on-prem IT vendors that transition their customers to the cloud? It would be a very short list. >> So you buy the whole cloud-broker Dell technologies? >> They don't have a cloud. I think customers want to consume cloud. >> Bing cloud air network now has 4,000 cloud providers. >> All slightly different, all slightly different. >> All working together with hypervisor. >> It's like a big portfolio management company. >> Is that a chess game, or is that just hail Mary? >> Vshpere was designed for the data centers. EMC bombed 10 years ago. Our tech's designed for the data center, and it wasn't designed for a world where people don't want data centers anymore. So I think VM ware's very challenged because their technology and business model is standing up viable public cloud options. The last big one was, oh no, we can't do it. We'll go to IBM. What's your cloud strategy, VM ware? Call IBM? That's kind of a rough deal on a sales call. >> Well, if you put it in the context of a V-cloud air network, you could argue that they're giving up the cloud. Basically, VM world, they said, "we're done with the cloud." they yielded -- >> Peter: I don't think they said that, John. >> They yielded that they weren't going to have their own cloud. >> Absolutely they yielded. >> They yielded on not having their own cloud. >> Okay, they yielded on their own cloud, that's what I meant. >> Nothing more than kind of a boutique offering, and certainly there's a market for small regional service providers around the world. No argument there. And there's a natural tendency, but as I look at people going to cloud, the sticking point isn't the hypervisor, the sticking point is the database and the applications, the middleware. This is something Microsoft has done brilliantly with Azure. >> Larry pointed out that's Ernie's call. Microsoft's well ahead of Oracle on migrating their install base half into their cloud. >> And that's what you guys have to try to figure out how to do as well. >> We're well along the way. But the point is that without that franchise, that's a tough road to hoe, right? The infrastructure guys maybe, the applications guys are the ones you want to talk to. >> Peter said, I'd like to get your thoughts on a comment Peter made on our intro with Matt Eastwood from IDC, everything's on the table. Ecosystems, channel partners, >> Chuck: And we're shaking the table apart. >> So if you have the gravity, an Oracle face of the world that's a suite, which I think is a little bit orthogonal to where the cloud is, but I get the language of Oracle the suite. Is it gravity around the suite, not a winner-take-all? >> You got to be able to pick off pieces and they have to stand on their own. >> You could build a ecosystem around that, and open ecosystem, so that means a new lock-in spec is stickyness, or pure performance, or not, am I getting that right? >> I think Oracle's going to try to play on both sides. If you appreciate the value of the suite, the IAS working with a pass, working with a Sass, great, we have all those pieces; pick and choose. Larry made it pretty clear. He wanted to go head-to-head on iops, memory and core, and dollars per whatever. Oracle intends to feed on that as well, so it'll be interesting to see how this plays out. Nothing like a low price to get an IT buyer -- >> Well he said, and the word he called this is interesting, he was overselling in my opinion, I've heard Larry. >> Chuck: Larry? I can't imagine he'd do that. >> Larry was overselling on their earnings call, but I don't think the analysts understand, they don't see the long game. You look down the 20-mile stare, it just hasn't even started for Oracle. >> Larry is a master at the long game in ways that I'm just now starting to appreciate. >> Well, let's be honest. What is the most sticky thing in the industry? Your applications, that's the stickiest thing in the industry. After that, the developer ecosystem and then you get down to the hypervisor, and you get down to the first -- >> Chuck: And then you get to the wires that connect it together and all that kind of stuff. >> But the most sticky thing is the businesses are still run around some of these floor applications. >> Well, that's why I brought up the suite angle, because I think that the developer angle is sticky because agility has proven that not everyone can build a killer app, so for instance, with an HCM there's probably some feature of HCM that is sub-par relative to some genius entrepreneur that eats, breathes that one feature, has an app, that could be integrated into that feature. >> I think that's your point, and with the platform-as-a-service offering, oh, you want to add it, do something different, great. Yes, exactly. >> It's all a continuous development, continuous integration, but that continuity still is close to the application. >> Yeah, ecosystem to me is, I've heard talks about what the developers' market, go-to-market strategy is. If that's in place, Oracle could have a very robust -- >> We're seeing the both the same thing on the hardware and the software. So hardware, build-your-own, is starting to get out of bow, ya know? Less and less popular buying servers and storage and knitting them together. A lot of guys still buy into that, but that market's going down. I think you're going to see the same thing with software and applications. Rather than starting with a blank piece of paper, where are the big chunks of enterprise functionality that I can grab out of the box and build the thing -- >> Reused, preexisting applications. >> Yes, yes! >> Everybody's talking about business capabilities, right? And the idea is that this capability is the things that I have to do to perform the activities to fit my business needs. And those activities are people, and increasingly, software. And being able to grab those capabilites and pick parts of them from the industry and weave them together quickly, continuously sustained, the match with the marketplace, to your point -- >> Well, we're going to have Juan Luzon next, and we're going to go deep on this, but I think -- >> That was a great guy. >> The API economy, if anything, showed us one, security is FUBARed and needs to be fixed fast, and the encryption on a chip thing has been downplayed. I don't know why Fowler's not getting more airtime on that. That's a really huge thing, but the API economy has proven that this ability to pull stuff that someone else has already done, not assembling like a junkyard kind of situation, why build it if someone's got to get it though an API? >> You talk about giving capital management, right? And you know, there's 175 functions, I don't know, some large number of function there, they're fine. I need this one little thing, so I'm just going to extend it, and still do it in such a way that I'm not developing -- >> And a developer who does that becomes a feature in a bigger pie. I mean, he'll make more money, doesn't go out of business, doesn't try to go public. >> So I wanted to share, before we wrapped up, one interesting thought. We all talked about cloud is coming, cloud is coming. I actually got tangible evidence at the beginning of the year that it's here. So a new word was given to me, cloud quotas. Cloud quotas, and it was kind of funny. This is happening mostly in the larger banks. Senior management, executive management, you're a little slow on this cloud thing. Let me help you out. We'll set a strategic objective. Five years from now, how much did we cloud-spend? This year, your cloud quota is 15% between cloud and non-cloud spent. Next year, etc, and I think what we're seeing is that kind of like the gears are starting to rub, between the businesses says, guys, this can't be so hard. Let's get on with it. >> I'm sure your sales guys have cloud quotas, too. >> Different kind of cloud quota. Different kind of cloud quota. >> On that point, 20 years ago, when it became very popular to pay executives on the basis of RONA, return on net assets, it was right about that time that outsourcing got popular. >> Shocking, isn't that, your mess for less, right? >> Sounds like cloud. >> Okay, bottom line, for the folks at home, Oracle's infrastructure stuff that you're involved in is not new, but it's growing now because it didn't have a lot of nurturing. It was always kind of like that back office secret sauce. What's the update, give a quick update. >> We want to give people a strategy for their enterprise applications for cloud. If they want to consume on-prem, great. Engineered system's cloud equivalence. You want to consume off-prem, same set of capabilites and more in our public cloud. You want to consume the public cloud in your data center, that's a cloud machine, and it oughtta be the technology stack and the set of capabilities. Geographical location, the consumption model really doesn't matter, and when we put this in front of large IT shops, and even smaller ones, they're like, this is great. I can build my architecture, I can build my strategy. I don't have to make a cloud decision now, and if I do make one, then I can undo it later. That agility has become very very attractive to people. >> I could invest in options but have a future. >> Chuck Hollis, Senior Vice-President of infrastructure, congratulations, and then Larry Ellison got to the end of his keynote, didn't have a lot of time, but there's a lot of meat on the bone in the keynote, that he kind of, he couldn't hit. Welcome to the cloud, too many product announcements. Welcome to Amazon's world. >> Peter: Seems excited. >> There's a lot of stuff coming down. It was great talking to you guys, thanks for your time. >> Thanks for sharing your insight and the data and the bits here. Here at theCUBE, we're always sending out the packets of content out to the network, live, original content. I'm John for Peter Burris with SiliconANGLE theCUBE. We'll be right back with more live coverage after this short break. >> Hi, I'm John Furrier, the co-founder of

>> Narrator: Live from San Francisco, it's the Cube. Covering Oracle OpenWorld 2016. Brought to you by Oracle. Now here's your hosts, John Furrier and Peter Burris. >> Hey welcome back everyone, we are here live at Oracle OpenWorld in San Francisco on the show floor. This is the Cube, SiliconANGLE's flagship program. We go out to the events and extract the entrepreneurs I'm John Furrier. Co-seated with me is going to be Peter Burris Head of Research for SiliconANGLE. Also the GM of Wikibon.com our research arm. Our next guest is Reggie Bradford who is a Senior Vice President of Product Development for Oracle Cloud. Reporting for Thomas Kurian who could not make the keynote last night but he did send in the tape during the Diane Bryant thing so that was really good. So hope everything is going well with his family. Welcome to the Cube. >> Thank you, glad to be here. >> Okay so you're a product guy which is great 'cause you're now on the product road map. You get to look at the holistic picture, not so much the go to market which Oracle has that separation. >> Yeah. >> The Cloud is really a conversion of Ironman when you think about it. The stack has to be set up in a way that enables innovation, at the same time preserves the value of what's moving to the Cloud or what get's started >> Yeah. >> in the Cloud. Cloud-Native. Take a minute to describe what Oracle's doing in this regard because you do have a huge install base. >> Yeah. >> And Larry pointed out in the earnings call, they're not yet moving all over yet so Microsoft started their progression. >> Yeah. >> You guys got a huge tsunami coming. >> Yeah. Well I think it's an evolution. It's not a revolution, first of all. So we have 420,000 customers worldwide as you mentioned of substantials installed base. As Larry has mentioned before with Thomas, we've been working on the Cloud applications for over 10 years. We started with a SaaS layer. Now the PaaS layer and the infrastructure layer. I think that if I was to use a sports analogy, we're in the first inning of a nine inning game so we're just getting started. >> First of all we love sports metaphors. I had it at top of the second but okay we'll give early innings. But Fusion ten years ago was kind of pre-cloud although Larry had the famous Churchill Club video I think about ten years ago with the Sun guy saying the Cloud is just a data center with an address that nobody knows. It's essentially that kind of concept. So I can see the progression of the ten year run but something happened four years ago. We could feel it when we were covering it, the show here. You saw Larry on stage almost knowing what's coming. They had not yet released the Fusion base. What's changed in your mind internally? And share with the folks, what's the internal pulse? 'Cause some say you're late to the game. Which you guys refute. You're in top of the second, how late can you be? But how much more work needs to get done? Can you share >> Yeah. >> the internal mojo, mindset, vibe and what work needs to get done? >> Well, I think let's start with it's a 135,000 person company. Not that there's corporate inertia but it's a very large company with a lot of customers that have an existing installed base. I think that we're I definitely don't think that we're late to the Cloud. I think if you look at the work loads that have been done to date. Something like six percent of workloads had been done in the Cloud. But I think that I can't speak for the past. I've been here for four years. My company was acquired. I agree with you. The energy and momentum, the acceleration, the sense of urgency is palpable. And it continues to accelerate. I think there's just this recognition that we feel like we've got a very strong position and a strong hand and we're going to play that. >> One of the things Larry mentioned on his keynote yesterday and which came up today is that Amazon is an environment where you can go to and you've got to do all this work. Oracle, you can just move stuff to Oracle, and it moves to the Cloud instantly. But it still brings up the integration game because it's still a lot more kind of point solutions out there. >> Yeah. >> You can call a startup doing something. >> Yeah. >> An ecosystem as a feature, not a company that would might want to plug into that so how do you bring friction-less integration with a suite mindset? Because essentially Oracle has that gravity. >> Yeah. >> But at the same time you don't want to get stuck in that as an Oracle only solution. >> Absolutely. >> How do you integrate well with others? >> Well first of all I'd say it starts with a mentality that when I was running my startup in 2011, if you think about just the marketing cloud alone, Scott Brinker has the landscape of startups in the Cloud. There was 100 I think back then and I think there's almost 4,000 now. Just in Marketing Cloud alone so the Cloud has opened up a huge era of new startups and innovation and credit card swiping and companies can get into that. That challenge for that is, as Larry said last night, nobody wants to integrate 50 different applications from 50 different companies. I think that we come in with this, we've got all the layers of the stack but we've also got to have a mindset and a mentality being able to be open to best and be -- to bring in end solutions from startups via APIs and making it easy for them to work with us and want to partner with us. I think that's the future for Oracle. >> Do you see Oracle inside or do you see Oracle facilitating other brands? So is it more, going back to what John's point was, is it customary to sit down with an Oracle screen and access stuff? Or is a customer going to sit down with an Oracle framework and know what they're accessing through the partners? >> Well I think it depends on the product and the customer. I'd say we're going to be both. I mean I think we've got the breadth and depth and capability to be it an inside platform type approach or infrastructure but also if you look at an HCM or ERP or Marketing Cloud, we're going to be on the front end. >> Do you anticipate that you're going to go more horizontal as opposed to vertical? So you're going to go from the one infrastructure to the horizontal and then let other folks verticalize? Is that kind of how the thinking is? >> I don't know that we see necessarily a distinction at this point. We obviously have a big industry. You guys have probably talked to them before. Go to Market Group, our entire business unit, but I think we're going to continue to take the market, what the market gives us, and continue to push out our solutions. >> So Reggie, I've talked to a lot of your customers all the time on the Cube and channel partners at Oracle and one of the things that keeps on coming up is that the swim lanes of the database is a big part of the business and you start to see now with the Cloud being more of an integrated IaaS, PaaS and SaaS, the role of database is certainly changing. But it still makes up a big bulk of the business, however you kind of consolidate the numbers. However they fall, the database is 70% roughly the business. My opinion. My analysis. Okay so I wrote a tweet last night, kind of being snarky, but kind of on the whole Tom Brady thing, bringing up sports a analogy. "Free Tom Brady," I said, "Free Database." (laughing) Meaning that one of the thesis that the Wikibon research have come up with is that with free data, you have more data exposed for potential use and by having locked in data, you can still manage the systems of record and then start getting at a whole new set of engagement data that could then spawn another set of innovation on top of it. This is clearly where the market's going in memory and whatnot. So question: What's going on with the database? How do you talk the customers into saying okay the database is going to be fine, use some other databases, they'll still work with Oracle. How do you have that conversation? Are you breaking down that swim lane, broadening the swim lane for the database team? >> Yeah I think from a business model standpoint, to use your Tom Brady analogy tweet. >> The database is suspended for four games? (laughing) >> I don't want to make any declarations on the future business models but I think the fundamental underpinnings of a lot of these new capabilities that are enabled with data, whether it's AI, machine learning, internet of things, I think that those are going to be best leveraged when you've got the broadest and deepest swath of internal and external third party data. And we feel like we've been in that business from day one forty years ago and now we have the broadest suite or capability and data and integrating that data and making it easy to consume and use. >> It just seems that the old move that Oracle made, which I thought was brilliant, the database business, and web logic was a nice sticky component of that, allowed the extensibility. So we're seeing that same dynamic in the Cloud where we want some reliability, we want some high quality. As Larry said yesterday, it's hard to do what you do, I get that. But at the same time there's a growth opportunity, an innovation strategy for Oracle and your partners. Developing an ecosystem or potentially a channel partner. >> Yeah. >> We need to see that thing so do you guys talk about that in the product group and what specifically does that translate to in terms of new features, new focus? >> Yeah we've got a very robust data cloud strategy which is leading in the internal and external third party. Marrying those data sets together and integrating those into our product suite on the applications side. Specifically around the database, opening that up to third parties and how that evolves, I think there's going to be more to come on that. >> What's the biggest thing that people are missing when they try to understand and squint through the Oracle breadth of massive size? When you look at the Cloud strategy, the numbers aren't really killing it. It's four billionth for Oracle. I mean it's still small relative to the big piece of the pie with a lot more growth to go. If that should be comforting to Wall Street at least but from a market perspective, what does the Cloud mean? I mean how do you have that conversation? >> I think that what's missing is that if this was a stand alone company it would be an incredibly viable IPO-able company with a growth rate faster than anybody would scale on the Cloud. Growing double the growth rate. I think another unknown is that we sell a lot of product to companies under 500 million in revenue. 75% of the user base, of the customer base, is under 500 million in revenue so we call that small to mid-size business so everybody thinks Oracle is only an enterprise class company. We are enterprise class but we also scale down and I think that's part of the announcement you've seen today. >> And the new net, new customers are higher than they were. >> Exactly. >> And the growth thing I'm like, okay you know 77% from where you were, I doubled my market share from one to two percent. I mean, percentage is a good benchmark, I get that, but at the end of the day the numbers are the numbers so Cloud-Native is attractive But it's hard. We were talking about it in our intro. It's hard for companies to get there. >> Yeah. >> They have their own inertia. So we're really trying to understand, what's the path for the customer? When you talk to a customer, and think about the customer from a product that you just roll out products, they want that bridge. Is that the past layer? How do you guys -- >> Well we try not to define it. I think it can be challenging because we do have so much product. >> Yeah. >> And we cover so much ground but that's what makes us so successful and that's why companies rely on us. I think the good new is, we have the most flexible platform. If you want to move some of your workloads, you want to move some of your applications to the Cloud, you want to do a hybrid, you want to transition over time, we've said a 100% of our customers are going to move to the Cloud and a 100% of our applications are going to run in the Cloud. But we haven't set a time table on that. >> I want to spend the rest of the time of the segment, to talk about your entrepreneurial background. You sold the company to Oracle four years ago, so you've been in the system for four years but prior to that, you had to be nimble and you also did some acquisitions. I don't know if they were hires but ultimately you're putting together a lot of stuff. So dealing with different product road maps. So two questions: One is how does that go on today? Is there any agile to that in terms of doing that? Is it hard? Is it easier today than it was before? And two: For startups out there, there are a lot of startups that aren't going to make it. They're not going to be the unicorn. They're not going to be that big company but they might be a nice 10 million dollar business. But it's looking for an ecosystem. >> Yeah. So the second part, which I'm going to take first is I'm a startup, I'm not going to make it to the IPO. Maybe it's a lifestyle business, cash small business whatever the word, I need a home. I need an ecosystem. It would appear that you guys would be a good fit for those kinds of companies. Can you share your thoughts on what those entrepreneurs should be thinking? Actions they can take? >> Yeah I mean I think first of all this is Reggie speaking and not Oracle but as a startup, find a big addressable market opportunity that nobody has. I know it's easier said than done. Surround yourself with great people and focus. I think the biggest challenge that startups face is they try to do too many things or be all things to all people. If you can find that niche, yes you know I've been part of -- We've acquired, personally speaking we've acquired many, many, many companies that fill a particular niche. It's easier for us to acquire and to integrate than it is for us to go build it ourselves. I think that's the mantra. >> So the first part of the question, now that you have that entrepreneurs, how's that translate inside Oracle? Because if you think about it, Oracle also does a lot of M&A. A lot of organic growth as well with R&D but you have to kind of pull those together. Does the data cloud, is there new fabric that gets developed so it's not like -- You know some startups go, you be by yourself for a little while and then some get integrated in quickly. Is there a way for an environment to be agile in the sense that you can just plug these new opportunities in. >> Well I would say again having gone through three acquisitions, and this is the God's honest truth, Oracle knows how to acquire companies better than -- certainly it's been the best experience that I've had over the other two. We can always improve. You know a lot of times you read about or follow the big tape deals. You know the NetSuites of the world and that but there's a lot of smaller companies that get acquired and I think that there's a very solid methodology and approach that we take that enables us to capture the value of these startups and make them feel like they're part of a broader company. More than half of the employees at Oracle have come through acquisitions. >> Yeah. Reggie, final question for the folks watching. What's the one thing they may not know about the Oracle Cloud that they should know about? >> Again, what I think that they probably don't know is we are working with companies that have as few as one or two employees that are using our cloud right now. So we're not just a company that's only available to enterprise. We are contemporizing our offering for companies of all sizes that want to deliver better quality and to lower cost. >> Cloud for all. >> Cloud for all. Exactly. (laughing) Democratizing the Cloud. (laughing) >> I wish we had more time. I'd love to dig into the developer conversation. How you guys were with developers. Any quick comment on the developer angle? >> We've always >> You own Java so it's like-- >> Yeah, we've always sought developers and I think if anything, you're going to see us push further towards that community. It's so vital and important for us to develop new products to integrate and create more capability. >> Looking forward to following up on that. Thanks for coming in and sharing your insight inside the Cube. Really appreciate it. >> Thanks for having me. >> Reggie Bradford here inside the Cube at Oracle OpenWorld live in San Francisco. More coverage. Three days of wall to wall live coverage. 35 segments. I just saw CNBC packing it up. They only had a few interviews and they go. So it looks like we won first round. Day one of the bake off between Bloomberg and CNBC. You're watching the Cube. Be right back with more after this short break. (upbeat electronic music) >> I remember

>> live from the Mandalay Bay Convention Center in Las Vegas. It's the king covering via World 2016 brought to you by IBM Wear and its ecosystem sponsors. Now here's your host stool minimum, >> including I Welcome back to the Q bomb stew. Minuteman here with my co host for this segment, Mark Farley, and we'll get the emerald 2016 here in Las Vegas. It's been five years since we've been in Vegas, and a lot of changes in five years back Elsa do this morning was talking about five years from now. They expect that to be kind of a crossover between public Cloud becomes majority from our research. We think that flash, you know, capacities. You know, you really are outstripping, You know, traditional hard disk drives within five years from now. So the two guests I have for this program, Brian Vials, is the CEO of Day Tree. Um, it's been a year since we had you on when you came out of stealth on really excited cause your customer along. We love having customers on down from Alaska, you know, within sight view of of of Russia. Maybe on Did you know Ben Craig, who's the c i O of Northern Bank. Thank you so much for coming. All right, so we want to talk a lot to you, but real quick. Ryan, why do you give us kind of the update on the company? What's happened in the last year where you are with the product in customer deployments? >> Sure. Last year, when we talked, daydream was just coming out of stealth mode. So we were introducing the notion of what we're doing. Starting in kind of mid Q. One of this year, we started shipping and deploying. Thankfully, one of our first customers was Ben. And, uh, you know, our our model of, ah, sort of convergence is different from anything else that you'll see a v m world. I think hearing Ben tell about his experience in deployment philosophy. What changed for him is probably the best way to understand what we do. >> All right, so and great leading. Start with first. Can you tell us a little bit about north from bank? How many locations you have your role there. How long you've been there? Kind of a quick synopsis. >> Sure. Where we're growing. Bank one of three publicly traded publicly held companies in the state of Alaska. We recently acquired residential mortgage after acquiring the last Pacific Bank. And so we have locations all the way from Fairbanks, Alaska, where it gets down to negative 50 negative, 60 below Fahrenheit down to Bellevue, Washington. And to be perfectly candid, what's helped propel some of that growth has been our virtual infrastructure and our virtual desktop infrastructure, which is predicated on us being able to grow our storage, which kind of ties directly into what we've got going on with a tree and >> that that that's great. Can you talk to you know what we're using before what led you to day tree? Um, you know, going with the startup is you know, it's a little risky, right? I thought, Cee Io's you buy on risk >> Well, and as a very conservative bank that serves a commercial market, risk is not something that way by into a lot. But it's also what propels some of our best customers to grow with us. And in this case, way had a lot of faith in the people that joined the company. From an early start, I personally knew a lot of the team from sales from engineering from leadership on That got us interested. Once we kind of got the hook way learned about the technology and found out that it was really the I dare say we're unicorn of storage that we've been looking for. And the reason is because way came from a ray based systems and we have the same revolution that a lot of customers did. We started out with a nice, cosy, equal logic system. We evolved into a nimble solution the hybrid era, if you will, of a raise. And we found that as we grew, we ran into scalability problems. A soon as we started tackling beady eye, we found that we immediately needed to segregate our workloads. Obviously, because servers and production beauty, I have a completely different read right profile. As we started looking at some of the limitations as we grew our video structure, we had to consider upgrading all our processors, all of our solid state drives, all of the things that helped make that hybrid array support our VD infrastructure, and it's costly. And so we did that once and then we grew again because maybe I was so darn popular. within our organization. At that time, we kind of caught wind of what was going on with the atrium, and it totally turned the paradigm on top of its head for what we were looking for. >> How did it? Well, I just heard that up, sir. How did the date Reum solution impact the or what did you talk about? The reed, Right balance? What was it about the day trim solution that solved what was the reed right? Balance you there for the >> young when we ran out of capacity with our equal logic, we had to go out and buy a whole new member when he ran out of capacity with are nimble, had to go out and buy a whole new controller. When we run out of capacity with day tree, um, solution, we literally could go out and get commoditized solid state drives one more into our local storage and end up literally impacting our performance by a magnifier. That's huge. So the big difference between day trim and these >> are >> my words I'm probably gonna screw this up, Bryant, So feel free to jump in, and in my opinion day trip starts out with a really good storage area network appliance, and then they basically take away all of you. I interface to it and stick it out on the network for durable rights. Then they move all of the logic, all of the compression, all of the D duplication. Even the raid calculations on to software that I call a hyper driver that runs the hyper visor level on each host. So instead of being bound by the controller doing all the heavy lifting, you now have it being done by a few extra processors, a few extra big of memory out on their servers. That puts the data as close as humanly possible, which is what hyper converging. But it also has this very durable back end that ensures that your rights are protected. So instead of having to span my storage across all of my hosts, I still have all the best parts of a durable sand on all the best parts of high performance. By bringing that that data closer to where the host. So that's why Atrium enabled us to be able to grow our VD I infrastructure literally overnight. Whenever we ran out of performance, we just pop in another drive and go and the performances is insane. We just finished writing a 72 page white paper for VM, where we did our own benchmarking. Um, using my OMETER sprayers could be using our secondary data center Resource is because they were, frankly, somewhat stagnant, and we knew that we'd be able to get with most level test impossible. And we found that we were getting insane amounts of performance, insane amounts of compression. And by that I can quantify we're getting 132,000 I ops at a little bit over a gig a sec running with two 0.94 milliseconds of late and see that's huge. And one of the things that we always used to compare when it came to performance was I ops and throughput. Whenever we talk to any storage vendor, they're always comparing. But we never talked about lately because Leighton See was really network bound and their storage bender could do anything about that. But by bringing the the brain's closer to the hosts, it solves that problem. And so now our latent C that was like a 25 minutes seconds using a completely unused, nimble storage sand was 2.94 milliseconds. What that translated into was about re X performance increase. So when we went from equal logic to nimble, we saw a multiplier. There we went from nimble toed D atrium. We saw three Export Supplier, and that translated directly into me being able to send our night processors home earlier. Which means less FT. Larger maintenance window times, faster performance for all of our branches. So it went on for a little bit there. But that's what daydreams done for us, >> right? And just to just to amplify that part of the the approached atrium Staking is to assume that host memory of some kind or another flash for now is going to become so big and so cheap that reads will just never leave the host at some point. And we're trying to make that point today. So we've increased our host density, for example, since last year, flash to 16 terabytes per host. Raw within line di Dupin compression. That could be 50 a 100 terabytes. So we have customers doing fairly big data warehouse operations where the reeds never leave the host. It's all host Flash Leighton see and they can go from an eight hour job to, ah, one hour job. It's, you know, and in our model, we sell a system that includes a protected repositories where the rights go. That's on a 10 big network. You buy hosts that have flash that you provisions from your server vendor? Um, we don't charge extra for the software that we load on the host. That does all the heavy lifting. It does the raid compression d do cloning. What have you It does all the local cashing. So we encourage people to put as much flash and as many hosts as possible against that repositories, and we make it financially attractive to do that. >> So how is the storage provisioned? Is it a They're not ones. How? >> So It all shows up, and this is one of the other big parts that is awesome for us. It shows up his one gigantic NFS datastore. Now it doesn't actually use NFS. Itjust presents that way to be anywhere. But previously we had about 34 different volumes. And like everybody else on the planet who thin provisions, we had to leave a buffer zone because we'd have developers that would put a bm where snapshot on something patches. Then forget about it, Philip. The volume bring the volume off lying panic ensues. So you imagine that 30 to 40% of buffer space times each one of those different volumes. Now we have one gigantic volume and each VM has its performance and all of its protection managed individually at the bm level. And that's huge because no longer do you have to set protection performance of the volume level. You can set it right in the B m. Um, >> so you don't even see storage. >> You don't ever have to log into the appliance that all you >> do serve earless storage lists. Rather, this is what we're having. It's >> all through the place. >> And because because all the rights go off, host the rights, don't interrupt each other the host on interrupt together. So we actually going to a lot of links to make sure that happens. So there's an isolation host, a host. That means if you want a provisional particular host for a particular set of demands, you can you could have VD I next door to data warehouse and you know the level of intensity doesn't matter to each other. So it's very specifically enforceable by host configuration or by managing the VM itself. Justus, you would do with the M where >> it gets a lot more flexibility than we would typically get with a hyper converge solution that has a very static growth and performance requirements. >> So when you talk about hyper convergence, the you know, number one, number two and number three things that we usually talk about is, you know, simplicity. So you're a pretty technical guy. You obviously understand this. Well, can you speak to beyond the, you know, kind of ecological nimble and how you scale that house kind of the day's your experience. How's the ongoing, how much you after, you know, test and tweak and adjust things? And how much is it? Just work? >> Well, this is one of the reasons that we went with the atrium is well, you know, when it comes down to it with a hyper converge solution, you're spanning all of your storage across your host, right? We're trying to make use of those. Resource is, but we just recently had one of our server's down because it had a problem with his bios for a little over 10 days. Troubleshooting it. It just doesn't want to stay up. If we're in a full hyper converged infrastructure and that was part of the cluster, that means that our data would've had to been migrated off of that hostess. Well, which is kind of a big deal. I love the idea of having a rock solid, purpose built, highly available device that make sure that my rights are there for me, but allows me to have the elastic configuration that I need on my host to be able to grow them as I see fit. And also to be able to work directly with my vendors to get the pricing points that I need for each. My resource is so our Oracle Servers Exchange Server sequel servers. We could put in some envy Emmy drives. It'll screen like a scalded dog, and for all of our file print servers, I t monitoring servers. We can go with Cem Samsung 8 50 e b o. Drives pop him in a couple of empty days, and we're still able to crank out the number of I ops that we need to be able. Thio appreciate between those at a very low cost point, but with a maximum amount of protection on that data. So that was a big song. Points >> are using both envy. Emmy and Block. >> We actually going through a server? Refresh. Right now, it's all part of the white paper that way. Just felt we decided to go with Internal in Vienna drives to start with two two terabyte internal PC cards. And then we have 2.5 inch in Vienna ready on the front load. But we also plumbed it to be able to use solid state drive so that we have that flexibility in the future to be able to use those servers as we see fit. So again, very elastic architecture and allows us to be kind of a control of what performance is assigned to each individual host. >> So what APS beyond VD? I Do you expect to use this for? Are you already deploying it further? >> VD I is our biggest consumer of resource is our users have come to expect that instant access to all of their applications eventually way have the ability to move the entire data center onto the day trim and so One of the things that we're currently completing this year is the rollout of beady eye to the remaining 40% of our branches. 60% of them are already running through the eye. And then after that, we're probably gonna end up taking our core servers and migrating them off and kind of through attrition, using some of our older array based technology for testing death. All >> right, so I can't let you go without asking you a bit. Just you're in a relationship with GM Ware House Veum. We're meeting your needs. Is there anything from GM wear or the storage ecosystem around them that would kind of make your job easier? >> Yes. If they got rid of the the Sphere Web client, that would be great. I am not a fan of the V Sphere Web client at all, and I wish they'd bring back the C Sharp client like to get that on the record because I tried to every single chance I could get. No, the truth is the integration between the day tree, um and being where is it's super tight. It's something I don't have to think about. It makes it easy for me to be able to do my job at the end of the day. That's what we're looking for. So I think the biggest focus that a lot of the constituents that air the Anchorage being where user group leader of said group are looking for stability and product releases and trying to make sure that there's more attention given to que es on some of the recent updates that they have. Hyper visor Weber >> Brian, I'll give you the final word takeaways that you want people to know about your company, your customers coming out. >> Of'em World. We're thrilled to be here for the second year, thrilled to be here with Ben. It's a It's a great, you know, exciting period for us. As a vendor, we're just moving into sort of nationwide deployment. So check us out of here at the show. If you're not, check us out on the Web. There's a lot of exciting things happening in convergence in general and atriums leading the way in a couple of interesting ways. All >> right, Brian and Ben, thank you so much for joining us. You know, I don't think we've done a cube segment in Alaska yet. so maybe we'll have to talk to you off camera about that. Recommended. All right. We'll be back with lots more coverage here from the emerald 2016. Thanks for watching the Cube. >> You're good at this. >> Oh, you're good.

>> Voiceover: Live from the Mandalay Bay Convention Center in Las Vegas, it's theCUBE covering VMworld 2016, brought to you by VMware and its ecosystem sponsors. Now here's your host, John Furrier. >> Welcome back everyone. We're here live at VMworld 2016 here in Las Vegas. This is the seventh year of coverage for SiliconANGLE Media's theCUBE, it's our flagship program, we go out to the events and extract the signal from the noise. I'm John Furrier. My co-host John Troyer with TechReckoning. Our next guest is CUBE alumn, one of our favorite guests, Sanjay Poonen who runs the end user computing, he's the General Manager, End User Computing Division of VMware, and also Head of Global Marketing now. Congratulations. New job role to oversee all of marketing, to bring that unified view across the company. Good to see you again, welcome back. >> Thank you John, and the John and John Show. I'm happy, I always love being on your show. >> Yeah, we have another John Walls on the other set over there, so it's three Johns hosting here in theCUBE. >> My middle name is John, let me tell you that, so I fit in the community. >> So Sanjay I want to get right into it. So you're giving us a preview here, folks, for tomorrow, the Keynote, you're the main act kicking off the Keynote tomorrow. A lot of big announcements, a couple super secret announcements that you can't share but you've got some new stuff going on in terms of new announcements, in terms of enhancements and new technologies. So can you share a little bit about tomorrow's announcements and what we'd expect at the Keynote. >> Yeah, thank you. So for everybody watching, make sure you dial in at nine o'clock tomorrow. I mean, the reality is, a key part of this client server to mobile cloud transformation is preparing people for a public cloud, digitally transforming the datacenters and preparing for public cloud, that's what you heard today. And the second piece of that, it's almost like two halves of the egg shell, the bottom part being the datacenter, the top part is preparing end users for an increasingly mobile world. And there we have this concept of a digital workspace, Workspace ONE that we introduced, and we're going to announced some new innovations there which really allow you to bring three things together. >> New products or new enhancements? >> In today's day and age when you're going cloud first, we're moving so fast so we don't do things in one big whole. I mean, for example, with AirWatch, we're doing probably like one incremental big feature every five, ten days. So we are doing things a lot more in the pace of cloud type company. So we don't really bundle everything to one big release. But nonetheless, we really focus our efforts around three gears, we're going to hear about tomorrow, one is the entire basis of how people work is driven now by identity management, and access to apps and identity. So you're going to see that tomorrow. And identity management becomes the important piece of the puzzle that's a control point for people's access to apps. Secondly you're going to hear about unified endpoint management and the worlds of desktop and mobile coming together. A good example of that is Windows 10. I'm going to talk about that more tomorrow. And third is a very important area of management and security, and how we think about endpoint management and endpoint security 'coz security is becoming one of the key missing linchpins that we think we can actually bring together in this digital workspace. So Workspace ONE with key focuses on areas like management and security. >> So you've been kind of, we've been interviewing you now three years. Congratulations, now at VMware, came from SAP as an executive there, now three years in. We've been watching your career, the end user computing evolve. The big bold movement down the field was the AirWatch acquisition. We've then seen a variety of different integration points in there. Give us an update on where it's come from and where, now we see where it's going, you just laid that out, but what are some of the specifics on how it's evolving because now with the cloud decision for the company, to say, okay, public cloud is in our equation with that Pat's announcement today, you've been kind of waiting for that engine, you've been kind of like, hurry up and wait for that to happen. So that's now, it's happening. Take us through how AirWatch in this piece evolved. >> Yeah, when we acquired AirWatch, part of it was our fundamental recognition that without a mobile strategy, you could end user computing. That's the name of our group is end user computing. You could end it 'coz we really needed something. So we looked at the space and we wanted something that was cloud first. They were, I would say, a close number, two or three, Mobile Line, I think was technical lead or maybe Good was, but they had a cloud architecture. We liked that about them. And was about a hundred million-dollar business. We disclosed at the end of last year that business was over 370 million in all in bookings. So you could see how rapidly we've taken them, they're almost 4X in two years. And the overall end user computing business was about a half billion when I joined. We announced at the end of last year, was a 1.2 billion all in bookings run rate company. When I joined it was about 30,000 customers. We're now about 65,000 customers. So reality is, we're now one of the top major businesses within the company. There's a lot of momentum. And that's been, I think, one of the better software acquisitions anybody's done the last two or three years. >> And strategically speaking, the digital transformation framework is essentially around this digital workspace area. >> It came out of that mobile space. And the part that we are now starting to see with clearer lenses in the course of the last six to 12 months is that identity management becomes an important piece to add to VDI mobile management. So we've added a third pillar of focus. And we feel like CIOs shouldn't have to buy VDI from one set of vendors, mobile device management, mobile management from a second, and then identity management from a third. These are coalescing into a digital workspace. So a big focus there. And allows us to also expand into new areas, for example, Iot, we can talk about it this time, and areas like endpoint security. >> It seems like, talking about identity management, that to you is right out of your security story. It seems like identity then has to become the fundamental pillar of security of end users in today's enterprise. How does your security story play into-- >> Yeah that's a very good point John. And I would say you're absolutely right. When we are increasingly selling our end user computing solutions, we're finding a key influencing buyer is the CISO. 40% of people have come to our mobile connect conferences are important to the CISO. Identity is a security topic too. So if you pull up for a second, the VMware security story now is very simple. It's in three parts. Number one, we can protect the datacenter. NSX now, one of the key propositions is micro-segmentation. That's a security seller. Number two, we can protect the endpoint with solutions like AirWatch and TrustPoint, we can get to TrustPoint this time. And number three, we can protect the middle, the user. So protect the datacenter, protect the endpoint, and protect the middle, the user. And all of those make us a very strong story appealing to the CISO. And then we take a bevy of partners with us that have even stronger brands and security. For example, one of our lead partners is Palo Alto. We're working very closely with them in NSX. We're working very closely with them in AirWatch. We're working very closely with them in identity. Another example of partners, F5. So we picked the group of partners that have very strong brands and security. And we found things that we do well. We partner with them in things that they do well. It's a really good story to both the CIO and the CISO. >> So much of the cloud story, as well as the end user story, is also about timing. We've been waiting on public cloud. Pundits talk about the death of private cloud but they don't say what year really. And so a lot of the end user story kind of we had to wait on, VDI, we had to wait on the devices. How do you as a leader of this company look at timing and when the market is ready for something? >> Well, I mean John, I think you have to really look at trends. And I had a fundamental premise coming in that the two Cs, and I'll talk about this more on tomorrow's Keynote, that we really needed to attack with venom was cost and complexity in the VDI market. And part of the reason as I talked to customers that many VDI projects failed, were cost and complexity. So we took a chainsaw to cost and complexity. And it turns out with a lot of what we've invented in the software-defined datacenter, software-defined storage that we were among the first to drive, hyper converged infrastructure, NSX for micro-segmentation, the fundamental premise of this sphere and all that you can do in areas like 3D graphics, we could engineer a solution that was 30 to 40% cheaper than the competition from VDI and app promoting. Complexity. We decided that VDI and app promoting needed to be one platform as opposed to sort of a competition that had like a, two separate products for VDI and app promoting. So these all were things that lowered the total cost of ownership and made that easy. Similarly with mobile, the two S's we attack there was simplicity and security. And we've had some core, I would say, these are the type of things, as a leader, you have to keep telling your teams, is your north pole. We're attacking cost and complexity. Another example of cost and complexity is moving stuff to the cloud. Three years ago we were the first to announce desktop as a service. What was one of the messages this morning, IBM, now embracing that desktop as a service in their cloud, working with us both in IBM cloud and IBM GTS. It's come a long way in three years. >> So I got to ask you about the aspect of unification. We're hearing that tomorrow you're announcing a huge shift in how customers buy and that it ultimately will change the equation on their cost side which is eliminating these point solutions out there. This unification endpoint, I don't know what you're calling it, can you share, give a little bit of leg, as Dave Vellante would say, on this morning tomorrow on this announcement, this consolidation or unification. How should we think about this? >> I mean, I think, and hopefully it's not a surprise 'coz we've been building up this momentum as opposed to one big mega announcement. Workspace ONE is really the coming together of three core areas. VDI and everything related to the way in which we manage desktops and apps, mobile management, and identity management. And in each of those spaces, if you don't look at us, there are point vendors doing each of those. And our differentiation is one, it's unified, second, it's a cloud first solution, many cases the folks have not yet moved to the cloud, and then we extend the capabilities of things like Workspace ONE, optimized for our datacenter where it needs to, into new areas like, for example, security. So we think as you lay this out and then build a partnership ecosystem, with not just security vendors but apps vendors, we're going to have a very large apps vendor on stage with me tomorrow, for the first time on stage, so I'm not going to tell you who it is, but come tomorrow you'll hear that. >> Microsoft, SAP, Salesforce? >> You've got some obvious candidates but it's one of those folks. >> It is one of those folks? >> How many big ones left, right? Some of them have been buying everybody. >> We've got some scoop this year on theCUBE. >> But that's an example of where VMware is taking the lead at embracing an apps ecosystem. >> So I got to ask you, you're a student of history and text, so back in the old days, back in the 90s, when dial-up in internet, Office Connections, Radioservers was a buzzword, you'd have to dial up into a facility, and you have to be authenticated. Pretty straightforward back in the day. But now the authentication, if you will, is coming from endpoints that are, like, anything. Uber could be inside the enterprise and app. So this notion of endpoints is interesting. It's also complicated. So there's not only a security surface area, there's also a cost area to deploy these solutions. Is that the kind of what Workspace ONE does? I mean am I getting it right? Am I thinking it right as an access method? >> I think you've got one piece of it right and I think you're exactly right. In the world of mobile, my fingerprint now becomes, police know that that's unique usually-- >> So does Apple. >> Right. And my retina scan becomes it. So you've got very sophisticated phones, it doesn't have to be complicated ones, that can give you either the fingerprint or the retina scan. You'd have to physically cut my thumb off and pluck my eye. I dare you to do both of those to replicate me. So you can move away from a very-- >> That's two-factor authentication right there. >> Yes, multi-factor, right? So you can move away from tokens becoming your only avenue of multi-factor authentication. You can do things smoothly. But it doesn't end there. Endpoints security has to be re-thought to really work at speed and at scale, so that's why we partnered with this hot security company, you're going to see them also on display tomorrow, Tanium. And with them we built a product called TrustPoint. And we use it internally at VMware. In fact one of the things you're going to see in the demos I do tomorrow, there's going to be lots of demos in 25 minutes, of day of the life of how VMware uses technology both in Workspace ONE and endpoint security. Tanium's one of the hottest products that we internally use and we combine some of our IP with theirs, and created a product called TrustPoint in a Google-like interface. I can search to find all endpoints in the enterprise, what potential apps are running on them, what potential malware's on them, quarantine it and maybe even take action on them with some of the technologies we have from AirWatch. So we've combined the best of Tanium and VMware's technology and this is going to be a real hot solution for areas like Windows 10. >> And what's the uptake you're taking on traction given where you're business is going? You've got some good performance now. What's your expectation on uptake on some of these, this Workspace ONE and the end space? >> If you look at our success so far, I told them, when I joined the company, the business was about a half a billion. We announced the end of last year, it's on a 1.2 billion run rate. So we've effectively more than doubled the business, doubled the customer count. And I think that on our path from 1.2 to two billion over multiple number of years, these solutions are going to become very critical to our growth. Horizon in the desktop portfolio, AirWatch in the mobile portfolio, identity management, and TrustPoint. And when I talk to our sales guys, I say, "Listen, there's enough there to feed "a lot of potential customers," and when I look at our customer count, 65,000 customers, we're still about 9, 10% penetrated inside the overall VMware base. If we can double, triple our customer base, there's no reason why this couldn't be a multi-billion dollar business. >> Alright, so for CXOs whether that's CIOs, chief data officers, chief revenue officers, any CXO, chief security officers, CISOs, all that stuff, for they're watching out there and tomorrow's Keynote, how would you summarize if you have to boil out your point of view and your theme for tomorrow, and some of the key takeaways? >> Four words, consumer-simple, enterprise-secure. There's an element of simplicity that gives you all the productivity that you need with Workspace ONE and your end user world. And then there's a message of security that the IT wants. The users benefit from simplicity, IT benefits from security. Users benefit from choice, IT benefits from control. And you'll hear that very, hopefully, fairly clearly tomorrow. >> Sanjay, final question, your team, VMware, you've amassed quite a team, the performance have been great, when you go back to the ranch inside Palo Alto headquarters and throughout the world, what's your marching orders to the team? What's the guiding principle that you put forth with respect to keeping the pace of innovation to match up the cadence of what's expected, not only by potentially your customers, but also your potential partners and competitors? >> First off, I'm a big believer in serve and leadership. So you have to lead by values that replicate, there's no success without successors, so I'm a hound for talent, I'm always looking for ways by which, just like the warriors, we create the best end user computing team bar none, and I think we've been very fortunate to create that team in every area. There's more talent that we should be hiring. I hear about them and we go recruit them. But once we've got a good team, we keep them focused on the mission. I mean obviously we have a revenue growth goal, and at the core of it, beyond just selling things, we want to make the customers successful. So we keep customer as our north pole. Customer satisfaction for VMware has been the highest of any IT vendor. When you look at many of these, Temkin research does a survey of customer satisfaction, we're among the top five, almost consistently the last few years. And then we make sure that in the products that we build, customer first, serve and leadership at the top, customer-focused, and we are building products, I mean we're an engineering-centric company so we want to build the best products that have a leap factor over the competition. >> So the warriors have a style of play-outs. You have Steph Curry who's just, lights up. But they're not afraid to shoot the three. They're good on transition, great speed. What is your differentiation as an organization? What's that x factor? What's the one thing you can point to? >> I mean, I think, listen, we were probably a little bit lethargic in end user computing. John was joking about this before we just had the show. We want to build great factors and we're a little bit edgy. I mean I've been called everything on Twitter from the Nostradamus of EUC to all kinds of, but we're aggressive, but I will tell you that if people watch me in Twitter, it's never, in the words of The Godfather, it's never personal. It's strictly business. So we have fun. We're a little edgy out there. We're in your face, we want to compete, we want to win every deal but it's never personal. I mean it's just like Steph Curry. You're going to compete hard on the court, but after the game, you go and have a drink with Kobe Bryant or Lebron James or whoever-have-you. >> Well final question, I didn't get this 'coz it's such a good product conversation and organization with your group, now you're heading up marketing, as the VMware, a very community-driven, very data-driven company, thoughts on marketing, you have it on social media, do you see social as being a part of marketing? Do you look at that? Do you look at certain ideas that you see that you put forth? >> First off I think Robin Matlock, our CMO has been doing an amazing job, so I told her this as I took over marketing and communications. Oliver Roll, our Chief Communications Officer is also doing great. Listen, I'm just going to throw more wood in the fire. Things are going good. Let's just get them from good to great. This show is one of the most cultistic shows on the planet because of the way in which she and her team have built this thing. It just gets better and better. But there's a few things I think you're going to see us do more. Customer-based marketing, having customers become our spokespeople. I dream of a day where every ad that we have is the biggest companies in the world or the smallest companies using our technology to either make their business more efficient or save lives. And then increasingly over time, we're going to be also doing vertical-based marketing in certain industries. And social media is a great way of getting that work across. >> We'll you've been on theCUBE as an SAP executive, now three years at VMware, certainly this is seven years you've been with CUBE and you guys do it right, so Robin and team and now you. Thanks for your support, appreciate everything. >> Thank you John and John. >> Sanjay Poonen, the General Manager, End Use Computing, and Global Head of Marketing for VMware here inside theCUBE. I'm John Furrier with John Troyer. You're watching theCUBE. (upbeat music)