(bright upbeat music) >> Okay, we're back with theCUBE's continuous coverage of HPE's latest GreenLake announcement, the continuous cadence that we're seeing here. You know, when you're trying to figure out how to optimize workloads, it's getting more and more complex. Data-driven workloads are coming in to the scene, and so how do you know, with confidence, how to configure your systems, keep your costs down, and get the best performance and value for that? So we're going to talk about that. With me are Chris Shin, who is the founder of CloudPhysics and the senior director of HPE CloudPhysics, and Sandeep Singh, who's the vice-president of Storage Marketing. Gents, great to see you. Welcome. >> Dave, it's a pleasure to be here. >> So let's talk about the problem first, Sandeep, if we could. what are you guys trying to solve? What are you hearing from customers when they talk to you about their workloads and optimizing their workloads? >> Yeah, Dave, that's a great question. Overall, what customers are asking for is just to simplify their world. They want to be able to go faster. A lot of business is asking IT, let's go faster. One of the things that cloud got right is that overall cloud operational experience, that's bringing agility to organizations. We've been on this journey of bringing this cloud operational agility to customers for their data states, especially with HPE GreenLake Edge-to-Cloud platform. >> Dave: Right. >> And we're doing that with, you know, powering that with data-driven intelligence. Across the board, we've been transforming that operational support experience with HPE InfoSight. And what's incredibly exciting is now we're talking about how we can transform that experience in that upfront IT procurement portion of the process. You asked me what are customers asking about in terms of how to optimize those workloads. And when you think about when customers are purchasing infrastructure to support their app workloads, today it's still in the dark ages. They're operating on heuristics, or a gut feel. The data-driven insights are just missing. And with this incredible complexity across the full stack, how do you figure out where should I be placing my apps, whether on Prim or in the public cloud, and/or what's the right size infrastructure built upon what's actually being consumed in terms of resource utilization across the board. That's where we see a tremendous opportunity to continue to transform the experience for customers now with data-driven insights for smarter IT decisions. >> You know, Chris, Sandeep's right. It's like, it's like tribal knowledge. Well, Kenny would know how to do that, but Kenny doesn't work here anymore. So you've announced CloudPhysics. Tell us more about what that is, what impact it's going to have for customers. >> Sure. So just as Sandeep said, basically the problem that exists in IT today is you've got a bunch of customers that are getting overwhelmed with more and more options to solve their business problems. They're looking at cloud options, they're looking at new technologies, they're looking at new sub-technologies and the level at which people are competing for infrastructure sales is down at the very, very, you know, splitting hairs level in terms of features. And they don't know how much of these they need to acquire. Then on the other side, you've got partners and vendors who are trying to package up solutions and products to serve these people's needs. And while the IT industry has, for decades, done a good job of automating problems out of other technology spaces, hasn't done a good job of automating their own problems in terms of what does this customer need? How do I best service them? So you've got an unsatisfied customer and an inadequately equipped partner. CloudPhysics brings those two together in a common data platform, so that both those customers and their partners can look at the same set of data that came out of their data center and pick the solutions that will solve their problems most efficiently. >> So talk more about the partner angle, because it sounds like, you know, if they don't have a Kenny, they really need some help, and it's got to be repeatable. It's got to be consistent. So how have partners reacting to this? >> Very, very strongly. Over the course of the four or five years that that CloudPhysics has been doing this in market, we've had thousands and thousands of VARs, SIs and others, as well as many of the biggest technology providers in the market today, use CloudPhysics to help speed up the sales process, but also create better and more satisfied customers. >> So you guys made... Oh, go ahead, please. >> Well, I was just going to chime into that. When you think about partners that with HPE CloudPhysics, where it supports heterogeneous data center environments, partners all of a sudden get this opportunity to be much more strategic to their customers. They're operating on real world insights that are specific to that customer's environment. So now they can really have a tailored conversation as well as offer tailored solutions designed specifically for the areas, you know, where help is needed. >> Well, I think it builds an affinity with the customer as well, because if the partners that trust advisor, if you give a customer some advice and it's kind of the wrong advice, "Hey, we got to go back and reconfigure that workload. We won't charge you that much for it". You're now paying twice. Like when an accountant makes a mistake on your tax return, you got to pay for that again. But so, you guys acquired CloudPhysics in February of this year. What can you tell us about what's transpired since then? How many engagements that you've done? What kind of metrics can you share? >> Yeah. Chris, do you want to weigh in for that? >> Sure, sure. The start of it really has been to create a bunch of customized analytics on the CloudPhysics platform to target specific sales motions that are relevant to HPE partners. So what do I mean by that? You'll remember that in May, we announced the Alletra Series 6,000 and 9,000. In tandem with that, CloudPhysics released a new set of analytics that help someone who's interested in those technologies figure out what model might be best for them and how much firepower they would need from one or the other of those solutions. Similarly, we have a bunch solutions and a market strength in the HCI world, hyper converged, and that's both SimpliVity and dHCI. And we've set up some analytics that specifically help someone who's interested in that form factor to accelerate, and again, pick the right solutions that will serve their exact applications needs. >> When you talk to customers, are they able to give you a sense as to the cost impacts? I mean, even if it's subjective, "Hey, we think we, you know, we save 10% versus the way we used to do it", or more or less. I mean, just even gut feel metrics. >> So I'll start that one, Sandeep. So there's sort of two ways to look at it. One thing is, because we know everything that's currently running in the data center - we discovered that - we have a pretty good cost of what it is costing them today to run their workloads. So anything that we compare that to, whether it's a transition to public cloud or a transition to a hosted VMware solution, or a set of new infrastructure, we can compare their current costs to the specific solutions that are available to them. But on the more practical side of things, oftentimes customers know intuitively this is a set of servers I bought four years ago, or this is an old array that I know is loose. It's not keeping up anymore. So they typically have some fairly specific places to start, which gives that partner a quick win, solving a specific customer problem. And then it can often boil out into the rest of the data center, and continual optimization can occur. >> How unique is this? I mean, is it, you know, can you give us a little glimpse of the secret sauce behind it? Is this kind of table stakes for the industry? >> Yeah. I mean, look, it's unique in the sense that CloudPhysics brings along over 200 metrics across the spectrum of virtual machines and guest OSs, as well as the overall CPU and RAM utilization, overall infrastructure analysis, and built in cloud simulators. So what customers are able to do is basically, in real time, be able to: A - be aware of exactly what their environment looks like; B - be able to simulate if they were going to move and give an application workload to the cloud; C - they're able to just right-size the underlying infrastructure across the board. Chris? >> Well, I was going to say, yeah, along the same lines, there have been similar technology approaches to different problems. Most notably in the current HPE portfolio, InfoSight. Best in class, data lake driven, very highly analytical machine learning, geared predominantly toward an optimization model, right? CloudPhysics is earlier in the talk track with the customer. We're going to analyze your environment where HPE may not even have a footprint today. And then we're going to give you ideas of what products might help you based on very similar techniques, but approaching a very different problem. >> So you've got data, you've got experience, you know what best practice looks like. You get a sense as to the envelope as to what's achievable, right? And that is just going to get better and better and better over time. One of the things that that I've said, and we've said on theCUBE, is that the definition of cloud is changing. It's expanding, it's not just public cloud anymore. It's a remote set of services, it's coming on Prim, there's a hybrid connection. We're going across clouds, we're going out to the edge. So can CloudPhysics help with that complexity? >> Yeah, absolutely. So we have a set of analytics in the cloud world that range from we're going to price your on-premise IT. We also have the ability to simulate a transition, a set of workloads to AWS, Azure, or Google Cloud. We also have the ability to translate to VMware based solutions on many of those public clouds. And we're increasingly spreading our umbrella over GreenLake as well, and showing the optimization opportunities for a GreenLake solution when contrasted with some of those other clouds. So there's not a lot of... >> So it's not static. >> It's not static at all. And Dave, you were mentioning earlier in terms such as proven. CloudPhysics now has operated on trillions of data points over millions of virtual machines across thousands of overall data assessments. So there's a lot of proven learnings through that as well as actual optimizations that customers have benefited from. >> Yes. I mean, there's benchmarks, but it's more than that because benchmarks tend to be static, okay. We consider rules of thumb. We're living in an age with a lot more data, a lot more machine intelligence. And so this is organic, it'll evolve. >> Sandeep: Absolutely. >> And the partners who work with their customers on a regular basis over at CloudPhysics, and then build up a history over time of what's changing in their data center can even provide better service. They can look back over a year, if we've been collecting, and they can see what the operating system landscape has changed, how different workloads have lost popularity, how other ones have gained. And they really can become a much better solution provider to that customer the longer CloudPhysics is used. >> Yeah, it gives your partners a competitive advantage, it's a much stickier model because the customer is going to trust your partner more if they get it right. So we're not going to change horses in the middle of the street. We're going to go back to the partner that set us up, and they keep getting better and better and better each time, we've got a good cadence going. All right. Sandeep, bring us home. What's your sort of summary? How should we think about this going forward? >> Well, I'll bring us right back to the way I started is, and to end, we're looking at how we continue to deliver best in class cloud operational experience for customers across the board with HPE GreenLake. And earlier this year, we unveiled this cloud operation experience for data, and for customers, that experience starts with a cloud consult where they can essentially discover services, consume services, that overall operational and support experience is transformed with HPE InfoSight. And now we're transforming this experience where any organization out there that's looking to get data-driven insights into what should they do next? Where should they place their workloads? How to right-size the infrastructure? And in the process, be able to transform how they are working and collaborating with their partners. They're able to do that now with HPE CloudPhysics, bringing these data driven insights for smarter IT decision-making. >> I like this a lot, because a lot of the cloud is trial and error. And when you try and you make a mistake, you're paying each time. So this is a great innovation to really help clients focus on the things that matter, you know, helping them apply technology to solve their business problems. Guys, thanks so much for coming to theCUBE. Appreciate it. >> Dave, always a pleasure. >> Thanks very much for having us. >> And keep it right there. We got more content from HPE's GreenLake announcements. Look for the cadence. One of the hallmarks of cloud is the cadence of announcements. We're seeing HPE on a regular basis, push out new innovations. Keep it right there for more. (bright upbeat music begins) (bright upbeat music ends)

>>Edge computing is predict, projected to be a multi-trillion dollar business. You know, it's hard to really pinpoint the size of this market. Let alone fathom the potential of bringing software, compute, storage, AI, and automation to the edge and connecting all that to clouds and on-prem systems. But what, you know, what is the edge? Is it factories? Is it oil rigs, airplanes, windmills, shipping containers, buildings, homes, race cars. Well, yes and so much more. And what about the data for decades? We've talked about the data explosion. I mean, it's mind boggling, but guess what, we're gonna look back in 10 years and laugh. What we thought was a lot of data in 2020, perhaps the best way to think about edge is not as a place, but when is the most logical opportunity to process the data and maybe it's the first opportunity to do so where it can be decrypted and analyzed at very low latencies that that defines the edge. And so by locating compute as close as possible to the sources of data, to reduce latency and maximize your ability to get insights and return them to users quickly, maybe that's where the value lies. Hello everyone. And welcome to this cube conversation. My name is Dave Vellante and with me to noodle on these topics is Omar Assad, VP, and GM of primary storage and data management services at HPE. Hello, Omer. Welcome to the program. >>Hey Steve. Thank you so much. Pleasure to be here. >>Yeah. Great to see you again. So how do you see the edge in the broader market shaping up? >>Uh, David? I think that's a super important, important question. I think your ideas are quite aligned with how we think about it. Uh, I personally think, you know, as enterprises are accelerating their sort of digitization and asset collection and data collection, uh, they're typically, especially in a distributed enterprise, they're trying to get to their customers. They're trying to minimize the latency to their customers. So especially if you look across industries manufacturing, which is distributed factories all over the place, they are going through a lot of factory transformations where they're digitizing their factories. That means a lot more data is being now being generated within their factories. A lot of robot automation is going on that requires a lot of compute power to go out to those particular factories, which is going to generate their data out there. We've got insurance companies, banks that are creating and interviewing and gathering more customers out at the edge for that. >>They need a lot more distributed processing out at the edge. What this is requiring is what we've seen is across analysts. A common consensus is that more than 50% of an enterprise is data, especially if they operate globally around the world is going to be generated out at the edge. What does that mean? More data is new data is generated at the edge, but needs to be stored. It needs to be processed data. What is not required needs to be thrown away or classified as not important. And then it needs to be moved for Dr. Purposes either to a central data center or just to another site. So overall in order to give the best possible experience for manufacturing, retail, uh, you know, especially in distributed enterprises, people are generating more and more data centric assets out at the edge. And that's what we see in the industry. >>Yeah. We're definitely aligned on that. There's some great points. And so now, okay. You think about all this diversity, what's the right architecture for these deploying multi-site deployments, robo edge. How do you look at that? >>Oh, excellent question. So now it's sort of, you know, obviously you want every customer that we talk to wants SimpliVity, uh, in, in, and, and, and, and no pun intended because SimpliVity is reasoned with a simplistic edge centric architecture, right? So because let's, let's take a few examples. You've got large global retailers, uh, they have hundreds of global retail stores around the world that is generating data that is producing data. Then you've got insurance companies, then you've got banks. So when you look at a distributed enterprise, how do you deploy in a very simple and easy to deploy manner, easy to lifecycle, easy to mobilize and easy to lifecycle equipment out at the edge. What are some of the challenges that these customers deal with these customers? You don't want to send a lot of ID staff out there because that adds costs. You don't want to have islands of data and islands of storage and promote sites, because that adds a lot of States outside of the data center that needs to be protected. >>And then last but not the least, how do you push lifecycle based applications, new applications out at the edge in a very simple to deploy better. And how do you protect all this data at the edge? So the right architecture in my opinion, needs to be extremely simple to deploy. So storage, compute and networking, uh, out towards the edge in a hyperconverged environment. So that's, we agree upon that. It's a very simple to deploy model, but then comes, how do you deploy applications on top of that? How do you manage these applications on top of that? How do you back up these applications back towards the data center, all of this keeping in mind that it has to be as zero touch as possible. We at HBS believe that it needs to be extremely simple. Just give me two cables, a network cable, a power cable, tied it up, connected to the network, push it state from the data center and back up at state from the ed back into the data center. Extremely simple. >>It's gotta be simple because you've got so many challenges. You've got physics that you have to deal your latency to deal with. You got RPO and RTO. What happens if something goes wrong, you've gotta be able to recover quickly. So, so that's great. Thank you for that. Now you guys have hard news. W what is new from HPE in this space >>From a, from a, from a, from a deployment perspective, you know, HPE SimpliVity is just gaining like it's exploding, like crazy, especially as distributed enterprises adopt it as it's standardized edge architecture, right? It's an HCI box has got stories, computer networking, all in one. But now what we have done is not only you can deploy applications all from your standard V-Center interface, from a data center, what have you have now added is the ability to backup to the cloud, right? From the edge. You can also back up all the way back to your core data center. All of the backup policies are fully automated and implemented in the, in the distributed file system. That is the heart and soul of, of the SimpliVity installation. In addition to that, the customers now do not have to buy any third-party software into backup is fully integrated in the architecture and it's van efficient. >>In addition to that, now you can backup straight to the client. You can backup to a central, uh, high-end backup repository, which is in your data center. And last but not least, we have a lot of customers that are pushing the limit in their application transformation. So not only do we previously were, were one-on-one them leaving VMware deployments out at the edge sites. Now revolver also added both stateful and stateless container orchestration, as well as data protection capabilities for containerized applications out at the edge. So we have a lot, we have a lot of customers that are now deploying containers, rapid manufacturing containers to process data out at remote sites. And that allows us to not only protect those stateful applications, but back them up, back into the central data center. >>I saw in that chart, it was a light on no egress fees. That's a pain point for a lot of CEOs that I talked to. They grit their teeth at those entities. So, so you can't comment on that or >>Excellent, excellent question. I'm so glad you brought that up and sort of at that point, uh, uh, pick that up. So, uh, along with SimpliVity, you know, we have the whole green Lake as a service offering as well. Right? So what that means, Dave, is that we can literally provide our customers edge as a service. And when you compliment that with, with Aruba wired wireless infrastructure, that goes at the edge, the hyperconverged infrastructure, as part of SimpliVity, that goes at the edge, you know, one of the things that was missing with cloud backups is the every time you backup to the cloud, which is a great thing, by the way, anytime you restore from the cloud, there is that breastfeed, right? So as a result of that, as part of the GreenLake offering, we have cloud backup service natively now offered as part of HPE, which is included in your HPE SimpliVity edge as a service offering. So now not only can you backup into the cloud from your edge sites, but you can also restore back without any egress fees from HBS data protection service. Either you can restore it back onto your data center, you can restore it back towards the edge site and because the infrastructure is so easy to deploy centrally lifecycle manage, it's very mobile. So if you want to deploy and recover to a different site, you could also do that. >>Nice. Hey, uh, can you, Omar, can you double click a little bit on some of the use cases that customers are choosing SimpliVity for, particularly at the edge, and maybe talk about why they're choosing HPE? >>What are the major use cases that we see? Dave is obviously, uh, easy to deploy and easy to manage in a standardized form factor, right? A lot of these customers, like for example, we have large retailer across the us with hundreds of stores across us. Right now you cannot send service staff to each of these stores. These data centers are their data center is essentially just a closet for these guys, right? So now how do you have a standardized deployment? So standardized deployment from the data center, which you can literally push out and you can connect a network cable and a power cable, and you're up and running, and then automated backup elimination of backup and state and BR from the edge sites and into the data center. So that's one of the big use cases to rapidly deploy new stores, bring them up in a standardized configuration, both from a hardware and a software perspective, and the ability to backup and recover that instantly. >>That's one large use case. The second use case that we see actually refers to a comment that you made in your opener. Dave was where a lot of these customers are generating a lot of the data at the edge. This is robotics automation that is going to up in manufacturing sites. These is racing teams that are out at the edge of doing post-processing of their cars data. Uh, at the same time, there is disaster recovery use cases where you have, uh, you know, campsites and local, uh, you know, uh, agencies that go out there for humanity's benefit. And they move from one site to the other. It's a very, very mobile architecture that they need. So those, those are just a few cases where we were deployed. There was a lot of data collection, and there's a lot of mobility involved in these environments. So you need to be quick to set up quick, to up quick, to recover, and essentially you're up to your next, next move. >>You seem pretty pumped up about this, uh, this new innovation and why not. >>It is, it is, uh, you know, especially because, you know, it is, it has been taught through with edge in mind and edge has to be mobile. It has to be simple. And especially as, you know, we have lived through this pandemic, which, which I hope we see the tail end of it in at least 2021, or at least 2022. They, you know, one of the most common use cases that we saw, and this was an accidental discovery. A lot of the retail sites could not go out to service their stores because, you know, mobility is limited in these, in these strange times that we live in. So from a central center, you're able to deploy applications, you're able to recover applications. And, and a lot of our customers said, Hey, I don't have enough space in my data center to back up. Do you have another option? So then we rolled out this update release to SimpliVity verse from the edge site. You can now directly back up to our backup service, which is offered on a consumption basis to the customers, and they can recover that anywhere they want. >>Fantastic Omer, thanks so much for coming on the program today. >>It's a pleasure, Dave. Thank you. >>All right. Awesome to see you. Now, let's hear from red bull racing and HPE customer, that's actually using SimpliVity at the edge. Countdown really begins when the checkered flag drops on a Sunday. It's always about this race to manufacture >>The next designs to make it more adapt to the next circuit to run those. Of course, if we can't manufacture the next component in time, all that will be wasted. >>Okay. We're back with Matt kudu, who is the CIO of red bull racing? Matt, it's good to see you again. >>Great to say, >>Hey, we're going to dig into a real-world example of using data at the edge and in near real time to gain insights that really lead to competitive advantage. But, but first Matt, tell us a little bit about red bull racing and your role there. >>Sure. So I'm the CIO at red bull racing and that red bull race. And we're based in Milton Keynes in the UK. And the main job job for us is to design a race car, to manufacture the race car, and then to race it around the world. So as CIO, we need to develop the ITT group needs to develop the applications is the design, manufacturing racing. We also need to supply all the underlying infrastructure and also manage security. So it's really interesting environment. That's all about speed. So this season we have 23 races and we need to tear the car apart and rebuild it to a unique configuration for every individual race. And we're also designing and making components targeted for races. So 20 a movable deadlines, um, this big evolving prototype to manage with our car. Um, but we're also improving all of our tools and methods and software that we use to design and make and race the car. >>So we have a big can do attitude of the company around continuous improvement. And the expectations are that we continuously make the car faster. That we're, that we're winning races, that we improve our methods in the factory and our tools. And, um, so for, I take it's really unique and that we can be part of that journey and provide a better service. It's also a big challenge to provide that service and to give the business the agility, agility, and needs. So my job is, is really to make sure we have the right staff, the right partners, the right technical platforms. So we can live up to expectations >>That tear down and rebuild for 23 races. Is that because each track has its own unique signature that you have to tune to, or are there other factors involved there? >>Yeah, exactly. Every track has a different shape. Some have lots of strengths. Some have lots of curves and lots are in between. Um, the track surface is very different and the impact that has some tires, um, the temperature and the climate is very different. Some are hilly, some, a big curves that affect the dynamics of the power. So all that in order to win, you need to micromanage everything and optimize it for any given race track. >>Talk about some of the key drivers in your business and some of the key apps that give you a competitive advantage to help you win races. >>Yeah. So in our business, everything is all about speed. So the car obviously needs to be fast, but also all of our business operations needed to be fast. We need to be able to design a car and it's all done in the virtual world, but the, the virtual simulations and designs need to correlate to what happens in the real world. So all of that requires a lot of expertise to develop the simulation is the algorithms and have all the underlying infrastructure that runs it quickly and reliably. Um, in manufacturing, um, we have cost caps and financial controls by regulation. We need to be super efficient and control material and resources. So ERP and MES systems are running and helping us do that. And at the race track itself in speed, we have hundreds of decisions to make on a Friday and Saturday as we're fine tuning the final configuration of the car. And here again, we rely on simulations and analytics to help do that. And then during the race, we have split seconds, literally seconds to alter our race strategy if an event happens. So if there's an accident, um, and the safety car comes out, or the weather changes, we revise our tactics and we're running Monte Carlo for example. And he is an experienced engineers with simulations to make a data-driven decision and hopefully a better one and faster than our competitors, all of that needs it. Um, so work at a very high level. >>It's interesting. I mean, as a lay person, historically we know when I think about technology and car racing, of course, I think about the mechanical aspects of a self-propelled vehicle, the electronics and the light, but not necessarily the data, but the data's always been there. Hasn't it? I mean, maybe in the form of like tribal knowledge, if somebody who knows the track and where the Hills are and experience and gut feel, but today you're digitizing it and you're, you're processing it and close to real time. >>It's amazing. I think exactly right. Yeah. The car's instrumented with sensors, we post-process at Virgin, um, video, um, image analysis, and we're looking at our car, our competitor's car. So there's a huge amount of, um, very complicated models that we're using to optimize our performance and to continuously improve our car. Yeah. The data and the applications that can leverage it are really key. Um, and that's a critical success factor for us. >>So let's talk about your data center at the track, if you will. I mean, if I can call it that paint a picture for us, what does that look like? >>So we have to send, um, a lot of equipment to the track at the edge. Um, and even though we have really a great wide area network linked back to the factory and there's cloud resources, a lot of the trucks are very old. You don't have hardened infrastructure, don't have ducks that protect cabling, for example, and you could lose connectivity to remote locations. So the applications we need to operate the car and to make really critical decisions, all that needs to be at the edge where the car operates. So historically we had three racks of equipment, like a safe infrastructure, um, and it was really hard to manage, um, to make changes. It was too flexible. Um, there were multiple panes of glass, um, and, um, and it was too slow. It didn't run her applications quickly. Um, it was also too heavy and took up too much space when you're cramped into a garage with lots of environmental constraints. >>So we, um, we'd, we'd introduced hyperconvergence into the factory and seen a lot of great benefits. And when we came time to refresh our infrastructure at the track, we stepped back and said, there's a lot smarter way of operating. We can get rid of all the slow and flexible, expensive legacy and introduce hyperconvergence. And we saw really excellent benefits for doing that. Um, we saw a three X speed up for a lot of our applications. So I'm here where we're post-processing data, and we have to make decisions about race strategy. Time is of the essence in a three X reduction in processing time really matters. Um, we also, um, were able to go from three racks of equipment down to two racks of equipment and the storage efficiency of the HPE SimpliVity platform with 20 to one ratios allowed us to eliminate a rack. And that actually saved a hundred thousand dollars a year in freight costs by shipping less equipment, um, things like backup, um, mistakes happen. >>Sometimes the user makes a mistake. So for example, a race engineer could load the wrong data map into one of our simulations. And we could restore that VDI through SimpliVity backup at 90 seconds. And this makes sure it enables engineers to focus on the car to make better decisions without having downtime. And we sent them to, I take guys to every race they're managing 60 users, a really diverse environment, juggling a lot of balls and having a simple management platform like HPE SimpliVity gives us, allows them to be very effective and to work quickly. So all of those benefits were a huge step forward relative to the legacy infrastructure that we used to run at the edge. >>Yeah. So you had the nice Petri dish and the factory. So it sounds like your, your goals, obviously your number one KPI is speed to help shave seconds time, but also costs just the simplicity of setting up the infrastructure. >>Yeah. It's speed. Speed, speed. So we want applications absolutely fly, you know, get to actionable results quicker, um, get answers from our simulations quicker. The other area that speed's really critical is, um, our applications are also evolving prototypes, and we're always, the models are getting bigger. The simulations are getting bigger and they need more and more resource and being able to spin up resource and provision things without being a bottleneck is a big challenge in SimpliVity. It gives us the means of doing that. >>So did you consider any other options or was it because you had the factory knowledge? It was HCI was, you know, very clearly the option. What did you look at? >>Yeah, so, um, we have over five years of experience in the factory and we eliminated all of our legacy, um, um, infrastructure five years ago. And the benefits I've described, um, at the track, we saw that in the factory, um, at the track we have a three-year operational life cycle for our equipment. When into 2017 was the last year we had legacy as we were building for 2018. It was obvious that hyper-converged was the right technology to introduce. And we'd had years of experience in the factory already. And the benefits that we see with hyper-converged actually mattered even more at the edge because our operations are so much more pressurized time has even more of the essence. And so speeding everything up at the really pointy end of our business was really critical. It was an obvious choice. >>Why, why SimpliVity? What why'd you choose HPE SimpliVity? >>Yeah. So when we first heard about hyperconverged way back in the, in the factory, um, we had, um, a legacy infrastructure, overly complicated, too slow, too inflexible, too expensive. And we stepped back and said, there has to be a smarter way of operating. We went out and challenged our technology partners. We learned about hyperconvergence within enough, the hype, um, was real or not. So we underwent some PLCs and benchmarking and, and the, the PLCs were really impressive. And, and all these, you know, speed and agility benefits, we saw an HP for our use cases was the clear winner in the benchmarks. So based on that, we made an initial investment in the factory. Uh, we moved about 150 VMs in the 150 VDI into it. Um, and then as, as we've seen all the benefits we've successfully invested, and we now have, um, an estate to the factory of about 800 VMs and about 400 VDI. So it's been a great platform and it's allowed us to really push boundaries and, and give the business, um, the service that expects. >>So w was that with the time in which you were able to go from data to insight to recommendation or, or edict, uh, was that compressed, you kind of indicated that, but >>So we, we all telemetry from the car and we post-process it, and that reprocessing time really it's very time consuming. And, um, you know, we went from nine, eight minutes for some of the simulations down to just two minutes. So we saw big, big reductions in time and all, ultimately that meant an engineer could understand what the car was during a practice session, recommend a tweak to the configuration or setup of it, and just get more actionable insight quicker. And it ultimately helps get a better car quicker. >>Such a great example. How are you guys feeling about the season, Matt? What's the team's sentiment? >>Yeah, I think we're optimistic. Um, we w we, um, uh, we have a new driver >>Lineup. Uh, we have, um, max for stopping his carries on with the team and Sergio joins the team. So we're really excited about this year and, uh, we want to go and win races. Great, Matt, good luck this season and going forward and thanks so much for coming back in the cube. Really appreciate it. And it's my pleasure. Great talking to you again. Okay. Now we're going to bring back Omer for quick summary. So keep it real >>Without having solutions from HB, we can't drive those five senses, CFD aerodynamics that would undermine the simulations being software defined. We can bring new apps into play. If we can bring new them's storage, networking, all of that can be highly advises is a hugely beneficial partnership for us. We're able to be at the cutting edge of technology in a highly stressed environment. That is no bigger challenge than the formula. >>Okay. We're back with Omar. Hey, what did you think about that interview with Matt? >>Great. Uh, I have to tell you I'm a big formula one fan, and they are one of my favorite customers. Uh, so, you know, obviously, uh, one of the biggest use cases as you saw for red bull racing is Trackside deployments. There are now 22 races in a season. These guys are jumping from one city to the next, they've got to pack up, move to the next city, set up, set up the infrastructure very, very quickly and average formula. One car is running the thousand plus sensors on that is generating a ton of data on track side that needs to be collected very quickly. It needs to be processed very quickly, and then sometimes believe it or not, snapshots of this data needs to be sent to the red bull back factory back at the data center. What does this all need? It needs reliability. >>It needs compute power in a very short form factor. And it needs agility quick to set up quick, to go quick, to recover. And then in post processing, they need to have CPU density so they can pack more VMs out at the edge to be able to do that processing now. And we accomplished that for, for the red bull racing guys in basically two are you have two SimpliVity nodes that are running track side and moving with them from one, one race to the next race, to the next race. And every time those SimpliVity nodes connect up to the data center collector to a satellite, they're backing up back to their data center. They're sending snapshots of data back to the data center, essentially making their job a whole lot easier, where they can focus on racing and not on troubleshooting virtual machines, >>Red bull racing and HPE SimpliVity. Great example. It's agile, it's it's cost efficient, and it shows a real impact. Thank you very much. I really appreciate those summary comments. Thank you, Dave. Really appreciate it. All right. And thank you for watching. This is Dave Volante. >>You.

>> So as folks make their way over from Javits I'm going to give you the least interesting part of the evening and that's my segment in which I welcome you here, introduce myself, lay out what what we're going to do for the next couple of hours. So first off, thank you very much for coming. As all of you know Wikibon is a part of SiliconANGLE which also includes theCUBE, so if you look around, this is what we have been doing for the past couple of days here in the TheCUBE. We've been inviting some significant thought leaders from over on the show and in incredibly expensive limousines driven them up the street to come on to TheCUBE and spend time with us and talk about some of the things that are happening in the industry today that are especially important. We tore it down, and we're having this party tonight. So we want to thank you very much for coming and look forward to having more conversations with all of you. Now what are we going to talk about? Well Wikibon is the research arm of SiliconANGLE. So we take data that comes out of TheCUBE and other places and we incorporated it into our research. And work very closely with large end users and large technology companies regarding how to make better decisions in this incredibly complex, incredibly important transformative world of digital business. What we're going to talk about tonight, and I've got a couple of my analysts assembled, and we're also going to have a panel, is this notion of software is eating the Edge. Now most of you have probably heard Marc Andreessen, the venture capitalist and developer, original developer of Netscape many years ago, talk about how software's eating the world. Well, if software is truly going to eat the world, it's going to eat at, it's going to take the big chunks, big bites at the Edge. That's where the actual action's going to be. And what we want to talk about specifically is the entangling of the internet or the industrial internet of things and IoT with analytics. So that's what we're going to talk about over the course of the next couple of hours. To do that we're going to, I've already blown the schedule, that's on me. But to do that I'm going to spend a couple minutes talking about what we regard as the essential digital business capabilities which includes analytics and Big Data, and includes IIoT and we'll explain at least in our position why those two things come together the way that they do. But I'm going to ask the august and revered Neil Raden, Wikibon analyst to come on up and talk about harvesting value at the Edge. 'Cause there are some, not now Neil, when we're done, when I'm done. So I'm going to ask Neil to come on up and we'll talk, he's going to talk about harvesting value at the Edge. And then Jim Kobielus will follow up with him, another Wikibon analyst, he'll talk specifically about how we're going to take that combination of analytics and Edge and turn it into the new types of systems and software that are going to sustain this significant transformation that's going on. And then after that, I'm going to ask Neil and Jim to come, going to invite some other folks up and we're going to run a panel to talk about some of these issues and do a real question and answer. So the goal here is before we break for drinks is to create a community feeling within the room. That includes smart people here, smart people in the audience having a conversation ultimately about some of these significant changes so please participate and we look forward to talking about the rest of it. All right, let's get going! What is digital business? One of the nice things about being an analyst is that you can reach back on people who were significantly smarter than you and build your points of view on the shoulders of those giants including Peter Drucker. Many years ago Peter Drucker made the observation that the purpose of business is to create and keep a customer. Not better shareholder value, not anything else. It is about creating and keeping your customer. Now you can argue with that, at the end of the day, if you don't have customers, you don't have a business. Now the observation that we've made, what we've added to that is that we've made the observation that the difference between business and digital business essentially is one thing. That's data. A digital business uses data to differentially create and keep customers. That's the only difference. If you think about the difference between taxi cab companies here in New York City, every cab that I've been in in the last three days has bothered me about Uber. The reason, the difference between Uber and a taxi cab company is data. That's the primary difference. Uber uses data as an asset. And we think this is the fundamental feature of digital business that everybody has to pay attention to. How is a business going to use data as an asset? Is the business using data as an asset? Is a business driving its engagement with customers, the role of its product et cetera using data? And if they are, they are becoming a more digital business. Now when you think about that, what we're really talking about is how are they going to put data to work? How are they going to take their customer data and their operational data and their financial data and any other kind of data and ultimately turn that into superior engagement or improved customer experience or more agile operations or increased automation? Those are the kinds of outcomes that we're talking about. But it is about putting data to work. That's fundamentally what we're trying to do within a digital business. Now that leads to an observation about the crucial strategic business capabilities that every business that aspires to be more digital or to be digital has to put in place. And I want to be clear. When I say strategic capabilities I mean something specific. When you talk about, for example technology architecture or information architecture there is this notion of what capabilities does your business need? Your business needs capabilities to pursue and achieve its mission. And in the digital business these are the capabilities that are now additive to this core question, ultimately of whether or not the company is a digital business. What are the three capabilities? One, you have to capture data. Not just do a good job of it, but better than your competition. You have to capture data better than your competition. In a way that is ultimately less intrusive on your markets and on your customers. That's in many respects, one of the first priorities of the internet of things and people. The idea of using sensors and related technologies to capture more data. Once you capture that data you have to turn it into value. You have to do something with it that creates business value so you can do a better job of engaging your markets and serving your customers. And that essentially is what we regard as the basis of Big Data. Including operations, including financial performance and everything else, but ultimately it's taking the data that's being captured and turning it into value within the business. The last point here is that once you have generated a model, or an insight or some other resource that you can act upon, you then have to act upon it in the real world. We call that systems of agency, the ability to enact based on data. Now I want to spend just a second talking about systems of agency 'cause we think it's an interesting concept and it's something Jim Kobielus is going to talk about a little bit later. When we say systems of agency, what we're saying is increasingly machines are acting on behalf of a brand. Or systems, combinations of machines and people are acting on behalf of the brand. And this whole notion of agency is the idea that ultimately these systems are now acting as the business's agent. They are at the front line of engaging customers. It's an extremely rich proposition that has subtle but crucial implications. For example I was talking to a senior decision maker at a business today and they made a quick observation, they talked about they, on their way here to New York City they had followed a woman who was going through security, opened up her suitcase and took out a bird. And then went through security with the bird. And the reason why I bring this up now is as TSA was trying to figure out how exactly to deal with this, the bird started talking and repeating things that the woman had said and many of those things, in fact, might have put her in jail. Now in this case the bird is not an agent of that woman. You can't put the woman in jail because of what the bird said. But increasingly we have to ask ourselves as we ask machines to do more on our behalf, digital instrumentation and elements to do more on our behalf, it's going to have blow back and an impact on our brand if we don't do it well. I want to draw that forward a little bit because I suggest there's going to be a new lifecycle for data. And the way that we think about it is we have the internet or the Edge which is comprised of things and crucially people, using sensors, whether they be smaller processors in control towers or whether they be phones that are tracking where we go, and this crucial element here is something that we call information transducers. Now a transducer in a traditional sense is something that takes energy from one form to another so that it can perform new types of work. By information transducer I essentially mean it takes information from one form to another so it can perform another type of work. This is a crucial feature of data. One of the beauties of data is that it can be used in multiple places at multiple times and not engender significant net new costs. It's one of the few assets that you can say about that. So the concept of an information transducer's really important because it's the basis for a lot of transformations of data as data flies through organizations. So we end up with the transducers storing data in the form of analytics, machine learning, business operations, other types of things, and then it goes back and it's transduced, back into to the real world as we program the real world and turning into these systems of agency. So that's the new lifecycle. And increasingly, that's how we have to think about data flows. Capturing it, turning it into value and having it act on our behalf in front of markets. That could have enormous implications for how ultimately money is spent over the next few years. So Wikibon does a significant amount of market research in addition to advising our large user customers. And that includes doing studies on cloud, public cloud, but also studies on what's happening within the analytics world. And if you take a look at it, what we basically see happening over the course of the next few years is significant investments in software and also services to get the word out. But we also expect there's going to be a lot of hardware. A significant amount of hardware that's ultimately sold within this space. And that's because of something that we call true private cloud. This concept of ultimately a business increasingly being designed and architected around the idea of data assets means that the reality, the physical realities of how data operates, how much it costs to store it or move it, the issues of latency, the issues of intellectual property protection as well as things like the regulatory regimes that are being put in place to govern how data gets used in between locations. All of those factors are going to drive increased utilization of what we call true private cloud. On premise technologies that provide the cloud experience but act where the data naturally needs to be processed. I'll come a little bit more to that in a second. So we think that it's going to be a relatively balanced market, a lot of stuff is going to end up in the cloud, but as Neil and Jim will talk about, there's going to be an enormous amount of analytics that pulls an enormous amount of data out to the Edge 'cause that's where the action's going to be. Now one of the things I want to also reveal to you is we've done a fair amount of data, we've done a fair amount of research around this question of where or how will data guide decisions about infrastructure? And in particular the Edge is driving these conversations. So here is a piece of research that one of our cohorts at Wikibon did, David Floyer. Taking a look at IoT Edge cost comparisons over a three year period. And it showed on the left hand side, an example where the sensor towers and other types of devices were streaming data back into a central location in a wind farm, stylized wind farm example. Very very expensive. Significant amounts of money end up being consumed, significant resources end up being consumed by the cost of moving the data from one place to another. Now this is even assuming that latency does not become a problem. The second example that we looked at is if we kept more of that data at the Edge and processed at the Edge. And literally it is a 85 plus percent cost reduction to keep more of the data at the Edge. Now that has enormous implications, how we think about big data, how we think about next generation architectures, et cetera. But it's these costs that are going to be so crucial to shaping the decisions that we make over the next two years about where we put hardware, where we put resources, what type of automation is possible, and what types of technology management has to be put in place. Ultimately we think it's going to lead to a structure, an architecture in the infrastructure as well as applications that is informed more by moving cloud to the data than moving the data to the cloud. That's kind of our fundamental proposition is that the norm in the industry has been to think about moving all data up to the cloud because who wants to do IT? It's so much cheaper, look what Amazon can do. Or what AWS can do. All true statements. Very very important in many respects. But most businesses today are starting to rethink that simple proposition and asking themselves do we have to move our business to the cloud, or can we move the cloud to the business? And increasingly what we see happening as we talk to our large customers about this, is that the cloud is being extended out to the Edge, we're moving the cloud and cloud services out to the business. Because of economic reasons, intellectual property control reasons, regulatory reasons, security reasons, any number of other reasons. It's just a more natural way to deal with it. And of course, the most important reason is latency. So with that as a quick backdrop, if I may quickly summarize, we believe fundamentally that the difference today is that businesses are trying to understand how to use data as an asset. And that requires an investment in new sets of technology capabilities that are not cheap, not simple and require significant thought, a lot of planning, lot of change within an IT and business organizations. How we capture data, how we turn it into value, and how we translate that into real world action through software. That's going to lead to a rethinking, ultimately, based on cost and other factors about how we deploy infrastructure. How we use the cloud so that the data guides the activity and not the choice of cloud supplier determines or limits what we can do with our data. And that's going to lead to this notion of true private cloud and elevate the role the Edge plays in analytics and all other architectures. So I hope that was perfectly clear. And now what I want to do is I want to bring up Neil Raden. Yes, now's the time Neil! So let me invite Neil up to spend some time talking about harvesting value at the Edge. Can you see his, all right. Got it. >> Oh boy. Hi everybody. Yeah, this is a really, this is a really big and complicated topic so I decided to just concentrate on something fairly simple, but I know that Peter mentioned customers. And he also had a picture of Peter Drucker. I had the pleasure in 1998 of interviewing Peter and photographing him. Peter Drucker, not this Peter. Because I'd started a magazine called Hired Brains. It was for consultants. And Peter said, Peter said a number of really interesting things to me, but one of them was his definition of a customer was someone who wrote you a check that didn't bounce. He was kind of a wag. He was! So anyway, he had to leave to do a video conference with Jack Welch and so I said to him, how do you charge Jack Welch to spend an hour on a video conference? And he said, you know I have this theory that you should always charge your client enough that it hurts a little bit or they don't take you seriously. Well, I had the chance to talk to Jack's wife, Suzie Welch recently and I told her that story and she said, "Oh he's full of it, Jack never paid "a dime for those conferences!" (laughs) So anyway, all right, so let's talk about this. To me, things about, engineered things like the hardware and network and all these other standards and so forth, we haven't fully developed those yet, but they're coming. As far as I'm concerned, they're not the most interesting thing. The most interesting thing to me in Edge Analytics is what you're going to get out of it, what the result is going to be. Making sense of this data that's coming. And while we're on data, something I've been thinking a lot lately because everybody I've talked to for the last three days just keeps talking to me about data. I have this feeling that data isn't actually quite real. That any data that we deal with is the result of some process that's captured it from something else that's actually real. In other words it's proxy. So it's not exactly perfect. And that's why we've always had these problems about customer A, customer A, customer A, what's their definition? What's the definition of this, that and the other thing? And with sensor data, I really have the feeling, when companies get, not you know, not companies, organizations get instrumented and start dealing with this kind of data what they're going to find is that this is the first time, and I've been involved in analytics, I don't want to date myself, 'cause I know I look young, but the first, I've been dealing with analytics since 1975. And everything we've ever done in analytics has involved pulling data from some other system that was not designed for analytics. But if you think about sensor data, this is data that we're actually going to catch the first time. It's going to be ours! We're not going to get it from some other source. It's going to be the real deal, to the extent that it's the real deal. Now you may say, ya know Neil, a sensor that's sending us information about oil pressure or temperature or something like that, how can you quarrel with that? Well, I can quarrel with it because I don't know if the sensor's doing it right. So we still don't know, even with that data, if it's right, but that's what we have to work with. Now, what does that really mean? Is that we have to be really careful with this data. It's ours, we have to take care of it. We don't get to reload it from source some other day. If we munge it up it's gone forever. So that has, that has very serious implications, but let me, let me roll you back a little bit. The way I look at analytics is it's come in three different eras. And we're entering into the third now. The first era was business intelligence. It was basically built and governed by IT, it was system of record kind of reporting. And as far as I can recall, it probably started around 1988 or at least that's the year that Howard Dresner claims to have invented the term. I'm not sure it's true. And things happened before 1988 that was sort of like BI, but 88 was when they really started coming out, that's when we saw BusinessObjects and Cognos and MicroStrategy and those kinds of things. The second generation just popped out on everybody else. We're all looking around at BI and we were saying why isn't this working? Why are only five people in the organization using this? Why are we not getting value out of this massive license we bought? And along comes companies like Tableau doing data discovery, visualization, data prep and Line of Business people are using this now. But it's still the same kind of data sources. It's moved out a little bit, but it still hasn't really hit the Big Data thing. Now we're in third generation, so we not only had Big Data, which has come and hit us like a tsunami, but we're looking at smart discovery, we're looking at machine learning. We're looking at AI induced analytics workflows. And then all the natural language cousins. You know, natural language processing, natural language, what's? Oh Q, natural language query. Natural language generation. Anybody here know what natural language generation is? Yeah, so what you see now is you do some sort of analysis and that tool comes up and says this chart is about the following and it used the following data, and it's blah blah blah blah blah. I think it's kind of wordy and it's going to refined some, but it's an interesting, it's an interesting thing to do. Now, the problem I see with Edge Analytics and IoT in general is that most of the canonical examples we talk about are pretty thin. I know we talk about autonomous cars, I hope to God we never have them, 'cause I'm a car guy. Fleet Management, I think Qualcomm started Fleet Management in 1988, that is not a new application. Industrial controls. I seem to remember, I seem to remember Honeywell doing industrial controls at least in the 70s and before that I wasn't, I don't want to talk about what I was doing, but I definitely wasn't in this industry. So my feeling is we all need to sit down and think about this and get creative. Because the real value in Edge Analytics or IoT, whatever you want to call it, the real value is going to be figuring out something that's new or different. Creating a brand new business. Changing the way an operation happens in a company, right? And I think there's a lot of smart people out there and I think there's a million apps that we haven't even talked about so, if you as a vendor come to me and tell me how great your product is, please don't talk to me about autonomous cars or Fleet Managing, 'cause I've heard about that, okay? Now, hardware and architecture are really not the most interesting thing. We fell into that trap with data warehousing. We've fallen into that trap with Big Data. We talk about speeds and feeds. Somebody said to me the other day, what's the narrative of this company? This is a technology provider. And I said as far as I can tell, they don't have a narrative they have some products and they compete in a space. And when they go to clients and the clients say, what's the value of your product? They don't have an answer for that. So we don't want to fall into this trap, okay? Because IoT is going to inform you in ways you've never even dreamed about. Unfortunately some of them are going to be really stinky, you know, they're going to be really bad. You're going to lose more of your privacy, it's going to get harder to get, I dunno, mortgage for example, I dunno, maybe it'll be easier, but in any case, it's not going to all be good. So let's really think about what you want to do with this technology to do something that's really valuable. Cost takeout is not the place to justify an IoT project. Because number one, it's very expensive, and number two, it's a waste of the technology because you should be looking at, you know the old numerator denominator thing? You should be looking at the numerators and forget about the denominators because that's not what you do with IoT. And the other thing is you don't want to get over confident. Actually this is good advice about anything, right? But in this case, I love this quote by Derek Sivers He's a pretty funny guy. He said, "If more information was the answer, "then we'd all be billionaires with perfect abs." I'm not sure what's on his wishlist, but you know, I would, those aren't necessarily the two things I would think of, okay. Now, what I said about the data, I want to explain some more. Big Data Analytics, if you look at this graphic, it depicts it perfectly. It's a bunch of different stuff falling into the funnel. All right? It comes from other places, it's not original material. And when it comes in, it's always used as second hand data. Now what does that mean? That means that you have to figure out the semantics of this information and you have to find a way to put it together in a way that's useful to you, okay. That's Big Data. That's where we are. How is that different from IoT data? It's like I said, IoT is original. You can put it together any way you want because no one else has ever done that before. It's yours to construct, okay. You don't even have to transform it into a schema because you're creating the new application. But the most important thing is you have to take care of it 'cause if you lose it, it's gone. It's the original data. It's the same way, in operational systems for a long long time we've always been concerned about backup and security and everything else. You better believe this is a problem. I know a lot of people think about streaming data, that we're going to look at it for a minute, and we're going to throw most of it away. Personally I don't think that's going to happen. I think it's all going to be saved, at least for a while. Now, the governance and security, oh, by the way, I don't know where you're going to find a presentation where somebody uses a newspaper clipping about Vladimir Lenin, but here it is, enjoy yourselves. I believe that when people think about governance and security today they're still thinking along the same grids that we thought about it all along. But this is very very different and again, I'm sorry I keep thrashing this around, but this is treasured data that has to be carefully taken care of. Now when I say governance, my experience has been over the years that governance is something that IT does to make everybody's lives miserable. But that's not what I mean by governance today. It means a comprehensive program to really secure the value of the data as an asset. And you need to think about this differently. Now the other thing is you may not get to think about it differently, because some of the stuff may end up being subject to regulation. And if the regulators start regulating some of this, then that'll take some of the degrees of freedom away from you in how you put this together, but you know, that's the way it works. Now, machine learning, I think I told somebody the other day that claims about machine learning in software products are as common as twisters in trail parks. And a lot of it is not really what I'd call machine learning. But there's a lot of it around. And I think all of the open source machine learning and artificial intelligence that's popped up, it's great because all those math PhDs who work at Home Depot now have something to do when they go home at night and they construct this stuff. But if you're going to have machine learning at the Edge, here's the question, what kind of machine learning would you have at the Edge? As opposed to developing your models back at say, the cloud, when you transmit the data there. The devices at the Edge are not very powerful. And they don't have a lot of memory. So you're only going to be able to do things that have been modeled or constructed somewhere else. But that's okay. Because machine learning algorithm development is actually slow and painful. So you really want the people who know how to do this working with gobs of data creating models and testing them offline. And when you have something that works, you can put it there. Now there's one thing I want to talk about before I finish, and I think I'm almost finished. I wrote a book about 10 years ago about automated decision making and the conclusion that I came up with was that little decisions add up, and that's good. But it also means you don't have to get them all right. But you don't want computers or software making decisions unattended if it involves human life, or frankly any life. Or the environment. So when you think about the applications that you can build using this architecture and this technology, think about the fact that you're not going to be doing air traffic control, you're not going to be monitoring crossing guards at the elementary school. You're going to be doing things that may seem fairly mundane. Managing machinery on the factory floor, I mean that may sound great, but really isn't that interesting. Managing well heads, drilling for oil, well I mean, it's great to the extent that it doesn't cause wells to explode, but they don't usually explode. What it's usually used for is to drive the cost out of preventative maintenance. Not very interesting. So use your heads. Come up with really cool stuff. And any of you who are involved in Edge Analytics, the next time I talk to you I don't want to hear about the same five applications that everybody talks about. Let's hear about some new ones. So, in conclusion, I don't really have anything in conclusion except that Peter mentioned something about limousines bringing people up here. On Monday I was slogging up and down Park Avenue and Madison Avenue with my client and we were visiting all the hedge funds there because we were doing a project with them. And in the miserable weather I looked at him and I said, for godsake Paul, where's the black car? And he said, that was the 90s. (laughs) Thank you. So, Jim, up to you. (audience applauding) This is terrible, go that way, this was terrible coming that way. >> Woo, don't want to trip! And let's move to, there we go. Hi everybody, how ya doing? Thanks Neil, thanks Peter, those were great discussions. So I'm the third leg in this relay race here, talking about of course how software is eating the world. And focusing on the value of Edge Analytics in a lot of real world scenarios. Programming the real world for, to make the world a better place. So I will talk, I'll break it out analytically in terms of the research that Wikibon is doing in the area of the IoT, but specifically how AI intelligence is being embedded really to all material reality potentially at the Edge. But mobile applications and industrial IoT and the smart appliances and self driving vehicles. I will break it out in terms of a reference architecture for understanding what functions are being pushed to the Edge to hardware, to our phones and so forth to drive various scenarios in terms of real world results. So I'll move a pace here. So basically AI software or AI microservices are being infused into Edge hardware as we speak. What we see is more vendors of smart phones and other, real world appliances and things like smart driving, self driving vehicles. What they're doing is they're instrumenting their products with computer vision and natural language processing, environmental awareness based on sensing and actuation and those capabilities and inferences that these devices just do to both provide human support for human users of these devices as well as to enable varying degrees of autonomous operation. So what I'll be talking about is how AI is a foundation for data driven systems of agency of the sort that Peter is talking about. Infusing data driven intelligence into everything or potentially so. As more of this capability, all these algorithms for things like, ya know for doing real time predictions and classifications, anomaly detection and so forth, as this functionality gets diffused widely and becomes more commoditized, you'll see it burned into an ever-wider variety of hardware architecture, neuro synaptic chips, GPUs and so forth. So what I've got here in front of you is a sort of a high level reference architecture that we're building up in our research at Wikibon. So AI, artificial intelligence is a big term, a big paradigm, I'm not going to unpack it completely. Of course we don't have oodles of time so I'm going to take you fairly quickly through the high points. It's a driver for systems of agency. Programming the real world. Transducing digital inputs, the data, to analog real world results. Through the embedding of this capability in the IoT, but pushing more and more of it out to the Edge with points of decision and action in real time. And there are four capabilities that we're seeing in terms of AI enabled, enabling capabilities that are absolutely critical to software being pushed to the Edge are sensing, actuation, inference and Learning. Sensing and actuation like Peter was describing, it's about capturing data from the environment within which a device or users is operating or moving. And then actuation is the fancy term for doing stuff, ya know like industrial IoT, it's obviously machine controlled, but clearly, you know self driving vehicles is steering a vehicle and avoiding crashing and so forth. Inference is the meat and potatoes as it were of AI. Analytics does inferences. It infers from the data, the logic of the application. Predictive logic, correlations, classification, abstractions, differentiation, anomaly detection, recognizing faces and voices. We see that now with Apple and the latest version of the iPhone is embedding face recognition as a core, as the core multifactor authentication technique. Clearly that's a harbinger of what's going to be universal fairly soon which is that depends on AI. That depends on convolutional neural networks, that is some heavy hitting processing power that's necessary and it's processing the data that's coming from your face. So that's critically important. So what we're looking at then is the AI software is taking root in hardware to power continuous agency. Getting stuff done. Powered decision support by human beings who have to take varying degrees of action in various environments. We don't necessarily want to let the car steer itself in all scenarios, we want some degree of override, for lots of good reasons. They want to protect life and limb including their own. And just more data driven automation across the internet of things in the broadest sense. So unpacking this reference framework, what's happening is that AI driven intelligence is powering real time decisioning at the Edge. Real time local sensing from the data that it's capturing there, it's ingesting the data. Some, not all of that data, may be persistent at the Edge. Some, perhaps most of it, will be pushed into the cloud for other processing. When you have these highly complex algorithms that are doing AI deep learning, multilayer, to do a variety of anti-fraud and higher level like narrative, auto-narrative roll-ups from various scenes that are unfolding. A lot of this processing is going to begin to happen in the cloud, but a fair amount of the more narrowly scoped inferences that drive real time decision support at the point of action will be done on the device itself. Contextual actuation, so it's the sensor data that's captured by the device along with other data that may be coming down in real time streams through the cloud will provide the broader contextual envelope of data needed to drive actuation, to drive various models and rules and so forth that are making stuff happen at the point of action, at the Edge. Continuous inference. What it all comes down to is that inference is what's going on inside the chips at the Edge device. And what we're seeing is a growing range of hardware architectures, GPUs, CPUs, FPGAs, ASIC, Neuro synaptic chips of all sorts playing in various combinations that are automating more and more very complex inference scenarios at the Edge. And not just individual devices, swarms of devices, like drones and so forth are essentially an Edge unto themselves. You'll see these tiered hierarchies of Edge swarms that are playing and doing inferences of ever more complex dynamic nature. And much of this will be, this capability, the fundamental capabilities that is powering them all will be burned into the hardware that powers them. And then adaptive learning. Now I use the term learning rather than training here, training is at the core of it. Training means everything in terms of the predictive fitness or the fitness of your AI services for whatever task, predictions, classifications, face recognition that you, you've built them for. But I use the term learning in a broader sense. It's what's make your inferences get better and better, more accurate over time is that you're training them with fresh data in a supervised learning environment. But you can have reinforcement learning if you're doing like say robotics and you don't have ground truth against which to train the data set. You know there's maximize a reward function versus minimize a loss function, you know, the standard approach, the latter for supervised learning. There's also, of course, the issue, or not the issue, the approach of unsupervised learning with cluster analysis critically important in a lot of real world scenarios. So Edge AI Algorithms, clearly, deep learning which is multilayered machine learning models that can do abstractions at higher and higher levels. Face recognition is a high level abstraction. Faces in a social environment is an even higher level of abstraction in terms of groups. Faces over time and bodies and gestures, doing various things in various environments is an even higher level abstraction in terms of narratives that can be rolled up, are being rolled up by deep learning capabilities of great sophistication. Convolutional neural networks for processing images, recurrent neural networks for processing time series. Generative adversarial networks for doing essentially what's called generative applications of all sort, composing music, and a lot of it's being used for auto programming. These are all deep learning. There's a variety of other algorithm approaches I'm not going to bore you with here. Deep learning is essentially the enabler of the five senses of the IoT. Your phone's going to have, has a camera, it has a microphone, it has the ability to of course, has geolocation and navigation capabilities. It's environmentally aware, it's got an accelerometer and so forth embedded therein. The reason that your phone and all of the devices are getting scary sentient is that they have the sensory modalities and the AI, the deep learning that enables them to make environmentally correct decisions in the wider range of scenarios. So machine learning is the foundation of all of this, but there are other, I mean of deep learning, artificial neural networks is the foundation of that. But there are other approaches for machine learning I want to make you aware of because support vector machines and these other established approaches for machine learning are not going away but really what's driving the show now is deep learning, because it's scary effective. And so that's where most of the investment in AI is going into these days for deep learning. AI Edge platforms, tools and frameworks are just coming along like gangbusters. Much development of AI, of deep learning happens in the context of your data lake. This is where you're storing your training data. This is the data that you use to build and test to validate in your models. So we're seeing a deepening stack of Hadoop and there's Kafka, and Spark and so forth that are driving the training (coughs) excuse me, of AI models that are power all these Edge Analytic applications so that that lake will continue to broaden in terms, and deepen in terms of a scope and the range of data sets and the range of modeling, AI modeling supports. Data science is critically important in this scenario because the data scientist, the data science teams, the tools and techniques and flows of data science are the fundamental development paradigm or discipline or capability that's being leveraged to build and to train and to deploy and iterate all this AI that's being pushed to the Edge. So clearly data science is at the center, data scientists of an increasingly specialized nature are necessary to the realization to this value at the Edge. AI frameworks are coming along like you know, a mile a minute. TensorFlow has achieved a, is an open source, most of these are open source, has achieved sort of almost like a defacto standard, status, I'm using the word defacto in air quotes. There's Theano and Keras and xNet and CNTK and a variety of other ones. We're seeing range of AI frameworks come to market, most open source. Most are supported by most of the major tool vendors as well. So at Wikibon we're definitely tracking that, we plan to go deeper in our coverage of that space. And then next best action, powers recommendation engines. I mean next best action decision automation of the sort of thing Neil's covered in a variety of contexts in his career is fundamentally important to Edge Analytics to systems of agency 'cause it's driving the process automation, decision automation, sort of the targeted recommendations that are made at the Edge to individual users as well as to process that automation. That's absolutely necessary for self driving vehicles to do their jobs and industrial IoT. So what we're seeing is more and more recommendation engine or recommender capabilities powered by ML and DL are going to the Edge, are already at the Edge for a variety of applications. Edge AI capabilities, like I said, there's sensing. And sensing at the Edge is becoming ever more rich, mixed reality Edge modalities of all sort are for augmented reality and so forth. We're just seeing a growth in certain, the range of sensory modalities that are enabled or filtered and analyzed through AI that are being pushed to the Edge, into the chip sets. Actuation, that's where robotics comes in. Robotics is coming into all aspects of our lives. And you know, it's brainless without AI, without deep learning and these capabilities. Inference, autonomous edge decisioning. Like I said, it's, a growing range of inferences that are being done at the Edge. And that's where it has to happen 'cause that's the point of decision. Learning, training, much training, most training will continue to be done in the cloud because it's very data intensive. It's a grind to train and optimize an AI algorithm to do its job. It's not something that you necessarily want to do or can do at the Edge at Edge devices so, the models that are built and trained in the cloud are pushed down through a dev ops process down to the Edge and that's the way it will work pretty much in most AI environments, Edge analytics environments. You centralize the modeling, you decentralize the execution of the inference models. The training engines will be in the cloud. Edge AI applications. I'll just run you through sort of a core list of the ones that are coming into, already come into the mainstream at the Edge. Multifactor authentication, clearly the Apple announcement of face recognition is just a harbinger of the fact that that's coming to every device. Computer vision speech recognition, NLP, digital assistance and chat bots powered by natural language processing and understanding, it's all AI powered. And it's becoming very mainstream. Emotion detection, face recognition, you know I could go on and on but these are like the core things that everybody has access to or will by 2020 and they're core devices, mass market devices. Developers, designers and hardware engineers are coming together to pool their expertise to build and train not just the AI, but also the entire package of hardware in UX and the orchestration of real world business scenarios or life scenarios that all this intelligence, the submitted intelligence enables and most, much of what they build in terms of AI will be containerized as micro services through Docker and orchestrated through Kubernetes as full cloud services in an increasingly distributed fabric. That's coming along very rapidly. We can see a fair amount of that already on display at Strata in terms of what the vendors are doing or announcing or who they're working with. The hardware itself, the Edge, you know at the Edge, some data will be persistent, needs to be persistent to drive inference. That's, and you know to drive a variety of different application scenarios that need some degree of historical data related to what that device in question happens to be sensing or has sensed in the immediate past or you know, whatever. The hardware itself is geared towards both sensing and increasingly persistence and Edge driven actuation of real world results. The whole notion of drones and robotics being embedded into everything that we do. That's where that comes in. That has to be powered by low cost, low power commodity chip sets of various sorts. What we see right now in terms of chip sets is it's a GPUs, Nvidia has gone real far and GPUs have come along very fast in terms of power inference engines, you know like the Tesla cars and so forth. But GPUs are in many ways the core hardware sub straight for in inference engines in DL so far. But to become a mass market phenomenon, it's got to get cheaper and lower powered and more commoditized, and so we see a fair number of CPUs being used as the hardware for Edge Analytic applications. Some vendors are fairly big on FPGAs, I believe Microsoft has gone fairly far with FPGAs inside DL strategy. ASIC, I mean, there's neuro synaptic chips like IBM's got one. There's at least a few dozen vendors of neuro synaptic chips on the market so at Wikibon we're going to track that market as it develops. And what we're seeing is a fair number of scenarios where it's a mixed environment where you use one chip set architecture at the inference side of the Edge, and other chip set architectures that are driving the DL as processed in the cloud, playing together within a common architecture. And we see some, a fair number of DL environments where the actual training is done in the cloud on Spark using CPUs and parallelized in memory, but pushing Tensorflow models that might be trained through Spark down to the Edge where the inferences are done in FPGAs and GPUs. Those kinds of mixed hardware scenarios are very, very, likely to be standard going forward in lots of areas. So analytics at the Edge power continuous results is what it's all about. The whole point is really not moving the data, it's putting the inference at the Edge and working from the data that's already captured and persistent there for the duration of whatever action or decision or result needs to be powered from the Edge. Like Neil said cost takeout alone is not worth doing. Cost takeout alone is not the rationale for putting AI at the Edge. It's getting new stuff done, new kinds of things done in an automated consistent, intelligent, contextualized way to make our lives better and more productive. Security and governance are becoming more important. Governance of the models, governance of the data, governance in a dev ops context in terms of version controls over all those DL models that are built, that are trained, that are containerized and deployed. Continuous iteration and improvement of those to help them learn to do, make our lives better and easier. With that said, I'm going to hand it over now. It's five minutes after the hour. We're going to get going with the Influencer Panel so what we'd like to do is I call Peter, and Peter's going to call our influencers. >> All right, am I live yet? Can you hear me? All right so, we've got, let me jump back in control here. We've got, again, the objective here is to have community take on some things. And so what we want to do is I want to invite five other people up, Neil why don't you come on up as well. Start with Neil. You can sit here. On the far right hand side, Judith, Judith Hurwitz. >> Neil: I'm glad I'm on the left side. >> From the Hurwitz Group. >> From the Hurwitz Group. Jennifer Shin who's affiliated with UC Berkeley. Jennifer are you here? >> She's here, Jennifer where are you? >> She was here a second ago. >> Neil: I saw her walk out she may have, >> Peter: All right, she'll be back in a second. >> Here's Jennifer! >> Here's Jennifer! >> Neil: With 8 Path Solutions, right? >> Yep. >> Yeah 8 Path Solutions. >> Just get my mic. >> Take your time Jen. >> Peter: All right, Stephanie McReynolds. Far left. And finally Joe Caserta, Joe come on up. >> Stephie's with Elysian >> And to the left. So what I want to do is I want to start by having everybody just go around introduce yourself quickly. Judith, why don't we start there. >> I'm Judith Hurwitz, I'm president of Hurwitz and Associates. We're an analyst research and fault leadership firm. I'm the co-author of eight books. Most recent is Cognitive Computing and Big Data Analytics. I've been in the market for a couple years now. >> Jennifer. >> Hi, my name's Jennifer Shin. I'm the founder and Chief Data Scientist 8 Path Solutions LLC. We do data science analytics and technology. We're actually about to do a big launch next month, with Box actually. >> We're apparent, are we having a, sorry Jennifer, are we having a problem with Jennifer's microphone? >> Man: Just turn it back on? >> Oh you have to turn it back on. >> It was on, oh sorry, can you hear me now? >> Yes! We can hear you now. >> Okay, I don't know how that turned back off, but okay. >> So you got to redo all that Jen. >> Okay, so my name's Jennifer Shin, I'm founder of 8 Path Solutions LLC, it's a data science analytics and technology company. I founded it about six years ago. So we've been developing some really cool technology that we're going to be launching with Box next month. It's really exciting. And I have, I've been developing a lot of patents and some technology as well as teaching at UC Berkeley as a lecturer in data science. >> You know Jim, you know Neil, Joe, you ready to go? >> Joe: Just broke my microphone. >> Joe's microphone is broken. >> Joe: Now it should be all right. >> Jim: Speak into Neil's. >> Joe: Hello, hello? >> I just feel not worthy in the presence of Joe Caserta. (several laughing) >> That's right, master of mics. If you can hear me, Joe Caserta, so yeah, I've been doing data technology solutions since 1986, almost as old as Neil here, but been doing specifically like BI, data warehousing, business intelligence type of work since 1996. And been doing, wholly dedicated to Big Data solutions and modern data engineering since 2009. Where should I be looking? >> Yeah I don't know where is the camera? >> Yeah, and that's basically it. So my company was formed in 2001, it's called Caserta Concepts. We recently rebranded to only Caserta 'cause what we do is way more than just concepts. So we conceptualize the stuff, we envision what the future brings and we actually build it. And we help clients large and small who are just, want to be leaders in innovation using data specifically to advance their business. >> Peter: And finally Stephanie McReynolds. >> I'm Stephanie McReynolds, I had product marketing as well as corporate marketing for a company called Elysian. And we are a data catalog so we help bring together not only a technical understanding of your data, but we curate that data with human knowledge and use automated intelligence internally within the system to make recommendations about what data to use for decision making. And some of our customers like City of San Diego, a large automotive manufacturer working on self driving cars and General Electric use Elysian to help power their solutions for IoT at the Edge. >> All right so let's jump right into it. And again if you have a question, raise your hand, and we'll do our best to get it to the floor. But what I want to do is I want to get seven questions in front of this group and have you guys discuss, slog, disagree, agree. Let's start here. What is the relationship between Big Data AI and IoT? Now Wikibon's put forward its observation that data's being generated at the Edge, that action is being taken at the Edge and then increasingly the software and other infrastructure architectures need to accommodate the realities of how data is going to work in these very complex systems. That's our perspective. Anybody, Judith, you want to start? >> Yeah, so I think that if you look at AI machine learning, all these different areas, you have to be able to have the data learned. Now when it comes to IoT, I think one of the issues we have to be careful about is not all data will be at the Edge. Not all data needs to be analyzed at the Edge. For example if the light is green and that's good and it's supposed to be green, do you really have to constantly analyze the fact that the light is green? You actually only really want to be able to analyze and take action when there's an anomaly. Well if it goes purple, that's actually a sign that something might explode, so that's where you want to make sure that you have the analytics at the edge. Not for everything, but for the things where there is an anomaly and a change. >> Joe, how about from your perspective? >> For me I think the evolution of data is really becoming, eventually oxygen is just, I mean data's going to be the oxygen we breathe. It used to be very very reactive and there used to be like a latency. You do something, there's a behavior, there's an event, there's a transaction, and then you go record it and then you collect it, and then you can analyze it. And it was very very waterfallish, right? And then eventually we figured out to put it back into the system. Or at least human beings interpret it to try to make the system better and that is really completely turned on it's head, we don't do that anymore. Right now it's very very, it's synchronous, where as we're actually making these transactions, the machines, we don't really need, I mean human beings are involved a bit, but less and less and less. And it's just a reality, it may not be politically correct to say but it's a reality that my phone in my pocket is following my behavior, and it knows without telling a human being what I'm doing. And it can actually help me do things like get to where I want to go faster depending on my preference if I want to save money or save time or visit things along the way. And I think that's all integration of big data, streaming data, artificial intelligence and I think the next thing that we're going to start seeing is the culmination of all of that. I actually, hopefully it'll be published soon, I just wrote an article for Forbes with the term of ARBI and ARBI is the integration of Augmented Reality and Business Intelligence. Where I think essentially we're going to see, you know, hold your phone up to Jim's face and it's going to recognize-- >> Peter: It's going to break. >> And it's going to say exactly you know, what are the key metrics that we want to know about Jim. If he works on my sales force, what's his attainment of goal, what is-- >> Jim: Can it read my mind? >> Potentially based on behavior patterns. >> Now I'm scared. >> I don't think Jim's buying it. >> It will, without a doubt be able to predict what you've done in the past, you may, with some certain level of confidence you may do again in the future, right? And is that mind reading? It's pretty close, right? >> Well, sometimes, I mean, mind reading is in the eye of the individual who wants to know. And if the machine appears to approximate what's going on in the person's head, sometimes you can't tell. So I guess, I guess we could call that the Turing machine test of the paranormal. >> Well, face recognition, micro gesture recognition, I mean facial gestures, people can do it. Maybe not better than a coin toss, but if it can be seen visually and captured and analyzed, conceivably some degree of mind reading can be built in. I can see when somebody's angry looking at me so, that's a possibility. That's kind of a scary possibility in a surveillance society, potentially. >> Neil: Right, absolutely. >> Peter: Stephanie, what do you think? >> Well, I hear a world of it's the bots versus the humans being painted here and I think that, you know at Elysian we have a very strong perspective on this and that is that the greatest impact, or the greatest results is going to be when humans figure out how to collaborate with the machines. And so yes, you want to get to the location more quickly, but the machine as in the bot isn't able to tell you exactly what to do and you're just going to blindly follow it. You need to train that machine, you need to have a partnership with that machine. So, a lot of the power, and I think this goes back to Judith's story is then what is the human decision making that can be augmented with data from the machine, but then the humans are actually training the training side and driving machines in the right direction. I think that's when we get true power out of some of these solutions so it's not just all about the technology. It's not all about the data or the AI, or the IoT, it's about how that empowers human systems to become smarter and more effective and more efficient. And I think we're playing that out in our technology in a certain way and I think organizations that are thinking along those lines with IoT are seeing more benefits immediately from those projects. >> So I think we have a general agreement of what kind of some of the things you talked about, IoT, crucial capturing information, and then having action being taken, AI being crucial to defining and refining the nature of the actions that are being taken Big Data ultimately powering how a lot of that changes. Let's go to the next one. >> So actually I have something to add to that. So I think it makes sense, right, with IoT, why we have Big Data associated with it. If you think about what data is collected by IoT. We're talking about a serial information, right? It's over time, it's going to grow exponentially just by definition, right, so every minute you collect a piece of information that means over time, it's going to keep growing, growing, growing as it accumulates. So that's one of the reasons why the IoT is so strongly associated with Big Data. And also why you need AI to be able to differentiate between one minute versus next minute, right? Trying to find a better way rather than looking at all that information and manually picking out patterns. To have some automated process for being able to filter through that much data that's being collected. >> I want to point out though based on what you just said Jennifer, I want to bring Neil in at this point, that this question of IoT now generating unprecedented levels of data does introduce this idea of the primary source. Historically what we've done within technology, or within IT certainly is we've taken stylized data. There is no such thing as a real world accounting thing. It is a human contrivance. And we stylize data and therefore it's relatively easy to be very precise on it. But when we start, as you noted, when we start measuring things with a tolerance down to thousandths of a millimeter, whatever that is, metric system, now we're still sometimes dealing with errors that we have to attend to. So, the reality is we're not just dealing with stylized data, we're dealing with real data, and it's more, more frequent, but it also has special cases that we have to attend to as in terms of how we use it. What do you think Neil? >> Well, I mean, I agree with that, I think I already said that, right. >> Yes you did, okay let's move on to the next one. >> Well it's a doppelganger, the digital twin doppelganger that's automatically created by your very fact that you're living and interacting and so forth and so on. It's going to accumulate regardless. Now that doppelganger may not be your agent, or might not be the foundation for your agent unless there's some other piece of logic like an interest graph that you build, a human being saying this is my broad set of interests, and so all of my agents out there in the IoT, you all need to be aware that when you make a decision on my behalf as my agent, this is what Jim would do. You know I mean there needs to be that kind of logic somewhere in this fabric to enable true agency. >> All right, so I'm going to start with you. Oh go ahead. >> I have a real short answer to this though. I think that Big Data provides the data and compute platform to make AI possible. For those of us who dipped our toes in the water in the 80s, we got clobbered because we didn't have the, we didn't have the facilities, we didn't have the resources to really do AI, we just kind of played around with it. And I think that the other thing about it is if you combine Big Data and AI and IoT, what you're going to see is people, a lot of the applications we develop now are very inward looking, we look at our organization, we look at our customers. We try to figure out how to sell more shoes to fashionable ladies, right? But with this technology, I think people can really expand what they're thinking about and what they model and come up with applications that are much more external. >> Actually what I would add to that is also it actually introduces being able to use engineering, right? Having engineers interested in the data. Because it's actually technical data that's collected not just say preferences or information about people, but actual measurements that are being collected with IoT. So it's really interesting in the engineering space because it opens up a whole new world for the engineers to actually look at data and to actually combine both that hardware side as well as the data that's being collected from it. >> Well, Neil, you and I have talked about something, 'cause it's not just engineers. We have in the healthcare industry for example, which you know a fair amount about, there's this notion of empirical based management. And the idea that increasingly we have to be driven by data as a way of improving the way that managers do things, the way the managers collect or collaborate and ultimately collectively how they take action. So it's not just engineers, it's supposed to also inform business, what's actually happening in the healthcare world when we start thinking about some of this empirical based management, is it working? What are some of the barriers? >> It's not a function of technology. What happens in medicine and healthcare research is, I guess you can say it borders on fraud. (people chuckling) No, I'm not kidding. I know the New England Journal of Medicine a couple of years ago released a study and said that at least half their articles that they published turned out to be written, ghost written by pharmaceutical companies. (man chuckling) Right, so I think the problem is that when you do a clinical study, the one that really killed me about 10 years ago was the women's health initiative. They spent $700 million gathering this data over 20 years. And when they released it they looked at all the wrong things deliberately, right? So I think that's a systemic-- >> I think you're bringing up a really important point that we haven't brought up yet, and that is is can you use Big Data and machine learning to begin to take the biases out? So if you let the, if you divorce your preconceived notions and your biases from the data and let the data lead you to the logic, you start to, I think get better over time, but it's going to take a while to get there because we do tend to gravitate towards our biases. >> I will share an anecdote. So I had some arm pain, and I had numbness in my thumb and pointer finger and I went to, excruciating pain, went to the hospital. So the doctor examined me, and he said you probably have a pinched nerve, he said, but I'm not exactly sure which nerve it would be, I'll be right back. And I kid you not, he went to a computer and he Googled it. (Neil laughs) And he came back because this little bit of information was something that could easily be looked up, right? Every nerve in your spine is connected to your different fingers so the pointer and the thumb just happens to be your C6, so he came back and said, it's your C6. (Neil mumbles) >> You know an interesting, I mean that's a good example. One of the issues with healthcare data is that the data set is not always shared across the entire research community, so by making Big Data accessible to everyone, you actually start a more rational conversation or debate on well what are the true insights-- >> If that conversation includes what Judith talked about, the actual model that you use to set priorities and make decisions about what's actually important. So it's not just about improving, this is the test. It's not just about improving your understanding of the wrong thing, it's also testing whether it's the right or wrong thing as well. >> That's right, to be able to test that you need to have humans in dialog with one another bringing different biases to the table to work through okay is there truth in this data? >> It's context and it's correlation and you can have a great correlation that's garbage. You know if you don't have the right context. >> Peter: So I want to, hold on Jim, I want to, >> It's exploratory. >> Hold on Jim, I want to take it to the next question 'cause I want to build off of what you talked about Stephanie and that is that this says something about what is the Edge. And our perspective is that the Edge is not just devices. That when we talk about the Edge, we're talking about human beings and the role that human beings are going to play both as sensors or carrying things with them, but also as actuators, actually taking action which is not a simple thing. So what do you guys think? What does the Edge mean to you? Joe, why don't you start? >> Well, I think it could be a combination of the two. And specifically when we talk about healthcare. So I believe in 2017 when we eat we don't know why we're eating, like I think we should absolutely by now be able to know exactly what is my protein level, what is my calcium level, what is my potassium level? And then find the foods to meet that. What have I depleted versus what I should have, and eat very very purposely and not by taste-- >> And it's amazing that red wine is always the answer. >> It is. (people laughing) And tequila, that helps too. >> Jim: You're a precision foodie is what you are. (several chuckle) >> There's no reason why we should not be able to know that right now, right? And when it comes to healthcare is, the biggest problem or challenge with healthcare is no matter how great of a technology you have, you can't, you can't, you can't manage what you can't measure. And you're really not allowed to use a lot of this data so you can't measure it, right? You can't do things very very scientifically right, in the healthcare world and I think regulation in the healthcare world is really burdening advancement in science. >> Peter: Any thoughts Jennifer? >> Yes, I teach statistics for data scientists, right, so you know we talk about a lot of these concepts. I think what makes these questions so difficult is you have to find a balance, right, a middle ground. For instance, in the case of are you being too biased through data, well you could say like we want to look at data only objectively, but then there are certain relationships that your data models might show that aren't actually a causal relationship. For instance, if there's an alien that came from space and saw earth, saw the people, everyone's carrying umbrellas right, and then it started to rain. That alien might think well, it's because they're carrying umbrellas that it's raining. Now we know from real world that that's actually not the way these things work. So if you look only at the data, that's the potential risk. That you'll start making associations or saying something's causal when it's actually not, right? So that's one of the, one of the I think big challenges. I think when it comes to looking also at things like healthcare data, right? Do you collect data about anything and everything? Does it mean that A, we need to collect all that data for the question we're looking at? Or that it's actually the best, more optimal way to be able to get to the answer? Meaning sometimes you can take some shortcuts in terms of what data you collect and still get the right answer and not have maybe that level of specificity that's going to cost you millions extra to be able to get. >> So Jennifer as a data scientist, I want to build upon what you just said. And that is, are we going to start to see methods and models emerge for how we actually solve some of these problems? So for example, we know how to build a system for stylized process like accounting or some elements of accounting. We have methods and models that lead to technology and actions and whatnot all the way down to that that system can be generated. We don't have the same notion to the same degree when we start talking about AI and some of these Big Datas. We have algorithms, we have technology. But are we going to start seeing, as a data scientist, repeatability and learning and how to think the problems through that's going to lead us to a more likely best or at least good result? >> So I think that's a bit of a tough question, right? Because part of it is, it's going to depend on how many of these researchers actually get exposed to real world scenarios, right? Research looks into all these papers, and you come up with all these models, but if it's never tested in a real world scenario, well, I mean we really can't validate that it works, right? So I think it is dependent on how much of this integration there's going to be between the research community and industry and how much investment there is. Funding is going to matter in this case. If there's no funding in the research side, then you'll see a lot of industry folk who feel very confident about their models that, but again on the other side of course, if researchers don't validate those models then you really can't say for sure that it's actually more accurate, or it's more efficient. >> It's the issue of real world testing and experimentation, A B testing, that's standard practice in many operationalized ML and AI implementations in the business world, but real world experimentation in the Edge analytics, what you're actually transducing are touching people's actual lives. Problem there is, like in healthcare and so forth, when you're experimenting with people's lives, somebody's going to die. I mean, in other words, that's a critical, in terms of causal analysis, you've got to tread lightly on doing operationalizing that kind of testing in the IoT when people's lives and health are at stake. >> We still give 'em placebos. So we still test 'em. All right so let's go to the next question. What are the hottest innovations in AI? Stephanie I want to start with you as a company, someone at a company that's got kind of an interesting little thing happening. We start thinking about how do we better catalog data and represent it to a large number of people. What are some of the hottest innovations in AI as you see it? >> I think it's a little counter intuitive about what the hottest innovations are in AI, because we're at a spot in the industry where the most successful companies that are working with AI are actually incorporating them into solutions. So the best AI solutions are actually the products that you don't know there's AI operating underneath. But they're having a significant impact on business decision making or bringing a different type of application to the market and you know, I think there's a lot of investment that's going into AI tooling and tool sets for data scientists or researchers, but the more innovative companies are thinking through how do we really take AI and make it have an impact on business decision making and that means kind of hiding the AI to the business user. Because if you think a bot is making a decision instead of you, you're not going to partner with that bot very easily or very readily. I worked at, way at the start of my career, I worked in CRM when recommendation engines were all the rage online and also in call centers. And the hardest thing was to get a call center agent to actually read the script that the algorithm was presenting to them, that algorithm was 99% correct most of the time, but there was this human resistance to letting a computer tell you what to tell that customer on the other side even if it was more successful in the end. And so I think that the innovation in AI that's really going to push us forward is when humans feel like they can partner with these bots and they don't think of it as a bot, but they think about as assisting their work and getting to a better result-- >> Hence the augmentation point you made earlier. >> Absolutely, absolutely. >> Joe how 'about you? What do you look at? What are you excited about? >> I think the coolest thing at the moment right now is chat bots. Like to be able, like to have voice be able to speak with you in natural language, to do that, I think that's pretty innovative, right? And I do think that eventually, for the average user, not for techies like me, but for the average user, I think keyboards are going to be a thing of the past. I think we're going to communicate with computers through voice and I think this is the very very beginning of that and it's an incredible innovation. >> Neil? >> Well, I think we all have myopia here. We're all thinking about commercial applications. Big, big things are happening with AI in the intelligence community, in military, the defense industry, in all sorts of things. Meteorology. And that's where, well, hopefully not on an every day basis with military, you really see the effect of this. But I was involved in a project a couple of years ago where we were developing AI software to detect artillery pieces in terrain from satellite imagery. I don't have to tell you what country that was. I think you can probably figure that one out right? But there are legions of people in many many companies that are involved in that industry. So if you're talking about the dollars spent on AI, I think the stuff that we do in our industries is probably fairly small. >> Well it reminds me of an application I actually thought was interesting about AI related to that, AI being applied to removing mines from war zones. >> Why not? >> Which is not a bad thing for a whole lot of people. Judith what do you look at? >> So I'm looking at things like being able to have pre-trained data sets in specific solution areas. I think that that's something that's coming. Also the ability to, to really be able to have a machine assist you in selecting the right algorithms based on what your data looks like and the problems you're trying to solve. Some of the things that data scientists still spend a lot of their time on, but can be augmented with some, basically we have to move to levels of abstraction before this becomes truly ubiquitous across many different areas. >> Peter: Jennifer? >> So I'm going to say computer vision. >> Computer vision? >> Computer vision. So computer vision ranges from image recognition to be able to say what content is in the image. Is it a dog, is it a cat, is it a blueberry muffin? Like a sort of popular post out there where it's like a blueberry muffin versus like I think a chihuahua and then it compares the two. And can the AI really actually detect difference, right? So I think that's really where a lot of people who are in this space of being in both the AI space as well as data science are looking to for the new innovations. I think, for instance, cloud vision I think that's what Google still calls it. The vision API we've they've released on beta allows you to actually use an API to send your image and then have it be recognized right, by their API. There's another startup in New York called Clarify that also does a similar thing as well as you know Amazon has their recognition platform as well. So I think in a, from images being able to detect what's in the content as well as from videos, being able to say things like how many people are entering a frame? How many people enter the store? Not having to actually go look at it and count it, but having a computer actually tally that information for you, right? >> There's actually an extra piece to that. So if I have a picture of a stop sign, and I'm an automated car, and is it a picture on the back of a bus of a stop sign, or is it a real stop sign? So that's going to be one of the complications. >> Doesn't matter to a New York City cab driver. How 'about you Jim? >> Probably not. (laughs) >> Hottest thing in AI is General Adversarial Networks, GANT, what's hot about that, well, I'll be very quick, most AI, most deep learning, machine learning is analytical, it's distilling or inferring insights from the data. Generative takes that same algorithmic basis but to build stuff. In other words, to create realistic looking photographs, to compose music, to build CAD CAM models essentially that can be constructed on 3D printers. So GANT, it's a huge research focus all around the world are used for, often increasingly used for natural language generation. In other words it's institutionalizing or having a foundation for nailing the Turing test every single time, building something with machines that looks like it was constructed by a human and doing it over and over again to fool humans. I mean you can imagine the fraud potential. But you can also imagine just the sheer, like it's going to shape the world, GANT. >> All right so I'm going to say one thing, and then we're going to ask if anybody in the audience has an idea. So the thing that I find interesting is traditional programs, or when you tell a machine to do something you don't need incentives. When you tell a human being something, you have to provide incentives. Like how do you get someone to actually read the text. And this whole question of elements within AI that incorporate incentives as a way of trying to guide human behavior is absolutely fascinating to me. Whether it's gamification, or even some things we're thinking about with block chain and bitcoins and related types of stuff. To my mind that's going to have an enormous impact, some good, some bad. Anybody in the audience? I don't want to lose everybody here. What do you think sir? And I'll try to do my best to repeat it. Oh we have a mic. >> So my question's about, Okay, so the question's pretty much about what Stephanie's talking about which is human and loop training right? I come from a computer vision background. That's the problem, we need millions of images trained, we need humans to do that. And that's like you know, the workforce is essentially people that aren't necessarily part of the AI community, they're people that are just able to use that data and analyze the data and label that data. That's something that I think is a big problem everyone in the computer vision industry at least faces. I was wondering-- >> So again, but the problem is that is the difficulty of methodologically bringing together people who understand it and people who, people who have domain expertise people who have algorithm expertise and working together? >> I think the expertise issue comes in healthcare, right? In healthcare you need experts to be labeling your images. With contextual information where essentially augmented reality applications coming in, you have the AR kit and everything coming out, but there is a lack of context based intelligence. And all of that comes through training images, and all of that requires people to do it. And that's kind of like the foundational basis of AI coming forward is not necessarily an algorithm, right? It's how well are datas labeled? Who's doing the labeling and how do we ensure that it happens? >> Great question. So for the panel. So if you think about it, a consultant talks about being on the bench. How much time are they going to have to spend on trying to develop additional business? How much time should we set aside for executives to help train some of the assistants? >> I think that the key is not, to think of the problem a different way is that you would have people manually label data and that's one way to solve the problem. But you can also look at what is the natural workflow of that executive, or that individual? And is there a way to gather that context automatically using AI, right? And if you can do that, it's similar to what we do in our product, we observe how someone is analyzing the data and from those observations we can actually create the metadata that then trains the system in a particular direction. But you have to think about solving the problem differently of finding the workflow that then you can feed into to make this labeling easy without the human really realizing that they're labeling the data. >> Peter: Anybody else? >> I'll just add to what Stephanie said, so in the IoT applications, all those sensory modalities, the computer vision, the speech recognition, all that, that's all potential training data. So it cross checks against all the other models that are processing all the other data coming from that device. So that the natural language process of understanding can be reality checked against the images that the person happens to be commenting upon, or the scene in which they're embedded, so yeah, the data's embedded-- >> I don't think we're, we're not at the stage yet where this is easy. It's going to take time before we do start doing the pre-training of some of these details so that it goes faster, but right now, there're not that many shortcuts. >> Go ahead Joe. >> Sorry so a couple things. So one is like, I was just caught up on your incentivizing programs to be more efficient like humans. You know in Ethereum that has this notion, which is bot chain, has this theory, this concept of gas. Where like as the process becomes more efficient it costs less to actually run, right? It costs less ether, right? So it actually is kind of, the machine is actually incentivized and you don't really know what it's going to cost until the machine processes it, right? So there is like some notion of that there. But as far as like vision, like training the machine for computer vision, I think it's through adoption and crowdsourcing, so as people start using it more they're going to be adding more pictures. Very very organically. And then the machines will be trained and right now is a very small handful doing it, and it's very proactive by the Googles and the Facebooks and all of that. But as we start using it, as they start looking at my images and Jim's and Jen's images, it's going to keep getting smarter and smarter through adoption and through very organic process. >> So Neil, let me ask you a question. Who owns the value that's generated as a consequence of all these people ultimately contributing their insight and intelligence into these systems? >> Well, to a certain extent the people who are contributing the insight own nothing because the systems collect their actions and the things they do and then that data doesn't belong to them, it belongs to whoever collected it or whoever's going to do something with it. But the other thing, getting back to the medical stuff. It's not enough to say that the systems, people will do the right thing, because a lot of them are not motivated to do the right thing. The whole grant thing, the whole oh my god I'm not going to go against the senior professor. A lot of these, I knew a guy who was a doctor at University of Pittsburgh and they were doing a clinical study on the tubes that they put in little kids' ears who have ear infections, right? And-- >> Google it! Who helps out? >> Anyway, I forget the exact thing, but he came out and said that the principle investigator lied when he made the presentation, that it should be this, I forget which way it went. He was fired from his position at Pittsburgh and he has never worked as a doctor again. 'Cause he went against the senior line of authority. He was-- >> Another question back here? >> Man: Yes, Mark Turner has a question. >> Not a question, just want to piggyback what you're saying about the transfixation of maybe in healthcare of black and white images and color images in the case of sonograms and ultrasound and mammograms, you see that happening using AI? You see that being, I mean it's already happening, do you see it moving forward in that kind of way? I mean, talk more about that, about you know, AI and black and white images being used and they can be transfixed, they can be made to color images so you can see things better, doctors can perform better operations. >> So I'm sorry, but could you summarize down? What's the question? Summarize it just, >> I had a lot of students, they're interested in the cross pollenization between AI and say the medical community as far as things like ultrasound and sonograms and mammograms and how you can literally take a black and white image and it can, using algorithms and stuff be made to color images that can help doctors better do the work that they've already been doing, just do it better. You touched on it like 30 seconds. >> So how AI can be used to actually add information in a way that's not necessarily invasive but is ultimately improves how someone might respond to it or use it, yes? Related? I've also got something say about medical images in a second, any of you guys want to, go ahead Jennifer. >> Yeah, so for one thing, you know and it kind of goes back to what we were talking about before. When we look at for instance scans, like at some point I was looking at CT scans, right, for lung cancer nodules. In order for me, who I don't have a medical background, to identify where the nodule is, of course, a doctor actually had to go in and specify which slice of the scan had the nodule and where exactly it is, so it's on both the slice level as well as, within that 2D image, where it's located and the size of it. So the beauty of things like AI is that ultimately right now a radiologist has to look at every slice and actually identify this manually, right? The goal of course would be that one day we wouldn't have to have someone look at every slice to like 300 usually slices and be able to identify it much more automated. And I think the reality is we're not going to get something where it's going to be 100%. And with anything we do in the real world it's always like a 95% chance of it being accurate. So I think it's finding that in between of where, what's the threshold that we want to use to be able to say that this is, definitively say a lung cancer nodule or not. I think the other thing to think about is in terms of how their using other information, what they might use is a for instance, to say like you know, based on other characteristics of the person's health, they might use that as sort of a grading right? So you know, how dark or how light something is, identify maybe in that region, the prevalence of that specific variable. So that's usually how they integrate that information into something that's already existing in the computer vision sense. I think that's, the difficulty with this of course, is being able to identify which variables were introduced into data that does exist. >> So I'll make two quick observations on this then I'll go to the next question. One is radiologists have historically been some of the highest paid physicians within the medical community partly because they don't have to be particularly clinical. They don't have to spend a lot of time with patients. They tend to spend time with doctors which means they can do a lot of work in a little bit of time, and charge a fair amount of money. As we start to introduce some of these technologies that allow us to from a machine standpoint actually make diagnoses based on those images, I find it fascinating that you now see television ads promoting the role that the radiologist plays in clinical medicine. It's kind of an interesting response. >> It's also disruptive as I'm seeing more and more studies showing that deep learning models processing images, ultrasounds and so forth are getting as accurate as many of the best radiologists. >> That's the point! >> Detecting cancer >> Now radiologists are saying oh look, we do this great thing in terms of interacting with the patients, never have because they're being dis-intermediated. The second thing that I'll note is one of my favorite examples of that if I got it right, is looking at the images, the deep space images that come out of Hubble. Where they're taking data from thousands, maybe even millions of images and combining it together in interesting ways you can actually see depth. You can actually move through to a very very small scale a system that's 150, well maybe that, can't be that much, maybe six billion light years away. Fascinating stuff. All right so let me go to the last question here, and then I'm going to close it down, then we can have something to drink. What are the hottest, oh I'm sorry, question? >> Yes, hi, my name's George, I'm with Blue Talon. You asked earlier there the question what's the hottest thing in the Edge and AI, I would say that it's security. It seems to me that before you can empower agency you need to be able to authorize what they can act on, how they can act on, who they can act on. So it seems if you're going to move from very distributed data at the Edge and analytics at the Edge, there has to be security similarly done at the Edge. And I saw (speaking faintly) slides that called out security as a key prerequisite and maybe Judith can comment, but I'm curious how security's going to evolve to meet this analytics at the Edge. >> Well, let me do that and I'll ask Jen to comment. The notion of agency is crucially important, slightly different from security, just so we're clear. And the basic idea here is historically folks have thought about moving data or they thought about moving application function, now we are thinking about moving authority. So as you said. That's not necessarily, that's not really a security question, but this has been a problem that's been in, of concern in a number of different domains. How do we move authority with the resources? And that's really what informs the whole agency process. But with that said, Jim. >> Yeah actually I'll, yeah, thank you for bringing up security so identity is the foundation of security. Strong identity, multifactor, face recognition, biometrics and so forth. Clearly AI, machine learning, deep learning are powering a new era of biometrics and you know it's behavioral metrics and so forth that's organic to people's use of devices and so forth. You know getting to the point that Peter was raising is important, agency! Systems of agency. Your agent, you have to, you as a human being should be vouching in a secure, tamper proof way, your identity should be vouching for the identity of some agent, physical or virtual that does stuff on your behalf. How can that, how should that be managed within this increasingly distributed IoT fabric? Well a lot of that's been worked. It all ran through webs of trust, public key infrastructure, formats and you know SAML for single sign and so forth. It's all about assertion, strong assertions and vouching. I mean there's the whole workflows of things. Back in the ancient days when I was actually a PKI analyst three analyst firms ago, I got deep into all the guts of all those federation agreements, something like that has to be IoT scalable to enable systems agency to be truly fluid. So we can vouch for our agents wherever they happen to be. We're going to keep on having as human beings agents all over creation, we're not even going to be aware of everywhere that our agents are, but our identity-- >> It's not just-- >> Our identity has to follow. >> But it's not just identity, it's also authorization and context. >> Permissioning, of course. >> So I may be the right person to do something yesterday, but I'm not authorized to do it in another context in another application. >> Role based permissioning, yeah. Or persona based. >> That's right. >> I agree. >> And obviously it's going to be interesting to see the role that block chain or its follow on to the technology is going to play here. Okay so let me throw one more questions out. What are the hottest applications of AI at the Edge? We've talked about a number of them, does anybody want to add something that hasn't been talked about? Or do you want to get a beer? (people laughing) Stephanie, you raised your hand first. >> I was going to go, I bring something mundane to the table actually because I think one of the most exciting innovations with IoT and AI are actually simple things like City of San Diego is rolling out 3200 automated street lights that will actually help you find a parking space, reduce the amount of emissions into the atmosphere, so has some environmental change, positive environmental change impact. I mean, it's street lights, it's not like a, it's not medical industry, it doesn't look like a life changing innovation, and yet if we automate streetlights and we manage our energy better, and maybe they can flicker on and off if there's a parking space there for you, that's a significant impact on everyone's life. >> And dramatically suppress the impact of backseat driving! >> (laughs) Exactly. >> Joe what were you saying? >> I was just going to say you know there's already the technology out there where you can put a camera on a drone with machine learning within an artificial intelligence within it, and it can look at buildings and determine whether there's rusty pipes and cracks in cement and leaky roofs and all of those things. And that's all based on artificial intelligence. And I think if you can do that, to be able to look at an x-ray and determine if there's a tumor there is not out of the realm of possibility, right? >> Neil? >> I agree with both of them, that's what I meant about external kind of applications. Instead of figuring out what to sell our customers. Which is most what we hear. I just, I think all of those things are imminently doable. And boy street lights that help you find a parking place, that's brilliant, right? >> Simple! >> It improves your life more than, I dunno. Something I use on the internet recently, but I think it's great! That's, I'd like to see a thousand things like that. >> Peter: Jim? >> Yeah, building on what Stephanie and Neil were saying, it's ambient intelligence built into everything to enable fine grain microclimate awareness of all of us as human beings moving through the world. And enable reading of every microclimate in buildings. In other words, you know you have sensors on your body that are always detecting the heat, the humidity, the level of pollution or whatever in every environment that you're in or that you might be likely to move into fairly soon and either A can help give you guidance in real time about where to avoid, or give that environment guidance about how to adjust itself to your, like the lighting or whatever it might be to your specific requirements. And you know when you have a room like this, full of other human beings, there has to be some negotiated settlement. Some will find it too hot, some will find it too cold or whatever but I think that is fundamental in terms of reshaping the sheer quality of experience of most of our lived habitats on the planet potentially. That's really the Edge analytics application that depends on everybody having, being fully equipped with a personal area network of sensors that's communicating into the cloud. >> Jennifer? >> So I think, what's really interesting about it is being able to utilize the technology we do have, it's a lot cheaper now to have a lot of these ways of measuring that we didn't have before. And whether or not engineers can then leverage what we have as ways to measure things and then of course then you need people like data scientists to build the right model. So you can collect all this data, if you don't build the right model that identifies these patterns then all that data's just collected and it's just made a repository. So without having the models that supports patterns that are actually in the data, you're not going to find a better way of being able to find insights in the data itself. So I think what will be really interesting is to see how existing technology is leveraged, to collect data and then how that's actually modeled as well as to be able to see how technology's going to now develop from where it is now, to being able to either collect things more sensitively or in the case of say for instance if you're dealing with like how people move, whether we can build things that we can then use to measure how we move, right? Like how we move every day and then being able to model that in a way that is actually going to give us better insights in things like healthcare and just maybe even just our behaviors. >> Peter: Judith? >> So, I think we also have to look at it from a peer to peer perspective. So I may be able to get some data from one thing at the Edge, but then all those Edge devices, sensors or whatever, they all have to interact with each other because we don't live, we may, in our business lives, act in silos, but in the real world when you look at things like sensors and devices it's how they react with each other on a peer to peer basis. >> All right, before I invite John up, I want to say, I'll say what my thing is, and it's not the hottest. It's the one I hate the most. I hate AI generated music. (people laughing) Hate it. All right, I want to thank all the panelists, every single person, some great commentary, great observations. I want to thank you very much. I want to thank everybody that joined. John in a second you'll kind of announce who's the big winner. But the one thing I want to do is, is I was listening, I learned a lot from everybody, but I want to call out the one comment that I think we all need to remember, and I'm going to give you the award Stephanie. And that is increasing we have to remember that the best AI is probably AI that we don't even know is working on our behalf. The same flip side of that is all of us have to be very cognizant of the idea that AI is acting on our behalf and we may not know it. So, John why don't you come on up. Who won the, whatever it's called, the raffle? >> You won. >> Thank you! >> How 'about a round of applause for the great panel. (audience applauding) Okay we have a put the business cards in the basket, we're going to have that brought up. We're going to have two raffle gifts, some nice Bose headsets and speaker, Bluetooth speaker. Got to wait for that. I just want to say thank you for coming and for the folks watching, this is our fifth year doing our own event called Big Data NYC which is really an extension of the landscape beyond the Big Data world that's Cloud and AI and IoT and other great things happen and great experts and influencers and analysts here. Thanks for sharing your opinion. Really appreciate you taking the time to come out and share your data and your knowledge, appreciate it. Thank you. Where's the? >> Sam's right in front of you. >> There's the thing, okay. Got to be present to win. We saw some people sneaking out the back door to go to a dinner. >> First prize first. >> Okay first prize is the Bose headset. >> Bluetooth and noise canceling. >> I won't look, Sam you got to hold it down, I can see the cards. >> All right. >> Stephanie you won! (Stephanie laughing) Okay, Sawny Cox, Sawny Allie Cox? (audience applauding) Yay look at that! He's here! The bar's open so help yourself, but we got one more. >> Congratulations. Picture right here. >> Hold that I saw you. Wake up a little bit. Okay, all right. Next one is, my kids love this. This is great, great for the beach, great for everything portable speaker, great gift. >> What is it? >> Portable speaker. >> It is a portable speaker, it's pretty awesome. >> Oh you grabbed mine. >> Oh that's one of our guys. >> (lauging) But who was it? >> Can't be related! Ava, Ava, Ava. Okay Gene Penesko (audience applauding) Hey! He came in! All right look at that, the timing's great. >> Another one? (people laughing) >> Hey thanks everybody, enjoy the night, thank Peter Burris, head of research for SiliconANGLE, Wikibon and he great guests and influencers and friends. And you guys for coming in the community. Thanks for watching and thanks for coming. Enjoy the party and some drinks and that's out, that's it for the influencer panel and analyst discussion. Thank you. (logo music)

(upbeat music) >> Narrator: From theCUBE Studios in Palo Alto and Boston, connecting with thought leaders all around the world, this is theCUBE Coversation. >> All right everybody, this is Dave Vellante and welcome to this special CUBE Conversation. I first met Frank Slootman in 2007 when he was the CEO of Data Domain. Back then he was the CEO of a disruptive company and still is. Data Domain, believe or not back then, was actually replacing tape drives as the primary mechanism for backup. Yes, believe it or not, it used to be tape. Fast forward several years later, I met Frank again at VMworld when he had become the CEO of ServiceNow. At the time ServiceNow was a small company, about 100 plus million dollars. Frank and his team took that company to 1.2 billion. And Gartner, at the time of IPO said "you know, this doesn't make sense. "It's a small market, it's a very narrow help desk market, "it's maybe a couple billion dollars." The vision of Slootman and his team was to really expand the total available market and execute like a laser. Which they did and today, ServiceNow a very, very successful company. Snowflake first came into my line of sight in 2015 when SiliconANGLE wrote an article, "Why Snowflake is Better "Than Amazon Redshift, Re-imagining Data". Well last year Frank Slootman joined Snowflake, another disruptive company. And he's here today to talk about how Snowflake is really participating in this COVID-19 crisis. And I really want to share some of Frank's insights and leadership principles, Frank great to see you, thanks for coming on. >> Yeah, thanks for having us Dave. >> So when I first reported earlier this year on Snowflake and shared some data with the community, you reached back out to me and said "Dave, I want to just share with you. "I am not a playbook CEO, I am a situational CEO. "This is what I learned in the military." So Frank, this COVID-19 situation was thrown at you, it's a black swan, what was your first move as a leader? >> Well, my first move is let's not overreact. Take a deep breath. Let's really examine what we know. Let's not jump to conclusions, let's not try to project things that we're not capable of projecting. That's hard because we tend to have sort of levels of certainty about what's going to happen in the week, in the next month and so on and all of a sudden that's out of the window. It creates enormous anxiety with people. So in other words you got to sort of reset to okay, what do we know, what can we do, what do we control? And not let our minds sort of go out of control. So I talk to our people all the time about maintain a sense of normalcy, focus on the work, stay in the moment and by the way, turn the newsfeed off, right, because the hysteria you get fed through the media is really not helpful, right? So just cool down and focus on what we still can do. And then I think then everybody takes a deep breath and we just go back to work. I mean, we're in this mode now for three weeks and I can tell you, I'm on teleconferencing calls, whatever, eight, nine hours a day. Prospects, customers, all over the world. Pretty much what I was doing before except I'm not traveling right now. So it's not, >> Yeah, so it sounds clear-- >> Not that different than what it was before. (laughs) >> It sounds very Bill Belichickian, you know? >> Yeah. >> Focus on those things of which you can control. When you were running ServiceNow I really learned it from you and of course Mike Scarpelli, your then and current CFO about the importance of transparency. And I'm interested in how you're communicating, it sounds like you're doing some very similar things but have you changed the way in which you've communicated to your team, your internal employees at all? >> We're communicating much more. Because we can no longer rely on sort of running into people here, there and everywhere. So we have to be much more purposeful about communications. For example, I mean I send an email out to the entire company on Monday morning. And it's kind of a bunch of anecdotes. Just to bring the connection back, the normalcy. It just helps people get connected back to the mothership and like well, things are still going on. We're still talking in the way we always used to be. And that really helps and I also, I check in with people a lot more, I ask all of our leadership to constantly check in with people because you can't assume that everybody is okay, you can't be out of sight, out of mind. So we need to be more purposeful in reaching out and communicating with people than we were previously. >> And a lot of people obviously concerned about their jobs. Have you sort of communicated, what have you communicated to employees about layoffs? I mean, you guys just did a large raise just before all this, your timing was kind of impeccable. But what have you communicated in that regard? >> I've said, there's no layoffs on our radar, number one. Number two, we are hiring. And number three is we have a higher level of scrutiny on the hires that we're making. And I am very transparent. In other words I tell people look, I prioritize the roles that are closest to the direct train of the business. Right, it's kind of common sense. But I wanted to make sure that this is how we're thinking about it. There are some roles that are more postponable than others. I'm hiring in engineering without any reservation because that is the long term strategic interest of the company. One the sales side, I want to know that sales leaders know how to convert to yields, that we're not just sort of bringing capacity online. And the leadership is not convinced or confident that they can convert to yield. So there's a little bit finer level of scrutiny on the hiring. But by and large, it's not that different. There's this saying out there that we should suspend all non-essential spending and hiring, I'm like you should always do that. Right? I mean what's different today? (both laugh) If it's non-essential, why do it, right? So all of this comes back to this is probably how we should operate anyways, yep. >> I want to talk a little bit about the tech behind Snowflake. I'm very sensitive when CEOs come on my program to make sure that we're not, I'm not trying to bait CEOs into ambulance chasing, that's not what it's about. But I do want to share with our community kind of what's new, what's changed and how companies like Snowflake are participating in this crisis. And in particular, we've been reporting for awhile, if you guys bring up that first slide. That the innovation in the industry is really no longer about Moore's Law. It's really shifted. There's a new, what we call an innovation cocktail in the business and we've collected all this data over the last 10 years. With Hadoop and other distributed data and now we have Edge Data, et cetera, there's this huge trove of data. And now AI is becoming real, it's becoming much more economical. So applying machine intelligence to this data and then the Cloud allows us to do this at scale. It allows us to bring in more data sources. It brings an agility in. So I wonder if you could talk about sort of this premise and how you guys fit. >> Yeah, I would start off by reordering the sequence and saying Cloud's number one. That is foundational. That helps us bring scale to data that we never had to number two, it helps us bring computational power to data at levels we've never had before. And that just means that queries and workloads can complete orders of magnitude faster than they ever could before. And that introduces concepts like the time value of data, right? The faster you get it, the more impactful and powerful it is. I do agree, I view AI as sort of the next generation of analytics. Instead of using data to inform people, we're using data to drive processes and businesses directly, right? So I'm agreeing obviously with these strengths because we're the principal beneficiaries and drivers of these platforms. >> Well when we talked about earlier this year about Snowflake, we really brought up the notion that you guys were one of the first if not the first. And guys, bring back Frank, I got to see him. (Frank chuckles) One of the first to really sort of separate the notion of being able to scale, compute independent of storage. And that brought not only economics but it brought flexibility. So you've got this Cloud-native database. Again, what caught my attention in that Redshift article we wrote is essentially for our audience, Redshift was based on ParAccel. Amazon did a great job of really sort of making that a Cloud database but it really wasn't born in the Cloud and that's sort of the advantage of Snowflake. So that architectural approach is starting to really take hold. So I want to give an example. Guys if you bring up the next chart. This is an example of a system that I've been using since early January when I saw this COVID come out. Somebody texted me this. And it's the Johns Hopkins dataset, it's awesome. It shows you, go around the map, you can follow it, it's pretty close to real time. And it's quite good. But the problem is, all right thank you guys. The problem is that when I started to look at, I wanted to get into sort of a more granular view of the counties. And I couldn't do that. So guys bring up the next slide if you would. So what I did was I searched around and I found a New York Times GitHub data instance. And you can see it in the top left here. And basically it was a CSV. And notice what it says, it says we can't make this file beautiful and searchable because it's essentially too big. And then I ran into what you guys are doing with Star Schema, Star Schema's a data company. And essentially you guys made the notion that look, the Johns Hopkins dataset as great as it is it's not sort of ready for analytics, it's got to be cleaned, et cetera. And so I want you to talk about that a little bit. Guys, if you could bring Frank back. And share with us what you guys have done with Star Schema and how that's helping understand COVID-19 and its progression. >> Yeah, one of the really cool concepts I've felt about Snowflake is what we call the data sharing architecture. And what that really means is that if you and I both have Snowflake accounts, even though we work for different institutions, we can share data optics, tables, schema, whatever they are with each other. And you can process against that in place if they are residing in a local, to your own platform. We have taken that concept from private also to public. So that data providers like Star Schema can list their datasets, because they're a data company, so obviously it's in their business interest to allow this data to be profiled and to be accessible by the Snowflake community. And this data is what we call analytics ready. It is instantly accessible. It is also continually updated, you have to do nothing. It's augmented with incremental data and then our Snowflake users can just combine this data with supply chain, with economic data, with internal operating data and so on. And we got a very strong reaction from our customer base because they're like "man, you're saving us weeks "if not months just getting prepared to start to do an al, let alone doing them." Right? Because the data is analytics ready and they have to do literally nothing. I mean in other words if they ask us for it in the morning, in the afternoon they'll be running workloads again. Right, and then combining it with their own data. >> Yeah, so I should point out that that New York Times GitHub dataset that I showed you, it's a couple of days behind. We're talking here about near realtime, or as close as realtime as you can get, is that right? >> Yep. Yeah, every day it gets updated. >> So the other thing, one of the things we've been reporting, and Frank I wondered if you could comment on this, is this new emerging workloads in the Cloud. We've been reporting on this for a couple of years. The first generation of Cloud was IS, was really about compute, storage, some database infrastructure. But really now what we're seeing is these analytic data stores where the valuable data is sitting and much of it is in the Cloud and bringing machine intelligence and data science capabilities to that, to allow for this realtime or near realtime analysis. And that is a new, emerging workload that is really gaining a lot of steam as these companies try to go to this so-called digital transformation. Your comments on that. >> Yeah, we refer to that as the emergence or the rise of the data Cloud. If you look at the Cloud landscape, we're all very familiar with the infrastructure clouds. AWS and Azure and GCP and so on, it's just massive storage and servers. And obviously there's data locked in to those infrastructure clouds as well. We've been familiar for it for 10, 20 years now with application clouds, notably Salesforce but obviously Workday, ServiceNow, SAP and so on, they also have data in them, right? But now you're seeing that people are unsiloing the data. This is super important. Because as long as the data is locked in these infrastructure clouds, in these application clouds, we can't do the things that we need to do with it, right? We have to unsilo it to allow the scale of querying and execution against that data. And you don't see that any more clear that you do right now during this meltdown that we're experiencing. >> Okay so I learned long ago Frank not to argue with you but I want to push you on something. (Frank laughs) So I'm not trying to be argumentative. But one of those silos is on-prem. I've heard you talk about "look, we're a Cloud company. "We're Cloud first, we're Cloud only. "We're not going to do an on-prem version." But some of that data lives on-prem. There are companies out there that are saying "hey, we separate compute and storage too, "we run in the Cloud. "But we also run on-prem, that's our big differentiator." Your thoughts on that. >> Yeah, we burnt the ship behind us. Okay, we're not doing this endless hedging that people have done for 20 years, sort of keeping a leg in both worlds. Forget it, this will only work in the public Cloud. Because this is how the utility model works, right? I think everybody is coming to this realization, right? I mean excuses are running out at this point. We think that it'll, people will come to the public Cloud a lot sooner than we will ever come to the private Cloud. It's not that we can't run on a private cloud, it just diminishes the potential and the value that we bring. >> So as sort of mentioned in my intro, you have always been at the forefront of disruption. And you think about digital transformation. You know Frank we go to all of these events, it used to be physical and now we're doing theCUBE digital. And so everybody talks about digital transformation. CEOs get up, they talk about how they're helping their customers move to digital. But the reality is is when you actually talk to businesses, there was a lot of complacency. "Hey, this isn't really going to happen in my lifetime" or "we're doing pretty well." Or maybe the CEO might be committed but it doesn't necessarily trickle down to the P&L managers who have an update. One of the things that we've been talking about is COVID-19 is going to accelerate that digital transformation and make it a mandate. You're seeing it obviously in retail play out and a number of other industries, supply chains are, this has wreaked havoc on supply chains. And so there's going to be a rethinking. What are your thoughts on the acceleration of digital transformation? >> Well obviously the crisis that we're experiencing is obviously an enormous catalyst for digital transformation and everything that that entails. And what that means and I think as a industry we're just victims of inertia. Right, I mean haven't understood for 20 years why education, both K through 12 but also higher ed, why they're so brick and mortar bound and the way they're doing things, right? And we could massively scale and drop the cost of education by going digital. Now we're forced into it and everybody's like "wow, "this is not bad." You're right, it isn't, right but we haven't so the economics, the economic imperative hasn't really set in but it is now. So these are all great things. Having said that, there are also limits to digital transformation. And I'm sort of experiencing that right now, being on video calls all day. And oftentimes people I've never met before, right? There's still a barrier there, right? It's not like digital can replace absolutely everything. And that is just not true, right? I mean there's some level of filter that just doesn't happen when you're digital. So there's still a need for people to be in the same place. I don't want to sort of over rotate on this concept, that like okay, from here on out we're all going to be on the wires, that's not the way it will be. >> Yeah, be balanced. So earlier you made a comment, that "we should never "be spending on non-essential items". And so you've seen (Frank laughs) back in 2008 you saw the Rest in Peace good times, you've seen the black swan memos that go out. I assume that, I mean you're a very successful investor as well, you've done a couple of stints in the VC community. What are you seeing in the Valley in regard to investments, will investments continue, will we continue to feed innovation, what's your sense of that? Well this is another wake up call. Because in Silicon Valley there's way too much money. There's certainly a lot of ideas but there's not a lot of people that can execute on it. So what happens is a lot of things get funded and the execution is either no good or it's just not a valid opportunity. And when you go through a downturn like this you're finding out that those businesses are not going to make it. I mean when the tide is running out, only the strongest players are going to survive that. It's almost a natural selection process that happens from time to time. It's not necessarily a bad thing because people get reallocated. I mean Silicon Valley is basically one giant beehive, right? I mean we're constantly repurposing money and people and talent and so on. And that's actually good because if an idea is not worth in investing in, let's not do it. Let's repurpose those resources in places where it has merit, where it has viability. >> Well Frank, I want to thank you for coming on. Look, I mean you don't have to do this. You could've retired long, long ago but having leaders like you in place in these times of crisis, but even when in good times to lead companies, inspire people. And we really appreciate what you do for companies, for your employees, for your customers and certainly for our community, so thanks again, I really appreciate it. >> Happy to do it, thanks Dave. >> All right and thank you for watching everybody, Dave Vellante for theCUBE, we will see you next time. (upbeat music)

[Narrator] - You are a CUBE Alumni. (cheerful music) Live, from Silicon Valley, it's theCUBE. Covering Google cloud Next '17 (rhythmic electronic music) >> Welcome back everyone. We're live here in the Palo Alto Studio for theCUBE, our new 4500 square foot studio we just moved into a month and a half ago. I'm John Furrier here, breaking down two days of live coverage in-studio of Google Next 2017, we have reporters and analysts in San Francisco on the ground, getting all the details, we had some call-ins. We're also going to call in at the end of the day to find out what the reaction is to the news, the key-notes, and all the great stuff on Day one and certainly Day two, tomorrow, here in the studio as well as in San Francisco. My next guest is Tyler Bell, good friend, industry guru, IOT expert, he's been doing a lot of work with IOT but also has a big data background, he's been on theCUBE before. Tyler, great to see you and thanks for coming in today. >> Thanks, great to be here. >> So, data has been in your wheelhouse for long time. You're a product guy, and The cloud is the future hope, it's happening big-time. Data, the Edge, with IOT is certainly part of this network transformation trend. And, certainly now, machine-learning and AI is now the big buzzword. AI, kind of a mental-model. Machine-learning, using the data. You've been at the front-end of this for years, with data and Factual and Mapbox, your other companies you worked for. Now you have data sets. So before it was like a ton of data, and now it's data sets. And then you got the IOT Edge, a car, smart city, a device. What's you take on the data intersecting with the cloud? What are the key paradigms that are colliding together? >> Yeah, I mean the reason IOT is so hot right now is really 'cause it's connecting a number of things that are also hot. So, together, you get this sort of conflagration of fires, technology fires. So, on one side you've got massive data sets. Just huge data sets about people, places and things that allow systems to learn. So, on the other end, you've got, basically, large-scale computation, which isn't only just available, it's actually accessible and it's affordable. Then, on the other end, you've got massive data collection mechanisms. So, this is anything from the mobile phone that you'll hold in your pocket, to a LIDAR, a laser-based sensor on a car. So, this combination of massive, hardware derived data collection mechanisms, combined with a place to process it, on the cloud, do so affordably. In addition to all the data, means that you get this wonderful combination of the advent of AI and machine-learning, and basically the development of smart systems. And that's really what everybody's excited about. >> It's kind of intoxicating to think about, from a computer science standpoint, this is the nirvana we've been thinking about for generations. With the compute now available, we have, it's just kind of coming together. What are the key things that are merging in your mind? 'Cause you've been doing a lot of this big data stuff. When I say big, I mean large amounts, large-scale data. But as it comes in, as they say, the world's, the future's here, but it's evenly distributed. You could also say that same argument for data. Data's everywhere, but it's not evenly distributed. So, what are some of the key things that you see happening that are important for people to understand with data, in terms of using it, applying it, commercializing it, leveraging it? >> Yeah, what you see, or what you have seen previously is the idea of data, in many people's minds, has been a data base or it's been sort of a CSV file of rows and columns and it's been this sort of fixed entity. And what you're seeing now is that, and that's sort of known as structure data, and what you're seeing now is the advent of data analytics that allow people to understand and analyze loose collections of data and begin to sort of categorize and classify content. In ways that people haven't been able to do so previously. And so, whereas you used to have just a data base of sort of all the places on the globe or a whole bunch of people, right now you can have information about, say, the images that camera sensors on your car sees. And because the systems have been trained about how to identify objects or street signs or certain behaviors and actions, it means that your systems are getting smarter. And so what's happening here is that data itself is driving this trend, where hardware and sensors, even though they're getting cheaper and they're getting increasingly commoditized, they're getting more intelligent. And that intelligence is really driven by, fundamentally, it's driven by data. >> I was having a conversation, yesterday, at Stanford there was a conference going on around bias and data. Algorithms now have bias, gender bias, male bias, but it brings up this notion of programmability and one of the things that some of the early thinkers around data, including yourself, and also we extend that out to IOT, is how do you make data available for software programs, for the learning piece? Because that means that data's now an input into the software development process, whether that's algorithms on the fly being developed in the future or data being part of the software development kit, if you will. Is that a fantasy or is that gettable, is that in reach? Is it happening? Making data part of that agile process, not just a call to a data base? >> Exactly, a lot of the things, the most valuable assets now are called basically labeled data sets, where you could say that this event or this photo or this sound even has been classified as such. And so it's the bark of a dog or the ring of a gunshot. And those labeled data sets are hugely valuable in actually training systems to learn. The other thing is, if you look at it from, say, AV, which has a lot in common with IOT, but the data set is less about a specific sort of structured or labeled event or entity. And instead, it's doing something like putting, there's one company where you can put your camera on the dashboard of your car and then you drive around and all this does is just records the images and records which way your car goes, and, that's actually collecting and learning data. And so, that kind of information is being used to teach cars how to drive and how to react in different circumstances. And so, on one hand, you've got this highly-structured labeled data, on the other hand, it's almost machine behavioral data, where to teach a car how to drive, cars need to understand what that actually entails. >> Yeah, one of the things we talked about on Google Next earlier in the day, when we saw a couple earlier segments. I was talking about, I didn't mean this as a criticism to the enterprise, but I was just saying, Google might want to throttle back their messaging or their concepts. Because the enterprise kind of works at a different pace. Google is just this high-energy, I won't say academic, but they're working on cutting-edge stuff. They have things like Maps, and they're doing things that are just really off the charts, technically. It's just great technical prowess. So, there's a disconnect between enterprise stuff and what I call 'pure' Google cloud. The question that's now on the table is, now with the advent of the IOT, industrial IOT, in particular, enterprises now have to be smarter about analog data, meaning, like the real world. How do you get the data into the cloud from a real-world perspective? Do you have any insight on that? it's something that hard to kind of get, but you mentioned that cam on the car, you're essentially recording the world, so that's the sky, that's not digitized. You're digitizing an analog signal. >> Yeah, that's right. I think I'd have two notes there. The first is that, everything that's going on that's exciting, is really at this nexus between the real world, that you and I operate in now and how that's captured and digitized, and actually collected online so it can be analyzed and processed and then affected back in the real world. And so, when you hear about IOT and cars, of course there are sensors, which basically do a read type analysis of the real world, but you also have affecters which change it and servos, which turn your tires or affect the acceleration or the braking of a vehicle. And so, all these interesting things that are happening now, and it really kicked off, of course, with the mobile phone, is how the online, data-centric, electric world connect with the real world. And all of that's really, all that information is being collected is through an explosion of sensors. Because you just have, the mobile phone supply chains are making cameras, and barometers, and magnetometers, all of these things are now so increasingly inexpensive that when people talk about sensors, they don't talk about one thousand dollar sensor that's designed to do one thing, instead there's thousands of $1 sensors. >> So, you've been doing a lot of work with IOT, almost the past year, you've been out in the IOT world. Thoughts on how the cloud should be enabled or set up for ingesting data or to be architected properly for IOT-related activities, whether it's Edge data store, or Edge Data, I mean, we have little things as boring as backup and recovery are impacted by the cloud. I can imagine that the IOT world, as it collides in with IT, is going to have some reinvention and reconstruction. Thoughts on what the cloud needs to do to be truly IOT ready? >> Yeah, there's some very interesting things that are happening here and some of them seem to be in conflict with each other. So, the cloud is a critical part of the IOT entire stack and it really goes from the device of a sensor, all the way to the cloud. And what you're getting is you are getting providers, including Google and Amazon and SAP and there's over 370, last count, IOT platform providers. Which are basically taken their particular skill set and adjusted it and tweaked it and they now say that we now have an IOT platform. And in traditional cloud services, the distinguishing features are things like being able to have record digital state of sensors and devices, sort of 'shadow' states, increased focus on streaming technology over MAP-reduced batch technology, which you got in the last 10 years, through the big data movement, and the conversations that you and I have had previously. So, there is that focus on streaming, there is a IOT-specific feature stack. But what's happening is that because so much data is being corrected. Let's imagine that you and I are doing something where we're monitoring the environment, using cameras, and we have 10,000 cameras out there. And, this could be within a vehicle, it could be in a building, or smart city, or in a smart building. Cameras are, the cloud traditionally accepts data from all these different resources, be it mobile phones, or terminals and collects it, analyzes it, and spits it back out in some kind of consumable format. But what's happening now is that IOT and the availability of these sensors is generating so much data that it's inefficient and very expensive to send it all back to the cloud. And so all of these-- >> And, it's physics, too. There's a lot of physics, right? >> Exactly, and all these cameras sending full raster images and videos back to the cloud for analysis. Basically the whole idea of real time goes away if you have that much data, you can't analyze it. So, instead of just the cameras sending out a single dumb raster image back, you teach the camera to recognize something, So you could say "I recognize a vehicle in this picture" or "I recognize a stop sign" or a street light. And instead of sending that image back to be analyzed on the cloud, the analysis is done on the device and then that entity is sent back. And so, the sensor says "I saw this stop sign "at this point, at this time in my process." >> So this cuts back to the earlier point you were making about the learning piece, and the libraries, and these data sets. Is that kind of where that thread connects? >> Exactly, so to build the intelligence on the device, that intelligence happens on the cloud. And so, you need to have the training sets and you need to have massive GPUs and huge computational power to instruct. >> Thanks Intel and NVIDIA, we need more of those, right? >> Indeed, and so, that's what's happening on the cloud, and then those learnings are basically consolidated and then put up on the device. And, the device doesn't need the GPUs, but the device does need to be smart. And so, in IOT, especially look for companies that understand, especially hardware companies, that understand that the product, as such, is no longer just a device, it's no longer just a sensor, it's an integral combination of device, intelligence platform in the cloud, and data. >> So, talk about the notion of, let's talk about the reconstruction of some of the value creation or value opportunities with what you just talked about 'cause if you believe what you just said, which I do believe is right on the money, that this new functionality, vis-a-vis, the cloud, and the smart ads and learning ads, and software, is going to change the nature of the apps. So, if I'm a cloud provider, like Google or Amazon, I have to then have the power in the cloud, but it's really the app game, it's the software game that we're talking about here. It's the apps themselves. So, yeah, you might have an atom processor has two cores versus 72 cores, and xeon, and the cloud. Okay, that's a device thing, but the software itself, at the app level, changes. Is that kind of what's happening? Where's the real disruption? I guess what I'm trying to get at is that, is it still about the apps? >> Yeah, so, I tend not to think about apps much anymore, and I guess, if you talk to some VCs, they won't think about apps much anymore either. It's rather, it tends to, you and I still think, and I think so many of us in Silicone Valley, still think of mobile phones as being the end point for both data collection and data effusion. But, really one of the exciting things about IOT now, is that it's moving away from the phone. So, it's vehicles, it's the sensors in the vehicles, it's factories, and the sensors in the factories, and smart cities. And so, what that means is you're collecting so much more data, but also, you're also being more intelligent about how you collect it. And so, it's less about the app and it's much more about the actual intelligence, that's baked into the silicon layer, or the firmware of the device. >> Yeah, I tried to get you on their Mobile World Congress special last week and we're just booked out. But I know you go to Mobile World Congress, you've been there a lot. 5G was certainly a big story there. They had the new devices, the new LG phones, all the sexy glam. But, the 5G and the network transformation becomes more than the device, so you're getting at the point which is it's not about the device anymore, it's beyond the device, more about the interplay between the back at the network. >> It is, it's the full stack, but also it's not just from one device, like the phone is one human, one device, and then that pipeline goes into the cloud, usually. The exciting thing about IOT and the general direction that things are moving now, it's what can thousands of sensors tell us? What can millions of mobile phones, driven over a 100 million miles of road surface, what can that tell us about traffic patterns or our cities? So, the general trend that you're seeing here is that it's less about two eyeballs and one phone and much more about thousands and millions of sensors. And then how you can develop data-centric products built on that conflagration of all of that data coming in. And how quickly you can build them. >> We're here with Tyler Bell, IOT Expert, but also data expert, good friend. We both have kids who play Lacrosse together, who are growing up in front of our eyes, but let's talk about them for a second, Tyler. Because they're going to grow up in a world where it's going to be completely different, so kind of knowing what we know, and as we tease-out the future and connect the dots, what are you excited about this next generation's shift that happening? If you could tease-out some of the highlights in your mind for, as our kids grow up, right, you got to start thinking about the societal impact from algorithms that might have gender bias, or smart cities that need to start thinking about services for residents that will require certain laning for autonomous vehicles, or will cargo (mumbles). Certainly, car buying might shift. They're cloud-native, they're digital-native. What are you excited about, about this future? >> Yeah, I think it's, the thing that's, I think, so huge that I have difficulty looking away from it, is just the impact, the societal impact that autonomous vehicles are going to have. And so, really, not only as our children grow up, but certainly their children, our grandchildren, will wonder how in the heck we were allowed to drive massive metal machines, and just anywhere-- >> John: With no software. >> Yeah, with really just our eyeballs and our hands, and no guidance and no safety. Safety's going to be such a critical part of this. But, it's not just the vehicle, although that's what's getting everybody's attention right now, it's really, what's going to happen to parking lots in the cities? How are parking lots and curb sides going to be reclaimed by cities? How will accessibility and safety within cities be affected by the ability to, at least in principle, just call an autonomous vehicle at any time, have it arrive at your doorstep, and take you where you need to go? What does that look like? It's going to change how cars are bought and sold, how they're leased. It's going to change the impact of brands, the significance of, are these things going to be commoditized? But, ultimately, I think, in terms of societal impact, we have, for generations, grown up in an automotive world, and our grandchildren will grow up in an automotive world, but it will be so changed 'cause it will impact entirely what our cities and our urban spaces look like. >> The good news is when they take our drivers licenses away when we're 90, we'll, at least be able to still get into a car. >> There's places we can go. >> We can still drive (laughs) >> Exactly, exactly, the time is right. We may not have immortality, but we will be able to get from one place to another in our senility. >> We might be a demographic to buy a self-driving car. Hey, you're over 90, you should buy a self-driving car. >> Well, it'll be more like a consortium. Like you, I, and maybe 30 other people. We have access to a car or fleet. >> A whole new man cave definition to bring to the auto,. Tyler, thanks for sharing the insight, really appreciated the color commentary on the cloud, the impact of data, appreciate it. We're here for the two days of coverage of Google Next here inside theCUBE. I'm John Furrier, thanks for watching. More coverage coming up after this short break. (cheerful music) (rhythmic electronic music) >> I'm George--