>> Narrator: From the Fairmont Hotel in the heart of Silicon Valley, it's theCUBE. Covering When IoT met AI: The Intelligence of Things. Brought to you by Western Digital. >> Hey welcome back here everybody Jeff Frick here with theCUBE. We're in downtown San Jose at the Fairmont Hotel. At a little event it's When IoT Met AI: The Intelligence of Things. As we hear about the Internet of Things all the time this is really about the data elements behind AI, and machine learning, and IoT. And we're going to get into it with some of the special guests here. We're excited to get the guy that's going to kick off this whole program shortly is Tom Stuermer. He is the I got to get the new title, the Global Managing Director, Ecosystem and Partnership, from Accenture. Tom, welcome-- >> Thank you, Jeff. >> And congrats on the promotion. >> Thank you. >> So IoT, AI, buzz words, a lot of stuff going on but we're really starting to see stuff begin to happen. I mean there's lots of little subtle ways that we're seeing AI work its way in to our lives, and machine learning work our way into its life, but obviously there's a much bigger wave that's about to crest here, shortly. So as you kind of look at the landscape from your point of view, you get to work with a lot of customers, you get to see this stuff implemented in industry, what's kind of your take on where we are? >> Well, I would say that we're actually very early. There are certain spaces with very well-defined parameters where AI's been implemented successfully, industrial controls on a micro level where there's a lot of well-known parameters that the systems need to operate in. And it's been very easy to be able to set those parameters up. There's been a lot of historical heuristic systems to kind of define how those work, and they're really replacing them with AI. So in the industrial spaces a lot of take up and we'll even talk a little bit later about Siemens who's really created a sort of a self-managed factory. Who's been able to take that out from a tool level, to a system level, to a factory level, to enable that to happen at those broader capabilities. I think that's one of the inflection points we're going to see in other areas where there's a lot more predictability and a lot of other IoT systems. To be able to take that kind of system level and larger scale factors of AI and enable prediction around that, like supply chains for example. So we're really not seeing a lot of that yet, but we're seeing some of the micro pieces being injected in where the danger of it going wrong is lower, because the training for those systems is very difficult. >> It's interesting, there's so much talk about the sensors, and the edge, and edge computing, and that's interesting. But as you said it's really much more of a system approach is what you need. And it's really kind of the economic boundaries of the logical system by which you're trying to make a decision in. We talk all the time, we optimizing for one wind turbine? Are you optimizing for one field that contains so many wind turbines? Are you optimizing for the entire plant? Or are you optimizing for a much bigger larger system that may or may not impact what you did on that original single turbine? So a systems approach is a really critical importance. >> It is and what we've seen is that IoT investments have trailed a lot of expectations as to when they were going to really jump in the enterprise. And what we're finding is that when we talk to our customers a lot of them are saying, look I've already got data. I've got some data. Let's say I'm a mining company and I've got equipment down in mines, I've got sensors around oxygen levels, I just don't get that much value from it. And part of the challenge is that they're looking at it from a historical data perspective. And they're saying well I can see the trajectory over time of what's happening inside of my mind. But I haven't really been able to put in prediction. I haven't been able to sort of assess when equipment might fail. And so we're seeing that when we're able to show them the ability to affect an eventual failure that might shut down revenue for a day or two when some significant equipment fails, we're able to get them to start making those investments and they're starting to see the value in those micro pockets. And so I think we're going to see it start to propagate itself through in a smaller scale, and prove itself, because there's a lot of uncertainty. There's a lot of work that's got to be done to stitch them together, and IoT infrastructure itself is already a pretty big investment as it is. >> Short that mine company, because we had Caterpillar on a couple weeks ago and you know their driving fleets of autonomous vehicles, they're talking about some of those giant mining trucks who any unscheduled downtime the economic impact is immense well beyond worrying about a driver being sick, or had a fight with his wife, or whatever reason is bringing down the productivity of those vehicles. So it's actually amazing the little pockets where people are doing it. I'm curious to get your point of view too on kind of you managed to comment the guy's like I'm not sure what the value is because the other kind of big topic that we see is when will the data and the intelligence around the data actually start to impact the balance sheet? Because data used to be kind of a pain, right? You had to store it, and keep it, and it cost money, and you had to provision servers, and storage, but really now and the future the data that you have, the algorithms you apply to it will probably be an increasing percentage of your asset value if not the primary part of you asset value, you seeing some people start to figure that out? >> Well they are. So if you look, if step back away from IoT for a minute and you look at how AI is being applied more broadly, we're finding some transformational value propositions that are delivering a lot of impacts to the bottom line. And it's anywhere from where people inside of a company interact with their customers, being able to anticipate their next move, being able to predict given these parameters of this customer what kind of customer care agent should I put on the phone with them before you even pick up the phone to anticipate some of those expectations. And we're seeing a lot of value in things like that. And so, excuse me, and so when you zoom it back in to IoT some of the challenges are that the infrastructure to implement IoT is very fragmented. There's 360 some IoT platform providers out in the world and the places where we're seeing a lot of traction in using predictive analytics and AI for IoT is really coming in the verticals like industrial equipment manufacturers where they've kind of owned the stack and they can define everything from the bottom up. And what they're actually being able to do is to start to sell product heavy equipment by the hour, by the use, because they're able to get telemeter off of that product, see what's happening, be able to see when a failure is about to come, and actually sell it as a service back to a customer and be able to predictably analyze when something fails and get spares there in time. And so those are some of the pockets where it's really far ahead because they've got a lot of vertical integration of what's happening. And I think the challenge on adoption of broader scale for companies that don't sell very expensive assets into the market is how do I as a company start to stitch my own assets that are for all kinds of different providers, and all kinds of the different companies, into a single platform? And what the focus has really been in IoT lately for the past couple of years is what infrastructure should I place to get the data? How do I provision equipment? How do I track it? How do I manage it? How do I get the data back? And I think that's necessary but completely insufficient to really get a lot of value IoT, because really all your able to do then is get data. What do you do with it? All the value is really in the data itself. And so the alternative approach a lot of companies are taking is starting to attack some of these smaller problems. And each one of them tends to have a lot of value on its own, and so they're really deploying that way. And some of them are looking for ways to let the battles of the platforms, let's at least get from 360 down to 200 so that I can make some bets. And it's actually proving to be a value, but I think that is one of the obstacles that we have to adoption. >> The other thing you mentioned interesting before we turned on the cameras is really thinking about AI as a way to adjust the way that we interact with the machines. There's two views of the machines taking over the world, is it the beautiful view, or we can freeze this up to do other things? Or certainly nobody has a job, right? The answer is probably somewhere in the middle. But clearly AI is going to change the way, and we're starting to see just the barely the beginnings with Alexa, and Siri, and Google Home, with voice interfacing and the way that we interact with these machines which is going to change dramatically with the power of, as you said, prescriptive analytics, presumptive activity, and just change that interaction from what's been a very rote, fixed, hard to change to putting as you said, some of these lighter weight, faster to move, more agile layers on the top stack which can still integrate with some of those core SAP systems, and systems of record in a completely different way. >> Exactly, you know I often use the metaphor of autonomous driving and people seem to think that that's kind of way far out there. But if you look at how driving an autonomous vehicle's so much different from driving a regular car, right? You have to worry about at the minutia of executing the driving process. You don't have to worry about throttle, break. You'd have to worry about taking a right turn on red. You'd have to worry about speeding. What you have to worry about is the more abstract concepts of source, destination, route that I might want to take. You can offload that as well. And so it changes what the person interacting with the AI system is actually able to do, and the level of cognitive capability that they're able to exercise. We're seeing similar things in medical treatment. We're using AI to do predictive analytics around injury coming off of medical equipment. It's not only starting to improve diagnoses in certain scenarios, but it's also enabling the techs and the doctors involved in the scans to think on a more abstract level about what the broader medical issues are. And so it's really changing sort of the dialogue that's happening around what's going on. And I think this is a good metaphor for us to look at when we talk about societal impacts of AI as well. Because there are some people who embrace moving forward to those higher cognitive activities and some who resist it. But I think if you look at it from a customer standpoint as well, no matter what business you're in if you're a services business, if you're a product business, the way you interact with your employees and the way you interact with your customers can fundamentally be changed with AI, because AI can enable the technology to bend it to your intentions. Someone at the call center that we talked about. I mean those are subtle activities. It's not just AI for voice recognition, but it's also using AI to alter what options are given to you, and what scenarios are going to be most beneficial. And more often than not you get it right. >> Well the other great thing about autonomous vehicles, it's just a fun topic because it's something that people can understand, and they can see, and they can touch in terms of a concept to talk about, some of these higher level concepts. But the second order impacts which most people don't even begin to think, they're like I want to drive my car is, you don't need parking lots anymore because the cars can all park off site. Just Like they do at airports today at the rental car agency. You don't need to build a crash cage anymore, because the things are not going to crash that often compared to human drivers. So how does the interior experience of a car change when you don't have to build basically a crash cage? I mean there's just so many second order impacts that people don't even really begin to think about. And we see this time and time again, we saw it with cloud innovation where it's not just is it cheaper to rent a server from Amazon than to buy one from somebody else? It's does the opportunity for innovation enable more of your people to make more contributions than they could before because they were too impatient to wait to order the server from the IT guy? So that's where I think too people so underestimate kind of the big Moore's Law my favorite, we overestimate in the short term and completely underestimate in the long term, the impacts of these things. >> It's the doubling function, exactly. >> Jeff: Yeah, absolutely. >> I mean it's hard for people, human kind is geared towards linear thinking, and so when something like Moore's Law continues to double every 18 months price performance continues to increase. Storage, compute, visualization, display. >> Networking, 5G. >> You know the sensors in MEMS, all of these things have gotten so much cheaper. It's hard for human of any intelligence to really comprehend what happens when that doubling occurs for the next 20 years. Which we're now getting on the tail end of that fact. And so those manifest themselves in ways that are a little bit unpredictable, and I think that's going to be one of our most exciting challenges over the next five years is what does an enterprise look like? What does a product look like? One of the lessons that, I spent a lot of time in race car engineering in my younger days and actually did quants and analytics, what we learned from that point is as you learned about the data you started to fundamentally change the architecture of the product. And I think that's going to be a whole new series of activities that are going to have to happen in the marketplace. Is people rethinking fundamental product. There's a great example of a company that's completely disrupted an industry. On the surface of it it's been disrupted because of the fact that they essentially disassociated the consumption from the provision of the product. And didn't have to own those assets so they could grow rapidly. But what they fundamentally did was to use AI to be able to broker when should I get more cars, where should the cars go? And because they're also we're on the forefront of being able to drive, this whole notion of consumption of cars, and getting people's conceptual mindset shifted to having owned a car to I know an Uber's going to be there. It becomes like a power outlet. I can just rely on it. And now people are actually starting to double think about should I even own a car? >> Whole different impact of the autonomous vehicles. And if I do own a car why should it be sitting in the driveway when I'm not driving it? Or I send it out to go work for me make it a performing asset. Well great conversation. You guys Accenture's in a great spot. You're always at the cutting edge. I used to tease a guy I used to work with at Accenture you've got to squeeze out all the fat in the supply chain (laughs) your RP days and again a lot of these things are people changing the lens and seeing fat and inefficiency and then attacking it in a different way whether it's Uber, Airbnb, with empty rooms in people's houses. We had Paul Doherty on at the GE Industrial Internet launch a few years back, so you guys are in a great position because you get to sit right at the forefront and help these people make those digital transformations. >> I appreciate that. >> I will tell you I mean supply chains is another one of those high level systems opportunities for AI where being able to optimize, think about it a completely automated distribution chain from factory all the way to the drone landing at your front doorstep as a consumer. That's a whole nother level of efficiency that we can't even contemplate right now. >> Don't bet against Bezos that's what I always say. All right, Tom Stuermer thanks for spending a few minutes and good luck with the keynote. >> I appreciate it Jeff. >> All right, I'm Jeff Frick you're watching theCUBE. We are at The Intelligence of Things, When IoT met AI. You're watching theCUBE. Thanks for watching. (upbeat music)

>> Male Voice: From the Fairmont Hotel in the heart of Silicon Valley, it's the Cube covering when IoT Met AI: the Intelligence of Things brought to you by Western Digital. >> Hey welcome back here everybody. Jeff Frick with the Cube. We're in downtown San Jose at the Fairmont Hotel at a small event talking about data and really in IoT and the intersection of all those things and we're excited to have a little startup boutique here and one of the startups is great enough to take the time to sit down with us. This is Shaun Moore, he's the founder and CEO of the recently renamed Trueface.ai. Shaun, welcome. >> Thank you for having me. >> So you've got a really cool company, Trueface,ai. I looked at the site. You have facial recognition software so that's cool but what I think is really more interesting is you're really doing facial recognition as a service. >> Shaun: Yes. >> And you a have a freemium model so I can go in and connect to your API and basically integrate your facial recognition software into whatever application that I built. >> Right so we were thinking about what we wanted to do in terms of pricing structure. We wanted to focus on the developer community so we wanted tinkers, people that just want to play with technology to help us improve it and then go after the kind of bigger clients and so we'll be hosting hack-a-thons. We just actually had one this past week in San Francisco. We had great feedback. We're really trying to get a base of you know, almost outsource engineers to help us improve this technology and so we have to offer it to them for free so we can see what they build from there. >> Right but you don't have an opensource component yet so you haven't gone that route? >> Not quite yet, no. >> Okay. >> We're thinking about that though. >> Okay, and still really young company, angel-funded, haven't taken it the institutional route yet. >> Right, yeah, we've been around since 2013, end of 2013, early 2014, and we were building smart home hardware so we had built the technology around originally to be a smart doorbell that used facial recognition to customize the smart home. From the the trajectory went, we realized our clients were using it more for security purposes and access-control, not necessarily personalization. We made a quick pivot to a quick access control company and continue to learn about how people are using facial recognition in practice. Could it be a commercial technology that people are comfortable with? And throughout that thought process and going through and testing a bunch of other facial recognition technologies, we realized we could actually build our own platform and reach a larger audience with it and essentially be the core technology of a lot cooler and more innovative products. >> Right, and not get into the hardware business of doorbells >> Yeah, the hardware business is tough. >> That's a tough one. >> We were going to through manufacturing one and I'm glad we don't have to do that again. >> So what are some of the cool ways that people are using facial recognition that maybe we would never have thought about? >> Sure, so for face matching - The API is four components. It's face matching, face detection, face identification, and what we call spoof detection. Face matching is what it sounds like: one-to-one matching. Face detection is just detecting that someone is in the frame. The face identification is your one to act so your going into a database of people. And your spoof detection is if someone holds up a picture of me or of you and tries to get it, we'll identify that as an attack attempt and that's kind of where we differentiate our technology from most is not a lot of technology out there can do that piece and so we've packaged that all up into essentially the API for all these developers to use and some of the different ideas that people have come up with for us have been for banking logins, so for ATMs, you walk up to an ATM, you put your card in and set up a PIN so to prevent against fraud it actually scans your face and does a one-to-one match. For ship industries, so for things like cruise ships, when people get off and then come back on, instead of having them show ID, they use quick facial recognition scans. So we're seeing a lot of different ideas. One of the more funny ones is based off a company out in LA that is doing probation monitoring for drunk drivers and so we've built technology that's drunk or not drunk. >> Drunk or not drunk? >> Right so we can actually measure based on historical data if your face appears to be drunk and so you know, the possibilities are truly endless. And that's why I said we went after the development community first because >> Right right >> They're coming to use with these creative ideas. >> So it's interesting with this drunk or not drunk, of course, not to make fun of drunk driving, it's not a funny subject but obviously you've got an algorithm that determines anchor points on the eyes and the nose and certain biometric features but drunk, you're looking for much softer, more subtle clues, I would imagine because the fundamental structure of your face hasn't changed. >> Right so it's a lot of training data, so it's a lot of training data. >> Well a lot of training data, yeah. We don't want to go down that path. >> So a lot of research on our team's part. >> Well then the other thing too is the picture, is the fraud attempt. You must be looking around and shadowing and really more 3D-types of things to look over something as simple as holding up a 2D picture. >> Right so a lot of the technology that's tried to do it, that's tried to prevent against picture attacks has done so with extra hardware or extra sensors. We're actually all cloud-based right now so it isn't our software and that is what is special to us is that picture attack detection but we've a got a very very intelligent way to do it. Everything is powered by deep learning so we're constantly understanding the surroundings, the context, and making an analysis on that. >> So I'm curious from the data side, obviously you're pulling in kind of your anchor data and then for doing comparisons but then are you constantly updating that data? I mean, what's kind of your data flow look like in terms of your algorithms, are you constantly training them and adjusting those algorithms? How does that work kind of based on real time data versus your historical data? >> So we have to continue to innovate and that is how we do it, is we continue to train every single time someone shows up we train their profile once more and so if you decide to grow a beard, you're not going to grow a beard in one day, right? It's going to take you a week, two weeks. We're learning throughout those two weeks and so it's just a way for use to continue to get more data for us but also to ensure that we are identifying you properly. >> Right, do you use any external databases that you pull in as some type of you know, adding more detail or you know, kind of, other public sources or it's all your own? >> It's all our own. >> Okay and I'm curious too on the kind of opening up to the developer community, how has that kind of shaped your product roadmap and your product development? >> It - we've got to be very very conscious of not getting sidetracked because we get to hear cool ideas about what we could do but we've got our core focus of building this API for more people to use. So you know, we continue to reach out them and ask for help and you know if they find flaw or they find something cool that we want to continue to improve, we'll keep working on that so I think it's more of a - we're finding the developer community likes to really tinker and to play and because they're doing it out of passion, it helps us drive our product. >> Right right. Okay, so priorities for the rest of the year? What's at the top of the list? >> We'll be doing a bigger rollout with a couple of partners later on this year and those will be kind of our flagship partners. But again, like I said, we want to continue to support those development communities so we'll be hosting a lot of hack-a-thons and just really pushing the name out there. So we launched our product yesterday and that helped generate some awareness but we're going to have to continue to have to get the brand out there as it's now one day old. >> Right right, well good. Well it was Chui before and it's Trueface.ai so we look forward to keeping an eye on progress and congratulations on where you've gotten to date. >> Thank you very much. I appreciate that. >> Absolutely. Alrighty, Shaun Moore, it's Trueface.ai. Look at the cameras, smile, it will know it's you. You're watching Jeff Frick down at the Cube in downtown San Jose at the When IoT Met AI: The Intelligence of Things. Thanks for watching. We'll be right back after this short break.

>> Announcer: From the Fairmont Hotel in the heart of Silicon Valley, it's The Cube. Covering when IOT met AI, the intelligence of things. Brought to you by Western Digital. >> Hey, welcome back everybody. Jeff Rick here with The Cube. We're in downtown San Jose at the Fairmont Hotel at a little show called when IOT Met AI, the Intelligence of Things. Talking about big data, IOT, AI and how those things are all coming together with virtual reality, artificial intelligence, augmented reality, all the fun buzz words, but this is where it's actually happening and we're real excited to have a pioneer in this space. He's Jack McCauley. He was a co-founder at Occulus VR, now spending his time at UC Berkeley as an innovator in residence. Jack welcome. >> Thank you. >> So you've been watching this thing evolve, obviously Occulus, way out front in kind of the VR space and I think augmented a reality in some ways is even more exciting than just kind of pure virtual reality. >> Right. >> So what do you think as you see this thing develop from the early days when you first sat down and started putting this all together? >> Well, I come from a gaming background. That's what I did for 30 years. I worked in video game development, particularly in hardware and things, console hardware. >> That's right, you did the Guitar Hero. >> Guitar Hero. Yeah, that's right. >> We got that one at home. >> I built their guitars and designed and built their guitars for Activision. And when were part of Red Octane, which is a studio. I primarily worked in the studio, not the headquarters, but I did some of the IP work with them too, so, to your question, you know when you produce a product and put it on the market, you never really know how it's going to do. >> Jeff: Right. >> So we make, we made two developer kits, put them out there and they exceeded our expectations and that was very good. It means that there is a market for VR, there is. We produce a consumer version and sales are not what we expected for that particular product. That was designated towards PC gamers and hopefully console games. But what has done well is the mobile stuff has exceeded everyone's mildest expectations. I heard numbers, Gear VR, which is Occulus designed product for me, sold 7 million of those. That's a smash hit. Now, worldwide for phone mounted VR goggles, it's about 20 million and that's just in two years, so that's really intriguing. So, what has happened is it's shifted away from an expensive PC based rig with $700 or whatever it costs, plus $1,500 for the computer to something that costs $50 and you just stick your cell phone in it and that's what people, it doesn't give you the best experience, but that's what has sold and so if I were doing a start-up right now, I would not be working on PC stuff, I'd be working on mobile stuff. >> Jeff: Right. >> And the next thing I think, which will play out of this is, and I think you mentioned it prior to the interview, is the 360 cameras and Google has announced a camera that they're going to come out and it's for their VR 180 initiative, which allows you to see 180 video in stereo with a cell phone strapped to your face. And that's very intriguing. There's a couple of companies out there working on similar products. Lucid Cam, which is a start-up company here has a 180 camera that's very, very good and they have one coming out that's in 4K. They just launched their product. So to answer your question, it looks like what is going to happen is for VR, is that it's a cell phone strapped to your face and a camera somewhere else that you can view and experience. A concert. Imagine taking it to a sporting event where 5,000 people can view your video, 10,000 from your seat. That's very intriguing. >> Yeah, it's interesting I had my first kind of experience just not even 360 or live view, but I did a periscope from the YouTube concert here at Levi Stadium a couple of months ago, just to try it out, I'd never really done it and it was fascinating to watch the engagement of people on that application who had either seen them the prior week in Seattle or were anticipating them coming to the Rose Bowl, I think, you know, within a couple of days, and to have an interaction just based on my little, you know, mobile phone, I was able to find a rail so I had a pretty steady vantage point, but it was a fascinating, different way to experience media, as well as engagement, as well as kind of a crowd interaction beyond the people that happened to be kind of standing in a circle. >> You, what's intriguing about VR 180 is that anybody can film the concert and put the video on YouTube or stream it through their phone. And formerly it would require a $10,000 camera, a stereo camera set up professionally, but can you imagine though that a crowd, you know, sourced sort of thing where the media is sourced by the crowd and anyone can watch it with a mobile phone. That's what's happening, I think, and with Google's announcement, it even that reinforces my opinion anyways that that is where the market will be. It's live events, sporting events. >> Right, it's an experience, right? It all comes back to kind of experience. People are so much more experience drive these days than I think thing driven from everything from buying cars versus taking a new Uber and seeing it over and over and over again. People want the experience, but not necessarily, as the CEO of Zura said, the straps and straddles of ownership, let me have the fun, I don't necessarily want to own it. But I think the other thing that gets less talked about, get your opinion, is really the kind of combination of virtual reality plus the real world, augmented reality. We see the industrial internet of things all the time where, you know, you go take a walk on that factory before you put your goggles on and not only do you see what you see that's actually in front of you, but now you can start to see, it's almost like a heads up display, certain characteristics of the machinery and this and that are now driven from the database side back into the goggles, but now the richness of your observation has completely changed. >> Yes, and in some ways when you think of what Google did with Google Glass, not as well as we had liked. >> But for a first attempt. >> Yeah. They're way ahead of their time and there will come a time when, you know, Snap has their specs, right? Have you seen those? It's not augmented reality, but, there will come a time when you can probably have a manacle on your face and see the kinds of things you need to see if your driving a car for instance that, I mean, a heads up display or a projector projecting right into your retina. So, and, so I think that's the main thing for augmented reality. Will people, I mean, your Pokemon Go, that's kind of a AR game in a way. You look through your cell phone and the character stays fixed on the table or wherever you're looking for it. I mean that uses a mobile device to do that and I can imagine other applications that use a mobile device to do that and I'm aware of people working on things like that right now. >> So do you think that the breakthrough on the mobile versus the PC-based system was just good enough? In being able to just experience that so easily, you know, I mean, Google gave out hundreds and hundreds of thousands of the cardboard boxes, so wow. >> Yeah. Well, it didn't mean that Gear VR didn't move into the market, it did. You know, it did anyways, but to answer your question about AR, you know, I think that, you know, without having good locals, I mean the problem with wearing the Google Glass and the Google cardboard and Gear VR is it kind of makes you sick a little bit and nobody's working on the localization part. Like how to get rid of the nausea effect. I watched a video that was filmed with Lucid Cam at the Pride Parade in San Francisco and I put it on and somebody was moving with the crowd and I just felt nauseous, so that problem probably probably is one I would attempt to attack if I were going to build a company or something like that right now. >> But I wonder too, how much of that is kind of getting used to the format because people when they first put them on for sure, there's like, ah, but you know, if you settle in a little bit and our eyes are pretty forgiving, you get used to things pretty quickly. Your mind can get accustomed to it to a certain degree, but even I get nauseous and I don't get nauseous very easily. >> Okay, so you're title should just be tinkerer. I looked at your Twitter handle. You're building all kinds of fun stuff in your not a garage, but your big giant lab and you're working at Berkeley. What are some of the things that you can share that you see coming down the road that people aren't necessary thinking about that's going to take some of these technologies to the next level. >> I got one for you. So you've heard of autonomous vehicles, right? >> Jeff: Yep, yep. >> And you've heard of Hollow Lens, right. Hollow Lens is an augmented reality device you put on your had and it's got built in localization and it creates what's, it's uses what's know as SLAM or S-L-A-M to build a mesh of the world around you. And with that mesh, the next guy that comes into that virtual world that you mapped will be away ahead. In other words, the map will already exists and he'll modify upon that and the mesh always gets updated. Can you imagine getting that into a self-driving vehicle just for safety's sake, mapping out the road ahead of you, the vehicle ahead of you has already mapped the road for you and you're adding to the mesh and adjusting the mesh, so I think that that's, you know, as far as Hollow Lens is concerned and their localization system, that's going to be really relevant to self-driving cars. Now whether or not it'll be Microsoft's SLAM or somebody else's, I think that that's probably the best, that's the good thing that came out of Hollow Lens and that will bleed into the self-driving car market. It's a big data crunching number and in Jobs, he was actually looking at this a long time ago, like what can we do with self-driving vehicles and I think he had banned the idea because he realized he had a huge computing and data problem. That was 10 years ago. Things have changed. But I think that that's the thing that will possibly come out of, you know, this AR stuff is that localization is just going to be transported to other areas of technology and self-driving cars and so forth. >> I just love autonomous vehicles because everything gets distilled and applied into that application, which is a great application for people to see and understand it's so tangible. >> Yeah, it may change the way we think about cars and we may just not ever own a car. >> I think absolutely. The car industry, it's ownership, it's usage, it's frequency of usage, how they're used. It's not a steel cage anymore for safety as the crash rates go down significantly. I think there's a lot of changes. >> Yeah, you buy a car and it sits for 20 hours a day. >> Right. >> Unutilized. >> All right. Well, Jack I hope maybe I get a chance to come out and check out your lab one time because you're making all kinds of cool stuff. When's that car going to be done? >> I took it upon myself to remodel a house the same time I was doing that, but the car is moving ahead. In September I think I can get it started. Get the engine running and get the power train up and running. Right now I'm working on the electronics and we have an interesting feature on that car that we're going to do an announcement on later. >> Okay, we'll look out for that. We'll keep watching the Twitter. All right, thanks for taking a few minutes. All right, let's check with Cauley. I'm Jeff Rick. You're watching The Cube from When IOT Met AI, the Intelligence of Things in San Jose. We'll be right back after this short break. Thanks for watching. (technological jingle)

>> Presenter: From the Fairmont Hotel, in the heart of Silicon Valley, it's theCUBE. Covering When IoT Met AI The Intelligence of Things. Brought to you by Western Digital. >> Hey welcome back everybody, Jeff Frick here with theCUBE. We're in downtown San Jose at the Fairmont Hotel, at an event called When IoT Met AI The Intelligence of Things. You've heard about the internet of things, and on the intelligence of things, it's IoT, it's AI, it's AR, all this stuff is really coming to play, it's very interesting space, still a lot of start-up activity, still a lot of big companies making plays in this space. So we're excited to be here, and really joined by our host, big thanks to Western Digital for hosting this event with WDLabs' Dave Tang. Got newly promoted since last we spoke. The SVP of corporate marketing and communications, for Western Digital, Dave great to see you as usual. >> Well, great to be here, thanks. >> So I don't think the need for more storage is going down anytime soon, that's kind of my takeaway. >> No, no, yeah. If this wall of data just keeps growing. >> Yeah, I think the term we had yesterday at the Ag event that we were at, also sponsored by you, is really the flood of data using an agricultural term. But it's pretty fascinating, as more, and more, and more data is not only coming off the sensors, but coming off the people, and used in so many more ways. >> That's right, yeah we see it as a virtual cycle, you create more data, you find more uses for that data to harness the power and unleash the promise of that data, and then you create even more data. So, when that virtual cycle of creating more, and finding more uses of it, and yeah one of the things that we find interesting, that's related to this event with IoT and AI, is this notion that data is falling into two general categories. There's big data, and there's fast data. So, big data I think everyone is quite familiar with by this time, these large aggregated likes of data that you can extract information out of. Look for insights and connections between data, predict the future, and create more prescriptive recommendations, right? >> Right. >> And through all of that you can gain algorithms that help to make predictions, or can help machines run based on that data. So we've gone through this phase where we focused a lot on how we harness big data, but now we're taking these algorithms that we've gleaned from that, and we're able to put them in real time applications, and that's sort of been the birth of fast data, it's been really-- >> Right, the streaming data. We cover Spark Summit, we cover Flink, and New, a new kind of open source project that came out of Berlin. That some people would say the next generation of Spark, and the other thing, you know, good for you guys, is that it used to be, not only was it old data, but it was a sampling of old data. Now on this new data, and the data stream that's all of the data. And I would actually challenge, I wonder if that separation as you describe, will stay, because I got to tell you, the last little drive I bought, just last week, was an SSD drive, you know, one terabyte. I needed some storage, and I had a choice between spinning disc and not, and I went with the flat. I mean, 'cause what's fascinating to me, is the second order benefits that we keep hearing time, and time, and time again, once people become a data-driven enterprise, are way more than just that kind of top-level thing that they thought. >> Exactly, and that's sort of that virtual cycle, you got to taste, and you learn how to use it, and then you want more. >> Jeff: Right, right. >> And that's the great thing about the breadth of technologies and products that Western Digital has, is from the solid state products, the higher performance flash products that we have, to the higher capacity helium-filled drive technologies, as well as devices going on up into systems, we cover this whole spectrum of fast data and big data. >> Right, right. >> I'll give an example. So credit card fraud detection is an interesting area. Billions of dollars potentially being lost there. Well to learn how to predict when transactions are fraudulent, you have to study massive amounts of data. Billions of transactions, so that's the big data side of it, and then as soon as you do that, you can take those algorithms and run them in real time. So as transactions come in for authorization, those algorithms can determine, before they're approved, that one's fraudulent, and that one's not. Save a lot of time and processing for fraud claims. So that's a great example of once you learn something from big data, you apply it to the real-time realm, and it's quite dire right? And then that spawned you to collect even more data, because you want to find new applications and new uses. >> Right, and too kind of this wave of computing back and forth from the shared services computer, then the desktop computer, now it's back to the cloud, and then now it's-- >> Dave: Out with the edge. >> IoT, it's all about the edge. >> Yeah, right. >> And at the end of the day, it's going to be application-specific. What needs to be processed locally, what needs to be processed back at the computer, and then all the different platforms. We were again at a navigation for autonomous vehicles show, who knew there was such a thing that small? And even the attributes of the storage required in the ecosystem of a car, right? And the environmental conditions-- >> That's right. >> Is the word I'm looking for. Completely different, new opportunity, kind of new class of hardware required to operate in that environment, and again that still combines cloud and Edge, sensors and maps. So just the, I don't think that the man's going down David. >> Yeah, absolutely >> I think you're in a good spot. (Jeff laughing) >> You're absolutely right, and even though we try to simplify into fast data, and big data, and Core and Edge, what we're finding is that applications are increasingly specialized, and have specialized needs in terms of the type of data. Is it large amounts of data, is it streaming? You know, what are the performance characteristics, and how is it being transformed, what's the compute aspect of it? And what we're finding, is that the days of general-purpose compute and storage, and memory platforms, are fading, and we're getting into environments with increasingly specialized architectures, across all those elements. Compute, memory and storage. So that's what's really exciting to be in our spot in the industry, is that we're looking at creating the future by developing new technologies that continue to fuel that growth even further, and fuel the uses of data even further. >> And fascinating just the ongoing case of Moore's law, which I know is not, you know you're not making microprocessors, but I think it's so powerful. Moore's law really is a philosophy, as opposed to an architectural spec. Just this relentless pace of innovation, and you guys just continue to push the envelope. So what are your kind of priorities? I can't believe we're halfway through 2017 already, but for kind of the balance of the year kind of, what are some of your top-of-mind things? I know it's exciting times, you're going through the merger, you know, the company is in a great space. What are your kind of top priorities for the next several months? >> Well, so, I think as a company that has gone through serial acquisitions and integrations, of course we're continuing to drive the transformation of the overall business. >> But the fun stuff right? It's not to increase your staff (Jeff laughing). >> Right, yeah, that is the hardware. >> Stitching together the European systems. >> But yeah, the fun stuff includes pushing the limits even further with solid state technologies, with our 3D NAND technologies. You know, we're leading the industry in 64 layer 3D NAND, and just yesterday we announced a 96 layer 3D NAND. So pushing those limits even further, so that we can provide higher capacities in smaller footprints, lower power, in mobile devices and out on the Edge, to drive all these exciting opportunities in IoT an AI. >> It's crazy, it's crazy. >> Yeah it is, yeah. >> You know, terabyte SD cards, terabyte Micro SD cards, I mean the amount of power that you guys pack into these smaller and smaller packages, it's magical. I mean it's absolutely magic. >> Yeah, and the same goes on the other end of the spectrum, with high-capacity devices. Our helium-filled drives are getting higher and higher capacity, 10, 12, 14 terabyte high-capacity devices for that big data core, that all the data has to end up with at some point. So we're trying to keep a balance of pushing the limits on both ends. >> Alright, well Dave, thanks for taking a few minutes out of your busy day, and congratulations on all your success. >> Great, good to be here. >> Alright, he's Dave Tang from Western Digital, he's changing your world, my world, and everyone else's. We're here in San Jose, you're watching theCUBE, thanks for watching.

(upbeat electronic music) >> Narrator: From the Fairmont Hotel in the heart of Silicon Valley, it's theCUBE. Covering when IoT met AI, The Intelligence of Things. Brought to you by Western Digital. >> Welcome back here everybody, Jeff Frick here with theCUBE. We are at downtown San Jose at the Fairmont Hotel. When IoT met AI it happened right here, you saw it first. The Intelligence of Things, a really interesting event put on by readwrite and Western Digital and we are really excited to welcome back a many time CUBE alumni and always a fan favorite, she's Janet George. She's Fellow & Chief Data Officer of Western Digital. Janet, great to see you. >> Thank you, thank you. >> So, as I asked you when you sat down, you're always working on cool things. You're always kind of at the cutting edge. So, what have you been playing with lately? >> Lately I have been working on neural networks and TensorFlow. So really trying to study and understand the behaviors and patterns of neural networks, how they work and then unleashing our data at it. So trying to figure out how it's training through our data, how many nets there are, and then trying to figure out what results it's coming with. What are the predictions? Looking at how the predictions are, whether the predictions are accurate or less accurate and then validating the predictions to make it more accurate, and so on and so forth. >> So it's interesting. It's a different tool, so you're learning the tool itself. >> Yes. >> And you're learning the underlying technology behind the tool. >> Yes. >> And then testing it actually against some of the other tools that you guys have, I mean obviously you guys have been doing- >> That's right. >> Mean time between failure analysis for a long long time. >> That's right, that's right. >> So, first off, kind of experience with the tool, how is it different? >> So with machine learning, fundamentally we have to go into feature extraction. So you have to figure out all the features and then you use the features for predictions. With neural networks you can throw all the raw data at it. It's in fact data-agnostic. So you don't have to spend enormous amounts of time trying to detect the features. Like for example, If you throw hundreds of cat images at the neural network, the neural network will figure out image features of the cat; the nose, the eyes, the ears and so on and so forth. And once it trains itself through a series of iterations, you can throw a lot of deranged cats at the neural network and it's still going to figure out what the features of a real cat is. >> Right. >> And it will predict the cat correctly. >> Right. So then, how does that apply to, you know, the more specific use case in terms of your failure analysis? >> Yeah. So we have failures and we have multiple failures. Some failures through through the human eye, it's very obvious, right? But humans get tired, and over a period of time we can't endure looking at hundreds and millions of failures, right? And some failures are interconnected. So there is a relationship between these failure patterns or there is a correlation between two failures, right? It could be an edge failure. It could a radial failure, eye pattern type failure. It could be a radial failure. So these failures, for us as humans, we can't escape. >> Right. >> And we used to be able to take these failures and train them at scale and then predict. Now with neural networks, we don't have to take and do all that. We don't have to extract these labels and try to show them what these failures look like. Training is almost like throwing a lot of data at the neural networks. >> So it almost sounds like kind of the promise of the data lake if you will. >> Yes. >> If you have heard about, from the Hadoop Summit- >> Yes, yes, yes. >> For ever and ever and ever. Right? You dump it all in and insights will flow. But we found, often, that that's not true. You need hypothesis. >> Yes, yes. >> You need to structure and get it going. But what you're describing though, sounds much more along kind of that vision. >> Yes, very much so. Now, the only caveat is you need some labels, right? If there is no label on the failure data, it's very difficult for the neural networks to figure out what the failure is. >> Jeff: Right. >> So you have to give it some labels to understand what patterns it should learn. >> Right. >> Right, and that is where the domain experts come in. So we train it with labeled data. So if you are training with a cat, you know the features of a cat, right? In the industrial world, cat is really what's in the heads of people. The domain knowledge is not so authoritative. Like the sky or the animals or the cat. >> Jeff: Right. >> The domain knowledge is much more embedded in the brains of the people who are working. And so we have to extract that domain knowledge into labels. And then you're able to scale the domain. >> Jeff: Right. >> Through the neural network. >> So okay so then how does it then compare with the other tools that you've used in the past? In terms of, obviously the process is very different, but in terms of just pure performance? What are you finding? >> So we are finding very good performance and actually we are finding very good accuracy. Right? So once it's trained, and it's doing very well on the failure patterns, it's getting it right 90% of the time, right? >> Really? >> Yes, but in a machine learning program, what happens is sometimes the model is over-fitted or it's under-fitted or there is bias in the model and you got to remove the bias in the model or you got to figure out, well, is the model false-positive or false-negative? You got to optimize for something, right? >> Right, right. >> Because we are really dealing with mathematical approximation, we are not dealing with preciseness, we are not dealing with exactness. >> Right, right. >> In neural networks, actually, it's pretty good, because it's actually always dealing with accuracy. It's not dealing with precision, right? So it's accurate most of the time. >> Interesting, because that's often what's common about the kind of difference between computer science and statistics, right? >> Yes. >> Computers is binary. Statistics always has a kind of a confidence interval. But what you're describing, it sounds like the confidence is tightening up to such a degree that it's almost reaching binary. >> Yeah, yeah, exactly. And see, brute force is good when your traditional computing programing paradigm is very brute force type paradigm, right? The traditional paradigm is very good when the problems are simpler. But when the problems are of scale, like you're talking 70 petabytes of data or you're talking 70 billion roles, right? Find all these patterns in that, right? >> Jeff: Right. >> I mean you just, the scale at which that operates and at the scale at which traditional machine learning even works is quite different from how neural networks work. >> Jeff: Okay. >> Right? Traditional machine learning you still have to do some feature extraction. You still have to say "Oh I can't." Otherwise you are going to have dimensionality issues, right? It's too broad to get the prediction anywhere close. >> Right. >> Right? And so you want to reduce the dimensionality to get a better prediction. But here you don't have to worry about dimensionality. You just have to make sure the labels are right. >> Right, right. So as you dig deeper into this tool and expose all these new capabilities, what do you look forward to? What can you do that you couldn't do before? >> It's interesting because it's grossly underestimating the human brain, right? The human brain is supremely powerful in all aspects, right? And there is a great deal of difficulty in trying to code the human brain, right? But with neural networks and because of the various propagation layers and the ability to move through these networks we are coming closer and closer, right? So one example: When you think about driving, recently, Google driverless car got into an accident, right? And where it got into an accident was the driverless car was merging into a lane and there was a bus and it collided with the bus. So where did A.I. go wrong? Now if you train an A.I., birds can fly, and then you say penguin is a bird, it is going to assume penguin can fly. >> Jeff: Right, right. >> We as humans know penguin is a bird but it can't fly like other birds, right? >> Jeff: Right. >> It's that anomaly thing, right? Naturally when are driving and a bus shows up, even if it's yield, the bus goes. >> Jeff: Right, right. >> We yield to the bus because it's bigger and we know that. >> A.I. doesn't know that. It was taught that yield is yield. >> Right, right. >> So it collided with the bus. But the beauty is now large fleets of cars can learn very quickly based on what it just got from that one car. >> Right, right. >> So now there are pros and cons. So think about you driving down Highway 85 and there is a collision, it's Sunday morning, you don't know about the collision. You're coming down on the hill, right? Blind corner and boom that's how these crashes happen and so many people died, right? If you were driving a driverless car, you would have knowledge from the fleet and from everywhere else. >> Right. >> So you know ahead of time. We don't talk to each other when we are in cars. We don't have universal knowledge, right? >> Car-to-car communication. >> Car-to-car communications and A.I. has that so directly it can save accidents. It can save people from dying, right? But people still feel, it's a psychology thing, people still feel very unsafe in a driverless car, right? So we have to get over- >> Well they will get over that. They feel plenty safe in a driverless airplane, right? >> That's right. Or in a driveless light rail. >> Jeff: Right. >> Or, you know, when somebody else is driving they're fine with the driver who's driving. You just sit in the driver's car. >> But there's that one pesky autonomous car problem, when the pedestrian won't go. >> Yeah. >> And the car is stopped it's like a friendly battle-lock. >> That's right, that's right. >> Well good stuff Janet and always great to see you. I'm sure we will see you very shortly 'cause you are at all the great big data conferences. >> Thank you. >> Thanks for taking a few minutes out of your day. >> Thank you. >> Alright she is Janet George, she is the smartest lady at Western Digital, perhaps in Silicon Valley. We're not sure but we feel pretty confident. I am Jeff Frick and you're watching theCUBE from When IoT meets AI: The Intelligence of Things. We will be right back after this short break. Thanks for watching. (upbeat electronic music)

>> Announcer: From the Fairmont Hotel in the heart of Silicon Valley, it's The Cube. Covering When IoT met AI: The Intelligence of Things. Brought to you by Western Digital. >> Hey, welcome back, everybody. Jeff Frick here with The Cube. We're in downtown Los Angeles at the Fairmont Hotel at a interesting little show called When IoT Met AI: The Intelligence of Things. A lot of cool startups here along with some big companies. We're really excited go have our next guest, taking a little different angle. He's Scott Noteboom. He is the co-founder and CEO of a company called Litbit. First off, Scott, welcome. >> Yeah, thank you very much. >> Absolutely. For folks that aren't familiar, what is Litbit, what's your core mission? >> Well, probably, the simplest way to put it is, is in business we enable our users who have a lot of experience in a lot of different areas to take their expertise and experience which may not be coding software, or understanding, or even being able to spell what an algorithm is on the data science perspective, and being able to give them an easy interface so they can kind of create their own Siro or Alexa, an AI but an AI that's based on their own subject matter expertise that they can put to work in a lot of different ways. >> So, there's often a lot of talk about kind of tribal knowledge, and how does tribal knowledge get passed down so people know how to do things. Whether it's with new employees, or as you were talking about a little bit off camera, just remote locations for this or that. And there hasn't really been a great system to do that. So, you're really attacking that, not only with the documentation, but then making an AI actionable piece of software that can then drive machines and using IoT to do things. Is that correct? >> That's right. So, if you created, say an AI that I've been passionate about 'cause I ran data centers for a lot of years, is DAC. So, DAC's an AI that has a lot of expertise, and how to run a data center by, and kind of fueled and mentored by a lot of the experts in the industry. So, how can you take DAC and put Dak to work in a lot of places? And the people who need the best trained DAC aren't people who are building apps. They are people who have their area of subject matter expertise, and we view these AI personas that can be put to work as kind of apps of the future, where can people can prescribe to personas that are build directly by the experts, which is a pretty pure way to connect AIs with the right people, and then be able to get them and put them-- >> So, there's kind of two steps to the process. How does the information get from the experts into your system? How's that training happen? >> So, where we spend a lot of attention is, a lot of people question and go, "Well, an AI lives in this virtual logical world "that's disconnected from the physical world." And I always questions for people to close their eyes and imagine their favorite person that loves them in the world. And when they picture that person hear that person's voice in their head, that's actually a very similar virtual world as what AIs working. It's not the physical world. And what connects us as people to the physical world, our senses, our sight, our hearing, our touch, our feeling. And what we've done is we've enabled using IoT sensors, the ability of combining those sensors with AI to turn sensors into senses, which then provide the ability for the AI to connect really meaningful ways to the physical world. And then the experts can teach the AI this is what this looks like, this is what this sounds like, this is what it's supposed to feel like. If it's greater than 80 degrees in an office location, it's hot. Really teaching the AI to be able to form thoughts based on a specific expertise and then be able to take the right actions to do the right things when those thoughts are formed. >> How do you deal with nuance, 'cause I'm sure there's a lot of times where people, as you said, are sensing or smelling or something, but they don't even necessarily consciously know that that's an input into their decision process, even though it really is. They just haven't really thought of it as a discrete input. How do you separate out all these discreet inputs so you get a great model that represents your best of breed technicians? >> Well, to try to answer the question, first of all, the more training the better. So, the good way to think of the AI is, unlike a lot of technologies that typically age and go out of life over time, an AI continuously gets smarter the more it's mentored by people, which would be supervised learning. And the more it can adjust and learn on it's own combined with real day to day data activity combined with that supervised learning and unsupervised learning approach, so enabling it to continuously get better over time. We've figure out some ways that it can produce some pretty meaningful results with a small amount of training. So, yeah. >> Okay. What are some of the applications, kind of your initial go to market? >> We're a small startup, and really, what we've done is we've developed a platform that we really like to, our goal is for it to be very horizontal in nature. And then the applications or the AI personas can be very vertical or subject matter experts across different silos. So, what we're doing is, is we're working with partners right now in different silos developing AIs that have expertise in the oil and gas business, in the pharmaceutical space, in the data center space, in the corporate facilities manage space, and really making sure that people who aren't technologists in all of those spaces, whether you're a very specific scientists who're running a lab, or a facilities guy in a corporate building, can successfully make that experiential connection between themselves and the AI, and put it to practical use. And then as we go, there's a lot of efforts that can be very specific to specific silos, whatever they may be. >> So, those personas are actually roles of individuals, if you will, performing certain tasks within those verticals. >> Absolutely. What we call them is coworkers, and the way things are designed is, one of the things that I think is really important in the AI world is that we approach everything from a human perspective because it's a big disruptive shift, and there's a lot of concern over it. So, if you get people to connect to it in a humanistic way, like coworker Viv works along with coworker Sophia, and Viv has this expertise, Sophia has this expertise, and has better improving ways to interface with people who have names that aren't a lot different from them and have skillsets that aren't a lot different. When you look at the AIS, they don't mind working longer hours. Let them work the weekends so I can spend hours with my family. Let them work the crazy shifts. So, things are different in that regard. But the relationship aspect of how the workplace works, try not to disrupt that too much. >> So, then on a consumption side, with the person coworker that's working with the persona, how do they interact with it, how do they get the data out, and I guess even more importantly, maybe, how do they get the new data back in to continue to train the model? >> So, the biggest thing you have to focus on with a human and machine learning interface that doesn't require a program or a data science, is that the language that the AI is taught in is human language, natural human language. So, we developed a lot of natural human language files that are pretty neat because a human coworker in California here could be interfacing in english to their coworker, and at the same time, someone speaking Mandarin in Shanghai could be interfacing with the same coworker speaking mandarin unless you can get multilingual functionality. Right now, to answer your question, people are doing it in a text based scenario. But the future vision, I think when the industry timing is right, is we view that every one of the coworkers we're developing will have a very distinct unique fingerprint of a voice. So, therefor, when you're engaging with your coworker using voice, you'll begin to recognize, oh, that's Dax, or that's Viv, or that's Sophia, based on their voice. So, like many people, this is how we're communicating with voice, and we believe the same thing's going to occur. And a lot of that's in timing. That's the direction where things are headed. >> Interesting. The whole voice aspect is just a whole 'nother interesting thing in terms of what type of voice personality attributes associated with voice. That's probably going to be a huge piece in terms of the adoption, in terms of having a true coworker experience, if you will. >> One of the things we haven't figure out, and these are important questions, and there's so many unknowns, is we feel really confident that the AI persona should have a unique voice because then I know who I'm engaging with, and I can connect by ear without them saying what their name is. But what does an AI persona look like? That's something where actually we don't know that, and we explore different things and, oh, that looks scary, or oh, that doesn't make sense. Should it look like anything? Which has largely been the approach of what does an Alexa or a Siri look like. As you continue to advance those engagements, and particularly when augmented reality comes into play, through augmented reality, if you're able to look and say, "Oh, a coworker's working over there," there's some value in that. But what is it going to look like? That's interesting, and we don't know that. >> Hopefully, better than those things at the San Jose Airport that are running around. >> Yeah, exactly. >> Classic robot. All right, Scott, very interesting story. I look forward to watching you grow and develop over time. >> Awesome, it's good to talk. >> Absolutely, all right, he's Scott Noteboom, he's from Litbit. I'm Jeff Frick, you're watching The Cube. We're at When IoT met AI: The Intelligence of Things, here at San Jose California. We'll be right back after the short break. Thanks for watching. (upbeat music)

>> Narrator: From the Fairmont Hotel in the heart of Silicon Valley it's theCUBE covering when IoT met AI, The Intelligence of Things. Brought to you by Western Digital. >> Hey welcome back here everybody Jeff Frick here with theCUBE. We're in San Jose, California at the Fairmont Hotel, at the when IoT met AI show, it's all about the intelligence of things. A lot of really interesting start ups here, we're still so early days in most of this technology. Facial recognition gets a lot of play, iris recognition, got to get rid of these stupid passwords. We're really excited to have our next guest, he's Modar Alaoui, he's the CEO and founder of Eyeris. And it says here Modar that you guys are into face analytics and emotion recognition. First off welcome. >> Thank you so much for having me. >> So face analytics, I'm a clear customer I love going to clear at the airport, I put my two fingers down, I think they have my iris, they have different things but what's special about the face compared to some of these other biometric options that people have? >> We go beyond just the biometrics, we do pretty much the entire suites of face analytics. Anything from eye openness, face, gender, emotion recognition, head bows, gaze estimation, et cetera et cetera. So it is pretty much anything and everything you can derive from the face including non verbal clues, yawning, head nod, head shake, et cetera. >> That was a huge range of things, so clearly just the face recognition to know that I am me probably relatively straight forward. A couple anchor points, does everything measure up and match the prior? But emotion that's a whole different thing, not only are there lots of different emotions, but the way I express my emotion might be different than the way you express the very same emotion. Right, everybody has a different smile. So how do you start to figure out the algorithms to sort through this? >> Right, so you're right. There are some nuances between cultures, ages, genders, ethnicities and things like that. Generally they've been universalized for the past three and a half decades by the scholars the psychologists et cetera. So what they actually have a consensus on is that there are only seven or six universal emotions plus neutral. >> Six, what are the six? >> Joy, surprise, anger, disgust, fear, sadness, and neutral. >> Okay and everything is some derivation of that, you can kind of put everything into little buckets. >> That is correct so think of them as seven universal colors or seven primary colors and then everything else is a derivative of that. The other thing is that emotions are hard wired into our brain they happen in a 1/15th or a 1/25th of a second, particularly micro expressions. And they can generally give up a lot of information as to whether a person has suppressed the certain emotion or not or whether they are thinking about something negatively before they could respond positively, et cetera. >> Okay so now you've got the data, you know how I'm feeling, what are you doing with it? It must tie back to all types of different applications I would assume. >> That's right there are a number of applications. Initially when we created this, what we call, enabling technology we wanted to focus on two things. One, is what type of application could have the biggest impact but also the quickest adoption in terms of volumes. Today we focus on driver monitoring AI as well as occupants monitoring AI so we focus on Autonomous and semi autonomous vehicles. And a second application is social robotics, but in essence if you think of a car it's also another robot except that social robotics are those potentially AI engines, or even AI engines in form of an actual robot that communicates with humans. Therefore, the word social. >> Right, so I can see a kind of semi autonomous vehicle or even a not autonomous vehicle you want to know if I'm dosing off. And some of those things have been around in a basic form for a little while. But what about in an autonomous vehicle is impacted by my emotion as a passenger, not necessarily a driver if it's a level five? >> That's right, so when we talk about an autonomous vehicle I think what you're referring to is level five autonomy where a vehicle does not actually have a steering wheel or gas pedal or anything like that. And we don't foresee that those will be on a road for at least another 10 years or more. The focus today is on level two, three, and four, and that's semi autonomy. Even for autonomous, fully autonomous vehicles, you would see them come out with vision sensors or vision AI inside the vehicle. So that these sensors could, together with the software that could analyze everything that's happening inside, cater to the services towards what is going to be the ridership economy. Once the car drives itself autonomously, the focus shifts from the driver to the occupants. As a matter of a fact it's the occupants that would be riding in these vehicles or buying them or sharing them, not the driver. And therefore all these services will revolve around who is inside the vehicle like age, gender emotion, activity, et cetera. >> Interesting, so all these things the age, gender emotion, activity, what is the most important do you think in terms of your business and kind of where as you say you can have a big impact. >> We can group them into two categories, the first one is safety obviously, eye openness, head bows, blinking, yawning, and all these things are utmost importance especially focused on the driver at this point. But then there is a number of applications that relates to comfort and personalization. And so those could potentially take advantage of the emotions and the rest of the analytics. >> Okay, so then where are you guys, Eyeris as a company? Where do have some installations I assume out there? Are you still early days kind of? Where are you in terms of the development of the company? >> We have quite a mature product, what I can disclose is we have plans to go into mass production starting 2018. Some plans for Q4 2017 have been pushed out. So we'll probably start seeing some of those in Q1, Q2 2018. >> Okay. >> We made some announcements earlier this year at CS with Toyota and Honda. But then we'll be seeing some mass volume starting 2019 and beyond. >> Okay, and I assume you're a cloud based solution. >> We do have that as well, but we are particularly a local processing solution. >> Jeff: Oh you are? >> Yes so think of it as an edge computing type of solution. >> Okay and then you work with other peoples sensors and existing systems or are you more of a software component that plugs in? Or you provide the whole system in terms of the, I assume, cameras to watch the people? >> So we're a software company only, we however, are hardware processor camera diagnostic. And of course for everything to succeed there will have to be some components of sensor fusion. And therefore we can work and do work with other sensor companies in order to provide higher confidence level of all the analytics that we provide. >> Pretty exciting, so is it commercially available you're GA now or not quite yet? >> We'll be commercially available, you'll start seeing it on the roads or in the market sometime early next year. >> Sometime early next year? Alright well we will look forward to it. >> Thank you so much. >> Very exciting times, alright, he's Modar Alaoui. And he's going to be paying attention to you to make sure you're paying attention to the roads. So you don't fall asleep, or doze off and go to sleep. So I'm Jeff Frick, you're watching theCUBE at IoT met AI, The Intelligence of Things. San Jose, California, we'll be right back after this short break, thanks for watching. (bright techno music)

(upbeat music) >> Announcer: From the Fairmont Hotel, in the heart of Silicon Valley, it's theCUBE. Covering, When IoT met AI: The Intelligence of Things. Brought to you by Western Digital. >> Welcome back everybody. Jeff Frick here with theCUBE. We're at Downtown San Jose at the Fairmont Hotel at a small little conference, very intimate affair, talking about IoT and AI, The Intelligence of Things. When IoT met AI. Now, they've got a cool little start up, kind of expo hall. We're excited to have our next guest here from that. It's Mike Wilson, he's the CEO of BriteThings. Mike, welcome. >> Good to be here, Jeff, how you doin'? >> Absolutely. So, BriteThings. What are BriteThings? >> BriteThings are intelligent plugs, power strips, wall sockets, anything that fits into the plug load space. It learns users behavior and then provides them an intelligent on-off schedule. The goal here is to turn stuff off when it's on and not being needed. >> Right. >> So wasted energy. Nights and weekends in the workspace, for example. >> It sounds like such a simple thing. >> Totally. >> But we were talking before we turned the cameras on, this actually has giant economic impact >> It does. >> in building maintenance, which is a huge category >> Yup. >> as you said, I'll let you kind of break down the numbers as to where >> Sure. >> that energy's being spent and the impact that you guys are having. >> Well our customers are building owners and operators, and they pay an electrical bill to run that building. It's a cost of running the building. About 27% of it goes to lighting, about 38% goes to heating and cooling, and all the rest goes to plug loads. And where we come to the market it, of course there's huge lighting companies, famous names, same with HVAC, but no one's doing anything about plug loads, and the reason is is because plug loads are distributed, they're hard to control. And so what we bring to the market is a product that is small, inexpensive, and can suddenly give owners and operators all the control that they enjoy with lighting and HVAC over their plug loads. >> So it's kind of like Dest, in that it takes a relatively simple function, now because of the cloud, because of the internet, you can add a lot more intelligence into a relatively, I don't want to say dumb device, but the device itself doesn't have to have that much power 'cause you can put the application somewhere else. >> Exactly, so if you just imagine, you're sitting here with me right now. Probably at your workplace and at home there's a bunch of stuff turned on, you're not using it, >> Right >> but you're spending money to keep it powered up, and that's causing CO2 to be generated at power plant down the road. So that's bad for your pocket, it's bad for the environment. So if we can automatically turn that stuff off, then people don't have to worry about it. We can measure it, so here's where the money is. >> Right. >> Not only energy savings, but data. So I can tell you when you turned your stuff on and off, so that means human presence. When you're at work, there's a value to that. If you're going to put a floor of an office building out there and heat it or light it, we can tell you if people are there or not. So you can look at that and make, and save even more money. >> Jeff: Right. >> We've got one customer that uses our product for inventory management. If it plugs in, you can see it on our screen, and you can see if it's on or off, if it's connected and how it's running. So that kind of data ends up being valuable, not only for energy savings, because we turn stuff on and off, but human presence, inventory control, the list goes on and on. Our customers actually every year are coming up with new ways to use our device. >> Right. And just for the baseline savings, you just basically plug it in and turn it on, and you're reporting some huge savings just by just the basic operation of your strips versus a regular strip. >> Exactly. So just imagine, this device is learning your behavior, so that's part of our, you know, that's kind of our core competency here, is these devices measure the amount of energy you're using. When you're not using something, it goes into standby mode, or sleep mode. Then we turn that off to save you the money. But the way we're able to do that is using artificial intelligence to learn patterns, and take those patterns and you can basically guess the best optimized schedule for your devices to be turned and off. >> Right. >> On and off. So if you imagine you've got 100,000 employees, 100,000 different schedules, this thing has to be smart and it can't affect worker productivity. >> Right. >> So we have to be smart enough to know when to turn it on before you come into work, when to turn it off to save you the max amount of money, and be able to measure all of that so you can roll that up and see how much money you're saving. How much CO2 are you reducing? >> Right. >> You know, so sustainability officers love our product too. >> So do you integrate with other types of intelligent systems in that space? The lightings, and the HVAC? >> Yeah. Exactly. So one of the most important things is, I've got a portfolio, my office building is a portfolio of devices and systems, so just one of them is our plug load management, right? So I want to be able to see my plug load in my current control panel. So we've got APIs where our cloud technology is able to take that reporting and stick it into, for example, a Lucid control panel. We're working with Trane right now to integrate their BACnet solution for their building control management. >> Right, right. >> So that their customers are able to see lighting, HVAC, and plug load, >> Just what I was going to say. >> right off the same old screen and operating tools that they've always used. >> Right, right. What's kind of the typical ROI that you pitch people just for the straight-up money savings that they're going to get? >> We got our foot in the door by saying we can reduce your plug load cost a minimum of 30%, and what we're seeing on average is about 40 to 45%. >> Wow. >> It's a huge huge reduction. >> Now where do you go next? >> Well, conquer the world. (Jeff laughs) You know, so imagine this, anywhere in the commercial office space where there's a plug, so let your mind go, how many power strips are out there? >> Right, right. >> How many of those-- >> We're using about 20 of them right here. >> Yeah, so, just, you know, every person at every desk is a potential customer. Every time there's a coffeemaker or a break room, a fax machine, you know, any piece of equipment that's plugged in, we can save you money. Vending machines. We have a customer with these, you know, raise and lower desks. Crazy, they want to just see, they don't want to save energy, they want to know who's using that and how often. >> Jeff: Right, right. >> Our device can do that, too. >> Right. >> And that's that data I was telling you about. Once you start collecting data of how people use plugged-in devices, I'm collecting information about you, how you use your laptop, how you use your charger, how often. >> Because the signature on the draw is different depending on the activity of the device. >> You got it. Exactly. >> I love this. You know, it's so funny because the second-order impact of all these types of things is so much more significant than people give it credit, I think. >> It's about the data. >> Jeff: Yeah. >> And our customer's just love that, because the data gives them control, and when you have control, cost savings. >> And is it just commercial, or you sell them for regular retail customers as well? Or do you-- >> I imagine some day in the future that's a potential, but you know, our focus right now, 'cause the big problem out there is that buildings use 40% of all the energy generated in the United States, and commercial space is the big opportunity, because nights and weekends. >> Right. >> Stuff should be turned off, and we can do that right now. >> Right, right. >> We're the market doing it. >> Buildings with big, big POs. >> Yup. (Jeff laughs) >> Alright, Michael, sounds like exciting stuff, can't wait til I can get one at Best Buy or Office Depot, or something. >> Coming to a store near you, or www.britethings.com. >> Alright, thanks a lot, he's Mike Wilson. Save some energy, get one of these things when they're available, or at least tell the boss to get one at the office. (Michael laughs) >> Definitely. >> Alright, I'm Jeff Frick, you're watching theCUBE. When IoT meets AI in San Jose, California. Thanks for watching. (upbeat music)

>>from around the globe. It's the Cube with digital coverage of AWS reinvent 2020. Special coverage sponsored by AWS Worldwide Public sector Welcome to the cubes Coverage of AWS 2020. This is specialized programming for the worldwide public sector. I'm Lisa Martin, and I'm joined by Mick Boccaccio, the security advisor at Splunk Met. Welcome to the Q Virtual Oh, >>thank you for having me. It's great to be here. >>So you have a really interesting background that I wanted to share with our audience. You were the first see so in the history of U. S presidential campaigns with Mayor Pete, you were also branch shape of Threat intelligence at the executive office of the President. Tell us something about about your background is so interesting. >>Uh, yeah, those and I'm a gonna Def con and I teach lock picking for funds. Ease working for Mayor Pete A. C. So the campaign was really, really unique opportunity and I'm glad I did it. I'm hoping that, you know, on both sides of the aisle, no matter what your political preference, people realize that security and campaigns can only be married together. That was an incredible experience and worked with Mayor P. And I learned so much about how campaigns work and just the overall political process. And then previous to that being at the White House and a threat intelligence, role of branch chief they're working over the last election, the 2016 election. I think I learned probably more than any one person wants Thio about elections over that time. So, you know, I'm just a security nerd. That kind of fell into those things. And and and here I am and really, really, really just fortunate to have had those experiences. >>Your phone and your email must have been blowing up the last couple of weeks in the wake of the US presidential election, where the word fraud has brought up many times everyday. But election security. When I saw that you were the first, see so for Pete Buddha Judge, that was so recent, I thought, Really, Why? Why are they just now getting folks like yourself? And you are a self described a cybersecurity nerd? Why are they Why were they just recently starting to catch on to this? >>I think it's, uh like security on the campaign and security anywhere else on credit to the Buddha Judge campaign. There is no federal or mandate or anything like that that says your campaign has toe have a security person at the head of it or any standards to implement those security. So you know that the Buddha Judge campaign kind of leaned into it. We wanna be secure. We saw everything that happened in 2016. We don't want that to be us. And I think Mawr campaigns are getting on that plane. Definitely. You know, you saw recently, uh, Trump's campaign, Biden's campaign. They all had a lot of security folks in, and I think it's the normal. Now people realize how important security is. Uh, not only a political campaign, but I guess the political process overall, >>absolutely. We've seen the rise of cyber attacks and threats and threat vectors this year alone, Ransomware occurring. Everyone attack every 11 seconds or so I was reading recently. So give me an other view of what the biggest threats are right now. >>Two elections and I think the election process in general. You know, like I said, I'm just a security nerd. I've just got a weird background and done some really unique things. Eso I always attack the problems like I'm a security nerd and it comes down to, you know that that triumvirate, the people process and technology people need had to have faith in the process. Faith in the technology. You need to have a a clear source to get their information from the process. To me, I think this year, more than previous elections highlighted the lack of a federal uniforms standard for federal elections. State the state. We have different, different standards, and that kind of leads to confusion with people because, hey, my friend in Washington did it this way. But I'm in Texas and we do it this way. And I think that that standard would help a lot in the faith in the system. And then the last part of that. The technology, uh, you know, voting machines campaigns like I mentioned about campaigns. There's nothing that says a campaign has toe have a security person or a security program, and I think those are the kind of standards for, you know, just voting machines. Um, that needs to be a standard across the board. That's uniforms, so people will will have more faith because It's not different from state to state, and it's a uniformed process. >>E think whole country could have benefited from or uniformed processes in 2020. But one of the things that I like I did my first male and fellow this year always loved going and having that in person voting experience and putting on my sticker. And this year I thought in California we got all of our But there was this massive rise in mainland ballots. I mean, think about that and security in terms of getting the public's confidence. What are some of the things that you saw that you think needs to be uniforms going forward >>again? I think it goes back to when When you look at, you know, you voted by mail and I voted absentee and your ballot was due by this date. Um, you know where I live? Voting absentee. It's Dubai. This state needs we received by the state. Andi, I think this year really highlighted the differences between the states, and I'm hoping that election security and again everyone has done a super fantastic job. Um, sister has done incredible. If you're all their efforts for the working with election officials, secretaries of states on both sides of the aisle. It's an incredible work, and I hope it continues. I think the big problem election security is you know, the election is over, so we don't care again until 2022 or 2024. And I think putting something like a federalized standard, whether it be technology or process putting that in place now so that we're not talking about this in two or four years. I'm hoping that moment, um, continues, >>what would your recommendation be from building security programs to culture and awareness? How would you advise that they start? >>So, uh, one of the things that when I was on the Buddha Judge campaign, you know, like I said, we was the first person to do security for a campaign. And a lot of the staffers didn't quite have the background of professional background of work with security person. No, you know why? What I was doing there Eso my hallmark was You know, I'm trying to build a culture heavy on the cult. Um, you got to get people to buy in. I think this year when you look at what What Krebs and siesta and where the team over there have done is really find a way to tell us. Security story and every facet of the election, whether it be the machines themselves, the transporting the votes, counting the votes, how that information gets out to people websites I started like rumor control, which were were amazing amazing efforts. The public private partnerships that were there I had a chance to work with, uh, MJ and Tanya from from AWS some election project. I think everyone has skin in the game. Everyone wants to make it better. And I hope that moment, um, continues. But I think, you know, embracing that there needs to be a centralized, uniformed place, uh, for every state. And I think that would get rid of a lot of confusion >>when you talk about culture and you mentioned specifically called Do you think that people and agencies and politicians are ready to embrace the culture? Is there enough data to support that? This is really serious. We need to embrace this. We need to buy in a You said, um >>I hope right. I don't know what it could take. I'm hoping so after seeing everything you know, being at the White House from that aperture in 2016. Seeing all of that, I would, you know, think right away. Oh, my gosh. 2018, The midterms, We're gonna be on the ball. And that really didn't happen like we thought it would. 2020. We saw a different kind of technical or I guess, not as technical, uh, security problem. And I think I'm kind of shifting from that to the future. People realize. And I think, uh, both sides of the aisle are working towards security programs and security posture. I think there's a lot of people that have bought into the idea. Um, but I think it kind of starts from the top, and I'm hoping it becomes a standard, so there's not really an option. You will do this just for the security and safety of the campaigns and the electoral process. But I do see a lot more people leaning into it, and a lot more resource is available for those people that are >>talk to me about kind of the status of awareness of security. Needing to combat these issues, be able to remediate them, be able to defend against them where our folks in that awareness cycle, >>I think it ebbs and flows like any other process. Any other you know, incident, event. That happens. And from my experience in the info SEC world, normally there's a compromise. There's an incident, a bunch of money gets thrown at it and then we forget about it a year or two later. Um, I think that culture, that awareness comes in when you have folks that would sustain that effort. And again, you know, on the campaign, um, even at the White House, we try to make everyone apart of security. Security is and all the time thing that everyone has a stake in. Um, you know, I can lock down your email at work. I can make sure this system is super super secure, but it's your personal threat model. You know, your personal email account, your personal social media, putting more security on those and being aware of those, I think that's that awareness is growing. And I Seymour folks in the security community just kind of preaching that awareness more and more and something I'm really, really excited about. >>Yeah, the biggest thing I always think when we talk about security is people that were the biggest threat vector and what happened 89 months ago when so many businesses, um, in any, you know, public sector and private went from on site almost maybe 100% on site to 100% remote people suddenly going, I've got to get connected through my home network. Maybe I'm on my own personal device and didn't really have the time of so many distractions to recognize a phishing email just could come in and propagate. So it's that the people challenge e always seems to me like that might be the biggest challenge. Besides, the technology in the process is what do you think >>I again it goes back. I think it's all part of it. I think. People, um, I've >>looked at it >>slightly. Ah, friend of mine made a really good point. Once he was like, Hey, people gonna click on the link in the email. It's just I think 30% of people dio it's just it's just the nature of people after 20 some odd years and info sec, 20 some odd years and security. I think we should have maybe done a better job of making that link safer, to click on, to click on to make it not militias. But again it goes back, Thio being aware, being vigilant and to your point. Since earlier this year, we've seen a tax increase exponentially specifically on remote desktop protocols from Cove. It related themes and scams and, you know, ransomware targeting healthcare systems. I think it's just the world's getting smaller and we're getting more connected digitally. That vigilance is something you kind of have to building your threat model and build into the ecosystem. When we're doing everything, it's just something you know. I quit a lot, too. You've got junk email, your open your mailbox. You got some junk mail in there. You just throw it out. Your email inbox is no different, and just kind of being aware of that a little more than we are now might go a long way. But again, I think security folks want to do a better job of kind of making these things safer because malicious actors aren't going away. >>No, they're definitely not going away that we're seeing the threat surfaces expanding. I think it was Facebook and TIC Tac and Instagram that were hacked in September. And I think it was unsecured cloud database that was the vehicle. But talking about communication because we talk about culture and awareness communication from the top down Thio every level is imperative. How how do we embrace that and actually make it a standard as possible? >>Uh, in my experience, you know, from an analyst to a C So being able to communicate and communicate effectively, it's gonna save your butt, right? It's if you're a security person, you're You're that cyber guy in the back end, something just got hacked or something just got compromised. I need to be able to communicate that effectively to my leadership, who is gonna be non technical people, and then that leadership has to communicate it out to all the folks that need to hear it. I do think this year just going back to our elections, you saw ah lot of rapid communication, whether it was from DHS, whether it was from, you know, public partners, whether was from the team over Facebook or Twitter, you know, it was ah, lot of activity that they detected and put out as soon as they found it on it was communicated clearly, and I thought the messaging was done beautifully. When you look at all the work that you know Microsoft did on the block post that came out, that information is put out as widely as possible on. But I think it just goes back to making sure that the people have access to it whenever they need it, and they know where to get it from. Um, I think a lot of times you have compromised and that information is slow to get out. And you know that DeLay just creates a confusion, so it clearly concisely and find a place for people, could get it >>absolutely. And how do you see some of these challenges spilling over into your role as the security advisor for Splunk? What are some of the things that you're talking with customers about about right now that are really pressing issues? >>I think my Rolex Plunkett's super super weird, because I started earlier in the year, I actually started in February of this year and a month later, like, Hey, I'm hanging out at home, Um, but I do get a chance to talk to ah, lot of organizations about her security posture about what they're doing. Onda about what they're seeing and you know everything. Everybody has their own. Everybody's a special snowflakes so much more special than others. Um, credit to Billy, but people are kind of seeing the same thing. You know, everybody's at home. You're seeing an increase in the attack surface through remote desktop. You're seeing a lot more fishing. You're singing just a lot. People just under computer all the time. Um, Zoom WebEx I've got like, I don't know, a dozen different chat clients on my computer to talk to people. And you're seeing a lot of exploits kind of coming through that because of that, people are more vigilant. People are adopting new technologies and new processes and kind of finding a way to move into a new working model. I see zero trust architecture becoming a big thing because we're all at home. We're not gonna go anywhere. And we're online more than we're not. I think my circadian rhythm went out the window back in July, so all I do is sit on my computer more often than not. And that caused authentication, just, you know, make sure those assets are secure that we're accessing from our our work resource is I think that gets worse and worse or it doesn't. Not worse, rather. But that doesn't go away, no matter what. Your model is >>right. And I agree with you on that circadian rhythm challenge. Uh, last question for you. As we look at one thing, we know this uncertainty that we're living in is going to continue for some time. And there's gonna be some elements of this that air gonna be permanent. We here execs in many industries saying that maybe we're going to keep 30 to 50% of our folks remote forever. And tech companies that air saying Okay, maybe 50% come back in July 2021. As we look at moving into what we all hope will be a glorious 2021 how can businesses prepare now, knowing some amount of this is going to remain permanent? >>It's a really interesting question, and I'll beyond, I think e no, the team here. It's Plunkett's constantly discussions that start having are constantly evaluating, constantly changing. Um, you know, friends in the industry, it's I think businesses and those executives have to be ready to embrace change as it changes. The same thing that the plans we would have made in July are different than the plans we would have made in November and so on. Andi, I think, is having a rough outline of how we want to go. The most important thing, I think, is being realistic with yourself. And, um, what, you need to be effective as an organization. I think, you know, 50% folks going back to the office works in your model. It doesn't, But we might not be able to do that. And I think that constant ability Thio, adjust. Ah, lot of company has kind of been thrown into the fire. I know my backgrounds mostly public sector and the federal. The federal Space has done a tremendous shift like I never well, rarely got to work, uh, vert remotely in my federal career because I did secret squirrel stuff, but like now, the federal space just leaning into it just they don't have an option. And I think once you have that, I don't I don't think you put Pandora back in that box. I think it's just we work. We work remote now. and it's just a new. It's just a way of working. >>Yep. And then that couldn't be more important to embrace, change and and change over and over again. Make. It's been great chatting with you. I'd love to get dig into some of that secret squirrel stuff. I know you probably have to shoot me, so we will go into that. But it's been great having you on the Cube. Thank you for sharing your thoughts on election security. People processes technology, communication. We appreciate it. >>All right. Thanks so much for having me again. >>My pleasure for McClatchy. Oh, I'm Lisa Martin. You're watching the Cube virtual.

from the cube studios in Palo Alto in Boston connecting with thought leaders all around the world this is a cube conversation we've been reporting that the Kovan pandemic has created a bifurcated IT spending outlook legacy on print on-prem infrastructure in traditional software licensing models they're giving away two approaches that enable more flexibility in business agility automation initiatives that reduce human labor labor that's not value add has really been gaining traction for the past 18 months the pandemic has only accelerated to focus on such efforts and robotic process automation or RPA along with machine intelligence have been the beneficiaries relative to other segments of the IT stack welcome to this week's wiki Vaughn cube insights powered by ETR my name is Dave Volante and in this breaking analysis we're gonna update you on the latest demand picture for the red-hot RP a sector will also focus on two main areas today first we're gonna review the basics of the RP a space for those that may not be as familiar with the market next we'll share with you the spending data and outlook in the RT ARPA space from ETR and we're really dig into the kovat impact on this market segment and take a look at the competitive outlook we're gonna pay particular attention to the leaders in this space and then we're gonna wrap up so let me start with kind of the RPI basics if you're not familiar with our PA here's what you really need to know happy hour PA gained traction by taking software robots and pointing them at existing applications to mimic human behavior and automate repeatable and well understood processes keyboard behavior that is now a challenge with early RPA implementations is that most customers chose to point these bots at legacy backend office systems now that the open emails and fill out forms and the like so that's great because it digitizes processes around legacy systems awesome ROI but the problem is that these bots will they interact with a user interface of that application and many of these apps they really don't have an API so any change in data or the interface breaks the automation down now more recently automations are interacting to apps through api's that makes them less brittle but of course you know the quality of api's as you well know will vary so enter your machine intelligence into the equation there's been a lot of discussion around the intersection of our PA and AI and that's allowed organizations to automate more processes that do so in a way that takes an augmentation approach using things like natural language processing or speech recognition and machine learning to iterate and improve automations and you know this trend holds a lot of promise and is a lot of talk about it in the marketplace particularly in the form of really trying to understand which processes to automate and where the best ROI can be achieved for organization but it's important to note it's really still early days with this AI intersection nonetheless investors you know they're ahead of the game they've they've poured money into this space as we've been reporting now for you know well over a year or two uipath an automation anywhere have raised close to two billion dollars and have been growing very very rapidly we're gonna talk more about that existing players like blue prism they've actually benefited from the automation tailwind and other you know process business process players take for example like Pegasus Toombs I mean they started in the early 80s they've added our PA to their platform as have many others by the way including Microsoft who has barely been trying to crack into this market for a while in fact Microsoft just bought a small company called soft emotive and to really try to shore up its RP a game but you know just a quick aside in our view Microsoft is their well behind the leaders it's gonna take years for them to get where the leaders are today yeah but it's Microsoft so you don't want to ignore them now the big buzzword here is hyper automation evidently it's a torrent a coin term coined by Gartner and uipath has picked up on this in a big way and so is automation anywhere now those both those companies are in hyper growth so it plays more established companies for example pega yeah they look at the term differently you know of course their vision is Rp a is a small portion of their their their vision these established firms they want to incorporate their business process automation z' that have been built over decades into a systems view of the organization using existing platforms the upstarts of course they want to build from new platforms what's really happening in the marketplace and like in many situations is this emergence of a hybrid you know quasi-equilibrium here we saw this in mainframes who certainly you know saw it in middleware enterprise data warehouses and we've seen it in the cloud you know where most companies don't just throw away the investments that they've made in legacy systems now they're stable they're operationalized and rather what they do is they overlay the more modern technologies and they kind of create an abstraction layer of their business that incorporates the old and the new but the growth is much much higher in the new as we know it and that leads me to the TAM the total available market let's look at the RPM you know we think the TAM expansion opportunity is pretty substantial we put this chart together awhile back that really underscores that the progression of our PA from you know simple BOTS automating back-office functions to really infusing automations in virtually all applications you know if you expand the definition beyond our PA software into the broader automation opportunities the other thing about it this this could be a much much larger than depicted here maybe well over a hundred billion dollar Tam as a I powered automation becomes fundamental to every organization in their operating model anyway it's a big opportunity and the data suggests that it's growing rapidly so let's turn to the data let's look at the spending and bring ETR into the equation so which technologies are showing new adoptions in tech on balance the tech sector has done pretty well despite this pandemic at the time of this video the Nasdaq Composite is up about a point and a half year to date and as we know from previous surveys that heading into 2020 there was a pullback in a narrowing of new technology adoptions as organizations began to operationalize their digital initiatives and place bets this chart shows new adoptions across three survey dates the gray is April last year the blue is January which is pre-pandemic really and the survey of more than 1,200 IT buyers is really the latest one which is the April so this survey took place at the height of the US lockdown and you can see look at all PA it's got 22% new adoptions what does that mean it means that 22% of the customers in the survey we're planning our PA spend there that are planning for our PA spend are planning new adoptions now that's a figure that says hi as machine learning and artificial intelligence and of course as we said these two technologies are increasingly playing a role together so our PA adoptions more than containers more than videoconferencing which has had this tailwind from work from home and more than cloud more than mobile device management so it's really one of the hottest sectors in terms of new adoptions now let's look at some of the players in our PA and try to really better understand their positions here's a chart that uses the two primary met work net metrics that we've been sharing over the past year net score or spending momentum is on the y-axis and market share which is a measure of pervasiveness in the data set is on the x-axis the chart plots are PA players in the et our data set and you can see uipath in automate anyway our the to market leaders they show both spending momentum and market awareness then you see blue prism and peg is in there and the rest of the pack and I'll say this about pegye systems I recently spoke to their CEO Alan trifler he's an amazing self-made billionaire he's got a great business you know peg that really doesn't see you know itself anyway as an RPA play and I don't either our PA is really a small part of their story but they're in the data set and certainly automation related so it's what's showing but it's a bit of an oranges and tangerines comparison now notice in the upper right of this chart you can see that the net scores are in the green shade and there's a little bit of red in there but remember net score is a simple metric sort of like Net Promoter Score in PS it subtracts customer spending less from those spending more and that's the difference and you can see very very strong net scores for both uipath in automation anywhere and I'm gonna discuss that more in a moment but there's lots of green in the chart and even pega or as I said it's really not an RPA specialist they've got a solid net score now let's look at a time series of this net score in the spending momentum what we do here is this chart takes the three leaders uipath automation anywhere and blue prism and it plots their net scores over time goes all the way back to the January 18 survey now let me make a couple of points here uipath in automation anywhere 70% plus net scores is very impressive and amongst the highest in the data set even though you see some of the Lawson momentum in the UI path line and the convergence with automation anywhere they're both very very strong and you can see in the upper right you can see the shared end which is an indicator of the presence of the company in the data set how many response is out of the 1200 plus so you might say well wait a minute you I passed the I had they had layoffs last fall and automation anywhere they more recently just recently had layoffs how can they show such strength well I make a few points first fast-growing companies like this that have raised you know nearly a billion dollars each they've got investors to serve and they're going to course-correct when they feel like there's some slack in the system yet to me it's not a sign of fundamental trouble second both of these companies are going to continue to invest heavily on research and development uipath has 60 openings on its website mostly in engineering automation anywhere they only have nine openings but I would expect both companies to up their engineering hiring especially given the Microsoft acquisition today third remember this is not an indicator of the amount of money spent in absolute dollars rather it looks at spending momentum of the doll in dollar terms as well if you were to cut the data by larger companies let's say the Fortune 1000 where the average contract values are higher you'd see that you I pass a net score jumps to 77% automation anywhere would drop into the 60s and blue prison would stay about the same where it is today today so let's look for example in the global 2000 so we'll expand that notion of a fortune 1000 let's go to the global 2000 where there's more of an end slice and you can see the picture changes from the overall data sample this chart shows the net scores in the global 2000 where the ends are more than 25 responses across all the three surveys gray as last April blue was January yellow is April 2020 and you can see the year-on-year decline and the modest step down during the the Colvin lockdown which again surveyed in April but still very elevated net scores for uipath and automation anywhere and respectable for the other so the point is Co vyd has not really crushed the RPA market I mean if anything is witnessed by the new adoptions it's maybe it's certainly better off than most IT sectors now let's dig into the net scores of the two leaders a little bit more uipath and automation anywhere remember net scores of very important metric and I want to spend the moment explaining how we use it you see this wheel chart this red green gray it really shows how the net score method is applied now we've taken the UI path example from the April survey net score works by asking buyers relative to last year are you adopting new that's the 28% are you increasing spend by 6 percent or greater that's 51 percent are you expecting flat spending that's 15 percent or a decrease in spend of 6 percent or more or finally are you replacing the vendor checking them out so look at this you can see for UI path added up 79 percent of respondents expect to increase spending in 2020 relative to 2019 and again remember this survey was taken at the height of the kovat lockdown let me show you the data for automation anywhere same exact methodology 72 percent of automation anywhere a customer's plan to spend more only 1 percent plan to spend less with zero replacements so very strong fundamentals as it relates to spending momentum for both UI path and automation anywhere now how is presents or what we call market share in the data set changing on a year-on-year basis well this is the last data point that I want to show and it relates to that metric of market share which again is the measure of pervasiveness it's calculated by dividing the number of mentions of a vendor in a sector by the total mentions of that sector in this case RP a and this chart shows the year-on-year change in customer growth comparing market share from the April 20 survey with that from the April 19 data and you can see the yellow line at 11% is the sector average uipath has the fastest growth automation anywhere is growing faster than the market average and blue prism is below the average now this looks back to last year and it'll be interesting to see how this picture changes with the next survey based on what we're seeing with the next net scores which is a forward-looking metric all right let's wrap so we're seeing that the bifurcated market is high that the automation trend generally is real and that the RP a drill down specifically shows us an example in action we think that kovat 919 not hit these numbers would actually be higher by maybe as much as 10% but in the near near to mid term we would expect a pretty fast return to normal patterns of demand if I put normal and air quotes for our PA in fact you know we don't expect a real v-shaped recovery across the board but our PA is you know one of those areas where we actually may see such a rebound the pandemic really underscores the need to accelerate digital transformations our PA we think is going to be a central player in that movie along with AI the cloud all right we have to leave it there for now so remember these episodes they're all available as podcasts just all you got to do is search breaking analysis podcasts please subscribe to the series would appreciate that and check out ETR dot plus for all the data I also publish a full report every week on wiki bound comm tons of data there as well and Silicon angle comm has all the news and I published there alright this is Dave Volante thanks for watching this episode of the cube insights powered by ETR we'll see you next time [Music]

>>buy from San Francisco. It's the queue covering our essay conference 2020. San Francisco Brought to you by Silicon Angle Media >>Hey, welcome back here. Ready? Jeff Frick here with the Cube. We're in downtown San Francisco. It is absolutely spectacular. Day outside. I'm not sure why were incited. Mosconi. That's where we are. It's the RCC conference, I think 50,000 people the biggest security conference in the world here in Mosconi this week. We've been here, wall to wall coverage. We'll be here all the way till Thursday. So thanks for joining us. We're excited to have our next guest. He's got a lot of great data to share, so let's jump into it. It's hard mode. He's a VP engineering threat and mitigation products for nets. Cowhearted. Great to meet you. >>Thank you. Good to be here, >>too. So for people who aren't familiar with Net Scout, give em kind of the basic overview. What do you guys all about? Yes, and that's what we consider >>ourselves their guardians of the connected world. And so our job is to protect, like, you know, companies, enterprises, service providers, anybody who has on the Internet and help keep their services running your applications and things returned deliver to your customers would make sure that it's up there performing to, like, you know the way you want them to, but also kind of give you visibility and protect you against DDOS attacks on other kind of security threats. That's basically in a nutshell. What we do as a company and, yeah, wear the garden of connected world. >>So So I just from a vendor point of the I always I feel so sorry for >>buyers in this environment because you walk around. I don't know how many vendors are in here. A lot of >>big boost, little boost. So how do you kind of help separate? >>You know, Netsch out from the noise? How what's your guys? Secret sauce? What's your kind of special things? >>Really, it's like 30 years >>off investment in like, network based visibility, and >>we truly >>believe in the network. Our CEO, he says, like you know the network like, you know, actually, when you monitor the network, it's like taking a blood test. It tells you the truth, right? And it's really like how you find out, like, you know, some things right or wrong. I mean, I actually, for my background to like network monitoring. There's a lot of our what we think of as like the endpoint is actually contested territory. That's where the adversary is. When you're on the network and your monitoring all activity, it really gives you a vantage point. You know, that's >>really special. So we really focus on the network. Our heritage and the network is is one of our key strengths and then, you know, as part of >>us as a company like Arbor Arbor. Networks with coming in that's got acquired some years ago were very much part of Net Scout with our brand of products. Part of that, you know, the Arbor legacy includes huge visibility into what's happening across the Internet and visibility like nobody else like in terms of the number of service providers and large enterprises who work with us, help us understand what's happening across the landscape. That's like nobody else out here. And that is what we consider a key differentiator. >>Okay, great. So one of the things you guys do >>a couple times years, I understand his publisher reporting solution, gift people. Some information as to what's going on. So we've got the We've >>got the version over four here. Right Net scout threat, intelligence report. So you said this comes out twice a year, twice a year. So what is the latest giving some scoop >>here, Hot off the presses we published last week. Okay, so it's really just a few days old and, you know, our focus here is what happened in the last six months of last year. So that and then what we do is we compare it against data that we've collected a year prior. >>So really a few things >>that we want you to remember if you're on the right, you know, the first number is 8.4 million. That's the number of D DOS attacks that >>we saw. This doesn't mean that >>we've seen every attack, you know, in the world, but that's like, you know just how many DDOS attacks we saw through the eyes of our customers. That's >>in this in six months. 8.4 number is >>actually for the entire year here in an entire year of 2019. There's a little bit of seasonality to it. So if you think of it like a 4.4, maybe something that that was the second half of the year. But that's where I want to start. That's just how many DDOS attacks we observed. And so, in the >>course of the report, what we can do a >>slice and dice that number talk about, like, different sizes, like, what are we seeing? Between zero and 100 gigabits per 2nd 102 104 100 above and >>kind of give you a sense of just what kind of this separation there is who is being targeted >>like we had a very broad level, like in some of the verticals and geographies. We kind of lay out this number and give you like, a lot of contact. So if you're if you're in finance and you're in the UK, you want to know like, Hey, what happened? What happened in Europe, for example, In the past 66 months, we have that data right, and we've got to give you that awareness of what's happening now. The second number I want you to remember is seven seven or the number of new attack vectors reflection application attack vectors that we observed being used widely in in in the second half. >>Seven new 17 new ones. So that now kind of brings our tally >>up to 31 like that. We have those listed out in here. We talk about >>just how much? Uh huh. Really? Just how many of these vectors, how they're used. Also, these each of these vectors >>leverage vulnerabilities in devices that are deployed across the Internet. So we kind of laid out like, you know, just how many of them are out there. But that's like, You know that to us seven is reflecting how the adversary is innovating. They're looking for new ways to attack us. They've found 71 last year. They're going to war, right? Right. And that's that's kind of what we focus on. >>Let's go back to the 8.4. So of those 8.4 million, how many would you declare >>successful from the attacker point of view? >>Yeah, You know something that this is always >>like, you know, you know, it's difficult to go estimate precisely or kind of get within some level of >>precision. I think that you know, the the adversaries, always trying to >>of course, they love to deliver a knockout blow and like all your services down but even like every attack inflicts a cost right and the cost is whether it's, you know, it's made its way all the way through to the end target. And now you know, they're using more network and computing resource is just to kind of keep their services going while they're under attack. The attack is low, You're still kind of you. You're still paying that cost or, you know, the cost of paid upstream by maybe the service provider. Somebody was defending your network for you. So that way, like, you know, there's like there's a cost to every one of these, right? In >>terms of like outages. I should also point out that the attacks that you might think >>that this attack is like, you know, hey, you know, there was a specific victim and that victim suffered as a result of but >>in many cases, the adversaries going after people who are providing services to others. So I mean, if a Turkish bank >>goes down right, like, you know, our cannot like services, customers for a month are maybe even a few hours, right, And you know, the number of victims in this case is fairly broad. Might be one attacks that might be one target, however, like the impact is fairly, >>is very large. What's interesting is, have begs a question. Kind of. How do you >>define success or failure from both the attacker's point of view as well as the defender? >>Yeah, I mean, I mean and again, like there's a lot of conversation in the industry about for every attack, right? Any kind of attack. What? When do I say that? You know what? I was ready for it. And, you know, I was I was fine. I mean, I don't care about, you know, ultimately, there's a cost to each of these things. I'd say that everybody kind of comes at it with their You know, if you're a bank, that you might go. Okay. You know what? If my if I'm paying a little bit extra to keep the service up and running while the Attackers coming at me, No problem. If I if my customers air aren't able to log in, some subset of my customers aren't able to log in. Maybe I can live through that. A large number of my customers can't log in. That's actually a really big problem. And if it's sustained, then you make your way into the media or you're forced to report to the government by like, outages are like, You know, maybe, you know, you have to go to your board and go like a sorry, right? Something just happened. >>But are the escalation procedures >>in the definition of consistency? Right? Getting banged all the time right? And there's something like you said, there's some disruption at some level before it fires off triggers and remediation. So so is there some level of okay, that's kind of a cost of doing business versus, you know, we caught it at this. They're kind of like escalation points that define kind of very short of a full line. >>I think when we talk to our service provider customers, we talked to the very large kind of critical enterprises. They tend to be more methodical about how they think of like, Okay, you know, degradation of the service right now, relative to the attack. I think I think for a lot of people, it's like in the eyes of the beholder. Here's Here's something. Here's an S L. A. That I missed the result of the attack at that point. Like you know, I have, I certainly have a failure, but, you know, it's it's up until there is kind of like, Okay, you're right >>in the eyes the attacker to delay service >>at the at the Turkish bank because now their teams operate twice, twice the duration per transaction. Is it? Just holding for ransom is what benefit it raises. A range >>of motivations is basically the full range of human nature. There's They're certainly like we still see attacks that are straight journalism. I just I just cause I could just I wanted I wanted to write. I wanted to show my friend like, you know, that I could do this. There's there's definitely a lot of attacks that have that are like, you know, Hey, I'm a gamer and I'm like, you know, there's I know that person I'm competing with is coming from this I p address. Let me let me bombard them with >>an attack. And you know, there's a huge kind of it could be >>a lot of collateral damage along the way because, you know, you think you're going after this one person in their house. But actually, if you're taking out the network upstream and there's a lot of other people that are on that network, like you know, there's certain competitive element to it. They're definitely from time to time. There are extortion campaigns pay up or we'll do this again right in some parts of the world, like in the way we think of it. It's like cost of doing business. You are almost like a business dispute resolution. You better be. You know, you better settle my invoice or like I'm about, Maybe maybe I'll try and uses take you out crazy. Yeah, >>it, Jeff. I mean things >>like, you know the way talked about this in previous reports, and it's still true. There's especially with d dos. There's what we think of it, like a democratization off the off the attack tools where you don't have to be technical right. You don't have to have a lot of knowledge, you know, their services available. You know, like here's who I'm going to the market by the booth, so I'd like to go after and, you know, here's my $50 or like a big point equivalent. All right, >>let's jump to >>the seven. We talked about 8.4 and the seven new attack vectors and you outline, You know, I think, uh, the top level themes I took from the summary, right? Weaponizing new attack vectors, leveraging mobile hot spots targeting compromised in point >>about the end points. I o t is >>like all the rage people have mess and five G's just rolling out, which is going to see this huge i o t expansion, especially in industrial and all these connected devices and factories in from that power people. How are people protecting those differently now, as we're getting to this kind of exponential curve of the deployment of all these devices, >>I mean, there are a lot of serious people thinking about how to protect individual devices, but infrastructure and large. So I'm not gonna go like, Hey, it's all bad, right? Is plenty back on it all to be the next number, like 17 and 17 as the number of architectures for which Amir, I mean, I was really popular, like in a bar right from a few years ago. That still exists. But over time, what's happened is people have reported Mirai to different architectures so that, you know, think of it like, you know, if you have your your refrigerator connected to the Internet, it comes. It's coming with a little board, has CPU on it like >>running a little OS >>runs and runs in the West on it. Well, there's a Mirai variant ready for that. Essentially, as new devices are getting deployed like, you know, there's, you know, that's kind of our observation that there's even as new CPUs are introduced, a new chips or even the West they're introduced. There's somebody out there. We're ready to port it to that very now, Like, you know, the next level challenges that these devices, you know, they don't often get upgraded. There's no real. In many cases, they're not like, you know, there's very little thought given to really kind of security around it. Right? There are back doors and, like default passwords used on a lot of them. And so you take this combination. I have a whole you know, we talk about, you know, large deployments of devices every year. So you have these large deployments and now, you know, bought is just waiting for ready for it Now again, I will say that it's not. It's not all bad, but there are serious people who were thinking about this and their devices that are deployed on private networks. From the get go, there was a VPN tunnel back to a particular control point that the the commercial vendor operates. I mean, there are things like that, like, hardening that people have done right, So not every device is gonna find its way into a botnet. However, like, you know, you feel like you're getting a toy like Christmas and against $20 you know, and it can connect to the Internet. The odds are nobody's >>thinking not well. The thing we've heard, too, about kind of down the i t and kind of bringing of operations technology and I t is. A lot of those devices weren't developed for upgrades and patches, and Lord knows what Os is running underneath the covers was a single kind of use device. It wasn't really ever going to be connected to the outside world. But now you're connecting with the I t. Suddenly exposing a whole host of issues that were never kind of part of the plan when whoever designed that thing in the first place for sure for sure is crazy. Alright, so that's that. Carpet bombing tactics, increased sector attack, availability. What is there's carpet bomb and carpet bombing generally? What's going on in this space? >>Well, so carpet bombing is a term that we applied a few years ago to a kind of a variation of attack which, like >>traditionally, you know, we see an attack >>against a specific I P address or a specific domain, right? That's that's where that's what I'm targeting. Carpet bombing is taking a range of API's and go like, you know, hey, almost like cycling through every single one of them. So you're so if your filters, if your defense is based on Hey, if my one server sees a spike, let me let me block traffic while now you're actually not seeing enough of a spike on an individual I p. But across a range there's a huge you know, there's a lot of traffic that you're gonna be. >>So this is kind of like trips people >>up from time to time, like are we certainly have defensive built for it. But >>now what? We're you know, it's it's really like what we're seeing is the use >>off Muehr, our other known vectors. We're not like, Okay, C l dap is a protocol feel that we see we see attacks, sealed up attacks all the time. Now what we're >>seeing is like C l >>dap with carpet bombing. Now we're seeing, like, even other other reflection application protocols, which the attack isn't like an individual system, but instead the range. And so that's that's what has changed. Way saw a lot of like, you know, TCP kind of reflection attacks, TCP reflection attacks last year. And then and then the novelty was that Now, like okay, alongside that is the technique, right? Carpet bombing technique. That's that's a pipe >>amounts never stops right? Right hard. We're out of time. I give you the final word. One. Where can people go get the information in this report? And more importantly, for people that aren't part of our is a matter that you know kind of observers or they want to be more spark. How should they be thinking about security when this thing is such a rapidly evolving space? >>So let me give you two resource is really quickly. There's this this >>report available Dub dub dub dub dot com slash threat report. That's that's that's what That's where this report is available on Google Next Threat report and you'll find your way there. We've also, you know, we made another platform available that gives you more continuous visibility into the landscape. So if you read this and like Okay, what's happening now? Then you would go to what we call Met Scout Cyber Threat Horizon. So that's >>kind of tell you >>what's happening over the horizon. It's not just like, you know, Hey, what's what am I seeing? What are people like me seeing maybe other people other elsewhere in the world scene. So that's like the next dot com slash horizon. Okay, to find >>that. And I think like between those two, resource is you get >>access to all of our visibility and then, you know, really, in terms of like, our focus is not just to drive awareness, but all of this knowledge is being built into our products. So the Net's got like arbor line of products. We're continually innovating and evolving and driving like more intelligence into them, right? That's that's really? How We help protect our customers. Right >>hearted. Thanks for taking a few minutes >>and sharing the story. Thank you. 18 Scary. But I'm glad you said it's not all bad. So that's good. >>Alright, he started. I'm Jeff. You're watching the Cube. We're at the RSA conference 2020 >>Mosconi. Thanks for watching. We'll see you next time. >>Yeah, yeah, yeah.

>> Live from San Diego, California It's the queue covering Sisqo Live US 2019 Tio by Cisco and its ecosystem. Barker's >> back to San Diego. Everybody watching the Cube, the leader and live tech coverage. My name is Dave Volante, and I'm with my co host, Steuben. Amanda, this is Day two for Sisqo. Live 2019. We're in the definite. So still. I was walking around earlier in the last interview, and I think I saw Ron Burgundy out there. Stay classy Sleeping Gow is here. He's the founder and CEO of Met Net Brain Technology's just outside of Boston. Thanks very much for coming on the Q. Thank you there. So you're very welcome. So I want to ask you, I always ask Founders passion for starting companies. Why did you start? >> Well, maybe tired of doing things, Emmanuel. Well, that's alongside the other side of Yes, I used Teo took exam called a C C. I a lot of folks doing here. I failed on my first try. There was a big blow to my eagle, so I decided that we're gonna create a softer help them the past. This is actually the genesis of nettle. I met a friend help people three better doing their network management. >> That's a great story. So tell us more about that brain. What do you guys all about? >> Sure, we're the industry. First chasing time. Little confirmations after our mission is to Democrat ties. Merrick Automation. Every engineer, every task. They should've started with automation before human being touched. This task, >> you know, way go back. Let's say, 10 years ago people were afraid of automation. You know, they thought I was going to take away their jobs. They steal and they still are. We'll talk about that. You get this and I want to ask you about the blockers. They were fearful they wanted the touch thing. But the reality is people talk about digital transformation. And it's really all about how you use data, how your leverage data. And you can't be spending your time doing all this stuff that doesn't add value to your business. You have to automate that and move up to more valuable test. But so people are still afraid of automation. Why, what's the blocker there? >> They have the right reason to be afraid. Because so many automation was created a once used exactly wass right. And then you have the cost ofthe tradition automation. You have the complexity to create in their dark automation. You guys realize that middle confirmation You cannot have little gotta measure only work on a portion of your little way. You have to walk on maturity if not all of your narrow right. So that's became very complex. Just like a You wanna a self driving car? 10 You can't go buy a Tesla a new car. You can drive on a song. But if you want to your Yoder Puta striving always song Richard feared it. That's a very complex Well, let's today, Netto. Condemnation had to deal with you. Had a deal with Marty Venna Technology Marty, years of technology. So people spent a lot of money return are very small. There's so they have a right to a fair afraid of them. But the challenges there is what's alternative >> way before you're there. So there, if I understand it, just playing back there, solving a very narrow problem, they do it once, maybe twice. Maybe a rudimentary example would be a script. Yeah, right, right. And then it breaks or it doesn't afford something else in the network changes, and it really doesn't affect that, right? >> Yeah. I mean, you know, I think back to money network engineers. It's like, Well, I'm sitting there, I've got all my keep knobs and I get everything done and they say, No, don't breathe on it because it's just the way I want it less. It can't be that doesn't scale. It doesn't respond to the business. I need to be able to, you know, respond fast what is needed. And things are changing in every environment. So it's something that I couldn't, as you know, a person or a team keep up with myself, and therefore I need to have more standardized components, and I need to have intelligence that can help me. >> Let's sit and let's >> s so we've laid out the generalized way that we've laid out the problem. What's what's the better approach? >> Well, give you looking out of the challenge today is you have to have Dave ups, which a lot of here they have not engineer know howto script and the mid off the engineer who know how little cooperates walk together. So there's a date, a part of it. There's a knowledge. A part of this too has to meet to create a narrow coordination and that Ned Ogata may have to be a scale. So the challenge traditional thoracotomy here, why is for short lie on if you're going down? Technical level is wise A terra, too many data and structure and the otherwise Our knowledge knowledge cannot be codified. So you have the knowledge sitting people's head, right, Eh Programa had to walk in with a narrow canyon near together. You make it a cost hire. You make it a very unskilled apple. So those are the challenge. So how fast Motor way have to do so neither brand for last 15 years You decide to look differently that we created some saying called operating system off total network and actually use this to manage over 1,000 of mental models technology. And he threw problem. You can't continually adding new savings into this problem. So the benefit of it is narrow. Canyon near anybody can create automation. They don't have to know how to writing a code. Right? And Deborah, who knows the code can also use this problem. All the people who are familiar with technology like and people they can integrate that never >> pray. Okay, so you have all this data I wish I could say is unstructured So he doesn't have any meaning. Data's plentiful insights aren't, uh And then you have this what I call tribal knowledge. Joe knows how to do it, but nobody else knows how to do it. So you're marrying those two. How are you doing that? Using machine intelligence and and iterating building models, can you get that's amore colors? Tow How you go about that? What's the secret sauce >> way? Took a hybrid approach. First call on you have to more than the entire network. With this we'll kind of operating system called on their own way have about 20 12,000 valuables modeling a device and that 12,000 valuable adults across your let's say 1,000 known there or there will be 12,000,000 valuables describing your medal. That's that's first. Zang on top of 12,000,000 valuables will be continually monitored. A slow aye aye, and the machine learning give something called a baseline data. But on top of it, the user, the human being will have the knowledge young what is considered normal what is considered abnormal. They can add their intelligence through something called excludable rumble on couple of this system, and their system now can be wrong at any time. Which talking about where somebody attacking you when that OK is un afford all you through a human being, all our task Now the automation can be wrong guessing time. So >> this the expert, the subject matter expert, the main expert that the person with the knowledge he or she can inject that neck knowledge into your system, and then it generates and improves overtime. That's right, >> and it always improve, and other people can open the hood. I can't continue improving. Tell it so the whole automation in the past, it was. Why is the writer wants only used once? Because it's a colossal? It's a script. You I you input and output just text. So it wasn't a designer with a company, has a motive behind it. So you do it, You beauty your model. You're writing a logical whizzing a same periods off, we decided. We think that's you. Cannot a scale that way. >> OK, so obviously you can stop Dave from inputting his lack of knowledge into the system with, you know, security control and access control. Yeah, but there must be a bell curve in terms of the quality of the knowledge that goes into the system. You know, Joe might be a you know, a superstar. And, you know, stew maybe doesn't know as much about it. No offense, too. Student. So good. So how do you sort of, you know, balance that out? Do you tryto reach an equilibrium or can you wait? Jos Knowledge more than Stu's knowledge. How does that work? >> So the idea that this automation platform has something called excludable Rambo like pseudo Rambo can sure and implacably improved by Sri source One is any near themselves, right? The otherwise by underlying engine. So way talk about a I and the machine learning we have is that we also have a loo engine way. Basically, adjusting that ourselves certainly is through Claverie Partner, for example, Sisko, who run many years of Qatar where they have a lot of no house. Let's attack that knowledge can be pushed to the user. We actually have a in our system that a partnership with Cisco attack South and those script can be wrong. slow. Never prayer without a using woman getting the benefit of without talking with attack. Getting the answer? >> Yes, I think you actually partially answered. The question I have is how do you make sure we don't automata bad process? Yeah. So And maybe talk a little bit about kind of the training process to your original. Why of the company is to make things easier. You know, What's the ramp up period for someone that gets in giving me a bit of a how many engineers you guys have >> worked with? The automatic Allied mission. Our mission statement of neda prayer is to Democrat ties. Network automation, you know, used to be network automation on ly the guru's guru to it. Right, Dave off. Send a satchel. And a young generation. My generation who used come, Ally, this is not us, right? This is the same, you know. But we believe nowadays, with the complicity of middle with a cloud, computing with a cybersecurity demand the alternative Genetic automation is just no longer viable. So way really put a lot of starting to it and say how we can put a network automation into everyone's hand. So the things we tell as three angle of it, while his other missions can be created by anyone, the second meaning they've ofthe net off. Anyone who know have knowledge on metal can create automation. Second piece of automation can lunched at any time. Somebody attacking you middle of the night. They don't tell you Automation can lunch to protect Theo, and they're always out. You don't have people the time of the charter. Automation can lunch the tax losses, so it's called a lunch. Any time certain want is can adapt to any work follow. You have trouble shooting. You have nettle changes. You have compliance, right? You have documentation workflow. The automation should be able to attack to any of this will clothe topping digression tomorrow. We have when service now. So there's a ticket. Human being shouldn't touches a ticket before automation has dies, she'll write. Is a human should come in and then use continually use automation. So >> So you talk about democratizing automation network automation. So it's so anybody who sees a manual process that's wasting time. I can sort of solve that problem is essentially what you're >> doing. That's what I did exactly what we >> know So is there, uh, is there a pattern emerging in terms of best practice in terms of how customers are adopting your technology? >> Yes. Now we see more animal customer creating This thing's almost like a club, the power user, and we haven't caught it. Normal user. They have knowledge in their heads. Pattern immunity is emergent. We saw. Is there now work proactively say, How can I put that knowledge into a set of excludable format so that I don't get escalate all the time, right? So that I can do the same and more meaningful to me that I be repeating the same scene 10 times a month? Right? And I should want it my way. Caught a shift to the left a little while doing level to the machine doing the Level one task level two. Level three are doing more meaningful sex. >> How different is what you're doing it net brain from what others are doing in the marketplace. What's the differentiation? How do you compete? >> Yeah, Little got 1,000,000 so far has being a piecemeal, I think, a fragment. It's things that has done typical in a sweeping cracker. Why is wholesale Hardaway approach you replace the hardware was esti N S P. Where's d? Let there's automation Capitol Building Fifth, I caught a Tesla approached by a Tesla, and you can drive and a self driving. The second approaches softer approach is as well. We are leading build a model of your partner or apply machine learning and statistics and was behind but also more importantly, open architecture. Allow a human being to put their intelligence into this. Let's second approach and insert approaches. Actually service little outsourcer take you, help you We're moving way or walk alone in the cloud because there's a paid automation there, right so way are focusing on the middle portion of it. And the landscaper is really where we have over 2,000 identifies customer and they're automating. This is not a just wall twice a week, but 1,000 times a day. We really excited that the automation in that escape scale is transforming how metal and is being managed and enable things like collaboration. But I used to be people from here. People from offshore couldn't walk together because knowledge, data and knowledge is hard to communicate with automation. We see collaboration is happening more collaboration happening. So we've >> been talking about automation in the network for my entire career. Feels like the promise has been there for decades. That site feels like over the last couple of years, we've really seen automation. Not just a networking, but we've been covering a lot like the robotic process automation. All the different pieces of it are seeing automation. Bring in, gives a little bit look forward. What? What do you predict is gonna happen with automation in I t over the next couple of years? A >> future that's great Way have a cloud computing. We have cyber security. We have the share of scale middle driving the network automation to the front and center as a solution. And my prediction in the next five years probably surrounded one izing automation gonna be ubiquitous. Gonna be everywhere. No human being should touch a ticket without automation through the first task. First right second way. Believe things called a collaborative nature of automation will be happy. The other was a local. Automation is following the packet from one narrow kennedy to the other entity. Example would be your manager service provider and the price they collaborated. Manager Nettle common little But when there's something wrong we don't know each part Which part? I have issues so automation define it by one entity Could it be wrong Across multiple So is provider like cloud provider also come Automation can be initiated by the Enterprise Client way also see the hado A vendor like Cisco and their customer has collaborated Automation happening So next five years will be very interesting The Manu away to manage and operate near Oca will be finally go away >> Last question Give us the business update You mentioned 2,000 customers You're hundreds of employees Any other business metrics you Khun, you can share with us Where do you want to take this company >> way really wanted behind every enterprise. Well, Misha is a Democrat. Eyes network automation way Looking at it in the next five years our business in a girl 10 times. >> Well, good luck. Thank you. Thanks very much for coming on the queue of a great story. Thank you. Thank you for the congratulations For all your success. Think Keep right! Everybody stew and I will be back. Lisa Martin as well as here with an X guest Live from Cisco Live 2019 in San Diego. You watching the cube right back

>> Live from San Francisco, it's theCUBE, covering DockerCon '18, brought to you by Docker and it's ecosystem partners. >> Welcome back to theCUBE, we are live at DockerCon 2018 in San Francisco on a spectacular day. I am Lisa Martin with my with my co-host for the day, John Troyer, and we're very pleased to welcome back to theCUBE a distinguished CUBE alumni and Docker veteran, Steve Johnston, Chief Product Officer at Docker. Welcome back. >> Thank you, thank you very much. That's Scott Johnston but that's okay. >> What did I say? Steve? >> Steve. That's okay. >> Oh, I gave you a new name. >> You know, I get that all the time. >> I'm sorry, Scott. >> That's alright. >> This event, between five and six thousand people. >> Yes. >> You were saying in your general session in keynote this morning, that this is the fifth DockerCon. You started a few years ago with just 300 people and when I was walking out of the keynote this morning, I took a photograph, incredible. People as far as the eye can see. It was literally standing room only. >> It's crazy, right? And you think about four years ago, June 2014 when we did our very first DockerCon, here in San Francisco, 300 people, right? And we've gone from 300 to over 5,000 in that time, grown the community, grown the products, grown the partnerships and it's just, it's very humbling, honestly, to be part of something that's literally industry changing. >> You gave some great numbers during your keynote. You talked about 500 customers using Docker Enterprise Edition. >> Yes. >> Some big names. >> Yes. >> MET Life, Visa, PayPal, McKesson, who was on stage and that was a really interesting. McKesson is what, 183 years old? >> Healthcare company, yeah. >> Talking about data, life and death type of data. >> Right. >> Their transformation working with Docker and containers was really pretty impressive. >> It's exciting that companies get their hands on the technology and they start maybe on a small project or a small team but very quickly they see the potential impact of the solution and very quickly, it's almost infectious inside the organization and more and more teams want to jump on, understand how they can use it to help with their applications, their business to get impact in their operations and it just spreads, spreads like wildflower. That was really the story that McKesson was sharing, just how quickly they were seeing the adoption throughout their org. >> I thought that was really interesting and they did point it out on stage, how that developer adoption did help them go to the next level. >> Yes. >> And kind of transform their whole pipeline. >> Yes. >> Now Scott, you've been here the whole line of time and that through line has been, for Docker, that developer experience. >> That's exactly right. >> Now, as Product Lead here, you've got the Docker Desktop side and the Docker EE side and it's clear, there were some great announcements about desktop here, previews today but how do you balance the enterprise side with the developer centric desktop side and that developer experience idea? >> No, it's a great question, John. I'd reshape it almost to say, it's a continuous platform from developer experience to the operation side and you have to stand back and kind of see it as one and less about trading off one versus the other and how do you create an experience that carries all the way through. So a lot of Gareth's demonstration and the Lily Mason play, was showing how you can create apps in Docker very easily as a developer but those same artifacts that they put their apps in to carry all the way through into production, all the way through into operations. So it's about providing a consistent user experience, consistent set of artifacts that can be used by all the different personas that are building software so that they can be successful moving these Docker applications through the entire application development life cycle. Does that make sense? >> It does, thank you. I'd love to get your perspective, when you're talking with enterprises who might have some trepidation about the container journey, they probably know they have to do it to stay agile and competitive. I think in the press release, I believe it was you, that was quoted saying, "An estimated 85% of enterprise organizations are in a multi Cloud world." >> That's right. >> In a multi Cloud strategy. >> That's right. >> So when you're talking with customers, what's that executive conversation like? C level to C level, what are some of the main concerns that you hear and how influential are the developers in that C suite saying, "Hey guys, we've got to go this direction"? >> No, that's right. That's a great question, Lisa and what we hear again, and again, and again, is a realization going on in the C suite, that having software capabilities is strategic to their business, right? That was not always the case, as much as a decade ago, as recently as a decade ago, inside kind of big manufacturing businesses or big verticals that weren't kind of tech first, IT was a back office, right? It was not front and center but now they're seeing the disruption that software can have in other verticals and they're saying, "Wait a minute, we need to make software capabilities a core capability in our business." And who starts that whole cycle? It's the developers, right? If the developers can integrate with the lines of business, understand their objectives, understand how software can help them achieve those objectives, that's where it kicks off the whole process of, "Okay, we're going to build competitive applications. We then need an operations team to manage and deploy those applications to help us deploy them in a competitive way by taking them to the Cloud." So developers are absolutely pivotal in that conversation and core to helping these very large, Fortune 500, hundred year old companies, transform into new, agile, software driven businesses. >> Modernizing enterprise apps has been a theme >> Yes. >> also at Docker for a few years now. >> Yup. >> Up on stage Microsoft demonstrating the results of a multiyear partnership >> That's right. >> between Microsoft and Docker both with Docker integrating well with Windows server as well as, you talked about, Kubernetes now. >> That's right. >> Can you talk a little bit about what the implications of this are? The demo on stage, of course, was a very old enterprise app written in dot net, with just a few clicks, up and running in the Cloud on Kubernetes no less. >> That's right. >> Managed by Docker, that's actually very cool. You want to talk a little bit about, again, your conversations? >> Absolutely. >> Is this all about Cloud native or how much of your conversations are also supporting enterprise apps? >> Tying back to Lisa's question, so how do we help these organizations get started on their transformation? So they realize they need to transform, where do you start? Well guess what? 90% of their IT budget right now is going into these legacy applications and these legacy infrastructures, so if you start there and it can help modernize what they already have and bring it to modern platforms like Docker and Kubernetes, modern platforms like Window Server 2016, it's a modern operating system, modern platforms like Clouds, that's where you can create a lot of value out of existing application assets, reduce your costs, make these apps agile, even though they're thirteen years old and it's a way for the organization to start to get comfortable with the technology, to adopt it in a surface area that's very well known, to see results very, very quickly and then they gain the confidence to then spread it further into new applications, to spread it further into IOT, to spread it further into big data. But you've got to start it somewhere, right? So the MTA, Modernized Traditional Apps, is a very practical, pragmatic but also high, very quick, return way to get started. >> Oh, go ahead. >> Well I just, the other big announcement involving Kubernetes was managing Kubernetes in the Cloud and I wanted to make sure we hit that. >> That's right, that's right. >> Because I think if people aren't paying attention, they're just going to hear multi Cloud and they're going to go on and say, "Well everybody does multi Cloud, Docker's no different, Docker's just kind of catching up." Actually, this tech preview, I think, is a step forward. I think it's something- >> Thank you. >> I haven't actually seen in practice, so I'm kind of curious, again, how you as an engineering leader make those trade offs. Kind of talk a little bit about what you did and how deciding, "Well there's multi Cloud but the devil's in the details." You actually have integrated now with the native Kubernetes in these three Clouds, EKS, AKS and GKE. >> GKE, no that's right. No, it's a great question, John. The wonderful and fascinating but double edged sword of technology is that the race is always moving the abstraction up, right? You're always moving the abstraction up and you're always having to stay ahead and find where you can create real value for your customers. There was two factors that were going on, that you saw us kind of lean in to that and realize there's an opportunity here. One is, the Cloud providers are doing a wonderful job investing in Kubernetes and making it a manage service on their platforms, great. Now, let's take advantage of that because that's a horizontal infrastructure piece. At parallel we were seeing customers want to take advantage of these different Clouds but getting frustrated that every time they went to a different Cloud they were setting up another stack of process and tooling and automation and management and they're like, "Wait a minute. This is going to slow us down if we have to maintain these stacks." So we leaned in to that and said, "Okay, great. Let's take advantage of commoditized infrastructure, hosting Kubernetes. Let's also then take advantage of our ability to ingest and onboard them into Docker Enterprise Edition, and provide a consistent experience user based APIs, so that the enterprise doesn't get tied into these individual silos of tools, processes and stacks." Really, it's the combination of those two that you see a product opportunity emerged that we leaned heavily into and you saw the fruits of this morning. >> I saw a stat on the docker.com website that said that customers migrating to EE containers can reduce total cost by around 50%? >> Yes. >> That's a significant number. >> It's huge, right? You're reducing your cost of maintaining a ten year old app by 50% and you've made it Cloud portable, and you've made it more secure by putting it in the Docker container than outside and so it's like, "Why wouldn't you invest in that?" It shows a way to get comfortable with the technology, free up some cashflow that then you can pour back into additional innovation, so it's really a wonderful formula. That again, is why we start a lot of customers with their legacy applications because it has these types of benefits that gets them going in other parts of their business. >> And as you mentioned, 90% of an enterprise IT budget is spent keeping the lights on. >> That's right. >> Which means 10% for innovation and as we've talked about before, John, it's the aggressively innovating organizations that are the winners. >> That's exactly right and we're giving them tools, we're giving them a road map even, on how they can become an aggressively innovating organization. >> What about the visibility, in terms of, you know, an organization that's got eight different IT platforms, on prem, public Cloud, hybrid- >> Right. >> What are you doing with respect to being able to deliver visibility across containers and multiple clusters? >> That's right. Well that's a big part of today's announcement, was being able ... Every time we ingest one of these clusters, whether it's on prem, whether it's in the Cloud, whether it's a hosted Kubernetes cluster, that gives us that visibility of now we can manage applications across that, we can aggregate the logging, aggregate the monitoring. You can see, are your apps up, down, are they running out of resources? Do you need to load balance them to another cluster? So it's very much aligned with the vision that we shared on stage, which is fully federated management of the applications across clusters which includes visibility and all the tools necessary for that. >> Scott, I wanted to ask about culture and engineering culture >> Thank you. >> The DockerCon here is very, I think we called it humane in our intro, right? There's childcare on site, there's spoustivities, there's other places to take care of the people who are here and give them a great experience and a lot of training, of course, and things like that. But internally, engineering, there's a war for talent. Docker is very small compared to the Googles of the world but yet you have a very ambitious agenda. The theme of choice today, CLI versus GUI, Kubernetes versus Swarm, Lennox and Windows, not versus, Lennox and Windows, you know and, and, and, and now all these different Clouds and on prem. That's very ambitious and each "and" there takes engineering resources, so I'm kind of curious how the engineering team is growing, how you want to build the culture internally and how you use that to attract the right people? >> Well it certainly helps to be the start up that kicked off this entire movement, right? So a lot of credit to Solomon Hykes, our founder, and the original crew that ... Docker was a Skunkworks project in the previous version of our company and they had the vision to bring it forward and bring it to the world in an opensource model which at the time was a brand new language, go language. That was a catalyst that really got the company off and running in 2013/2014. We're staying true to that in that there's still a very strong opensource culture in the company and that attracts a lot of talent, as well as continuing to balance enterprise features and innovation and you see a combination of that on stage. You're also going to see a wonderful combination of that on the show floor, both from our own employees but also from the community. And I think that's the third dimension, John, which is being humble and call it "aware" that innovation doesn't just come from inside our four walls but that we give our engineers license to bring things in from the outside that add value to their projects. The Kubernetes is a great example of that, right? Our team saw the need for orchestration, we had our own IP in the form of Swarm, but they saw the capabilities of Kubernetes is very complimentary to that, or some customers were preferring to deploy that. So, no ifs, ands or buts, let's take advantage of that innovation, bring it inside the four walls and go. So, it's that kind of flexibility and awareness to attract great engineers who want to work on cutting edge, industry building technologies but also who are aware enough of, there's exciting things happening outside with the community and partnering with that community to bring those into the platform as well. >> So Scott, you guys are doing a lot of collaboration internally, but you're also doing a lot of collaboration with customers. How influential are customers to the development of Docker technologies? >> At ground zero, literally and we have at DockerCon, we call it a customer advisor group, where the customers who have been with us, who have deployed with us in production, we have them. And it's a very select group, it's about twelve to sixteen, and they tell us straight talk in terms of where it's working, where we need to improve. They give us feedback on the road map and so that happens every DockerCon, so that's once every six months. But then we actually have targets inside engineering and product management to be out in the field on a regular basis to make sure we're continuing to get that customer feedback. Innovation's a tricky balance, right? Because you want to be out in front and go where folks aren't asking you to, but you know there's opportunity, at the same time here, where they are today, and make sure you're not getting too far ahead. It's the old joke, Henry Ford, where if he's just listened to his customers, he would have made faster horses but instead he was listening to their problems, their real problems which was transportation and his genius, or his innovation, was to give them the Model T, right? We're trying to balance that ourselves inside Docker. Listen to customers but also know where the innovation, where the technology can take you to give you new solutions, hopefully many of which you saw on stage today. >> We did, well Scott, thanks so much for stopping by theCUBE again and sharing some of the exciting announcements that Docker has made and what you're doing to innovate internally and for the external enterprise community. We appreciate your time. >> Thank you, Lisa. Thank you, John. >> We want to thank you for watching theCUBE. Again, Lisa Martin with John Troyer, live in San Francisco at DockerCon 2018. Stick around, John and I will be right back with our next guest. (upbeat techno music)

>> Narrator: Live from Las Vegas, its the Cube, covering NAB 2017, brought to you by HGST. >> Hi, welcome back to NAB, I'm Lisa Martin. We have had a very exciting day so far, talking with lots of great leaders. Very excited to be joined next by Joan Wrabetz, the VP of Marketing for HGST. Joan welcome to the Cube. >> Joan: Thank you, its good to be here. >> We're very excited to have you here. NAB 2017, this is our first time here. An event with over 100,000 people, overwhelming, walked a lot of miles so far today. Tell me your first impressions of NAB this year. >> Yes, well this is also my first year at NAB. And it's a little overwhelming. Yeah, a lot of people and the technology is everything from suit to nuts. I heard you can buy an, you can go upstairs and buy a helicopter. >> Wow. >> Yeah, which apparently is important in media and entertainment. >> >> So our little piece of it, which is IT, I think is a growing part, but yeah there's a little bit of everything here. >> There really, really is. I haven't seen the helicopters yet, I'll have to make my way upstairs. >> Yeah you can get a demo, a 3-D demo. >> Wow. Oh my goodness. >> >> So one of the pervasive, overarching themes of the event this year is the M-E-T effect, the MET effect, this convergence of media, entertainment and technology, which is so interesting, and as we were talking before, technology is both the bane of the existence of a lot of companies, in any industry, as well as something that provides tremendous opportunity. >> Yeah. >> As you lead Marketing for HGST, when you're talking to major studios, how are CTOs reacting to market trends that are going on, whether it's virtual reality, or, now we've got so much more data that we need to keep because there's more IP there? How is that CTO journey changing in response to technology proliferation? >> Right, well it's interesting. Some CTOs are very aware that they're in the middle of a big disruption, for which data related technologies are going to be the key to surviving. Others, not so sure. And they'll say, in the first meeting, "You know, I'm not the storage guy, I don't worry about, you know, asset archivings. So, there's probably nothing you can sell me." And it only takes about ten minutes for them to realize that that's probably not true, that underneath most of their biggest challenges there's some aspect of how to manage a large amount of data. They're in the middle of a big disruption in their industry. One CTO told me that, you know, new people don't go to movies anymore. There's a loyal audience of people who go to the movie theater, and I'm one of them, I'm a complete videophile. But a lot of younger people just don't go to movies. They get the same content in other ways, whether it's Netflix, whether its movies at home. And so there's a huge disruption going on there, that the way people want to consume the content that studios create is really changing. They tend to want to consume it in ways that require more than just the storyline, which is kind of what a film in a movie theater is. It's got to be interactive. So if I buy a DVD or the digital version of it to take home, I often want added scenes, or extra stuff. If I might be playing a video game, that's based on an actual real piece of content, that's going to be an interactive form of it. Some of the new technologies that are coming out, like virtual reality, are really interactive. That's where they shine. It's not a format for theaters. So, they recognize that they have this core content, they have to deliver it in a very different set of ways. And delivering it in a different set of ways means curating it and keeping it and producing it in different ways. So not only is the accuracy and the quality of the content going up, right, I've got these 8K cameras and everything, the delivery mechanism is changing so that I'm going to keep and create this content in a lot of different ways. Underneath all of that is about how do I keep it, how do I store it? So all these disruptive changes are driving a proliferation of massive amounts of data. So that's one side of it, they just have to keep this stuff. >> Right. >> For insurance purposes, for historical purposes, they're keeping it. The flip side is monetizing, right. So you mentioned before Netflix. So Netflix is in a very interesting position in the industry in that they own so much information about you and me, the people who watch movies, they know all about our preferences. I can come right back into a show at the exact place I left off on a different TV in another location. And I love that about Netflix. And they also know a lot about my habits. When I go to a movie theater the guys that produce that movie aren't getting that from me. >> It's more of a qualitative reaction. >> Exactly. >> That they're employing. >> Right. But the flip-side is that Netflix, historically, has only been able to give out content as it's been provided to them. Whereas if you're in a film production and you can get feedback from your users, you can really modify your movie. You can change the storyline, they change the endings. I mean they take characters in and out. So I think on the one hand, the film industry's understanding that knowing the behaviors and the preferences of individual consumers is critical to success in the future. Meanwhile Netflix, who has that information, knows that curating and making their own content is critical to their success, otherwise having personalized information is of no value if I can't customize the content. So they're all figuring out that there's some core thing that they need in this disruptive world that they don't have. And in both cases it's about content. And it's about the storage and the data and the ways that they can take that data and find useful, basically mine useful information out of it. >> Right, and new revenue streams. So imagine that the conversation with the studio that ten minutes later, you said, understands, "Oh wait, this actually is a conversation about archiving this." There's so much data, but it must be, to put it bluntly, overwhelming. How do you help really transform the culture from the C suite down to say IT? Help them understand what are the steps that we need to take, given that we've got petabyte scale data that needs to be archived and needs to be easily accessible. You were mentioning something before we went live about, you know, studios that go, "We shot this this way and it's great but man wouldn't that be great with VR?" >> Right >> So it's really interactive. What is that? Walk us through that evolution that you help CTOs and their teams understand with respect to getting an archive strategy that allows them a lot of capability and functionality. >> And that's exactly kind of a key word is strategy. Right, so historically the CTO might have looked at keeping copies of a movie that's no longer being created as an insurance policy almost. Today, its a strategic asset and some of those assets they recognize as being strategic and others they're not sure, right. So the first part of developing a strategy for getting use out of that data and monetizing it et cetera is understanding where the value might be. So the example I give is I was sitting with this CTO of one of the major studios, and we were just talking about virtual reality and how is virtual reality going to change their business. And he commented that, "Well we had just finished this beautiful ballroom scene, we finished the scene we said, 'Oh my gosh, that would've been a great VR scene.'" And so that scene had 250 extras, all in costume, for this ballroom dance, right. So they kept all those people in costume for four extra hours, went back and re-shot that scene. And I said to them, "Too bad you don't keep your dailies, you would've had that scene." >> Right >> So he kind of looked at me and said, "Wow, we never thought of the dailies," which is the film that they throw away that doesn't get kept from each days worth of filming. They don't see that as a critical asset, but if you start to rethink your problem, it is. So part of this strategy is having them start to think about what is and might be a critical asset going forward, and then how could they cost effectively save that information, because the reason they don't save dailies today is because it's another two petabytes. If I don't know the value of that that's pretty expensive, even at very low cost of storage. So it's both understanding how they can keep their cost down for certain types of information and get the value up, know that that value could be there. Then they build a different type of strategy than the one they have today which is really more of a defensive strategy. So we see, as we're talking to the major companies in this industry, they're all moving from a somewhat defensive to a very offensive strategy with digital assets. They have to or they'll be out of business. >> Absolutely. You recently spoke last month at the virtual NAB conference on hybrid redefined and the future of digital assets. What were some of the takeaways from you in terms of, or recommendations on how can the media and the entertainment industry really preserve these digital assets in hybrid workflows. >> Sure, well and the hybrid comes in because not only is there an explosion of data, but there's an explosion of processing requirements. So the cheapest, people think the cheapest way to do extra processing is to send it up to the cloud and do a lot of processing up there. Unfortunately the data has to go with it. So the challenge that they face is how to keep some of the data assets on site, where they are more protected, they're not subject to risk. And by the way if you take data and you move it in the cloud and you're moving it around it becomes very expensive. It's not expensive to keep data in the cloud, but it's very expensive if you touch it and move it. That's where they start making their money. So how can I keep my data where it's protected and where I don't have to pay to move it around, but get all the benefits of on demand processing with thousands of processors in the cloud. And the answer is you do need to make the data accessible to the workflow that might happen in the crowd. So most of these guys that do rendering, for example, they burst 20 to 50 percent of their rendering to the public cloud because they're on very tight schedules. And it just doesn't make sense to buy the extra equipment when it's only going to be used for three weeks. So hybrid workflows are about moving the processing around but not necessarily having to move all the data around and keep the data secure. And that's a big priority right now for all these media and entertainment companies to figure out. >> Is one of the benefits that they can get from that accelerating production workflows? >> Yes, I mean they're 100 percent deadline oriented. Right so you look at these animated movies, whether it's Despicable Me and the minions. It's all about how long it's going to take to produce that. So the workflow that is used to create and then render the animated information is, I mean that's it, that is the critical path. I had these guys in animation space talk to me about simple mistakes where they changed the hair on an animated figure and they look at it in the morning after it's all been re-rendered and the hair is half an inch behind the person. >>That's a problem. >> I mean and then they have to run it again and it takes eight hours. That's a whole day lost in a production schedule. And you know movies we see today, so much more of it is CGI than at any time in the past that even if it's not an animated movie, there's a whole lot of processing that's going on on that movie and that all is critical path. So yeah time is, of the workflow around that, that whole processing workflow, is absolutely time critical. Every minute that's spent costs them a lot of money. >> I can't imagine. And something that you mentioned, I want to ask the last question on collaboration. You talked about the benefits that Netflix has and then some of the challenges, or the opportunities, and the same thing on the side of the filmmakers. And it seems like it's this sort of circle. What did you call it? >> A virtuous cycle. >> The virtuous cycle. Do you see collaboration happening between some of the streaming providers and film? Is that a two-way street that is starting to become viable? >> Yes, and we do hear stories of them collaborating directly and indirectly. So indirectly where they have these common overlapping technology problems they're working together in industry organizations in media and entertainment whether its Simpty or Etsy, and trying to develop technologies that are for everyone's benefit. And then directly I think they do work together, and they see the benefits of, you know, that each other has, and try to learn and adopt some of the similar technologies. So I don't know if it's always been this way, but you get this feeling when you're here at NAB that there is this intense desire for everybody to learn from everyone else's strengths across the industry, it's not just film, it includes people who are doing sports, we talked about interactive gaming, and now we have video games being played in tournaments on live TV. So yeah we see a real sort of sharing of information and collaborating around best technology practices across all of media and entertainment in ways that I think are probably much, much more intense than in the past. >> Fantastic. Well it just shows the momentum that we're feeling around this event, this convergence of media, entertainment technology is incredibly viable. But to have this feeling that you're sharing of sharing best practices in collaboration is probably really, as this event, which has been going on for many, many years, evolved, really the direction that it should go into. >> Yep. >> Thank you so much, Joan, for joining us on the Cube. >> You're welcome. >> It's been so delightful speaking with you. And it sounds like never a dull moment, >> Nope. >> In the day in the life of you. >>Nope, it's always changing. >> Excellent. Well we wish you have a great time at the rest of the show. >> Thank you. >> And we thank you for watching The Cube. We are live at NAB 2017 in Las Vegas. I'm Lisa Martin, stick around, we'll be right back.

>> Live from Las Vegas, it's The Cube, covering NAB 2017. Brought to you by HGST. >> Hey welcome back everybody, Jeff here with The Cube. We're at NAB 2017, 100 thousand plus people running around the Las Vegas Convention center. North, south, east, west, upstairs, downstairs. I'm joined by my co-host for the week, Lisa Martin. Lisa, have you ever seen anything like this? >> I haven't Jeff, It's incredible. Just, the buzz that's here, the two miles I walked to get here, the amount >> That's just from the front door >> Exactly! The amount of technology that's here and the, everything about the MET effect, the M-E-T effect, the convergence of media, entertainment, technology, we're living it, literally. Even just in our studio alone, it's very exciting to be here. >> It's kind of an interesting contrast, cause on one hand you have this, a bunch of super high end gear here, there's a bunch of green screen sets all over the place that are, you know, really top end, on the other hand though you have kind of this democratization of distribution, >> Yes. >> The studio no longer hold the keys to the kingdom and actually I just heard that Netflix is the biggest movie or production house going anymore, so the industry is changing a lot. But here right now, if you're a gear head, if you're a video gear head this is the place to be. >> It is the place to be, but you're right about the transformation. The interesting concept behind METs, this convergence of things that used to be distinct, so not only are we seeing this convergence, but the, at the nucleus of this media transformation is content. You talk about Netflix, you talk about content proliferation, everyone is doing everything content. >> Right. >> There's so many different platforms that are generating content and these organizations, whether they're filmmakers or big studios or broadcast news has to be able to find where the audience is, deliver content that's relevant to them. You know before, back in the day, filmmakers and Hollywood, this was a qualitative sort of assessment of what people liked and where to deliver it. Now a days, you mentioned Netflix, the Netflix of the world know so much about all of us that are using this streaming service >> Right. >> And so, it's very much a data-driven experience that delivers content. That, oh, I think Jeff might like this on Netflix or Lisa might like this. So, it's interesting that with the audience in control, really very empowered, how technology can really help enable filmmakers, studio, writers, news broadcasters, to be much more data-driven. So, I'm excited to hear about machine learning, artificial intelligence, how all of this data is being stored, taking advantage of the latest technologies. >> Right. Because the consumer has so many choices, they have so many options that if you don't know who your audience is and get your product to that audience the chance of you holding them is pretty slim, so >> Absolutely >> So, no longer just for four channels or even hundreds like there used to be on early cable days, now it's literally thousands of options that are one swipe away, so you really need to engage with your audience and there's some talk about that, actually, we've got a guest later today who will be talking about, kind of, a little more sophistication about audience identification and getting in contact with that audience, cause if you don't there's just too many choices. >> There are too many choices, additionally one of the challenges that all of us that are cord-cutters deliver to, especially like traditional news is they've got to find us where we are, you've got to deliver the right content, monetize the content and there are new business opportunities, new revenue streams that can be generated there and finding them and delivering that relevant content is key. Another thing that's interesting is, you know, we talk about security, Jeff and I were just at AWS summit in San Francisco last week and we think, well security with respect to public cloud now isn't nearly the concern that it was in the beginning, but we look at, in the entertainment and media industry and just a few months ago there was leaked screeners of LA La Land, the near best picture winner from a few months ago and there's a lot of data that show that things that are leaked online like that can actually dramatically impact, negatively impact box office sales. So, cyber security is a very, very crucial element that all of these media and entertainment companies need to design for, it's really sort of becoming a creative problem. So I'm very curious to talk to, we've got an expert on a little bit later on in the show from a security company, would love to get his perspective on how do media and entertainment companies really transform their culture so that cyber security is really woven into the fabric of what they're doing. >> Right, right. A lot more than just DVDs being copied overseas right, >> It is, 20 years after Napster piracy, even on Netflix, it's still a problem. >> Absolutely. Alright, Lisa Martin. We got three full days, we're excited to be here, I'm Jeff Rick, you're watching The Cube. Stay tuned, we've got a wall-to-wall schedule, we'll be back with our next guest after this short break, thanks for watching. (electronic music)