>> Voiceover: Live, from Atlanta, Georgia, it's theCUBE, covering CITRIX Synergy, Atlanta 2019. Brought to you by: CITRIX. >> Welcome back to theCUBE. Lisa Martin with Keith Townsend coming to you live from Atlanta Georgia, our first day of coverage of Citrix Synergy 2019. Keith and I are very pleased to welcome you to theCUBE. For the first time, Bob O'Donnell, the founder and president of Technalysis. Bob, it's great to have you on theCUBE. >> Thank you. Great to be here I really appreciate it. It's my first chance to do theCUBE. It's exciting. >> We're so excited because you are no stranger to TV. Bloomberg, CNN, CNBC, Squawk Box, now theCUBE! >> Bob: And the now theCUBE! >> Keith: Most importantly- >> Bob: It completes the circle. >> He's a friend of Leo Laporte, which makes him a super star. >> All: (laughing) >> Well there you go. >> We're sitting in the presence of greatness. >> Oh, I don't know about that. But anyway, no, it's a pleasure to be here and it's nice to chat with you guys. It's a very interesting time that we're in. I mean, when we think about what's happening in the world. For years we've seen this move to cloud-based computing, and SaaS, and everything else. And everybody's excited about all of this stuff, and there's all these tools. And then on top of that, we thought, we have all these devices, right? We've got this amazing range of different devices we can use. But ironically, what it is, is we're in a state of too much of a good thing. It's too much. Even though if you think about it, you'd say, "Well, objectively, there's so much that "we could potentially do here. "I mean, we've got these tools that can do "this and this and this." But all of a sudden, "Well, except I got this one and this one, and this one. "And oh, by the way, if I want to send a message, "I can send it five different ways to Sunday, "and therefore if I want to read a message, "I have to be able to read it "five different ways from Sunday." And so, the challenge that you face is, and Citrix talked about it, I thought, quite nicely in their keynote this morning, is people get overwhelmed. And they just can't get productive with what they're trying to do. And so, what you need to do it figure out ways to turn that chaos into structure and order. And that's what they're trying to do with the workspace. And it looks pretty cool. >> Yeah, one of the offline conversations I had was you get all these tools. It's like somebody took a box of 10,000 Legos and just jumped it on your desk and said, "Build a masterpiece." And what I head this morning was the equivalent of what was like a Star Wars kid of like, "This is what you can build. Here's the directions, "and now you can start to deviate and customize it "for your environment." So one of the things that I'd love to get your input on is this concept of AI ML. This ideal of taking tasks and automating them. It's nothing new. We've tried this with macros and other areas. But the thing that was missing was, these tools were pretty dumb. >> Bob: Right. So the promise of ML AI should make these tools become real. What's your impression of the state of the technology versus what was presented today. >> Well, look, we're in very early days of AI and ML. There are some fascinating things out there. There's a lot of the high profile things that we hear about. The ImageNet and the ability to recognize every kind of dog known to mankind, and all the demos we've all seen at every other trade show. It really is, the fascinating part, exactly, to your point, is that the goal with AI and machine learning is to actually makes things understand. And it's fascinating because... I'll take a bit of a sidetrack but bring it back. When devices started to be able to recognize our words, we assumed, because we're human beings, that they recognized what we meant. But, no. There's a big jump between the words that you can transcribe, and what you actually mean. >> Yeah. That context. >> Context is everything. And context is something that, again as human beings, we take it for granted. But you can't take that for granted when it comes to technology and products. So, the beauty of AI as it starts to get deployed is how do we get the context around what it is that we're trying to do, what we meant to say. Of course, we all want that in real life: "What I meant to say was..." But, "what I meant to do was this." Or, "the task I want to do is that." So, taking that back to what Citrix is talking about is there are a lot of rote procedural things that people do in most organizations. And they gave the classic examples of proving the expense reports and this and that. So, clearly, some of those things they can pre-build. The micro apps, in a lot of ways, they really are macros. It's kind of a fancy macro. And that's fine, but the question is are they smart enough to kind of deviate, "Oh, well, there's a conditional branch "that it automatically builds in a macro "that I didn't have to think about "because it realizes in the context of what I'm doing "that it means something else." Or something like that. >> At the end of the day, I want to get the account balance, however that translates. As opposed to: take this column from row A and put it in row B. No, sometimes row A won't be the correct destination. I want the account balance. >> Right, right. >> And the other truth of the matter is we're still getting used to actually talking to our devices. We do that at home to some degree for people who have Alexas, unless they've decided to stop recording everything, and then that's a whole different subject. But, at work we don't. Interestingly, I remember when I first saw Cortana, for example, on a Windows machine. I thought, in a weird way, Cortana makes more sense because I should want... But it hasn't really happened. It hasn't played out. So there's some level of discomfort of talking to our devices and recognizing these things. So, I think there are cultural issues you still have to overcome. There are physical issues in the workplace, now. Now, when you have these open office environments, which doesn't take a rocket scientist to know that that was going to be a disaster. Whoever thought that was smart, man, let's take a look at where their degree came from. But that's the reality that people are in. So, you've got the physical environment challenges. You've got the cultural "how do I work with this?" environment. And then just starting to realize what it can actually do. And then, of course, you have the problem that it didn't recognize what it actually said. That's something stupid, and the original Siri problems that we all had. But, all of these things tie together because they're all different takes on what machine learning has the potential to do and what we think it should do, and what it can actually do. The one thing I will say is as we head towards 2020, I think we're going to start to finally see some of these things do what we thought they were going to do. They're going to start to have the context. They're going to start to have the intelligence. So, in the work space, it's going to have the ability to know what I mean when I say, "I need the account balance." Or, "I need to know where in the sales pipeline "this particular project is," or whatever task it is that I've got to deal with. And so, understanding that and then building the plumbing to do that is critical. One of the interesting things, if you look at what Citrix does, they're really all about plumbing. They have this ability to pull together all these different elements. From the beginning, what we started talking about. All these different applications over different types of network speeds and connections and make them all work. And yet, they present this very simplified, beautiful, nice little, you're like, "oh, this is great!" But, man, buried beneath there is a lot of stuff. And that's, to give them credit, that's what they're really good at doing. And companies now, the challenge is, a lot of companies have really old applications that they've got to kind of modernize in some way shape or form. And some of them are doing it on their own. They're doing the containerization and all the things we hear about as well. Some of them are wrapping them. Citrix, some of their original business, XenApp, was about app virtualization. Taking an old app and giving access in a modern way. So, again, it's doing that, but the other problem you have to bear in mind, excuse me, is that every company has a different combination of apps. They said 500 apps is normal. A lot of companies have more than that. >> Keith: Mhm. (affirmative) >> The problem is, it's not the same five hundred apps. This company has this set of 500 apps. This company has this set of 500 apps. This company has this set of 500 apps, and maybe 150 of them overlap, which means the long tail of 350 per company has to be dealt with and figured out. And that's, again, those are the problems that they're trying to solve and bring in to a unified environment. >> And also manage these growing expectations that all of us that are workers have from the consumer side of our lives. You mentioned Alexa and Siri, and we have these growing experiences that whether I'm talking to a device or I'm going on Amazon, I want it to know what I want. Don't show me something I've already purchased. And we have these expectations as humans and consumers that we want the apps when we get to work to understand the context and of course, we're asking a lot. In your opinion, where is Citrix in starting to help manage, helping their customers, rather, manage those growing expectations? >> I think Citrix has done a lot in that area. Even many, many years ago they were the first to come up with the notion of an enterprise app store. In the early days of the app store, they came out with this concept of, "We want to do an enterprise equivalent of that." When I download an app that I need to install on a work PC, make it easy to get at. So, from way back when they've been building on that. And then, the examples they gave today, the notification from the airline that your flight has changed, or whatever. Those are all the experiences that we're now used to thanks to cloud-based services. And their point is like, "Hey, why shouldn't we "have that at work, as well?" And so that's exactly what they're trying to work towards, is that notion of cloud-based notifications and services, and things, but related to the specific tasks I have to do. Because at the end of the day, they want to drive productivity. Because we all waste stupid amounts of time, and truth be told, the bigger the company you're at, the more time you waste because of just keeping up. I used to work at a big research firm of 1200 people, and literally half my day, every day, was just procedural stuff. I didn't actually work on the stuff that I thought I was hired to do, except for maybe half the day. And with a lot of people, that's very common. So, anything that can be done to reduce that and allow people to get through the procedural stuff a little bit more efficiently, and then actually let them do the work that they were hired to do and that they'd like to do, and oh, by the way, gives them more satisfaction. All of these things tie together. People tend to say, "Oh well, you know, "that's nice to do, this consumerization of IT, "that's nice." It's not just nice. It's actually practical. It's actually a real productivity enhancing capability. And I think Citrix has done an excellent job of driving that message. It's hard to to do because, again, the complexity of the plumbing necessary is super difficult. But their head and their heart are in the right place in terms of trying to achieve that. >> Well, it sounds absolutely like not a "nice to have," but business-critical. One of the stats that David Henshall, their CEO, said this morning, and Keith's been mentioning a number of times, is that he said there's 7 trillion dollars wasted on output because employees are not able to get to their functions that they were hired for in a timely manner. >> Right. >> So, there's a huge addressable market there of opportunity but also the consumerization that's personalization expectation is huge to not just making me, Lisa Martin, as an employee happy, but my business's customers that I'm dealing with. I think of a sales person, or even a call center support person. If they don't have access to that information, "She already called in about this problem 'with her cable ISP," that person is going to go turn, and go find another option that's going to fulfill their needs much better. >> That's absolutely right. And that was the interesting point that they made. And that's what they're trying to do with the intelligent work space is to move beyond just providing these apps, but actually personalizing it to each individual and being able to say, "All right, each of us are going to have a workspace." Sort of, it looks kind of like a news feed kind of a thing. Each one is going to be different though, based upon, obviously, the different tasks that we do, the order with which we do them, the manner with which we do them." So it does get personalized. The notifications, you know, I may want certain notifications that you don't really care about as much. But that's fine. We can each create that level of personalization and customization. And again, what Citrix is trying to do, and it was a key point that P.J. made, is, "Look, we're not just building an application. "We're building a platform." And that's... The significance of that is big. And remember, he came from Microsoft. He worked on Windows. He worked on Office. So, he's got a long history of working on building platform based tools that have tools that you can build on. That have APIs and ways for other people to add to. So, all of those are critical parts of how they tell that story, and how they get people enthralled enough to say, "Hey, I'm going to make the commitment to do it." Because look, it's a lot of work. Let's not kid ourselves. If I'm not a Citrix shop, but I go, "Damn, that's cool!" There's a fair amount of effort to make all this stuff actually happen. So, it's a commitment. But, once they get them hooked it's a pretty sticky type of environment. Especially as they continue to deliver value and personalization and customization. That, at the end of the day, drives productivity. And that's a pretty straight forward message: "Hey, we can save your workers time "and make them happier." Well, who doesn't want that, right? >> So, let's talk about engaging your customers. Like, I can look at this, and I can easily, say I can come to a conference like this and say, "Wow, I really want the output. I don't want "any of that employee experience stuff. "That stuff just sounds hard, "but the output I definitely want." Talk to me about the evolution of your customers as you walk them through if you want the output, here's what you have to do. And talk to me about, specifically, the success stories of where they didn't get it, and then after you've engaged them, they got it. >> Well, there's so many different variations out there. But, at the end of the day, every company out there is dealing with the fact that they have workers that work in a lot of places on a lot of devices and they have to allow them to get stuff done. And so, it's about how much are they willing to do to make that happen? But there's the psychology of it. There is the whole, "how much of this am I willing to outsource?" Versus, "I really want to keep it inside." So, it depends on the industry and the level of if they are a regulated industry, and all those things have an enormous impact on how they do this. But, if you think back, Citrix's original business was, a lot of it, was again, around desktop virtualization, and actually trying to get really old school stuff, I'm taking mainframe green screen stuff, to actually run on an old Windows PC. And that was kind of a lot of what they did, initially. And then, of course, they've built on from there. So, all along the way, you see different organizations. Citrix has been thought of more as more of the old school kind of enterprise software. Along with an SAP or an Oracle so something like that. I think they've done a particularly good job of being cloud native, cloud aware, and working with these cloud-based tools. Because early on, when we think about what happened with SaaS applications, people thought that was going to dramatically change how anybody did software. And it did, but not in the way people expected. So, I'm trying to get an answer, specifically, to your question, but I think what it is is what they're doing, and what companies who deploy it find is that they can take even these completely different types of software and services, and ServiceNow, and Salesforce, and Workday, and all these kinds of things that are dramatically different, but still, again, have overlapping functionality if I use all of them, and conflict or counteract or interact, or need to interact with other tools I already have that I'm working to change. So, again, what I think that what Citrix has done a good job is they're able to look at the wide range of stuff that people have in that 500 group of apps, or whatever it is, and be able to say, "All right, ten of those are cloud-based services. "But we've got 490 other ones we've got to deal with." And they have different levels of technologies to deal with those. So, what companies can do is they can also pick and choose. They can say, "Look, we're not going to get all 500 apps in our workspace." Maybe they just decide, "But we're going to do these twelve, "five of which are SaaS-based, "and then we've got a couple other critical ones "that we have to do, and that hits 80% of our workers." And they can tackle it that way. So, the bottom line is companies who... Look, it's a big investment up front. So the process is you have to psychologically say, "I'm willing to make an investment in," not obviously, just now, but their roadmap. What they're doing. What they're talking about. That's why they talk a lot about the future because if I buy into this ecosystem, I'm committed. Right? Again, I talking about that earlier: The stickiness question. So, companies who are doing this kind of thing, companies who are trying to make sense of all these applications have to be willing to make those big investments. It used to be, it used to have a huge Citrix server farms, as well. Obviously, with the development of the Cloud and Citrix Cloud, that's all changed. But, it's still a big investment, and they have to work to figure out ways to do this. And if they do, to finally get to, you know, they do see productivity savings. I mean, Citrix is, I don't remember the numbers, but they can qualify actual time saved when their solutions are installed, and that's the benefits that these companies get. So, they have to measure how much is my employee time worth versus the cost of getting these things deployed? >> Well, and I think that's going to be a differentiator for them. I wish we had more time because we could keep talking to you for a long time, but you got to add theCUBE to your list of TV: Bloomberg, CNBC, >> Bob: It's all there. Hey, I'm excited. >> Squawk Box, Now, theCUBE. Bob, it has been such a pleasure to have you on theCUBE. >> Thank you. >> We appreciate your time. >> Thanks so much. Appreciate being here, thank you. >> Our pleasure. For Keith Townsend, I am Lisa Martin. You're watching theCUBE, live from CITRIX Synergy 2019. Thanks for watching. (upbeat techno music)

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