(upbeat music) >> Good afternoon, fellow cloud nerds, and welcome back to theCUBE's live coverage of AWS re:Invent, here in a fabulous Sin City, Las Vegas, Nevada. My name is Savannah Peterson, joined by my fabulous co-host, John Furrier. John, how you feeling? >> Great, feeling good Just getting going. Day one of four more, three more days after today. >> Woo! Yeah. >> So much conversation. Talking about business transformation as cloud goes next level- >> Hot topic here for sure. >> Next generation. Data's classic is still around, but the next gen cloud's here, it's changing the game. Lot more AI, machine learning, a lot more business value. I think it's going to be exciting. Next segment's going to be awesome. >> It feels like one of those years where there's just a ton of momentum. I don't think it's just because we're back in person at scale, you can see the literally thousands of people behind us while we're here on set conducting these interviews. Our bold and brave guests, just like the two we have here, combating the noise, the libations, and everything else going on on the show floor. Please help me welcome Mike from Science Logic and Ajay from Zebrium. Gentlemen, welcome to the show floor. >> Thank you. >> Thank you Savannah. It's great to be here. >> How you feeling? Are you feeling the buzz, Mike? Feeling the energy? >> It's tough to not feel and hear the buzz, Savannah >> Savannah: Yeah. (all laughing) >> John: Can you hear me? >> Savannah: Yeah, yeah, yeah. Can you hear me now? What about you, Ajay? How's it feel to be here? >> Yeah, this is high energy. I'm really happy it's bounced back from COVID. I was a little concerned about attendance. This is hopping. >> Yeah, I feel it. It just, you can definitely feel the energy, the sense of community. We're all here for the right reasons. So I know that, I want to set the stage for everyone watching, Zebrium was recently acquired by Science Logic. Mike, can you tell us a little bit about that and what it means for the company? >> Mike: Sure, sure. Well, first of all, science logic, as you may know, has been in the monitoring space for a long time now, and what- >> Savannah: 20 years I believe. >> Yeah. >> Savannah: Just about. >> And what we've seen is a shift from kind of monitoring infrastructure, to monitoring these increasingly complex modern cloud native applications, right? And so this is part of a journey that we've been on at Science Logic to really modernize how enterprises of all sizes manage their IT estate. Okay? So, managing, now workloads that are increasingly in the public cloud, outside the four walls of the enterprise, workloads that are increasingly complex. They're microservices based, they're container based. >> Mhmm. >> Mike: And the rate of change, just because of things like CICD, and agile development has also increased the complexity in the typical IT environment. So all these things have conspired to make the traditional tools and processes of managing IT and IT applications much more difficult. They just don't scale. One of the things that we've seen recently, Savannah is this shift in sort of moving to cloud native applications, right? >> Huge shift. >> Mike: Today it only incorporates about roughly 25% of the typical IT portfolio, but most of the projections we've seen indicate that that's going to invert in about three years. 75% of applications will be what I call cloud native. And so this really requires different technologies to understand what's going on with those applications. And so Zebrium interested us when we were looking at partners at the beginning of this year as they have a super innovative approach to understanding really what's going on with any cloud native application. And they really distill, they separate the complexity out of the equation and they used machine learning to tremendous effect to rapidly understand the root cause of an application failure. And so I was introduced to Ajay, beginning of this year, actually. It feels like it's been a long time now. But we've been on this journey together throughout 2022, and we're thrilled to have Zebrium now, part of the Science Logic family. >> Ajay, Zebrium saves people a lot of time. Obviously, I've worked with developers and seen that struggle when things break, shortening that time to recovery and understanding is so critical. Can you tell us a little bit about what's under the hood and how the ML works to make that happen? >> Ajay: Yeah. So the goal is to figure out not just that something went wrong, but what went wrong. >> Savannah: Right. >> And we took, you know, based on a couple of decades of experience from my co-founders- >> Savannah: Casual couple of decades, came into went into this product just to call that out. Yeah, great. >> Exactly. It took some general learnings about the nature of software and when software breaks, what tends to happen, you tend to see unusual things happen, and they lead to bad things happening. It's very simple. >> Yes. >> It turns out- >> Savannah: Mutations lead to bad things happening, generally speaking. >> So what Zebrium's really good at is identifying those rare things accurately and then figuring out how they connect, or correlate to the bad things, the errors, the warnings, the alerts. So the machine learning has many stages to it, but at its heart it's classifying the event, catalog of any application stack, figuring out what's rare, and when things start to break it's telling you this cluster of events is both unusual, and unlikely to be random, and it's very likely the root cause report for the problem you're trying to solve. We then added some nice enhancements, such as correlation with knowledge spaces in, on the public internet. If someone's ever solved that problem before, we're able to find a match, and pull that back into our platform. But the at the heart, it was a technology that can find rare events and find the connections with other events. >> John: Yeah, and this is the theme of re:Invent this year, data, the role of data, solving end-to-end complexities. One, you mentioned that. Two, I think the Mike, your point about developers and the CICD pipeline is where DevOps is. That is what IT now is. So, if you take digital transformation to its conclusion, or its path and continue it, IT is DevOps. So the developers are actually doing the IT in their coding, hence the shift to autonomous IT. >> Mike: Right, right. Now, those other functions at IT used to be a department, not anymore, or they still are, so, but they'll go away, is security and data teams. You're starting to see the formation of- >> Mike: Yep. >> New replacements to IT as a function to support the developers who are building the applications that will be the company. >> That's right. Yeah. >> John: I mean that's, and do you agree with that statement? >> Yeah, I really do. And you know, collectively independent of whether it's like traditional IT, or it's DevOps, or whatever it is, the enterprise as a whole needs to understand how the infrastructure is deployed, the health of that infrastructure, and more importantly the applications that are hosted in the infrastructure. How are they doing? What's the health? And what we are seeing, and what we're trying to facilitate at Science Logic is really changed the lens of IT, from being low level compute, storage, and networking, to looking at everything through a services lens, looking at the services being delivered by IT, back to the business, and understanding things through a services lens. And Zebrium really compliments that mission that we've been on, by providing, cause a lot of cases, service equal equal application, and they can provide that kind of very real time view of service health in, you know, kind of the IT- >> And automation is beautiful there too, because, as you get into some of the scale- >> Yeah >> Ajay's. understanding how to do this fast is a key component. >> Yeah. So scale, you, you've pinpointed one of the dimensions that makes AI really important when it comes to troubleshooting. The humans just can't scale as fast as data, nor can they keep up with complexity of modern applications. And the third element that we feel is really important is the velocity with which people are now rolling out changes. People develop new features within hours, push them out to production. And in a world like that, the human has just no ability or time to understand what's normal, what's bad, to update their alert rules. And you need a machine, or an AI technology, to go help you with that. And that's basically what we're about. >> So this is where AI Ops comes in, right? Perfectly. Yeah. >> Yeah. You know, and John started to allude to it earlier, but having the insight on what's going on, we believe is only half of the equation, right? Once you understand what's going on, you naturally want to take action to remediate it or optimize it. And we believe automation should not be an exercise that's left to the reader. >> Yeah. >> As a lot of traditional platforms have done. Instead, we have a very robust, no-code, low-code automation built into our platform that allows you to take action in context with what you're seeing right then and there with the service. >> John: Yeah. Essentially monitoring, a term you use observability, some used as a fancy word today, is critical in all operating environments. So if we, if we kind of holistically, hey we're a distributed computing system, aka cloud, you got to track stuff at scale and you got to understand what it, what the impact is from a systems perspective. There's consequences to understanding what goes wrong. So as you look at that, what's the challenge for customers to do that? Because that seems to be the hard part as they lift and shift to the cloud, run their apps on the cloud, now they got to go take it to the next level, which is more developer velocity, faster productivity, and secure. >> Yeah. >> I mean, that seems to be the table stakes now. >> Yeah. >> How are companies forming around that? Are they there yet? Are they halfway there? Are they, where are they in the progression of, one, are they changing? And if so- >> Yeah that's a great question. I mean, I think whether it's an IT use case or a security use case, you can't manage what you don't know about. So visibility, discoverability, understanding what's going on, in a lot of ways that's the really hard problem to solve. And traditionally, we've approached that by like, harvesting data off of all these machines and devices in the infrastructure. But as we've seen with Zebrium and with related machine learning technologies, there's multiple ways of gaining insight as to what's going on. Once you have the insight be it an IT issue, like a service outage, or a security vulnerability, then you can take action. And the idea is you want to make that action as seamless as possible. But I think to answer your question, John, enterprises are still kind of getting their heads around how can we break down all the silos that have built up over the last decade or two, internally, and get visibility across the estate that really matters. And I think that's the real challenge. >> And I mean, and, at the velocity that applications are growing, just looking at our notes here, number of applications scaling from 64 million in 2017 to 147 million in 2021. That goes to what you were talking about, even with those other metrics earlier, 582 million by 2026 is what Morgan Stanley predicts. So, not only do we need to get out of silos we need to be able to see everything all the time, all at once, from the past legacy, as well as as we extend at scale. How are you thinking about that, Ajay? You're now with a big partner as an umbrella. What's next for you all? How, how are you going to help people solve problems faster? >> Yeah, so one of the attractions to the Zebrium team about Science Logic, aside from the team, and the culture, was the product portfolio was so complimentary. As Mike mentioned, you need visibility, you need mapping from low level building blocks to business services. And the end, at the end of the spectrum, once you know something's wrong you need to be able to take action automatically. And again, Science Logic has a very strong product, set of product capabilities and automated actions. What we bring to the table is the middle layer, which is from visibility, understanding what went wrong, figuring out the root cause. So to us, it was really exciting to be a very nice tuck in into this broader platform where we helped complete the story. >> Savannah: Yeah, that's, that's exciting. >> John: Should we do the Insta challenge? >> I was just getting ready to do that. You go for it John. You go ahead and kick it off. >> So we have this little tradition now, Instagram real, short and sweet. If you were going to see yourself on Instagram, what would be the Instagram reel of why this year's re:Invent is so important, and why people should pay attention to what's going on right now in the industry, or your company? >> Well, I think partly what Ajay was saying it's good to be back, right? So seeing just the energy and being back in 3D, you know en mass, is awesome again. It really is. >> Yeah. >> Mike: But, you know, I think this is where it's happening. We are at an inflection point of our industry and we're seeing a sea change in the way that applications and software delivered to businesses, to enterprises. And it's happening right here. This is the nexus of it. And so we're thrilled to be here as a part of all this, and excited about the future. >> All right, Ajay- >> Well done. He passes >> Your Instagram reel. >> Knowing what's happening in the broader economy, in the business context, it's, it feels even more important that companies like us are working on technologies that empower the same number of people to do more. Because it may not be realistic to just add on more headcount given what's going on in the world. But your deliverables and your roadmaps aren't slowing down. So, still the same amount of complexity, the same growth rates, but you're going to have to deal with all of that with fewer resources and be smarter about it. So, the approaches we're taking feel very much off the moment, you know, given what's going on in the real world. >> I love it. I love it. I've got, I've got kind of a finger to the wind, potentially hardball question for you here to close it out. But, given that you both have your finger really on the pulse right here, what percentage of current IT operations do you think will eventually be automated by AI and ML? Or AI ops? >> Well, I think a large percentage of traditional IT operations, and I'm talking about, you know, network operating center type of, you know, checking heartbeat monitors of compute storage and networking health. I think a lot of those things are going to be automated, right? Machine learning, just because of the scale. You can't scale, you can't hire enough NOC engineers to scale that kind of complexity. But I think IT talents, and what they're going to be focusing on is going shift, and they're going to be focusing on different parts. And I believe a lot of IT is going to be a much more of an enabler for the business, versus just managing things when they go wrong. So that's- >> All right. >> That's what I believe is part of the change. >> That's your, all right Ajay what about your hot take? >> Knowing how error-prone predictions are, (all laughing) I'll caveat my with- >> Savannah: We're allowing for human error here. >> I could be wildly wrong, but if I had to guess, you know, in 10 years you know, as much as 50% of the tasks will be automated. >> Mike: Oh, you- >> I love it. >> Mike: You threw a number out there. >> I love it. I love that he put his finger out- >> You got to see, you got to say the matrix. We're all going to be part of the matrix. >> Well, you know- >> And Star Trek- >> Skynet >> We can only turn back to this footage in a few years and quote you exactly when you have the, you know Mackenzie Research or the Morgan Stanley research that we've been mentioning here tonight and say that you've called it accurately. So I appreciate that. Ajay, it was wonderful to have you here. Congratulations on the acquisition. Thank you. Mike, thank you so much for being here on the Science Logic side, and congratulations to the team on 20 years. That's very exciting. John. Thank you. >> I try, I tried. Thank you. >> You try, you succeed. And thank you to all of our fabulous viewers out there at home. Be sure and tweet us at theCUBE. Say hello, Furrier, Sav is savvy. Let us know what you're thinking of AWS re:Invent where we are live from Las Vegas all week. You're watching theCUBE, the leader in high tech coverage. My name's Savannah Peterson, and we'll see you soon. (upbeat music)

(bright music) >> Hey, welcome back everyone, theCUBE coverage here at AWS re:Mars 2022. I'm John, your host of the theCUBE. re:Mars, part of the three re big events, re:Invent is the big one, re:Inforce the security, re:MARS is the confluence of industrial space, of automation, robotics and machine learning. Got a great guest here, Muhammad Faisal senior consultant solutions architect at Capgemini. Welcome to theCUBE. Thanks for coming on. >> Thank you. >> So we, you just we're hearing the classes we had with the professor from Okta ML from Washington. So he's in the weeds on machine learning. He's down getting dirty with all the hardcore, uncoupling it from hardware. Machine learning has gone really super nova in the past couple years. And this show points to the tipping point where machine learning's driving space, it's driving robotics industrial edge at unprecedented rates. So it's kind of moving from the old I don't want to say old, couple years ago and the legacy AI, I mean, old school AI is kind of the same new school with a twist it's just modernized and has faster, cheaper, smaller chips. >> Yeah. I mean, but there is a change also in the way it's working. So you had the classical AI, where you are detecting something and then you're making an action. You are perceiving something, making an action, you're detecting something, and you're assuming something that has been perceived. But now we are moving towards more deeper learning, deep. So AI, where you have to train your model to do things or to detect things and hope that it will work. And there's like, of course, a lot of research going on into explainable AI to help facilitate that. But that's where the challenges come into play. >> Well, Muhammad , first let's take, what do you do over there? Talk about your role specifically. You're doing a lot of student architecting around AI machine learning. What's your role? What's your focus. >> Yeah. So we basically are working in automotive to help OEMs and tier-one suppliers validate ADAS functions that they're working on. So advanced driving assistance systems, there are many levels that are, are when we talk about it. So it can be something simple, like, you know, a blind spot detection, just a warning function. And it goes all the way. So SAE so- >> So there's like the easy stuff and then the hard stuff. >> Muhammad : Exactly. >> Yeah. >> That's what you're getting at. >> Yeah. Yeah. And, and the easy stuff you can test validate quite easily because if you get it wrong. >> Yeah. >> The impact is not that high. The complicated stuff, if you have it wrong, then that can be very dangerous. (John laughs) >> Well, I got to say the automotive one was one was that are so fascinating because it's been so archaic and just in the past recent years, and Tesla's the poster child for this. You see that you go, oh my God, I love that car. I want to have a software driven car. And it's amazing. And I don't get a Tesla on now because that's, it's more like I should have gotten it earlier. Now I'm going to just hold my ground. >> Everyone has- >> Everyone's got it in Palo Alto. I'm not going to get another car, no way. So, but you're starting to see a lot of the other manufacturers, just in the past five years, they're leveling up. It may not be as cool and sexy as the Tesla, but it's, they're there. And so what are they dealing with when they talk about data and AI? What's the, what's some of the challenges that you're seeing that they're grappling with in terms of getting things integrated, developing pipelines, R and D, they wrangling data. Take us through some of the things. >> Muhammad: I mean, like when I think about the challenges that autonomous or the automakers are facing, I can think of three big ones. So first, is the amount of data they need to do their training. And more importantly, the validation. So we are talking about petabytes or hundred of petabytes of data that has to be analyzed, validated, annotated. So labeling to create gen, ground truth processed, reprocessed many times with every creation of a new software. So that is a lot of data, a lot of computational power. And you need to ensure that all of the processing, all of handling of the data allows you complete transparency of what is happening to the data, as well as complete traceability. So your, for home allocations, so approval process for these functions so that they can be released in cars that can be used on public roads. You need to have traceability. Like you can, you are supposed to be able to reproduce the data to validate your work that was done. So you can, >> John: Yeah >> Like, prove that your function is successful or working as expected. So this, the big data is the first challenge. I see that all the automotive makers are tackling. The second big one I see is understanding how much testing is enough. So with AI or with classical approach, you have certain requirements, how a function is supposed to work. You can test that with some test cases based on your architecture, and you have a successful or failed result. With deep learning, it gets more complicated. >> John: What are they doing with deep learning? Give an example of some of things. >> I mean, so you are, you need to then start thinking about statistics that I will test enough data with like a failure rate of potentially like 0.0, 0.1%. How much data do I need to test to make sure that I am achieving that rate. So then we are talking about, in terms of statistics, which requires a lot of data, because the failure rate that we want to have is so low. And it's not only like, failure in terms of that something is always detected, and if it's there, but it's also having like, a low false positive rate. So you are only detecting objects which are there and not like, phantom objects. >> What's some of the trends you're seeing across the client base, in terms of the patterns that they're all kind of, what, where's the state of their mindset and position with AI and some of the work they're doing, are they feeling, you feel like they're all crossed over across the chasm so to speak, in terms of executing, are they still in experimental mode in driving with the full capabilities is conservative or is it progressive? >> Muhammad: I mean, it's a mixture of both. So I'm in German automotive where I'm from, there is for functions, which are more complicated ones. There's definitely hesitancy to release them too early in the car, unless we are sure that they are safe. But of course, for functions which are assisting the drivers everyday usage they are widely available. Like one of the things like, so when we talk about this complex function. >> John: Highly available or available? >> Muhammad: I would say highly available. >> Higher? Is that higher availability and highly available. >> Okay. Yeah. (both laughing) >> Yeah, so. >> I know there's a distinction. >> Yeah. I mean >> I bring up as a joke cuz of the Jedi contract. (Muhammad laughs) >> I mean, in like, our architecture. So when we are developing our solution, high availability is one of our requirements. It is highly available, but the ADAS functions are now available in more and more cars. >> John: Well, latency, man. I mean, it's kind of a joke of storage, but it's a storage joke, but you know, it's latency, you got it, okay. (Muhammad laughs) But these are decisions that have to be made. >> Muhammad: They... >> I mean. >> Muhammad: I mean, they are still being made. >> So I mean, we are... >> John: Good. >> We haven't reached like, level five, which is the highest level of autonomous driving yet on public roads. >> John: That's hard. That's hard to do. >> Yeah. And I mean, the biggest difference, like, as you go above these levels is in terms of availability. So are they these functions? >> John: Yeah. >> Can they handle all possible scenarios or are they only available in certain scenarios? And of course the responsibility. So, it's, in the end, so with Tesla, you would be like, if you had a one you would be the person who is in control or responsible to monitor it. >> John: Yeah. But as we go >> John: Actually the reason I don't have a Tesla all my family would want one. I don't want to get anyone a Tesla. >> But I mean, but that's the sort the liabilities is currently on you, if like, you're not monitoring. >> Allright, so, talk about AWS, the relationship that Capgemini has with AWS, obviously, the partnerships there, you're here and this show is really a commitment to, this is a future to me, this is the future. >> Muhammad: Yeah. >> This is it. All right here, industrial, innovation's going to come massive. Back-office cloud, done deal. Data centers, hybrid somewhat multi-cloud, I guess. But hybrid is a steady state in the back-office cloud, game over. >> Muhammad: Yeah. >> Amazon, Azure, Google, Alibaba done. So super clouds underneath. Great. This is a digital transformation in the industrial area. >> Muhammad: Yeah. >> This is the big thing. What's your relationship with AWS >> Muhammad: So, as I mentioned, the first challenge, data, like, we have so much data, so much computational power and it's not something that is always needed. You need it like on demand. And this is where like a hyperscale or cloud provider, like AWS, can be the key to achieve, like, the higher, the acceleration that we are providing to our customers using our technology built on top of AWS services. We did a breakout session, this during re:MARS, where we demonstrated a couple of small tools that we have developed out of our offering. One of them was ability to stream data from the vehicle that is collecting data worldwide. So during the day when we did it from Vegas, driving on the strip, as well as from Germany, and while we are while this data is uploaded, it's at the same time real time anonymized to make sure it you're privacy aligned with the, the data privacy >> Of course. Yeah. That's hard to do right there. >> Yeah. And so the faces are blurred. The licenses are blurred. We also, then at the same time can run object detection. So we have real time monitoring of what our feed is doing worldwide. And... >> John: Do you, just curious, do you do that blurring? Is that part of a managed service, you call an API or is that built into the go? >> Muhammad: So from like part of our DSV, we have many different service offerings, so data production, data test strategy orchestration. So part of data production is worldwide data collection. And we can then also offer data management services, which include then anonymization data, quality check. >> John: And that's service you provide. >> Yeah. >> To the customer. Okay. Got it. Okay. >> So of course, like, in collaboration with the customer, so our like, platform is very modular. Microservices based the idea being if the customer already has a good ML model for anonymization, we can plug it into our platform, running on AWS. If they want to use it, we can develop one or we can use one of our existing ones or something off the shelf or like any other supplier can provide one as well. And we all integrate. >> So you are, you're tight with Amazon web services in terms of your cloud, your service. It's a cloud. >> Yeah. >> It's so Capgemini Super Cloud, basically. >> Exactly. >> Okay. So this we call we call it Super Cloud, we made that a thing and re:Invent Charles Fitzgerald would disagree but we will debate him. It's a Super Cloud, but okay. You got your Super Cloud. What's the coolest thing that you think you're doing right now that people should pay attention to. >> I mean, the cool thing that we are currently working on, so from the keynote today, we talked about also synthetic data for validation. >> John: Now That was phenomenal. So that was phenomenal. >> We are working on digital twin creation. So we are capturing data in real world creating a virtual identity of it. And that allows you the freedom to create multiple scenarios out of it. So that's also something where we are using machine learning to determine what are the parameters you need to change between, or so, you have one scenario, such as like, the cut-in scenario and you can change. >> John: So what scenario? >> A cut-in scenario. So someone is cutting in front of you or overtake scenario. And so, I mean, in real world, someone will do it in probably a nicer way, but of course, in, it is possible, at some point. >> Cognition to the cars. >> Yeah. >> It comes up as a vehicle. >> I mean, at some point some might, someone would be very aggressive with it. We might not record it. >> You might be able to predict too. I mean, the predictions, you could say this guy's weaving, he's a potential candidate. >> It it is possible. Yes. But I mean, but to, >> That's a future scenario. >> Ensure that we are testing these scenarios, we can translate a real world scenario into a digital world, change the parameters. So the distance between those two is different and use ML. So machine learning to change these parameters. So this is exciting. And the other thing we are... >> That is pretty cool. I will admit that's very cool. >> Yeah. Yeah. The other thing we like are trying to do is reduce the cost for the customer in the end. So we are collecting petabytes of data. Every time they make updates to the software, they have to re-simulate it or replay this data, so that they can- >> Petabytes? >> Petabytes of data. And, and physically sometimes on a physical hardware in loop device. And then this >> That's called a really heavy edge. You got to move, you don't want to be moving that around the Amazon cloud. >> Yeah. That that's, that's the challenge. And once we have replayed this or re-simulated it. we still have to calculate the KPIs out of it. And what we are trying to do is optimize this test orchestration, so that we are minimizing the REAP simulation. So you don't want the data to be going to the edge, >> Yeah. >> Unnecessarily. And once we get this data back to optimize the way we are doing the calculation, so you're not calculating- >> There's a huge data, integrity management. >> Muhammad: Yeah. >> New kind of thing going on here, it's kind of is it new or is it? >> Muhammad: I mean, it's- >> Sounds new to me. >> The scale is new, so- >> Okay, got it. >> The management of the data, having the whole traceability, that has been in automotive. So also Capgemini involved in aerospace. So in aerospace. >> Yeah. >> Having this kind of high, this validation be very strictly monitored is norm, but now we have to think about how to do it on this large scale. And that's why, like, I think that's the biggest challenge and hopefully what we are trying to, yeah, solve with our DSV offering. >> All right, Muhammad, thanks for coming on theCUBE. I really appreciate it. Great way to close out re:MARS, our last interview our the show. Thanks for coming on. Appreciate your time. >> I mean like just one last comment, like, so I think in automotive, like, so part of the automation the future is quite exciting, and I think that's where like- >> John: Yeah. >> It's, we have to be hopeful that like- >> John: Well, the show is all about hope. I mean, you had, you had space, moon habitat, you had climate change, potential solutions. You have new functionality that we've been waiting for. And, you know, I've watch every episode of Star Trek and SkyNet and kind of SkyNet going on air. >> The robots. >> Robots running cubes, robot cubes host someday. >> Yeah. >> You never know. Yeah. Thanks for coming on. Appreciate it. >> Thank you. Okay. That's theCUBE here. Wrapping up re:MARS. I'm John Furrier You're watching theCUBE, stay with us for the next event. Next time. Thanks for watching. (upbeat music)

(upbeat music) >> Okay, welcome back everyone to theCube's coverage of AWS re:Mars here in Las Vegas. I'm John Furrier, host of theCube. Got Alexey Surkov, Partner at Deloitte joining me today. We're going to talk about AI biased AI trust, trust in the AI for the, to save the planet to save us from the technology. Alexey thanks for coming on. >> Thank you for having me. >> So you had a line before you came on camera that describe the show, and I want you to say it if you don't mind because it was the best line that for me, at least from my generation. >> Alexey: Sure. >> That describes the show and then your role at Deloitte in it. >> Alexey: Sure. Listen, I mean, I, you know, it may sound a little corny, but to me, like I look at this entire show, at this whole building really, and like everybody here is trying to build a better Skynet, you know, better, faster, stronger, more potent, you know, and it's like, we are the only ones, like we're in this corner of like Deloitte trustworthy AI. We're trying to make sure that it doesn't take over the world. So that's, you know, that's the gist of it. How do you make sure that AI serves the good and not evil? How do you make sure that it doesn't have the risk? It doesn't, you know, it's well controlled that it does what we're, what we're asking it to do. >> And of course for all the young folks out there the Terminator is the movie and it's highly referenced in the nerd circles Skynet's evil and helps humanity goes away and lives underground and fights for justice and I think wins at the end. The Terminate three, I don't, I can't remember what happened there, but anyway. >> Alexey: I thought the good guys win, but, you know, that's. >> I think they do win at the end. >> Maybe. >> So that brings up the whole point because what we're seeing here is a lot of futuristic positive messages. I mean, three areas solve a lot of problems in the daily lives. You know, machine learning day to day hard problems. Then you have this new kind of economy emerging, you know, machine learning, driving new economic models, new industrial capabilities. And then you have this whole space save the world vibe, you know, like we discover the moon, new water sources maybe save climate change. So very positive future vibe here at re:Mars. >> Alexey: Absolutely. Yeah, and it was really exciting just watching, you know, watching the speakers talk about the future, and conquering space, and mining on the moon like it's happening already. It's really exciting and amazing. Yeah. >> Let's talk about what you guys are working at Deloitte because I think it's fascinating. You starting to see the digital transformation get to the edge. And when I say edge, I mean back office is done with cloud and you still have the old, you know, stuff that the old models that peoples will use, but now new innovative things are happening. Pushing software out there that's driving you with the FinTech, these verticals, and the trust is a huge factor. Not only do the consumers have a trust issues, who owns my data, there's also trust in the actual algorithms. >> Exactly. >> You guys are in the middle of this. What's your advice to clients, 'cause they want to push the envelope hard be cutting edge, >> Alexey: Right. >> But they don't want to pull back and get caught with their, you know, data out there that might been a misfire or hack. >> Absolutely. Well, I mean the simple truth is that, you know, with great power comes great responsibility, right? So AI brings a lot of promise, but there are a lot of risks, you know. You want to make sure that it's fair, that it's not biased. You want to make sure that it's explainable, that you can figure out and tell others what it's doing. You might want to make sure that it's well controlled, that it's responsible, that it's robust, that, you know, if somebody feeds it bad data, it doesn't produce results that don't make sense. If somebody's trying to provoke it, to do something wrong, that it's robust to those types of interactions. You want to make sure that it preserves privacy. You know, you want to make sure that it's secure, that nobody can hack into it. And so all of those risks are somewhat new. Not all of them are entirely new. As you said, the concept of model risk management has existed for many years. We want to make sure that each black box does what it's supposed to do. Just AI machine learning just raises it to the next level. And we're just trying to keep up with that and make sure that we develop processes, you know, controls that we look at technology that can orchestrate all this de-risking of transition to AI. >> Deloitte's a big firm. You guys saw you in the US open sponsorship was all over the TV. So that you're here at re:Mars show that's all about building up this next infrastructure in space and machine learning, what's the role you have with AWS and this re:Mars. And what's that in context of your overall relationship to the cloud players? >> Alexey: Well, we are, we're one of the largest strategic alliances for AWS, and AWS is one of the largest ones for Deloitte. We do a ton of work with AWS related to cloud, related to AI machine learning, a lot of these new areas. We did a presentation here just the other day on conversational AI, really cutting edge stuff. So we do all of that. So in some ways we participate in that part of the, the part of the room that I mentioned that is trying to kind of push the envelope and get the new technologies out there, but at the same time, Deloitte is a brand that carries a lot of, you know, history of trust, and responsibility, and controls, and compliance, and all of that comes, >> John: You get a lot of clients. I mean, you have big names. Get a lot of big name enterprises >> Right. >> That relied on you. >> Right, and so >> They rely on you now. >> Exactly, yeah. And so, it is natural for us to be in the marketplace, not only with the message of, you know, let's get to the better mouse trap in AI and machine learning, but also let's make sure that it's safe, and secure, and robust, and reliable, and trustworthy at the end of the day. And so, so this trustworthy message is intertwined with everything that we do in AI. We encourage companies to consider trustworthiness from the start. >> Yeah. >> It shouldn't be an afterthought, you know. Like I always say, you know, if you have deployed a bot and it's been deciding whether to issue loans to people, you don't want to find out that it was like, you know, biased against a certain type of (indistinct) >> I can just see in the boardroom, the bot went rogue. >> Right, yeah. >> Through all those loans you know. >> And you don't want to find out about it like six months later, right? That's too late, right? So you want to build in these controls from the beginning, right? You want to make sure that, you know, you are encouraging innovation, you're not stifling any development, and allowing your- >> There's a lot of security challenges too. I mean, it's like, this is the digital transformation sweet spot you're in right now. So I have to ask you, what's the use case, obviously call center's obvious, and bots, and having, you know, self-service capabilities. Where is the customers at right now on psychology and their appetite to push the envelope? And what do you guys see as areas that are most important for your customers to pay attention to? And then where do you guys ultimately deliver the value? >> Sure. Well, our clients are, I think, are aware of the risks of AI. They are not, that's not the first thing that they're thinking about for the most part. So when we come to them with this message they listen, they're very interested. And a lot of them have begun this journey of putting in kind of governance, compliance, controls, to make sure that as they are proceeding down this path of building out AI, that they're doing it responsibly. So it is in a nascent stage. >> John: What defines responsibility? >> Well, you want to, okay, so responsibility is really having governance. Like you have a, you build a robot dog, right? So, but you want to make sure that it has a leash, right? That it doesn't hurt anybody, right? That you have processes in place that at the end of the day, humans are in control, right? I don't want to go back to the Skynet analogy, right? >> John: Yeah. >> But humans should always be in control. There should always be somebody responsible for the functioning of the algorithm that can throw the switch at the right time, that can tweak it at the right time, that can make sure that you nudge it in the right direction that at no point should somebody be able to say, oh, well, it's not my fault. The algorithm did it, and that's why we're in the papers today, right? So that's the piece that's really complex, and what we try to do for our clients as Deloitte always does is kind of demystify that, right? >> John: Yeah. >> So what does it actually mean from a procedures, policies, >> John: Yeah, I mean, I think, >> Tools, technology, people. >> John: Yeah, I mean, this is like the classic operationalizing a new technology, managing it, making sure it doesn't get out of control if you will. >> Alexey: Exactly. >> Stay on the leash if you will. >> Alexey: Exactly. Yeah. And I guess one piece that I always like to mention is that, it's not to put breaks on these new technologies, right? It's not to try to kind of slow people down in developing new things. I actually think that making AI trustworthy is enabling the development of these technologies, right? The way to think about it is that, we have, you know, seat belts, and abs brakes, and, you know, airbags today. And those are all things that didn't exist like 100 years ago, but our cars go a lot faster, and we're a lot safer driving them. So, you know, when people say, oh, I hate seatbelts, you know, you're like, okay, yes, but first of all, there are some safety technologies that you don't even notice, which is how a lot of AI controls work. They blend into the background. And more importantly, the idea is for you to go faster, not slower. And that's what we're trying to enable our clients to do. >> Well, Alexey, great to have you on theCube. We love Deloitte come on to share their expertise. Final question for you is, where do you see this show going? Where do you guys, obviously you here, you're participating, you got a big booth here, where's this going? And what's next, where's the next dots that connect? Share your vision for this show, and kind of how it, or the ecosystem, and this ecosystem, and where you're going to intersect that? >> Wow. I mean, this show is already kind of pushing the boundaries. You know, we're talking about machine learning, artificial intelligence, you know, robotics, space. You know, I guess next thing I think, you know, we'll be probably spending a lot of time in the metaverse, right? So I can see like next time we come here, you know, half of us are wearing VR headsets and walking around and in meta worlds, but, you know, it's been an exciting adventure and, you know I'm really excited to partner and spend, you know spend time with AWS folks, and everybody here because they're really pushing the envelope on the future, and I look forward to next year >> The show is small, so it feels very intimate, which is actually a good feeling. And I think the other thing in metaverse I heard that too. I heard quantum. I said next, I heard, I've heard both those next year quantum and metaverse. >> Okay. >> Well, why not? >> Why not? Exactly, yeah. >> Thanks for coming on theCube. Appreciate it. >> Thank you. >> All right. It's theCube coverage here on the ground. Very casual Cube. Two days of live coverage. It's not as hot and and heavy as re:Invent, but it's a great show bringing all the best smart people together, really figure out the future, you know, solving problems day to day problems, and setting the new economy, the new industrial economy. And of course, a lot of the world problems are going to be helped and solved, very positive message space among other things here at re:Mars. I'm John furrier. Stay with us for more coverage after this short break. (upbeat music)

>>AWS public sector summit here in person in Washington, D. C. For two days live. Finally a real event. I'm john for your host of the cube. Got a great guest Howard Levinson from data bricks, regional vice president and general manager of the federal team for data bricks. Uh Super unicorn. Is it a decade corn yet? It's uh, not yet public but welcome to the cube. >>I don't know what the next stage after unicorn is, but we're growing rapidly. >>Thank you. Our audience knows David bricks extremely well. Always been on the cube many times. Even back, we were covering them back when big data was big data. Now it's all data everything. So we watched your success. Congratulations. Thank you. Um, so there's no, you know, not a big bridge for us across to see you here at AWS public sector summit. Tell us what's going on inside the data bricks amazon relationship. >>Yeah. It's been a great relationship. You know, when the company got started some number of years ago we got a contract with the government to deliver the data brooks capability and they're classified cloud in amazon's classified cloud. So that was the start of a great federal relationship today. Virtually all of our businesses in AWS and we run in every single AWS environment from commercial cloud to Govcloud to secret top secret environments and we've got customers doing great things and experiencing great results from data bricks and amazon. >>The federal government's the classic, I call migration opportunity. Right? Because I mean, let's face it before the pandemic even five years ago, even 10 years ago. Glacier moving speed slow, slow and they had to get modernized with the pandemic forced really to do it. But you guys have already cleared the runway with your value problems. You've got lake house now you guys are really optimized for the cloud. >>Okay, hardcore. Yeah. We are, we only run in the cloud and we take advantage of every single go fast feature that amazon gives us. But you know john it's The Office of Management and Budget. Did a study a couple of years ago. I think there were 28,000 federal data centers, 28,000 federal data centers. Think about that for a minute and just think about like let's say in each one of those data centers you've got a handful of operational data stores of databases. The federal government is trying to take all of that data and make sense out of it. The first step to making sense out of it is bringing it all together, normalizing it. Fed aerating it and that's exactly what we do. And that's been a real win for our federal clients and it's been a real exciting opportunity to watch people succeed in that >>endeavour. We have another guest on. And she said those data center huggers tree huggers data center huggers, majority of term people won't let go. Yeah. So but they're slowly dying away and moving on to the cloud. So migrations huge. How are you guys migrating with your customers? Give us an example of how it's working. What are some of the use cases? >>So before I do that I want to tell you a quick story. I've I had the luxury of working with the Air Force Chief data officer Ailene vedrine and she is commonly quoted as saying just remember as as airmen it's not your data it's the Air Force's data. So people were data center huggers now their data huggers but all of that data belongs to the government at the end of the day. So how do we help in that? Well think about all this data sitting in all these operational data stores they're getting it's getting updated all the time. But you want to be able to Federated this data together and make some sense out of it. So for like an organization like uh us citizenship and immigration services they had I think 28 different data sources and they want to be able to pull that data basically in real time and bring it into a data lake. Well that means doing a change data capture off of those operational data stores transforming that data and normalizing it so that you can then enjoy it. And we've done that I think they're now up to 70 data sources that are continually ingested into their data lake. And from there they support thousands of users doing analysis and reports for the whole visa processing system for the United States, the whole naturalization environment And their efficiency has gone up I think by their metrics by 24 x. >>Yeah. I mean Sandy carter was just on the cube earlier. She's the Vice president partner ecosystem here at public sector. And I was coming to her that federal game has changed, it used to be hard to get into you know everybody and you navigate the trip wires and all the subtle hints and and the people who are friends and it was like cloak and dagger and so people were locked in on certain things databases and data because now has to be freely available. I know one of the things that you guys are passionate about and this is kind of hard core architectural thing is that you need horizontally scalable data to really make a I work right. Machine learning works when you have data. How far along are these guys in their thinking when you have a customer because we're seeing progress? How far along are we? >>Yeah, we still have a long way to go in the federal government. I mean, I tell everybody, I think the federal government's probably four or five years behind what data bricks top uh clients are doing. But there are clearly people in the federal government that have really ramped it up and are on a par were even exceeding some of the commercial clients, U. S. C. I. S CBP FBI or some of the clients that we work with that are pretty far ahead and I'll say I mentioned a lot about the operational data stores but there's all kinds of data that's coming in at U S. C. I. S. They do these naturalization interviews, those are captured in real text. So now you want to do natural language processing against them, make sure these interviews are of the highest quality control, We want to be able to predict which people are going to show up for interviews based on their geospatial location and the day of the week and other factors the weather perhaps. So they're using all of these data types uh imagery text and structure data all in the Lake House concept to make predictions about how they should run their >>business. So that's a really good point. I was talking with keith brooks earlier directive is development, go to market strategy for AWS public sector. He's been there from the beginning this the 10th year of Govcloud. Right, so we're kind of riffing but the jpl Nasa Jpl, they did production workloads out of the gate. Yeah. Full mission. So now fast forward today. Cloud Native really is available. So like how do you see the the agencies in the government handling Okay. Re platform and I get that but now to do the reef acting where you guys have the Lake House new things can happen with cloud Native technologies, what's the what's the what's the cross over point for that point. >>Yeah, I think our Lake House architecture is really a big breakthrough architecture. It used to be, people would take all of this data, they put it in a Hadoop data lake, they'd end up with a data swamp with really not good control or good data quality. And uh then they would take the data from the data swamp where the data lake and they curate it and go through an E. T. L. Process and put a second copy into their data warehouse. So now you have two copies of the data to governance models. Maybe two versions of the data. A lot to manage. A lot to control with our Lake House architecture. You can put all of that data in the data lake it with our delta format. It comes in a curated way. Uh there's a catalogue associated with the data. So you know what you've got. And now you can literally build an ephemeral data warehouse directly on top of that data and it exists only for the period of time that uh people need it. And so it's cloud Native. It's elastically scalable. It terminates when nobody's using it. We run the whole center for Medicaid Medicare services. The whole Medicaid repository for the United States runs in an ephemeral data warehouse built on Amazon S three. >>You know, that is a huge call out, I want to just unpack that for a second. What you just said to me puts the exclamation point on cloud value because it's not your grandfather's data warehouse, it's like okay we do data warehouse capability but we're using higher level cloud services, whether it's governance stuff for a I to actually make it work at scale for those environments. I mean that that to me is re factoring that's not re platform Ng. Just re platform that's re platform Ng in the cloud and then re factoring capability for on uh new >>advantages. It's really true. And now you know at CMS, they have one copy of the data so they do all of their reporting, they've got a lot of congressional reports that they need to do. But now they're leveraging that same data, not making a copy of it for uh the center for program integrity for fraud. And we know how many billions of dollars worth of fraud exist in the Medicaid system. And now we're applying artificial intelligence and machine learning on entity analytics to really get to the root of those problems. It's a game >>changer. And this is where the efficiency comes in at scale. Because you start to see, I mean we always talk on the cube about like how software is changed the old days you put on the shelf shelf where they called it. Uh that's our generation. And now you got the cloud, you didn't know if something is hot or not until the inventory is like we didn't sell through in the cloud. If you're not performing, you suck basically. So it's not working, >>it's an instant Mhm. >>Report card. So now when you go to the cloud, you think the data lake and uh the lake house what you guys do uh and others like snowflake and were optimized in the cloud, you can't deny it. And then when you compare it to like, okay, so I'm saving you millions and millions if you're just on one thing, never mind the top line opportunities. >>So so john you know, years ago people didn't believe the cloud was going to be what it is. Like pretty much today, the clouds inevitable. It's everywhere. I'm gonna make you another prediction. Um And you can say you heard it here first, the data warehouse is going away. The Lake house is clearly going to replace it. There's no need anymore for two separate copies, there's no need for a proprietary uh storage copy of your data and people want to be able to apply more than sequel to the data. Uh Data warehouses, just restrict. What about an ocean house? >>Yeah. Lake is kind of small. When you think about this lake, Michigan is pretty big now, I think it's I >>think it's going to go bigger than that. I think we're talking about Sky Computer, we've been a cloud computing, we're going to uh and we're going to do that because people aren't gonna put all of their data in one place, they're going to have, it spread across different amazon regions or or or amazon availability zones and you're going to want to share data and you know, we just introduced this delta sharing capability. I don't know if you're familiar with it but it allows you to share data without a sharing server directly from picking up basically the amazon, you RLS and sharing them with different organizations. So you're sharing in place. The data actually isn't moving. You've got great governance and great granularity of the data that you choose to share and data sharing is going to be the next uh >>next break. You know, I really loved the Lake House were fairly sing gateway. I totally see that. So I totally would align with that and say I bet with you on that one. The Sky net Skynet, the Sky computing. >>See you're taking it away man, >>I know Skynet got anything that was computing in the Sky is Skynet that's terminated So but that's real. I mean I think that's a concept where it's like, you know what services and functions does for servers, you don't have a data, >>you've got to be able to connect data, nobody lives in an island. You've got to be able to connect data and more data. We all know more data produces better results. So how do you get more data? You connect to more data sources, >>Howard great to have you on talk about the relationship real quick as we end up here with amazon, What are you guys doing together? How's the partnership? >>Yeah, I mean the partnership with amazon is amazing. We have, we work uh, I think probably 95% of our federal business is running in amazon's cloud today. As I mentioned, john we run across uh, AWS commercial AWS GovCloud secret environment. See to us and you know, we have better integration with amazon services than I'll say some of the amazon services if people want to integrate with glue or kinesis or Sagemaker, a red shift, we have complete integration with all of those and that's really, it's not just a partnership at the sales level. It's a partnership and integration at the engineering level. >>Well, I think I'm really impressed with you guys as a company. I think you're an example of the kind of business model that people might have been afraid of which is being in the cloud, you can have a moat, you have competitive advantage, you can build intellectual property >>and, and john don't forget, it's all based on open source, open data, like almost everything that we've done. We've made available to people, we get 30 million downloads of the data bricks technology just for people that want to use it for free. So no vendor lock in. I think that's really important to most of our federal clients into everybody. >>I've always said competitive advantage scale and choice. Right. That's a data bricks. Howard? Thanks for coming on the key, appreciate it. Thanks again. Alright. Cube coverage here in Washington from face to face physical event were on the ground. Of course, we're also streaming a digital for the hybrid event. This is the cubes coverage of a W. S. Public sector Summit will be right back after this short break.

(upbeat music) (music fades) >> Welcome to today's session of theCUBE's presentation of the AWS Startup Showcase, I'm your host Natalie Erlich. Today, we're going to feature Olive, in the life sciences track. And of course, this is part of the future of AI, security, and life sciences. Here we're joined by our very special guest Rohan D'Souza, the Chief Product Officer of Olive. Thank you very much for being with us. Of course, we're going to talk today about building the internet of healthcare. I do you appreciate you joining the show. >> Thanks, Natalie. My pleasure to be here, I'm excited. >> Yeah, likewise. Well tell us about AI and how it's revolutionizing health systems across America. >> Yeah, I mean, we're clearly living around, living at this time of a lot of hype with AI, and there's a tremendous amount of excitement. Unfortunately for us, or, you know, depending on if you're an optimist or a pessimist, we had to wait for a global pandemic for people to realize that technology is here to really come into the aid of assisting everybody in healthcare, not just on the consumer side, but on the industry side, and on the enterprise side of delivering better care. And it's a truly an exciting time, but there's a lot of buzz and we play an important role in trying to define that a little bit better because you can't go too far today and hear about the term AI being used/misused in healthcare. >> Definitely. And also I'd love to hear about how Olive is fitting into this, and its contributions to AI in health systems. >> Yeah, so at its core, we, the industry thinks of us very much as an automation player. We are, we've historically been in the trenches of healthcare, mostly on the provider side of the house, in leveraging technology to automate a lot of the high velocity, low variability items. Our founding and our DNA is in this idea of, we think it's unfair that healthcare relies on humans as being routers. And we have looked to solve the problem of technology not talking to each other, by using humans. And so we set out to really go in into the trenches of healthcare and bring about core automation technology. And you might be sitting there wondering, well why are we talking about automation under the umbrella of AI? And that's because we are challenging the very status quo of siloed-based automation, and we're building, what we say, is the internet of healthcare. And more importantly what we've done is, we've brought in a human, very empathetic approach to automation, and we're leveraging technology by saying when one Olive learns, all Olives learn, so that we take advantage of the network effect of a single Olive worker in the trenches of healthcare, sharing that knowledge and wisdom, both with her human counterparts, but also with her AI worker counterparts that are showing up to work every single day in some of the most complex health systems in this country. >> Right. Well, when you think about AI and, you know, computer technology, you don't exactly think of, you know, humanizing kind of potential. So how are you seeking to make AI really humanistic, and empathetic, potentially? >> Well, most importantly the way we're starting with that is where we are treating Olive just like we would any single human counterpart. We don't want to think of this as just purely a technology player. Most importantly, healthcare is deeply rooted in this idea of investing in outcomes, and not necessarily investing in core technology, right? So we have learned that from the early days of us doing some really robust integrated AI-based solutions, but we've humanized it, right? Take, for example, we treat Olive just like any other human worker would, she shows up to work, she's onboarded, she has an obligation to her customers and to her human worker counterparts. And we care very deeply about the cost of the false positive that exists in healthcare, right? So, and we do this through various different ways. Most importantly, we do it in an extremely transparent and interpretable way. By transparent I mean, Olive provides deep insights back to her human counterparts in the form of reporting and status reports, and we even, we even have a term internally, that we call is a sick day. So when Olive calls in sick, we don't just tell our customers Olive's not working today, we tell our customers that Olive is taking a sick day, because a human worker that might require, that might need to stay home and recover. In our case, we just happened to have to rewire a certain portal integration because a portal just went through a massive change, and Olive has to take a sick day in order to make that fix, right? So. And this is, you know, just helping our customers understand, or feel like they can achieve success with AI-based deployments, and not sort of this like robot hanging over them, where we're waiting for Skynet to come into place, and truly humanizing the aspects of AI in healthcare. >> Right. Well that's really interesting. How would you describe Olive's personality? I mean, could you attribute a personality? >> Yeah, she's unbiased, data-driven, extremely transparent in her approach, she's empathetic. There are certain days where she's direct, and there are certain ways where she could be quirky in the way she shares stuff. Most importantly, she's incredibly knowledgeable, and we really want to bring that knowledge that she has gained over the years of working in the trenches of healthcare to her customers. >> That sounds really fascinating, and I love hearing about the human side of Olive. Can you tell us about how this AI, though, is actually improving efficiencies in healthcare systems right now? >> Yeah, not too many people know that about a third of every single US dollar is spent in the administrative burden of delivering care. It's really, really unfortunate. In the capitalistic world, of, just us as a system of healthcare in the United States, there is a lot of tail wagging the dog that ends up happening. Most importantly, I don't know that the last time, if you've been through a process where you have to go and get an MRI or a CT scan, and your provider tells you that we first have to wait for the insurance company in order to give us permission to perform this particular task. And when you think about that, one, there's, you know the tail wagging the dog scenario, but two, the administrative burden to actually seek the approval for that test, that your provider is telling you that you need to perform. Right? And what we've done is, as humans, or as sort of systems, we have just put humans in the supply chain of connecting the left side to the right side. So what we're doing is we're taking advantage of massive distributing cloud computing platforms, I mean, we're fully built on the AWS stack, we take advantage of things that we can very quickly stand up, and spin up. And we're leveraging core capabilities in our computer vision, our natural language processing, to do a lot of the tasks that, unfortunately, we have relegated humans to do, and our goal is can we allow humans to function at the top of their license? Irrespective of what the license is, right? It could be a provider, it could be somebody working in the trenches of revenue cycle management, or it could be somebody in a call center talking to a very anxious patient that just learned that he or she might need to take a test in order to rule out something catastrophic, like a very adverse diagnosis. >> Yeah, really fascinating. I mean, do you think that this is just like the tip of the iceberg? I mean, how much more potential does AI have for healthcare? >> Yeah, I think we're very much in the early, early, early days of AI being applied in a production in practical sense. You know, AI has been talked about for many, many many years, in the trenches of healthcare. It has found its place very much in challenging status quos in research, it has struggled to find its way in the trenches of just the practicality on the application of AI. And that's partly because we, you know, going back to the point that I raised earlier, the cost of the false positive in healthcare is really high. You know, it can't just be a, you know, I bought a pair of shoes online, and it recommended that I buy a pair of socks, and I happen to get the socks and I returned them back because I realized that they're really ugly and hideous and I don't want them. In healthcare, you can't do that. Right? In healthcare you can't tell a patient or somebody else oops, I really screwed up, I should not have told you that. So, what that's meant for us, in the trenches of delivery of AI-based applications, is we've been through a cycle of continuous pilots and proof of concepts. Now, though, with AI starting to take center stage, where a lot of what has been hardened in the research world can be applied towards the practicality to avoid the burnout, and the sheer cost that the system is under, we're starting to see this real upwards tick of people implementing AI-based solutions, whether it's for decision-making, whether it's for administrative tasks, drug discovery, it's just, is an amazing, amazing time to be at the intersection of practical application of AI and really, really good healthcare delivery for all of us. >> Yeah, I mean, that's really, really fascinating, especially your point on practicality. Now how do you foresee AI, you know, being able to be more commercial in its appeal? >> I think you have to have a couple of key wins under your belt, is number one, number two, the standard, sort of outcomes-based publications that is required. Two, I think we need, we need real champions on the inside of systems to support the narrative that us as vendors are pushing heavily on the AI-driven world or the AI-approachable world, and we're starting to see that right now. You know, it took a really, really long time for providers, first here in the United States, but now internationally, on this adoption and move away from paper-based records to electronic medical records. You know, you still hear a lot of pain from people saying oh my God, I used an EMR, but try to take the EMR away from them for a day or two, and you'll very quickly realize that life without an EMR is extremely hard right now. AI is starting to get to that point where, for us, we, you know, we treat, we always say that Olive needs to pass the Turing test. Right? So when you clearly get this, this sort of feeling that I can trust my AI counterpart, my AI worker to go and perform these tasks, because I realized that, you know, as long as it's unbiased, as long as it's data-driven, as long as it's interpretable, and something that I can understand, I'm willing to try this out in a routine basis, but we really, really need those champions on the internal side to promote the use of this safe application. >> Yeah. Well, just another thought here is, you know, looking at your website, you really focus on some of the broken systems in healthcare, and how Olive is uniquely prepared to shine the light on that, where others aren't. Can you just give us an insight onto that? >> Yeah. You know, the shine the light is a play on the fact that there's a tremendous amount of excitement in technology and AI in healthcare applied to the clinical side of the house. And it's the obvious place that most people would want to invest in, right? It's like, can I bring an AI-based technology to the clinical side of the house? Like decision support tools, drug discovery, clinical NLP, et cetera, et cetera. But going back to what I said, 30% of what happens today in healthcare is on the administrative side. And so what we call as the really, sort of the dark side of healthcare where it's not the most exciting place to do true innovation, because you're controlled very much by some big players in the house, and that's why we we provide sort of this insight on saying we can shine a light on a place that has typically been very dark in healthcare. It's around this mundane aspects of traditional, operational, and financial performance, that doesn't get a lot of love from the tech community. >> Well, thank you Rohan for this fascinating conversation on how AI is revolutionizing health systems across the country, and also the unique role that Olive is now playing in driving those efficiencies that we really need. Really looking forward to our next conversation with you. And that was Rohan D'Souza, the Chief Product Officer of Olive, and I'm Natalie Erlich, your host for the AWS Startup Showcase, on theCUBE. Thank you very much for joining us, and look forward for you to join us on the next session. (gentle music)

>> Narrator: From theCUBE Studios in Palo Alto and Boston connecting with thought leaders all around the world, this is theCUBEConversation. >> Hey, welcome back everybody Jeffrey here with theCUBE we're in our Palo Alto Studios the calendar has turned to late September I still can't believe it. We're still getting through the COVID issue and as we've seen in the news companies are taking all different types of tacts and how they're announcing kind of their go forward strategy with the many of them saying they're going to continue to have work from home or work from anywhere policies. And we're really excited to have our next guest from Citrix. He's Tim Minahan, the EVP of Strategy and the CMO of Citrix, Tim great to see you. >> Jeff, thanks for having me. >> Yeah so love having you guys on we had Tamara on and Amy Haworth this back in April when this thing was first starting and you know we had this light switch moment and everyone had to deal with a work from anywhere world. Now, it's been going on for over six months, people are making announcements, Google, Facebook, Twitter I'm out in the Valley so a lot of the companies here locally saying we're probably not going to have you back for a very long period of time. You guys have been in the supporting remote workers for a really long time, you're kind of like Zoom right place, right time, right market and then suddenly this light switch moment, it's a whole lot more important than it was before. We're six months into this thing what can you share that you've seen from your customers and kind of the transition that we've gone from kind of the shock and awe back in March to now we're in late September almost to October and this is going to continue for a while. >> Yeah, Jeff well, if there is any silver lining to the global crisis that we're all living through, it's that it has indeed caused organizations in all industries really to accelerate their digital transformation and to rethink how they work. And so at Citrix we've done considerable crisis scenario modeling. Engaging with our own customers, with government officials, with influencers around the globe really to determine how will the current environment change, cause companies to change their operating models and to prioritize their IT investments. And it really boils down to while there's variations by geography and sector, our modeling points to three major shifts in behavior. The first is looking for greater agility in their operations companies are adopting more variable operating models, literally in everything from their workforce strategy to the real estate strategy, to their IT strategy to allow them to scale up quickly to the next inevitable, unplanned event or opportunity. And for IT this typically means modernizing their application environment and taking that kind of one to three year cloud transition plan and accelerating it into a few months. The second thing we're seeing is because of the pandemic companies are realizing they need to prioritize employee experience to provide a consistent and secure work experience wherever work needs to get done. Whether that's in the office, whether that's on the road or increasingly whether that's at home and that goes beyond just traditional virtualization applications but it's also for delivering in a secure and unified environment. Your virtual apps alongside your SaaS apps, your web apps, your mobile apps, et cetera. And then finally, as companies rapidly move to the cloud and they adopt SaaS and they moved to these more distributed IT operating models, their attack surface from a security standpoint expands and they need to evolve their security model to one that is much more contextual and understands the behaviors and the access behaviors of individuals so if you're going to apply security policies and you'll keep your company information and application secure no matter where work is getting done. >> That's a great summary and you know there's been lots of conversation about security and increased attack surface but now you had a blog post that you published last month, September 15th, really interesting. And you talked about kind of COVID being this accelerant in work from home and we talk a lot about consumerization of IT and apps but we haven't talked a lot about it in the context of the employee experience. And you outlined some really great specific vocabulary those people need to be able to sit and think and create and explore the way they want so they can become what they can be free from the distractions at the same time you go through the plethora of I don't know how many business apps we all have to interact with every single day from Salesforce to Asana to Slack to Outlook to Google Drive to Box to et cetera, et cetera. And as you point out here the distractions in I think you said, "People are interrupted by a text, a chat or application alert every two minutes." So that there's this real battle between trying to do higher value work and less minutiae versus this increasing number of applications that are screaming for my attention and interrupting me anytime I'm trying to get something done. So how do you guys look at that and say, hey, we've got an opportunity to make some serious improvements so that you can get to that and cut the employee experience so they can deliver the higher value stuff and not just moving paper down the line. >> Yeah, absolutely Jeff, to your point you know a lot of the tools that we've introduced and adopted and the devices we've used in the like over the years certainly provide some advantages in helping us collaborate better, helping us execute business transactions and the like. However, they've also added a lot of complexity, right? As you said, typical employees use more than a dozen apps to get work done often four or more just to complete a single business process like submitting an expense or a purchase order or approving time off. They spend another 20% of their time searching for information they need to do their jobs across all of these different applications and collaboration channels and they are interrupted by alerts and texts and chats every few minutes. And that really keeps them from doing their core jobs and so Citrix is committed to delivering a digital workspace solutions that help companies transform employee experience to drive better business outcomes. And we do that in three ways. Number one is leveraging our heritage around delivering a unified and secure work environment. We bring all of the resources and employee needs together, your virtual apps and desktops, your SaaS apps, your web apps, your mobile apps, your information and your content into one unified experience. We wrapper that in a contextualized security model that doesn't get in the way of employees getting their job done but understands that employees, their behavior, their access protocols and assigns additional security policies, maybe a second level of authentication or maybe turning off certain features if they're behaving a little bit differently. But the key thing I think is that the third component we've also over the past several years infused within this unified workspace, intelligence, machine learning, workflows or micro apps that really remove that noise from your day, providing a personalized work stream to that individual employee and only offering up the individual tasks or the insights that they need to get their job done. Really guiding them through their day and automating some of that noise out of their day so they can really focus on being creative, focus on being innovative and to your point, giving them that space they need to succeed. >> Yeah, it's a great point, Tim and you know one of the hot buzz words that we hear all the time right now is artificial intelligence and machine learning. And people talk about it, it's kind of like big data where that's not really where the opportunity is in kind of general purpose AI as we've talked to people in natural language processing and video processing. It's really about application specific uses of AI to do something and I know you guys commissioned looks like a report called Work 2035. There's a nice summary that I was able to pull off the internet and there's some really positive things in here. It's actually, you know it got some good news in it about work being more flexible and new jobs will be created and productivity will get a major boost but the piece  I wanted to focus on which piggybacks on what you're just talking is the application of AI around a lot of specific tasks whether that's nudges, personal assistance, wearables that tell you to get up and stretch. And as I think and what triggered as you said, as this person is sitting at their desk trying to figure out what to do now, you've got your calendar, you've got your own tasks but then you've got all these notifications. So the opportunity to apply AI to help me figure out what I should be focusing on that is a tremendous opportunity and potential productivity enhancer, not to mention my mental health and positive attitude and engagement. >> Yeah, absolutely Jeff, and this Work 2035 project that we undertook is from a year long effort of research, quantitative research of business executives, IT executives supplemented with qualitative research with futurist work experts and the like to really begin a dialogue together with governments, with enterprises, with other technology companies about how we should be leveraging technology, how we should be changing our operating models and how we should be adapting our business culture to facilitate a new and better way to work. And to your point, some of the key findings are it's not going to be Skynet out there in the future. AI is not going to overtake all of our jobs and the like it is going to actually help us, you're going to see more of the augmented worker that really not only offers up the insights and the tasks like we just talked about when they're needed but actually helps us through decision-making helps us actually assess massive amounts of data to better engage with customers, better service healthcare to patients and the like. To your point, because of this some jobs certainly will be lost but new jobs will be created, right? And some people will need to be the coaches or trainers for these bots and robots. You'll see things like advanced data scientists becoming more in demand, virtual reality managers, privacy and trust managers. And then to your point, work is going to be more flexible we already talked about this but the ability to allow employees to perform at their best and give them all the resources they need to do so wherever work needs to happen, whether that's in the office, in the field or at home but importantly for businesses and even for employees this actually changes the dynamic of what we think about as a workforce. We can now tap into new pools of talent not just in remote locations but entire segments that had because of our traditional work hub model where I build a big office building or a call center and people have to commute there. Now they can work anywhere so you think about recent retirees that have a lot of domain expertise can get back into the workforce, stay at home parents or stay at home caregivers can actually engage and use their skills and expertise to reengage in that workforce. These are really, really exciting things and then the last thing is, it will help us improve employee engagement, improve wellness and improve productivity by having AI help us throughout our day, guiding us to the right decisions and automating tasks that typically added noise to our day so that we can focus on where we as humans are great which is some of the key decision-making, the creativity, the innovation to drive that next wave of growth for our companies. >> Yeah it's really interesting the kind of divergence that you're seeing with people in this opportunity, right? One of the benefits is that there is no script in how to move forward today, right? This has never happened before, especially at the scale so people are trying all kinds of things and you're talking about is a lot of positive uses of technology to an aide or to get blockers out of the way and help people do a better job. Unfortunately, there's this whole other track that we hear about, you know monitoring, are you in front of your desk, monitoring how many Zoom calls are you on a day, monitoring all these silly things that are kind of old school management of activity versus kind of new school managing of output. And we've done a lot of interviews on this topic, one of Darren Murph from GitLab great comments, does it now as a boss, your job should be removing blockers from your people to help them do a better job, right? That's such a different kind of mentality than managing their tasks and managing the minutiae. So really a lot of good stuff and we could go for a very long time and maybe we'll have a followup, but I want to shift gears a little bit here and talk about the other big delta that impacts both of you and I pretty dramatically and that's virtual events or the fact that basically March 15th there was no more gatherings of people, period. And you guys we've covered Citrix Synergy in the past but this year you guys have gone a different kind of tact. And again, I think what's so interesting about it is there is no right answer and everyone is trying to experiment and we're seeing all different ways to get your message to the market. But then the other really important part of events is getting leads, right? And getting engagement with your audience whether that's customers, whether that's partners, whether it's prospects, whether it's press and analysts and everything else. So I wonder if you can share with us kind of the thinking you had the benefit of kind of six months into this thing versus a couple of weeks which a few people had in early May, you know how did you kind of look at the landscape and how did you come to the conclusion that for you guys, it's this three event you've got Citrix Cloud on October 8th, Citrix Workspace Summit on October 22nd and Citrix Security Summit on October 29th. What did you think about before you came to this decision? >> Yeah, it's a great question, Jeff and certainly we put a lot of thought into it and to your point what helped clarify things for us is we always put the customer first. And so, like many other companies we did have our Big User Conference scheduled for the May timeframe, but you know considering the environment at that time and companies were just figuring out how to get their employees home and working securely and safely, how to maintain business continuity. We felt the inappropriate at time to be able to be talking about future innovations and so on and so forth. So we made the decision to kind of put an end to our Citrix Synergy for the year and instead, we went through all this scenario modeling as I mentioned and we've accelerated our focus and our investments and our partnerships to develop new innovations to help our customers achieve the three things that they prioritize which is accelerating that cloud transition, that hybrid multicloud transition plan, advancing their digital workspace and employee experience strategies and embracing a new, more contextual security framework. And so when we thought about how do we bring those announcements to market, how do we help educate our customers around these topics? It became very clear that we needed to design for digital attention spans which means it's not everything in the kitchen sink and we hope that we're bringing a whole bunch of different buying segments together and customer segments together and hope that they glean out the key insights we want. Instead, we wanted to be very focused around the cloud acceleration, the workspace and employee experience strategies and the security strategies is we created three separate summits. And even within the summits we've designed them for digital attention spans, no individual segment is going to be more than 20 minutes long. There'll be very descriptive so you can almost choose your own pathway as you go through the conference rather than having to commit a whole day or the likes you can get the information you need, it's supplemented by knowledge centers so you can go deeper if you want to and talk to some of our experts, if you want to. And it's certainly something we'll use to facilitate ongoing dialogue long after the day of event. >> Really interesting 20 minutes is the longest session. That is really progressive and again I think it's great to hear you say that you started from the perspective of the customer. I think so many people have basically started from the perspective of what did we do for the SaaS convention May five through eight in 2019 and then try to replicate that kind of almost one-to-one in a digital format which isn't really doing justice to either of the formats, I think and not really looking at the opportunity that digital affords that physical doesn't and we just getting together and grabbing a coffee or a drink or whatever in those hallways but there's a whole lot of things that you can do on a digital event that you can't do in a physical event. And we're seeing massive registration and more importantly, massive registration of new people that didn't have the ability couldn't afford it, couldn't get away from the shop whatever the reason is that that the physical events really weren't an option. So I think instead of focusing on the lack of hallway chatter spend your time focusing on the things you can do with this format that you couldn't before. And I think removing the space-time bounds of convention space availability and the limited number of rooms that you can afford, blah, blah, blah, blah, blah, and the budget this really does open up a very different way to get your message to market. >> It does, Jeff and what I'm excited about is what does it mean for the future of events overall? I think there's going to be some very valuable lessons learned for all of us in the industry and I expect just like work won't be the same when we return back to the office, post-pandemic. I don't think the events approach that companies take is going to be quite the same as it was previous and I think that'll be a good thing. There'll be a lot of lessons learned about how people want to engage, how to reach new segments, as you mentioned. And so I think you'll see a blended events strategy from companies across the industry going forward. >> Yeah. And to your point, event was part of your communication strategy, right? It was part of your marketing strategy it is part of your sales strategy so that doesn't necessarily all have to again be bundled into one week in May and can be separated. Well, Tim really, really enjoyed the conversation I have to say your blog posts had some really kind of really positive things in it in terms of the way people should be thinking about their employees not as resources but as people which is one of my pet peeves I'm not a big fan of the human resources word and I really was encouraged by some of the stuff coming out of this 2035 I think you said it's going to be an ongoing project so it'll be great to see what continues to come out because I don't know how much of that was done prior to COVID or kind of augmented after COVID but I would imagine the acceleration on the Delta is going to go up dramatically over the next several months or certainly over the next couple of years. >> Yeah, Jeff, I would say I think Winston Churchill said it best "Never waste a good crisis." And smart companies are doing that right now. I think there's going to be a lot of lessons learned there's going to be a lot of acceleration of the digital transformation and the work model transformations and the business model transformations that companies have had on the radar but haven't really been motivated to do so. And they're really accelerating those now I think that the world of work and the world of IT is going to look a heck of a lot different when we emerge from all of this. >> Yep, yep. I agree well, Tim thank you again for sharing your insight, sharing your information and is great to catch up. >> You too. >> Alright, take care. >> I know. >> He's Tim, I'm Jeff you're watching theCUBE. Thanks for watching we'll see you next time.

>>Well, welcome everybody to our last session of the day. I want to introduce you to Sean O'Meara. Orfield Cto. Hey, Sean. >>Hey, Nick. Good afternoon. It's been a crazy day. It has. It's been a busy run up to today in a busy day with a lot of great things going on. You know, we've heard from Adrian on his strategy this morning. The great way the Marantz is moving forward. We announced our new product line. You know, we spoke about the new doctor Enterprise Container Cloud line, New future for Mirant. Us. We had a great lineup of customers share their story. We introduced lanes following on the lanes launch a couple of weeks ago. Andi, we're introducing new great projects like our mosque project. New way to deliver open stack going into the future on then in parallel sel. This We ran a great tutorial tracker teachers you all about how to use these new products, and hopefully you'll go and everyone had opportunity to go and look through guys. Yeah. What's next? What is next? Yeah, lots going on. A lot of new things that we're thinking about for the future. Obviously, a lot of work to do on what we have right now. A lot of great things coming. But, you know, we've had this opportunity to talk about all these cool things that are coming down the road. And everybody these days seems to be talking about topics like edge computing or hybrid cloud. Or, you know, hyper scale data centers, even things like disaster recovery is a service. Andi, you know, we talk a lot about things like hyper converged, but frankly, it's boring. It's one thing a little. Good morning. Uh, you know, you and I have been talking about these topics for a while now, and I think it's about time when we spoke about some of the cool things that we're thinking about for the future, not necessarily looking out for the road map, but ideas for the future. Things that may could have an impact on the way we do business going to. So today we're gonna talk a little bit about things like pervasive computing. A nick, what is pervasive computing. >>Well, basically pervasive computing is when everything that you interact with, for the most part, is computerized. So in some ways, we're already there in that You know your phone is a computer. Your refrigerator may have a computer in it. Um, your smart watch your car has a computer in it. And the the most obvious sign of that is this whole Internet of things where, you know your vacuum is, uh is connected to your phone and all of that. And so pervasive computing is this, uh is this sense of you don't even really think about it. You just kind of assume that everything is computerized. >>So how is that different from ubiquitous computing? >>Oh, God. You hit, You hit my hot button. Okay, so if you look, there are a lot of places that will say that pervasive computing and ubiquitous computing are the same thing, but not the same thing. Don't use them interchangeably. They're not the same thing. You big. What is computing is where you can do your computing virtually anywhere. So, for example, you know, I've got, uh I've got a document. I started it on my laptop. I can then go and finish it sitting on the beach on my phone. Or, you know, I can go and do it in a coffee shop or a library. or wherever. So the idea of ubiquitous computing is similar in that, yes, there's computing everywhere, but it's more about your data being universally accessible. So essentially it is cloud computing. That is what this whole ubiquitous computing thing is about. >>Okay on that then differs from pervasive computing in the fact that pervasive is the devices that we have all around us versus the access to those devices. >>Exactly. It's it's really it's more about the data. So ubiquitous computing is more about My data is stored in some central place, and I could hit it from anywhere. There is a device, whereas pervasive computing is there is a device almost everywhere. Okay, so yeah, >>So why Why do we as Moran takes care about the vice of computing? >>Well, pervasive computing brings up a whole lot of new issues, and it's coming up really fast. I mean, you last night I was watching, you know, commercial where you know, somebody a woman's coming out and starting her car with her phone. Um, which sounds really cool. Um, but you know what they say Anything that you can access, you know, with your computer is hackable. So, you know, there are security issues that need to be considered when it comes to all of this, but that's that's the downside. But there's just this huge upside on pervasive computing that it's so exciting when you think about this. I mean, think about a world where remember I said your refrigerator might be attached to the network. Well, what if you could rent out space on your refrigerator to somebody someplace else in a secure way? Of course. You know what? If you could define your personal network as all of these devices that you own and it doesn't matter where your workloads run or, you know, you could define all of this stuff in such a way that the connectivity between objects is really huge. Um, so you know, I mean, you look at things like, you know, I f t t you know, it's like get a notification when the International space station passes over your house. Okay? I don't know why I would need that. Um, but it's the kind of thing that people >>would have a nine year old. You can run him outside and show Z. Oh, >>there you go. There you go. So I mean, that kind of level of connectivity between objects is really really it gives us this new level off. Uh, this new level of functionality that we would never even considered even 10 years ago. Um, it also extends the life of objects that we already have. So, you know, maybe you've got that, uh, that computerized vacuum cleaner, and you don't like the way that it you don't like the pattern that uses in your house. So you re program it or, uh Or I watched. I watched a guy decide that he didn't want to buy multiple vacuums for his house. So he programmed his programa will act Hume to fly between floors. It was actually pretty funny. Um, I it's some people just have too much time. >>It's driving the whole world of programmable at all levels. Really? Like the projects coming out of the car industry of creating a programmable car would fit into that category. Then, I >>suppose absolutely, absolutely needs developer tool kits. Um, that make it possible for anybody to re program these devices that you never would have thought of reprogramming before. So it's important. So do >>we want to talk about the questions. We would love people to give us some feedback on at this stage. >>I would love to talk about these questions. So what we did is we put together, uh, we put together a place for you to answer questions. If you're not watching this live. If you're watching this live, please go ahead. Drop your ideas in the chat. We would love to discuss them, you know. Do you want to see more of this? Or does it? Conversely, Does it scare you, Sean? You What? >>What do you >>think about these questions? >>Well, I mean, for me, the idea of the connected world at one level, the engineering me loves the idea. Another level. It comes to these questions of privacy. Vegas questions off. How do I control this going into the future? What prevents somebody from taking over my flying vacuum cleaner? I'm using it, you know? So it's an interesting question. I think there's a lot of cool, cool ideas. Yeah, and a lot of work to be done. I really want to hear other people's ideas as well and see how we can take this into the future. >>Definitely, definitely. I mean, look I mean, we're joking about it, but, you know, when somebody hacks into your grandmother's insulin pump, maybe not so funny. >>Yeah, a very real risk. >>A very real risk. A very real risk. But yeah, I mean, we'd love Thio. We'd love to hear how you'd like to see this used. So that's that's my That's kind of what I've been thinking about thes days. Um, but, you know, Sean, uh, now, you I know you are really concerned about this whole issue of developers and how they feel about infrastructure. So I would love to hear what you've got to say on that. >>Yeah, I'd like to sex, but a bit about that. You know, we we've done a lot of work over the last few years looking at how developing our history has been very focused on operations, but without big drive towards supporting developers providing better infrastructure for developers. One of the interesting things that keeps coming up to the four on Do you know, the way the world is changing is that big question is, do developers actually give a damn about infrastructure in any way, shape or form? Um, you know, ultimately more and more development languages and tools abstract that underlying infrastructure. What communities does is basically abstract. The infrastructure away, Um, mawr and more options. They're coming to market, which you can quite literally creating application without out of a writing a line of code. Um, so this morning, way Dio, we're doing it all the time, sometimes without even realizing it on. I think the definition of what a developer is is also changing to a certain extent. So you know the big question, which I have on which I'd like to understand Maureen, from talking to low developers is due. Developers care about infra What is it that you expect from infrastructure? What do they want going into the future? How are they going to interact with that infrastructure? I My personal opinion is that they don't really care about infrastructure, that they're going to find more ways to completely abstract away from that. And they just want to focus on delivering applications faster and getting value to market. But I might be wrong, and I'd really like to hear people's impact ideas and thoughts on that >>on. And that's exactly and that's why we're asking this question. Developers out there. Do you care? Or do you just want the whole thing completely abstracted away from you >>on? If you do care why, If you don't, what would you like to see? Another. It's a couple of questions to ask, but really like to hear those opinions on bond. You know, Do you just want the operations guys to live with it? You never want to hear about it again, just fine. It's actually good to say that we'll work it out. >>Yes, and that there's nothing. There's nothing wrong with pushing that up stack >>pretty much what we're trying to do here. >>Well, it is what we're trying to do. But at the same time, we want to do what's good for developers. And if you developers or like No, don't don't do that. Well, we want to know because, you know, we don't wanna work away here and some ivory tower and wind up with something that's not good for >>you after school. So cool. So, yeah, there are some other interesting things we're talking about. >>I know, I know. This is This is one of my favorites. This is one of my favorites. >>Zoo this? Yes. While >>we're on the subject of not getting involved with the infrastructure. Go ahead, Sean. Tell us about it. >>Thing is a pet topic of mine and something that that we've spoken about a lot. And thanks something that we we have spent many nights talking about. The idea is AI ops using artificial intelligence to drive operations within our infrastructure. And so a lot of people ask me, You know why? Um, essentially, What the hell is a I out on? I have answered this question many times, and it does often seem that we all take this AI ops thing for granted or look at it in a different way. To me, it is essentially, it's it's automation on steroids. That's what it boils down Thio. It's using intelligence systems that to replace the human cerebellum. I mean, let's just be blunt about this. We're trying to replace humans. Onda reason for that is we humans less meat sacks are airplane. We make mistakes all the time and compared to computers were incredibly slow. Um, you know, that's really the simplest point with the scale of modern infrastructure that we're dealing with the sheer volume. I mean, we've gone from, you know, thousands to tens of thousands of the EMS to now hundreds and thousands of containers spread across multiple time zones. Multiple places. We need to come up with better ways of managing this on the old fashioned stick through mechanism of automation. It's just too limited for that. Right >>when we say we want to replace meat sacks, we mean in a good way. >>We mean in a good way. I know it's a bit of a harsh way of putting it. Um, ultimately, humans have ability for creativity that machines just don't have. But machines can do other things, and they could do analysis of data a lot faster than we can. Quite often, we have to present that data to humans to have invalidate that information. But, you know, one of the options for us is to use artificial intelligence, quantified data, um, correlated, you know, look for root cause and then provide that information to us in such a way that we can make valid decisions based on that information a lot faster than we could otherwise, >>right? So what are the what are the implications? What are the practical implications of doing this so >>practically we can analyze massive amounts of data a lot faster than a single human. Could we even just a normal type system that's searching? We We have the tools to learn by looking at data and have machines do it a lot faster than we can. We can take action faster based on that data, because we get the data foster. We can take action and much more complex action that involves maybe many different layers of tasking much, much faster. Um, on we could start to do maintenance operations and maintenance tasks without having to wait for human beings to wake up or get to an office. But more importantly, we could start making tasks happen very complex tasks in a very specific orders, with much less potential for error. And those are the kinds of areas we're looking at. >>That's that's true. So how do you kind of see this moving forward? I mean, obviously, we're not gonna go from nothing to Skynet, and hopefully we never get to Skynet. Well, >>depends if you are in control of Skynet or not. Ultimately, Dionysus little computer. Um, practically speaking, we have a few things Thio hoops to jump through our suppose before we can look at where else is going to be really effective on the first one is a trust issue. We have to learn to trust it. And to do that, we have to put in a position where it can learn and start providing us that data analysis on that inference and then having humans validated. That's practically the very first step. No, it's a trust issue. You know, we've seen been watching sci fi for the last 30 years. Class on. Do you know the computers take over? Well, ultimately, is that real or not? Um, if we look at how we gonna get there? Probably midterm. Adaptive maintenance, maybe infrastructure orchestration. Smart allocation of resource is across cloud services. Well, >>we can talk for a minute About what that would would actually look like. So, I mean, we could talk about, you know, abs, midterms. I mean, in a practical sense, how would that actually work? >>Yeah, Okay. It's a great question. So, practically speaking, the first thing we're gonna do is we're going to start to collect all this data. We're gonna find all this data. I mean, the modern computer systems that we have infrastructure systems. We are producing many hundreds of gigabytes, sometimes terabytes of logging data every day. The majority of it gives far 13. I mean, we throw the majority of their logging information away or if it's not thrown away, it's stored some way for security purposes and never analyzed. So let's start by taking their data and actually analyzing it. To do that, we have laid and correlated, >>so we >>gotta put it all together. We've got a match it and we've got to start building patents. We're going to start looking for the patterns. This is where I is particularly good at starting to help us. Bold patterns start to look for those patterns. Initially, humans will have to do some training. Um, once we have that patent, once we've got that working, we can now start having the AI systems start to do some affairs. E, here's the recalls. So we the system can tell us based on the data based on the patterns we've been learning. We know from the past debt. If those three network links get full bad example, we're gonna have a failure in Region X, right. So start telling us while those network links of filling up tell us before they fall rather than after their full always they're falling up as we see trending information now seems like a simple I could do trending information with just normal monitoring systems. But if I can start to correlate that with greater users in, you know, Beijing Office versus Users in California office filling up those links and different times of the day, I can now start to make much more clever decisions, which is a human on its own, to try and correlate that information, which is be insane once you've done that way to go to the next stage, which is not to have the system act do actions for us. Based on that information right now, we're starting to get close to the scan it. Speaking of this doesn't have to be a big, complex pile of change. Smart ai solution. I have data on that AI solution is talking to my existing automation solutions to action. That change. That's how I see this moving forward, >>right? So essentially you, instead of saying, you know, deploy this too. Uh, this workload to AWS, you would say deploy this. Yeah, And then the system would look and go. Okay, It's this kind of workload. At this time of day at this size, it's gonna interact with this and this and this. And so it's gonna be best off in this region of this cloud provider on then. Uh, you know, two days from now, when the prices drop, we're gonna put it over there, >>even taking a different different. Spoken exactly that it could be. The Beijing office is coming online. Let's move the majority of the workload to a cloud that's closer to them. Reducing the network bandwidth. Yeah, and inference. Andi Also reducing the impact on international lines as Beijing winds down for the day, I can just move the majority of the workload into California on board Europe. In between, it's very simple examples, but have humans do that would be very complex and very time consuming >>exactly. And end. Just having humans notice those patterns would be difficult. But once you have the system noticing those patterns, then the humans could start to think, How can I take advantage of this, you know, So as you are talking about much longer term in the actual applicant patients themselves. So you know, everything can be optimized that way so >>everybody may optimized way can optimize down to the way we even potentially write applications in the future. Humans were still deciding the base logic. Humans were still deciding the creative components of that. Right as we as we build things, we can start to optimize them, breaking down into smaller and smaller units that are much more specific. But the complexity goes up. When we do that right. I want to use AI and AI solutions to start to manage that complexity across multiple spaces. Multiple time zones, etcetera. >>Exactly. Exactly. So. So that's the question, you know. What do you guys think? You know, we really want to know >>on Dhere again. You know, we mentioned this around the beginning, but do you think you could trust in a iob sedition? What would it take for you to trust in our absolution? And where do you practically see it being used in the short term? >>Yeah, that's that's the big question is where do you see it being used? Where would you like it to be used, you know? Is there something that you don't think would be possible, but you would like to see it, you know. But the main thing is, on a practical matter, what would you like to see? >>Let me ask. The question is like a different way. Do you have a problem that we could solve within a isolation today? E, They're really well >>right. A re a world problem. And And assuming that, you know, we are not gonna, you know, take over the world. >>Yeah. Important. My evil plan is to take over the world with >>man. I'm so sorry. First >>had to let that draw. >>I did. I did. I'm so sorry. Okay, Alright. So that's so That's a I ops. And we like I said, if you're watching this live, throw in the chat. We want to hear your ideas. If your, um if you're doing this, if you're watching this on the replay, go to the survey because we way, we really want to hear your ideas and your opinions. All right, So moving right along. All right. What the heck are you know, kernels? >>Uh, lovely questions. So, you know, the whole world is talking about containers today way we're talking about containers today. But containers like VMS or just one way to handle compute Andi. They're more and more ideas that are out there today, and people have been trying different ways off, shrinking the size of the compute environment. COMPUTER Paxil Another cool way of looking at this and saying That's been around for a little while. But it's getting your attraction to learn to sing called unique kernels, and what they are is they're basically highly optimized. Execute a bles that include the operating system, Um, there on OS settle libraries, um, and some very simplified application code all mixed into a very, very tiny package. Easiest way to describe them. They're super simplified. And I were talking about in the eye ops discussion this idea off taking everything into smaller and smaller individual functions but creating a certain level of complexity. Well, if we look at uni kernels, those are those smaller and smaller bundles and functions. They interact directly with the hardware or through a hyper visor. Um, so actually, no overhead. I mean the overhead If you just look at what a modern you clinics operating system is made up of these days, there are so many different parts and components. Even just the colonel has got anything from, you know, 5 to 7 different parts to it. Plus, of course, drivers and a boot loader. Then we look at the system libraries that set on top of that, you know? And then they're demons and utilities and shells and scream components and, you know, additional colonel stacks that go on top of that for hyper visors. What we're trying to say is, what, This text of space, I'm >>getting tired. Just listening, >>Thio. I'm tired talking about it. You know that the unique colonel, really, it just takes over their complexity. It puts the application the OS on the basic libraries necessary. That application in tow, one really tiny package. Um, yeah. Give you an idea what we're talking about here. We're talking about memory footprints or time package footprints in the kilobytes. You know, a small container is considered 100 make plus, we're talking kilobytes. We're talking memory utilization in the kilobyte two megabytes space because there's no no fact, no fluff, no unnecessary components. And then only the CPU that it needs. >>So Bill Gates was right 6. 40 k is all anybody will ever need >>Potentially. Yeah, right. E, there was there was an IBM CEO who said even less at some point. So we'll see >>how that go. What goes around comes around. >>But one of the really interesting things about this small size, which is really critical, is how fast they can boot. Yeah, we're talking boot times measured in 30 seconds. Wow, We're talking the ability to spin up specific functions only when you need them. Now, if we look at the knock on effect of that, we're looking at power saving. Who knew? Run the app when I need it because there's no Leighton. See to start it up. The app is tiny so I can pack a lot mawr into a lot less space game power seconds. But when I start looking at where you were talking about earlier, which the basic compute idea in the world all of a sudden that tiny little arm chips it in my raspberry pi that's running my fridge, My raspberry pi equivalent that's running my fridge no longer has a fact operating system around it. I can run tens thousands, potentially off these very tiny specific devices when I need them. Wow, I'm kind of excited about it. I'm excited by the idea. You >>can hear that >>I'm a hardware geek from from many, many moons ago on DSO. I kind of like the idea of being able to better utilize along this very low powered hardware that we have lying around and really take it into the future. Well, that's good. Yeah. So I'm not going to kill, not going to kill containers. But it is a parallel technology that I'm very interested in >>that that is true. Now what does it I mean in terms of, like, attack surface. That means it's got a much smaller attack surface, though, right? >>Yeah. Great. Great point. I mean, there's no there's no fluff. There's no extra components in the system. Therefore, the attack surface is very, very small. Um, you know, and because they're so small and can be distributed much, much faster and much more easily updating and upgrading them as much easier way can we can upgrade a 60 k b file across a GPRS connection on which I certainly can't do with 600 make, uh, four gig VM 600 made container. You know, just unrealistic. Um, e >>I was just going to say so. So now these. You know, kernels, they're they're so small. And they have on Lee what they absolutely need. Now, how do you access the hardware? >>So the hardware is accessed via hyper visor. So you have to have some kind of hyper visor running on top of the hard way. But because Because we need very little from their type adviser, we don't actually need to interact with that very much. It could be a very cut down operating system. Very, very simplified operating system. We're also not trying to run another layer on top of that. We're not We're not ending up with multiple potential VMS or something underneath it were completely removed. That layer, um, the the drivers, the necessary drivers are built into that particular colonel device. >>Oh, okay. That makes sense. >>Tiny footprint easily distributed, um, and once again, very specialized, >>right? Right. Well, that makes sense. Okay. So, yeah, I mean, I guess so. These these individual stacks, you know, comparing virtual machines to containers to unit colonels, there just a completely different architecture. But I can see how that would How That would work where you have the hi perverse. A little hyper buys are on top of rented teeth. OK, so moving right along certain. Where do we see these being used? >>Um, it's early days, although there are some very good practical applications out there. There's a big, big ecosystem of people trying different ways for this I o ts off the obvious immediate place. I i o t s a quick, easy place for something very specialized. Um, what's interesting to me? And you mentioned this earlier. You know, we're talking about medical devices. We're talking about potentially disposable medical devices. Now, if I can keep those devices to run on really low power very, very cheap, um, CPUs and all of a sudden I've got a device that is available to a lot more people. I don't need a massive, powerful CPU. I just need saying that runs a very specific function really fast, A very small scale. I could do well disposable devices. I can build medical devices that are so small we can potentially swallow them and other areas which are really interesting. And I spoke a little bit about it, but it's energy efficiency. Where We need to be very, very energy efficient. No. And that can also impact on massively scalable systems where I want to deal with tens of thousands of potential transactions from users going into a system. I can spin them up only when I need them. I don't need to keep them running all the time again. It comes back to that low latency on then. Anyway, that an incredibly fast food time is valuable. Um, a car, you know, Think about it. If if my if my electric car is constantly draining that battery when it's parked in the garage and I'm traveling or if it takes 20 minutes from my car to boot up its clinics. Colonel, when I wanted, I'm going to get very irritated. Well, >>that and if you have a specific function, you know, like, identify that thing, Yeah, it would be good if you haven't smashed into it before. Identified it as a baby carriage e dark today. Yes. >>So, Nick, you know, these is all really interesting topics. Um, yeah. We spoke about air ops. We spoke about the impact is gonna have on humans. Um, all of these changes to the world that we're living in from computer systems, the impact it's having on our lives biggest. An interesting question about the ethics of all of this >>ethics of all of this. Yes, because let's be let's be realistic. There are actual riel concerns when it comes to privacy, when it comes to how corporations operate, when it comes to how governments operate. Um, there are areas of the world's where, how all of this has has moved, it's absolutely I'll be honest, absolutely terrifying the economic disparity. Um, but when you really come right down to it, um, it's all about the human control over the technology because all of these ethical issues are are in our hands. Okay, we could joke about Sky Net. We can joke about things like that, but this is one place that technology can't help us. We have to do this. We have to be aware of what's going on. We have to be aware. Are they using facial recognition? Uh, you know, when you go to X y Z, are they using recidivism algorithms in sentencing? And how is that? How is that going? Is it? Are those algorithms fair? Certain groups get longer sentences because historical data, uh, is skewed. Be educated. Know how this works? Don't be afraid of any of this. None of this is, uh, none of this is rocket science. Really? Come right down to it. I mean, it's it's not simple, but you can learn this. You can do it. >>Ask good questions. Be interested to be part of the part of the discussion. Not just a passive bystander. >>Exactly. Don't just complain about what you think is going on. Learn about what is actually going on and be active, where you see something that needs to be fixed. So that's what that's what we can do about it. We need to be aware that there's an issue or potential issues, and we need to step in and fix it. So that z myself box, I'll step down zone >>important topic. And it's one that we all can have influence on on bits one. Those who are us who are actually involved in building these systems for the future. We can help make sure that the rules are there. That's right. Systems are built correctly on that. We have open dialogues and discussions around these points and topics and on going away, was she? I think we're coming to the end of the time on hopefully we've kept everybody interested in some of the things that we think are cool for the future. And we're putting our efforts into E O. But I think we need to wrap this up now. So, Nick, great chatting to you is always >>always, always a pleasure, Sean. >>It's been an amazing week. Um, been amazing. Couple of weeks, everybody leading up to this event on bond. No, thank you, everybody for listening to us. Please go and download and try. Dr. Enterprise, Uh, the container card is available. Will post the links here to better understand what we've been doing. Go and have a look through the tutorial track. You'll hear my voice. I'm sure you'll hear next voice and make other people's voices through those tutorials. Hopefully, we keep you all interested and then going download and try lens, Please. Finally, we want your feedback. We're interested to hear what you think would be the great ideas. Good, Bad. Otherwise let us know what you think about products. We are striving to make them better all the time. >>Absolutely. And we want your involvement. Was it all right? Thank you all. Bye bye. Yeah,

from the cube studios in Palo Alto in Boston connecting with thought leaders all around the world this is a cube conversation hello and welcome to the cube conversation here in the Palo Alto studio I'm John four host of the cube we are here at the quarantine crew of the cube having the conversations that matter the most now and sharing that with you got a great guest here Phil Quaid was the chief information security officer of Fortinet also the author of book digital bing-bang which I just found out he wrote talking about the difference cybersecurity and the physical worlds coming together and we're living that now with kovat 19 crisis were all sheltering in place Phil thank you for joining me on this cube conversation so I want to get in this quickly that I think the main top thing is that we're all sheltering in place anxiety is high but people are now becoming mainstream aware of what we all in the industry have been known for a long time role of data cybersecurity access to remote tools and we're seeing the work at home the remote situation really putting a lot of pressure on as I've been reporting what I call at scale problems and one of them is security right one of them is bandwidth we're starting to see you know the throttling of the packets people are now living with the reality like wow this is really a different environment but it's been kind of a disruption and has created crimes of opportunity for bad guys so this has been a real thing everyone's aware of it across the world this is something that's now aware on everyone's mind what's your take on this because you guys are fighting the battle and providing solutions and we're doing for a long time around security this highlights a lot of the things in the surface area called the world with what's your take on this carbon 19 orton s been advocating for architectures and strategies that allow you to defend anywhere from the edge through the core all the way up to the cloud boom so with you know high speed and integration and so all the sudden what we're seeing not just you know in the US but the world as well is that that edge is being extended in places that we just hadn't thought about or our CV that people just hadn't planned for before so many people or telecommunication able to move that edge securely out to people's homes and more remote locations and do so providing the right type of security of privacy if those communications that are coming out of those delicate ears I noticed you have a flag in the background and for the folks that might not know you spent a lot of time at the NSA government agency doing a lot of cutting-edge work I mean going back to you know really you know post 9/11 - now you're in the private sector with Fortinet so you don't really speak with the agency but you did live through a time of major transformation around Homeland Security looking at data again different physical thing you know terrorist attacks but it did bring rise to large-scale data to bring to those things so I wanted to kind of point out I saw the flag there nice nice touch there but now that you're in the private sector it's another transformation it's not a transition we're seeing a transformation and people want to do it fast and they don't want to have disruption this is a big problem what's your reaction to that yeah I think what you're reporting out that sometimes sometimes there's catalysts that cause major changes in the way you do things I think we're in one of those right now that we're already in the midst of an evolutionary trend towards more distributed workforces and as I mentioned earlier doing so with the right type of security privacy but I would think what I think the global camp in debt endemic is showing is that we're all going to be accelerating that that thing is like it's gonna be a lot less evolutionary and a little bit more faster that's what happens when you have major world events like this being 911 fortunate tragedies it causes people to think outside the box or accelerate what they're already doing I think wearing that in that world today yeah it pulls forward a lot of things that are usually on the planning side and it makes them reality I want to get your thoughts because not only are CEOs and their employees all thinking about the new work environment but the chief information security officer is people in your role have to be more aware as more things happening what's on the minds of CISOs around the world these days obviously the pandemics there what are you seeing what are some of the conversations what are some of the thought processes what specifically is going on in the of the chief information security officer yeah I think there's probably a there's probably two different two different things there's the there's the emotional side and there's the analytic side on the emotional side you might say that some Caesars are saying finally I get to show how cyber security can be in an abler of business right I can allow you to to to maintain business continuity by allowing your workers to work from home and trying sustain business and allow you to keep paying their salary is very very important to society there's a very important time to step up as the seaso and do what's helpful to sustain mission in on the practical side you say oh my goodness my job's gotten a whole lot harder because I can rely less and less on someone's physical controls that use some of the physical benefits you get from people coming inside the headquarters facility through locked doors and there's personal congress's and personal identification authentication you need to move those those same security strategies and policies and you need to move it out to this broad eggs it's gotten a lot bigger and a lot more distributed so I want to ask you around some of the things they're on cyber screws that have been elevated to the top of the list obviously with the disruption of working at home it's not like an earthquake or a tornado or hurricane or flood you know this backup and recovery for that you know kind of disaster recovery this has been an unmitigated disaster in the sense of it's been unfor casted I was talking to an IT guy he was saying well we provisioned rvv lands to be your VPNs to be 30% and now they need a hundred percent so that disruption is causing I was an under forecast so in cyber as you guys are always planning in and protecting has there been some things that have emerged that are now top of mind that are 100 percent mindshare base or new solutions or new challenges why keep quite done what we're referring to earlier is that yep any good see so or company executive is going to prepare for unexpected things to a certain degree you need it whether it be spare capacity or the ability to recover from something an act of God as you mentioned maybe a flood or tornado or hurricane stuff like that what's different now is that we have a disruption who which doesn't have an end date meaning there's a new temporal component that's been introduced that most companies just can't plan for right even the best of companies that let's say Ronald very large data centers they have backup plans where they have spare fuel to run backup generators to provide electricity to their data centers but the amount of fuel they have might only be limited to 30 days or so it's stored on-site we might think well that's pretty that's a lot of for thinking by storing that much fuel on site for to allow you to sort of work your way through a hurricane or other natural disaster what we have now is a is a worldwide crisis that doesn't have a 30-day window on it right we don't know if it's gonna be 30 days or 120 days or or you know even worse than that so what's different now is that it's not just a matter of surging in doing something with band-aids and twine or an extra 30 days what we need to do is as a community is to prepare solutions that can be enduring solutions you know I have some things that if the absent I might like to provide a little color what those types of solutions are but that that would be my main message that this isn't just a surge for 30 days this is a surge or being agile with no end in sight take a minute explain some of those solutions what are you seeing whatever specific examples and solutions that you can go deeper on there yeah so I talked earlier about the the edge meaning the place where users interact with machines and company data that edge is no longer at the desktop down the hallway it could be 10 miles 450 miles away to where anyone where I'm telling you I'm commuting crumb that means we need to push the data confidentiality things out between the headquarters and the edge you do that with things like a secure secured tunnel it's called VPNs you also need to make sure that the user identification authentication this much is a very very secure very authentic and with high integrity so you do that with multi-factor authentication there's other things that we like that that are very very practical that you do to support this new architecture and the good news is that they're available today in the good news at least with some companies there already had one foot in that world but as I mentioned earlier not all companies had yet embraced the idea of where you're going to have a large percentage of your workforce - until a community so they're not quite so they're there they're reacting quickly to to make sure this edge is better protected by identification and authentication and begins I want to get to some of those edge issues that now translate to kind of physical digital virtualization of of life but first I want to ask you around operational technology and IT OT IT these are kind of examples where you're seeing at scale problem with the pandemic being highlighted so cloud providers etc are all kind of impacted and bring solutions to the table you guys at Foot are doing large scale security is there anything around the automation side of it then you've seen emerge because all the people that are taking care of being a supplier in this new normal or this crisis certainly not normal has leveraged automation and data so this has been a fundamental value proposition that highlights what we call the DevOps movement in the cloud world but automation has become hugely available and a benefit to this can you share your insights into how automation is changing with cyber I think you up a nice question for me is it allowed me to talk about not only automation but convergence so it's let's hit automation first right we all even even pre-crisis we need to be better at leveraging automation to do things that machines do best allow people to do higher-order things whether it's unique analysis or something else with a with a more distributed workforce and perhaps fewer resources automation is more important ever to automatically detect bad things that are about to happen automatically mitigating them before they get or they get to bad you know in the cybersecurity world you use things like agile segmentation and you use like techniques called soar it's a type of security orchestration and you want to eat leverage those things very very highly in order to leverage automation to have machines circum amount of human services but you also brought up on my favorite topics which is ot graceful technology though OTS you know are the things that are used to control for the past almost a hundred years now things in the physical world like electric generators and pipes and valves and things like that often used in our critical infrastructures in my company fort net we provide solutions that secure both the IT world the traditional cyber domain but also the OT systems of the world today where safety and reliability are about most important so what we're seeing with the co19 crisis is that supply chains transportation research things like that a lot of things that depend on OT solutions for safety and reliability are much more forefront of mine so from a cybersecurity strategy perspective what you want to do of course is make sure your solutions in the IT space are well integrated with you solutions in the OT space to the so an adversary or a mistake in cause a working to the crack in causing destruction that convergence is interesting you know we were talking before you came on camera around the fact that all these events are being canceled but that really highlights the fact that the physical spaces are no longer available the so-called ot operational technologies of events is the plumbing the face-to-face conversations but everyone's trying to move to digital or virtual eyes that it's not as easy as just saying we did it here we do it there there is a convergence and some sort of translation this new there's a new roles there's new responsibilities new kinds of behaviors and decision making that goes on in the physical and digital worlds that have to then come together and get reimagined and so what's your take on all this because this is not so much about events but although that's kind of prime time problem zooming it is not the answer that's a streaming video how do you replicate the value of physical into the business value in digital it's not a one-to-one so it's quite possible that that we might look back on this event to cover 19 experience we might look back at it in five or ten years and say that was simply a foreshadowing of our of the importance of making sure that our physical environment is appropriate in private what I mean is that with the with the rapid introduction of Internet of Things technologies into the physical world we're going to have a whole lot of dependencies on the thing inconveniences tendencies inconveniences on things an instrument our physical space our door locks or automobiles paths our temperatures color height lots of things to instrument the physical space and so there's gonna be a whole lot of data that's generated in that cyber in a physical domain increasingly in the future and we're going to become dependent upon it well what happens if for whatever reason in the in the future that's massively disruptive so all of a sudden we have a massive disruption in the physical space just like we're experiencing now with open 19 so again that's why it makes sense now to start your planning now with making sure that your safety and reliability controls in the physical domain are up to the same level security and privacy as the things in your IT delete and it highlights what's the where the value is to and it's a transformation I was just reading an article around spatial economics around distance not being together it's interesting on those points you wrote a book about this I want to get your thoughts because in this cyber internet or digital or virtualization of physical to digital whether it's events or actual equipment is causing people to rethink architectures you mentioned a few of them what's the state of the art thinking around someone who has the plan for this again is in its complex it's not just creating a gateway or a physical abstraction layer of software between two worlds there's almost a blending or convergence here what's your what's your thoughts on what's the state of the art thinking on this area yeah the book that I number of a very esteemed colleagues contribute to what we said is that it's time to start treating cybersecurity like a science let's not pretend it's a dark art that we have to relearn every couple years and what what we said in the in the digital Big Bang is that humankind started flourishing once we admitted our ignorance in ultimately our ignorance in the physical world and discovered or invented you can right word the disciplines of physics and chemistry and once we recognize that our physical world was driven by those scientific disciplines we started flourishing right the scientific age led to lots of things whether it would be transportation health care or lots of other things to improve our quality of life well if you fast forward 14 billion years after that cosmic Big Bang which was driven by physics 50 years ago or so we had a digital Big Bang where there was a massive explosion of bits with the invention of the internet and what we argue in the book is that let's start treating cybersecurity like a science or the scientific principle is that we ought to write down and follow a Rousseau's with you so we can thrive in the in the in a digital Big Bang in the digital age and one more point if you don't mind what we what we noted is that the internet was invented to do two things one connect more people or machines than ever imagined in to do so in speeds that were never imagined so the in the Internet is is optimized around speed in connectivity so if that's the case it may be a fundamental premise of cybersecurity science is make sure that your cyber security solutions are optimized around those same two things that the cyber domains are optimized around speed in integration continue from there you can you can build on more and more complex scientific principles if you focus on those fundamental things and speed and integration yeah that's awesome great insight they're awesome I wanted to throw in while you had the internet history lesson down there also was interesting was a very decentralization concept how does that factor in your opinion to some of the security paradigms is that helped or hurt or is it create opportunities for more secure or does it give the act as an advantage yeah I love your questions is your it's a very informed question and you're in a give me good segue to answer the way you know it should be answer yeah the by definition the distributed nature of the Internet means it's an inherently survivable system which is a wonderful thing to have for a critical infrastructure like that if one piece goes down the hole doesn't go down it's kind of like the power grid the u.s. the u.s. electrical power grid there's too many people who say the grid will go down well that's that's just not a practical thing it's not a reality thing the grades broken up into three major grades and there's AB ulis strategies and implementations of diversification to allow the grid to fail safely so it's not catastrophic Internet's the same thing so like my nipple like I was saying before we ought to de cyber security around a similar principle that a catastrophic failure in one partner to start cybersecurity architecture should result in cascading across your whole architecture so again we need to borrow some lessons from history and I think he bring up a good one that the internet was built on survivability so our cybersecurity strategies need to be the same one of the ways you do that so that's all great theory but one of the ways you do that of course is by making your cybersecurity solutions so that they're very well integrated they connect with each other so that you know speaking in cartoon language you know if one unit can say I'm about to fail help me out and another part of your architecture can pick up a slack and give you some more robust security in that that's what a connected the integrated cyber security architecture do for you yeah it's really fascinating insight and I think resiliency and scale are two things I think are going to be a big wave is going to be added into the transformations that going on now it's it's very interesting you know Phil great conversation I could do a whole hour with you and do a fish lead a virtual panel virtualize that our own event here keynote speech thanks so much for your insight one of things I want to get your thoughts on is something that I've been really thinking a lot lately and gathering perspectives and that is on biosecurity and I say biosecurity I'm referring to covet 19 as a virus because biology involves starting a lab or some people debate all that whether it's true or not but but that's what people work on in the biology world but it spreads virally like malware and has a similar metaphor to cybersecurity so we're seeing conversation starting to happen in Washington DC in Silicon Valley and some of my circles around if biology weapon or it's a tool like open-source software could be a tool for spreading cybersecurity Trojans or other things and techniques like malware spear phishing phishing all these things are techniques that could be deployed metaphorically to viral distribution a biohazard or bio warfare if you will will it look the same and how do you defend against the next covet 19 this is what you know average Americans are seeing the impact of the economy with the shelter in place is that what happens again and how do we prevent it and so a lot of people are thinking about this what is your thoughts because it kind of feels the same way as cybersecurity you got to see it early you got to know what's going on you got to identify it you got to respond to it time to close your contain similar concepts what's your thoughts on with BIOS we don't look with all due respect to the the the bio community let me make a quick analogy to the cyber security strategy right cyber security strategy starts with we start as an attacker so I parts of my previous career I'm an authorized had the opportunity to help develop tools that are very very precisely targeted against foreign adversaries and that's a harder job than you think I mean I think the same is true of anyone of a natural-born or a custom a buyer buyer is that not just any virus has the capability to do a lot of harm to a lot of people selling it so it's it's if that doesn't mean though you can sit back and say since it's hard it'll never happen you need to take proactive measures to look for evidence of a compromise of something whether it's a cyber cyber virus or otherwise you have to actively look for that you have to harm yourself to make sure you're not susceptible to it and once you detect one you need to make sure you have a the ability to do segmentation or quarantine very rapidly very very effectively right so in the cyber security community of course the fundamental strategy is about segmentation you keep different types of things separate that don't need to interact and then if you do have a compromise not everything is compromised and then lastly if you want to gradually say bring things back up to recover you can do some with small chunks I think it's a great analogy segmentation is a good analogy to I think what the nation is trying to do right now by warranty kneeing and gradually reopening up things in in segments in actually mention earlier that some of the other techniques are very very similar you want to have good visibility of where you're at risk and then you can automatically detect and then implement some some mitigations based on that good visibility so I agree with you that it turns out that the cyber security strategies might have a whole lot in common with biohazard I address it's interesting site reliability engineers which is a term that Google coined when they built out their large-scale cloud has become a practice that kind of mindset combined with some of the things that you're saying the cyber security mindset seemed to fit this at scale problem space and I might be an alarmist but I personally believe that we've been having a digital war for many many years now and I think that you know troops aren't landing but it's certainly digital troops and I think that we as a country and a global state and global society have to start thinking about you know these kinds of things where a virus could impact the United States shut down the economy devastating impact so I think Wars can be digital and so I may be an alarmist and a conspirators but I think that you know thinking about it and talking about it might be a good thing so appreciate your insights there Phil appreciated what one other point that might be interesting a few years back I was doing some research with the National Lab and we're looking for novel of cybersecurity analytics and we hired some folks who worked in the biology the bio the biomedical community who were studying a biome fires at the time and it was in recognition that there's a lot of commonality between those who are doing cybersecurity analytics and those reviewing bio biology or biomedical type analytics in you know there was a lot of good cross fertilization between our teams and it kind of helps you bring up one more there's one more point which is what we need to do in cybersecurity in general is have more diversity of workforces right now I don't mean just the traditional but important diversities of sex or color but diversity of experiences right some of the best people I've worked with in the cyber analytics field weren't computer science trained people and that's because they came in problems differently with a different background so one of the things that's really important to our field at large and of course the company my company fort net is to massively increase the amount of cyber security training that's available to people not just the computer scientists the world and the engineers but people in other areas as well the other degree to non-greek people and with that a you know higher level of cyber security training available to a more diverse community not only can we solve the problem of numbers we don't have enough cybersecurity people but we can actually increase our ability to defend against these things I have more greater diversity of thought experience you know that's such a great point I think I just put an exclamation point on that I get that question all the time and the skills gap is should I study computer science and like actually if you can solve problems that's a good thing but really diversity about diversity is a wonderful thing in the age of unlimited compute power because traditionally diversity whether it was protocol diversity or technical diversity or you know human you know makeup that's tend to slow things down but you get higher quality so that's a generalization but you get the point diversity does bring quality and if you're doing a data science you don't want have a blind spot I'm not have enough data so yeah I think a good diverse data set is a wonderful thing you're going to a whole nother level saying bringing diversely skill sets to the table because the problems are diverse is that what you're getting at it is it's one of our I'll say our platforms that we're talking about during the during the covered nineteen crisis which is perhaps there's perhaps we could all make ourselves a little bit better by taking some time out since we're not competing taking some time out and doing a little bit more online training where you can where you can either improve your current set of cybersecurity skills of knowledge or be introduced to them for the first time and so there's one or some wonderful Fortinet training available that can allow both the brand-new folks the field or or the the intermediate level folks with you become higher level experts it's an opportunity for all of us to get better rather than spending that extra hour on the road every day why don't we take at least you know 30 of those 60 minutes or former commute time and usually do some online soccer security treaty feel final question for you great insight great conversation as the world and your friends my friends people we don't know other members of society as they start to realize that the virtualization of life is happening just in your section it's convergence what general advice would you have for someone just from a mental model or mindset standpoint to alleviate any anxiety or change it certainly will be happening so how they can better themselves in their life was it is it thinking more about the the the experiences is it more learning how would you give advice to folks out there who are gonna come out of this post pandemic certainly it's gonna be a different world we're gonna be heightened to digital and virtual but as things become virtualized how can someone take this and make a positive outcome out of all this I I think that the future the future remains bright earlier we talked about sci-fi the integration of the cyber world in the physical world that's gonna provide great opportunities to make us more efficient gives us more free time detect bad things from happening earlier and hopefully mitigating those bad things from happening earlier so a lot of things that some people might use as scare tactics right convergence and Skynet in in robotics and things like that I believe these are things that will make our lives better not worse our responsibilities though is talking about those things making sure people understand that they're coming why they're important and make sure we're putting the right security and privacy to those things as these worlds this physical world and the soccer worlds converged I think the future is bright but we still have some work to do in terms of um making sure we're doing things at very high speeds there's no delay in the cybersecurity we put on top of these applications and make sure we have very very well integrated solutions that don't cause things to become more complex make make things easier to do certainly the winds of change in the big waves with the transformations happening I guess just summarize by saying just make it a head win I mean tailwind not a headwind make it work for you at the time not against it Phil thank you so much for your insights I really appreciate this cube conversation remote interview I'm John Ford with the cube talking about cybersecurity and the fundamentals of understanding what's going on in this new virtual world that we're living in to being virtualized as we get back to work and as things start to to evolve further back to normal the at scale problems and opportunities are there and of course the key was bringing it to you here remotely from our studio I'm John Ferrier thanks for watching [Music]

(upbeat music) >> From our studios in the heart of Silicon Valley, Palo Alto, California, this is a CUBE Conversation. >> Welcome everyone to this special CUBE Conversation Around the CUBE segment, Unpacking AI, number two, sponsored by Juniper Networks. We've got a great lineup here to go around the CUBE and unpack AI. We have Ken Jennings, all-time Jeopardy champion with us. Celebrity, great story there, we'll dig into that. John Hinson, director of AI at Evotek and Charna Parkey, who's the applied scientist at Textio. Thanks for joining us here for Around the CUBE Unpacking AI, appreciate it. First question I want to get to, Ken, you're notable for being beaten by a machine on Jeopardy. Everyone knows that story, but it really brings out the question of AI and the role AI is playing in society around obsolescence. We've been hearing gloom and doom around AI replacing people's jobs, and it's not really that way. What's your take on AI and replacing people's jobs? >> You know, I'm not an economist, so I can't speak to how easy it's going to be to retrain and re-skill tens of millions of people once these clerical and food prep and driving and whatever jobs go away, but I can definitely speak to the personal feeling of being in that situation, kind of watching the machine take your job on the assembly line and realizing that the thing you thought made you special no longer exists. If IBM throws enough money at it, your skill essentially is now obsolete. And it was kind of a disconcerting feeling. I think that what people need is to feel like they matter, and that went away for me very quickly when I realized that a black rectangle can now beat me at a game show. >> Okay John, what's your take on AI replacing jobs? What's your view on this? >> I think, look, we're all going to have to adapt. There's a lot of changes coming. There's changes coming socially, economically, politically. I think it's a disservice to us all to get to too indulgent around the idea that these things are going to change. We have to absorb these things, we have to be really smart about how we approach them. We have to be very open-minded about how these things are going to actually change us all. But ultimately, I think it's going to be positive at the end of the day. It's definitely going to be a little rough for a couple of years as we make all these adjustments, but I think what AI brings to the table is heads above kind of where we are today. >> Charna, your take around this, because the role of humans versus machines are pretty significant, they help each other. But is AI going to dominate over humans? >> Yeah, absolutely. I think there's a thing that we see over and over again in every bubble and collapse where, you know, in the automotive industry we certainly saw a bunch of jobs were lost, but a bunch of jobs were gained. And so we're just now actually getting into the phase where people are realizing that AI isn't just replacement, it has to be augmentation, right? We can't simply use images to replace recognition of people, we can't just use black box to give our FICO credit scores, it has to be inspectable. So there's a new field coming up now called explainable AI that actually is where we're moving towards and it's actually going to help society and create jobs. >> All right so let's stay on that next point for the next round, explainable AI. This points to a golden age. There's a debate around are we in a bubble or a golden age. A lot of people are negative right now on tech. You can see all the tech backlash. Amazon, the big tech companies like Apple and Facebook, there's a huge backlash around this so-called tech for society. Is this an indicator of a golden age coming? >> I think so, absolutely. We can take two examples of this. One would be where, you remember when Amazon built a hiring algorithm based upon their own resume data and they found that it was discriminating against women because they had only had men apply for it. Now with Textio we're building augmented writing across the audience and not from a single company and so companies like Johnson and Johnson are increasing the pipeline by more than nine percent which converts to 90,000 more women applying for their jobs. And so part of the difference there is one is explainable, one isn't, and one is using the right data set representing the audience that is consuming it and not a single company's hiring. So I think we're absolutely headed into more of a golden age, and I think these are some of the signs that people are starting to use it in the right way. >> John, what's your take? Obviously golden age doesn't look that to us right now. You see Facebook approving lies as ads, Twitter banning political ads. AI was supposed to solve all these problems. Is there light at the end of this dark tunnel we're on? >> Yeah, golden age for sure. I'm definitely a big believer in that. I think there's a new era amongst us on how we handle data in general. I think the most important thing we have here though is education around what this stuff is, how it works, how it's affecting our lives individually and at the corporate level. This is a new era of informing and augmenting literally everything we do. I see nothing but positives coming out of this. We have to be obviously very careful with our approaching all the biases that already exist today that are only going to be magnified with these types of algorithms at mass scale. But ultimately if we can get over that hurdle, which I believe collectively we all need to do together, I think we'd live in much better, less wasteful world just by approaching the data that's already at hand. >> Ken, what's your take on this? It's like a daily double question. Is it going to be a golden age? >> Laughs >> It's going to come sooner or later. We have to have catastrophe before, we have to have reality hit us in the face before we realize that tech is good, and shaping it? It's pretty ugly right now in some of the situations out there, especially in the political scene with the election in the US. You're seeing some negative things happening. What's your take on this? >> I'm much more skeptical than John and Charna. I feel like that kind of just blinkered, it's going to be great, is something you have to actually be in the tech industry and hearing all day to actually believe. I remember seeing kind of lay-person's exposure to Watson when Watson was on Jeopardy and hearing the questions reporters would ask and seeing the memes that would appear, and everyone's immediate reaction just to something as innocuous as a AI algorithm playing on a game show was to ask, is this Skynet from Terminator 2? Is this the computer from The Matrix? Is this HAL pushing us out of the airlock? Everybody immediately first goes to the tech is going to kill us. That's like everybody's first reaction, and it's weird. I don't know, you might say it's just because Hollywood has trained us to expect that plot development, but I almost think it's the other way around. Like that's a story we tell because we're deeply worried about our own meaning and obsolescence when we see how little these skills might be valued in 10, 20, 30 years. >> I can't tell you how much, by the way, Star Trek, Star Wars and Terminators probably affected the nomenclature of the technology. Everyone references Skynet. Oh my God, we're going to be taken over and killed by aliens and machines. This is a real fear. I thinks it's an initial reaction. You felt that Ken, so I've got to ask you, where do you think the crossover point is for people to internalize the benefits of say, AI for instance? Because people will say hey, look back at life before the iPhone, look at life before these tools were out there. Some will say society's gotten better, but yet there's this surveillance culture, things... And on and on. So what do you guys think the crossover point is for the reaction to change from oh my God, it's Skynet, gloom and doom to this actually could be good? >> It's incredibly tricky because as we've seen, the perception of AI both in and out of the industry changes as AI advances. As soon as machine learning can actually do a task, there's a tendency to say there's this no true Scotsman problem where we say well, that clearly can't be AI because I see how the trick worked. And yeah, humans lose at chess now. So when these small advances happen, the reaction is often oh, that's not really AI. And by the same token, it's not a game-changer when your email client can start to auto-complete your emails. That's a minor convenience to you. But you don't think oh, maybe Skynet is good. I really do think it's going to have to be, maybe the inflection point is when it starts to become so disruptive that actually public policy has to change. So we get serious about >> And public policy has started changing. >> whatever their reactions are. >> Charna, your thoughts. >> The public policy has started changing though. We just saw, I think it was in September, where California banned the use of AI in the body cameras, both real-time and after the fact. So I think that's part of the pivot point that we're actually seeing is that public policy is changing.` The state of Washington currently has a task force for AI who's making a set of recommendations for policy starting in December. But I think part of what we're missing is that we don't have enough digital natives in office to even attempt to, to your point Ken, predict what we're even going to be able to do with it, right? There is this fear because of misunderstanding, but we also don't have a respect of our political climate right now by a lot of our digital natives, and they need to be there to be making this policy. >> John, weigh in on this because you're director of AI, you're seeing positive, you have to deal with the uncertainty as well, the growth of machine learning. And just this week Google announced more TensorFlow for everybody. You're seeing Open Source. So there's a tech push, almost a democratization, going on with AI. So I think this crossover point might be sooner in front of us than people think. What's your thoughts? >> Yeah it's here right now. All these things can be essentially put into an environment. You can see these into products, or making business decisions or political decisions. These are all available right now. They're available today and its within 10 to 15 lines of code. It's all about the data sets, so you have to be really good stewards of the data that you're using to train your models. But I think the most important thing, back to the Skynet and all this science-fiction side, we have to collectively start telling the right stories. We need better stories than just this robots are going to take us over and destroy all of our jobs. I think more interesting stories really revolve around, what about public defenders who can have this informant augmentation algorithm that's going to help them get their job done? What about tailor-made medicine that's going to tell me exactly what the conditions are based off of a particular treatment plan instead of guessing? What about tailored education that's going to look at all of my strengths and weaknesses and present a plan for me? These are things that AI can do. Charna's exactly right, where if we don't get this into the right political atmosphere that's helping balance the capitalist side with the social side, we're going to be in trouble. So that's got to be embedded in every layer of enterprise as well as society in general. It's here, it's now, and it's real. >> Ken, before we move on to the ethics question, I want to get your thoughts on this because we have an Alexa at home. We had an Alexa at home; my wife made me get rid of it. We had an Apple device, what they're called... the Home pods, that's gone. I bought a Portal from Facebook because I always buy the earliest stuff, that's gone. We don't want listening devices in our house because in order to get that AI, you have to give up listening, and this has been an issue. What do you have to give to get? This has been a big question. What's your thoughts on all this? >> I was at an Amazon event where they were trumpeting how no technology had ever caught on faster than these personal digital assistants, and yet every time I'm in a use case, a household that's trying to use them, something goes terribly wrong. My friend had to rename his because the neighbor kids kept telling Alexa to do awful things. He renamed it computer, and now every time we use the word computer, the wall tells us something we don't want to know. >> (laughs) >> This is just anecdata, but maybe it speaks to something deeper, the fact that we don't necessarily like the feeling of being surveilled. IBM was always trying to push Watson as the star Trek computer that helpfully tells you exactly what you need to know in the right moment, but that's got downsides too. I feel like we're going to, if nothing else, we may start to value individual learning and knowledge less when we feel like a voice from the ceiling can deliver unto us the fact that we need. I think decision-making might suffer in that kind of a world. >> All right, this brings up ethics because I bring up the Amazon and the voice stuff because this is the new interface people want to have with machines. I didn't mention phones, Androids and Apple, they need to listen in order to make decisions. This brings up the ethics question around who sets the laws, what society should do about this, because we want the benefits of AI. John, you point out some of them. You got to give to get. Where are we on ethics? What's the opinion, what's the current view on this? John, we'll start with you on your ethics view on what needs to change now to move the ball faster. >> Data is gold. Data is gold at an exponential rate when you're talking about AI. There should be no situation where these companies get to collect data at no cost or no benefit to the end consumer. So ultimately we should have the option to opt out of any of these products and any of this type of surveillance wherever we can. Public safety is a little bit different situation, but on the commercial side, there is a lot of more expensive and even more difficult ways to train these models with a data set that isn't just basically grabbing everything our of your personal lives. I think that should be an option for consumers and that's one of those ethical check-marks. Again, ethics in general, the way that data's trained, the way that data's handled, the way models actually work, it has to be a primary reason for and approach of how you actually go about developing and delivering AI. That said, we cannot get over-indulgent in the fact that we can't do it because we're so fearful of the ethical outcomes. We have to find some middle ground and we have to find it quickly and collectively. >> Charna, what's your take on this? Ethics is super important to set the agenda for society to take advantage of all this. >> Yeah. I think we've got three ethical components here. We certainly have, as John mentioned, the data sets. However, it's also what behavior we're trying to change. So I believe the industry could benefit from a lot more behavioral science, so that we can understand whether or not the algorithms that we're building are changing behaviors that we actually want to change, right? And if we aren't, that's unethical. There is an entire field of ethics that needs to start getting put into our companies. We need an ethics board internally. A few companies are doing this already actually. I know a lot of the military companies do. I used to be in the defense industry, and so they've got a board of ethics before you can do things. The challenge is also though that as we're democratizing the algorithms themselves, people don't understand that you can't just get a set of data that represents the population. So this is true of image processing, where if we only used 100 images of a black woman, and we used 1,000 images of a white man because that was the distribution in our population, and then the algorithm could not detect the difference between skin tones for people of color, then we end up with situations where we end up in a police state where you put in an image of one black woman and it looks like ten of them and you can't distinguish between them. And yet, the confidence rate for the humans are actually higher, because they now have a machine backing their decision. And so they stop questioning, to your point, Ken, about what is the decision I'm making, they're like I'm so confident, this data told me so. And so there's a little bit of you need some expert in the loop and you also can't just have experts, because then you end up with Cambridge Analytica and all of the political things that happened there, not just in the US, but across 200 different elections and 30 different countries. And we are upset because it happened in the US, but this has been happening for years. So its just this ethical challenge of behavior change. It's not even AI and we do it all the time. Its why the cigarette industry is regulated (laughs). >> So Ken, what's your take on this? Obviously because society needs to have ethics. Who runs that? Companies? The law-makers? Someone's got to be responsible. >> I'm honestly a little pessimistic the general public will even demand this the way we're maybe hoping that they will. When I think about an example like Facebook, people just being able to, being willing to give away insane amounts of data through social media companies for the smallest of benefits: keeping in touch with people from high school they don't like. I mean, it really shows how little we value not being a product in this kind of situation. But I would like to see this kind of ethical decisions being made at the company-level. I feel like Google kind of surreptitiously moved away from it's little don't be evil mantra with the subtext that eh, maybe we'll be a little evil now. It just reminds me of Manhattan Project era thinking, where you could've gone to any of these nuclear scientists and said you're working on a real interesting puzzle here, it might advance the field, but like 200,000 civilians might die this summer. And I feel like they would've just looked at you and thought that's not really my bailiwick. I'm just trying to solve the fission problem. I would like to see these 10 companies actually having that kind of thinking internally. Not being so busy thinking if they can do something that they don't wonder if they should. >> That's a great point. This brings up the point of who is responsible. Almost as if who is less evil than the other person? Google, they don't do evil, but they're less evil than Amazon and Facebook and others. Who is responsible? The companies or the law-makers? Because if you look up some of the hearings in Washington, D.C., some of the law-makers we see up there, they don't know how the internet works, and it's pretty obvious that this is a problem. >> Yeah, well that's why Jack Dorsey of Twitter posted yesterday that he banned not just political ads, but also issue ads. This isn't something that they're making him do, but he understands that when you're using AI to target people, that it's not okay. At some point, while Mark is sitting on (laughs) this committee and giving his testimony, he's essentially asking to be regulated because he can't regulate himself. He's like well, everyone's doing it, so I'm going to do it too. That's not an okay excuse. We see this in the labor market though actually, where there's existing laws that prevent discrimination. It's actually the company's responsibility to make sure that the products that they purchase from any vendor isn't introducing discrimination into that process. So its not even the vendor that's held responsible, it's the company and their use of it. We saw in the NYPD actually that one of those image recognition systems came up and someone said well, he looked like, I forget the name of what the actor was, but some actor's name is what the perpetrator looked like and so they used an image of the actor to try and find the person who actually assaulted someone else. And that's, it's also the user problem that I'm super concerned about. >> So John, what's your take on this? Because these are companies are in business to make money, for profit, they're not the government. And who's the role, what should the government do? AI has to move forward. >> Yeah, we're all responsible. The companies are responsible. The companies that we work with, I have yet to interact with customers, or with our customers here, that have some insidious goal, that they're trying to outsmart their customers. They're not. Everyone's looking to do the best and deliver the most relevant products in the marketplace. The government, they absolutely... The political structure we have, it has to be really intelligent and it's got to get up-skilled in this space and it needs to do it quickly, both at the economy level, as well as for our defense. But the individuals, all of us as individuals, we are already subjected to this type of artificial intelligence in our everyday lives. Look at streaming, streaming media. Right now every single one of us goes out through a streaming source, and we're getting recommendations on what we should watch next. And we're already adapting to these things, I am. I'm like stop showing me all the stuff you know I want to watch, that's not interesting to me. I want to find something I don't know I want to watch, right? So we all have to get educated, we're all responsible for these things. And again, I see a much more positive side of this. I'm not trying to get into the fear-mongering side of all the things that could go wrong, I want to focus on the good stories, the positive stories. If I'm in a courtroom and I lose a court case because I couldn't afford the best attorney and I have the bias of a judge, I would certainly like artificial intelligence to make a determination that allows me to drive an appeal, as one example. Things like that are really creative in the world that we need to do. Tampering down this wild speculation we have on the markets. I mean, we are all victims of really bad data decisions right now, almost the worst data decisions. For me, I see this as a way to actually improve all those things. Fraud fees will be reduced. That helps everybody, right? Less speculation and these wild swings, these are all helpful things. >> Well Ken, John and Charna, thank- (audio feedback) >> Go ahead, finish. Get that word in. >> Sorry. I think that point you were making though John, is we are still a capitalist society, but we're no longer a shareholder capitalist society, we are a stakeholder capitalist society and the stakeholder is the society itself. It is us, it what we want to see. And so yes, I still want money. Obviously there are things that I want to buy, but I also care about well-being. I think it's that little shift that we're seeing that is actually you and I holding our own teams accountable for what they do. >> Yeah, culture first is a whole new shift going on in these companies that's a for-profit, mission-based. Ken, John, Charna, thanks for coming on Around the CUBE, Unpacking AI. Let's go around the CUBE Ken, John and Charna in that order, and just real quickly, unpacking AI, what's your final word? >> (laughs) I really... I'm interested in John's take that there's a democratization coming provided these tools will be available to everyone. I would certainly love to believe that. It seems like in the past, we've seen no, that access to these kind of powerful, paradigm-changing tools tend to be concentrated among a very small group of people and the benefits accrue to a very small group of people. But I hope that doesn't happen here. You know, I'm optimistic as well. I like the utopian side where we all have this amazing access to information and so many new problems can get solved with amazing amounts of data that we never could've touched before. Though you know, I think about that. I try to let that help me sleep at night, and not the fact that, you know... every public figure I see on TV is kind of out of touch about technology and only one candidate suggests the universal basic income, and it's kind of a crackpot idea. Those are the kind of things that keep me up at night. >> All right, John, final word. >> I think it's beautiful, AI's beautiful. We're on the cusp of a whole new world, it's nothing but positivity I see. We have to be careful. We're all nervous about it. None of us know how to approach these things, but as human beings, we've been here before. We're here all the time. And I believe that we can all collectively get a better lives for ourselves, for the environment, for everything that's out there. It's here, it's now, it's definitely real. I encourage everyone to hurry up on their own education. Every company, every layer of government to start really embracing these things and start paying attention. It's catching us all a little bit by surprise, but once you see it in production, you see it real, you'll be impressed. >> Okay, Charna, final word. >> I think one thing I want to leave people with is what we incentivize is what we end up optimizing for. This is the same for human behavior. You're training a new employee, you put incentives on the way that they sell, and that's, they game the system. AI's specifically find the optimum route, that is their job. So if we don't understand more complex cost functions, more complex representative ways of training, we're going to end up in a space, before we know it, that we can't get out of. And especially if we're using uninspectable AI. We really need to move towards augmentation. There are some companies that are implementing this now that you may not even know. Zillow, for example, is using AI to give you a cost for your home just by the photos and the words that you describe it, but they're also purchasing houses without a human in the loop in certain markets, based upon an inspection later by a human. And so there are these big bets that we're making within these massive corporations, but if you're going to do it as an individual, take a Coursera class on AI and take a Coursera class on ethics so that you can understand what the pitfalls are going to be, because that cost function is incredibly important. >> Okay, that's a wrap. Looks like we have a winner here. Charna, you got 18, John 16. Ken came in with 12, beaten again! (both laugh) Okay, Ken, seriously, great to have you guys on, a pleasure to meet everyone. Thanks for sharing on Around the CUBE Unpacking AI, panel number two. Thank you. >> Thanks a lot. >> Thank you. >> Thanks. I've been defeated by artificial intelligence again! (all laugh) (upbeat music)

(upbeat music) >> From our studios, in the heart of Silicon Valley, Palo Alto, California, this is a CUBE Conversation. >> Hello everyone, welcome to theCUBE studio here in Palo Alto. I'm John Furrier your host of theCUBE. We're here introducing a new format for CUBE panel discussions, it's called Around theCUBE and we have a special segment here called Get Smart: Unpacking AI with some great with some great guests in the industry. Gene Santos, Professor of Engineering in College of Engineering Dartmouth College. Bob Friday, Vice President CTO at Mist at Juniper Company. And Ed Henry, Senior Scientist and Distinguished Member of the Technical Staff for Machine Learning at Dell EMC. Guys this is a format, we're going to keep score and we're going to throw out some interesting conversations around Unpacking AI. Thanks for joining us here, appreciate your time. >> Yeah, glad to be here. >> Okay, first question, as we all know AI is on the rise, we're seeing AI everywhere. You can't go to a show or see marketing literature from any company, whether it's consumer or tech company around, they all have AI, AI something. So AI is on the rise. The question is, is it real AI, is AI relevant from a reality standpoint, what really is going on with AI, Gene, is AI real? >> I think a good chunk of AI is real there. It depends on what you apply it to. If it's making some sort of decisions for you, that is AI that's blowing into play. But there's also a lot of AI left out there potentially is just simply a script. So, you know, one of the challenges that you'll always have is that, if it were scripted, is it scripted because, somebody's already developed the AI and now just pulled out all the answers and just using the answers straight? Or is it active learning and changing on its own? I would tend to say that anything that's learning and changing on its own, that's where you're having the evolving AI and that's where you get the most power from. >> Bob what's your take on this, AI real? >> Yeah, if you look at Google, What you see is AI really became real in 2014. That's when the AI and ML really became a thing in the industry and when you look why did it become a thing in 2014? It's really back when we actually saw TensorFlow, open source technology really become available. It's all that Amazon Compute story. You know, you look what we're doing here at Mist, I really don't have to worry about compute storage, except for the Amazon bill I get every month now. So I think you're really seeing AI become real, because of some key turning points in the industry. >> Ed, your take, AI real? >> Yeah, so it depends on what lens you want to kind of look at it through. The notion of intelligence is something that's kind of ill defined and depending how how you want to interpret that will kind of guide whether or not you think it's real. I tend to all things AI if it has a notion of agency. So if it can navigate its problem space without human intervention. So, really it depends on, again, what lens you kind of want to look at it through? It's a set of moving goalposts, right? If you take your smartphone back to Turing When he was coming up with the Turing test and asked them if this intelligent, or some value intelligent device was AI, would that be AI, to him probably back then. So really it depends on how you kind of want to look at it. >> Is AI the same as it was in 1988? Or has it changed, what's the change point with AI because some are saying, AI's been around for a while but there's more AI now than ever before, Ed we'll start with you, what's different with AI now versus say in the late 80s, early 90s? >> See what's funny is some of the methods that we're using aren't different, I think the big push that happened in the last decade or so has been the ability to store as much data as we can along with the ability to have as much compute readily disposable as we have today. Some of the methodologies I mean there was a great Wired article that was published and somebody referenced called, method called Eigenvector Decomposition they said it was from quantum mechanic, that came out in 1888 right? So it really a lot of the methodologies that we're using aren't much different, it's the amount of data that we have available to us that represents reality and the amount of compute that we have. >> Bob. >> Yeah so for me back in the 80s when I did my masters I actually did a masters on neural networks so yeah it's been around for a while but when I started Mist what really changed was a couple things. One is this modern cloud stack right so if you're going to have to build an AI solution really have to have all the pieces ingest tons of data and process it in real time so that is one big thing that's changed that we didn't have 20 years ago. The other big thing is we had access to all this open source TensorFlow stuff right now. People like Google and Facebook have made it so easy for the average person to actually do an AI project right? You know anyone here, anyone in the audience here could actually train a machine learning model over the weekend right now, you just have to go to Google, you have to find kind of the, you know they have the data sets you want to basically build a model to recognize letters and numbers, those data sets are on the internet right now and you personally yourself could go become a data scientist over the weekend. >> Gene, your take. >> Yeah I think also on top of that because of all that availability on the open software anybody can come in and start playing with AI, it's also building a really large experience base of what works and what doesn't work and because they have that now you can actually better define the problem you're shooting for and when you do that you increase you know what's going to work, what's not going to work and people can also tell you that on the part that's not going to work, how's it going to expand but I think overall though this comes back to the question of when people ask what is AI, and a lot of that is just being focused on machine learning and if it's just machine learning that's kind of a little limited use in terms of what you're classifying or not. Back in the early 80s AI back then is really what people are trying to call artificial general intelligence nowadays but it's that all encompassing piece. All the things that you know us humans can do, us humans can reason about, all the decision sequences that we make and so you know that's the part that we haven't quite gotten to but there is all the things that's why the applications that the AI with machine learning classification has gotten us this far. >> Okay machine learning is certainly relevant, it's been one of the most hottest, the hottest topic I think in computer science and with AI becoming much more democratized you guys mentioned TensorFlow, a variety of other open source initiatives been a great wave of innovation and again motivation, younger generations is easier to code now than ever before but machine learning seems to be at the heart of AI and there's really two schools of thought in the machine learning world, is it just math or is there more of a cognition learning machine kind of a thing going on? This has been a big debate in the industry, I want to get your guys' take on this, Gene is machine learning just math and running algorithms or is there more to it like cognition, where do you guys fall on this, what's real? >> If I look at the applications and look what people are using it for it's mostly just algorithms it's mostly that you know you've managed to do the pattern recognition, you've managed to compute out the things and find something interesting from it but then on the other side of it the folks working in say neurosciences, the first people working in cogno-sciences. You know I have the interest in that when we look at that, that machine learning does it correspond to what we're doing as human beings, now because the reason I fall more on the algorithm side is that a lot of those algorithms they don't match what we're often thinking so if they're not matching that it's like okay something else is coming up but then what do we do with it, you know you can get an answer and work from it but then if we want to build true human intelligence how does that all stack together to get to the human intelligence and I think that's the challenge at this point. >> Bob, machine learning, math, cognition is there more to do there, what's your take? >> Yeah I think right now you look at machine learning, machine learning are the algorithms we use, I mean I think the big thing that happened to machine learning is the neural network and deep learning, that was kind of a mild stepping stone where we got through and actually building kind of these AI behavior things. You know when you look what's really happening out there you look at the self driving car, what we don't realize is like it's kind of scary right now, you go to Vegas you can actually get on a driving bus now, you know so this AI machine learning stuff is starting to happen right before our eyes, you know when you go to the health care now and you get your diagnosis for cancer right, we're starting to see AI in image recognition really start to change how we get our diagnosis. And that's really starting to affect people's lives. So those are cases where we're starting to see this AI machine learning stuff is starting to make a difference. When we think about the AI singularity discussion right when are we finally going to build something that really has human behavior. I mean right now we're building AI that can actually play Jeopardy right, and that was kind of one of the inspirations for my company Mist was hey, if they can build something to play Jeopardy we should be able to build something answer questions on par with network domain experts. So I think we're seeing people build solutions now that do a lot of behaviors that mimic humans. I do think we're probably on the path to building something that is truly going to be on par with human thinking right, you know whether it's 50 years or a thousand years I think it's inevitable on how man is progressing right now if you look at the technologically exponential growth we're seeing in human evolution. >> Well we're going to get to that in the next question so you're jumping ahead, hold that thought. Ed, machine learning just math, pattern recognition or is there more cognition there to be had? Where do fall in this? >> Right now it's, I mean it's all math, so we collect something some data set about the world and then we use algorithms and some representation of mathematics to find some pattern, which is new and interesting, don't get me wrong, when you say cognition though we have to understand that we have a fundamentally flawed perspective on how maybe the one guiding light that we have on what intelligence could be would be ourselves right. Computers don't work like brains, brains are what we determine embody our intelligence right, computers, our brains don't have a clock, there's no state that's actually between different clock cycles that light up in the brain so when you start using words like cognition we end up trying to measure ourselves or use ourselves as a ruler and most of the methodologies that we have today don't necessarily head down that path. So yeah that's kind of how I view it. >> Yeah I mean stateless those are API kind of mindsets, you can't run Kubernetes in the brain. Maybe we will in the future, stateful applications are always harder than stateless as we all know but again when I'm sleeping, I'm still dreaming. So cognition in the question of human replacement. This has been a huge conversation. This is one, the singularity conversation you know the fear of most average people and then some technical people as well on the job front, will AI replace my job will it take over the world is there going to be a Skynet Terminator moment? This is a big conversation point because it just teases out what could be and tech for good tech for bad. Some say tech is neutral but it can be shaped. So the question is will AI replace humans and where does that line come from. We'll start with Ed on this one. What do you see this singularity discussion where humans are going to be replaced with AI? >> So replace is an interesting term, so there I mean we look at the last kind of Industrial Revolution that happened and people I think are most worried about the potential of job loss and when you look at what happened during the Industrial Revolution this concept of creative destruction kind of came about and the idea is that yes technology has taken some jobs out of the market in some way shape or form but more jobs were created because of that technology, that's kind of our one again lighthouse that we have with respect to measuring that singularity in and of itself. Again the ill defined definition, or the ill defined notion of intelligence that we have today, I mean when you go back and you read some of the early papers from psychologists from the early 1900s the experiment specifically who came up with this idea of intelligence he uses the term general intelligence as kind of the first time that all of civilization has tried to assign a definition to what is intelligent right? And it's only been roughly 100 years or so or maybe a little longer since we have had this understanding that's been normalized at least within western culture of what this notion of intelligence is so singularity this idea of the singularity is interesting because we just don't understand enough about the one measure ruler or yardstick that we have that we consider intelligence ourselves to be able to go and then embed that inside of a thing. >> Gene what's your thoughts on this, reasoning is a big part of your research you're doing a lot of research around intent and contextual, all these cool behavioral things you know this is where machines are there to augment or replace, this is the conversation, your view on this? >> I think one of the things with this is that that's where the downs still lie, if we have bad intentions, if we can actually start communicating then we can start getting the general intelligence yeah I mean sort of like what Ed was referring to how people have been trying to define this but I think one of the problems that comes up is that computers and stuff like that don't really capture that at this time, the intentions that they have are still at a low level, but if we start tying it to you know the question of the terminator moment to the singularity, one of the things is that autonomy, you know how much autonomy that we give to the algorithm, how much does the algorithm have access to? Now there could be you know just to be on an extreme there could be a disaster situation where you know we weren't very careful and we provided an API that gives full autonomy to whatever AI we have to run it and so you can start seeing elements of Skynet that can come from that but I also tend to come to analysis that hey even with APIs, while it's not AI, APIs a lot of that also we have the intentions of what you're going to give us to control. Then you have the AI itself where if you've defined the intentions of what it is supposed to do then you can avoid that terminator moment in terms of that's more of an act. So I'm seeing it at this point. And so overall singularity I still think we're a ways off and you know when people worry about job loss probably the closest thing that I think that can match that in recent history is the whole thing on automation, I grew up at the time in Ohio when the steel industry was collapsing and that was a trade off between automation and what the current jobs are and if you have something like that okay that's one thing that we go forward dealing with and I think that this is something that state governments, our national government something we should be considering. If you're going to have that job loss you know what better study, what better form can you do from that and I've heard different proposals from different people like, well if we need to retrain people where do you get the resources from it could be something even like AI job pack. And so there's a lot of things to discuss, we're not there yet but I do believe the lower, repetitive jobs out there, I should say the things where we can easily define, those can be replaceable but that's still close to the automation side. >> Yeah and there's a lot of opportunities there. Bob, you mentioned in the last segment the singularity, cognition learning machines, you mentioned deep learning, as the machines learn this needs more data, data informs. If it's biased data or real data how do you become cognitive, how do you become human if you don't have the data or the algorithms? The data's the-- >> I mean and I think that's one of the big ethical debates going on right now right you know are we basically going to basically take our human biases and train them into our next generation of AI devices right. But I think from my point of view I think it's inevitable that we will build something as complex as the brain eventually, don't know if it's 50 years or 500 years from now but if you look at kind of the evolution of man where we've been over the last hundred thousand years or so, you kind of see this exponential rise in technology right from, you know for thousands of years our technology was relatively flat. So in the last 200 years where we've seen this exponential growth in technology that's taking off and you know what's amazing is when you look at quantum computing what's scary is, I always thought of quantum computing as being a research lab thing but when you start to see VC's and investing in quantum computing startups you know we're going from university research discussions to I guess we're starting to commercialize quantum computing, you know when you look at the complexity of what a brain does it's inevitable that we will build something that has basic complexity of a neuron and I think you know if you look how people neural science looks at the brain, we really don't understand how it encodes, but it's clear that it does encode memories which is very similar to what we're doing right now with our AI machine right? We're building things that takes data and memories and encodes in some certain way. So yeah I'm convinced that we will start to see more AI cognizance and it starts to really happen as we start with the next hundred years going forward. >> Guys, this has been a great conversation, AI is real based upon this around theCUBE conversation. Look at I mean you've seen the evidence there you guys pointed it out and I think cloud computing has been a real accelerant with the combination of machine learning and open source so you guys have illustrated and so that brings up kind of the final question I'd love to get each of you's thought on this because Bob just brought up quantum computing which as the race to quantum supremacy goes on around the world this becomes maybe that next step function, kind of what cloud computing did for revitalizing or creating a renaissance in AI. What does quantum do? So that begs the question, five ten years out if machine learning is the beginning of it and it starts to solve some of these problems as quantum comes in, more compute, unlimited resource applied with software, where does that go, five ten years? We'll go start with Gene, Bob, then Ed. Let's wrap this up. >> Yeah I think if quantum becomes a reality that you know when you have the exponential growth this is going to be exponential and exponential. Quantum is going to address a lot of the harder AI problems that were from complexity you know when you talk about this regular search regular approaches of looking up stuff quantum is the one that allows you now to potentially take something that was exponential and make it quantum. And so that's going to be a big driver. That'll be a big enabler where you know a lot of the problems I look at trying to do intentions is that I have an exponential number of intentions that might be possible if I'm going to choose it as an explanation. But, quantum will allow me to narrow it down to one if that technology can work out and of course the real challenge if I can rephrase it into say a quantum program while doing it. But that's I think the advance is just beyond the step function. >> Beyond a step function you see. Okay Bob your take on this 'cause you brought it up, quantum step function revolution what's your view on this? >> I mean your quantum computing changes the whole paradigm right because it kind of goes from a paradigm of what we know, this binary if this then that type of computing. So I think quantum computing is more than just a step function, I think it's going to take a whole paradigm shift of you know and it's going to be another decade or two before we actually get all the tools we need to actually start leveraging quantum computing but I think that is going to be one of those step functions that basically takes our AI efforts into a whole different realm right? Let us solve another whole set of classic problems and that's why they're doing it right now because it starts to let you be able to crack all the encryption codes right? You know where you have millions of billions of choices and you have to basically find that one needle in the haystack so quantum computing's going to basically open that piece of the puzzle up and when you look at these AI solutions it's really a collection of different things going underneath the hood. It's not this one algorithm that you're doing and trying to mimic human behavior, so quantum computing's going to be yet one more tool in the AI toolbox that's going to move the whole industry forward. >> Ed, you're up, quantum. >> Cool, yeah so I think it'll, like Gene and Bob had alluded to fundamentally change the way we approach these problems and the reason is combinatorial problems that everybody's talking about so if I want to evaluate the state space of anything using modern binary based computers we have to kind of iteratively make that search over that search space where quantum computing allows you to kind of evaluate the entire search space at once. When you talk about games like AlphaGo, you talk about having more moves on a blank 19 by 19 AlphaGo board than you have if you put 1,000 universes on every proton of our universe. So the state space is absolutely massive so searching that is impossible. Using today's binary based computers but quantum computing allows you to evaluate kind of search spaces like that in one big chunk to really simplify the aspect but I think it will kind of change how we approach these problems to Bob and Gene's point with respect to how we approach, the technology once we crack that quantum nut I don't think will look anything like what we have today. >> Okay thank you guys, looks like we have a winner. Bob you're up by one point, we had a tie for second but Ed and Gene of course I'm the arbiter but I've decided Bob you nailed this one so since you're the winner, Gene you guys did a great job coming in second place, Ed good job, Bob you get the last word. Unpacking AI, what's the summary from your perspective as the winner of Around theCUBE. >> Yeah no I think you know from a societal point of view I think AI's going to be on par with kind of the internet. It's going to be one of these next big technology things. I think it'll start to impact our lives and people when you look around it it's kind of sneaking up on us, whether it's the self driving car the healthcare cancer, the self driving bus, so I think it's here, I think we're just at the beginnings of it. I think it's going to be one of these technologies that's going to basically impact our whole lives or our next one or two decades. Next 10, 20 years is just going to be exponentially growing everywhere in all our segments. >> Thanks so much for playing guys really appreciate it we have an inventor entrepreneur, Gene doing great research at Dartmouth check him out, Gene Santos at Dartmouth Computer Science. And Ed, technical genius at Dell, figuring out how to make those machines smarter and with the software abstractions growing you guys are doing some good work over there as well. Gentlemen thank you for joining us on this inaugural Around theCUBE unpacking AI Get Smart series, thanks for joining us. >> Thank you. >> Thank you. >> Okay, that's a wrap everyone this is theCUBE in Palo Alto, I'm John Furrier thanks for watching. (upbeat funk music)

>> From Gillette Stadium in Foxboro, Massachusetts, it's the CUBE. Covering VTUG Winter Warmer 2019. Brought to you by SiliconANGLE Media. >> I'm Stu Miniman, and this is theCUBE's coverage of the VTUG Winter Warmer 2019. Just had Rob Ninkovich from the New England Patriots on the program. And, happy to bring on the program, one of the co-leaders of this VTUG event, Chris Williams. Whose day job is as a cloud architect with GreenPages, but is co-leader here at VTUG, does some user groups, and many other things, and actually a CUBE alum, even. Back four years ago, the first year-- >> That's right. >> -that we did this, we had you on the program, but a few things have changed, you know... You have a little less hair. >> This got a little longer. A little less here. >> More gray hair. Things like that. We were talking, >> Funny how that works out. you know, Rob was, you know, talking about how he's 35, and we were, like, yeah, yeah, 35, I remember 35. >> A child. (laughing) >> Things like that. Just wait til you hit your 40's and stuff starts breaking. >> Oh, so much to look forward to. >> So, Chris, first of all, thank you. We love coming to an event like this. I got to talk to a few users on-air, and I talked to, you know, get a, just, great pulse of what's going on in the industry. Virtualization, cloud computing, and beyond. So, you know, we know these, you know, local events are done, you know, a lot of it is the passion of the people that do it, and therefore we know a lot goes into it. >> I appreciate it, thanks for having me on. >> Alright, so bring people up to speed. What's your life like today? What do you do for work? What do you do for, you know, the passion projects? >> Ah, so the passion projects recently have been a lot of, we're doing a Python for DevOp series on vBrownBag. For the AWS Portsmouth User Group, we're also doing a machine learning and robotics autonomous car driving project, using Python as well. And for VTUG, we're looking at a couple of different tracks, also with the autonomous driving, and some more of the traditional, like, VMware to CAS Cloud Hybrid training kind of things. >> Excellent, so in the near future, the robots will be replacing the users here, and we'll have those running around. >> I have my Skynet t-shirt on underneath here. >> Ah, yes, Skynet. (laughing) You know if you Tweet that out, anything about Skynet, there's bots that respond to you with, like, things from The Terminator movies. >> I built one of them. >> Did you? (laughter) Well, thank you. They always make me laugh, and if there's not a place for snark on Twitter, then, you know, all we have left is kind of horrible politics, so. >> That's true, that's true. >> Great, so, yeah, I mean, Cloud AI, robotics, you know, what's the pulse? When you talk to users here, you know, they started out, you know, virtualization. There's lots of people that are, "I'm rolling out my virtualization, "I'm expanding what use-cases I can use it on, "I might be thinking about how cloud fits into that, "I'm looking at, you know, VmMare and Amazon especially, "or Microsoft, how all those fit together." You know, what are you hearing, what drives some of those passion projects other than, you know, you're interested in 'em? >> So, a lot of what my passion projects are driven, it's kind of a confluence of a couple of different events. I'm passionate about the things that I work on, and when I get into a room with customers, or whatever like that, or with the end users, getting together and talking about, you know, what's the next step? So, we as users, as a user group and as a community, we're here to learn about not just what today is... what's happening today, but, what's going to keep us relevant in the future, what are the new things coming down the pipe. And, a lot of that is bending towards the things that I'm interested in, fortuitously. Learning how to take my infrastructure knowledge and parlay that into a DevOps framework. Learning how to take Python and some of the stuff that I'm learning from the devs on the AWS side, and teaching them the infrastructure stuff. So, it's a bi-directional learning thing, where we all come together to that magical DevOps unicorn in the middle, that doesn't really exist, but... >> Yeah, I tell you, we've had this conversation a few times here, and many times over the last few years especially, is that, there's lots of opportunities to learn. And, you know, >> Too many. >> is your job threatened? And, the only reason your job should be threatened, is if you think you can keep doing, year after year, what you were doing before, because chances are either you will be disrupted in the job, or if not, the people you're working for might be disrupted, because if they're not pushing you along those tracks, and the tools and the communities to be able to learn stuff is, I can learn stuff at a fraction of the cost in faster times. >> Yep. >> Might not learn as much, but I'm saying I can pick up new skills, I can start getting into cloud. You know, it's not $1000 and six months to get the first piece of it. >> Exactly. >> It might be 40 to 60 hours online. >> Yep. >> And, you know, cost you 30 to 100 bucks, so, it's... >> Yeah, the lift in training, is a lot easier because, you're basically swiping your credit card, and with AWS, you have a free tier for 12 months, that you can play with and just, you know, doodle around, and then... And figure things out. You don't have to buy a home lab, you don't have to buy NFR license, or get NFR licenses from Vmware. But, the catch to that is, you do have to do it. There's a... remember Charlie and the Chocolate Factory? >> Of course. >> Remember the dad was doing the toothpaste tubes, he was the guys screwing the toothpaste tubes onto the machines. At the end of the story, he got, you know, automated out of a job, because they had a machine screwing the toothpaste tubes on. And then, at the end, he was the guy fixing the machine that was screwing the toothpaste tubes on. >> Right. >> So, in our world, that infrastructure guy, who's been deploying manually virtual machines, there's a piece of code, there's an infrastructure code, that will do that for them now. They've got to know how to modify and refactor that piece of code, and get good. And, get good at that. >> Yeah, you know, I've talked to a couple of people, we talk about, you know, there's big, you know, vendor shows, and then there's, you know, regional user groups and meet-up's, and the like. Give us a little insight into, you know, let's start with VTUG specifically, and, you know, what you're doin' up in the Portland area. Would love to hear some of the dynamics now, you know, it feels like there's just been a ground swell for many years now, to drive those, you know, local, and many times, more specialized events, as opposed to bigger, broader events. >> Yeah, it's interesting, because we like the bigger, broader events, because it gets everybody together to talk about, things across a broad spectrum. So, here we have the infrastructure guys, and we have the DevOps guys, and we have a couple of Developers, and stuff like that. And so, getting that group think, that mind share, into one room together, gets everybody's creative juices flowing. So, people are starting to learn from each other, that the Dev's, are getting some ideas about how infrastructure works, the infrastructure guys are getting some ideas about, you know, how to, how to automate a certain piece of their job. To make that, you know, minimize and maximize a thousand times, you know, go away. So, I love... I love the larger groups because of that. The smaller groups are more specialized, more niche. So, like, when you get into a smaller version, then, it's mostly infrastructure guys, or mostly Dev's, or some mixture thereof. So, they both definitely have their place, and that's why I love doing both of them. >> Yeah, and, you know, what can you share, kind of, speeds and feeds of this show here. I know, it's usually over a thousand people >> Yep. >> You know, had, you know, bunch of keynotes going on. You know, we talked about The Patriots, in, you know, quite a number of, you know, technology companies, people that are the, kind of, SI's or VAR's in the mix. >> Yeah, so, we had, I think, 35 sponsers. We had, six different keynotes, or six general sessions. We talked about everything from Azure to AWS, to Vmware. We covered the gamate of the things that the users are interested in. >> You had... don't undersell the general sessions there. (laughing) There was one that was on, like, you know, Blockchain and Quantum computing, I heard. >> Yep, yep. >> There was, an Amazon session, that was just, geekin' out on the database stuff, I think, there. >> Yes, yeah, Graph tier, yep. >> So, I mean, you know, it's not just marketing slideware up there, I saw a bunch of code in many of the sessions. >> Oh yeah, yeah. >> You know, this definitely is, you know, I was talkin' with the Amazon... Randell earlier, here on the program, and said that-- >> The Amazon Randall. (laughing) >> Yeah, yeah, sorry, Randall from Amazon, here. >> He's a very large weber. >> Gettin' at the end of the day, I've done a few of these, but, you know, remember like, four years ago, the first, like, cloud 101 session here? >> Yeah, yep. >> And, I was like, you know, I probably could have given that session, but, everybody here was like, "Oh, my gosh", you know, I just found out about that electricity. >> Right. >> You know, that, this is amazing. And, today, most people, understand a little bit more of... We've gotten the 101, so, you know, I'm getting into more of the pieces of it, but. >> Yeah, it was really gratifiying because, the one that he gave was, all of the service, all of the new services, of which, there were like, more than 100, in 50 minutes or less. And, he talks really, really fast. And, everybody was riveted, we... I mean, people were coming in, even up until the last minute. And, they all got it. It wasn't like, what am I do... what am I going to do with this? It's, this is what I need to know, and this is valuable information. >> Yeah, we were having a lunch conversation, about, like, when you listen to a Podcast, what speed do you listen on? So, I tend to listen at about one and a half speed, normally. >> Me too, yep. >> You know, Frappe was sayin', he listens at 2x, normally. >> Does he really? >> Somebody like, Randall, I think I would, put the video up, and you can actually go into YouTube, and things like that, and adjust the speed settings, I might hit, put him down to 0.75, or something like that, >> Yeah, absolutely. >> Because absolutely, you know, otherwise, you can listen to it at full speed, and just like, pause and rewind, and then things like that. But, definitely, someone... I respect that, I'm from New Jersey, originally, I tend to talk a little faster, on camera I try to keep a steady pace, so that, people can keep up with my excitement. >> I do, I speed up too. He actually, does this everyday. He flies to a new city, does it once a day. So, he's, he's gotten... This is like rapid fire now. >> Alright, want to give you the final word, you know, VTUG, you know, I think, people that don't know it, you go to VTUG.com, A Big Winter Warmer, here. There's The Big Summer one, >> The Summer Slam. >> With the world famous, you know, Lobster Bake Fest, there, I've been to that one a few times. I know people that fly from other countries, to come to that one. What else should we know about? >> So, we're about to revamp the website, we've got some new and interesting stuff coming up on there. Now that, we also have our slack channel, everybody communicates on the backhand through that. We're going to start having some user content, for the website. So, people can start posting blog articles, and things of that nature, there. I'm going to start doing, like a little, AW... like learn AWS, on the VTUG blog, so, people can start, you know, ramping up on some of the basics and everything. And, and if, that gains traction, then, we'll maybe get into some more advanced topics, from Azure, and AwS, and Vmware of course, Vmware is always going to be there, that's... Some of the stuff that Cody is doing, Cody Jarklin is doing, over at Vmware, like the CAS stuff, where it's the shim layer, and the management of all the different clouds. That's some really, really cool stuff. So, I'm excited to showcase some of that on the website. >> Alright, wow. Chris Williams, really appreciate you coming. And, as always, appreaciate the partnership with the VTUG, to have us here. >> Thanks for havin' me. >> Alright, and thank you as always for watching. We always love to bring you the best community content, we go out to all the shows, help extract the signal for the noise. I'm Stu Miniman, thanks for watchin' The CUBE. (energetic music) (energetic music) (energetic music)

>> Live from New York City it's theCube. Covering Lenovo Transform 2.0. Brought to you by Lenovo. >> Welcome back to theCube's live coverage of Lenovo Transform, I'm your host Rebecca Knight along with my co-host Stu Miniman. We're joined by Madhu Matta; He is the VP and GM High Performance Computing and Artificial Intelligence at Lenovo and Dr. Daniel Gruner the CTO of SciNet at University of Toronto. Thanks so much for coming on the show gentlemen. >> Thank you for having us. >> Our pleasure. >> So, before the cameras were rolling, you were talking about the Lenovo mission in this area to use the power of supercomputing to help solve some of society's most pressing challenges; and that is climate change, and curing cancer. Can you talk a little bit, tell our viewers a little bit about what you do and how you see your mission. >> Yeah so, our tagline is basically, Solving humanity's greatest challenges. We're also now the number one supercomputer provider in the world as measured by the rankings of the top 500 and that comes with a lot of responsibility. One, we take that responsibility very seriously, but more importantly, we work with some of the largest research institutions, universities all over the world as they do research, and it's amazing research. Whether it's particle physics, like you saw this morning, whether it's cancer research, whether it's climate modeling. I mean, we are sitting here in New York City and our headquarters is in Raleigh, right in the path of Hurricane Florence, so the ability to predict the next anomaly, the ability to predict the next hurricane is absolutely critical to get early warning signs and a lot of survival depends on that. So we work with these institutions jointly to develop custom solutions to ensure that all this research one it's powered and second to works seamlessly, and all their researchers have access to this infrastructure twenty-four seven. >> So Danny, tell us a little bit about SciNet, too. Tell us what you do, and then I want to hear how you work together. >> And, no relation with Skynet, I've been assured? Right? >> No. Not at all. It is also no relationship with another network that's called the same, but, it doesn't matter. SciNet is an organization that's basically the University of Toronto and the associated research hospitals, and we happen to run Canada's largest supercomputer. We're one of a number of computer sites around Canada that are tasked with providing resources and support, support is the most important, to academia in Canada. So, all academics, from all the different universities, in the country, they come and use our systems. From the University of Toronto, they can also go and use the other systems, it doesn't matter. Our mission is, as I said, we provide a system or a number of systems, we run them, but we really are about helping the researchers do their research. We're all scientists. All the guys that work with me, we're all scientists initially. We turned to computers because that was the way we do the research. You can not do astrophysics other than computationally, observationally and computationally, but nothing else. Climate science is the same story, you have so much data and so much modeling to do that you need a very large computer and, of course, very good algorithms and very careful physics modeling for an extremely complex system, but ultimately it needs a lot of horsepower to be able to even do a single simulation. So, what I was showing with Madhu at that booth earlier was results of a simulation that was done just prior us going into production with our Lenovo system where people were doing ocean circulation calculations. The ocean is obviously part of the big Earth system, which is part of the climate system as well. But, they took a small patch of the ocean, a few kilometers in size in each direction, but did it at very, very high resolution, even vertically going down to the bottom of the ocean so that the topography of the ocean floor can be taken into account. That allows you to see at a much smaller scale the onset of tides, the onset of micro-tides that allow water to mix, the cold water from the bottom and the hot water from the top; The mixing of nutrients, how life goes on, the whole cycle. It's super important. Now that, of course, gets coupled with the atmosphere and with the ice and with the radiation from the sun and all that stuff. That calculation was run by a group from, the main guy was from JPL in California, and he was running on 48,000 cores. Single runs at 48,000 cores for about two- to three-weeks and produced a petabyte of data, which is still being analyzed. That's the kind of resolution that's been enabled... >> Scale. >> It gives it a sense of just exactly... >> That's the scale. >> By a system the size of the one we have. It was not possible to do that in Canada before this system. >> I tell you both, when I lived on the vendor side and as an analyst, talking to labs and universities, you love geeking out. Because first of all, you always have a need for newer, faster things because the example you just gave is like, "Oh wait." "If I can get the next generation chipset." "If the networking can be improved." You know you can take that petabyte of data and process it so much faster. >> If I could only get more money to buy a bigger one. >> We've talked to the people at CERN and JPL and things like that. - Yeah. >> And it's like this is where most companies are it's like, yeah it's a little bit better, and it might make things a little better and make things nice, but no, this is critical to move along the research. So talk a little bit more about the infrastructure and what you look for and how that connects to the research and how you help close that gap over time. >> Before you go, I just want to also highlight a point that Danny made on solving humanity's greatest challenges which is our motto. He talked about the data analysis that he just did where they are looking at the surface of the ocean, as well as, going down, what is it, 264 nautical layers underneath the ocean? To analyze that much data, to start looking at marine life and protecting marine life. As you start to understand that level of nautical depth, they can start to figure out the nutrients value and other contents that are in that water to be able to start protecting the marine life. There again, another of humanity's greatest challenge right there that he's giving you... >> Nothing happens in isolation; It's all interconnected. >> Yeah. >> When you finally got a grant, you're able to buy a computer, how do you buy the computer that's going to give you the most bang for your buck? The best computer to do the science that we're all tasked with doing? It's tough, right? We don't fancy ourselves as computer architects; we engage the computer companies who really know about architecture to help us do it. The way we did our procurement was, 'Ok vendors, we have a set pot of money, we're willing to spend every last penny of this money, you give us the biggest and the baddest for our money." Now, it has to have a certain set of criteria. You have to be able to solve a number of benchmarks, some sample calculations that we provided. The ones that give you the best performance that's a bonus. It also has to be able to do it with the least amount of power, so we don't have to heat up the world and pay through the nose with power. Those are objective criteria that anybody can understand. But then, there's also the other criteria, so, how well will it run? How is it architected? How balanced is it? Did we get the iOS sub-system for all the storage that was the one that actually meets the criteria? What other extras do we have that will help us make the system run in a much smoother way and for a wide variety of disciplines because we run the biologists together with the physicists and the engineers and the humanitarians, the humanities people. Everybody uses the system. To make a long story short, the proposal that we got from Lenovo won the bid both in terms of what we got for in terms of hardware and also the way it was put together, which was quite innovative. >> Yeah. >> I want to hear about, you said give us the biggest, the baddest, we're willing to empty our coffers for this, so then where do you go from there? How closely do you work with SciNet, how does the relationship evolve and do you work together to innovate and kind of keep going? >> Yeah. I see it as not a segment or a division. I see High Performance Computing as a practice, and with any practice, it's many pieces that come together; you have a conductor, you have the orchestra, but the end of the day the delivery of that many systems is the concert. That's the way to look at it. To deliver this, our practice starts with multiple teams; one's a benchmarking team that understands the application that Dr. Gruner and SciNet will be running because they need to tune to the application the performance of the cluster. The second team is a set of solution architects that are deep engineers and understand our portfolio. Those two work together to say against this application, "Let's build," like he said, "the biggest, baddest, best-performing solution for that particular application." So, those two teams work together. Then we have the third team that kicks in once we win the business, which is coming on site to deploy, manage, and install. When Dr. Gruner talks about the infrastructure, it's a combination of hardware and software that all comes together and the software is open-source based that we built ourselves because we just felt there weren't the right tools in the industry to manage this level of infrastructure at that scale. All this comes together to essentially rack and roll onto their site. >> Let me just add to that. It's not like we went for it in a vacuum. We had already talked to the vendors, we always do. You always go, and they come to you and 'when's your next money coming,' and it's a dog and pony show. They tell you what they have. With Lenovo, at least the team, as we know it now, used to be the IBM team, iXsystems team, who built our previous system. A lot of these guys were already known to us, and we've always interacted very well with them. They were already aware of our thinking, where we were going, and that we're also open to suggestions for things that are non-conventional. Now, this can backfire, some data centers are very square they will only prescribe what they want. We're not prescriptive at all, we said, "Give us ideas about what can make this work better." These are the intangibles in a procurement process. You also have to believe in the team. If you don't know the team or if you don't know their track record then that's a no-no, right? Or, it takes points away. >> We brought innovations like DragonFly, which Dr. Dan will talk about that, as well as, we brought in for the first time, Excelero, which is a software-defined storage vendor and it was a smart part of the bid. We were able to flex muscles and be more creative versus just the standard. >> My understanding, you've been using water cooling for about a decade now, maybe? - Yes. >> Maybe you could give us a little bit about your experiences, how it's matured over time, and then Madhu will talk and bring us up to speed on project Neptune. >> Okay. Our first procurement about 10 years ago, again, that was the model we came up with. After years of wracking our brains, we could not decide how to build a data center and what computers to buy, it was like a chicken and egg process. We ended up saying, 'Okay, this is what we're going to do. Here's the money, here's is our total cost of operation that we can support." That included the power bill, the water, the maintenance, the whole works. So much can be used for infrastructure, and the rest is for the operational part. We said to the vendors, "You guys do the work. We want, again, the biggest and the baddest that we can operate within this budget." So, obviously, it has to be energy efficient, among other things. We couldn't design a data center and then put in the systems that we didn't know existed or vice-versa. That's how it started. The initial design was built by IBM, and they designed the data center for us to use water cooling for everything. They put rear door heat exchanges on the racks as a means of avoiding the use of blowing air and trying to contain the air which is less efficient, the air, and is also much more difficult. You can flow water very efficiently. You open the door of one of these racks. >> It's amazing. >> And it's hot air coming out, but you take the heat, right there in-situ, you remove it through a radiator. It's just like your car radiator. >> Car radiator. >> It works very well. Now, it would be nice if we could do even better by doing the hot water cooling and all that, but we're not in a university environment, we're in a strip mall out in the boonies, so we couldn't reuse the heat. Places like LRZ they're reusing the heat produced by the computers to heat their buildings. >> Wow. >> Or, if we're by a hospital, that always needs hot water, then we could have done it. But, it's really interesting how the option of that design that we ended up with the most efficient data center, certainly in Canada, and one of the most efficient in North America 10 years ago. Our PUE was 1.16, that was the design point, and this is not with direct water cooling through the chip. >> Right. Right. >> All right, bring us up to speed. Project Neptune, in general? >> Yes, so Neptune, as the name suggests, is the name of the God of the Sea and we chose that to brand our entire suite of liquid cooling products. Liquid cooling products is end to end in the sense that it's not just hardware, but, it's also software. The other key part of Neptune is a lot of these, in fact, most of these, products were built, not in a vacuum, but designed and built in conjunction with key partners like Barcelona Supercomputer, LRZ in Germany, in Munich. These were real-life customers working with us jointly to design these products. Neptune essentially allows you, very simplistically put, it's an entire suite of hardware and software that allows you to run very high-performance processes at a level of power and cooling utilization that's like using a much lower processor, it dissipates that much heat. The other key part is, you know, the normal way of cooling anything is run chilled water, we don't use chilled water. You save the money of chillers. We use ambient temperature, up to 50 degrees, 90% efficiency, 50 degree goes in, 60 degree comes out. It's really amazing, the entire suite. >> It's 50 Celsius, not Fahrenheit. >> It's Celsius, correct. >> Oh. >> Dr. Bruner talked about SciNet with the rado-heat exchanger. You actually got to stand in front of it to feel the magic of this, right? As geeky as that is. You open the door and it's this hot 60-, 65-degree C air. You close the door it's this cool 20-degree air that's coming out. So, the costs of running a data center drop dramatically with either the rado-heat exchanger, our direct to node product, which we just got released the SE650, or we have something call the thermal-transfer module, which replaces a normal heat sink. Where for an air cool we bring water cool goodness to an air cool product. >> Danny, I wonder if you can give us the final word, just the climate science in general, how's the community doing? Any technological things that are holding us back right now or anything that excites you about the research right now? >> Technology holds you back by the virtual size of the calculations that you need to do, but, it's also physics that hold you back. >> Yes. Because doing the actual modeling is very difficult and you have to be able to believe that the physics models actually work. This is one of the interesting things that Dick Peltier, who happens to be our scientific director and he's also one of the top climate scientists in the world, he's proven through some of his calculations that the models are actually pretty good. The models were designed for current conditions, with current data, so that they would reproduce the evolution of the climate that we can measure today. Now, what about climate that started happening 10,000 years ago, right? The climate was going on; it's been going on forever and ever. There's been glaciations; there's been all these events. It turns out that it has been recorded in history that there are some oscillations in temperature and other quantities that happen about every 1,000 years and nobody had been able to prove why they would happen. It turns out that the same models that we use for climate calculations today, if you take them back and do what's called paleoclimate, you start with approximating the conditions that happened 10,000 years ago, and then you move it forward, these things reproduce, those oscillations, exactly. It's very encouraging that the climate models actually make sense. We're not talking in a vacuum. We're not predicting the end of the world, just because. These calculations are right. They're correct. They're predicting the temperature of the earth is climbing and it's true, we're seeing it, but it will continue unless we do something. Right? It's extremely interesting. Now he's he's beginning to apply those results of the paleoclimate to studies with anthropologists and archeologists. We're trying to understand the events that happened in the Levant in the Middle East thousands of years ago and correlate them with climate events. Now, is that cool or what? >> That's very cool. >> So, I think humanity's greatest challenge is again to... >> I know! >> He just added global warming to it. >> You have a fun job. You have a fun job. >> It's all the interdisciplinarity that now has been made possible. Before we couldn't do this. Ten years ago we couldn't run those calculations, now we can. So it's really cool. - Amazing. Great. Well, Madhu, Danny, thank you so much for coming on the show. >> Thank you for having us. >> It was really fun talking to you. >> Thanks. >> I'm Rebecca Knight for Stu Miniman. We will have more from the Lenovo Transform just after this. (tech music)

(Upbeat music) >> Announcer: Live from Orlando Florida, It's theCUBE covering PentahoWorld 2017. Brought to you by Hitachi Vantara. >> Welcome back to theCUBE's coverage of PentahoWorld, brought to you, of course, by Hitachi Vantara, I'm your host Rebecca Knight along with my cohost James Kobielius. We're joined by Charles Gaddy, he is the Business Development Manager at Melissa Data. Thanks so much for joining us. >> Great, thank you for having me. >> So tell us, tell our viewers a little bit about Melissa Data and what you do there. >> Well, Melissa is a data quality and identity assurance company, so we have been around for 30 years. And we're a 30 year old start up you might say. Very innovative in what we do, and the way we address our problems. We are the strategic partner for Pentaho as it relates to data quality. So most of our data quality solutions are embedded and available within the Pentaho stack. So my particular role there is to facilitate global sales and alliances, and Pentaho is one of our global alliances. >> Okay, so that's the, it's a strategic alliance, and so what is your relationship now with Hitachi Vantara? >> That's a great question, because now that we're with Hitachi Vantara, one of the things we're focusing on is a strategy around data quality blue prints. Data quality blueprints are something that Pentaho brought in to that relationship, or that new company, right? And it's a powerful way that they sell their solutions, and craft the message around their solutions in a way that sounds less technical and more engaging, I think. And I'll give you a bit of an opinion there, and so we're very excited to be one of the first companies, from a partner perspective, to do a blueprint that's not strictly Pentaho based. >> Is it, you're talking about blueprints, is it a consultative marketing and sales tool? Or is it a solution accelerator template, or a bit of both? >> You stole my thunder, I was going to say I think it's a bit of both actually, yes. The nice thing that I've seen about the other ones they've done and the one that we're crafting is, you're taking a use case, effectively, and you're breaking down what you're bringing to that use case, with a sprinkle of technology, so that they know it is a technical solution, as well as a consultative sale. Then you're telling them about the problem you're going to solve with it, and the expected outcomes after you've solved that problem. So, the first use case is around customer data quality, within online retail, right. So, everything from preventing packages from being misplaced by using address verification, and geocoding in order to improve the quality of address data that you're shipping, all the way through to customer demographics, so you can understand and overlay demographic information about the customers you're targeting online. All of these solutions, we bring the data piece of that, and Pentaho brings the other elements to make that combined blueprint. >> So just in hearing you say those things, I'm thinking back to what we heard on the main stage today, about the potential of the dark side, in the sense of the models maybe being used for nefarious reasons, I mean, how do you guard against that? >> Well, you know, there's that AI component, which was very much of the Skynet comment I believe, and then there's data quality, which, having been around data quality for quite a while, there's a rules based element to that, that isn't necessarily AI based, so you don't necessarily have as much of that dark side to deal with, what you are rightfully pointing out, is the idea that you're using elements of data that represent someone's identity potentially, right. And how do you protect and safeguard that? And our 30 years in the business really gives us an insight on how to protect the data in ways that insure the quality of it, but then also insure that it's not used for nefarious purposes, like you said. >> Okay, so as you know, Pentaho co-founder James Dixon coined the term "the data lake". So how has Melissa partnered and integrated with Pentaho in that way? >> And how does data governance and quality ride upon and leverage the data lake to be effective? >> Okay, so it's a two part question. Looking at it from the perspective of what was described in the data lake, things are going in to the data lake. Well, you can take two approaches to it, I guess. You can try to boil that data lake, which is very challenging, you know. Or you can extract quality information out of it, and so, data quality, whether you're pushing data quality into the lake, or whether you're trying to extract actionable intelligence out of the lake, fits on both sides and gives you that step towards analytics and intelligence that you need. Right, otherwise it's a lake. The other side you mentioned is the governance side of it. So, our components that run, and our services that run as a part of what is offered with Pentaho, give elements of a feature like profiling, so you're able to profile the data as it's moving between these different places, see the anomalies, potentially address the anomalies, if that's something you need to do, or at least be aware of them so you know what's going on, right, and you're constantly monitoring. >> Does that involve AI or machine learning on your end to do that, the anomaly detection within the data lake? >> There's elements of our technology that leverage pieces of that for sure. I wouldn't call it full blown AI from that perspective, but there are some patents and some proprietary technology that we have, that gives us a unique approach on how to profile that data, and how to make that profiled information actionable within Pentaho. >> So, you talked about the retailer use case, and that's how we can make sure the packages are delivered to the right places, and the demographic. What are some other examples of ways that we can use Melissa Data? >> Okay, so as luck would have it, the first blueprint we're doing is the customer one I just mentioned, but we're already talking with Hitachi Vantara about the idea of doing a financial services one, right. And so in that fin tech space, not only would you be able to leverage matching deduplication, which they call more of an identity resolution in that element, but you'd also be able to leverage the elements of data that we bring to bear to say that you are who you say you are. So you bundle those together in a fin tech, or a financial services model, and you've got a different use case from customers and online retail, but you still have a very compelling joint offering as you're pushing data through. >> Which is particularly relevant in light of the Equifax breach, which will haunt us for the rest of our lives, we keep hearing about this. >> Yes, you have to be very careful with the data that you utilize, absolutely. >> One of the terms we keep hearing a lot is future proofing. What does that mean to you at Melissa Data? How do you describe your approach to future proofing your business? >> So, it's interesting because, as I mentioned, we're pretty much a 30 year old start up, so as a function of that, we future proofed ourselves. Because we've evolved and adapted, you have to be nimble, you have to be agile, as well as embracing agile concepts, which, there's two different meanings there, if you will. And so, in looking at that, you want to make sure that you've got the right technology set, and that that technology set can be easily adapted and evolve over time, right. I think those are they key things we've done as a company, with the solutions we've built, and much like, I heard today on the keynote, that Hitachi had focused to do, we've done a very similar thing, because we started in direct marketing, with a database of zip codes. And now we offer matching, and we offer these cloud solutions and identity. So we've had a very similar track to that story you heard earlier. >> You've said it a couple of times, you're a 30 year old start up. How do you stay innovative? I mean, you're a 30 year old start up that now has employees in four locations across the U.S. dealing in huge businesses. How do you keep that start up mentality? The hungry mentality, and the hack-y mentality, I guess I should say too? >> One of the real advantages we've got there, is our CEO and founder has always innovated. From the first company before Melissa, all the way up through today, he's always been one to say we need to try that next thing, right. Pentaho, five or six years ago, was that next thing that he and our VP of strategy said we should try, and now I'm sitting here with you today. There's a top down, bottom up approach, if that makes sense to you, because if you have an idea, you can bring that idea forward as well. >> You consider the next thing, and Hitachi Vantara's been saying that in spades today here at this event, it's also a Wikibon research focus, the Edge, Edge computing, Edge analytics, data, machine data coming from Edge devices, how is Melissa Data, in partnership with Pentaho, moving towards this Edge to outcome frame of reference, or frame for building innovative solutions, where does that fit with your roadmap going forward? >> So our perspective on that, much like when we first engaged with them, data was going into the data lake, let's just get it all in there, get it all in there, get it all in there, get it all in there, right. Well, eventually you have to make that data actionable. You're going to have a reverse scenario with the Edge. There's a lot of data, small amounts, small chunks, that are going to be everywhere, I think it was talked about being on cell phones, and everywhere else. The idea that you can extend the reach of data quality along with the reach of analytics, to actually make sure you're getting the best data you can, to feed those microanalytics, to feed that, that's a critical part that we see as potential. >> Looking ahead, what are some of the problems that you want to solve, just sort of in the next year, the next five years, what are some of the things that you're thinking about and keeping you up at night right now. >> We're doing some very interesting things with globally unique identifiers, I'll call them that, not a GUID in that sense, but the idea that every address on the planet could be indexed, right. And then the idea beyond that was every email and every phone and every identity around that could be indexed. Then when you're dealing with a massive amount of indexes, becomes a lot faster and a lot easier to match, to dedupe, to do other data quality tasks. So, it's one of the projects that our CEO is very interested in, is this sort of indexing or massive indexing table concept. And so that's one of the things I know we're very focused on as an organization, and how that can feed all of our other technologies. >> How would that work, I mean, I know it's a research process in motion, but >> And keep in mind I am the head of global sales and alliances, so don't bust out all the too technical a question. (laughter) >> Yeah, so this is identity resolution at a massive scale, does it involve an internet of things, almost like a, slap me on the wrist, a graph, a social graph of you and all the identities you may have running on various Edge devices? You meaning a user. >> I think there is the potential for pieces. >> Remember, I'm a geek here so. >> Yeah, yeah there's a potential for pieces of that to be used in that way. Like an example we got approached about was, someone who wanted to have a cookie that represented the address that they just captured from this particular interaction on the web, right. Well, imagine if you could use this table of addresses that was indexed, right, to get that number back, and you just store that number constantly with that cookie, you'd never have to store that address data again, you could match that index against other indexes, and the uses go on and on and on. >> James: Right. >> So it's not complete in any way, so I wouldn't want to venture to answer the implete part of your question, but the idea that you can represent things with a series of numbers is how the internet got started, effectively, right, so you could look at something similar. >> Right. >> So you're here at PentahoWorld, and you said you're a biz dev manager, what is your, what do you hope to take away from it? I mean, are you talking? >> You mean outside of business? (laughter) >> Get some deals done, exactly. But what are you learning, what are you hearing, are you sharing best practices, and how do you do that here? >> Well, we're pretty tightly connected into different elements of what is now Hitachi Vantara, right, so we work with their office in Singapore, we work with them engaged all over the world, on many different fronts, and so it's nice to be here one, so you can literally put some faces with some names, right. And as you look at some of their different initiatives, like cyber security that I've seen, over there somewhere, and some of the other initiatives they've got going, they march a bit in lock step with what we're doing, and the nice thing about being here, is the ability to sort of reconcile that and see and talk about how we can go forward together with those elements, if that makes sense. >> James: Right. >> Absolutely. Well Charles, thanks so much for coming on theCUBE, it's been a great talking to you. >> James: Yeah absolutely. >> Thank you for having me, I appreciate it. >> We will have more from theCUBE's live coverage of PentahoWorld in just a little bit. (upbeat music)

>> Narrator: From Hell's Kitchen in New York City, it's The Cube on the ground at Serverlessconf. Brought to you by SilliconANGLE Media. >> Hi, I'm Stu Miniman with The Cube, and we're here are Serverlessconf in Hell's Kitchen New York City, really happy to welcome to the program, another one of the keynote speakers. Ben Kehoe, who's the Cloud Robotics research scientist at iRobot. >> Yeah. >> Ben, great to see you. >> Great to see you too. >> All right, so tell us a little bit about how you got involved with Serverless. >> Yeah, I mean it all started, I was a grad student in robotics, and I started thinking about, you know, we have all these robotics algorithms. And as the cloud can enable robots to do more and better things, how do we help turn those robotics algorithms into web services. And I didn't get very far in that, right towards the end of my PHD, and then that was 2014, LAMBDA was released, and it was like hey, that looks like it does the kind of thing that I was thinking about that we needed. So then I joined iRobot, and we were developing a cloud solution, a cloud application for our connected robots and apps, and to help us scale that to stay lean. Serverless was the right choice, and we've been doing that since 2015. >> Yeah, so Ben, what is it about Serverless that made it a fit for this? You know, I think about, doesn't their responsiveness, performance, latency if I have to go >> Yeah. >> up to the cloud and back like that way. I think some of this needs to kind of live locally. And some that goes there, maybe you can just briefly tease through some of those dynamics for us. >> Yeah, when you're talking about robots, you definitely have to keep things local. You want a robot to be responsive to its environment. You want, that even if its cloud connection disappears, that it can still accomplish all of its tasks. So it's always a mix of keeping it as a timeless robot that is enabled to do better things through the cloud, in terms of additional computational power, or accessing libraries of information to help it understand its world better. And of course, when one robot learns something, all robots can benefit from that experience. >> Excellent, so this is the first step for Skynet is what you're saying, right? >> Could be. >> All right, bring us in a little bit. Your keynote, what were you looking to share? You know, some of the key points. >> Yeah, I think in the talks that I've given at Serverlessconf, they tend to be as much as I am enthusiastic about Serverless, fully bodying, I try and pull us back a little bit to say, "What are we still missing? "What's not here yet? "Where do we need to go?" And so I had some frowny face emoji in my talk about event driven programming, event driven Serverless, and Serverless without event driven programming. Now we're still, you know, we have areas to improve in each one of those. And then that transitioned really into, "How do we start bringing in people who "are just starting into Serverless?" Larger organizations, more traditional architectures, and people who are experienced with that, and understand traditional architectures well. How do we get them on board with Serverless? And so that starts with just the gateway drug, which is infrastructure automation at the edges of their application, taking scripts that they run from developer machines with Cron jobs, and moving those into a function that's triggered by some cloud event. And then from there, starting to bring them over in terms of you can reduce your costs by eliminating idle resources. You can start to simplify and strengthen by refactoring some of that. And then once you really get them thinking about, "Oh, this is really working for the things "that we're doing." New features will start to be developed. Serverless native or event driven native. And then sort of at the end of the talk, the key is that because Serverless architectures look different from traditional architectures, there's something called Conway's law that says, "The design of your application will follow "the communication patterns in your organization." >> Stu: Right. >> And so you have to sort of flip that around to say, "Well if our design is changing, then we have "to make our organization change as well." >> Right, does that mean we're going to have, micro-employees you know? Instead of micro services we have, you know, employees that we hire them, and then we fire them pretty quick when we don't need them, or? >> I hope not. >> Yeah. >> I hope not. >> (crosstalk) that that's the part time, the uber's >> Yes. >> nation of the workforce. >> Yes. That would be, I think an inefficient way of going about it. >> Yeah. >> But I think we do need to reset expectations around what we have control over, and what we don't, because when you're on a traditional architecture with servers, you can reach in and fix problems that you have. And recognizing that when you're running on functions as a service platform, and using managed services, that when the provider has some sort of incident, you're out of control of that. It's a very uncomfortable place to be of not being in control of your own destiny, even though when you look at the big picture, that's going to happen less often, then if you were doing it yourself. >> Stu: Yeah. >> And so that's making sure that the mindset inside the organization, and the way that people communicate, is appropriately tuned to that sort of new paradigm. >> Okay, yeah. Ben, some of those frowny faces, what are things that the community is working on that you're hopeful for? What are some of the areas that we need for the maturation of this space? >> Yeah, I think something that I talked about previously that's coming around, is monitoring. So there's much more tools out there to monitor the infrastructure to know what's going on inside these functions and these managed services. And there's now some security analysis tools that are coming out, that some of these people are present here. And that was a big aspect that I've harped on for a long time of... We have a lot of mature traditional tools, that will do network analysis of your servers. Well it's like, "I don't have any servers." And those vendors then say, "Well, we can't help you." And there's static code analysis vendors who say we look at your whole application, and the flows inside it. And we say, well most of my application exists outside of code that I've written. I just write little bits, that glue it together in the way that my business works. And they say, "Oh, well we can't help you." >> Yeah. It reminds me, I think you know for so many years, people were really excited about how they could build their infrastructure. >> Yeah. >> And now they look to environments, well I can get out of that. So it caught my eye. You know, you put out on twitter, said "Maybe we need to have, you know, my next talk will be, "Work dumber not harder." Maybe explain that a little bit. >> Yeah, so I think, >> Yeah. >> I've been thinking about, you know, with some of the talks here about how it's not building it yourself. That in some ways, there's not invented here syndrome. And we kind of want to go a little bit down the road of invented here syndrome, of if you're building something that is not business logic, you're probably ideally thinking, "Maybe I shouldn't be doing this." So turning it into, I don't want to have to be clever in setting up my architecture, because being clever and like writing, it's always interesting to do, right? When you're developing, you're solving a computer science problem. But often that mean you're not delivering business value. And so, in Paul Johnson's talk, he was talking about the kind of people he looks like. What the kind of people he looks for, look like. >> Yeah. >> And he was saying, you know, "It's people "who want to get stuff out the door. "And who think about good enough." And I think that's really the thing of, how do we, when the people you hire are people who just want to ship features, they're going to say, "I can pull together services to do that "without having to actually solve any hard problems." And that means that you're delivering value, and you're operating more in your business space then in a technology space." >> All right, Ben I want to give you the final word. >> Thank you. >> You know, only 460 people here, which is good growth for the show, but a lot of people out there that are still learning about Serverless, what tips do you give them? You know, first steps to get involved, get involved with the community, (mumbles) some early wins they can have? >> I think there's a couple of things. There is training out there, there's blogs. There's twitter. Ask questions. You know, ping me on twitter if you wonder about something. And there's a Serverless slack that's very active, and if you ask basically anybody, the link is floating around. >> All right, well Ben Kehoe, thanks so much. Great to meet you, and thanks for sharing in this community. >> Yeah, thanks for having me. >> And our community, I'm Stu Miniman and thanks for watching The Cube. (upbeat, exciting music bumper)

(futuristic music) >> Hello and welcome to theCUBE Studios here in Palo Alto, CA I'm John Furrier, co-host of theCUBE and co-founder of Silicon Angle Media, Inc. I'm joined here with Tom Joyce, Cube alumni. Some big news, new role as the CEO of Pensa. Welcome back to the Cube. You've been freelancing out there as an entrepreneur in residence, CEO in residence, you've been on theCUBE commentating. Great to see you. >> Good to see you, too. Thanks for having me back. You know, fully employed. >> Congratulations. You know, finding where you land is really critical. I've talked to a lot of friends, and they want to get a good fit in a gig, they want to have a good team to work with it's a cultural issue, but also you want to sink your teeth into something good, so you found Pensa. You're the CEO now of the company and you've got some news which we'll get to in a minute, but what's going on? Why the change, why these guys? >> You know, last time we talked, last time I was in here, I was running a consulting business, and I did that for almost a year so that I could look at a lot of options and you know, kind of reset my understanding of where the industry is and where the problems are. And it was good to do that. These were some of the best people that I met, and I got interested in what they were doing. They're smart, technical people, I wanted to work with them It was a good fit in terms of skills because when I joined Pensa just a couple of months ago now they were all technical people, and they'd been heads-down developing core technology and some early product stuff for almost three years. So they needed somebody like me to come in and help them get to the next level and it was a really good fit. And the other thing is, frankly, in my last job I was running an IT shop and I also had a thousand people out there selling, and about 300 pre-sales people, and when I saw this, I saw a product that I could've used in both of those areas. So sometimes when you resonate with something like that you start to think well geeze, this is something that I could, that a lot of people are going to need. And so there are many aspects of the technology that are interesting, but ultimately, I saw that this is a useful thing that I could go make a big business out of. So that's why I did it. >> You've had a great career, you know we know each other going way back, EMC days, and certainly at HP, even during the corporate developments work that Meg Whitman was doing at HP but involved in a lot of M&A activities, so you seen the landscape, you are talking about all the VCs, and all the conversations we've talked about in the past on other interviews you can check it out on YouTube, Tom Joyce, if you're interested in checking those conversations out. Worth looking at. So you landed at Pensa. What do they do? What was the itch for you? What was the, why are they relavant? What do they doing? >> Well, the first thing is, the company was founded about three years ago by people that had hardcore experience in big networking and virtualization environments. And they've been tackling some of the hardest problems in virtual infrastructure as you move from the hardware to everything being virtualized on multiple clouds. These guys were tackling the scale problem. And they'd also drilled down into how to make this work in the largest network environments in the world. So they had gotten business out of one of the largest service providers in the world as their first customer. So you look at that, and you say, alright these are smart people. And they're focusing on hard problems and there's a lot of, a lot of longevity in the technology that they're going out and building. And basically, what they're trying to do is help customers go to the next level with all software-based or software-defined, if you will, infrastructure, so that you can take technology from a whole bunch of different sources. It's going to be VMware, OpenStack, DevOps, the DevOps Stack as well as the whole constellation of people in the security industry. How do you make all those software parts work together at scale, with the people that you have? Rather than going out an hiring a whole new IT staff to plug all this stuff together and hope it works, these guys wanted to solve that. So it's without a lot of expertise, this product can go design, validate that it works, build and deploy complete software-defined environments, and it can do it faster than you could do it any other way that I'm aware of, and I've been around this industry for a long time. So that's what I saw when I said, geeze, I could have used this before, I could have used it in my own IT where our exposures were things like we had all this old software that we needed to update and we're scared to touch any of it, right? You look at things like Equifax. I was exposed in the Equifax breach, and that was exactly that scenario. >> Yeah, and they had four months in there playing around. Who knows what they got? >> To be honest with you, in my business we were doing the same thing because we weren't comfortable with upgrading our software cause we couldn't validate that it worked. How do you move from the old stuff to VMware six-dot-five and make sure nothing else breaks? We're kind of in the era of needing machine learning, intelligent technologies, autonomous kinds of ways to deploy this stuff, cause you can't hire enough smart people to go do it. And that's what I saw. >> Well, we'll do a breakdown or a tear-down, however you want to look at it, of the company in a second, but you guys have some news. Let's get to the news. What's the big news that you're sharing today? >> Okay, great. Well, there's a couple of key parts of it. First, we're formally launching the company. We've been heads down in development and I've been there for a few months, but the company hasn't been launched. So we're doing that, we're introducing Pensa to the world and the new website is Pensa.ai. The second thing is we've completed our Series A financing so we've got the financing under our belt. Third thing is we've been hiring a team. We've brought in certainly me, I've brought in a fella named Jim Chapel as the VP of marketing, long-time industry guy in both large and small software companies. And we're rolling out the first product. So the technology is called-- >> In terms of shipping? >> Yeah, it's going to be shipping as a SaaS offering and it's available now. It's built on our technology which is called Maestro, which is this smart machine, and the first offering is called Pensa Lab. And I can describe to you what it's used for, but it's for helping people go figure out how do I design, build, run, try new scenarios, and roll out stuff that's actually going to work and do it a lot faster than people can do with traditional technologies. >> Congratulations for launching the company, congratulations on the new role, great job. I'm looking forward to seeing you, But let's get into company, Pensa. >> Alright. >> So let's just go in market you guys are targeting. Take a minute to go into the market. What's the market, what's going on in the market, what trends, what's the bet in the market for you guys? >> With a early company like this, there's always a lot of things you can do and the battle is figuring out what is the first thing we're going to do? So I think over time we're going to be relevant to a lot of people, the first customers we're going to be focusing on are people in IT that are trying to manage complex virtualized networks. So a lot of them are people using VMware today. >> So the category is virtualization cloud? What's the category? >> It's a SaaS product for design, build, run. So it's really designing autonomous IT systems that are built on software-defined environments. So it's VMware, OpenStack, DevOps stack, and being able to kind of bring all those parts together in a way that from an operational standpoint you can deploy quickly. In the first version of the product is going to be designed for test in depth. And next year, we intend to bring out production versions of it, but virtually every one of these folks has environments for test today to figure out alright, I want to go do my update, my upgrade, my change I want to try a different security policy, cause I've got a hack happening and I want to do that fast, we're going to go after that. The other side of it is folks in the vendor community. Almost anybody that's selling a solution, again, like me and the job that I used to have, has people out there doing proofs of concept, demos, building systems for customers. And what we can do is give you the ability to spin up complete working environments and do it (snaps finger) basically like that. If you got a call this afternoon to go show VMware NSX running with some customer application with some other technology from a third we can make that work for you, and then you can tear it down and do the next one at four o'clock in the afternoon. >> So that a VMware customer-based you're targeting, I mean, it sounds like, and clarify if I don't get this right, you don't really care if it's private cloud, or hybrid cloud, or public cloud. >> We don't care. No, we don't. And there's a lot of folks-- >> And VMware, is that a target market, VMware buyers? >> Absolutely. Yup. And frankly, we've had people inside of VMware working with us as a number of the beta testers on this and demonstrating that they can spin up their own environments faster, so that kind of proof point is what we're after. Then there's a lot of folks in DevOps, right? DevOps is one of the hot targets for our business and a lot of businesses and what we see is folks that are focusing on the app development side of DevOps and then they get to the point where they got to call IT and say alright, give me a platform to run my new application on and they get the old answers. So a lot of these folks are looking for the ability to spin up environments very very quickly, with a lot of flexibility where they don't need to be and expert in alright, how's the storage going to work and how do I build a network, right? >> So are you targeting IT and DevOps hybrid, or is it one of the other DevOps developers? >> It's both. >> Okay and you don't care which cloud so you're going to draft off the success that VMware's seeing right now with their cloud strategy with AWS >> Absolutely. I mean look, there's a lot of ways >> Software design is booming. >> We can help those customers figure out how do I do VSAN faster? How do I do NSX faster? How do I set up applications that I can move to AWS faster? It's kind of bringing-- >> So software-defined clouds, software-defined data center, all this is in your wheelhouse. >> Yes, that's exactly right. >> This is what you're targeting. >> And that's the opportunity and the challenge. Again when you're doing a small company, the world is your oyster but you have to kind of focus on the first thing first. So we're going to go in and try to help people that have, are dealing with alright, I need to kind of update my software so that I don't have an Equifax, or I need to fix my security policies, I need an environment like, today that I can use to test that. Or, I want to go from the old VMware to to the new VMware, I got to make sure it works. That's good for the customer, that's good for VMware, it's good for us. >> And the outcome is digital productivity for the developer. >> Absolutely. >> OK, so let's talk about the business, and the business model. So you guys raised some money, can you talk about the amount, or is that confidential? >> It's confidential at this point and we have some additional-- >> Is it bigger than 10 million? Less than 10 million? >> It's been less than 10 million. We're going to go lean and mean, but we're set up to make the run we need to run. >> OK, good I got that out of the way. Employees, how many people do you guys have? What's the strategy? >> Just over 20 now, and we have a few more folks that we're going to be adding. We're going to go fairly lean from here. >> Okay, in terms of business model, you said SaaS Can you just explain a little bit more about thee business model, and then some of the competition that you have? >> Yeah, this product was designed from day one to be a SaaS product, so we're not going to go on-premise software or old models, we're going with a SaaS model for everything we're doing now and everything we intend to do in the future, so the product sits in the cloud, and you can access it basically on demand. We're going to make it very easy for people to get in and give this a try. It's going to be simple pricing, starting at about 15 hundred dollars a month. >> So a little bit of low-cost entry, not freemium, so it's going to some cost to get in, right? Try before you buy, POC, however that goes, right? >> Yeah, it's see a demo, do a trial, give it a shot. I'll give you an example, right. When I was at my last job, I had 300 pre-sales people >> Where's this? >> This was at Dell Software. >> Dell Software, okay, got it. >> Now it's called Quest. They would go out and they'd use cloud-based resources to spin up their demo environment. Well, I'm going to give them, and I'm calling them, by the way, the ability to buy it for a very short amount of money and you're not committed to it forever, you can use it as much as you want. And get the ability to say alright, let's spin up VMware, let's spin up OpenStack, let's spin up F5 Palo Alto Networks whatever security I want, get my app running on that without being an expert in all those parts. >> You can stand up stuff pretty quickly, it's a DevOps ethos but it's about the app and the developer productivity. >> Right. And from a business model standpoint, it's how do I make this really, really easy? Because the more of those folks that use it in this phase, next year, when we get to say alright, let's punch that thing you built into production on your cloud, we'll be ready to go. Our goal is to grab space quickly. >> Talk about competition. >> I think the competition for this part of it this kind of dev test lab spin up scenario, the Pensa lab that I just described, the biggest competition is going to be people that build their own. So in the corner you've got your test environment running on your old hardware, right? So that doesn't come with this automated software capability. The other ones are going to be people like Skytap, as an example, that a lot of people use, and I've used in the past, that gives you a platform to run on, but again, a lot more cost and not the automated software capabilities. So there are a lot of scenarios like that that we can go after, and it's almost universal. Everybody's got a need to have some sort of a test or dev environment, right? And we are going to prove to them that the software is better. >> So not a lot of competition. It's not like there's a zillion players out there. >> No, it's a big target, but there's not a lot of players. And for the most part, you're going to go into scenarios where customers have something they've cobbled together that isn't working as well as they'd like. >> And Pensa AI hints a little bit of a automation piece which is really all our people know in the enterprise. Let's talk about the technology. What's under the hood, is there AI involved, also you've got the domain name .ai, which I love those domain names, by the way, but what's the tech? What's driving the innovation and story differentiation? >> To be honest with you, inside that's something you debate because that's what it is. If AI is a way to use technology, to do things as well or better than people used to do before, that's what it is. And if you take all the hype, and nonsense out of the conversation, you say it's not about SkyNet and computers taking over the world, it's really about doing stuff better than we can do and making people more effective, that's what we have. Now, under AI there's a bunch of different techniques and we're going to be focused on primarily modeling and the core IP of this is how we build the model for all of those components and how they interact and how they behave, and then machine learning. How do we apply techniques to actually-- >> So you're writing software that's innovating on technology and configuration, tying that together and then using that instrumentation to make changes and/or adaptive-like capabilities-- >> Exactly, but rather than go spend a month building the template that you're going to go deploy the system will build that for you. And that's where the smarts are. And we'll use machine learning techniques over time to make that model better. So that's kind of where we're digging, and frankly it's a big problem for people. >> So software you're main technology. >> It's 100% a software platform. >> Okay, well, Wikibon Research was going viral at VMworld and I'll make a note cause I think this is important cause automation is our and it's a key point of your thing is that Wikibon showed that about 1.5 billion dollars are going to be taken out of the market as automation takes non-differentiated labor out of the equation, which essentially is stacking servers and racking, stacking and racking. That plays right into your trend. >> That's exactly what we're doing. And what we want to do is-- >> By the way that value shifts, too, all the parts. >> Yeah, and I think we're trying to focus-- automation isn't new. It's not new in IT. Certainly there's been a lot of focus on it the last 10 years. The question is how do you make the automation smarter? So you don't have to do the design and say push play. Cause the problem with automation in these really complicated microservices, multi-- the problem is, if you automate it, if you build that template wrong, you can make the same mistake a thousand times in a row. And I've had products in the past where they've worked great as long as that template was correct. Well what if the template changes? What if I need to put new security policies in there, changes? Maestro is going to build it for you. That's what the story is all about. >> That's your product, that's your product name. >> Yep. >> Well, that's what DevOps is all about. Programming the infrastructure, and that's always going to change. So that's really the DevOps ethos. >> Yeah, and that's why if you expand out from the first play run, this test dev scenario, well, frankly, we'll learn a lot. We'll learn a ton about different patterns that we see, we'll learn a lot about the Interop environment that customers want, I want you to add this or add that, the system is going to get smarter to the point where when we punch it into production, it's going to know a lot more than it does today. >> Well congratulations on the launch. My final question for you is really the most important one which is, if I'm a customer, why do I care? What's in it for me? What's the value? Why should I pay attention to Pensa.ai? What's going on, what's the value to me, why should I care, why should I call you? Gimme that bottom line. >> It's about risk reduction. It's about making sure that the things you need to change you can actually do it without it blowing up in your face. And it's also, frankly, the other side of the AI-- >> What, the infrastructure blowing up in my face? Or just apps? >> If you make changes to your environment and you're not sure if they're going to work, but you know, again, take the Equifax thing. If they had made those changes and put them into their environment, it wouldn't be on the front page of every newspaper in the world. Frankly, my information wouldn't have been hacked. >> What would you guys have done if I was Equifax and I knew that potentially I had to move fast? How could you guys solve that problem? >> If you have a problem, upgrade the software today. And what we would've done is give them the ability-- >> Do you think they knew they had a problem? >> Uh... I don't know if they did or not, but you can see this scenario over and over and over again in other companies, where they say, we know we need to do an update, but we're not doing it. We're going to wait for the six months-- >> Cause it breaks stuff. >> Cause we're scared. >> Scared, or that it breaks stuff, or both? >> It breaks stuff and we need to test it, right? So we're going to bring test velocity into that, we're going to bring intelligence to make sure the design is right, right? So that you can do it more quickly. In many different scenarios. >> It's interesting in the old days, it was like, patch management was a big thing, that was the on-premise software, but with DevOps, you need, essentially, test and dev all the time on? >> You do. If you're developing these applications with DevOps in the front end, and you're dropping new versions of 'em in hours, rather than quarters, the infrastructure in the back end has to kind of speed up to DevOps speed. And that's where we're going to focus our attention. >> Alright, here's the hard question for you and we'll end the segment, is when does a customer, your potential customer, know they need you? What's the environment look like? What's the pain points? What are the signals that they need to be calling Pensa.ai? What's the deal there? >> Yeah, I think we're going to talk to the DevOps people that are looking to get their applications out and get them built and deployed-- >> So, need for application pushing, that's one. >> That's one. The other ones are going to be folks inside any IT organization that need better velocity, need to be able to test one and take money, cost out of it, cause we're going to do it for a lot less than what it costs you to do now. And the third one is the vendor community. Folks out there selling software. VARs, pre-solicit people. >> So I guess the question is more specific. What is the signs inside the customer that make them want to call you? Stuff's breaking, upgrades not happening fast enough, I'm trying to get to the heart of it. If I'm a customer-- >> On the IT customer side, it's all about velocity. We need to push our apps faster, we need infrastructure faster, we need to test security policies faster, we're not going fast enough-- >> So basically if you're going slow, not getting the job done, they call you. >> Pretty much, that's our guys. >> Tom, congratulations on the launch, congratulations on the new CEO job, we'll be tracking you guys. Series A funding, congratulations, who's the VC involved? >> We have The Fabric, which was the seed funding source, and then March Capital has been very helpful to us in this A round. >> Great, well they got a great pro in you as CEO. We'll keep in touch. Cube alumni, good friend Tom Joyce here inside theCUBE Studios on the conversation around the launch of the company, Series A funding, new team members, and Pensa.ai. This is theCUBED. Cubed.net is our URL, check it out. Siliconangle.com and wikibon.com is where you can go check out our stuff. I'm John Furrier, thanks for watching. (futuristic music)