>> From the Salesforce Tower in downtown San Francisco, it's theCUBE, covering Accenture Tech Vision 2019. Brought to you by SiliconANGLE Media. (upbeat techno music) >> Hey welcome back everybody, Jeff Frick here with theCUBE. We are live in downtown San Francisco, the Salesforce office in the brand new Accenture Innovation Hub. It's the grand opening, like I say the soft opening, but we had the ribbon cutting, we're presenting the Accenture Technology Vision 2019 and we're excited to have somebody who's not a technologist who's very important to technology, she's Doctor Rumman Chowdhury, she's the Global Lead For Responsible AI at Accenture. >> I am. >> Great to see you. >> Thank you for having me on your program. >> Absolutely. So I was doing some background research on you and I love you introduce a lot of your talks about the fact that you're not a technologist, you come at this from a very, very different point of view. >> I do. So I am a social scientist by background. I've been working as a data scientist in artificial intelligence for some years but I'm not a computer scientist by trade. I come more from a stats background, which gives me a different perspective. So when I think of AI or data science, I literally think of it as information about people meant to understand trends in human behavior. >> So there's so many issues around responsible AI. We can talk, probably, to all these people, go on above, you know. >> Yeah. >> We don't have too much... And the first one is really a lot in the news right now, about AI is simply a codification of existing biases often, unless you really take a very proactive stance to make sure you're not just codifying biases in software. What are you seeing? >> Absolutely. So we really have to think about two kinds of bias. There's one that comes from our data, from our models. This can mean incomplete data, poorly trained models. But the second one to think about is you can have great data and a perfect model but we come from an imperfect world. We know that the world is not a fair place, some people just get a poor lot in life. We don't want to codify that into our systems and processes, so as we think about ethics and AI it's not just about improving the technology, it's about improving the society behind the technology. >> Right. >> Yeah. Another big topic I think that's really important is if you're doing a project and you want to think through some of the ethical issues, should we be collecting this data, why are we collecting this data, why are we running these algorithms and you make a decision it's for a particular person, purpose and the value outweighs the cost. But I think where the challenge really comes into is the next people that use that data or the next use that you don't necessarily have in mind and I think we hear that a lot in terms of kind of the complaints about the current state of big tech, where everyone is doing their little piece. >> Right. >> But what happens over time as those get rolled into maybe bigger pieces that weren't necessarily what they were starting with in the first place. >> Right. >> Absolutely, it's something I called moral outsourcing. Because what we build is often, we feel like a cog in a machine, we feel sometimes as technologists people aren't willing to take the responsibility for their actions, even though we should be. If we build something that is fundamentally unethical, we need to stop and ask ourselves, just because we can doesn't mean we should. >> Right. >> And think about the implications on society. Right now there's often not enough accountability because everybody feels like they're contributing to this larger machine, who am I to question it and the system will crush me anyway. So we need to empower people to be able to speak their minds and have an ethical conscience. >> So I'm curious in term of the reception of your message when you're talking to clients because clearly there's a lot of pressure to innovate fast. Everyone is telling everybody that data's the new oil and we've got to leverage these micro-experiences, et cetera, et cetera, et cetera. And they don't necessarily take a minute to step back and reflect >> Right. >> Is this the right thing, is this the right way? Are we collecting more data than we really need to achieve the objective? So how receptive are companies to your message? Do they get it? Do they have >> Yeah. >> To get hit upside the head with some problem before they really understand the value? >> So I'll give you a phrase that everybody understands and then they get the point of ethics in AI. Brakes help a car go faster. If we have the right kinds of guard rails, warning mechanisms, systems, to tell us if something is going to derail or get out of control, we feel more comfortable taking risks. So think about driving on the freeway. Because you know you can stop your car if the car in front of you stops abruptly, you feel comfortable driving 90 miles an hour. If you could not stop your car, nobody would go faster than 15. So I actually think of ethics and AI are an ethical implementation of technology as a way of helping companies be more innovative. It sounds contradictory but it actually works very well. If I know where my safe space is, I'm more capable of making true innovations. >> Right. So I want to get your take on another kind of topic, which is really kind of STEM education versus not STEM, or ethics. >> Right. >> And it's interesting, huge push on STEM, it's very, very important thing that's going on now. But as you look not that far down the road, and this events all about looking down the future, reinventing the future. As more and more of those kind of engineering functions are taken over by the machines >> Right. >> It seems like where the void is is really more talking about what are the implications, what are the deeper questions we should be asking, what are the ethics and the moral questions before just building a better mousetrap. >> Right. So you're raising a very hot button issue in the ethics and AI space. Is it simply enough to say all technologists should take an ethics course? I think it is very important to have an interdisciplinary education but, no, I don't think one ethics course, taken out of context in college will help you. So I think that there's a few things to think about. One is that corporations need to have an ethical culture. It needs to be a good thing to be ethical, number one. Number two, we need interdisciplinary teams. Often technologists will say, and rightfully so, "How was I supposed to know thing X would happen?" It's something very specific to a neighborhood or a country or a socio-economic group. And that's absolutely true. So what you should do is bring in a local community, the ACLU, some sort of a regional expert. So we do also need to move towards creating interdisciplinary teams. >> Right. So you brought up another really cool thing I think in one of your talks, FAITH. Fairness, Accountability, Transparency and Explainability >> Yes. >> Which is a, you know nobody likes black box algorithms. >> Yep. >> But fairness, specifically, is such an interesting concept. We all feel very slighted if we perceive things not to be fair. >> Yes. >> The reality is life is not fair, a lot of things are not fair. So as people try to incorporate some of these things into the way they do business, how can they do a better job, what are some of the things they should be thinking about >> Yeah. >> So they can have the faith? >> Fairness is a very complicated, complex thing and I invite you, or whenever someone asks, "What does it mean to be fair?" I point them towards this really great talk from this conference called Fat Star and it's called, 21 Definitions of Fairness. And it's all these different ways in which we can quantify and measure the concept of fairness. Well at Accenture, we took that talk and some other papers and created something called the Fairness Tool. So it's a tool to help guide discussion and show solutions on algorithmic bias and fairness. Now, the way we think about it is not as a decision maker but a decision enabler. So how can you communicate as a data scientist to a non-technical person to explain the potential flaws and problems and then take collective action? So the algorithm can help you make that decision but it's not automating the decision for you. So what it does is it helps smooth conversation and helps pinpoint where there might be bias or unfairness in your algorithm. >> Right. Well we don't have time tonight but another time we're going to >> Sure. >> Dig deeper into this and all the biomechanics and bioengineering >> Yes. >> And a lot of great topics that you've covered in a number of your talks. So I really enjoy getting to meet you and you do terrific work, really enjoy it. >> Thank you, thank you very much. >> Alright, thank you. She's Rumman, I'm Jeff, you're watching theCUBE. We're at the Accenture Innovation Hub in downtown San Francisco. Thanks for watching, see you next time. (upbeat techno music)

>> Announcer: Live from Stanford University. It's theCUBE, covering Stanford Women in Data Science 2020, brought to you by SiliconANGLE Media. (upbeat music) >> Hi, and welcome to theCUBE. I'm your host, Sonia Tagare. And we're live at Stanford University covering the fifth annual WiDS Women in Data Science Conference. Joining us today is Lucy Bernholz, who is the Senior Research Scholar at Stanford University. Lucy, welcome to theCUBE. >> Thanks for having me. >> So you've led the Digital Civil Society Lab at Stanford for the past 11 years. So tell us more about that. >> Sure, so the Digital Civil Society Lab actually exists because we don't think digital civil society exists. So let me take that apart for you. Civil society is that weird third space outside of markets and outside of government. So it's where we associate together, it's where we as people get together and do things that help other people could be the nonprofit sector, it might be political action, it might be the eight of us just getting together and cleaning up a park or protesting something we don't like. So that's civil society. But what's happened over the last 30 years really is that everything we use to do that work has become dependent on digital systems and those digital systems, some tier, I'm talking gadgets, from our phones, to the infrastructure over which data is exchanged. That entire digital system is built by companies and surveilled by governments. So where do we as people get to go digitally? Where we could have a private conversation to say, "Hey, let's go meet downtown and protest x and y, or let's get together and create an alternative educational opportunity 'cause we feel our kids are being overlooked, whatever." All of that information that get exchanged, all of that associating that we might do in the digital world, it's all being watched. It's all being captured (laughs). And that's a problem because both history and political science, history and democracy theory show us that when there's no space for people to get together voluntarily, take collective action, and do that kind of thinking and planning and communicating it just between the people they want involved in that when that space no longer exists, democracies fall. So the lab exists to try to recreate that space. And in order to do that, we have to first of all recognize that it's being closed in. Secondly, we have to make real technological process, we need a whole set of different kind of different digital devices and norms. We need different kinds of organizations, and we need different laws. So that's what the lab does. >> And how does ethics play into that. >> It's all about ethics. And it's a word I try to avoid actually, because especially in the tech industry, I'll be completely blunt here. It's an empty term. It means nothing the companies are using it to avoid being regulated. People are trying to talk about ethics, but they don't want to talk about values. But you can't do that. Ethics is a code of practice built on a set of articulated values. And if you don't want to talk about values, you don't really having conversation about ethics, you're not having a conversation about the choices you're going to make in a difficult situation. You're not having a conversation over whether one life is worth 5000 lives or everybody's lives are equal. Or if you should shift the playing field to account for the millennia of systemic and structural biases that have been built into our system. There's no conversation about ethics, if you're not talking about that thing and those things. As long as we're just talking about ethics, we're not talking about anything. >> And you were actually on the ethics panel just now. So tell us a little bit about what you guys talked about and what were some highlights. >> So I think one of the key things about the ethics panel here at WiDS this morning was that first of all started the day, which is a good sign. It shouldn't be a separate topic of discussion. We need this conversation about values about what we're trying to build for, who we're trying to protect, how we're trying to recognize individual human agency that has to be built in throughout data science. So it's a good start to have a panel about it, the beginning of the conference, but I'm hopeful that the rest of the conversation will not leave it behind. We talked about the fact that just as civil society is now dependent on these digital systems that it doesn't control. Data scientists are building data sets and algorithmic forms of analysis, that are both of those two things are just coated sets of values. And if you try to have a conversation about that, at just the math level, you're going to miss the social level, you're going to miss the fact that that's humanity you're talking about. So it needs to really be integrated throughout the process. Talking about the values of what you're manipulating, and the values of the world that you're releasing these tools into. >> And what are some key issues today regarding ethics and data science? And what are some solutions? >> So I mean, this is the Women and Data Science Conference that happens because five years ago or whenever it was, the organizers realize, "Hey, women are really underrepresented in data science and maybe we should do something about that." That's true across the board. It's great to see hundreds of women here and around the world participating in the live stream, right? But as women, we need to make sure that as you're thinking about, again, the data and the algorithm, the data and the analysis that we're thinking about all of the people, all of the different kinds of people, all of the different kinds of languages, all of the different abilities, all of the different races, languages, ages, you name it that are represented in that data set and understand those people in context. In your data set, they may look like they're just two different points of data. But in the world writ large, we know perfectly well that women of color face a different environment than white men, right? They don't work, walk through the world in the same way. And it's ridiculous to assume that your shopping algorithm isn't going to affect that difference that they experience to the real world that isn't going to affect that in some way. It's fantasy, to imagine that is not going to work that way. So we need different kinds of people involved in creating the algorithms, different kinds of people in power in the companies who can say we shouldn't build that, we shouldn't use it. We need a different set of teaching mechanisms where people are actually trained to consider from the beginning, what's the intended positive, what's the intended negative, and what is some likely negatives, and then decide how far they go down that path? >> Right and we actually had on Dr. Rumman Chowdhury, from Accenture. And she's really big in data ethics. And she brought up the idea that just because we can doesn't mean that we should. So can you elaborate more on that? >> Yeah well, just because we can analyze massive datasets and possibly make some kind of mathematical model that based on a set of value statements might say, this person is more likely to get this disease or this person is more likely to excel in school in this dynamic or this person's more likely to commit a crime. Those are human experiences. And while analyzing large data sets, that in the best scenario might actually take into account the societal creation that those actual people are living in. Trying to extract that kind of analysis from that social setting, first of all is absurd. Second of all, it's going to accelerate the existing systemic problems. So you've got to use that kind of calculation over just because we could maybe do some things faster or with larger numbers, are the externalities that are going to be caused by doing it that way, the actual harm to living human beings? Or should those just be ignored, just so you can meet your shipping deadline? Because if we expanded our time horizon a little bit, if you expand your time horizon and look at some of the big companies out there now, they're now facing those externalities, and they're doing everything they possibly can to pretend that they didn't create them. And that loop needs to be shortened, so that you can actually sit down at some way through the process before you release some of these things and say, in the short term, it might look like we'd make x profit, but spread out that time horizon I don't know two x. And you face an election and the world's largest, longest lasting, stable democracy that people are losing faith in. Set up the right price to pay for a single company to meet its quarterly profit goals? I don't think so. So we need to reconnect those externalities back to the processes and the organizations that are causing those larger problems. >> Because essentially, having externalities just means that your data is biased. >> Data are biased, data about people are biased because people collect the data. There's this idea that there's some magic debias data set is science fiction. It doesn't exist. It certainly doesn't exist for more than two purposes, right? If we could, and I don't think we can debias a data set to then create an algorithm to do A, that same data set is not going to be debiased for creating algorithm B. Humans are biased. Let's get past this idea that we can strip that bias out of human created tools. What we're doing is we're embedding them in systems that accelerate them and expand them, they make them worse (laughs) right? They make them worse. So I'd spend a whole lot of time figuring out how to improve the systems and structures that we've already encoded with those biases. And using that then to try to inform the data science we're going about, in my opinion, we're going about this backwards. We're building the biases into the data science, and then exporting those tools into bias systems. And guess what problems are getting worse. That so let's stop doing that (laughs). >> Thank you so much for your insight Lucy. Thank you for being on theCUBE. >> Oh, thanks for having me. >> I'm Sonia Tagare, thanks for watching theCUBE. Stay tuned for more. (upbeat music)

(bright music) >> Hey welcome back, everybody. Jeff Frick here with theCUBE We are high atop San Francisco in the Sales Force Tower in the new Accenture offices, it's really beautiful and as part of that, they have their San Francisco Innovation Hubs. So it's five floors of maker's labs, and 3D printing, and all kinds of test facilities and best practices, innovation theater, and this studio which is really fun to be at. So we're talking about hybrid cloud and the development of cloud and multi-cloud and continuing on this path. Not only are customers on this path, but everyone is kind of on this path as things kind of evolve and transform. We are excited to have a couple of experts in the field we've got Larry Socher, he's the Global Managing Director of Intelligent Cloud Infrastructure Services growth and strategy at Accenture. Larry, great to see you again. >> Great to be here, Jeff. And Ajay Patel, he's the Senior Vice President and General Manager at Cloud Provider Software Business Unit at VMWare and a theCUBE alumni as well. >> Excited to be here, thank you for inviting me. >> So, first off, how do you like the digs up here? >> Beautiful place, and the fact we're part of the innovation team, thank you for that. >> So let's just dive into it. So a lot of crazy stuff happening in the marketplace. Lot of conversations about hybrid cloud, multi-cloud, different cloud, public cloud, movement of back and forth from cloud. Just want to get your perspective today. You guys have been in the middle of this for a while. Where are we in this kind of evolution? Everybody's still kind of feeling themselves out, is it, we're kind of past the first inning so now things are settling down? How do you kind of view the evolution of this market? >> Great question and I think Pat does a really nice job of defining the two definitions. What's hybrid versus multi? And simply put, we look at hybrid as when you have consistent infrastructure. It's the same infrastructure regardless of location. Multi is when you have disparate infrastructure, but are using them in a collective. So just from a from a level setting perspective, the taxonomy is starting to get standardized. Industry is starting to recognize hybrid is the reality. It's not a step in the long journey. It is an operating model that going to exist for a long time. So it's not about location. It's about how do you operate in a multi-cloud and a hybrid cloud world. And together at Accenture VMware have a unique opportunity. Also, the technology provider, Accenture, as a top leader in helping customers figure out where best to land their workload in this hybrid, multi-cloud world. Because workloads are driving decisions. >> Jeff: Right. >> We are going to be in this hybrid, multi-cloud world for many years to come. >> Do I need another layer of abstraction? 'Cause I probably have some stuff that's in hybrid and I probably have some stuff in multi, right? 'Cause those are probably not mutually exclusive, either. >> We talked a lot about this, Larry and I were chatting as well about this. And the reality is the reason you choose a specific cloud, is for those native differentiator capability. So abstraction should be just enough so you can make workloads portable. To be able to use the capability as natively as possible. And by fact that we now at VMware have a native VMware running on every major hyperscaler and on pram, gives you that flexibility you want of not having to abstract away the goodness of the cloud while having a common and consistent infrastructure while tapping into the innovations that the public cloud brings. So, it is the evolution of what we've been doing together from a private cloud perspective to extend that beyond the data center, to really make it an operating model that's independent of location. >> Right, so Larry, I'm curious your perspective when you work with customers, how do you help them frame this? I mean I always feel so sorry for corporate CIAOs. I mean they got security going on like crazy, they go GDPR now I think, right? The California regs that'll probably go national. They have so many things to be worried about. They go to keep up on the latest technology, what's happening in containers. I thought it was doc, now you tell me it's Kubernetes. It's really tough. So how do you help them kind of, put a wrapper around it? >> It's got to start with the application. I mean you look at cloud, you look at infrastructure more broadly I mean. It's there to serve the applications and it's the applications that really drive business value. So I think the starting point has to be application led. So we start off, we have our intelligent engineering guys, our platform guys, who really come in and look and do an application modernization strategy. So they'll do an assessment, you know, most of our clients given their scale and complexity usually have from 500 to 20,000 applications. You know, very large estates. And you got to start to figure out okay what's my current applications? A lot of times they'll use the six Rs methodology and they say hey okay what is it? I'm going to retire this, I no longer need it. It no longer has business value. Or I'm going to replace this with SaaS. I move it to sales force for example, or service now, etcetera . Then they're going to start to look at their workloads and say okay, hey, do I need to re-fact of reformat this. Or re-host it. And one of the things obviously, VMware has done a fantastic job is allowing you to re-host it using their software to find data center, you know, in the hyperscaler's environment. >> We call it just, you know, migrate and then modernize. >> Yeah, exactly. But the modernized can't be missed. I think that's where a lot of times we see clients kind of get in the trap, hey, i'm just going to migrate and then figure it out. You need to start to have a modernization strategy and then, 'cause that's ultimately going to dictate your multi and your hybrid cloud approach, is how those apps evolve and you know the dispositions of those apps to figure out do they get replaced. What data sets need to be adjacent to each other? >> Right, so Ajay, you know we were there when Pat was with Andy and talking about VMware on AWS. And then, you know, Sanjay is showing up at everybody else's conference. He's at Google Cloud talking about VMware on Google Cloud. I'm sure there was a Microsoft show I probably missed you guys were probably there, too. You know, it's kind of interesting, right, from the outside looking in, you guys are not a public cloud, per se, and yet you've come up with this great strategy to give customers the options to adopt VMware in a public cloud and then now we're seeing where even the public cloud providers are saying, "Here, stick this box in your data center". It's like this little piece of our cloud floating around in your data center. So talk about the evolution of the strategy, and kind of what you guys are thinking about 'cause you know you are clearly in a leadership position making a lot of interesting acquisitions. How are you guys see this evolving and how are you placing your bets? >> You know Pat has been always consistent about this and any strategy. Whether it's any cloud or any device. Any workload, if you will, or application. And as we started to think about it, one of the big things we focused on was meeting the customer where he was at in his journey. Depending on the customer, they may simply be trying to figure out working out to get on a data center. All the way, to how to drive an individual transformation effort. And a partner like Accenture, who has the breadth and depth and sometimes the vertical expertise and the insight. That's what customers are looking for. Help me figure out in my journey, first tell me where I'm at, where am I going, and how I make that happen. And what we've done in a clever way in many ways is, we've created the market. We've demonstrated that VMware is the only, consistent infrastructure that you can bet on and leverage the benefits of the private or public cloud. And I often say hybrid's a two-way street now. Which is they are bringing more and more hybrid cloud services on pram. And where is the on pram? It's now the edge. I was talking to the Accenture folks and they were saying the metro edge, right? So you're starting to see the workloads And I think you said almost 40 plus percent of future workloads are now going to be in the central cloud. >> Yeah, and actually there's an interesting stat out there. By 2022, seventy percent of data will be produced and processed outside the cloud. So I mean the edge is about to, as we are on the tipping point of IOT finally taking off beyond smart meters. We're going to see a huge amount of data proliferate out there. So the lines between between public and private have becoming so blurry. You can outpost, you look at, Antheos, Azure Stack for ages. And that's where I think VMware's strategy is coming to fruition. You know they've-- >> Sometimes it's great when you have a point of view and you stick with it against the conventional wisdom. And then all of a sudden everyone is following the herd and you are like, "This is great". >> By the way, Anjay hit on a point about the verticalization. Every one of our clients, different industries have very different paths there. And to the meaning that the customer where they're on their journey. I mean if you talk to a pharmaceutical, you know, GXP compliance, big private cloud, starting to dip their toes into public. You go to Mians and they've been very aggressive public. >> Or in manufacturing with Edge Cloud. >> Exactly. >> So it really varies by industry. >> And that's a very interesting area. Like if you look at all the OT environments of the manufacturing. We start to see a lot of end of life of environments. So what's that next generation of control systems going to run on? >> So that's interesting on the edge because and you've brought up networking a couple times while we've been talking as a potential gate, right, when one of them still in the gates, but we're seeing more and more. We were at a cool event, Churchill Club when they had psy links, micron, and arm talking about shifting more of the compute and store on these edge devices to accommodate, which you said, how much of that stuff can you do at the edge versus putting in? But what I think is interesting is, how are you going to manage that? There is a whole different level of management complexity when now you've got this different level of distributing computing. >> And security. >> And security. Times many, many thousands of these devices all over the place. >> You might have heard recent announcements from VMware around the Carbon Black acquisition. >> Yeah. >> That combined with our workspace one and the pulse IOT, we are now giving you the management framework whether it's for people, for things, or devices. And that consistent security on the client, tied with our network security with NSX all the way to the data center security. We're starting to look at what we call intrinsic security. How do we bake security into the platform and start solving these end to end? And have our partner, Accenture, help design these next generation application architectures, all distributed by design. Where do you put a fence? You could put a fence around your data center but your app is using service now and other SaaS services. So how do you set up an application boundary? And the security model around that? So it's really interesting times. >> You hear a lot about our partnership around software defined data center, around networking. With Villo and NSX. But we've actually been spending a lot of time with the IOT team and really looking and a lot of our vision aligns. Actually looking at they've been working with similar age in technology with Liota where, ultimately the edge computing for IOT is going to have to be containerized. Because you're going to need multiple modalware stacks, supporting different vertical applications. We were actually working with one mind where we started off doing video analytics for predictive maintenance on tires for tractors which are really expensive the shovels, et cetera. We started off pushing the data stream, the video stream, up into Azure but the network became a bottleneck. We couldn't get the modality. So we got a process there. They're now looking into autonomous vehicles which need eight megabits load latency band width sitting at the edge. Those two applications will need to co-exist and while we may have Azure Edge running in a container down doing the video analytics, if Caterpillar chooses Green Grass or Jasper, that's going to have to co-exist. So you're going to see the whole containerization that we are starting to see in the data center, is going to push out there. And the other side, Pulse, the management of the Edge, is going to be very difficult. >> I think the whole new frontier. >> Yeah absolutely. >> That's moving forward and with 5G IntelliCorp. They're trying to provide value added services. So what does that mean from an infrastructure perspective? >> Right, right. >> When do you stay on the 5G radio network versus jumping on a back line? When do you move data versus process on the edge? Those are all business decisions that need to be there into some framework. >> So you guys are going, we can go and go and go. But I want to follow up on your segway on containers. 'Cause containers is such an important part of this story and an enabler to this story. And you guys made and aggressive move with Hep TO. We've had Craig McLuckie on when he was still at Google and Dan, great guys. But it's kind of funny right? 'Cause three years ago, everyone was going to DockerCon right? That was like, we're all about shows. That was the hot show. Now Docker's kind of faded and Kubernetes is really taking off. Why, for people that aren't familiar with Kubernetes, they probably hear it at cocktail parties if they live in the Bay area. Why is containers such an important enabler and what's so special about Kubernetes specifically? >> Do you want to go on the general or? >> Why don't your start off? >> I brought my products stuff for sure. >> If you look at the world its getting much more dynamic. Particularly as you start to get more digitally decoupled applications, you're starting, we've come from a world where a virtual machine might have been up for months or years to all the sudden you have containers that are much more dynamic, allowed to scale quickly, and then they need to be orchestrated. And that's essentially what Kubernetes does, is really start to orchestrate that. And as we get more distributed workloads, you need to coordinate them. You need to be able to scale up as you need for performance etcetera So Kubernetes is an incredible technology that allows you really to optimize the placement of that. So just like the virtual machine changed how we compute, containers now gives us a much more flexible, portable, you can run on any infrastructure at any location. Closer to the data etcetera to do that. >> I think the bold move we made is, we finally, after working with customers and partners like Accenture, we have a very comprehensive strategy. We announced Project Tanzu at our last VM World. And Project Tanzu really focused on three aspects of containers, How do you build applications, which is what Pivotal and the acquisition of Pivotal was driven around. How do we run these on a robust enterprise class run time? And what if you could take every vSphere ESX out there and make it a container platform. Now we have half a million customers. 70 million VM's. All the sudden, that run time we are container enabling with a Project Pacific. So vSphere 7 becomes a common place for running containers and VMs. So that debate of VMs or containers? Done, gone. One place or just spend up containers and resources. And then the more important part is how do I manage this? As you have said. Becoming more of a platform, not just an orchestration technology. But a platform for how do I manage applications. Where I deploy them where it makes more sense. I've decoupled my application needs from the resources and Kubernetes is becoming that platform that allows me to portably. I'm the Java Weblogic guy, right? So this is like distributed Weblogic Java on steroids, running across clouds. So pretty exciting for a middleware guy, this is the next generation middleware. >> And to what you just said, that's the enabling infrastructure that will allow it to roll into future things like edge devices. >> Absolutely. >> You can manage an Edge client. You can literally-- >> the edge, yeah. 'Cause now you've got that connection. >> It's in the fabric that you are going to be able to connect. And networking becomes a key part. >> And one of the key things, and this is going to be the hard part is optimization. So how do we optimize across particularly performance but even cost? >> And security, rewiring security and availability. >> So still I think my all time favorite business book is Clayton Christensen, "Innovator's Dilemma". One of the most important lessons in that book is what are you optimizing for? And by rule, you can't optimize for everything equally. You have to rank order. But what I find really interesting in this conversation and where we're going and the complexity of the size of the data, the complexity of what am I optimizing for now just begs for plight AI. This is not a people problem to solve. This is AI moving fast. >> Smart infrastructure going to adapt. >> Right, so as you look at that opportunity to now apply AI over the top of this thing, opens up tremendous opportunity. >> Absolutely, I mean standardized infrastructure allows you, sorry, allows you to get more metrics. It allows you to build models to optimize infrastructure over time. >> And humans just can't get their head around it. I mean because you do have to optimize across multiple dimensions as performance, as cost. But then that performance is compute, it's the network. In fact the network's always going to be the bottleneck. So you look at it, even with 5G which is an order magnitude more band width, the network will still lag. You go back to Moore's Law, right? It's a, even though it's extended to 24 months, price performance doubles, so the amount of data potentially can exponentially grow our networks don't keep pace. So that optimization is constantly going to have to be tuned as we get even with increases in network we're going to have to keep balancing that. >> Right, but it's also the business optimization beyond the infrastructure optimization. For instance, if you are running a big power generation field of a bunch of turbines, right, you may want to optimize for maintenance 'cause things are running in some steady state but maybe there's an oil crisis or this or that, suddenly the price rises and you are like, forget the maintenance right now, we've got a revenue opportunity that we want to tweak. >> You just talked about which is in a dynamic industry. How do I real time change the behavior? And more and more policy driven, where the infrastructure is smart enough to react, based on the policy change you made. That's the world we want to get to and we are far away from that right now. >> I mean ultimately I think the Kubernetes controller gets an AI overlay and then operators of the future are tuning the AI engines that optimize it. >> Right, right. And then we run into the whole thing which we talked about many times in this building with Dr. Rumman Chowdhury from Accenture. Then you got the whole ethics overlay on top of the business and the optimization and everything else. That's a whole different conversation for another day. So, before we wrap I just want to give you kind of last thoughts. As you know customers are in all different stages of their journey. Hopefully, most of them are at least off the first square I would imagine on the monopoly board. What does, you know, kind of just top level things that you would tell people that they really need just to keep always at the top as they're starting to make these considerations? Starting to make these investments? Starting to move workloads around that they should always have at the top of their mind? >> For me it's very simple. It's really about focus on the business outcome. Leverage the best resource for the right need. And design architectures that are flexible that give you choice, you're not locked in. And look for strategic partners, whether it's technology partners or services partners that allow you to guide. Because if complexity is too high, the number of choices are too high, you need someone who has the breadth and depth to give you that platform which you can operate on. So we want to be the ubiquitous platform from a software perspective. Accenture wants to be that single partner who can help them guide on the journey. So, I think that would be my ask is start thinking about who are your strategic partners? What is your architecture and the choices you're making that give you the flexibility to evolve. Because this is a dynamic market. Once you make decisions today, may not be the ones you need in six months even. >> And that dynanicism is accelerating. If you look at it, I mean, we've all seen change in the industry, of decades in the industry. But the rate of change now, the pace, things are moving so quickly. >> And we need to respond to competitive or business oriented industry. Or any regulations. You have to be prepared for that. >> Well gentleman, thanks for taking a few minutes and great conversation. Clearly you're in a very good space 'cause it's not getting any less complicated any time soon. >> Well, thank you again. And thank you. >> All right, thanks. >> Thanks. >> Larry and Ajay, I'm Jeff, you're watching theCUBE. We are top of San Francisco in the Sales Force Tower at the Accenture Innovation Hub. Thanks for watching. We'll see you next time.