>> Narrator: From around the globe, it's theCUBE, covering HPE Discover Virtual Experience brought to you by HPE. >> Hi, and welcome back to theCUBE's coverage of HPE Discover 2020 the virtual experience. I'm your host Stu Miniman. I'm really happy to be joined on the program two of our CUBE alumni, we have the Daves from Hewlett Packard labs. Sitting in the screen next to me is Dave Husak he is a fellow and general manager for the Cloudless Initiative. And on the other side of the screen, we have Dave Larson, vice president and CTO of the Cloudless Initiative. Dave and Dave, thank you so much for joining us again. >> Delighted to be here. >> All right, so specifically we're going to be talking a bit about security, obviously, you know, very important in the cloud era. And as we build our native architect, you know, Dave Husak, I guess, why don't you set the stage for us a little bit, of you know, where security fits into, you know, HPE overall and, you know, the mission that you know, last year a lot of buzz and discussion and interest around Cloudless. So just put that as a start and then we'll, get into a lot of discussion about security. >> Right yeah, last year we did, you know, launch the initiative and, you know, we framed it as, it composed of three components, one of which in fact, the most important aspect of which it was the trust fabric Cloudless Trust Fabric, which was you know, built on the idea of intrinsic security for all workload end points, right. And this is a theme that you see playing out, you know, a year later playing out, I think across the industry. You hear that language and that, you know, that kind of idea of being promoted in the context of zero trust, you know, new capabilities being launched by VMware and other kinds of runtime environments, right. And you know, the way I like to say it is that we have entered an era of security first in IT infrastructure. It's no longer going to be practical to build IT infrastructure and then, you know, have products that secure it, right. You know, build perimeters, do micro-segment or anything like that. Workload end points need to be intrinsically secure. And you know, the upshot of that really at this point is that all IT infrastructure companies are security companies now. The you know it, acknowledge it, like it or not, we're all security companies now. And so, you know, a lot of the principles applying in the Cloudless Trust Fabric are those zero trust principles are based on cryptographic, workload, identity, leverage unique aspects of HPs products and infrastructure that we've already been delivering with hardware and Silicon root of trust built into our reliance servers and other capabilities like that. And you know, our mission, my mission is to propel that forward and ensure that HP is, you know, at the forefront of securing everything. >> Yeah, excellent definitely, you know love the security first discussion. Every company we've talked to absolutely security is not only a sea level, but you know, typically board level discussion, I guess my initial feedback, as you would say, if every company today is a security company, many of them might not be living up to the expectation just yet So Dave Larson, let's say, you know, applications are, you know, at the core of what we've look at it in cloud native. It's new architectures, new design principles. So give us some, what is HPE thoughts and stuff, how security fits into that, and what's different from how we might've thought about security in the past the applications? Well, I think Dave touched on it, right? From a trust fabric perspective, we have to think of moving to something where the end points themselves, whether their workloads or services are actually intrinsically secure and that we can instantiate some kind of a zero trust framework that really benefits the applications. It really isn't sufficient to do intermediate inspection. In fact, the real, the primary reason why that's no longer possible is that the world is moving too encryption everywhere. And as soon as all packets are encrypted in flight, not withstanding claims to the contrary, it's virtually impossible to do any kind of inference on the flows to apply any meaningful security. But the way we see it is that the transition is moving to a modality where all services, all workloads, all endpoints can be mutually attested, cryptographically identified in a way that allows a zero trust model to emerge so that all end points can know what they are speaking to on the remote end and by authorization principals determine whether or not they're allowed to speak to those. So from a HPE perspective, the area where we build is from the bottom up, we have a Silicon root of trust in our server platform. It's part of our ILO five Integrated lights out baseboard management controller. We can actually deliver a discreet and measurable identity for the hardware and projected up into the workload, into the software realm. >> Excellent, Ty I heard you mentioned identity makes me think of the Cytel acquisition that the HPE made early this year, people in the cloud native community into CubeCon you know, SPIFFE of course, is a project that had gotten quite a bit of attention. Can give us a little bit as to how that acquisition fits into this overall discussion we were just having? >> Oh yeah, so we acquired Cytel into the initiative, beginning of this year. As you, understand Stu, right. Cryptographic identity is fundamental to zero trust security because we're no longer, like Dave pointed out we're no longer relying, on intermediary devices, firewalls, or other kinds of functions to manage, you know, authorize those communications. So the idea of building cryptographic identity into all workload endpoints, devices and data is sort of a cornerstone of any zero trust security strategy. We were delighted to bring the team on board. Not only from the standpoint that they are the world's experts, original contributors, and moderators and committers in the stewardship of SPIFFE and SPIRE the two projects in the CNCF. But you know, the impact they're going to have on the HPs product development, hardware and software is going to be outsized. And it also, you know, as a, I'll have to point this out as well, you know, It is the, this is the most prominent open source project that HP is now stewarding, right. In terms of its acceptance, of SPIFFE and SPIRE, or both poised to be I have an announcement here shortly, probably. But we expect they're going to be promoted to the incubating phase of CNCF maturity from the Sandbox is actually one of the first Sandbox projects in the CNCF. And so it's going to join that Pantheon of know, you know, top few dozen out of I think 1,390 projects in the CNCF. So like you pointed out Stu you know, SPIFFE and SPIRE are right now, you know, the world's leading candidate as, you know, sort of the certificate standard for cryptographic workload endpoint identity. And we're looking at that as a very fundamental enabling technology for this transformation, that the industry is going to go through. >> Yeah, it's really interesting if we pull on that open source thread a little bit more, you know, I think back to earlier in my career, you know, 15, 20 years ago, and if you talk to a CIO, you know, security might be important to them, but they keep what they're building and how their IT infrastructure, is something that they keep very understood. And if you were a vendor supplying to them, you had to be under NDA to understand, because that was a differentiation. Now we're talking about lifting cloud, we're talking about open source, you know, even when I talked to the financial institutions, they're all talking amongst themselves the how do we share best practices because it's not, am I secure? It's we all need to be secure. I wonder if you can comment a little bit on that trend, you know, how the role of open source. Yeah, this is an extension of Kerckhoffs's principle, right? The idea that a security system has to be secure, even if you know the system, right. That's it's only the contents of the ease in the communication letter, that are important. And that is playing out, at the highest level in our industry now, right. So it is, like I said, cryptographic identity and identity based encryption are the cornerstones of building a zero trust fabric. You know, one of the other things is, cause you mentioned that, we also observed is that the CNCF, the Apache foundation. The other thing that's, I think a contrast to 15 years ago, right back 15, 20 years ago, open source was a software development phenomenon, right. Where, you know, the usual idea, you know, there's repositories of code, you pull them down, you modify them for your own particular purposes and you upstream this, the changes and such, right. It's less about that now. It is much more a model for open source operations than it is a model for open source development. Most of the people that are pulling down those repositories unless they are using them, they're not modifying them, right. And as you also, I think understand, right. The framework of the CNCF landscape comprehensive, right? You can build an entire IT infrastructure operations environment by you know, taking storage technologies, security technologies, monitoring management, you know, it's complete, right. And it is, you know, becoming really, you know, a major operational discipline out there in the world to harness all of that development harness, the open source communities. Not only in the software, not only in the security space, but I think you know comprehensively and that engine of growth and development is I think probably the largest, you know manpower and brainpower, and you know, operational kind of active daily users model out there now, right. And, it's going to be critical. I think for the decade, this decade that's coming. That the successful IT infrastructure companies have to be very tightly engaged with those communities in that process, because open source operations is the new thing. It's like, you know DevOps became OpsDev or something like that is the trend. >> Yeah, and I'm glad you brought that up you know I think about the DevOps movement, really fused security, it can't be a bolt on it can't be an afterthought. The mantra I've heard over the last few years, is security is everyone's responsibility. Dave Larson, you know, the question I have for you is, how do we make sure, you know, policy is enforced you know, even I think about an organization everyone's responsible for it, you know, who's actually making sure that things happen because, you know, if everybody's looking after it, it should be okay. But, you know, bring us down a little bit from the application standpoint. >> Well, I would say, you know, first of all, you have to narrow the problem down, right? The more we try to centralize security with discreet appliances, that's some kind of a choke point, the explosion, the common editorial explosion of policy declaratives that are necessary in order to achieve that problem to achieve the solution becomes untenable, right? There is no way to achieve the right kind of policy enforcement unless we get as close to the actual workloads themselves, unless we implement a zero trust model where only known and authorized end points are allowed to communicate with each other, you know. We've lived with a really unfortunate situation in the internet at large, for the last couple of decades where an IP address is both a location and an identifier. This is problem because that can be abused. it's something that can be changed. It's something that is easily spoofed, and frankly the nature of that element of the way we connect applications together is the way that almost virtually all exploits, get into the environment and cause problems. If we move to a zero trust model where the individual end points will only speak with only respond to something that is authorized and only things that are authorized and they trust nothing else, we eliminate 95 to 99% of them problem. And we are in an automated stance that will allow us to have much better assurance of the security of the connections between the various endpoints and services. >> Excellent, so, you know, one of the questions that always comes up, some of the pieces we're talking about here are open source. You talk about security and trust across multiple environments. How does HPE differentiate from, you know, everything else out there and, you know, how are you taking the leadership position? I'd love to hear both of your commentary on that. >> Yeah, well, like I said, initially, the real differentiation for us is that HPE was the market leader for industry standard servers, from a security perspective. Three years ago in our ProLiant gen 10 servers, when we announced them, they had the Silicon root of trust and we've shipped more than a million and a half servers into the market with this capability that is unique in the market. And we've been actively extending that capability so that we can project the identity, not just to the actual hardware itself, but that we can bind it in a multi-factor sense, the individual software components that are hosted on that server, whether it's the operating system, a hypervisor, a VM, a container framework, or an actual container, or a piece of it code from a serverless perspective. All of those things need to be able to be identified and we can bring a multi-factor identity capability to individual workloads that can be the underpinning for this zero across connection capability. >> Great and David, anything you'd like to add there? >> No, like what he said I think HP is uniquely positioned you know, the depth and the breadth of our installed base of platforms that are already zero trust ready, if you will, right. Coupled with the identity technology that we're developing in the context of the Cytel acquisition and David, my work in a building, the cloudless trust fabric, you know, are the, like I said, the cornerstones of these architectures, right? And HP has a couple of unfair advantages here you know, okay breadth and depth of our, the customer base and the installed base of the system is already put out there. While the world is transitioning, you know, inevitably to these, you know, these kinds of security architectures, these kinds of IT infrastructure architectures, HP has a, you know, a leadership team position by default here that we can take advantage of. And our customers can reap the benefits of without, well, you know, without you know, rebuilding forklift upgrading, or otherwise, you know, it is, yeah as Dave talked about, you know, a lot will change, right. There's more to do, right? As we move from, you know, IP addresses and port numbers, as identities for security, because we know that perimeter security, network security like that is busted, right. It is, you know, every headline making, you know, kind of advanced persistent threat kind of vulnerabilities it's all at the root of all those problems, right. There are technologies like OPA, right you know, policy has to be reframed in the context of workload identity, not in network identity know. Like call this legal sort of the microsegmentation fallacy, right. You know that, you know, perimeters are broken, not a valid security strategy anymore. So the answer can't be, let's just draw smaller perimeters, especially since we're now filling them up with evermore, you know, dynamic evanescent kind of workload endpoints, you know, containers coming and going at a certain pace. And serverless instances, right. All of those things springing up and, and being torn down, you know, on, you know, very short life cycle that's right. It is inconceivable that traditional, you know perimeter based micro-segmentation based security frameworks can keep up with the competent tutorial explosion and the pace with which we are going to be where, you know, orchestration frameworks are going to be deploying these end points. There are, you know, there's a lot more to do, you know, but this is, the transformation story. This is of the 2020s, you know, infrastructure, IT infrastructure school is very different in two, five, 10 years from now than it does today. And you know that's you know we believe HP has, like I said, a few unfair advantages to lead the world in terms of those transformations. >> Excellent, well, appreciate the look towards the future as well as where we are today. Dave and Dave, thanks so much for joining. Thank you, Stu. >> Thanks, dude, pleasure. >> All right, we'll be back with lots more coverage. HPE Discover 2020 the Virtual Experience. I'm Stu Miniman and thank you for watching theCUBE. (upbeat music)

>> Narrator: From around the globe, it's theCUBE, covering HPE Discover Virtual Experience, brought to you by HPE. >> Hello and welcome to theCUBE's coverage of HPE Discover 2020, The Virtual Experience. I'm Lisa Martin and I've got a couple of guests joining me, Stephen Laaper principal at Deloitte consulting and Brenna Sniderman the Executive Director for the Center of Integrated Research at Deloitte Services, Stephen and Brenna, nice to have you on the program today. >> Thank you, >> (mumbles) >> So we're going to be talking about The Smart Factory. I'd love for you to start Brenna, we'll start with you. Give our audience an overview of Deloitte's definition of The Smart Factory then we can dig into some of the very interesting research that Deloitte has been doing the last few years. >> Sure, absolutely. So the way we think about The Smart Factory is it is a system that's quite flexible that uses data and information from throughout, physical assets to optimized performance, to enable the facility to be more agile, to be proactive, to optimize its assets and to react and change as quickly as possible to shifts going on. It overall enables organizations to just be more intelligent about the way they use their assets to use data, to make more informed decisions and to drive a more optimized process. >> And Stephen for you, one of the things that I found interesting looking at some of Deloitte's research is that the last few years or so, there's been net zero growth in manufacturing labor productivity and labor productivity being an indicator of economic impact. Why in Deloitte's perspective, has that manufacturing labor productivity growth been flat? >> Yeah, it's a really interesting observation. And what we've seen is really decades and decades of management principles, companies using things like Lean, like Six Sigma, taking advantage of labor arbitrage in many cases. And the reality is that a lot of that low hanging fruit is gone. Those projects have been executed well and we're now seeing what we would consider to be diminishing returns as it relates to the investments in those same types of tools. And that is really what's leading many organizations now towards things like the capabilities that you'd find in a Smart Factory. Adding additional technologies to the capability set to really bring companies to that new productivity frontier. >> One of the things that I saw too, is that Smart Factory adoption in one of your studies, can result in a threefold productivity increase. So talk to me about in the last few years, some of the early adopters, Brenna we'll start with you, what are some of the trends that you've seen with those early adopters? any industries in particular that are leading in that respect? >> Well, that's a good question. I think when we recently published a study on lessons from early adopters in the Smart Factory and what we found was that a lot of the organizations that have adopted the Smart Factory have learned lessons that are not necessarily new but some that are new as well. Really I think the biggest challenge has been to figure out how to gather data from a lot of assets that maybe haven't had to produce data before to find out where all the information is from throughout the facility to bring together different groups and different cultures within the organization, whether it's IT and OT and have them figure out how to share information and data and really just to figure out what to do with that information once we've gotten it. Some of the organizations that we spoke with for our research really ran the gamut from aerospace to automotive, to consumer products, to industrial manufacturing. It really has been an interesting spread that we've looked at. >> Stephen walk us through the last three years or so of research that Deloitte has been doing into the Smart Factory from the 2017 study to the 2019 study, to the one that was just released, what's some of the progress that you've seen over the last three years? Is it what you anticipated it would be? >> Yeah, it's interesting. I mean, three years ago, I think a lot of people were talking about Industry 4.0, they were talking about the industrial internet of things, they were talking about The Smart Factory, but we saw relatively few very concentrated efforts to advance those. Now as we fast forward three years, we're seeing that the specific capabilities that each one of those topic areas can enable for organizations, has become much clearer. So correspondingly companies have been planning for these types of investments and they're taking action on much of the capability build and quite frankly, starting to see the value. One of the underlying kind of architectural elements that I think are critical as part of the modern Smart Factory is exactly what Brenna touched on. And that was as it relates to the data. Many assets out there even if they're several decades old likely have a wealth of data associated with them. The challenge is that data is either not readily accessible or it's not well understood. And much of the effort that organizations have now undertaken is not only how do they connect, extract and use that information many times on a real time or near real time basis, but now also combining that information with other assets, other parts of the manufacturing facility, or even their manufacturing network to generate that value. >> So Stephen follow on question, how does an organization, a company start that process, if as you said, there's myriad assets of varying age, some really advanced, some really old as well as even from, I guess, a generational perspective in the workforce, you've got multiple generations, for organizations that know we've got data that's hidden, where do they start? >> Yeah, absolutely. And I think a really important element of your question is how do you determine where to start? And the reality is that not all of these solutions are created equal. Not all of the assets have data that's interesting enough to be equal. And so really going through a very concerted effort to understand what are the capabilities we're trying to build And what value does it create for our organization? Aligning that to the objectives and the goals of the organization is critical right from the outset. And we see companies that are being most successful in their implementation of the Smart Factory, following that value orientation. And that might not mean that that value comes tomorrow, It might not come next month, but there's a very clear guidance in terms of how the particular capabilities that are being built will lead to value. Organizations that are not doing that, we tend to see random X visual. We see a lot of different efforts underway with very little tied value and correspondingly many of those efforts don't continue because the executive team, the shareholders aren't going to continue those investments in that space without showing them (mumbles). >> So Brenna walk us through, along what Stephen was just saying. I was reading in your 2020 study that positioning a Smart Factory initiative for value starts with human-centered design and I thought this was really interesting that Deloitte research demonstrated successful teams generally focus on the user first, not the technology. >> Well, yeah. And I think to follow on a little bit to what Stephen said about understanding the value and the goal of what you're trying to do before thinking about the technology you need to rush out and implement goes along with this as well. You want to think about what the user is actually going to be using that data for, what is their job, what information are they going to need and think about from their perspective, what is going to be most helpful and effective for them. And I think the value of this is twofold. One is if talent within your organization and folks on the shop floor, see the value of this data and information, they're going to be more inclined to adopt it because it makes their job easier. But also if you have a tremendous amount of data and information from all the different assets and parts of your facility, if an individual has to sift through all of that, to find what's going to be valuable to them, it's not really going to make their job easier. So human-centered design is really thinking about what that individual needs to do their role, and in a lot of the work that we've done, we've almost thought about it as personas where this particular persona or job needs this information, needs to go through these steps and here's the data information we need to show them to enable them to do that. It's just a way for people to leverage information, to make smarter decisions more quickly. >> How does a manufacturing company do that, Brenna, excuse me, without being siloed, like in business units, so I'm thinking, getting cross-functional support all the way up to the top level. >> Mhhh, that's something that we saw quite a bit in our research that many of the groups or organizations that have successfully enacted a Smart Factory have done so because it's not just coming down from the top, it's also coming up from the bottom. You know, although that may sound like a pejorative term, but coming from all angles of the organization. So we see from the strategic level, this is what we need to do to change the way our organization operates in a more effective way. But from the line of business individuals that are using this information and data every day, we need to think about sort of having a groundswell of support work there as well, so that our team members are using this information. So I think it has to be something that comes from throughout the organization. What we've also found your point about silos is bringing in diverse teams and individuals from throughout the organization who have different types of expertise, different perspectives, different things that they're looking at in different ways that they need to use this technology to do their job, will enable us to make sure that, what we're producing is something that's going to be of value to them. >> And along those lines, Stephen question for you, this must need to be looked at, not as what can we do today or the next six months, but over the long-term. So that ongoing enablement and education is going to be critical. >> Yeah, absolutely. Right. And you know, the reality is that some of these investments that organizations are making into Smart Factory, do take quite a bit of thought, research and assessment and those aren't investments that they're making for the short-term, many of them are long-term. The important part about those investments that organizations are making is that they're creating platforms by which teams can continue to evolve the persona-based type solutions that Brenna referred to, so critical. And so, the flexibility, the adaptability, the agility of those platforms and the investments that are being made, really is one of their critical factors. I did want to just revisit the user adoption of these types of solutions. And, I'm a engineer by education. And I could look up back to early in my career and say, "Hey, look, I built solutions, "using data for manufacturing shop floor equipment. "And I created those solutions for others." But the reality was that I created it in a way that an engineer would you consume that data. And the reality is the persona-based approach really lets us focus on how is a particular individual in their job going to consume that data in a way that enables them to make the best next decision which ultimately has a positive outcome for the company. And in some cases that might mean not exposing them to all the complexities that happen underneath the surface. The modern smartphone, for example, enormously complex device, yet intuitive to use, easy to pick up, easy to interact with. The modern Smart Factory is also very similar in that frame. >> Along those lines of agility, but also designing with certain mindset, culturally IT and OT are different. Brenna, one of the things that I found interesting in the research was the marriage of IT and OT, how do you advise or let's go to clients that were part of that 2020 study, what lessons can the next wave of adopters learn where it comes to bridging those two IT OT mentalities and different cultures? >> Yeah, that's a good question. And I think the different cultures is sort of, key insight that is helpful. With respect to IT, they work on different timeframes, they think about investments in a different way, they think about technology in a different way than individuals who are in OT, who are on the shop floor, who are using these tools every day. and what we found was that bridging that divide and bringing them together, is a challenge that many overlook and something that really the importance of it, can't be overstated. I think to get back to Stephen's point about adoption, if those within the OT space have an understanding of what IT is doing and why, they're just likelier to adopt and to use. And conversely, if those in IT have a deeper understanding of what those in OT are doing and what types of tools they need, they're likelier to come up with solutions that are going to be effective. I think the cultural divide is something that's practically important to understand, to address and not to overlook because I think the last thing that anyone implementing any sort of Smart Factory solution wants is to roll out a solution that was sort of baked in one area, but not taking into account the other as well. >> Great point. Stephen, I want to go back to you for a second. Just understanding along the lines of the cultural differences and the design principles that need to be factored in. When the COVID-19 pandemic hit in March of 2020, for clients that you were talking to that were in whatever stage process of rolling out Smart Factory initiatives, where are they now? And what are some of the advantages that you see that organizations that aren't yet adopting Smart Factory initiatives should be doing to prepare to thrive in this new normal? >> Yeah, absolutely. Let me start with some of those advantages right at the outset. So many organizations now have been looking at advanced solutions, perhaps, to enforce social distancing within the manufacturing environment or perhaps contact tracing within the manufacturing environment and the advantages organizations are seeing that are already on that Smart Factory journey is they're finding they have largely a lot of the infrastructure required to be able to do that already in place. So that has been an enormous accelerant for companies that are already on the journey. The reality is that many organizations, are unable to have their experts, their engineers, their vendors, many of the people that are supporting the equipment and the people in their manufacturing plants around the world, they're not able to get them there. And companies that have been on the Smart Factory journey, specifically as it relates to creating what we would call the digital twin of many of their assets, where they can now see not only visual representations of those assets, but can also see that the data flowing off those assets and in the most advanced solutions, being able to see those together, they'd be able to unlock remote support, in a way that organizations that have not been on this journey simply can't. And we're starting to see some very distinct results, as it relates to those who are able to continue running at scale, and those who are struggling in the COVID environment. >> And Stephen, last question for you. I know you've got a session or a demo on Smart Factory an AI that you're doing at Discover 2020. Tell us a little bit about that and what the participants can anticipate. >> Yeah. So we're really excited to be able to bring Factory AI as we call it, in a live virtualized session. That session is going to cover what we have built around we'll call it a mini manufacturing line. And usually we'd have that with you at the conference, or we take that around the country, to many of our manufacturing clients to really show them, the power of adopting many of these different types of capabilities in the manufacturing environment. So what we're going to be showing and what viewers can expect to see is a demonstration of edge capabilities, of computer vision, of advanced internet of things, all wrapped into several high-impact use cases. So we're looking forward to you're doing that. >> Excellent. Well, Stephen, Brenna, thank you so much for your time discussing The Smart Factor. This is such an interesting provocative topic. I wish we had more time, but appreciate you speaking with me today. >> Thanks for having us. >> Thank you. >> You're watching the cube, Lisa Martin for HPE Discover 2020, The Virtual Experience. Thanks for watching. (upbeat music)

>> Narrator: From around the globe its the Cube, covering HPE Discover Virtual Experience brought to you by HPE. >> Hi everybody this is Dave Vellante and welcome back to the Cube's coverage of HPE's Discover 2020 the Virtual Experience the Cube. The Cube has been virtualized We like to say Am very happy to welcome in Neil McDonalds, he's the General Manager for Compute at HPE. Great to see you again Neil, wish we were face to face, but this will have to do. >> Very well, it's great to see you Dave. Next time we'll do this face to face. >> Next time we have hopefully next year. We'll see how things are going, but I hope you're safe and your family's all good and I say it's good to talk to you, you know we've talked before many times you know, it's interesting just to know the whole parlance in our industry is changing even you know Compute in your title, and no longer do we think about it as just sort of servers or a box you guys are moving to this as a service notion, really it's kind of fundamental or, poignant that we see this really entering this next decade. It's not going to be the same as last decade, is it? >> No, I think our customers are increasingly looking at delivering outcomes to their customers in their lines of business, and Compute can take many forms to do that and it's exciting to see the evolution and the technologies that we're delivering and the consumption models that our customers are increasingly taking advantage of such as GreenLake. >> Yes so Antonio obviously in his Keynote made a big deal in housing previous Keynotes about GreenLake, a lot of themes on you know, the cloud economy and as a service, I wonder if you could share with our audience, you know what are the critical aspects that we should know really around GreenLake? >> Well, GreenLake is growing tremendously for us we have around a thousand customers, delivering infrastructure through the GreenLake offerings and that's backed by 5,000 people in the company around the world who are tuning an optimizing and taking care of that infrastructure for those customers. There's billions of dollars of total contract value under GreenLake right now, and it's accelerating in the current climate because really what GreenLake is all about is flexibility. The flexibility to scale up, to scale down, the ability to pay as you use the infrastructure, which in the current environment, is incredibly helpful for conserving cash and boosting both operational flexibility with the technology, but also financial flexibility, in our customer's operations. The other big advantage of course at GreenLake is it frees up talent most companies are in the world of challenges in freeing up their talent to work on really impactful business transformation initiatives, we've seen in the last couple of quarters, an even greater acceleration of digital transformation work for example and if all of your talent is tied up in managing the existing infrastructure, then that's a drain on your ability to transform and in some industries even survive right now, so GreenLake can help with all of those elements and, with all of the pressure from COVID, it's actually becoming even more consumed, by more and more customers around the world it's- >> Yeah right I mean that definitely ties into the whole as a service conversation as well I mean to your point, you know, digital transformation you know, the last couple of years has really accelerated, but I feel yeah, I feel like in the last 90 days, it's accelerated more than it has in the last three years, because if you weren't digital, you really had no way to do business and as a service has really played into that so I wonder if you could talk about yours as a service, you know, posture and thinking. >> Well you're absolutely right Dave organizations that had not already embarked on a digital transformation, have rapidly learned in our current situation that it's not an optional activity. Those that were already on that path are having to move faster, and those that weren't are having to develop those strategies very rapidly in order to transform their business and to survive. And the really new thing about GreenLake and the other service offerings that we provide in that context is how it can accelerate the deployment. Many companies for example, have had to deal with VDI deployments in order to enable many more of their workforce to be productive when they can't be in the office or in the facility and a solution like GreenLake can really help enable very rapid deployment and build up but not just VDI many other workloads in high performance Compute or in SAP HANA for example, are all areas that we're bringing value to customers through that kind of as a service offering. Yeah, a couple of examples Nokia software is using GreenLake to accelerate their research and development as they drive the leadership and the 5G revolution, and they're doing that at a fraction of the cost of the public cloud. We've got Zanotti, which has built a private cloud for artificial intelligence and HPC is being used to develop the next generation of autonomous software for cars. And finally, we've got also Portion from Arctic who have built a fully managed hybrid cloud environment to accelerate all the application development without having to bear the traditional costs of an over-provisioned complex infrastructure. So all of our customers are relying on that because Compute and Innovation is just at the core of the digital transformations that everybody is embarked on as they modernize their businesses right now and it's exciting to be able to be part of that and to be able to do there, to help. >> So of course in the tech business innovation is the you know the main spring of growth and change, which is constant in our industry and I have a panel this week with Doctor Go talking about swarm learning in AI, and that's some organic innovation that HPE is doing, but as well, you've done some, M&A as well. Recently, you guys announced and we covered it a pretty major investment in Pensando Systems. I wonder if you could talk a little bit about what, that means to the Compute business specifically in, HPE customers generally. >> So that partnership with Pensando was really exciting, and it's great to see the momentum that its building in delivering value to our customers, at the end of the day we've been successful with Pensando in building that momentum in very highly regulated industries and the value that is really intrinsic to Pensando is the simplifying of the network architecture. Traditionally, when you would manage an enterprise network environment, you would create centralized devices for services like load balancing or firewalls and other security functionality and all the traffic in the data center would be going back and forth, tromboning across the infrastructure as you sought to secure your underlying Compute. The beauty of the Pensando technology is that we actually push that functionality all the way out to the edge at the server so whether those servers are in a data center, whether they're in a colocation facility, whether they're on the edge, we can deliver all of that security service that would traditionally be required in centralized expensive, complex, unique devices that were specific to each individual purpose, and essentially make that a software defined set of services running in each node of your infrastructure, which means that as you scale your infrastructure, you don't have a bottleneck. You're just scaling that security capability with the scaling of your computer infrastructure. It takes traffic off your core networks, which gives you some benefits there, but fundamentally it's about a much more scalable, responsive cost-efficient approach to managing the security of the traffic in your networks and securing the Compute end points within your infrastructure. And it's really exciting to see that being picked up, in financial services and healthcare, and other segments that have you know, very high standards, with respect to security and infrastructure management, which is a great complement to the technology from Pensando and the partnership that we have with Pensando and HPE. >> And it's compact too we should share with our audience it's basically a card, that you stick inside of a server correct Neil? >> That's exactly right. Pensando's PCIe card together with HPE servers, puts that security functionality in the server, exactly where your data is being processed and the power of that is several fold, it avoids the tromboning that we talked about back across the whole network every time you've got to go to a centralized security appliance, it eliminates those complex single purpose appliances from the infrastructure, and that of course means that the failure domain is much smaller cause your failure demands a single server, but it also means that as you scale your infrastructure, your security infrastructure scales with the servers. So you have a much simpler network architecture, and as I say, that's being delivered in environments with very high standards for security, which is a really a great endorsement of the Pensando technology and the partnership that HPE and Pensando will have in bringing that technology to market for our customers. >> So if I understand it correctly, the Pensando is qualified for Pro-Lite, Appollo and in Edgelines. My question is, so if I'm one of those customers today, what's in it for me? Are they sort of hopping on this for existing infrastructure, or is it part of, sort of new digital initiatives, I wonder if you could explain. >> So if you were looking to build out infrastructure for the future, then you would ask yourself, why would you continue to carry forward legacy architectures in your network with these very expensive custom appliances for each security function? Why not embrace a software defined approach that pushes that to the edge of your network whether the edge are in course or are actually out on the edge or in your data centers, you can have that security functionality embedded within your Compute infrastructure, taking advantage of Pensandos technologies. >> So obviously things have changed is specifically in the security space, people are talking about this work from home, and this remote access being a permanent or even a quasi-permanent situation. So I wonder if we could talk about the edge and specifically where Aruba fits in the edge, how Pensando compliments. What's HPE's vision with regard to how this evolves and maybe how it's been supercharged with the COVID pandemic. >> So we're very fortunate to have the Aruba intelligent edge technology in the HPE portfolio. And the power of that technology is its focus on the analysis of data and the development of solutions at the site of the data generated. Increasingly the data volumes are such that they're going to have to be dealt with at the edge and given that, you need to be building edge infrastructure that is capable enough and secure enough for that to be the case. And so we've got a great compliment between the, intelligent edge technology within the Aruba portfolio, with all of the incredible management capabilities that are in those platforms combined with technologies like Pensando and our HPE Compute platforms, bring the ability to build a very cohesive, secure, scalable infrastructure that tackles the challenges of having to do this computer at the edge, but still being able to do it in both a secure and easily managed way and that's the power of the combination of Aruba, HPE Compute and Pensando. >> Well, with the expanded threat surface with people working from home organizations are obviously very concerned about compliance, and being able to enforce consistent policies across this sort of new network, so I think what you're talking about is it's very important that you have a cohesive system from a security standpoint you're not just bolting on some solution at the tail end, your comments. >> Well security, always depends on all the links in the chain and one of the most critical links in the chain is the security of the actual Compute itself. And within the HPE compliant platforms, we've done a lot of work to build very differentiated and exclusive capability with our hardware, a Silicon Root of Trust, which is built directly into Silicon. And that enables us to ensure the integrity of the entire boot chain on the security of the platform, drones up in ways that can't be done with some of the other hardware approaches that are prevalent in the industry, and that's actually brought some benefit, in financial terms to our customers because of the certifications that are enabled in the, Cyber Catalyst designations that we've earned for the platforms. >> So we also know from listening to your announcements with Pensando just observing security in general, that this notion of micro-segmentation is very important being able to have increased granularity as opposed to kind of a blob, maybe you could explain why that's important you know, the so what behind micro-segmentation if you will. >> Well it's all about minimizing the threat perimeter on any given device and if you can minimize the vectors through which your infrastructure will interact on the network, then you can provide additional layers of security and that's the power of having your security functionality right down at the edge, because you can have a security processor sitting right in the server and providing great security of the node level you're no longer relying on the network management and getting all of that right and you also have much greater flexibility because you can easily in a software defined environment, push the policies that are relevant for the individual pieces of infrastructure in an automated policy driven way, rather than having to rely on someone in network security, getting the manual configuration of that infrastructure, correct to protect the individual notes. And if you take that kind of approach, and you embed that kind of technology in servers, which are fundamentally robust in terms of security because of the Silicon Root of Trust that we've embedded across our platform portfolio whether that's Pro-line or Synergy or BladeSystem or Edgeline, you get a tremendous combination, as a result of these technologies, and as I mentioned, the being Cyber Catalyst designation is a proof point of that. Last year there we're over 150 security products, put forward for the Sovereign Capitalist designation, and the only a handful were actually awarded I think 17, of which two were HPE Compute and Aruba. And the power of is that many organizations are not having to deal with insurance for Cybersecurity events. And the Catalyst designation can actually lead to lower premiums for the choice of the infrastructure that you've made to such as HPE Compute, has actually enabled you to have a lower cost of insuring your organization against cybersecurity issues, because infrastructure matters and the choice of infrastructure with the right innovation in it is a really critical choice for organizations moving forward in security and in so many other ways. >> Yeah, you mentioned a lot of things there software defined, that's going to enable automation and scale, you talked about the perimeter you know, the perimeter of the traditional moat around the castle that's gone the perimeter, there is no perimeter anymore, it's everywhere so that whole you know, weakest link in the chain and the chain of events. And then the other thing you talked about was the layers you know very important when you're talking to security practitioners you know, building layers in so all of this really is factoring in security in particular, is factoring into customer buying decisions. Isn't it? >> Well security is incredibly important for so many of our customers across many industries. And having the ability to meet those security needs head on is really critical. We've been very successful in leveraging these technologies for many customers in many different industries, you know, one example is we've recently won multiple deals with the Defense Intelligence Systems Agency, who you will imagine have very high standards for security, worth hundreds of millions of dollars of that infrastructure so there's a great endorsement, from the customer set who are taking advantage of these technologies and finding that they deliver great benefits for them in the operational security of their infrastructure. >> Yeah what if I could ask you a question on the edge. I mean, as somebody who is you know, with a company that is really at the heart of technology, and I'm sure you're constantly looking at new companies, M&A you know et cetera, you know inventing tech, but I want to ask you about the architectures for the edge and just in thinking about a lot of data at the edge, not all the data is going to come back to the data center or the cloud, there's going to be a lot of AI influencing going on in real time or near real time. Do you guys see different architectures emerging to support that edge? I mean from a Compute standpoint or is it going to be traditional architectures that support that. >> It's clearly an evolving architectural approach because for the longest time, infrastructure was built with some kind of hub you know, whether or not some data center or in the cloud, around all of the devices at the edge would be essentially calling home, so edge devices historically have been very focused on connectivity on acquisition of data, and then sending that data back for some kind of processing and action at some centralized location. And the reality is that given the amount of data being generated at the edge now given the capability even of the most modern networks, it's simply not possible to be moving those kinds of data volumes all the way back to some remote processing environment, and then communicating a decision for action all the way back up to the edge. First of all, the networks kind of handle the volume data's involved if every device in the world was doing that, and secondly, the latencies are too slow. They're not fast enough in order to be able to take the action needed at the edge. So that means that you have to countenance systems at the edge that are not actually storing data, that are not actually computing upon data, and in a lot of edge systems historically, they would evolve from very proprietary, very vertically integrated systems to Brax PC controller based systems with some form of IP connectivity back to, some central processing environment. And the reality is that if you build your infrastructure that way, you finish up with a very unmanageable fleet, you finish up with a very fragmented, disjointed infrastructure and our perspective is that companies that are going to be successful in the future have to think themselves as an edge to cloud approach. They have to be pursuing this in a way that views, the edge, the data center, and the cloud as part of an integrated continuum, which enables the movement of data when needed you heard about the swarm learning that you talked about with my colleague Doctor Go, where there's a balance of what is computed, where in the infrastructure, and so many other examples, but you need to be able to move Compute to where the data is, and you need to be able to do that efficiently with a unified approach to the architecture. And that's where assets like the HPE Data Fabric come into play, which enable that kind of unification across the different locations of equipment. It also means you need to think differently about the actual building blocks themselves, in a lot of edge environments, if you take a Classic 19 interact mode Compute device, that was originally designed for the data center it's simply not the right kind of infrastructure. So that's why we have offerings like the Edgeline portfolio and the HPE products there, because they're designed to operate in those environments with different environmentals than you find the data center with different interfaces to systems of action and systems of control, than you'd typically find in a data center environment yet still bringing many of the security benefits and the manageability benefits that we've talked about earlier in our conversation today Dave. So it's definitely going to be an evolving, a new architectural approach at the edge, and companies that are thoughtful about their choice of infrastructure, are going to be much more successful than those that take a more incremental approach, and we were excited to be there to help our customers on that journey. >> Yeah Neil it's a very exciting time I mean you know, much of the innovation in the last decade was found inside the data center and in your world a lot of times you know, inside the server itself but what you're describing is this, end-to-end system across the network and that systems view, and then there's going to be a ton of innovation there and we're very excited for you thanks so much for coming on the Cube it was great to see you again. >> It is great to be here and we're just excited to be here to help our customers, and giving them the best volume for the workloads whether that's taking advantage of GreenLake, taking advantage of the innovative security technologies that we've talked about, or being the edge to cloud platform as a service company that can help our customers transform in this distributed world from the edge to the data center to the cloud. Thanks for having me Dave. >> You very welcome, awesome summary and its always good to see you Neil. Thank you for watching everybody this David Vellante, for the Cube our coverage of the HPE Discover 2020 Virtual Experience, will be right back to the short break. (soft upbeat music)

>> Male Voice: From around the globe, it's theCUBE covering HPE Discover Virtual Experience brought to you by HPE. >> Hi everybody, you're watching theCUBE. And this is Dave Vellante in our continuous coverage of the Discover 2020 Virtual Experience, HPE's virtual event, theCUBE is here, theCUBE virtual. We're really excited, we got a great session here. We're going to dig deep into machine intelligence and artificial intelligence. Dr. Arti Garg is here. She's the Head of Advanced AI Solutions and Technologies at Hewlett Packard Enterprise. And she's joined by Dr. Sorin Cheran, who is the Vice President of AI Strategy and Solutions Group at HPE. Folks, great to see you. Welcome to theCUBE. >> Hi. >> Hi, nice to meet you, hello! >> Dr. Cheran, let's start with you. Maybe talk a little bit about your role. You've had a variety of roles and maybe what's your current situation at HPE? >> Hello! Hi, so currently at HPE, I'm driving the Artificial Intelligence Strategy and Solution group who is currently looking at how do we bring solutions across the HPE portfolio, looking at every business unit, but also on the various geos. At the same time, the team is responsible for building the strategy around the AI for the entire company. We're working closely with the field, we're working closely with the things that are facing the customers every day. And we're also working very closely with the various groups in order to make sure that whatever we build holds water for the entire company. >> Dr. Garg, maybe you could share with us your focus these days? >> Yeah, sure, so I'm also part of the AI Strategy and Solutions team under Sorin as our new vice president in that role, and what I'm focused on is really trying to understand, what are some of the emerging technologies, whether those be things like new processor architectures, or advanced software technologies that could really enhance what we can offer to our customers in terms of AI and exploring what makes sense and how do we bring them to our customers? What are the right ways to package them into solutions? >> So everybody's talking about how digital transformation has been accelerated. If you're not digital, you can't transact business. AI infused into every application. And now people are realizing, "Hey, we can't solve all the world's problems with labor." What are you seeing just in terms of AI being accelerated throughout the portfolio and your customers? >> So that's a very good idea, because we've been talking about digital transformation for some time now. And I believe most of our customers believed initially that the one thing they have is time thinking that, "Oh yes I'm going to somehow at one point apply AI "and somehow at one point "I'm going to figure out how to build the data strategy, "or how to use AI in my different line of businesses." What happened with COVID-19 and in this area is that we lost one thing: time. So I think discussed what they see in our customers is the idea of accelerating their data strategy accelerating, moving from let's say an environment where they would compute center models per data center models trying to understand how do they capture data, how they accelerate the adoption of AI within the various business units, why? Because they understand that currently the way they are actually going to the business changed completely, they need to understand how to adapt a new business model, they need to understand how to look for value pools where there are none as well. So most of our customers today, while initially they spend a lot of time in an never ending POC trying to investigate where do they want to go. Currently they do want to accelerate the application of AI models, the build of data strategies, how then they use all of this data? How do they capture the data to make sure that they look at new business models, new value pools, new customer experience and so on and so forth. So I think what they've seen in the past, let's say three to six months is that we lost time. But the shift towards an adoption of analytics, AI and data strategy is accelerated a lot, simply because customers realize that they need to get ahead of the game. >> So Dr. Garg, what if you could talk about how HPE is utilizing machine intelligence during this pandemic, maybe helping some of your customers, get ahead of it, or at least trying to track it. How are you applying AI in this context? >> So I think that Sorin sort of spoke to one of the things with adopting AI is, it's very transformational for a business so it changes how you do things. You need to actually adopt new processes to take advantage of it. So what I would say is right now we're hearing from customers who recognize that the context in which they are doing their work is completely different. And they're exploring how AI can help them really meet the challenges of those context. So one example might be how can AI and computer vision be coupled together in a way that makes it easier to reopen stores, or ensures that people are distancing appropriately in factories. So I would say that it's the beginning of these conversations as customers as businesses try to figure out how do we operate in the new reality that we have? And I think it's a pretty exciting time. And I think just to the point that Sorin just made, there's a lot of openness to new technologies that there wasn't before, because there's this willingness to change the business processes to really take advantage of any technologies. >> So Dr. Cheran, I probably should have started here but help us understand HPE's overall strategy with regard to AI. I would certainly know that you're using AI to improve IT, the InfoSite product and capability via the Nimble acquisition, et cetera, and bringing that across the portfolio. But what's the strategy for HPE? >> So, yeah, thank you. That's (laughs) a good question. So obviously you started with a couple of our acquisition in the past because obviously Nimble and then we talked a lot about our efforts to bring InfoSite across the portfolio. But currently, in the past couple of months, let's say close to a year, we've been announcing a lot of other acquisitions and we've been talking about Tuteybens, we've been talking about Scytale we've been talking about Cray, and so on, so forth, and now what we're doing at HPE is to bring all of this IP together into one place and try to help our customers within their region out. If you're looking at what, for example, what did they actually get when Cray play was not only the receiver, but we also acquire and they also have a lot of software and a lot of IP around optimization and so on and so forth. Also within our own labs, we've been investigating AI around like, for example, some learning or accelerators or a lot of other activity. So right now what we're trying to help our customers with is to understand how do they lead from the production stage, from the POC stage to the production stage. So (mumbles) what we are trying to do is we are trying to accelerate their adoption of AI. So simply starting from an optimized platform infrastructure up to the solution they are actually going to apply or to use to solve their business problems and wrapping all of that around with services either consumed on-prem as a service and so on. So practically what we want to do is we want to help our customers optimize, orchestrate and operationalize AI. Because the problem of our customers is not to start in our PLC, the problem is how do I then take everything that I've been developing or working on and then put it in production at the edge, right? And then keep it, maintaining production in order to get insights and then actually take actions that are helping the enterprise. So basically, we want to be data driven assets in cloud enable, and we want to help our customers move from POC into production. >> Or do you work with obviously a lot of data folks, companies or data driven data scientists, you are hands on practitioners in this regard. One of the challenges that I hear a lot from customers is they're trying to operationalize AI put AI into production, they have data in silos, they spend all their time, munging data, you guys have made a number of acquisitions. Not a list of which is prey, obviously map of, data specialist, my friend Kumar's company Blue Data. So what do you see as HPE's role in terms of helping companies operationalize AI. >> So I think that a big part of operationalizing AI moving away from the PLC to really integrate AI into the business processes you have and also the sort of pre existing IT infrastructure you talked about, you might already have siloed data. That's sort of something we know very well at HPE, we understand a lot of the IT that enterprises already have the incumbent IT and those systems. We also understand how to put together systems and integrated systems that include a lot of different types of computing infrastructure. So whether that being different types of servers and different types of storage, we have the ability to bring all of that together. And then we also have the software that allows you to talk to all of these different components and build applications that can be deployed in the real world in a way that's easy to maintain, and scale and grow as your AI applications will almost invariably get more complex involved, more outputs involved and more input. So one of the important things as customers try to operationalize AI is think is knowing that it's not just solving the problem you're currently solving. It's not just operationalizing the solution you have today, it's ensuring that you can continue to operationalize new things or additional capabilities in the future. >> I want to talk a little bit about AI for good. We talked about AI taking away jobs, but the reality is, when you look at the productivity data, for instance, in the United States, in Europe, it's declining and it has for the last several decades and so I guess my point is that we're not going to be able to solve some of the world problems in the coming decades without machine intelligence. I mean you think about health care, you think about feeding populations, you think about obviously paying things like pandemics, climate change, energy alternatives, et cetera, productivity is coming down. Machines are potential opportunity. So there's an automation imperative. And you feel, Dr. Cheran, the people who are sort of beyond that machines replacing human's issue? Is that's still an item or has the pandemic sort of changed that? >> So I believe it is, so it used to be a very big item, you're right. And every time we were speaking at a conference and every time you're actually looking at the features of AI, right? Two scenarios are coming to plays, right? The first one where machines are here, actually take a walk, and then the second one as you know even a darker version where terminator is coming, yes and so forth, right? So basically these are the two, is the lesser evil in the greater evil and so on and so forth. And we still see that regular thing coming over and over again. And I believe that 2019 was the year of reckoning, where people are trying to realize that not only we can actually take responsible AI, but we can actually create an AI that is trustworthy, an AI that is fair and so on and so forth. And that we also understood in 2019 it was highly debated everywhere, which part of our jobs are going to be replaced like the parts that are mundane, or that can actually be easily automated and so on and so forth. With the COVID-19 what happened is that people are starting to look at AI differently, why? Because people are starting to look at data differently. And looking at data differently, how do I actually create this core of data which is trusted, secure and so on and so forth, and they are trying to understand that if the data is trusted and secure somehow, AI will be trusted and secure as well. Now, if I actually shifted forward, as you said, and then I try to understand, for example on the manufacturing floor, how do I add more machines? Or how do I replace humans with machines simply because, I need to make sure that I am able to stay in production and so on and so forth. From their perspective, I don't believe that the view of all people are actually looking at AI from the job marketplace perspective changed a lot. The view that actually changes how AI is helping us better certain prices, how AI is helping us, for example, in health care, but the idea of AI actually taking part of the jobs or automating parts of the jobs, we are not actually past yet, even if 2018 and even more so in 2019, it was the year also where actually AI through automation replaced the number of jobs but at the same time because as I was saying the first year where AI created more jobs it's because once you're displacing in one place, they're actually creating more work more opportunities in other places as well. But still, I don't believe the feeling changed. But we realize that AI is a lot more valuable and it can actually help us through some of our darkest hours, but also allow us to get better and faster insights as well. >> Well, machines have always replaced humans and now for the first time in history doing so in a really cognitive functions in a big way. But I want to ask you guys, I'll start with Dr. Arti, a series of questions that I think underscore the impact of AI and the central role that it plays in companies digital transformations, we talk about that a lot. But the questions that I'm going to ask you, I think will hit home just in terms of some hardcore examples, and if you have others I'd love to hear them but I'm going to start with Arti. So when do you think Dr. or machines will be able to make better diagnoses than doctors? We're actually there today already? >> So I think it depends a little bit on how you define that. And I'm just going to preface this by saying both of my parents are physicians. So I have a little bit of bias in this space. But I think that humans can bring creativity in a certain type of intelligence that it's not clear to me. We even know how to model with the computer. And so diagnoses have sometimes two components. One is recognizing patterns and being able to say, "I'm going to diagnose this disease that I've seen before." I think that we are getting to the place where there are certain examples. It's just starting to happen where you might be able to take the data that you need to make a diagnosis as well understood. A machine may be able to sort of recognize those subtle patterns better. But there's another component of doing diagnosis is when it's not obvious what you're looking for. You're trying to figure out what is the actual sort of setup diseases I might be looking at. And I think that's where we don't really know how to model that type of inspiration and creativity that humans still bring to things that they do, including medical diagnoses. >> So Dr. Cheran my next question is, when do you think that owning and driving your own vehicle will become largely obsolete? >> (laughs) Well, I believe my son is six year old now. And I believe, I'm working with a lot of companies to make sure that he will not get his driving license with his ID, right? So depending who you're asking and depending the level of autonomy that you're looking at, but you just mentioned the level five most likely. So there are a lot of dates out there so some people actually say 2030. I believe that my son in most of the cities in US but also most of the cities in Europe, by the time he's 18 in let's say 2035, I'll try to make sure that I'm working with the right companies not to allow them to get the driving license. >> I'll let my next question is from maybe both of you can answer. Do you take the traditional banks will lose control of payment system? >> So that's an interesting question, because I think it's broader than an AI question, right? I think that it goes into some other emerging technologies, including distributed ledgers and sort of the more secure forms of blockchain. I think that's a challenging question to my mind, because it's bigger than the technology. It's got Economic and Policy implications that I'm not sure I can answer. >> Well, that's a great answer, 'cause I agree with you already. I think that governments and banks have a partnership. It's important partnership for social stability. But similar we've seen now, Dr. Cheran in retail, obviously the COVID-19 has affected retail in a major way, especially physical retail, do you think that large retail stores are going to go away? I mean, we've seen many in chapter 11. At this point, how much of that is machine intelligence versus just social change versus digital transformation? It's an interesting question, isn't it? >> So I think most of the... Right now the retailers are here to stay I guess for the next couple of years. But moving forward, I think their capacity of adapting to stores like to walk in stores or to stores where basically we just go in and there are no shop assistants and just you don't even need the credit card to pay you're actually being able to pay either with your face or with your phone or with your small chips and so on and so forth. So I believe currently in the next couple of years, obviously they are here to stay. Moving forward then we'll get artificial intelligence, or robotics applied everywhere in the store and so on and so forth. Most likely their capacity of adapting to the new normal, which is placing AI everywhere and optimizing the walk in through predicting when and how to guide the customers to the shop, and so on and so forth, would allow them to actually survive. I don't believe that everything is actually going to be done online, especially from the retailer perspective. Most of the... We've seen a big shift at COVID-19. But what I was reading the other day, especially in France that the counter has opened again, we've seen a very quick pickup in the retailers of people that actually visiting the stores as well. So it's going to be some very interesting five to 10 years, and then most of the companies that have adapted to the digital transformation and to the new normal I think they are here to stay. Some of them obviously are going to take sometime. >> I mean, I think it's an interesting question too that you really sort of triggering in my mind is when you think about the framework for how companies are going to come back and come out of this, it's not just digital, that's a big piece of it, like how digital businesses, can they physically distance? I mean, I don't know how sports arenas are going to be able to physically distance that's going to be interesting to see how essential is the business and if you think about the different industries that it really is quite different across those industries. And obviously, digital plays a big factor there, but maybe we could end on that your final thoughts and maybe any other other things you'd like to share with our audience? >> So I think one of the things that's interesting anytime you talk about adopting a new technology, and right now we're happening to see this sort of huge uptick in AI adoption happening right at the same time but this sort of massive shift in how we live our lives is happening and sort of an acceptance, I think that can't just go back to the way things work as you mentioned, they'll probably be continued sort of desire to maintain social distancing. I think that it's going to force us to sort of rethink why we do things the way we do now, a lot, the retail, environments that we have the transportation solutions that we have, they were adapted in many cases in a very different context, in terms of what people need to do on a day-to-day basis within their life. And then what were the sort of state of technologies available. We're sort of being thrust and forced to reckon with like, what is it I really need to do to live my life and then what are the technologies I have available to meet to answer that and I think, it's really difficult to predict right now what people will think is important about a retail experience, I wouldn't be surprised if you start to find in person retail actually be much less, technologically aided, and much more about having the ability to talk to a human being and get their opinion and maybe the tactile sense of being able to like touch new clothes, or whatever it is. And so it's really difficult I think right now to predict what things are going to look like maybe even a year or two from now from that perspective. I think that what I feel fairly confident is that people are really starting to understand and engage with new technologies, and they're going to be really open to thinking about what those new technologies enable them to do in this sort of new way of living that we're going to probably be entering pretty soon. >> Excellent! All right, Sorin, bring us home. We'll give you the last word on this topic. >> Now, so I wanted to... I agree with Arti because what these three months of staying at home and of busy shutting down allowed us to do was to actually have a very big reset. So let's say a great reset but basically we realize that all the things we've taken from granted like our freedom of movement, our technology, our interactions with each other, and also for suddenly we realize that everything needs to change. And the only one thing that we actually kept doing is interacting with each other remotely, interacting with each other with our peers in the house, and so on and so forth. But the one thing that stayed was generating data, and data was here to stay because we actually leave traces of data everywhere we go, we leave traces of data when we put our watch on where we are actually playing with our phone, or to consume digital and so on and so forth. So what these three months reinforced for me personally, but also for some of our customers was that the data is here to stay. And even if the world shut down for three months, we did not generate less data. Data was there on the contrary, in some cases, more data. So the data is the main enabler for the new normal, which is going to pick up and the data will actually allow us to understand how to increase customer experience in the new normal, most likely using AI. As I was saying at the beginning, how do I actually operate new business model? How do I find, who do I partner with? How do I actually go to market together? How do I make collaborations more secure, and so on and so forth. And finally, where do I actually find new value pools? For example, how do I actually still enjoy for having a beer in a pub, right? Because suddenly during the COVID-19, that wasn't possible. I have a very nice place around the corner, but it's actually cheaply stuff. I'm not talking about beer but in general, I mean, so the finance is different the pools of data, the pools (mumbles) actually, getting values are different as well. So data is here to stay, and the AI definitely is going to be accelerated because it needs to use data to allow us to adopt the new normal in the digital transformation. >> A lot of unknowns but certainly machines and data are going to play a big role in the coming decade. I want to thank Dr. Arti Garg and Dr. Sorin Cheran for coming on theCUBE. It's great to have you. Thank you for a wonderful conversation. Really appreciate it. >> Thank you very much. >> Thanks so much. >> All right. And thank you for watching everybody. This is Dave Vellante for theCUBE and the HPE 2020 Virtual Experience. We'll be right back right after this short break. (upbeat music)