(upbeat music) >> Narrator: theCUBE's live coverage is made possible by funding from Dell Technologies: creating technologies that drive human progress. (upbeat music) >> Welcome back to the Fira in Barcelona. You're watching theCUBE's Coverage day two of MWC 23. Check out SiliconANGLE.com for all the news, John Furrier in our Palo Alto studio, breaking that down. But we're here live Dave Vellante, Dave Nicholson and Lisa Martin. We're really excited. We're going to talk qubits. Vanessa Diaz is here. She's CEO of LuxQuanta And Antonio Acin is a professor of ICFO. Folks, welcome to theCUBE. We're going to talk quantum. Really excited about that. >> Vanessa: Thank you guys. >> What does quantum have to do with the network? Tell us. >> Right, so we are actually leaving the second quantum revolution. So the first one actually happened quite a few years ago. It enabled very much the communications that we have today. So in this second quantum revolution, if in the first one we learn about some very basic properties of quantum physics now our scientific community is able to actually work with the systems and ask them to do things. So quantum technologies mean right now, three main pillars, no areas of exploration. The first one is quantum computing. Everybody knows about that. Antonio knows a lot about that too so he can explain further. And it's about computers that now can do wonder. So the ability of of these computers to compute is amazing. So they'll be able to do amazing things. The other pillar is quantum communications but in fact it's slightly older than quantum computer, nobody knows that. And we are the ones that are coming to actually counteract the superpowers of quantum computers. And last but not least quantum sensing, that's the the application of again, quantum physics to measure things that were impossible to measure in with such level of quality, of precision than before. So that's very much where we are right now. >> Okay, so I think I missed the first wave of quantum computing Because, okay, but my, our understanding is ones and zeros, they can be both and the qubits aren't that stable, et cetera. But where are we today, Antonio in terms of actually being able to apply quantum computing? I'm inferring from what Vanessa said that we've actually already applied it but has it been more educational or is there actual work going on with quantum? >> Well, at the moment, I mean, typical question is like whether we have a quantum computer or not. I think we do have some quantum computers, some machines that are able to deal with these quantum bits. But of course, this first generation of quantum computers, they have noise, they're imperfect, they don't have many qubits. So we have to understand what we can do with these quantum computers today. Okay, this is science, but also technology working together to solve relevant problems. So at this moment is not clear what we can do with present quantum computers but we also know what we can do with a perfect quantum computer without noise with many quantum bits, with many qubits. And for instance, then we can solve problems that are out of reach for our classical computers. So the typical example is the problem of factorization that is very connected to what Vanessa does in her company. So we have identified problems that can be solved more efficiently with a quantum computer, with a very good quantum computer. People are working to have this very good quantum computer. At the moment, we have some imperfect quantum computers, we have to understand what we can do with these imperfect machines. >> Okay. So for the first wave was, okay, we have it working for a little while so we see the potential. Okay, and we have enough evidence almost like a little experiment. And now it's apply it to actually do some real work. >> Yeah, so now there is interest by companies so because they see a potential there. So they are investing and they're working together with scientists. We have to identify use cases, problems of relevance for all of us. And then once you identify a problem where a quantum computer can help you, try to solve it with existing machines and see if you can get an advantage. So now the community is really obsessed with getting a quantum advantage. So we really hope that we will get a quantum advantage. This, we know we will get it. We eventually have a very good quantum computer. But we want to have it now. And we're working on that. We have some results, there were I would say a bit academic situation in which a quantum advantage was proven. But to be honest with you on a really practical problem, this has not happened yet. But I believe the day that this happens and I mean it will be really a game changing. >> So you mentioned the word efficiency and you talked about the quantum advantage. Is the quantum advantage a qualitative advantage in that it is fundamentally different? Or is it simply a question of greater efficiency, so therefore a quantitative advantage? The example in the world we're used to, think about a card system where you're writing information on a card and putting it into a filing cabinet and then you want to retrieve it. Well, the information's all there, you can retrieve it. Computer system accelerates that process. It's not doing something that is fundamentally different unless you accept that the speed with which these things can be done gives it a separate quality. So how would you characterize that quantum versus non quantum? Is it just so much horse power changes the game or is it fundamentally different? >> Okay, so from a fundamental perspective, quantum physics is qualitatively different from classical physics. I mean, this year the Nobel Prize was given to three experimentalists who made experiments that proved that quantum physics is qualitatively different from classical physics. This is established, I mean, there have been experiments proving that. Now when we discuss about quantum computation, it's more a quantitative difference. So we have problems that you can solve, in principle you can solve with the classical computers but maybe the amount of time you need to solve them is we are talking about centuries and not with your laptop even with a classic super computer, these machines that are huge, where you have a building full of computers there are some problems for which computers take centuries to solve them. So you can say that it's quantitative, but in practice you may even say that it's impossible in practice and it will remain impossible. And now these problems become feasible with a quantum computer. So it's quantitative but almost qualitative I would say. >> Before we get into the problems, 'cause I want to understand some of those examples, but Vanessa, so your role at LuxQuanta is you're applying quantum in the communication sector for security purposes, correct? >> Vanessa: Correct. >> Because everybody talks about how quantum's going to ruin our lives in terms of taking all our passwords and figuring everything out. But can quantum help us defend against quantum and is that what you do? >> That's what we do. So one of the things that Antonio's explaining so our quantum computer will be able to solve in a reasonable amount of time something that today is impossible to solve unless you leave a laptop or super computer working for years. So one of those things is cryptography. So at the end, when use send a message and you want to preserve its confidentiality what you do is you destroy it but following certain rules which means they're using some kind of key and therefore you can send it through a public network which is the case for every communication that we have, we go through the internet and then the receiver is going to be able to reassemble it because they have that private key and nobody else has. So that private key is actually made of computational problems or mathematical problems that are very, very hard. We're talking about 40 years time for a super computer today to be able to hack it. However, we do not have the guarantee that there is already very smart mind that already have potentially the capacity also of a quantum computer even with enough, no millions, but maybe just a few qubits, it's enough to actually hack this cryptography. And there is also the fear that somebody could actually waiting for quantum computing to finally reach out this amazing capacity we harvesting now which means capturing all this confidential information storage in it. So when we are ready to have the power to unlock it and hack it and see what's behind. So we are talking about information as delicate as governmental, citizens information related to health for example, you name it. So what we do is we build a key to encrypt the information but it's not relying on a mathematical problem it's relying on the laws of quantum physics. So I'm going to have a channel that I'm going to pump photons there, light particles of light. And that quantum channel, because of the laws of physics is going to allow to detect somebody trying to sneak in and seeing the key that I'm establishing. If that happens, I will not create a key if it's clean and nobody was there, I'll give you a super key that nobody today or in the future, regardless of their computational power, will be able to hack. >> So it's like super zero trust. >> Super zero trust. >> Okay so but quantum can solve really challenging mathematical problems. If you had a quantum computer could you be a Bitcoin billionaire? >> Not that I know. I think people are, okay, now you move me a bit of my comfort zone. Because I know people have working on that. I don't think there is a lot of progress at least not that I am aware of. Okay, but I mean, in principle you have to understand that our society is based on information and computation. Computers are a key element in our society. And if you have a machine that computes better but much better than our existing machines, this gives you an advantage for many things. I mean, progress is locked by many computational problems we cannot solve. We can want to have better materials better medicines, better drugs. I mean this, you have to solve hard computational problems. If you have machine that gives you machine learning, big data. I mean, if you have a machine that gives you an advantage there, this may be a really real change. I'm not saying that we know how to do these things with a quantum computer. But if we understand how this machine that has been proven more powerful in some context can be adapted to some other context. I mean having a much better computer machine is an advantage. >> When? When are we going to have, you said we don't really have it today, we want it today. Are we five years away, 10 years away? Who's working on this? >> There are already quantum computers are there. It's just that the capacity that they have of right now is the order of a few hundred qubits. So people are, there are already companies harvesting, they're actually the companies that make these computers they're already putting them. People can access to them through the cloud and they can actually run certain algorithms that have been tailor made or translated to the language of a quantum computer to see how that performs there. So some people are already working with them. There is billions of investment across the world being put on different flavors of technologies that can reach to that quantum supremacy that we are talking about. The question though that you're asking is Q day it sounds like doomsday, you know, Q day. So depending on who you talk to, they will give you a different estimation. So some people say, well, 2030 for example but perhaps we could even think that it could be a more aggressive date, maybe 2027. So it is yet to be the final, let's say not that hard deadline but I think that the risk, that it can actually bring is big enough for us to pay attention to this and start preparing for it. So the end times of cryptography that's what quantum is doing is we have a system here that can actually prevent all your communications from being hacked. So if you think also about Q day and you go all the way back. So whatever tools you need to protect yourself from it, you need to deploy them, you need to see how they fit in your organization, evaluate the benefits, learn about it. So that, how close in time does that bring us? Because I believe that the time to start thinking about this is now. >> And it's likely it'll be some type of hybrid that will get us there, hybrid between existing applications. 'Cause you have to rewrite or write new applications and that's going to take some time. But it sounds like you feel like this decade we will see Q day. What probability would you give that? Is it better than 50/50? By 2030 we'll see Q day. >> But I'm optimistic by nature. So yes, I think it's much higher than 50. >> Like how much higher? >> 80, I would say yes. I'm pretty confident. I mean, but what I want to say also usually when I think there is a message here so you have your laptop, okay, in the past I had a Spectrum This is very small computer, it was more or less the same size but this machine is much more powerful. Why? Because we put information on smaller scales. So we always put information in smaller and smaller scale. This is why here you have for the same size, you have much more information because you put on smaller scales. So if you go small and small and small, you'll find the quantum word. So this is unavoidable. So our information devices are going to meet the quantum world and they're going to exploit it. I'm fully convinced about this, maybe not for the quantum computer we're imagining now but they will find it and they will use quantum effects. And also for cryptography, for me, this is unavoidable. >> And you brought the point there are several companies working on that. I mean, I can get quantum computers on in the cloud and Amazon and other suppliers. IBM of course is. >> The underlying technology, there are competing versions of how you actually create these qubits. pins of electrons and all sorts of different things. Does it need to be super cooled or not? >> Vanessa: There we go. >> At a fundamental stage we'd be getting ground. But what is, what does ChatGPT look like when it can leverage the quantum realm? >> Well, okay. >> I Mean are we all out of jobs at that point? Should we all just be planning for? >> No. >> Not you. >> I think all of us real estate in Portugal, should we all be looking? >> No, actually, I mean in machine learning there are some hopes about quantum competition because usually you have to deal with lots of data. And we know that in quantum physics you have a concept that is called superposition. So we, there are some hopes not in concrete yet but we have some hopes that these superpositions may allow you to explore this big data in a more efficient way. One has to if this can be confirmed. But one of the hopes creating this lots of qubits in this superpositions that you will have better artificial intelligence machines but, okay, this is quite science fiction what I'm saying now. >> At this point and when you say superposition, that's in contrast to the ones and zeros that we're used to. So when someone says it could be a one or zero or a one and a zero, that's referencing the concept of superposition. And so if this is great for encryption, doesn't that necessarily mean that bad actors can leverage it in a way that is now unhackable? >> I mean our technologies, again it's impossible to hack because it is the laws of physics what are allowing me to detect an intruder. So that's the beauty of it. It's not something that you're going to have to replace in the future because there will be a triple quantum computer, it is not going to affect us in any way but definitely the more capacity, computational capacity that we see out there in quantum computers in particular but in any other technologies in general, I mean, when we were coming to talk to you guys, Antonio and I, he was the one saying we do not know whether somebody has reached some relevant computational power already with the technologies that we have. And they've been able to hack already current cryptography and then they're not telling us. So it's a bit of, the message is a little bit like a paranoid message, but if you think about security that the amount of millions that means for a private institution know when there is a data breach, we see it every day. And also the amount of information that is relevant for the wellbeing of a country. Can you really put a reasonable amount of paranoid to that? Because I believe that it's worth exploring whatever tool is going to prevent you from putting any of those piece of information at risk. >> Super interesting topic guys. I know you're got to run. Thanks for stopping by theCUBE, it was great to have you on. >> Thank you guys. >> All right, so this is the SiliconANGLE theCUBE's coverage of Mobile World Congress, MWC now 23. We're live at the Fira Check out silicon SiliconANGLE.com and theCUBE.net for all the videos. Be right back, right after this short break. (relaxing music)

>>Hello >>Everyone and welcome back to Dallas where we're live from, Super computing. My name is Savannah Peterson, joined with my co-host David, and we have a rocket of a show for you this afternoon. The doctors are in the house and we are joined by nasa, ladies and gentlemen. So excited. Please welcome Dr. Dan Duffy and Dr. Bill Putman. Thank you so much for being here, guys. I know this is kind of last minute. How's it to be on the show floor? What's it like being NASA here? >>What's exciting? We haven't, we haven't been here for three years, so this is actually really exciting to come back and see everybody, to see the showroom floor, see the innovations that have happened over the last three years. It's pretty exciting. >>Yeah, it's great. And, and so, because your jobs are so cool, and I don't wanna even remotely give even too little of the picture or, or not do it justice, could you give the audience a little bit of background on what you do as I think you have one of the coolest jobs ever. YouTube bill. >>I, I appreciate that. I, I, I run high Performance Computing Center at NASA Goddard for science. It's high performance information technology. So we do everything from networking to security, to high performance computing, to data sciences, artificial intelligence and machine learning is huge for us now. Yeah, large amounts of data, big data sets, but we also do scientific visualizations and then cloud and commercial cloud computing, as well as on premises cloud computing. And quite frankly, we support a lot of what Bill and his team does. >>Bill, why don't you tell us what your team >>Does? Yeah, so I'm a, I'm an earth scientist. I work as the associate chief at the global modeling assimilation office. And our job is to really, you know, maximize the use of all the observations that NASA takes from space and build that into a coherent, consistent physical system of the earth. Right? And we're focused on utilizing the HC that, that Dan and the folks at the nccs provide to us, to the best of our abilities to integrate those observations, you know, on time scales from hours, days to, to seasonal to to monthly time scales. That's, that's the essence of our focus at the GMA o >>Casual modeling, all of NASA's earth data. That, that in itself as a sentence is pretty wild. I imagine you're dealing with a ton of data. >>Oh, massive amounts of data. Yes, >>Probably, I mean, as much as one probably could, now that I'm thinking about it. I mean, and especially with how far things have to travel. Bill, sticking with you, just to open us up, what technology here excites you the most about the future and that will make your job easier? Let's put it that way. >>To me, it's the accelerator technologies, right? So there's the limited, the limiting factor for, for us as scientists is how fast we can get an answer. And if we can get our answer faster through accelerated technologies, you know, with the support of the, of the nccs and the computing centers, but also the software engineers enabling that for us, then we can do more, right. And push the questions even further, you know, so once we've gotten fast enough to do what we want to do, there's always something next that we wanna look for. So, >>I mean, at nasa you have to exercise such patience, whether that be data, coming back, images from a rover, doesn't matter what it is. Sometimes there's a lot of time, days, hours, years, depending on the situation. Right? I really, I really admire that. What about you, Dan? What's got you really excited about the future here? So >>Bill talked about the, the accelerated technology, which is absolutely true and, and, and is needed to get us not to only to the point where we have the compute resources to do the simulations that Bill wants to do, and also do it in a energy efficient way. But it's really the software frameworks that go around that and the software frameworks, the technology that dealing with how to use those in an energy efficient and and most efficient way is extremely important. And that's some of the, you know, that's what I'm really here to try to understand better about is how can I support these scientists with not just the hardware, but the software frameworks by which they can be successful. >>Yeah. We've, we've had a lot of kind of philosophical discussion about this, the difference between the quantitative increases in power in computing that we're seeing versus the question of whether or not we need truly qualitative changes moving forward. Where do you see the limits of, of, of, you know, if you, if you're looking at the ability to gather more data and process more data more quickly, what you can do with that data changes when you're getting updates every second versus every month seems pretty obvious. Is there a, is there, but is there, is there a near term target that you have specifically where once you reach that target, if you weren't thinking ahead of that target, you'd kind of be going, Okay, well we solved that problem, we're getting the data in so fast that you can, you can ask me, what is the temperature in this area? And you can go, Oh, well, huh, an hour ago the data said this. Beyond that, do you need a qualitative change in our ability to process information and tease insight into out of chaos? Or do you just need more quantity to be able to get to the point where you can do things like predict weather six months in advance? What are, what are your thoughts on that? Yeah, >>It's an interesting question, right? And, and you ended it with predicting whether six months in advance, and actually I was thinking the other way, right? I was thinking going to finer and finer scales and shorter time scales when you talk about having data more frequently, right? So one of the things that I'm excited about as a modeler is going to hire resolution and representing smaller scale processes at nasa, we're, we're interested in observations that are global. So our models are global and we'd like to push those to as fine a resolution as possible to do things like severe storm predictions and so forth. So the faster we can get the data, the more data we can have, and that area would improve our ability to do that as well. So, >>And your background is in meteorology, right? >>Yes, I'm a meteorologist. >>Excellent. Okay. Yeah, yeah, >>Yeah. So, so I have to ask a question, and I'm sure all the audience cares about this. And I went through this when I was talking about the ghost satellites as well. What, what is it about weather that makes it so hard to predict? >>Oh, it's the classic chaos problem. The, the butterfly effects problem, and it's just true. You know, you always hear the story of a butterfly in Africa flaps, its rings and wings, and the weather changes in, in New York City, and it's just, computers are an excellent example of that, right? So we have a model of the earth, we can run it two times in a row and get the exact same answer, but if we flip a bit somewhere, then the answer changes 10 days later significantly. So it's a, it's a really interesting problem. So, >>Yeah. So do you have any issue with the fact that your colleague believes that butterflies are responsible for weather? No, I does that, does that, is it responsible for climate? Does that bother you at all? >>No, it doesn't. As a matter of fact, they actually run those butterfly like experi experiments within the systems where they do actually flip some bits and see what the uncertainties are that happen out 7, 8, 9 days out in advance to understand exactly what he's saying, to understand the uncertainties, but also the sensitivity with respect to the observations that they're taking. So >>Yeah, it's fascinating. It is. >>That is fascinating. Sticking with you for a second, Dan. So you're at the Center for Climate Simulation. Is that the center that's gonna help us navigate what happens over the next decade? >>Okay, so I, no one center is gonna help us navigate what's gonna happen over the next decade or the next 50 or a hundred years, right. It's gonna be everybody together. And I think NASA's role in that is really to pioneer the, the, the models that that bill and others are doing to understand what's gonna happen in not just the seasonal sub, but we also work with G, which is the God Institute for Space Studies. Yeah. Which does the decatal and, and the century long studies. Our, our job is to really help that research, understand what's happening with the client, but then feed that back into what observations we need to make next in order to better understand and better quantify the risks that we have to better quantify the mitigations that we can make to understand how and, and, and affect how the climate is gonna go for the future. So that's really what we trying to do. We're trying to do that research to understand the climate, understand what mitigations we can have, but also feedback into what observations we can make for the future. >>Yeah. And and what's the partnership ecosystem around that? You mentioned that it's gonna take all of us, I assume you work with a lot of >>Partners, Probably both of you. I mean, obviously the, the, the federal agencies work huge amounts together. Nasa, Noah is our huge partnerships. Sgs, a huge partnerships doe we've talked to doe several times this, so this, this this week already. So there's huge partnerships that go across the federal agency. We, we work also with Europeans as much as we can given the, the, the, you know, sort of the barriers of the countries and the financials. But we do collaborate as much as we can with, And the nice thing about NASA, I would say is the, all the observations that we take are public, they're paid for by the public. They're public, everybody can down them, anybody can down around the world. So that's also, and they're global measurements as Bill said, they're not just regional. >>Do you have, do you have specific, when you think about improving your ability to gain insights from data that that's being gathered? Yeah. Do you set out specific milestones that you're looking for? Like, you know, I hope by June of next year we will have achieved a place where we are able to accomplish X. Yeah. Do you, do you, Yeah. Bill, do you put, what, >>What milestones do we have here? So, yeah, I mean, do you have >>Yeah. Are, are you, are you sort of kept track of that way? Do you think of things like that? Like very specific things? Or is it just so fluid that as long as you're making progress towards the future, you feel okay? >>No, I would say we absolutely have milestones that we like to keep in track, especially from the modeling side of things, right? So whether it's observations that exist now that we want to use in our system, milestones to getting those observations integrated in, but also thinking even further ahead to the observations that we don't have yet. So we can use the models that we have today to simulate those kind of observations that we might want in the future that can help us do things that we can do right now. So those missions are, are aided by the work that we do at the GBO and, and the nccs, but, >>Okay, so if we, if we extrapolate really to the, to the what if future is really trying to understand the entire earth system as best as we can. So all the observations coming in, like you said, in in near real time, feeding that into an earth system model and to be able to predict short term, midterm or even long term predictions with, with some degree of certainty. And that may be things like climate change or it may be even more important, shorter term effects of, of severe weather. Yeah. Which is very important. And so we are trying to work towards that high resolution, immediate impact model that we can, that we can, you know, really share with the world and share those results as best, as best we can. >>Yeah. I, I have a quick, I have a quick follow up on that. I I bet we both did. >>So, so if you think about AI and ml, artificial intelligence and machine learning, something that, you know, people, people talk about a lot. Yeah. There's the concept of teaching a machine to go look for things, call it machine learning. A lot of it's machine teaching we're saying, you know, hit, you know, hit the rack on this side with a stick or the other side with the stick to get it to, to kind of go back and forth. Do you think that humans will be able to guide these systems moving forward enough to tease out the insights that we want? Or do you think we're gonna have to rely on what people think of as artificial intelligence to be able to go in with this massive amount of information with an almost infinite amount of variables and have the AI figure out that, you know what, it was the butterfly, It really was the butterfly. We all did models with it, but, but you understand the nuance that I'm saying. It's like we, we, we think we know what all the variables are and that it's chaotic because there's so many variables and there's so much data, but maybe there's something we're not taking into >>A account. Yeah, I I, I'm, I'm, I'm sure that's absolutely the case. And I'll, I'll start and let Bill, Bill jump in here. Yeah, there's a lot of nuances with a aiml. And so the, the, the, the real approach to get to where we want to be with this earth system model approach is a combination of both AI ML train models as best as we can and as unbiased way as we can. And there's a, there's a big conversation we have around that, but also with a physics or physical based model as well, Those two combined with the humans or the experts in the loop, we're not just gonna ask the artificial intelligence to predict anything and everything. The experts need to be in the loop to guide the training in as best as we, as, as we can in an unbiased, equitable way, but also interpret the results and not just give over to the ai. But that's the combination of that earth system model that we really wanna see. The future's a combination of AI l with physics based, >>But there's, there's a, there's an obvious place for a AI and ML in the modeling world that is in the parameterizations of the estimations that we have to do in our systems, right? So when we think about the earth system and modeling the earth system, there are many things like the equations of motions and thermodynamics that have fixed equations that we know how to solve on a computer. But there's a lot of things that happen physically in the atmosphere that we don't have equations for, and we have to estimate them. And machine learning through the use of high resolution models or observations in training the models to understand and, and represent that, yeah, that that's the place where it's really useful >>For us. There's so many factors, but >>We have to, but we have to make sure that we have the physics in that machine learning in those, in those training. So physics informed training isn't very important. So we're not just gonna go and let a model go off and do whatever it wants. It has to be constrained within physical constraints that the, that the experts know. >>Yeah. And with the wild amount of variables that affect our, our earth, quite frankly. Yeah, yeah. Which is geez. Which is insane. My god. So what's, what, what technology or what advancement needs to happen for your jobs to get easier, faster for our ability to predict to be even more successful than it is currently? >>You know, I think for me, the vision that I have for the future is that at some point, you know, all data is centrally located, essentially shared. We have our applications are then services that sit around all that data. I don't have to sit as a user and worry about, oh, is this all this data in place before I run my application? It's already there, it's already ready for me. My service is prepared and I just launch it out on that service. But that coupled with the performance that I need to get the result that I want in time. And I don't know when that's gonna happen, but at some point it might, you know, I don't know rooting for you, but that's, >>So there are, there are a lot of technologies we can talk about. What I'd like to mention is, is open science. So NASA is really trying to make a push and transformation towards open science. 2023 is gonna be the year of open science for nasa. And what does that mean? It means a lot of what Bill just said is that we have equity and fairness and accessibility and you can find the data, it's findability, it's fair data, you know, a fair findability accessibility reproducibility, and I forget what the eye stands for, but these are, these are tools and, and, and things that we need to, as, as a computing centers and including all the HC centers here, as well as the scientists need to support, to be as transparent as possible with the data sets and the, and the research that we're doing. And that's where I think is gonna be the best thing is if we can get this data out there that anybody can use in an equitable way and as transparent as possible, that's gonna eliminate, in my opinion, the bias over time because mistakes will be found and mistakes will be corrected over time. >>I love that. Yeah. The open source science end of this. No, it's great. And the more people that have access people I find in the academic world, especially people don't know what's going on in the private sector and vice versa. And so I love that you just brought that up. Closing question for you, because I suspect there might be some members of our audience who maybe have fantasized about working at nasa. You've both been working there for over a decade. Is it as cool as we all think of it? It is on the outside. >>I mean, it's, it's definitely pretty cool. >>You don't have to be modest about it, you know, >>I mean, just being at Goddard and being at the center where they build the James web web telescope and you can go to that clean room and see it, it's just fascinating. So it, it's really an amazing opportunity. >>Yeah. So NASA Goddard as a, as a center has, you know, information technologist, It has engineers, it has scientists, it has support staff, support team members. We have built more things, more instruments that have flown in this space than any other place in the world. The James Lab, we were part of that, part of a huge group of people that worked on James. We and James, we came through and was assembled in our, our, our clean room. It's one of the biggest clean rooms in, in, in the world. And we all took opportunities to go over and take selfies with this as they put those loveness mirrors on them. Yeah, it was awesome. It was amazing. And to see what the James we has done in such a short amount of time, the successes that they've gone through is just incredible. Now, I'm not a, I'm not a part of the James web team, but to be a, to be at the same center, to to listen to scientists like Bill talk about their work, to listen to scientists that, that talk about James, we, that's what's inspiring. And, and we get that all the time. >>And to have the opportunity to work with the astronauts that service the, the Hubble Telescope, you know, these things are, >>That's literally giving me goosebumps right now. I'm sitting over >>Here just, just an amazing opportunity. And woo. >>Well, Dan, Bill, thank you both so much for being on the show. I know it was a bit last minute, but I can guarantee we all got a lot out of it. David and I both, I know I speak for us in the whole cube audience, so thank you. We'll have you, anytime you wanna come talk science on the cube. Thank you all for tuning into our supercomputing footage here, live in Dallas. My name is Savannah Peterson. I feel cooler having sat next to these two gentlemen for the last 15 minutes and I hope you did too. We'll see you again soon.

(upbeat music) >> Announcer: theCUBE presents UiPath Forward5. Brought to you by UiPath. >> Hi everybody, we're back in Las Vegas. We're live with Cube's coverage of Forward 5 2022. Dave Vellante with Dave Nicholson Ted Kumer this year is the Executive Vice President, product and engineering at UiPath. Brought on to do a lot of the integration and bring on new capabilities for the platform and we've seen that over the last several years. And he's joined by Dr. Edward Challis, who's the co-founder of the recent acquisition that UiPath made, company called Re:infer. We're going to learn about those guys. Gents, welcome to theCUBE. Ted, good to see you again. Ed, welcome. >> Good to be here. >> First time. >> Thank you. >> Yeah, great to be here with you. >> Yeah, so we have seen, as I said, this platform expanding. I think you used the term business automation platform. It's kind of a new term you guys introduced at the conference. Where'd that come from? What is that? What are the characteristics that are salient to the platform? >> Well, I see the, the evolution of our platform in three chapters. You understand the first chapter, we call that the RPA chapter. And that's where we saw the power of UI automation applied to the old problems of how do I integrate apps? How do I automate processes? That was chapter one. You know, chapter two gets us to Forward3 in 2019, and the definition of this end-to-end automation platform you know, with the capabilities from discover to measure, and building out that core platform. And as the platform's progressed, what we've seen happen with our customers is the use of it goes from being very heavy in automating the repetitive and routine to being more balanced, to now where they're implementing new brought business process, new capability for their organization. So that's where the name, Business Automation Platform, came from. Reflecting now that it's got this central role, as a strategic tool, sitting between their application landscape, their processes, their people, helping that move forward at the rate that it needs to. >> And process mining and task mining, that was sort of the enabler of chapter two, is that right? >> Well, I'd say chapter two was, you know, first the robots got bigger in terms of what they could cover and do. API integration, long running workflows, AI and ML skills integrated document processing, citizen development in addition to professional development, engaging end users with things like user interfaces built with UiPath apps. And then the discovery. >> So, more robustness of the? Yeah, okay. >> Yeah. Just an expansion of the whole surface area which opened up a lot of things for our customers to do. That went much broader than where core RPA started. And so, and the other thing about this progression to the business automation platform is, you know, we see customers now talking more about outcomes. Early on they talk a lot about hours saved and that's great, but then what about the business outcomes it's enabling? The transformations in their business. And the other thing we're doing in the platform is thinking about, well, where can we land with solutions capabilities that more directly land on business, measurable business outcomes? And so we had started, for example, offering an email automation solution, big business problem for a lot of our customers last year. And we'd started encountering this company Re:infer as we were working with customers. And then, and we encountered Re:infer being used with our platform together. And we saw we can accelerate this. And what that is giving us now is a solution now that aligns with a very defined business outcome. And this way, you know, we can help you process communications and do it efficiently and provide better service for your customers. And that's beginning of another important progression for us in our platform. >> So that's a nice segue, Ed. Tell about Re:infer. Why did you start the company? >> Right, yeah, so my whole career has been in machine learning and AI and I finished my PhD around 2013, it was a very exciting time in AI. And me and my co-founders come from UCL, this university in London, and Deep Mind, this company which Google acquired a few years later, came from our same university. So very exciting time amongst the people that really knew about machine learning and AI. And everyone was thinking, you know, how do we, these are just really big breakthroughs. And you could just see there was going to be a whole bunch of subsequent breakthroughs and we thought NLP would be the next breakthrough. So we were really focused on machine reading problems. And, but we also knew as people that had like built machine learning production systems. 'Cause I'd also worked in industry that built that journey from having a hypothesis that machine learning can solve a problem to getting machine learning into production. That journey is of painful, painful journey and that, you know, you can see that you've got these advances, but getting into broad is just way too hard. >> So where do you fit in the platform? >> Yeah, so I think when you look in the enterprise just so many processes start with a message start with a no, start with a case ticket or, you know, some other kind of request from a colleague or a customer. And so it's super exciting to be able to, you know, take automation one step higher in that process chain. So, you could automatically read that request, interpret it, get all the structured data you need to drive that process forward. So it's about bringing automation into these human channels. >> So I want to give the audience a sense here. So we do a lot of events at the Venetian Conference Center, and it's usually very booth heavy, you know, brands and big giant booths. And here the booths are all very small. They're like kiosks, and they're all pretty much the same size. So it's not like one vendor trying to compete with the other. And there are all these elements, you know I feel like there's clouds and there's, you know, of course orange is the color here. And one of the spots is, it has this really kind of cool sitting area around customer stories. And I was in there last night reading about Deutsche Bank. Deutsche Bank was also up on stage. Deutsche Bank, you guys were talking about a Re:infer. So share with our audience what Deutsche Bank are doing with UiPath and Re:infer. >> Yeah, so I mean, you know, before we automate something, we often like to do what we call communications mining. Which is really understanding what all of these messages are about that might be hitting a part of the business. And at Deutsche Bank and in many, you know, like many large financial services businesses, huge volumes of messages coming in from the clients. We analyze those, interpret the high volume query types and then it's about automating against those to free up capacity. Which ultimately means you can provide faster, higher quality service because you've got more time to do it. And you're not dealing with all of those mundane tasks. So it's that whole journey of mining to automation of the coms that come into the corporate bank. >> So how do I invoke the service? So is it mother module or what's the customer onboarding experience like? >> So, I think the first thing that we do is we generate some understanding of actually the communications data they want to observe, right? And we call it mining, but you know, what we're trying to understand is like what are these communications about? What's the intent? What are they trying to accomplish? Tone can be interesting, like what's the sentiment of this customer? And once you understand that, you essentially then understand categories of conversations you're having and then you apply automations to that. And so then essentially those individual automations can be pointed to sets of emails for them to automate the processing of. And so what we've seen is customers go from things they're handling a hundred percent manual to now 95% of them are handled basically with completely automated processing. The other thing I think is super interesting here and why communications mining and automation are so powerful together is communications about your business can be very, very dynamic. So like, new conversations can emerge, something happens right in your business, you have an outage, whatever, and the automation platform, being a very rapid development platform, can help you adapt quickly to that in an automated way. Which is another reason why this is such a powerful thing to put the two things together. >> So, you can build that event into the automation very quickly you're saying? >> Speaker 1: Yeah. >> Speaker 2: That's totally right. >> Cool. >> So Ed, on the subject of natural language processing and machine learning versus machine teaching. If I text my wife and ask her would you like to go to an Italian restaurant tonight? And she replies, fine. Okay, how smart is your machine? And, of course, context usually literally denotes things within the text, and a short response like that's very difficult to do this. But how do you go through this process? Let's say you're implementing this for a given customer. And we were just talking about, you know, the specific customer requirements that they might have. What does that process look like? Do you have an auditor that goes through? And I mean do you get like 20% accuracy, and then you do a pass, and now you're at 80% accuracy, and you do a pass? What does that look? >> Yeah, so I mean, you know when I was talking about the pain of getting a machine learning model into production one of the principle drivers of that is this process of training the machine learning model. And so what we use is a technique called active learning which is effectively where the AI and ML model queries the user to say, teach me about this data point, teach me about this sentence. And that's a dynamic iterative process. And by doing it in that way you make that training process much, much faster. But critically that means that the user has, when you train the model the user defines how you want to encode that interpretation. So when you were training it you would say fine from my wife is not good, right? >> Sure, so it might be fine, do you have a better suggestion? >> Yeah, but that's actually a very serious point because one of the things we do is track the quality of service. Our customers use us to attract the quality of service they deliver to their clients. And in many industries people don't use flowery language, like, thank you so much, or you know, I'm upset with you, you know. What they might say is fine, and you know, the person that manages that client, that is not good, right? Or they might say I'd like to remind you that we've been late the last three times, you know. >> This is urgent. >> Yeah, you know, so it's important that the client, our client, the user of Re:infer, can encode what their notions of good and bad are. >> Sorry, quick follow up on that. Differences between British English and American English. In the U.K., if you're thinking about becoming an elected politician, you stand for office, right? Here in the U.S., you run for office. That's just the beginning of the vagaries and differences. >> Yeah, well, I've now got a lot more American colleagues and I realize my English phrasing often goes amiss. So I'm really aware of the problem. We have customers that have contact centers, some of them are in the U.K., some of them are in America, and they see big differences in the way that the customers get treated based on where the customer is based. So we've actually done analysis in Re:infer to look at how agents and customers interact and how you should route customers to the contact centers to be culturally matched. Because sometimes there can be a little bit of friction just for that cultural mapping. >> Ted, what's the what's the general philosophy when you make an acquisition like this and you bring in new features? Do you just wake up one day and all of a sudden there's this new capability? Is it a separate sort of for pay module? Does it depend? >> I think it depends. You know, in this case we were really led here by customers. We saw a very high value opportunity and the beginnings of a strategy and really being able to mine all forms of communication and drive automated processing of all forms of communication. And in this case we found a fantastic team and a fantastic piece of software that we can move very quickly to get in the hands of our customer's via UiPath. We're in private preview now, we're going to be GA in the cloud right after the first of the year and it's going to continue forward from there. But it's definitely not one size fits all. Every single one of 'em is different and it's important to approach 'em that way. >> Right, right. So some announcements, StudioWeb was one that I think you could. So I think it came out today. Can't remember what was today. I think we talked about it yesterday on the keynotes anyway. Why is that important? What is it all about? >> Well we talked, you know, at a very top level. I think every development platform thinks about two things for developers. They think, how do I make it more expressive so you can do other things, richer scenarios. And how do I make it simpler? 'Cause fast is always better, and lower learning curves is always better, and those sorts of things. So, Re:infer's a great example of look the runtime is becoming more and more expressive and now you can buy in communications state as part of your automation, which is super cool. And then, you know StudioWeb is about kind of that second point and Studios and Studio X are already low code visual, but they're desktop. And part of our strategy here is to elevate all of that experience into the web. Now we didn't elevate all of studio there, it's a subset. It is API integration and web based application automation, Which is a great foundation for a lot of apps. It's a complete reimagining of the studio user interface and most importantly it's our first cross-platform developer strategy. And so that's been another piece of our strategy, is to say to the customers we want to be everywhere you need us to be. We did cross-platform deployment with the automation suite. We got cross-platform robots with linear robots, serverless robots, Mac support and now we got a cross-platform devs story. So we're starting out with a subset of capabilities maybe oriented toward what you would associate with citizen scenarios. But you're going to see more roadmap, bringing more and more of that. But it's pretty exciting for us. We've been working on this thing for a couple years now and like this is a huge milestone for the team to get to this, this point. >> I think my first conversation on theCUBE with a customer was six years ago maybe at one of the earlier Forwards, I think Forward2. And the pattern that I saw was basically people taking existing processes and making them better, you know taking the mundane away. I remember asking customers, yeah, aren't you kind of paving the cow path? Aren't there sort of new things that you can do, new process? And they're like, yeah, that's sort of the next wave. So what are you seeing in terms of automating existing processes versus new processes? I would see Re:infer is going to open up a whole new vector of new processes. How should we think about that? >> Yeah, I think, you know, I mean in some ways RPA has this reputation because there's so much value that's been provided in the automating of the repetitive and routine. But I'd say in my whole time, I've been at the company now for two and a half years, I've seen lots of new novel stuff stood up. I mean just in Covid we saw the platform being used in PPP loan processing. We saw it in new clinical workflows for COVID testing. We see it and we've just seen more and more progression and it's been exciting that the conference, to see customers now talking about things they built with UiPath apps. So app experiences they've been delivering, you know. I talked about one in healthcare yesterday and basically how they've improved their patient intake processing and that sort of thing. And I think this is just the front end. I truly believe that we are seeing the convergence happen and it's happening already of categories we've talked about separately, iPass, BPM, low-code, RPA. It's happening and it's good for customers 'cause they want one thing to cover more stuff and you know, I think it just creates more opportunity for developers to do more things. >> Your background at Microsoft probably well prepared you for a company that you know, was born on-prem and then went all in on the cloud and had, you know, multiple code bases to deal with. UiPath has gone through a similar transformation and we talked to Daniel last night about this and you're now cloud first. So how is that going just in terms of managing multiple code bases? >> Well it's actually not multiple Code bases. >> Oh, it's the same one, Right, deployment models I should say. >> Is the first thing, Yeah, the deployment models. Another thing we did along the way was basically replatform at an infrastructure level. So we now can deploy into a Kubernetes Docker world, what you'd call the cloud native platform. And that allows us to have much more of a shared infrastructure layer as we look to deliver to the automation cloud. The same workload to the automation cloud that we now deliver in the automation suite for deployment on-prem or deploying a public cloud for a customer to manage. Interesting and enough, that's how Re:infer was built, which is it was built also in the cloud native platform. So it's going to be pretty easy. Well, pretty easy, there's some work to do, but it's going to be pretty easy for us to then bring that into the platform 'cause they're already working on that same platform and provide those same services both on premises and in the cloud without having your developers have to think too much about both. >> Okay, I got to ask you, so I could wrap my stack in a container and put it into AWS or Azure or Google and it'll run great. As well, I could tap some of the underlying primitives of those respective clouds, which are different and I could run them just fine. Or/and I could create an abstraction layer that could hide those underlying primitives and then take the best of each and create an automation cloud, my own cloud. Does that resonate? Is that what you're doing architecturally? Is that a roadmap, or? >> Certainly going forward, you know, in the automation cloud. The automation cloud, we announced a great partnership or a continued partnership with Microsoft. And just Azure and our platform. We obviously take advantage of anything we can to make that great and native capabilities. And I think you're going to see in the Automation Suite us doing more and more to be in a deployment model on Azure, be more and more optimized to using those infrastructure services. So if you deploy automation suite on-prem we'll use our embedded distro then when we deploy it say on Azure, we'll use some of their higher level managed services instead of our embedded distro. And that will just give customers a better optimized experience. >> Interesting to see how that'll develop. Last question is, you know what should we expect going forward? Can you show us a little leg on on the future? >> Well, we've talked about a number of directions. This idea of semantic automation is a place where you know, you're going to, I think, continue to see things, shoots, green shoots, come up in our platform. And you know, it's somewhat of an abstract idea but the idea that the platform is just going to become semantically smarter. You know, I had to serve Re:infer as a way, we're semantically smarter now about communications data and forms of communications data. We're getting semantically smarter about documents, screens you know, so developers aren't dealing with, like, this low level stuff. They can focus on business problem and get out of having to deal with all this lower level mechanism. That is one of many areas I'm excited about, but I think that's an area you're going to see a lot from us in the next coming years. >> All right guys, hey, thanks so much for coming to theCUBE. Really appreciate you taking us through this. Awesome >> Yeah Always a pleasure. >> Platform extension. Ed. All right, keep it right there, everybody. Dave Nicholson, I will be back right after this short break from UiPath Forward5, Las Vegas. (upbeat music)

(gentle melody) >> Welcome back to theCUBE's live coverage of AWS Summit in San Francisco, California. Events are back. AWS Summit in New York City this summer, theCUBE will be there as well. Check us out there. I'm glad to have events back. It's great to have of everyone here. I'm John Furrier, host of theCUBE. Dr. Matt Wood is with me, CUBE alumni, now VP of Business Analytics Division of AWS. Matt, great to see you. >> Thank you, John. It's great to be here. I appreciate it. >> I always call you Dr. Matt Wood because Andy Jackson always says, "Dr. Matt, we would introduce you on the arena." (Matt laughs) >> Matt: The one and only. >> The one and only, Dr. Matt Wood. >> In joke, I love it. (laughs) >> Andy style. (Matt laughs) I think you had walk up music too. >> Yes, we all have our own personalized walk up music. >> So talk about your new role, not a new role, but you're running the analytics business for AWS. What does that consist of right now? >> Sure. So I work. I've got what I consider to be one of the best jobs in the world. I get to work with our customers and the teams at AWS to build the analytics services that millions of our customers use to slice dice, pivot, better understand their data, look at how they can use that data for reporting, looking backwards. And also look at how they can use that data looking forward, so predictive analytics and machine learning. So whether it is slicing and dicing in the lower level of Hadoop and the big data engines, or whether you're doing ETL with Glue, or whether you're visualizing the data in QuickSight or building your models in SageMaker. I got my fingers in a lot of pies. >> One of the benefits of having CUBE coverage with AWS since 2013 is watching the progression. You were on theCUBE that first year we were at Reinvent in 2013, and look at how machine learning just exploded onto the scene. You were involved in that from day one. It's still day one, as you guys say. What's the big thing now? Look at just what happened. Machine learning comes in and then a slew of services come in. You've got SageMaker, became a hot seller right out of the gate. The database stuff was kicking butt. So all this is now booming. That was a real generational change over for database. What's the perspective? What's your perspective on that's evolved? >> I think it's a really good point. I totally agree. I think for machine learning, there's sort of a Renaissance in machine learning and the application of machine learning. Machine learning as a technology has been around for 50 years, let's say. But to do machine learning right, you need like a lot of data. The data needs to be high quality. You need a lot of compute to be able to train those models and you have to be able to evaluate what those models mean as you apply them to real world problems. And so the cloud really removed a lot of the constraints. Finally, customers had all of the data that they needed. We gave them services to be able to label that data in a high quality way. There's all the compute you need to be able to train the models. And so where you go? And so the cloud really enabled this Renaissance with machine learning. And we're seeing honestly a similar Renaissance with data and analytics. If you look back five to ten years, analytics was something you did in batch, your data warehouse ran an analysis to do reconciliation at the end of the month, and that was it. (John laughs) And so that's when you needed it. But today, if your Redshift cluster isn't available, Uber drivers don't turn up, DoorDash deliveries don't get made. Analytics is now central to virtually every business, and it is central to virtually every business's digital transformation. And being able to take that data from a variety of sources, be able to query it with high performance, to be able to actually then start to augment that data with real information, which usually comes from technical experts and domain experts to form wisdom and information from raw data. That's kind of what most organizations are trying to do when they kind of go through this analytics journey. >> It's interesting. Dave Velanta and I always talk on theCUBE about the future. And you look back, the things we're talking about six years ago are actually happening now. And it's not hyped up statement to say digital transformation is actually happening now. And there's also times when we bang our fists on the table saying, say, "I really think this is so important." And David says, "John, you're going to die on that hill." (Matt laughs) And so I'm excited that this year, for the first time, I didn't die on that hill. I've been saying- >> Do all right. >> Data as code is the next infrastructure as code. And Dave's like, "What do you mean by that?" We're talking about how data gets... And it's happening. So we just had an event on our AWS startups.com site, a showcase for startups, and the theme was data as code. And interesting new trends emerging really clearly, the role of a data engineer, right? Like an SRE, what an SRE did for cloud, you have a new data engineering role because of the developer onboarding is massively increasing, exponentially, new developers. Data science scientists are growing, but the pipelining and managing and engineering as a system, almost like an operating system. >> Kind of as a discipline. >> So what's your reaction to that about this data engineer, data as code? Because if you have horizontally scalable data, you've got to be open, that's hard (laughs), okay? And you got to silo the data that needs to be siloed for compliance and reason. So that's a big policy around that. So what's your reaction to data's code and the data engineering phenomenon? >> It's a really good point. I think with any technology project inside of an organization, success with analytics or machine learning, it's kind of 50% technology and then 50% cultural. And you have often domain experts. Those could be physicians or drug design experts, or they could be financial experts or whoever they might be, got deep domain expertise, and then you've got technical implementation teams. And there's kind of a natural, often repulsive force. I don't mean that rudely, but they just don't talk the same language. And so the more complex a domain and the more complex the technology, the stronger their repulsive force. And it can become very difficult for domain experts to work closely with the technical experts to be able to actually get business decisions made. And so what data engineering does and data engineering is, in some cases a team, or it can be a role that you play. It's really allowing those two disciplines to speak the same language. You can think of it as plumbing, but I think of it as like a bridge. It's a bridge between the technical implementation and the domain experts, and that requires a very disparate range of skills. You've got to understand about statistics, you've got to understand about the implementation, you got to understand about the data, you got to understand about the domain. And if you can put all of that together, that data engineering discipline can be incredibly transformative for an organization because it builds the bridge between those two groups. >> I was advising some young computer science students at the sophomore, junior level just a couple of weeks ago, and I told them I would ask someone at Amazon this question. So I'll ask you, >> Matt: Okay. since you've been in the middle of it for years, they were asking me, and I was trying to mentor them on how do you become a data engineer, from a practical standpoint? Courseware, projects to work on, how to think, not just coding Python, because everyone's coding in Python, but what else can they do? So I was trying to help them. I didn't really know the answer myself. I was just trying to kind of help figure it out with them. So what is the answer, in your opinion, or the thoughts around advice to young students who want to be data engineers? Because data scientists is pretty clear on what that is. You use tools, you make visualizations, you manage data, you get answers and insights and then apply that to the business. That's an application. That's not the standing up a stack or managing the infrastructure. So what does that coding look like? What would your advice be to folks getting into a data engineering role? >> Yeah, I think if you believe this, what I said earlier about 50% technology, 50 % culture, the number one technology to learn as a data engineer is the tools in the cloud which allow you to aggregate data from virtually any source into something which is incrementally more valuable for the organization. That's really what data engineering is all about. It's about taking from multiple sources. Some people call them silos, but silos indicates that the storage is kind of fungible or undifferentiated. That's really not the case. Success requires you to have really purpose built, well crafted, high performance, low cost engines for all of your data. So understanding those tools and understanding how to use them, that's probably the most important technical piece. Python and programming and statistics go along with that, I think. And then the most important cultural part, I think is... It's just curiosity. You want to be able to, as a data engineer, you want to have a natural curiosity that drives you to seek the truth inside an organization, seek the truth of a particular problem, and to be able to engage because probably you're going to some choice as you go through your career about which domain you end up in. Maybe you're really passionate about healthcare, or you're really just passionate about transportation or media, whatever it might be. And you can allow that to drive a certain amount of curiosity. But within those roles, the domains are so broad you kind of got to allow your curiosity to develop and lead you to ask the right questions and engage in the right way with your teams, because you can have all the technical skills in the world. But if you're not able to help the team's truth seek through that curiosity, you simply won't be successful. >> We just had a guest, 20 year old founder, Johnny Dallas who was 16 when he worked at Amazon. Youngest engineer- >> Johnny Dallas is a great name, by the way. (both chuckle) >> It's his real name. It sounds like a football player. >> That's awesome. >> Rock star. Johnny CUBE, it's me. But he's young and he was saying... His advice was just do projects. >> Matt: And get hands on. Yeah. >> And I was saying, hey, I came from the old days where you get to stand stuff up and you hugged on for the assets because you didn't want to kill the project because you spent all this money. And he's like, yeah, with cloud you can shut it down. If you do a project that's not working and you get bad data no one's adopting it or you don't like it anymore, you shut it down, just something else. >> Yeah, totally. >> Instantly abandon it and move on to something new. That's a progression. >> Totally! The blast radius of decisions is just way reduced. We talk a lot about in the old world, trying to find the resources and get the funding is like, all right, I want to try out this kind of random idea that could be a big deal for the organization. I need $50 million and a new data center. You're not going to get anywhere. >> And you do a proposal, working backwards, documents all kinds of stuff. >> All that sort of stuff. >> Jump your hoops. >> So all of that is gone. But we sometimes forget that a big part of that is just the prototyping and the experimentation and the limited blast radius in terms of cost, and honestly, the most important thing is time, just being able to jump in there, fingers on keyboards, just try this stuff out. And that's why at AWS, we have... Part of the reason we have so many services, because we want, when you get into AWS, we want the whole toolbox to be available to every developer. And so as your ideas develop, you may want to jump from data that you have that's already in a database to doing realtime data. And then you have the tools there. And when you want to get into real time data, you don't just have kinesis, you have real time analytics, and you can run SQL against that data. The capabilities and the breadth really matter when it comes to prototyping. >> That's the culture piece, because what was once a dysfunctional behavior. I'm going to go off the reservation and try something behind my boss' back, now is a side hustle or fun project. So for fun, you can just code something. >> Yeah, totally. I remember my first Hadoop projects. I found almost literally a decommissioned set of servers in the data center that no one was using. They were super old. They're about to be literally turned off. And I managed to convince the team to leave them on for me for another month. And I installed Hadoop on them and got them going. That just seems crazy to me now that I had to go and convince anybody not to turn these servers off. But what it was like when you- >> That's when you came up with Elastic MapReduce because you said this is too hard, we got to make it easier. >> Basically yes. (John laughs) I was installing Hadoop version Beta 9.9 or whatever. It was like, this is really hard. >> We got to make it simpler. All right, good stuff. I love the walk down memory Lane. And also your advice. Great stuff. I think culture is huge. That's why I like Adam's keynote at Reinvent, Adam Selipsky talk about Pathfinders and trailblazers, because that's a blast radius impact when you can actually have innovation organically just come from anywhere. That's totally cool. >> Matt: Totally cool. >> All right, let's get into the product. Serverless has been hot. We hear a lot of EKS is hot. Containers are booming. Kubernetes is getting adopted, still a lot of work to do there. Cloud native developers are booming. Serverless, Lambda. How does that impact the analytics piece? Can you share the hot products around how that translates? >> Absolutely, yeah. >> Aurora, SageMaker. >> Yeah, I think it's... If you look at kind of the evolution and what customers are asking for, they don't just want low cost. They don't just want this broad set of services. They don't just want those services to have deep capabilities. They want those services to have as low an operating cost over time as possible. So we kind of really got it down. We got built a lot of muscle, a lot of services about getting up and running and experimenting and prototyping and turning things off and turning them on and turning them off. And that's all great. But actually, you really only in most projects start something once and then stop something once, and maybe there's an hour in between or maybe there's a year. But the real expense in terms of time and operations and complexity is sometimes in that running cost. And so we've heard very loudly and clearly from customers that running cost is just undifferentiated to them. And they want to spend more time on their work. And in analytics, that is slicing the data, pivoting the data, combining the data, labeling the data, training their models, running inference against their models, and less time doing the operational pieces. >> Is that why the service focuses there? >> Yeah, absolutely. It dramatically reduces the skill required to run these workloads of any scale. And it dramatically reduces the undifferentiated heavy lifting because you get to focus more of the time that you would have spent on the operations on the actual work that you want to get done. And so if you look at something just like Redshift Serverless, that we launched a Reinvent, we have a lot of customers that want to run the cluster, and they want to get into the weeds where there is benefit. We have a lot of customers that say there's no benefit for me, I just want to do the analytics. So you run the operational piece, you're the experts. We run 60 million instant startups every single day. We do this a lot. >> John: Exactly. We understand the operations- >> I just want the answers. Come on. >> So just give me the answers or just give me the notebook or just give me the inference prediction. Today, for example, we announced Serverless Inference. So now once you've trained your machine learning model, just run a few lines of code or you just click a few buttons and then you got an inference endpoint that you do not have to manage. And whether you're doing one query against that end point per hour or you're doing 10 million, we'll just scale it on the back end. I know we got not a lot of time left, but I want to get your reaction on this. One of the things about the data lakes not being data swamps has been, from what I've been reporting and hearing from customers, is that they want to retrain their machine learning algorithm. They need that data, they need the real time data, and they need the time series data. Even though the time has passed, they got to store in the data lake. So now the data lake's main function is being reusing the data to actually retrain. It works properly. So a lot of post mortems turn into actually business improvements to make the machine learnings smarter, faster. Do you see that same way? Do you see it the same way? >> Yeah, I think it's really interesting >> Or is that just... >> No, I think it's totally interesting because it's convenient to kind of think of analytics as a very clear progression from point A to point B. But really, you're navigating terrain for which you do not have a map, and you need a lot of help to navigate that terrain. And so having these services in place, not having to run the operations of those services, being able to have those services be secure and well governed. And we added PII detection today. It's something you can do automatically, to be able to use any unstructured data, run queries against that unstructured data. So today we added text queries. So you can just say, well, you can scan a badge, for example, and say, well, what's the name on this badge? And you don't have to identify where it is. We'll do all of that work for you. It's more like a branch than it is just a normal A to B path, a linear path. And that includes loops backwards. And sometimes you've got to get the results and use those to make improvements further upstream. And sometimes you've got to use those... And when you're downstream, it will be like, "Ah, I remember that." And you come back and bring it all together. >> Awesome. >> So it's a wonderful world for sure. >> Dr. Matt, we're here in theCUBE. Just take the last word and give the update while you're here what's the big news happening that you're announcing here at Summit in San Francisco, California, and update on the business analytics group. >> Yeah, we did a lot of announcements in the keynote. I encourage everyone to take a look at, that this morning with Swami. One of the ones I'm most excited about is the opportunity to be able to take dashboards, visualizations. We're all used to using these things. We see them in our business intelligence tools, all over the place. However, what we've heard from customers is like, yes, I want those analytics, I want that visualization, I want it to be up to date, but I don't actually want to have to go from my tools where I'm actually doing my work to another separate tool to be able to look at that information. And so today we announced 1-click public embedding for QuickSight dashboard. So today you can literally as easily as embedding a YouTube video, you can take a dashboard that you've built inside QuickSight, cut and paste the HTML, paste it into your application and that's it. That's what you have to do. It takes seconds. >> And it gets updated in real time. >> Updated in real time. It's interactive. You can do everything that you would normally do. You can brand it, there's no power by QuickSight button or anything like that. You can change the colors, fit in perfectly with your application. So that's an incredibly powerful way of being able to take an analytics capability that today sits inside its own little fiefdom and put it just everywhere. Very transformative. >> Awesome. And the business is going well. You got the Serverless detail win for you there. Good stuff. Dr. Matt Wood, thank you for coming on theCUBE. >> Anytime. Thank you. >> Okay, this is theCUBE's coverage of AWS Summit 2022 in San Francisco, California. I'm John Furrier, host of theCUBE. Stay with us for more coverage of day two after this short break. (gentle music)

>>Um, okay, thanks to the studio there for the handoff. Appreciate it. We're here for breaking news and it's exciting that we have who's the managing director. Google is breaking some hard news here, Dave. We want to bring him in and get commentary while we end up Dave too. Honestly, the story here is cloud city. We are in the cloud city and all, thanks for coming on remotely into our physical hybrid set here. Thanks for coming >>On. Thank you, John. And very excited to be here. What Juliet. >>Well, we got Bon Jovi ready to play. Everyone's waiting for that concert in the year. The only thing standing between bunch of LV and all the great stuff. So a lot of people watching. Thanks for coming on, sir. So you guys got some big news, um, first Erickson partners with you guys on 5g platform, deal with Anthem, as well as, uh, open ran Alliance. You guys are joining huge, a Testament to the industry. I see Google with all your innovation you guys have in the big three cloud hyperscalers. Obviously you guys invented SRE, so you know, you no stranger to large scale. What's the news. Let's tell us why this Erickson news is so important. Let's start with the Erickson announcement. >>Sure. So John, I mean, we are very excited today to finally bring to the market, the strategic partnership that we've been building with Erickson for the last few months, uh, the partnership to recent retreat, which is very important to the industry is you're actually doing this in conjunction with very large CSPs. So it's not been in isolation. You are in fact in the press release that we have already launched something to get the big telecom Italia in Italy, because you will see that also in the past. And really the partnership is on three pillars. Number one, how can CSBs monetize 5g and edge, which is the real team at the moment using Google clouds solutions like the edge computing platform and, and POS, and Erikson's cutting edge 5g components, 5g solutions. And if we can onboard these together at the CSP, such as telecom Italia, that creates massive pain to market efficiency. So that's 0.1 because speed and agility is key John, but then point to it also unlocks a lot of edge use cases for a bunch of verticals, retail, manufacturing, healthcare, so on, which are already starting to launch together with that. Excellent. And so that's the second pillar. And then the final pillar of course, is this continuously cloud native innovation that you just highlighted. John, we are going to try and double down on it between ourselves and Ericsson to really time created this cloud native application suite or 5g or whatever. >>Talk about the innovations around cloud, because the message we're hearing him this year at mobile world Congress, is that the public cloud is driving the innovation. And, you know, I can be a little bit over the top. So the telcos are slow. They're like glaciers, they move slow, but they're just moving packets. They are there. They're moving the network around. The innovation is happening on top. So there's some hardened operations operating the networks. Now you have a build concept cloud native enables that. So you've got containers. You can put that encapsulate that older technology and integrated in. So this is not a rip and replace. Someone has to die to win. This is a partnership with the tellers. Can you share your thoughts on that piece? >>Smart Antone's photo? We believe that it's a massive partnership opportunity. There's zero conflict or tensions in this sort of ecosystem. And the reason for that is when you talk about that containerization and right once and deploy everywhere type architecture that we are trying to do, that's where the cloud native really helps. Like when you create Ericsson 5g solutions with the operators, adjust telecom Italia, once you build a solution, you don't have to worry about, do I need to kick it back again and again, but every deployment, as long as your mantra, genetics and working, you shouldn't be able to have the same experience. >>Yeah, I'm John. I talk all the time in the cube about how developers are really going to drive the edge. You're clearly doing that with your distributed cloud, building out a telco cloud. I wonder if you could talk a little bit more about how you see that evolving. A lot of the AI that's done today is done in the cloud. A lot of modeling being done. When you think about edge, you think about AI inferencing, you think about all these monetization opportunities. How are you thinking about that? >>So I think David, first of all, it's a fan best six Sigma in how we are looked in at analytics at the edge, right? So we, uh, we have realized that is a very, very, uh, uh, uh, data computing, heavy operation. So certainly the training of the models is still going to stay in cloud for the foreseeable future. But the influencing part that you mentioned is there something that we can offer to the edge? Why is that so important in the pandemic era, think of running a shop or a factory floor, completely autonomously meeting zero minimal human intervention. And if you want to look at an assembly line and look at AI influencing as a way to find out assembly line defects on products in manufacturing, that's very difficult problem to solve unless you actually create those influencing models at the edge. So creating that ecosystem of an Erickson and a Google cloud carrier gives you that edge placement of the workloads that would fit right next to our factory floor in our manufacturing example. And then on top of that, you could run that AI influence thing to really put in the hands of the manufacturer, a visual inspection capability to just bring this to life. >>Great. Thank you for that. And now the other piece of the announcement of course, is the open, open ran. We've been talking about that all weekend and you know, you well, remember when cloud first came out, people were concerned about security. Of course. Now everybody's asking the question, can we still get the reliability and the security that we're used to with the telcos? And of course over time we learned that you guys actually pretty good at security. So how do you see the security component, maybe first talk about the open ran piece, why that's important and how security fits? >>Sure. So first of all, open trend is something that we have taken great interest in the last year or so as it started evolving. And the reason for that is fairly simple. Dave, this aggregation of networks has been happening for some time in the radio layer. We believe that's the final frontier of sort of unlocking and dis-aggregating that radio layer. And why is it so important? 80% of the operators spend globally is on radio. 80% is on radio. If you disaggregate that. And if the internet synergies for your CSP partners and clients, that meant you have standard purpose hardware standard for software with open interfaces, number one, massive difference in VCO. Number two, the supply chain gets streamlined and become still really, really simple way to manage a fairly large distribution. That's about to get larger in 5g and the capital clarity that 5g needs. >>You're thinking of tens of thousands of micro cells and radio cells going everywhere. And having that kind of standardized hardware software with openings of Essex is an extremely important cost dimension to every new site finished that the reason we got to exact open brand was you can now run for a lot of API APIs on the radio net, cetera, that then certainly brings a whole developer community on the radio later. That then helps you do a bunch of things like closed loop automation for network optimization, as well as potentially looking at monetization opportunities by hyper personalizing, yours and mine experiences at the waist level from the self-doubt. And so that really is what is driving us towards this open grind paper. Come on, we go and >>Got a minute and a half. I want to get your thoughts real quick on, on open source and the innovation. Um, Danielle Royston, who's the CEO of telco, Dr. She's at a keynote today. And she mentioned that the iPhone 14 years ago was launched. Okay. And you think about open and you mentioned proprietary with the 5g and having Iran be more commodity and industry standard. That's going to lower the costs increase the surface here of infrastructure. Everyone wins because everyone wants more connectivity options. Software is going to be the key to success for the telco industry. And open source is driving. That is Android. The playbook that you guys pioneered, obviously at Google with the smart phones was very successful. How is that a playbook or an indicator to what could happen at telecom? >>Absolutely. John and the parallel and analogy that you raised is photon. Be believed in the telco world and tossed multi cloud as a unifying software development layer. The app development platform is the way that people will start to drive this innovation, whether it's radio or whether it's in the core or whether it's on the side of pups, same software planning, everywhere that really allows you that whole development models that we are familiar with, but on the telecom side. And that's where we are seeing some massive innovation opportunities for systems to come on board. >>That's great stuff. And I was just heard someone in the hallway just yesterday and say, you want to be the smartphone. You don't want to be the Blackberry going forward. That's pretty much the consensus here at mobile world Congress. I'm all. Thank you for coming on and sharing the hard news and Google regulations on the Erickson Anthem platform, a deal as well as the open Ranton Alliance. Uh, congratulations. Good to see you. And by the way, you'll be keynoting tomorrow on the cube featured segment. So >>Watch that in there. Thank you, John. Thank you. Glad >>To be here. Benching director telecom, industry, solicitor, Google, obviously player. He's managing that business. Big opportunities for Google because they have the technology. They got the chops, Dave, and we're going to now bring this Daniel. Russia says here when to bring up on the stage, Bon Jovi is about to go on behind us Bon Jovi's here. And this is like a nightclub, small intimate setting here in cloud city. Dave Bon. Jovi's right there. He's going to come on stage after we close down here, but first let's bring up the CEO of telco. Dr. And yeah, it was great to see she's hot off the keynote. We're going to see you to Mike. Great to see you. Oh, it's great to be there. We're going to see you tomorrow for an official unpacking of the keynote, but thanks for coming by and closing, >>Swinging by. I never closed down the show. It's been a big, it's been a big day-to-day at MWC and in cloud city, really starting to get packed. I mean, everyone's coming in the band's warming up. You can kind of hear it. Um, I think Elon Musk is about to go on as well. So I mean, it's really happening all the buzz about cloud city out there in the hallway. Yeah. Yeah, no, I mean, I think everyone's talking about it. I'm really, really excited with how it's going. >>Well, this is awesome. While we got you here, we want to put you to work being the cube analyst for the segment. You just heard Google. Uh, we broke them in for a breaking news segment. So hard news Erickson partnership. We're in the factory, former Erickson booth. They're not even here, it's now the Calco VR booth, but that's a relation. And then open ran again, open source, you got five G you got open source all happening. What's your take on this? >>You see, you know, there's two big. And I, I talked about it, my keynote this morning, and there's two big technological changes that are happening in our industry simultaneously. And I don't think we could have had it MWC 21. I certainly wanted to make it about the public cloud. I think I'm sort of successful in doing that. And I think the other piece is open ramp, right? And I think these two big shifts are happening and, um, I'm really thrilled about it. And so, yeah, >>Well I loved your keynote. We were here, live. Chloe was here filling in for Dave while David was going to do some research and some breaking stories to you are on stage. And we were talking well, he's like, there's trillions of dollars, John on the table. And I was making the point, the money is at the middle of the table and it's changing hands if people don't watch it. And then you onstage that this trillions of dollars, this is a real competitive shift with dollars on the table. And you've got cultural collision. You got operators and builders trying to figure out it feels like dev ops is coming in here. Yeah. I mean, what's the, what's the holistic vibe. What's >>The, yeah, I think my message is about, we can use the software and specifically the software, the public cloud to double your ARPU without massive cap X expenditure. And I think the CSPs is always viewed to get the increase in ARPU. I got to build out the network. I got to spend a lot of money. And with these two technologies that require might be dropped. And then in exchange for doubling our poo, why not? We should do that. Absolutely. >>You know, your message has been pretty clear that you got to get on, on the wave that arrived the way you're going to become driftwood. As John said yesterday. And I think it's pretty, it's becoming pretty clear that that's the case for the telcos. I feel like Danielle, that they were entering this decade, perhaps with a little bit more humility than they have in the past. And then, you know, maybe, especially as it relates to developers, we're just talking about building out the edge. We always talk about how developers are really going to be a key factor in the edge. And that's not a wheelhouse necessarily. It's obviously they're going to have to partner for that to have going to have to embrace cloud native. I mean, it's pretty clear that your premise is right on it. We'll see how long it takes, but if it, if they don't move fast, you know, what's going to happen. Well, I >>Think you look at it from the enterprise's perspective. And I think we just heard Google talking about it. We need to provide a tech stack that the enterprises can write to now, historically they haven't had this opportunity historically that CSPs have provided it. Now you're going to be able to write against Google's tech stack. And that's something that is documented. It's available. There's developers out there that know it. Um, and so I think that's the big opportunity and this might be the big use case that they've been looking for with 5g and looking forward to 16th. And so it's a huge opportunity for CSS. >>I think that's an important point because you got to place bets. And if I'm betting on Google or Amazon, Microsoft, okay. Those are pretty safe bets, right. Those guys are going to be around. >>I mean, they're like, no, don't trust the hyperscalers. I'm like, um, are you guys nuts? If they're safe, right. Safe >>Bets in terms of your investment in technology, now you got to move fast. Yeah. That's the other piece of it. You've got to change your business model. >>Well, you gotta be in the right side of history too. I mean, I mean, what is trust actually really mean? The snowflake trust Amazon, it sure did to get them where they are. Um, but now that's a >>Great example, John. It really is because there's a company that can move fast, but at the same time they compete with the same time they add incremental value. And so yeah, >>Here, the, you can see the narrative like, oh no, we're partnering telcos. Aren't bad. No one needs to die to bring in the new containers. Do we'll help them manage that operational legacy. But if they don't move, they're going to have an asset. That'll get rolled up into a SPAC or some sort of private equity deal. And because the old model of building cap backs and extract rents is kind of shifting because the value shifting. So to me, I think this is what we're watching still kind of unknown. Danielle Love to get your thoughts on this because if the value shifts to services, which is a consumption model like cloud, yeah. Then you can, don't have to try and extract the rents out of the cap ex >>Yeah. I don't think you need to own the entire stack to provide value. And I think that's where we are today in telco, right there. I mean, nuts and bolts of the stack, the servers, you know, the cabling, everything. And I'm like, stand on the shoulders of these amazing tech giants that have solved, you know, mega data centers, right. Huge data centers at scale, and just leverage their, their investment and uh, for your own benefit, it starts to focus. And we heard, um, all talking about it starts to focus on your subscriber and driving a great experience for us. Right? Yeah. Well, you're >>Talking about that many times they can do, but you're right. If the conversation hasn't has to go beyond, okay, we're just conductivity. It's gotta be ongoing and be like, oh, it's $10 a month for roaming charges. Ah, great. Yeah. Tick that box, right? It's those value added services that you're talking about and it's an infinite number of those that can be developed. And that's where the partnerships come in a creativity in the industry. It's just >>A blank piece of paper for, well, we, you know, everyone thinks Google knows everything about you, right. We've had the experience on our phone where they're serving of ads and you're like, how did you write Facebook? But you know, who knows more about us than, than Google or your mother, even your telco, you take your phone with you everywhere. Right? And so it's time to start unlocking all of that knowledge and using it to provide >>A really great, by the way, congratulations on the CEO to Toby and the investment a hundred million dollars. That's a game changer statement again, back to the billing. And there's a good, there's a whole new chain, even all up and down the stack of solutions, great stuff. And I want to unpack that tomorrow. I don't hold that. We're going and we're going to meet tomorrow. I want, I wanna want to leave that to stay >>In the data for a second, because you made the point before in your keynote as well. That it's, that it's the data that drives the value of these companies. Why is it that apple, Amazon, Google Facebook now trillion dollar evaluations. It's all about the data and the telcos have the data, but they can't figure out how to turn that into valuation. >>There's two parts of the data problem, which is number one, the data is trapped in on-premise siloed systems that are not open. You can't connect them and they certainly can't do without. And we talked about it, I think yesterday, you know, millions of dollars of expenditure. And I think the other piece that's really interesting is that it's not connected to a mechanism to get it out in a timely manner, right? This is data that's aging by the minute. And when it takes you weeks to get the insight it's useless. Right? And so to Togi we announced the launch to Togi, I'll get a little to Tokyo plug in there, right. To Toby is connecting that insight to the charger, to the engagement engine and getting it out to subscribers. I think that's the beginning of this connection. I think it's a hard problem to solve and would have been solved already. >>But I think the key is leveraging the public cloud to get your data out of on-premise and, and mashing it up against these great services that Google and Azure and Amazon provides to drive it into the hands of the subscriber, make it very actionable, very monetizeable right at the end, that's what they want. More ARPU, more revenue. Right. And you know, we heard some keynotes from GSA yesterday, some big, big guys, you know, talking about how, you know, it's not fair that these other communication platforms are not regulated. You know, telco is heavily regulated and they're like, it's not fair. And I'm like, yep. It's not fair. That's like right. South complaining about it and start treating your customers better. So they are, they're happy to give you more. >>Yeah. And I think that's the message about the assets do, um, well, one thing I will say is this mobile world Congress is that we've been having a lot of fun here in cloud city. I have to ask you a personal question. Have you been having fun? You look great on the keynote of spring to your staff, cloud cities. Beautiful. Spectacular here. Give us some highlights, personal highlights from your trip. So far, >>Number one, I'm, I'm psyched that the keynote is delivered and, and done. I mean, I think it takes my blood pressure down a blind, um, you know, the spring in my step, I wore these fun little tennis shoes and, and that was really fun, but yeah, I'm having, I'm having, I think a lot of things, great conversations. Yes. The attendance has reduced, um, you know, usually you see hundreds of people from the big group carriers, especially the European groups and yeah, the attendance is reduced, but the senior guys are here, right. The senior leadership teams are in the booth or having meetings, running amazing conversations. I think the last year we really did live a decade in one year. I think they woke up to the power of the public cloud. I mean, there was no way that they got business done without cloud based tools. And I think the light bulb went off, I think I'm right in the right moment. Awesome. Do you think that, >>Do you think that they'd think in there, like left money on the table because you look at the pandemic, there were three categories of companies, losers, people who held the line struggled and then winners. Yeah. Big time tailwind booming. Obviously the zooms of the world telcos did well. They were up and running. Uh, this, this was good. You think we might've left some money on the table? They could have done more. >>Yeah. I think the ones that were, you know, people talk about digital transformation where digital telco we're digitally enabled, but I think the pandemic really tested this. Right. Can you deliver a contactless SIM or do you need to go to a store in person to get to go pick it up? And I had a broken SIM during the pandemic. My provider made me go to the store and I'm like, is it even open? And so I heard other stories of telcos that were very digitally enabled, right. They were using Uber to deliver Sims, all sorts of fun, crazy stuff and new ideas. And they were able to pivot right. Agile. And so I think, I think that was a really big telemedicine booming. So >>If you were in a digital business during the pandemic in general, you're out of business maybe unless you were telco, but I think you're right. I think the light bulb went off. It was an aha moment. And they said, oh, if >>We don't, I mean, I am not kidding. Right. As an ex CEO where I was trying to collect signatures on renewals, right. Here's a DocuSign, which for the world is like, duh. I mean, our school uses DocuSign. I had telcos that required an in-person signature, right. In some country once a month on Tuesday between 10 and two. And I'm like, how are you doing business? Like that? That's like the dark ages. >>Yeah. This is where the crypto guys got it right. With know your customer. Right. >>Because they have the data. Well, there's a lot of things that come in wrong. We don't want to get the whole show on that, but then you have great to have you drop biopsy Bon Jovi's here. How did you get Bon Jovi? Huge fan, New Jersey boy Patriots fan. We'd love it. Well, >>Yeah. I mean, who doesn't love Bon Jovi. Right. Um, we knew we wanted a rocker, right. Rock and roll is all about challenging the status quo. Um, that, I mean, since the beginning and that's what we're doing here, right. We're really challenging. Like the way things have been done in telco kind of just shattering the glass ceiling and lots of different ways. Right. Calling the old guys dinosaurs. I'm sure those guys love me. Right. I mean, how much do they hate me right now? Or they're like that girl? Oh, we're punk >>Rock. They're rock and roll. Right, right. I mean, maybe we should have gotten the clash >>Right. Black flag. Right. I'm a little bit old. >>Accessible. Still >>Edgy. Yeah. So really excited to get them here. Um, I've met him before. Um, and so hopefully he'll remember me. It's been a couple of years since I've seen him. So can't wait to connect with him again. I think we have Elon Musk coming up and that's going to be, it's always exciting to hear that guy talk. So >>Yeah, it could be inspiration off after you've talked to space, space X and kind to star lake. >>Right. I mean, those guys are launching rockets and deploying satellites. And >>I think that's really interesting for >>Rural right. In telco. Right. Being able to deploy very quickly in rural where the, maybe the cost, um, you know, per gig doesn't make sense. You know, the cost for deployment of tower. I think, I mean, that's an interesting idea right there. It's exciting. It's exciting. >>He's inspirational. I think a lot of people look at the younger generation coming into this issue. Why are we doing things? A lot of people are questioning and they see the cloud. They're saying, oh, Hey, you're a B, why are we doing this? This is such an easier, better way. Yeah. I think eventually the generation shifts >>It's coming. I'm so excited to be a part of it. Yeah. Great, >>Great leadership. And I want to say that you are real innovative, glad to have us here and presenting with you here. >>Awesome team. I'm psyched to have you guys. We talked last night about how great this partnership has said. Yeah. >>Cuba's keep us rocking inside the cloud city. The streets of the city are packed in here. All stuff. Great stuff. Thanks for coming on. Thanks. Bon Jovi is here. We've got a shot. A bunch of we do we have a screenshot of Bon Jovi? Yup. There it is. Okay. He's about to come on stage and uh, we're gonna take a break here. We're gonna take and send it back to Adam and the team in the studio. Thanks guys.

(upbeat music) >> Okay, we're back here in theCUBE, this is day three of our coverage right here in the middle of all the action of Cloud City at Mobile World Congress. This is the hit of the entire show in Barcelona, not only in person, but out on the interwebs virtually, this is a hybrid event. This is back to real life, and theCUBE is here. I'm John Furrier and Dave Vellante and DR is here, Danielle Royston. >> Totally. >> Welcome back to theCUBE for the fourth time now at the anchor desk, coming back, we love you. >> Well, it's been a busy day, it's been a busy week. It's been an awesome week. >> John: Feeling good? >> Oh my God. >> You made the call. >> I've made the call. >> You did on your podcast what, months ago. >> Yeah, right? >> You made the call. >> Made the call. >> You're on the right side of history. >> Right, and people were like, it's going to be canceled. COVID won't be handled, blahbity blah. >> She's crazy. >> Nope, I was just crazy, I'm okay with that, right? >> Crazy good. >> Right, I'm like I'm forward looking in a lot of ways. And we were looking towards June and we're like, I think this is going to be the first event back. >> You know, the crazy ones commercial that Apple ran is one of the best commercials of all time. You can't ignore the crazy ones in a good way. You can't ignore what you're doing. And I think to me, what I'm so excited about is cause we've been covering cloud we're cloud bigots, we love the cloud, public cloud. We've been on that train from day one. But when you hear the interviews we did here in theCUBE and interviews that we talked about with the top people, Google, Amazon Web Services. We're talking about the top people, both technology leaders like Bill Vass and the people who run the telecom verticals like Alfonzo, Adolfo, I mean, Hernandez. We had Google's top networking executive, we had their industry leader and the telecom, Microsoft and the Silicon all are validating, and it's like, surround sound to what you're saying here, and it cannot be ignored. >> I mean, we are coming to a big moment in Telco, right? And I mean, I've been saying it's coming. I called 2021, the year of Public Cloud and Telco. It helped that Erickson bailed. So thank you, Erickson people. >> It was a gift. >> It was a gift. >> It was. >> It really was a gift. And it was not just for me, but I think also for the vendors in the booth, I mean, we have a Cloud City army, right? Here we go, let's start marching, and it's awesome. >> He reminds me of that baseball player that took a break, cause he had a hangover and, Cal Ripkin. >> Cal Ripkin? >> Yeah, what was that guy's name? >> Did that really happen? >> Yeah, he took a break and uh- >> New guy stepped in. >> Yeah, and so well, not Cal Ripkin. >> No, no, so before, you want to know, who was it, Lou Gehrig? >> Lou Gehrig, yeah, Lou Gehrig. >> Right, so, Lou Gehrig was nobody, and we can't remember the guy's name, nobody knows the guy's name, what was that guy's name? Nobody knows, oh, there's Lou Gehrig, he got hurt. He sat out and Lou Gehrig replaced him and never hear of him again. >> Danielle: Love it, I'll take that. >> Never, never missed a game for his entire career. So again, this is what Erickson did, they just okay, take a break. >> Yeah, but I mean, it's been great again. I had a great day yesterday, my keynote was delivered. Things are going well with the booth, we had Jon Bon Jovi. I mean, that was just epic and it was acoustic and it was right after lockdown. I think everyone was really excited to be there. But I was talking to a vendor that said we'd been able to accomplish in three days, what normally it would take three years from a sales funnel perspective. I mean, that's big and that's not me. That's not my organization. That's other organizations that are benefiting from this energy. Oh, it's awesome. >> The post isolation economy has become a living metaphor for transformation, and I've been trying to sort of grok and put the pieces together as to how this thing progresses in my interview with Portal One in particular really brought it into focus for me, anyway, I'd love to get your thoughts. One of the things we haven't talked much about is public policy, and I think about all the time, all the discussion in the United States about infrastructure, this is critical infrastructure, right? And the spectrum is a country like South Africa saying, come on in, we want to open up. We want to innovate, to me, that's the model for these tier two and tier three Telcos that are just going to disrupt the big guys, whereas, maybe China's maybe on the other end of the spectrum, very controlling, but it's the former that is going to adopt the cloud sooner, and it's going to completely transform the next decade. >> Yeah, I think this is a great technology for a smaller challenge or CSP that still is a large successful company to challenge the incumbents that are, they are dinosaurs too, they move a little bit slow, and maybe if you're a little bit faster, quicker dinosaur you'll survive longer, maybe you'll be able to transform and, and a public cloud enables that. And I think, you know I'm playing the long game here, right? Is public cloud already for every Telco in every corner of the world, no. And there's a couple of things that are barriers to that. We don't really talk about the downsides, and so maybe we sort of wrap up with- there are challenges and acknowledge there are challenges, you know, in some cases their data regulations and issues, right? And you can't right? There's not a hyperscaler in your country, right? And so you're having a little bit of challenges, but you trend this out over 10 years and then pace it with the hyperscalers that are building new data centers. They're each at 25 plus each, you know, plus or minus a few, right? They're marching along, and you trend this out over 10 years, I think one of two things happened, your data regulations are eased or a hyperscaler appears in a place you can use it, and those points converge and hopefully the software's there, and that's my effort and (claps) yeah. >> Dave: You know what's an interesting trend, DR and John, that is maybe a harbinger to this, is you just mentioned something. If the hyperscalers might not have a presence in, in a country, you know what they're doing? And our data shows this, I do that weekly series breaking analysis and the data Openstack was popping up. Like where does OpenStack come from, well, guess what, when you cut the data, it was Telcos using open source to build clouds in regions where there was no hyperscalers. >> It's a gap filler. >> Yeah, it's a gap filler, it's a bandaid. >> But I think this is where, like. outpost is such a great idea, right? Like getting outposts, and I think Microsoft has the ability to do this as well, Google less so, right? They're not providing the staff, they're doing Anthos. So you're still managing this, the rack, but they're giving you the ability to tap into their services. But I was talking to a CTO in Bolivia. He was like, we have data privacy issues in our country. There's no hyperscaler, not sure Bolivia is like next on the list for AWS, right? But he's like, I'm going to build my own public cloud. And I'm like why would you do that when you can just use outposts? And then when your data regulations release, where they get to Bolivia, you can switch and you're on the stack, and you're ready to go. I think that's what you should do. You should totally do that. >> John: Yeah, one of the things that's come up on here in the interviews, in theCUBE and here, the show is that there are risk takers and innovators and there's operators. And this has been the consistent theme around, yeah, the on-premises world you mentioned this regulation reasons, and or some workflows just have to be on premise for security reasons, whatever, that's the corner case. But the operating model of the technology architecture is shifted. And that reality, I don't think is debatable, so I find it, I got to ask you this because I'm really curious. I know you get a lot of people staring at ya, oh the public cloud's just a hosting, but why aren't people getting this architectural shift? I mean, you mentioned outpost and wavelength, which Amazon has, is a game changer. It's Amazon cloud at the hub. >> Yeah, at the edge. >> Okay, that's a low latency, again, low-hanging fruit applications, real buys, whatnot. I mean, that's an architectural dot that's been connected. Why are people getting it. >> In our industry, I think it is a lot of not invented here syndrome, right? And that's a very sort of nineties thought and I have been advocating stand on the shoulders of the greatest technologists in the world, right, and you know, there's, there is a geopolitical US thing, I think we lived through a presidency that had a sort of nationalistic approach and a lot of those conversations pop up, but I've also looked to these guys and I'm like, you're still, you still have your Huawei kit installed. And there's concerns with that too. So, and you picked it because of cost, and it's really hard to switch off of, so give me a break with your public cloud USA stuff, right? You can use it, you're just making excuses, you're just afraid. What are you afraid of, the HR implications? Let's talk about that, right? And the minute I take it there, conversation changes. >> Yeah, I talked to Teresa Carlson when she was running the public sector at AWS, she's now president of Splunk. I call her a Renaissance woman. She's been a great leader and public sector for this weird little pocket of AWS where it's a guess a sales division, but it's still its own company. >> Danielle: Yeah. >> And she's, did the CIA deal, the DOD, and the public sector partnerships are now private, a lot more private relationships, So it's not like just governments, you mentioned government and national security, and these things, you started to see the ecosystem not, not just be about companies, >> Danielle: Yeah. >> Government and private sector. So this whole vibe of the telecom being regulated, unregulated, unbundled is an interesting kind of theory. What's your thoughts and reactions to this, kind of this, melting pot of ecosystem change and evolution? >> Danielle: Yeah, I mean. I think there's a very nationalistic approach by the Telcos, right? They sort of think about the countries that they operate in. There's a couple of groups that go across multiple countries, but can there be a global Telco? Can that happen, right? Just like we say, you were saying it earlier, Netflix, right? You can say Netflix, UK. Right, and so can we have a global Telco, right. That is challenging on a lot of different levels. But think about that in a public cloud start to enable that idea, right? Elon Musk is going to get to Mars. You need a planetary level Telco. And I can, I think that day is, I mean, I don't think it's tomorrow, but I think that's like 10, 20 years away. >> Dave: You're done, we're going to see it start this decade, it's already starting. We're going to see the fruits of that dividend. >> Danielle: Yeah, it's crazy. >> I've got to ask you, you're a student of the industry and you get so much experience, it's great to have you on theCUBE and chat about, riff about these things, but, the classic who's ready for disruption question comes up, and I think there's no doubt that the Telcos as an industry has been slow moving and the role and the importance has changed. People need the need to have the internet access they need to access. >> Yeah. >> So, and you've got the edge, now applications are now running on it, since the iPhone 14 years ago, as you pointed out, people now are interested in how packets move. That's fast whether it's a doctor or an emergency worker or someone. >> Danielle: What we have done in 2020 without the internet and broadband and our mobile phones, I mean? >> You know, I think about 1920 when the Spanish flu pandemic hit a hundred years ago, those guys did not have mobile phones and they must have been bored, right? I mean, what are you going to do, right? And so, yeah I think last year really moved a lot of thinking forward in this respect, so. >> Yeah, it's always like that, that animal out in the Serengeti that gets taken down, you know, by the cheetah or the lion. How do know when someone is going to be disrupted What's the, what's the tell sign in your mind, you look at the Telco landscape. What is someone waiting to be disrupted or replaced like? >> You know what they're ostriches, how do you say that word, right? They stick their head in the sand. Like I don't want to talk about it, la la la, I don't want to, I don't want to think about it. You know, they bring up all these like roadblocks, and I'm like, okay, I'm going to come visit you in another six months to a year, and let's see what happens when the guys that are moving fast that are open-minded to this, and it's, I mean, when you start to use the public cloud, you don't, like, turn it on overnight. You start experimenting, right? You start, you take an application that is non-threatening. You have, I mean, these guys are running thousands of apps inside their data centers. Pick some boring ones, pick some old ones that no one likes, and move that to the public cloud, play with it. Right, I'm not talking about moving a whole network overnight tomorrow. You got to learn, you have no, I mean, very little talent in the Telco that know how to program against the AWS stack. Start hiring, start doing it, and you're going to start to learn about the compensation, and I used to do compensation, right? I spent a lot of time in HR, right? The compensation points and structures, they compare AWS and Google, versus a Telco. Do you want Telco stock? Do you want Google stock? >> Dave: Right, where do you want to go? >> Right, right? like that's going to challenge the HR organization in terms of compensate. How do we compensate our people when they're learning these new valuable skills? >> When you think about disruption, you know, the master or the professor of disruption, Clay Christensen, one of the best lectures he ever gave was who at Cambridge, and he gave a lecture on the steel industry, and he was describing it, it was like four layers of value in the steel industry, the value chain, it started with rebar, like the lowest end, right? >> Danielle: Yeah yeah. >> And the Telco's actually the opposite, so that, you know, when, when the international companies came in, they went after rebar, and the higher end steel companies said, nah, let them have it, that's the low margin stuff. And then eventually, uh, when they got up to the high end. >> Danielle: It was over, yeah. >> The Telcos are the opposite. They're like, the, you know, in the, in the conductivity and they're hanging on to that because it's so big, but all the high value stuff, it's already gone to the, over the top players, right. >> It's being eaten away, and I'm like, what is going to wake you guys up to realize those are your competitors, that's where the battle is, right? >> John: That's really where the value is. >> The battle of the bastards, you're there by yourself, like "Game of Thrones" and they're coming at you. >> John: You need a dragon. >> What are you doing about it? >> John: I need a dragon to compete in this market. Riding a dragon would be a good strategy. >> I know, I was just watching. Cause I have a podcast, I have a podcast called "Telco In 20" and we always put like little nuggets in the show notes, I personally reviewed them, I was just reviewing the one for the keynote that we're putting out, and I had a dragon in my keynote, right? It was a really great moment, it was really fun to do, but there's, I don't know if you guys are "Game of Thrones" fans. >> Yeah. >> Sure. >> Right, but there's a great moment when Daenerys gets her dragons, the baby dragons, and she takes over the Unsullied Army, right? And it's just this, right? Like all of a sudden the tables turn in an instant where she has nothing, and she's like on her quest, right. I'm on a quest. >> Dave: Comes out of the fire. >> Right, comes out of the fire, the unburnt, right? She has her dragons, right? She has them hatch. She takes over the Unsullied Army, right? Slaves, it starts her march, right? And I'm like, we're putting that clip into the show notes because I think that's where we are. I think I've hatched some dragons, right? The Cloud City army, let's go, let's go take on Telco. >> John: Well, I mean, this to me. >> Easy. >> It definitely have made, made it happen because I heard many people talking about cloud, this is turning into a cloud show. The question is, when does this going to be a cloud show? That's just Cloud City, it's a big section of the show. I mean, all the big players are behind it. >> Danielle: Yeah, yeah. >> Amazon Web Services, Google Azure, Ecosystem, startups, thinking differently, but everyone's agreeing why aren't we doing this? >> I think, like I said, I mean, people are like, you're such a visionary, and how did, why do you think this will work, I'm like, it's worked in every other industry. Am I really that visionary, and like, these are the three best tech companies in the world, like, are, are you kidding me? And so I think we've shown the momentum here. I think we're looking forward to 2022, you know? And that we see 2022, you got to start planning this the minute we get back, right? Like I wouldn't recommend doing this in a hundred days again, that was a very painful, but you know, February, I was, there's a sign inside NWC, February 28th. Right, we're talking seven months. You got to get going now. >> John: Let's get on the phone. >> With Telco, I mean, I think you're right on. I mean, you know, remember Skype, in the early days, right? >> Danielle: Yeah, yeah. >> It wasn't regional. It was just, plug into the internet. >> Danielle: It was just Skype, it was just WhatsApp. >> Well this is a great location, if you can get a shot guys of the people behind us, I don't know if you can, if you're watching check out the scene here, It's winding down, a lot of people having happy hour. Now this is a social construct here at Cloud City, not only is it chock full of information, reporting that we're doing and getting all the data and with the presentations on the main stage, with Adam and the studio and the team, this is a place where people are meeting and there's deals being done face to face, intimate relationships, the best of the best are here, they make the trek. So there's been a successful formula. Of course theCUBE is in the middle of all the action, which we love, we're psyched to be back. I want to thank you personally, while we have you on stage here. >> I want to thank you guys, and the crew, the crew has been amazing, turning out videos on short order. We have all these crews in different cities, it's, our own show has been virtual. You know, Adam's in Bristol, right? We're here, this was an experiment, we talked about this a hundred days ago, 90 days ago. Could we get theCUBE there, do the show but also theCUBE. >> You are a visionary, you said made for TV hybrid event with your team, produce television shows, theCUBE, we're digital, we love you guys, great alignment, but it's magical because the content doesn't end here, the show might end, they might break down the beautiful plants and the exhibits, but the community is going to continue, the content and the conversations. >> Yeah. >> So, we were looking forward to it and- >> I'm super glad, super glad we did this. >> Awesome, well, any final moments that you would like to share in the last two minutes we have, favorite moments, observations, funny things that have happened to you, weird things that have happened to you, share something that people might not know, or a favorite moment? >> I think, I don't know that people know, we have a 3D printer in the coffee shops, and so you can upload any picture and they're 3d printing, coffee art, right? So I've been seeing lots of social posts around people uploading their, their logos and things like that. I think Jon Bon Jovi, he was super thankful to be back. He thanked me personally two different times of like, I'm just glad to be out in front of people. And I think just even just the people walking around, thank you for being brave, thank you for coming back. You've helped Barcelona and we're happy to be together. Even if it is with masks, it's hard to do business with masks on, everyone's happy and psyched. >> John: Well the one thing that people cannot do relative to you is they cannot ignore you. You are making a great big wave. >> Danielle: I shout pretty loud, It's kind of hard to ignore me. >> You're making a great big wave, you're on the right side, we believe, of history, public cloud is driving the bus down main street of Cloud City, and if people don't get out of the way, they will be under the bus. >> I'm, like I said, in my keynote, it's go time let's do it. >> Okay. Thank you so much for all your attention and mission behind the cloud and the success. >> Danielle: We'll do it again. We're going to do it again soon. >> After Togi's a hundred million dollar investment, you're the CEO of Togi that, let's follow that progress, and of course, Telco DR, Danielle Royston, the digital revolution. Thanks for coming on with you. >> Thank you guys, it was super fun. >> This is theCUBE I'm John Furrier with Dave Vallante, we're going to send it back to Adam in the studio. Thanks, the team here. >> Woo! (audience applauding) >> I want to thank the team, everyone here, Adam is great, Chloe. >> Great working with you guys. >> Awesome, and what a great crew. >> So great. >> Thank you everybody. That's it for theCUBE, here on the last day, Wednesday of theCUBE, stay tuned for tomorrow more action on the main stage, here in Cloud City. Thanks for watching.

(upbeat music) >> Okay, Chloe, thank you very much. Hey folks, in here in the Cloud City We with Danielle Royston. Great to see you. Watching you up on stage, I got to say, as the CEO of TelcoDR, leader and chief executive of that company. As well as a great visionary, you laid out the vision. It's hard to debate that. I mean, I think there's people who will say that vision, is like freedom, no one can debate it. It's not going to happen. >> Yeah, there's still a lot of debate in our industry about it. There's a lot of articles being written about it. I've referenced one about, you know, should we let the dragons into the castle? For me, I think it's super obvious. I think other industries are like "Duh, we've made the move." And Telco is still like, "Hmm, we're not sure." And so, am I a visionary, I don't know. I'm just sort of just Babe Ruth-ing it a little bit. I think that's where we're going. >> You know you do, you have a lot of content, podcasts, you write blogs, you do a lot of speaking. You brought it all together on stage, right? That has got to feel good. >> Yeah. >> You've got a body of work and it came together very nicely. How did you feel up there? >> Oh my God, it's absolutely nerve wrecking. I sort of feel like, you know, could you tell if my hands were shaking? Right, could you tell that my heart was racing? >> It's a good feeling. >> I don't know. >> Come on! >> I'll be honest, I'm happy it's over, I'm happy. I think I did a really great job and I'm really happy >> Yeah, you did a great job, I love the dragon reference-- >> Have it in the can. >> Fantastic, loved the Game of Thrones vibe there. It was cool-- >> Totally. >> One of the things I wanted pick up on, I thought it was very interesting and unique was the iPhone reference 14 years ago, because that really, to me, was a similar moment because that shifted the smartphone. A computer that happened to make phone calls. And then we all knew who was the leader at that time, Nokia, Blackberry with the phones, and they became toast. That ushered in a whole another era of change, wealth creation, innovation, new things. >> Yeah. Well, up until that moment, carriers had been designing the phones themselves. They were branded with their logos. And so Steve Jobs fought for the design of the iPhone. He designed it with the consumer, with the user in mind. But I think what it really, I mean, it's such a big pivotal moment in our industry because it singled the end of voice revenue and ushered in the era of data. But it also introduced the OTT players, right? That came in through the apps and started a siphon approved from the carriers. And this is like, it's a pivotal moment in the industry, like, changed the industry forever. >> It's a step function, it was a step function change, it's obvious, everyone knew it. But what's interesting is that we were riffing yesterday about O-RAN and Android. So you have iPhone, but Android became a very successful open source project that changed the landscape of the handset. Some are saying that that kind of phenomenon is coming here. Into Telco with software, kind of like an Android model where that'll come in. What's your thoughts on that, reaction to that? >> Yeah, well the dis-aggregation of the hardware, right? We're in the iconic Erickson booth, right? They get most of their revenue from RAN, from Radio Access Networks. And now with the introduction of Open RAN, right? With 50% less CapEx, 40% less OPEX, you know, I think it's easiest for Greenfield operators like Dish, that are building a brand new network. But just this month, Vodafone announced they're going to build the world's largest Open RAN network. Change is happening and the big operators are starting to adopt Open RAN in a real big way. >> So to me, riding the dragon means taking the advantage of new opportunities on top of that dragon. Developing apps like the iPhone did. And you mentioned Android, they got it right. Remember the Windows Phone, right? They tried to take Windows and shove it to the phone-- >> Barely. >> It was a kin phone too. >> I try to delete it from my, look here, beep! >> I'm going to take this old world app and I'm going to shove it into the new world, and guess what, it failed. So if the Telco is trying to do the same thing here, it will fail, but if they start building 5G apps in the cloud and pick the cloud native and think about the consumer, isn't really that the opportunity that you're talking about? >> Well, I think it is, absolutely. And I think it's a wake up call for the vendors in our space, right? And I'm certainly trying to become a vendor with Totogi. I'm really pushing my idea. But you can't take, using your Windows example on the Windows Phone, you can't take a Windows app and stuff it onto a phone and you can't take these old school applications that were written 20 years ago and just stuff them into the cloud, right? Cloud is not a place, it's a way to design applications and it all needs to be rewritten and let's go write, rewrite it. >> It's not a destination as we always say. Let's take a step back on the keynote 'cause I know we just did a couple of highlights there, wasn't the whole thing. We were watching it, by the way, we thought you did a great job, you were very cool and calm under pressure. But take us through the core ideas in the keynote. Break down the core elements of what the talk was about. >> Yeah, I think the headline really is, you know, just like there were good and bad things about the iPhone, right? It killed voice, but introduced data and all these other things. There's good and bad things about the public cloud, right? It's not going to be smooth sailing, no downsides. And so I acknowledge that, even though I'm the self appointed queen, you know? This self appointed evangelist. And so, I think that if you completely ignore the public cloud, try to stick your head in the sand and pretend it doesn't exist, I think there's nothing but downsides for Telcos. And so I think you need to learn how to maximize the advantage there, ride he dragon, like spew some fire and, you know, get some speed and height, and then you can double your ARPU. But I think, going from there, so the next three, I was trying to give examples of what I meant by that, of why it's a double-edged sword, why it's two sides of the coin. And I think there's three areas, which is the enterprise, the network, and a relationship with subscribers. And so that really what the talk, that's what the talk is about >> The three main pillars. >> Yeah, yeah! >> Future, work, enterprise, transition, Open RAN. >> The network and then the relationship with the subscribers. >> Those are the structural elements you see. >> Yeah, yeah, yeah. >> What's the most important one you think, right now, that people are focused on? >> I mean, I think the first one, with work, that's an easy one to do, because there's not too much downside, right? I think we all learned that we could work productively from home. The reason public cloud matter there is because we had tools like Zoom and G Suite and we didn't need to be, I mean, imagine if that this had happened even 20 years ago, right? Broadband at the home wasn't ready, the tools weren't ready. I mean, it would have been, I mean a bigger disaster than it was, right? And so this is an opportunity to sort of ride this work from home wave that a lot of CEOs are saying, we're not coming back or we're going to have smaller offices. And all of those employees need fiber to their home. They need 5G at their home. I mean, if I'm a head of enterprise in a Telco, I am shifting my 5G message from like random applications or whatever, to be like, how are you getting big pipes to the home so your workers can be productive there? And that, I don't hear Telco's talking about that and that's a really big idea. >> You know, you say it's a no brainer, but it's interesting you had your buildings crumbling, which was great, very nice effect in the talk. I heard a executive, Wall Street executive the other day, talking about how, "My people will be back in the office. "I'm going to mandate vaccinations, they're going to be back "in the office, you work for me. "Even though it's an employee friendly environment "right now, I don't care". And I was shocked. I go, okay, this is just an old guy. But, and it's not just the fact that it's an old guy, old guard doing that because I take two examples of old guys, Michael Dell and Frank Slootman. >> Yeah. >> Right, Michael Dell, you know, hundred billion dollar company, Frank Slootman, hottest, you know, software company. Both of them, sort of agree. It's a no brainer. >> Yeah. >> Why should I spend all this money on buildings? And my people are going to be more productive. They love it, so. Why fight the fashion? >> Well, I think the office and I can talk about this for a long time and I know we don't have that much time, but on offices, it's a way to see when did you come in and when did you leave, and look over your shoulder and what we're working on. And that's what offices are for. Now, we tell ourselves it's about collaboration and all this other stuff. And you know, these guys are saying, "come back to the office." It's because they don't have an answer on how to manage productivity. What are you working on? Are you off, are you authentically working 40 hours a week? I want to see, I know if at least you're here, you're here. Now, you might be playing, you know, Minesweeper. You might be playing Minesweeper on your computer, but at least you were, your butt was at your computer. So yeah, I think this is a pivotal moment in work. I think Telcos could push it, to work from home. We'll get you the pipes, we'll get you the cloud-based tools to help manage productivity, to change in work style. >> Yeah, and we've covered this in theCube many times, about how software is going to enable this virtual first model, no one's actually built software for virtual first. I think that's going to happen. Again, back to your team software, but I want to ask you about software defined infrastructure. You mentioned O-RAN, and as software eats the world and eats infrastructure, you still need infrastructure. So, talk about the relationship of how you see O-RAN competing and winning with the balance of software versus the commodity argument. >> Yeah, and I think this is really where people get scared in Telco. I mean, authentically nervous, right. Where you're like, okay, really the public cloud is at that network edge, right? We're really going to like, who are we? It's an identity crisis. We're not the towers anymore. We're renting space, right? We're now dis-aggregating the network, putting the edge cloud right there and it's AWS or Google. Who are we, what do we do, are we networks? Are we a tech company? Right, and so I'm like, guys, you are your subscribers and you don't focus on that. I mean, it's kind of like a last thought. >> So you're like a therapist then too, not just an evangelist. >> I'm a little bit of a therapist. >> Okay, lay down on the couch, Telco. >> Let's talk about what your problems are. (laughs) >> They have tower issues. >> All seriousness, no but, the tower is changing is backhauling. Look at direct connects for instance. The rise of direct and killed the exchanges. I mean, broadband, backhaul, last mile, >> Yeah. >> Completely, still issues, >> Yeah. >> But it's going to software and so that's there. The other thing I want to get to quickly, I know we don't have a lot of time, is the love relationship you talk about with subscribers. We had Peter Adderton on, from a Boost Mobile, formerly Boost Mobile, earlier. He was saying, if you don't have a focus on the customer, then you're just selling minutes and that's it. >> Yeah. >> And his point was, they don't really care. >> Yeah. Let's talk about organizational energy, right? How much energy is contained within any organization, not just Telco, but any organization. To some of your people time is the hours they work per week. And then you think of that as a sack on how you're allocating your time and spending your time, right? And so I think they spend 50% of their time, maybe more, fighting servers, machines, the network, right? And having all these battles. How much of that organizational energy is dedicated to driving great subscriber experiences? And it just shrunk, right? And I think that's where the public cloud can really help them. Like ride the dragon. Let the dragon deal with some of this underlying stuff. So that you can ride a dragon, survey the land, focus on your subscriber and back to the software. Use software, just like the OTT players are doing. They are taking away your ARPU. They're siphoning your ARPU, 'cause they're providing a better customer experience. You need to compete on that dimension. Not the network, not the three Telcos in the country. You're competing again, WhatsApp, Apple, Amazon, Facebook. And you spent how much of your organizational energy to focus on that? Very small. >> And that's where digital platforms roll by, it uses the word platform, why? Because everybody wants to be a platform. Why do you want to be a platform? Because I want to be like Amazon, they're a platform. And you think about Netflix, right? It's not, you know, you don't think about Netflix UK or Netflix Spain, right? >> It's global. >> There's one Netflix >> Yeah, yeah. >> You don't think about their marketing department or their sales department or their customer service, you think about the app. >> Yeah. >> You know. One interface. And that's what digital platforms allow you to do. And granted, there's a lot of public policy to deal with, but if you're shooting satellites up in space, >> Yeah. >> You know, now, you own that space, right, global network. >> And what makes Netflix so good, I think, is that it knows you, right? It knows what you're watching and recommends things, and you're like, "Oh, I would like that, that's great." Who knows more about you than your mobile phone? Carry it everywhere you go, right? What you're watching, what you're doing, who you're calling, what time did you wake up? And right now all of that data we talked about a couple of days ago, it's trapped in siloed old systems. And like why do people think Google knows so much about you? Telco knows about you. And to start to use that to drive a great experience. >> And you've got a great relationship with Netflix. The relationship we have with our our carrier is to your admin, "can you call these guys? "I don't know, I lost the password, I can't get in". >> Right. >> It's like-- >> Or you get SIM hacked-- >> I don't have an hour and a half to call your call center 'cause you don't have a chat bot, right. >> I don't have time. >> Chat bot, right. I can't even do the chat bot because my problem is, you're like, I got to talk to someone. All of their systems are built with the intention of a human being on the other side, and there's all this awesome chat bot AI that works. >> Yeah. >> Set it free. >> Yeah, yeah, right. You almost rather go to the dentist, then calling your carrier. >> Well, we're going to wrap things up here on the keynote review. Did you achieve what you wanted to achieve? I mean, controversy, bold vision, leadership, also that came across, but people they know who you are now. You're out there and that's great news. >> Yeah. I think I rocked the Telco universe and I'm really, that was my goal, and I think I accomplish it so, very excited. >> Well, we love having you on theCUBE. It's great to have great conversations, not only are you dynamic and smart, you're causing a lot of controversy, in a good way and getting, waking people up. >> Making people talk, that's a start. >> And I think, the conversations are there. People are talking and having relationships on the ecosystem open, it's all there. Danielle Royston, you are a digital revolution, DR. Telco DR, thanks for coming to theCube. >> Thank you so much, always fun. >> Good to see you. >> Thanks. >> Of course, back to the Cloud City studios. Adam is going to take it from here and continue on day three of theCube. Adam in studio, thanks for having us and take it from here.

(upbeat music) >> Hey, we're back here in theCube. This is day three of our coverage right here in the middle of all the action of Cloud City at Mobile World Congress. This is the hit of the entire show in Barcelona, not only in person, but out on the interwebs virtually. This is a hybrid event. This is back to real life, and theCube is here. I'm John Furrier with Dave Vellante and D. R. is here, Danielle Royston. >> Totally. >> Welcome back to theCube for fourth time. now at the anchor desk, coming back. >> I don't know. It's been a busy day. It's been a busy week. It's been an awesome week. >> Dave: Feeling good? >> Oh, my god. >> You made the call. >> I made the call. You finished your podcast, what months ago? >> Yeah. >> Made the call. >> Made the call. You're on the right side of history. >> Right? And people were like, "It's going to be canceled. COVID won't be handled." Blahbity blah. >> She's crazy. >> And I'm like, nope. She's crazy. I'm okay with that. Right? But I'm like... >> Crazy good. >> Right, I'm like, I'm forward-looking in a lot of ways. And we were looking towards June, and we're like, "I think this is going to be the first event back. We're going to be able to do it." >> You know, the crazy one's commercial that Apple ran, probably one of the best commercials of all time. You can't ignore the crazy ones in a good way. You can't ignore what you're doing. And I think to me, what I'm so excited about is, 'cause we've been covering cloud. We're cloud bigots. We love the cloud, public cloud. We've been on that train from day one. But when you hear the interviews we did here on theCube and interviews that we talked about with the top people, Google, Amazon Web Services. We're talking about the top people, both technology leaders like Bill Vass and the people who run the Telecom Verticals like Alf, Alfonzo. >> Danielle: Yeah. >> Adolfo, I mean, Hernandez. >> Danielle: Yeah. >> We had Google's top networking executive. We had their industry leader in the telecom, Microsoft, and the Silicon. All are validating, and it's like surround sound to what you're saying here. And it cannot be ignored. >> I mean, we are coming to a big moment in Telco, right? And I mean, I've been saying that it's coming. I called 2021, the year of public cloud and Telco. It helped that Ericcson bailed. So thank you, Ericcson people. >> Dave: It was a gift. >> It was a gift. >> John: It really was. >> It really was a gift. And it was not just for me, but I think also for the vendors in the booth. I mean, we have a Cloud City army, right? Here we go. Let's start marching. And it's awesome. >> He reminds me of that baseball player that took a break 'cause he had a hangover and Cal Ripken. >> Cal Ripken, right, yeah, yeah. What was that guy's name? >> Did it really happen? >> Yeah, he took a break and... >> The new guy stepped in? >> Yeah, and so we'll go to Cal Ripken. >> No, no, so before it was it? Lou Gehrig. >> Lou Gehrig, yeah. >> Right, so Lou Gehrig was nobody. And we can't remember the guy's name. Nobody knows the guy's name. >> Danielle: Yeah, yeah. >> What was that guy's name? Nobody knows. Oh, 'cause Lou Garrett, he got hurt. >> Danielle: And Lou Gehrig stepped in. >> He sat out, and Lou Gehrig replaced him. >> Danielle: Love it. >> And never heard of him again. >> Danielle: I'll take that. >> Never missed a game. Never missed a game for his entire career. So again, this is what Ericcson did. They just okay, take a break and... >> But I mean, it's been great. Again, I had a great day yesterday. My keynote was delivered. Things are going well with the booth. We had Jon Bon Jovi. I mean, that was just epic, and it was acoustic, and it was right after lockdown. I think everyone was really excited to be there. But I was talking to a vendor that said we'd been able to accomplish in three days what normally it would take three years from a sales funnel perspective. I mean, that is, that's big, and that's not me. That's not my organization. That's other organizations that are benefiting from this energy. Oh, that's awesome. >> The post-isolation economy has become a living metaphor for transformation. And I've been trying to sort of grok and put the pieces together as to how this thing progresses. And my interview with Portaone, in particular, >> Danielle: Yeah. >> really brought it into focus for me, anyway. I'd love to get your thoughts. One of the things we haven't talked much about is public policy. And I think about all the time, all the discussion in the United States about infrastructure, this is critical infrastructure, right? >> Danielle: Yeah. >> And the spectrum is a country like South Africa saying, "Come on in. We want to open up." >> Danielle: Yeah. >> "We want to innovate." And to me that's to me, that's the model for these tier two and tier three telcos that are just going to disrupt the big guys. Whereas, you know, China, may be using the other end of the spectrum, very controlling, but it's the former that is going to adopt the cloud sooner. It's going to completely transform the next decade. >> Yeah, I think this is a great technology for a smaller challenger CSP that still is a large successful company to challenge the incumbents that are, they are dinosaurs too. They move a little bit slow. And maybe if you're a little bit faster, quicker dinosaur you'll survive longer. Maybe it will be able to transform and a public cloud enables that. And I think, you know, I'm playing the long game here, right? >> Dave: Yeah. >> Is public cloud ready for every telco in every corner of the world? No. And there's a couple of things that are barriers to that. We don't really talk about the downsides, and so maybe we sort of wrap up with, there are challenges, and I acknowledge there are challenges. You know, in some cases there are data regulations and issues, right? And you can't, right? There's not a hyperscaler in your country, right? And so you're having a little bit of challenges, but you trend this out over 10 years and then pace it with the hyperscalers are building new data centers. They're each at 25 plus each, plus or minus a few, right? They're marching along, and you trend this out over 10 years, I think one of two things happens. Your data regulations are eased or you a hyperscaler appears in a place you can use it. And those points converge, and hopefully the software's there, and that's my effort. And, yeah. >> You know what's an interesting trend, D. R., John? That is maybe a harbinger to this. You just mentioned something. If the hyperscalers might not have a presence in a country, you know what they're doing? And our data shows this, I do that weekly series "Breaking Analysis," and the data, OpenStack was popping up. >> Danielle: Yeah. >> Like where does OpenStack come from? Well, guess what. When you cut the data, it was telcos using open source to build clouds in regions where there was no hyperscaler. >> Where it didn't exist, yeah. >> So it's a-- >> Gap-filler. >> Yeah, it's a gap-filler. It's a Band-aid. >> But I think this is where like Outpost is such a great idea, right? Like getting Outposts, and I think Microsoft has the ability to do this as well, Google less so, right. They're not providing the staff. They're doing Anthos, so you're still managing this, the rack, but they're giving you the ability to tap into those services. But I was talking to a CE, a CTO in Bolivia. He was like, "We have data privacy issues in our country. There's no hyperscaler." Not sure Bolivia is like next on the list for AWS, right? But he's like, "I'm going to build my own public cloud." And I'm like, "Why would you do that when you can just use Outposts?" And then when your data regulations release or there's a, they get to Bolivia, you can switch and you're on the stack and you're ready to go. I think that's what you should do. You should totally do that. >> Yeah, and one of the things that's come up here on the interviews and theCube and here, the show, is that there are risk takers and innovators and there's operators. And this has been the consistent theme around, yeah, the on-premises world. You mentioned this regulation reasons and/or some workflows just have to be on premise for security reasons, whatever. That's the corner case. >> Danielle: Yeah. >> But the operating model of the technology architecture is shifted. >> Danielle: Yep. >> And that reality, I don't think, is debatable. So I find it. I've got to ask you this because I'm really curious. I know you get a lot of people steering 'ya, oh the public cloud's just a hosting, but why aren't people getting this architectural shift? I mean, you mentioned Outpost, and Wavelength, which Amazon has, is a game changer. It's Amazon Cloud at the hub. >> Yeah, at the edge, yeah. >> Okay, that's a low latency again, low-hanging fruit applications, robotics, whatnot. I mean, that's an architectural dot that's been connected. >> Yeah. >> Why aren't people getting it? >> In our industry, I think it is a lot of not invented here syndrome, right? And that's a very sort of nineties thought, and I have been advocating stand on the shoulders of the greatest technologists in the world. Right? And you know, there is a geopolitical US thing. I think we lived through a presidency that had a sort of nationalistic approach and a lot of those conversations pop up, but I've also looked to these guys and I'm like, you still have your Huawei kit installed, and there's concerns with that, too. So, and you picked it because of cost. And it's really hard to switch off of. >> John: Yeah. >> So give me a break with your public cloud USA stuff, right? You can use it. You're just making excuses. You're just afraid. What are you afraid of? The HR implications? Let's talk about that, right? And the minute I take it there, conversation changes. >> I talked to Teresa Carlson when she was running the public sector at AWS. She's now president of Splunk. I call her a Renaissance woman. She's been a great leader. In public sector there's been this weird little pocket of AWS where it's, I guess, a sales division, but it's still its own company. >> Danielle: Yeah. >> And she just did the CIA deal. The DOD and the public sector partnerships are now private, a lot more private relationships. So it's not like just governments. You mentioned government and national security and these things. You start to see the ecosystem, not, not just be about companies, government and private sector. So this whole vibe of the telecomm being regulated, unregulated, unbundled is an interesting kind of theory. What's your thoughts and reactions to this kind melting pot of ecosystem change and evolution? >> Yeah, I mean, I think there's a very nationalistic approach by the telcos, right? They sort of think about the countries that they operate in. There's a couple of groups that go across multiple countries, but can there be a global telco? Can that happen, right? Just like we say, you were saying it earlier, Netflix. Right? You didn't say Netflix, UK, right? And so can we have a global telco, right? That is challenging on a lot of different levels. But think about that in a public cloud starts to enable that idea. Right? Elon Musk is going to get Mars. >> Dave: Yep. >> John: Yeah. >> You need a planetary level telco, and I think that day is, I mean, I don't think it's tomorrow, but I think that's like 10, 20 years away. >> You're done. We're going to see it start this decade. It's already starting. >> Danielle: Yeah. >> But we're going to see the fruits of that dividend. >> Danielle: Right, yeah. >> I got to ask you. You're a student of the industry and you got so much experience. It's great to have you on theCube and chat about, riff about, these things, but the the classic "Who's ready for disruption?" question comes up. And I think there's no doubt that the telcos, as an industry, has been slow moving, and the role and the importance has changed. People need the need to have the internet access. They need to access. >> Danielle: Yeah. >> So and you've got the Edge. Now applications are now running on a, since the iPhone 14 years ago, as you pointed out, people now are interested in how packets move. >> Danielle: Yeah. >> That's fast, whether it's a doctor or an emergency worker or someone. >> What would we have done in 2020 without the internet and broadband and our mobile phones? I mean. >> Dave: We would have been miserable. >> You know, I think about 1920 when the Spanish flu pandemic hit a hundred years ago. Those guys did not have mobile phones, and they must have been bored, right? I mean, what are you going to do? Right? And so, yeah, I think, I think last year really moved a lot of thinking forward in this respect, so. >> Yeah, it's always like that animal out in the Serengeti that gets taken down, you know, by the cheetah or the lion. How do you know when someone is going to be disrupted? What's the, what's the tell sign in your mind? You look at the telco landscape, what is someone waiting to be disrupted or replaced look like? >> Know what? They're ostriches. Ostriches, how do you say that word right? They stick their head in the sand. Like they don't want to talk about it. La, la, la, I don't want to. I don't want to think about it. You know, they bring up all these like roadblocks, and I'm like, okay, I'm going to come visit you in another six months to a year, and let's see what happens when the guys that are moving fast that are open-minded to this. And it's, I mean, when you start to use the public cloud, you don't like turn it on overnight. You start experimenting, right? You start. You take an application that is non-threatening. You have, I mean, these guys are running thousands of apps inside their data centers. Pick some boring ones. Pick some old ones that no one likes. Move that to the public cloud. Play with it, right? I'm not talking about moving your whole network overnight tomorrow. You got to learn. You have no, I mean, very little talent in the telco that know how to program against the AWS stack. Start hiring. Start doing it. And you're going to start to learn about the compensation. And I used to do compensation, right? I spent a lot of time in HR, right? The compensation points and structures, and they can bear AWS and Google versus a telco. You want Telco stock? Do you want Google stock? >> John: Right, where do you want to go? >> Right? Right? And so you need to start. Like that's going to challenge the HR organization in terms of compensate. How do we compensate our people when they're learning these new, valuable skills? >> When you think about disruption, you know, the master or the professor of disruption, Clay Christensen, one of the best lectures he ever gave is we were at Cambridge, and he gave a lecture on the steel industry and he was describing it. It was like four layers of value in the steel industry, the value chain. It started with rebar, like the lowest end. Right? >> Danielle: Yeah, yeah. >> And the telco's actually the opposite. So, you know, when the international companies came in, they went after rebar, and the higher end steel companies said, "Nah, let them have it." >> Danielle: Let it go. >> "That's the low margin stuff." And then eventually when they got up to the high end, they all got killed. >> Danielle: It was over, yeah. >> The telcos are the opposite. They're like, you know, in the connectivity, and they're hanging on to that because it's so big, but all the high value stuff, it's already gone to the over-the-top players, right? >> It's being eaten away. And I'm like, "What is going to wake you guys up to realize those are your competitors?" That's where the battle is, right? >> Dave: That's really where the value is. >> The battle of the bastards. You're there by yourself, the Game of Thrones, and they're coming at you. >> John: You need a dragon. >> What are you doing about it? >> I need a dragon. I need a dragon to compete in this market. Riding on the dragon would be a good strategy. >> I know. I was just watching. 'Cause I have a podcast. I have a podcast called "Telco in 20," and we always put like little nuggets in the show notes. I personally review them. I was just reviewing the one for the keynote that we're putting out. And I had a dragon in my keynote, right? It was a really great moment. It was really fun to do. But there's, I don't know if you guys are Game of Thrones fans. >> Dave: Oh, yeah. >> John: For sure. >> Right? But there's a great moment when Daenerys guts her dragons, the baby dragons, and she takes over the Unsullied Army. Right? And it's just this, right? Like all of a sudden, the tables turn in an instant where she has nothing, and she's like on her quest, right? I'm on a quest. >> John: Comes out of the fire. >> Right, comes out of the fire. The unburnt, right? She has her dragons, right? She has them hatch. She takes over the Unsullied Army, right? Slays and starts her march, right? And I'm like, we're putting that clip into the show notes because I think that's where we are. I think I've hatched some dragons, right? The Cloud City Army, let's go, let's go take on Telco. >> John: Well, I mean to me... >> Easy. >> I definitely have made it happen because I heard many people talking about cloud. This is turning into a cloud show. The question is, when does this be, going to be a cloud show? You know it's just Cloud City is a big section of the show. I mean, all the big players are behind it. >> Danielle: Yeah, yeah. >> Amazon Web Services, Google, Azure, Ecosystem, startups thinking differently, but everyone's agreeing, "Why aren't we doing this?" >> I think, like I said, I mean, people are like, you're such a visionary. And how did, why do you think this will work? I'm like, it's worked in every other industry. Am I really that visionary? And like, these are the three best tech companies in the world. Like, are you kidding me? And so I think we've shown the momentum here. I think we're looking forward to 2022, you know? And do we see 2022, you get to start planning this the minute we get back. Right? >> John: Yeah. >> Like I wouldn't recommend doing this in a hundred days again. That was a very painful, but you know, February, I was, there's a sign inside NWC, February 28th, right? We're talking seven months. You got to get going now. >> John: Let's get on the phone. (John and Dave talking at the same time) >> I mean, I think you're right on. I mean, you know, remember Skype in the early days? >> Danielle: Yeah, yeah, yeah, yeah. >> It wasn't regional. >> Danielle: Yeah. >> It was just plug into the internet, right? >> Danielle: It was just Skype. It was just WhatsApp. >> Well, this great location, and if you can get a shot, guys, of the people behind us. I don't know if you can. If you're watching, check out the scene here. It's winding down. A lot of people having happy hour now. This is a social construct here at Cloud City. Not only is it chock full of information, reporting that we're doing and getting all the data and with the presentations on the main stage with Adam and the studio and the team. This is a place where people are meeting and there's deals being done face to face, intimate relationships. The best of the best are here. They make the trek, so there's been a successful formula. Of course theCube is in the middle of all the action, which we love. We're excited to be back. I want to thank you personally while we have you on stage here. >> I want to thank you guys and the crew. The crew has been amazing turning out videos on short order. We have all these crews in different cities. It's our own show has been virtual. You know, Adam's at Bristol, right? We're here. This was an experiment. We talked about this a hundred days ago, 90 days ago. Could we get theCube there and do the show, but also theCube. >> You are a visionary. And you said, made for TV hybrid event with your team, reduced television shows, theCube. We're digital. We love you guys. Great alignment, but it's magical because the content doesn't end here. The show might end. They might break down the beautiful plants and the exhibits, but the community is going to continue. The content and the conversations. >> Yeah. >> So. >> We are looking forward to it and. >> Yeah, super-glad, super-glad we did this. >> Awesome. Well, any final moments that you would like to share? And the last two minutes we have, favorite moments, observations, funny things that have happened to you, weird things that have happened to you. Share something that people might not know or a favorite moment. >> I think, I mean I don't know that people know we have a 3D printer in the coffee shops, and so you can upload any picture, and there are three 3D printing coffee art, right? So I've been seeing lots of social posts around people uploading their, their logos and things like that. I think Jon Bon Jovi, he was super-thankful to be back. He thanked me personally two different times of like, I'm just glad to be out in front of people. And I think just even just the people walking around, thank you for being brave, thank you for coming back. You've helped Barcelona, and we're happy to be together even if it is with masks. It's hard to do business with masks on. Everyone's happy and psyched. >> The one thing that people cannot do relative to you is they cannot ignore you. You are making a great big waves. >> Danielle: I shout pretty loud. It's kind of hard to ignore me. >> Okay, you're making a great big wave. You're on the right side, we believe, of history. Public cloud is driving the bus down main street of Cloud City, and if people don't get out of the way, they will be under the bus. >> And like I said, in my keynote, it's go time. Let's do it. >> Okay, thank you so much for all your tension and mission behind the cloud and the success of... >> Danielle: We'll do it again. We're going to do it again soon. >> Ketogi's hundred million dollar investment. Be the CEO of Togi as we follow that progress. And of course, Telco D. R. Danielle Royston, the digital revolution. Thanks for coming on theCube. >> Thank you, guys. It was super-fun. Thank you so much. >> This is theCube. I'm John Furrier with Dave Vellante. We're going to send it back to Adam in the studio. Thanks the team here. (Danielle clapping and cheering) I want to thank the team, everyone here. Adam is great. Chloe, great working with you guys. Awesome. And what a great crew. >> So great. >> Thank you everybody. That's it for theCube here on the last day, Wednesday, of theCube. Stay tuned for tomorrow, more action on the main stage here in Cloud City. Thanks for watching.

>>Please >>welcome back to HPD discovered 2021. The cubes virtual coverage, continuous coverage of H P. S H. P. S. Annual customer event. My name is Dave Volonte and we're going to dive into the intersection of high performance computing data and AI with DR Eng limb go who is the senior vice president and CTO for AI Hewlett Packard enterprise Doctor go great to see you again. Welcome back to the cube. >>Hello Dave, Great to talk to you again. >>You might remember last year we talked a lot about swarm intelligence and how AI is evolving. Of course you hosted the day two keynotes here at discover and you talked about thriving in the age of insights and how to craft a data centric strategy. And you addressed you know some of the biggest problems I think organizations face with data that's You got a data is plentiful but insights they're harder to come by. And you really dug into some great examples in retail banking and medicine and health care and media. But stepping back a little bit with zoom out on discovered 21, what do you make of the events so far? And some of your big takeaways? >>Mm Well you started with the insightful question, Right? Yeah, data is everywhere then. But we like the insight. Right? That's also part of the reason why that's the main reason why you know Antonio on day one focused and talked about that. The fact that we are now in the age of insight, right? Uh and uh and and how to thrive thrive in that in this new age. What I then did on the day to kino following Antonio is to talk about the challenges that we need to overcome in order in order to thrive in this new asia. >>So maybe we could talk a little bit about some of the things that you took away in terms I'm specifically interested in some of the barriers to achieving insights when customers are drowning in data. What do you hear from customers? What we take away from some of the ones you talked about today? >>Oh, very pertinent question. Dave You know the two challenges I spoke about right now that we need to overcome in order to thrive in this new age. The first one is is the current challenge and that current challenge is uh you know stated is no barriers to insight. You know when we are awash with data. So that's a statement. Right? How to overcome those barriers. What are the barriers of these two insight when we are awash in data? Um I in the data keynote I spoke about three main things. Three main areas that received from customers. The first one, the first barrier is in many with many of our customers. A data is siloed. All right. You know, like in a big corporation you've got data siloed by sales, finance, engineering, manufacturing, and so on, uh supply chain and so on. And uh there's a major effort ongoing in many corporations to build a federation layer above all those silos so that when you build applications above they can be more intelligent. They can have access to all the different silos of data to get better intelligence and more intelligent applications built. So that was the that was the first barrier. We spoke about barriers to incite when we are washed with data. The second barrier is uh that we see amongst our customers is that uh data is raw and dispersed when they are stored and and uh and you know, it's tough to get tough to to get value out of them. Right? And I in that case I I used the example of uh you know the May 6 2010 event where the stock market dropped a trillion dollars in in tens of minutes. You know, we we all know those who are financially attuned with know about this uh incident, But this is not the only incident. There are many of them out there and for for that particular May six event, uh you know, it took a long time to get insight months. Yeah, before we for months we had no insight as to what happened, why it happened, right. Um, and and there were many other incidences like this and the regulators were looking for that one rule that could, that could mitigate many of these incidences. Um, one of our customers decided to take the hard road to go with the tough data right? Because data is rolling dispersed. So they went into all the different feeds of financial transaction information, took the took the tough took the tough road and analyze that data took a long time to assemble. And they discovered that there was quote stuffing right? That uh people were sending a lot of traits in and then cancelling them almost immediately. You have to manipulate the market. Um And why why why didn't we see it immediately? Well, the reason is the process reports that everybody sees the rule in there that says all trades, less than 100 shares don't need to report in there. And so what people did was sending a lot of less than 103 100 100 shares trades uh to fly under the radar to do this manipulation. So here is here the second barrier right? Data could be raw and dispersed. Um Sometimes you just have to take the hard road and um and to get insight And this is 1 1 great example. And then the last barrier is uh is has to do with sometimes when you start a project to to get insight to get uh to get answers and insight. You you realize that all the datas around you but you don't you don't seem to find the right ones to get what you need. You don't you don't seem to get the right ones. Yeah. Um here we have three quick examples of customers. 111 was it was a great example right? Where uh they were trying to build a language translator, a machine language translator between two languages. Right? By not do that. They need to get hundreds of millions of word pairs, you know, of one language compared uh with a corresponding other hundreds of millions of them. They say, well I'm going to get all these word pairs. Someone creative thought of a willing source. And you thought it was the United Nations, you see. So sometimes you think you don't have the right data with you, but there might be another source. And the willing one that could give you that data Right? The 2nd 1 has to do with uh there was uh the uh sometimes you you may just have to generate that data, interesting one. We had an autonomous car customer that collects all these data from their cars, right? Massive amounts of data, loss of sensors, collect loss of data. And uh, you know, but sometimes they don't have the data they need even after collection. For example, they may have collected the data with a car uh in in um in fine weather and collected the car driving on this highway in rain and also in stone, but never had the opportunity to collect the car in hill because that's a rare occurrence. So instead of waiting for a time where the car can dr inhale, they build a simulation you by having the car collector in snow and simulated him. So, these are some of the examples where we have customers working to overcome barriers, right? You have barriers that is associated the fact that data silo the Federated it various associated with data. That's tough to get that. They just took the hard road, right? And, and sometimes, thirdly, you just have to be creative to get the right data. You need, >>wow, I I'll tell you, I have about 100 questions based on what you just said. Uh, there's a great example, the flash crash. In fact, Michael Lewis wrote about this in his book The Flash Boys and essentially right. It was high frequency traders trying to front run the market and sending in small block trades trying to get on the front end it. So that's and they, and they chalked it up to a glitch like you said, for months. Nobody really knew what it was. So technology got us into this problem. I guess my question is, can technology help us get out of the problem? And that maybe is where AI fits in. >>Yes, yes. Uh, in fact, a lot of analytics, we went in to go back to the raw data that is highly dispersed from different sources, right, assemble them to see if you can find a material trend, right? You can see lots of trends, right? Like, uh, you know, we if if humans look at things right, we tend to see patterns in clouds, right? So sometimes you need to apply statistical analysis, um math to to be sure that what the model is seeing is is real. Right? And and that required work. That's one area. The second area is uh you know, when um uh there are times when you you just need to to go through that uh that tough approach to to find the answer. Now, the issue comes to mind now is is that humans put in the rules to decide what goes into a report that everybody sees. And in this case uh before the change in the rules. Right? But by the way, after the discovery, uh authorities change the rules and all all shares, all traits of different any sizes. It has to be reported. No. Yeah. Right. But the rule was applied uh you know, to say earlier that shares under 100 trades under 100 shares need not be reported. So sometimes you just have to understand that reports were decided by humans and and under for understandable reasons. I mean they probably didn't want that for various reasons not to put everything in there so that people could still read it uh in a reasonable amount of time. But uh we need to understand that rules were being put in by humans for the reports we read. And as such there are times you just need to go back to the raw data. >>I want to ask, >>it's gonna be tough. >>Yeah. So I want to ask a question about AI is obviously it's in your title and it's something you know a lot about but and I want to make a statement, you tell me if it's on point or off point. So it seems that most of the Ai going on in the enterprise is modeling data science applied to troves of data but but there's also a lot of ai going on in consumer whether it's you know, fingerprint technology or facial recognition or natural language processing will a two part question will the consumer market as has so often in the enterprise sort of inform us uh the first part and then will there be a shift from sort of modeling if you will to more you mentioned autonomous vehicles more ai influencing in real time. Especially with the edge you can help us understand that better. >>Yeah, it's a great question. Right. Uh there are three stages to just simplify, I mean, you know, it's probably more sophisticated than that but let's simplify three stages. All right. To to building an Ai system that ultimately can predict, make a prediction right or to to assist you in decision making, have an outcome. So you start with the data massive amounts of data that you have to decide what to feed the machine with. So you feed the machine with this massive chunk of data and the machine uh starts to evolve a model based on all the data is seeing. It starts to evolve right to the point that using a test set of data that you have separately kept a site that you know the answer for. Then you test the model uh you know after you trained it with all that data to see whether it's prediction accuracy is high enough and once you are satisfied with it, you you then deploy the model to make the decision and that's the influence. Right? So a lot of times depend on what what we are focusing on. We we um in data science are we working hard on assembling the right data to feed the machine with, That's the data preparation organization work. And then after which you build your models, you have to pick the right models for the decisions and prediction you wanted to make. You pick the right models and then you start feeding the data with it. Sometimes you you pick one model and the prediction isn't that robust, it is good but then it is not consistent right now. What you do is uh you try another model so sometimes it's just keep trying different models until you get the right kind. Yeah, that gives you a good robust decision making and prediction after which It is tested well Q eight. You would then take that model and deploy it at the edge. Yeah. And then at the edges is essentially just looking at new data, applying it to the model that you have trained and then that model will give you a prediction decision. Right? So uh it is these three stages. Yeah, but more and more uh your question reminds me that more and more people are thinking as the edge become more and more powerful. Can you also do learning at the edge? Right. That's the reason why we spoke about swarm learning the last time, learning at the edge as a swamp, right? Because maybe individually they may not have enough power to do so. But as a swamp they made >>is that learning from the edge? You're learning at the edge? In other words? >>Yes. >>Yeah, I understand the question. Yeah. >>That's a great question. That's a great question. Right? So uh the quick answer is learning at the edge, right? Uh and and also from the edge, but the main goal, right? The goal is to learn at the edge so that you don't have to move the data that the edge sees first back to the cloud or the core to do the learning because that would be the reason. One of the main reasons why you want to learn at the edge, right? Uh So so that you don't need to have to send all that data back and assemble it back from all the different Edge devices, assemble it back to the cloud side to to do the learning right. With someone you can learn it and keep the data at the edge and learn at that point. >>And then maybe only selectively send the autonomous vehicle example you gave us great because maybe there, you know, there may be only persisting, they're not persisting data that is inclement weather or when a deer runs across the front. And then maybe they they do that and then they send that smaller data set back and maybe that's where it's modelling done. But the rest can be done at the edges. It's a new world that's coming down. Let me ask you a question, is there a limit to what data should be collected and how it should be collected? >>That's a great question again, you know uh wow today, full of these uh insightful questions that actually touches on the second challenge. Right? How do we uh in order to thrive in this new age of insight? The second challenge is are you know the is our future challenge, right? What do we do for our future? And and in there is uh the statement we make is we have to focus on collecting data strategically for the future of our enterprise. And within that I talk about what to collect right? When to organize it when you collect and where will your data be, you know, going forward that you are collecting from? So what, when and where for the what data for the what data to collect? That? That was the question you ask. Um it's it's a question that different industries have to ask themselves because it will vary, right? Um Let me give you the, you use the autonomous car example, let me use that. And We have this customer collecting massive amounts of data. You know, we're talking about 10 petabytes a day from the fleet of their cars. And these are not production autonomous cars, right? These are training autonomous cars, collecting data so they can train and eventually deploy commercial cars. Right? Um, so this data collection cars they collect as a fleet of them collect 10 petabytes a day and when it came to us uh building a storage system yeah, to store all of that data, they realized they don't want to afford to store all of it. Now here comes the dilemma, right? Should what should I after I spent so much effort building all these cars and sensors and collecting data, I've now decide what to delete. That's a dilemma right now in working with them on this process of trimming down what they collected. You know, I'm constantly reminded of the sixties and seventies, right? To remind myself 16 seventies we call a large part of our D. N. A junk DNA. Today we realize that a large part of that what we call john has function as valuable function. They are not jeans, but they regulate the function of jeans, you know? So, so what's jumped in the yesterday could be valuable today or what's junk today could be valuable tomorrow. Right? So, so there's this tension going on right between you decided not wanting to afford to store everything that you can get your hands on. But on the other hand, you you know, you worry you you you ignore the wrong ones, right? You can see this tension in our customers, right? And it depends on industry here. Right? In health care, they say I have no choice. I I want it. All right. One very insightful point brought up by one health care provider that really touched me was, you know, we are not we don't only care. Of course we care a lot. We care a lot about the people we are caring for, right? But you also care for the people were not caring for. How do we find them? Mhm. Right. And that therefore they did not just need to collect data that is uh that they have with from their patients. They also need to reach out right to outside data so that they can figure out who they are not caring for. Right? So they want it all. So I tell us them. So what do you do with funding if you want it all? They say they have no choice but to figure out a way to fund it and perhaps monetization of what they have now is the way to come around and find out. Of course they also come back to us rightfully that, you know, we have to then work out a way to help them build that system, you know, so that health care, right? And and if you go to other industries like banking, they say they can't afford to keep them on, but they are regulated. Seems like healthcare, they are regulated as to uh privacy and such. Like so many examples different industries having different needs but different approaches to how what they collect. But there is this constant tension between um you perhaps deciding not wanting to fund all of that uh all that you can stall right on the other hand, you know, if you if you kind of don't want to afford it and decide not to store some uh if he does some become highly valuable in the future right? Don't worry. >>We can make some assumptions about the future, can't we? I mean, we know there's gonna be a lot more data than than we've ever seen before. We know that we know. Well notwithstanding supply constraints on things like nand, we know the prices of storage is gonna continue to decline. We also know and not a lot of people are really talking about this but the processing power but he says moore's law is dead. Okay, it's waning. But the processing power when you combine the Cpus and N. P. U. S. And Gpus and accelerators and and so forth actually is is increasing. And so when you think about these use cases at the edge, you're going to have much more processing power, you're going to have cheaper storage and it's going to be less expensive processing. And so as an ai practitioner, what can you do with that? >>So the amount of data that's gonna come in, it's gonna we exceed right? Our drop in storage costs are increasing computer power. Right? So what's the answer? Right? So so the the answer must be knowing that we don't and and even the drop in price and increase in bandwidth, it will overwhelm the increased five G will overwhelm five G. Right? Given amount 55 billion of them collecting. Right? So the answer must be that there might need to be a balance between you needing to bring all that data from the 55 billion devices data back to a central as a bunch of central. Cause because you may not be able to afford to do that firstly band with even with five G. M and and SD when you'll still be too expensive given the number of devices out there, Were you given storage costs dropping? You'll still be too expensive to try and store them all. So the answer must be to start at least to mitigate the problem to some leave both a lot of the data out there. Right? And only send back the pertinent ones as you said before. But then if you did that, then how are we gonna do machine learning at the core and the cloud side? If you don't have all the data, you want rich data to train with. Right? Some sometimes you wanna mix of the uh positive type data and the negative type data so you can train the machine in a more balanced way. So the answer must be eventually right. As we move forward with these huge number of devices out of the edge to do machine learning at the edge today, we don't have enough power. Right? The edge typically is characterized by a lower uh energy capability and therefore lower compute power. But soon, you know, even with lower energy they can do more with compute power, improving in energy efficiency, Right? Uh So learning at the edge today we do influence at the edge. So we data model deploy and you do in France at the age, that's what we do today. But more and more I believe given a massive amount of data at the edge, you, you have to have to start doing machine learning at the edge and, and if when you don't have enough power then you aggregate multiple devices, compute power into a swamp and learn as a swan. >>Oh, interesting. So now of course, if, if I were sitting and fly, fly on the wall in hp board meeting, I said okay. HB is as a leading provider of compute how do you take advantage of that? I mean we're going, we're, I know its future, but you must be thinking about that and participating in those markets. I know today you are, you have, you know, edge line and other products. But there's, it seems to me that it's, it's not the general purpose that we've known in the past. It's a new type of specialized computing. How are you thinking about participating in that >>opportunity for the customers? The world will have to have a balance right? Where today the default? Well, the more common mode is to collect the data from the edge and train at uh at some centralized location or a number of centralized location um going forward. Given the proliferation of the edge devices, we'll need a balance. We need both. We need capability at the cloud side. Right? And it has to be hybrid and then we need capability on the edge side. Yeah. That they want to build systems that that on one hand, uh is uh edge adapted, right? Meaning the environmentally adapted because the edge different. They are on a lot of times. On the outside. Uh They need to be packaging adapted and also power adapted, right? Because typically many of these devices are battery power. Right? Um, so you have to build systems that adapt to it. But at the same time they must not be custom. That's my belief. They must be using standard processes and standard operating system so that they can run a rich set of applications. So yes. Um that's that's also the insightful for that Antonio announced in 2018 Uh the next four years from 2018, right $4 billion dollars invested to strengthen our edge portfolio. Edge product lines, Right. Edge solutions. >>I can doctor go, I could go on for hours with you. You're you're just such a great guest. Let's close. What are you most excited about in the future? Of of of it. Certainly H. P. E. But the industry in general. >>Yeah. I think the excitement is uh the customers, right? The diversity of customers and and the diversity in a way they have approached their different problems with data strategy. So the excitement is around data strategy, right? Just like you know uh you know, the the statement made was was so was profound, right? Um And Antonio said we are in the age of insight powered by data. That's the first line, right. Uh The line that comes after that is as such were becoming more and more data centric with data, the currency. Now the next step is even more profound. That is um You know, we are going as far as saying that you know um data should not be treated as cost anymore. No. Right. But instead as an investment in a new asset class called data with value on our balance sheet, this is a this is a step change right? In thinking that is going to change the way we look at data, the way we value it. So that's a statement that this is the exciting thing because because for for me, a city of Ai right uh machine is only as intelligent as the data you feed it with data is a source of the machine learning to be intelligent. So, so that's that's why when when people start to value data, right? And and and say that it is an investment when we collect it, it is very positive for AI because an AI system gets intelligent, get more intelligence because it has a huge amounts of data and the diversity of data. So it would be great if the community values values data. Well, >>you certainly see it in the valuations of many companies these days. Um and I think increasingly you see it on the income statement, you know, data products and people monetizing data services and maybe eventually you'll see it in the in the balance. You know, Doug Laney, when he was a gardener group wrote a book about this and a lot of people are thinking about it. That's a big change, isn't it? Dr >>yeah. Question is is the process and methods evaluation right. But I believe we'll get there, we need to get started and then we'll get there. Believe >>doctor goes on >>pleasure. And yeah. And then the Yeah, I will well benefit greatly from it. >>Oh yeah, no doubt people will better understand how to align you know, some of these technology investments, Doctor goes great to see you again. Thanks so much for coming back in the cube. It's been a real pleasure. >>Yes. A system. It's only as smart as the data you feed it with. >>Excellent. We'll leave it there, thank you for spending some time with us and keep it right there for more great interviews from HP discover 21 this is Dave Volonte for the cube. The leader in enterprise tech coverage right back

>>Welcome back to HPD discovered 2021 the cubes virtual coverage, continuous coverage of H P. S H. P. S. Annual customer event. My name is Dave Volonte and we're going to dive into the intersection of high performance computing data and AI with DR Eng limb go who is the senior vice president and CTO for AI at Hewlett Packard enterprise Doctor go great to see you again. Welcome back to the cube. >>Hello Dave, Great to talk to you again. >>You might remember last year we talked a lot about swarm intelligence and how AI is evolving. Of course you hosted the day two keynotes here at discover you talked about thriving in the age of insights and how to craft a data centric strategy and you addressed you know some of the biggest problems I think organizations face with data that's You got a data is plentiful but insights they're harder to come by. And you really dug into some great examples in retail banking and medicine and health care and media. But stepping back a little bit with zoom out on discovered 21, what do you make of the events so far? And some of your big takeaways? >>Mm Well you started with the insightful question, right? Yeah. Data is everywhere then. But we like the insight. Right? That's also part of the reason why that's the main reason why you know Antonio on day one focused and talked about that. The fact that we are now in the age of insight. Right? Uh and and uh and and how to thrive thrive in that in this new age. What I then did on the day to kino following Antonio is to talk about the challenges that we need to overcome in order in order to thrive in this new age. >>So maybe we could talk a little bit about some of the things that you took away in terms I'm specifically interested in some of the barriers to achieving insights when you know customers are drowning in data. What do you hear from customers? What we take away from some of the ones you talked about today? >>Oh, very pertinent question. Dave you know the two challenges I spoke about right now that we need to overcome in order to thrive in this new age. The first one is is the current challenge and that current challenge is uh you know stated is you know, barriers to insight, you know when we are awash with data. So that's a statement right? How to overcome those barriers. What are the barriers of these two insight when we are awash in data? Um I in the data keynote I spoke about three main things. Three main areas that received from customers. The first one, the first barrier is in many with many of our customers. A data is siloed. All right. You know, like in a big corporation you've got data siloed by sales, finance, engineering, manufacturing, and so on, uh supply chain and so on. And uh, there's a major effort ongoing in many corporations to build a federation layer above all those silos so that when you build applications above they can be more intelligent. They can have access to all the different silos of data to get better intelligence and more intelligent applications built. So that was the that was the first barrier we spoke about barriers to incite when we are washed with data. The second barrier is uh, that we see amongst our customers is that uh data is raw and dispersed when they are stored and and uh and you know, it's tough to get tough to to get value out of them. Right? And I in that case I I used the example of uh you know the May 6 2010 event where the stock market dropped a trillion dollars in in tens of ministerial. We we all know those who are financially attuned with know about this uh incident But this is not the only incident. There are many of them out there and for for that particular May six event uh you know, it took a long time to get insight months. Yeah before we for months we had no insight as to what happened, why it happened, right. Um and and there were many other incidences like this. And the regulators were looking for that one rule that could, that could mitigate many of these incidences. Um one of our customers decided to take the hard road go with the tough data right? Because data is rolling dispersed. So they went into all the different feeds of financial transaction information. Uh took the took the tough uh took the tough road and analyze that data took a long time to assemble and they discovered that there was court stuffing right? That uh people were sending a lot of traits in and then cancelling them almost immediately. You have to manipulate the market. Um And why why why didn't we see it immediately? Well the reason is the process reports that everybody sees uh rule in there that says all trades. Less than 100 shares don't need to report in there. And so what people did was sending a lot of less than 103 100 100 shares trades uh to fly under the radar to do this manipulation. So here is here the second barrier right? Data could be raw and dispersed. Um Sometimes you just have to take the hard road and um and to get insight And this is 1 1 great example. And then the last barrier is uh is has to do with sometimes when you start a project to to get insight to get uh to get answers and insight. You you realize that all the datas around you but you don't you don't seem to find the right ones To get what you need. You don't you don't seem to get the right ones. Yeah. Um here we have three quick examples of customers. 111 was it was a great example right? Where uh they were trying to build a language translator, a machine language translator between two languages. Right? But not do that. They need to get hundreds of millions of word pairs, you know, of one language compared uh with the corresponding other hundreds of millions of them. They say we are going to get all these word pairs. Someone creative thought of a willing source and a huge, so it was a United Nations you see. So sometimes you think you don't have the right data with you, but there might be another source and a willing one that could give you that data right. The second one has to do with uh there was uh the uh sometimes you you may just have to generate that data, interesting one. We had an autonomous car customer that collects all these data from their cars, right, massive amounts of data, loss of senses, collect loss of data. And uh you know, but sometimes they don't have the data they need even after collection. For example, they may have collected the data with a car uh in in um in fine weather and collected the car driving on this highway in rain and also in stone, but never had the opportunity to collect the car in hale because that's a rare occurrence. So instead of waiting for a time where the car can dr inhale, they build a simulation you by having the car collector in snow and simulated him. So these are some of the examples where we have customers working to overcome barriers, right? You have barriers that is associated the fact that data is silo Federated, it various associated with data. That's tough to get that. They just took the hard road, right? And sometimes, thirdly, you just have to be creative to get the right data you need, >>wow, I tell you, I have about 100 questions based on what you just said. Uh, there's a great example, the flash crash. In fact, Michael Lewis wrote about this in his book, The Flash Boys and essentially right. It was high frequency traders trying to front run the market and sending in small block trades trying to get on the front end it. So that's and they, and they chalked it up to a glitch like you said, for months, nobody really knew what it was. So technology got us into this problem. I guess my question is, can technology help us get out of the problem? And that maybe is where AI fits in. >>Yes, yes. Uh, in fact, a lot of analytics, we went in, uh, to go back to the raw data that is highly dispersed from different sources, right, assemble them to see if you can find a material trend, right? You can see lots of trends right? Like, uh, you know, we, if if humans look at things right, we tend to see patterns in clouds, right? So sometimes you need to apply statistical analysis, um math to be sure that what the model is seeing is is real. Right? And and that required work. That's one area. The second area is uh you know, when um uh there are times when you you just need to to go through that uh that tough approach to to find the answer. Now, the issue comes to mind now is is that humans put in the rules to decide what goes into a report that everybody sees in this case uh before the change in the rules. Right? But by the way, after the discovery, the authorities change the rules and all all shares, all traits of different any sizes. It has to be reported. No. Yeah. Right. But the rule was applied uh you know, to say earlier that shares under 100 trades under 100 shares need not be reported. So sometimes you just have to understand that reports were decided by humans and and under for understandable reasons. I mean they probably didn't want that for various reasons not to put everything in there so that people could still read it uh in a reasonable amount of time. But uh we need to understand that rules were being put in by humans for the reports we read. And as such, there are times you just need to go back to the raw data. >>I want to ask, >>albeit that it's gonna be tough. >>Yeah. So I want to ask a question about AI is obviously it's in your title and it's something you know a lot about but and I want to make a statement, you tell me if it's on point or off point. So it seems that most of the Ai going on in the enterprise is modeling data science applied to troves of data >>but >>but there's also a lot of ai going on in consumer whether it's you know, fingerprint technology or facial recognition or natural language processing. Will a two part question will the consumer market has so often in the enterprise sort of inform us uh the first part and then will there be a shift from sort of modeling if you will to more you mentioned autonomous vehicles more ai influencing in real time. Especially with the edge. She can help us understand that better. >>Yeah, it's a great question. Right. Uh there are three stages to just simplify, I mean, you know, it's probably more sophisticated than that but let's simplify three stages. All right. To to building an Ai system that ultimately can predict, make a prediction right or to to assist you in decision making, have an outcome. So you start with the data massive amounts data that you have to decide what to feed the machine with. So you feed the machine with this massive chunk of data and the machine uh starts to evolve a model based on all the data is seeing. It starts to evolve right to the point that using a test set of data that you have separately campus site that you know the answer for. Then you test the model uh you know after you trained it with all that data to see whether it's prediction accuracy is high enough and once you are satisfied with it, you you then deploy the model to make the decision and that's the influence. Right? So a lot of times depend on what what we are focusing on. We we um in data science are we working hard on assembling the right data to feed the machine with, That's the data preparation organization work. And then after which you build your models, you have to pick the right models for the decisions and prediction you wanted to make. You pick the right models and then you start feeding the data with it. Sometimes you you pick one model and the prediction isn't that robust, it is good but then it is not consistent right now what you do is uh you try another model so sometimes it's just keep trying different models until you get the right kind. Yeah, that gives you a good robust decision making and prediction after which It is tested well Q eight. You would then take that model and deploy it at the edge. Yeah. And then at the edges is essentially just looking at new data, applying it to the model, you're you're trained and then that model will give you a prediction decision. Right? So uh it is these three stages. Yeah, but more and more uh you know, your question reminds me that more and more people are thinking as the edge become more and more powerful. Can you also do learning at the edge? Right. That's the reason why we spoke about swarm learning the last time, learning at the edge as a swamp, right? Because maybe individually they may not have enough power to do so. But as a swampy me, >>is that learning from the edge or learning at the edge? In other words? Yes. Yeah. Question Yeah. >>That's a great question. That's a great question. Right? So uh the quick answer is learning at the edge, right? Uh and also from the edge, but the main goal, right? The goal is to learn at the edge so that you don't have to move the data that the Edge sees first back to the cloud or the core to do the learning because that would be the reason. One of the main reasons why you want to learn at the edge, right? Uh So so that you don't need to have to send all that data back and assemble it back from all the different edge devices, assemble it back to the cloud side to to do the learning right? With swampland. You can learn it and keep the data at the edge and learn at that point. >>And then maybe only selectively send the autonomous vehicle example you gave us. Great because maybe there, you know, there may be only persisting, they're not persisting data that is inclement weather or when a deer runs across the front and then maybe they they do that and then they send that smaller data set back and maybe that's where it's modelling done. But the rest can be done at the edges. It's a new world that's coming down. Let me ask you a question, is there a limit to what data should be collected and how it should be collected? >>That's a great question again. You know uh wow today, full of these uh insightful questions that actually touches on the second challenge. Right? How do we uh in order to thrive in this new age of inside? The second challenge is are you know the is our future challenge, right? What do we do for our future? And and in there is uh the statement we make is we have to focus on collecting data strategically for the future of our enterprise. And within that I talk about what to collect right? When to organize it when you collect and then where will your data be, you know going forward that you are collecting from? So what, when and where for the what data for the what data to collect? That? That was the question you ask. Um it's it's a question that different industries have to ask themselves because it will vary, right? Um let me give you the you use the autonomous car example, let me use that. And you have this customer collecting massive amounts of data. You know, we're talking about 10 petabytes a day from the fleet of their cars. And these are not production autonomous cars, right? These are training autonomous cars collecting data so they can train and eventually deploy commercial cars, right? Um so this data collection cars they collect as a fleet of them collect temporal bikes a day. And when it came to us building a storage system to store all of that data, they realized they don't want to afford to store all of it. Now, here comes the dilemma, right? What should I after I spent so much effort building all these cars and sensors and collecting data, I've now decide what to delete. That's a dilemma right now in working with them on this process of trimming down what they collected. You know, I'm constantly reminded of the sixties and seventies, right? To remind myself 60 and seventies, we call a large part of our D. N. A junk DNA. Today. We realize that a large part of that what we call john has function as valuable function. They are not jeans, but they regulate the function of jeans, you know, So, so what's jump in the yesterday could be valuable today or what's junk today could be valuable tomorrow, Right? So, so there's this tension going on right between you decided not wanting to afford to store everything that you can get your hands on. But on the other hand, you you know, you worry you you you ignore the wrong ones, right? You can see this tension in our customers, right? And it depends on industry here, right? In health care, they say I have no choice. I I want it. All right. One very insightful point brought up by one health care provider that really touched me was, you know, we are not we don't only care. Of course we care a lot. We care a lot about the people we are caring for, right? But you also care for the people were not caring for. How do we find them? Mhm. Right. And that therefore, they did not just need to collect data. That is that they have with from their patients. They also need to reach out right to outside data so that they can figure out who they are not caring for, right? So they want it all. So I tell us them, so what do you do with funding if you want it all? They say they have no choice but to figure out a way to fund it and perhaps monetization of what they have now is the way to come around and find that. Of course they also come back to us rightfully that you know, we have to then work out a way to help them build that system, you know? So that's health care, right? And and if you go to other industries like banking, they say they can't afford to keep them off, but they are regulated, seems like healthcare, they are regulated as to uh privacy and such. Like so many examples different industries having different needs, but different approaches to how what they collect. But there is this constant tension between um you perhaps deciding not wanting to fund all of that uh all that you can store, right? But on the other hand, you know, if you if you kind of don't want to afford it and decide not to store some uh if he does some become highly valuable in the future, right? Yeah. >>We can make some assumptions about the future, can't we? I mean, we know there's gonna be a lot more data than than we've ever seen before. We know that we know well notwithstanding supply constraints on things like nand. We know the prices of storage is going to continue to decline. We also know, and not a lot of people are really talking about this but the processing power but he says moore's law is dead okay. It's waning. But the processing power when you combine the Cpus and NP US and GPUS and accelerators and and so forth actually is is increasing. And so when you think about these use cases at the edge, you're going to have much more processing power, you're gonna have cheaper storage and it's going to be less expensive processing And so as an ai practitioner, what can you do with that? >>Yeah, it's highly again, another insightful questions that we touched on our keynote and that that goes up to the why I do the where? Right, When will your data be? Right. We have one estimate that says that by next year there will be 55 billion connected devices out there. Right. 55 billion. Right. What's the population of the world? Of the other? Of 10 billion? But this thing is 55 billion. Right? Uh and many of them, most of them can collect data. So what do you what do you do? Right. Um So the amount of data that's gonna come in, it's gonna weigh exceed right? Our drop in storage costs are increasing computer power. Right? So what's the answer? Right. So, so the the answer must be knowing that we don't and and even the drop in price and increase in bandwidth, it will overwhelm the increased five G will overwhelm five G. Right? Given amount 55 billion of them collecting. Right? So, the answer must be that there might need to be a balance between you needing to bring all that data from the 55 billion devices of data back to a central as a bunch of central Cause because you may not be able to afford to do that firstly band with even with five G. M and and SD when you'll still be too expensive given the number of devices out there. Were you given storage cause dropping will still be too expensive to try and store them all. So the answer must be to start at least to mitigate the problem to some leave both a lot of the data out there. Right? And only send back the pertinent ones as you said before. But then if you did that, then how are we gonna do machine learning at the core and the cloud side? If you don't have all the data you want rich data to train with. Right? Some sometimes you want a mix of the uh positive type data and the negative type data so you can train the machine in a more balanced way. So the answer must be eventually right. As we move forward with these huge number of devices out of the edge to do machine learning at the edge. Today, we don't have enough power. Right? The edge typically is characterized by a lower uh, energy capability and therefore lower compute power. But soon, you know, even with lower energy, they can do more with compute power improving in energy efficiency, Right? Uh, so learning at the edge today, we do influence at the edge. So we data model deploy and you do influence at the age, that's what we do today. But more and more, I believe, given a massive amount of data at the edge, you you have to have to start doing machine learning at the edge. And and if when you don't have enough power, then you aggregate multiple devices, compute power into a swamp and learn as a swan, >>interesting. So now, of course, if I were sitting and fly on the wall in HP board meeting, I said, okay, HP is as a leading provider of compute, how do you take advantage of that? I mean, we're going, I know it's future, but you must be thinking about that and participating in those markets. I know today you are you have, you know, edge line and other products. But there's it seems to me that it's it's not the general purpose that we've known in the past. It's a new type of specialized computing. How are you thinking about participating in that >>opportunity for your customers? Uh the world will have to have a balance right? Where today the default, Well, the more common mode is to collect the data from the edge and train at uh at some centralized location or a number of centralized location um going forward. Given the proliferation of the edge devices, we'll need a balance. We need both. We need capability at the cloud side. Right. And it has to be hybrid. And then we need capability on the edge side. Yeah. That they want to build systems that that on one hand, uh is uh edge adapted, right? Meaning the environmentally adapted because the edge different they are on a lot of times on the outside. Uh They need to be packaging adapted and also power adapted, right? Because typically many of these devices are battery powered. Right? Um so you have to build systems that adapt to it, but at the same time they must not be custom. That's my belief. They must be using standard processes and standard operating system so that they can run rich a set of applications. So yes. Um that's that's also the insightful for that Antonio announced in 2018, Uh the next four years from 2018, right, $4 billion dollars invested to strengthen our edge portfolio, edge product lines, right Edge solutions. >>I get a doctor go. I could go on for hours with you. You're you're just such a great guest. Let's close what are you most excited about in the future of of of it? Certainly H. P. E. But the industry in general. >>Yeah I think the excitement is uh the customers right? The diversity of customers and and the diversity in a way they have approached their different problems with data strategy. So the excitement is around data strategy right? Just like you know uh you know the the statement made was was so was profound. Right? Um And Antonio said we are in the age of insight powered by data. That's the first line right? The line that comes after that is as such were becoming more and more data centric with data the currency. Now the next step is even more profound. That is um you know we are going as far as saying that you know um data should not be treated as cost anymore. No right. But instead as an investment in a new asset class called data with value on our balance sheet, this is a this is a step change right in thinking that is going to change the way we look at data the way we value it. So that's a statement that this is the exciting thing because because for for me a city of AI right uh machine is only as intelligent as the data you feed it with. Data is a source of the machine learning to be intelligent. So so that's that's why when when people start to value data right? And and and say that it is an investment when we collect it. It is very positive for ai because an Ai system gets intelligent, more intelligence because it has a huge amounts of data and the diversity of data. So it'd be great if the community values values data. Well >>you certainly see it in the valuations of many companies these days. Um and I think increasingly you see it on the income statement, you know data products and people monetizing data services and maybe eventually you'll see it in the in the balance. You know Doug Laney when he was a gardener group wrote a book about this and a lot of people are thinking about it. That's a big change isn't it? Dr >>yeah. Question is is the process and methods evaluation. Right. But uh I believe we'll get there, we need to get started then we'll get their belief >>doctor goes on and >>pleasure. And yeah and then the yeah I will will will will benefit greatly from it. >>Oh yeah, no doubt people will better understand how to align you know, some of these technology investments, Doctor goes great to see you again. Thanks so much for coming back in the cube. It's been a real pleasure. >>Yes. A system. It's only as smart as the data you feed it with. >>Excellent. We'll leave it there. Thank you for spending some time with us and keep it right there for more great interviews from HP discover 21. This is dave a lot for the cube. The leader in enterprise tech coverage right back.

(upbeat music) >> Welcome back to HPE Discover 2021, theCube's virtual coverage, continuous coverage of HPE's annual customer event. My name is Dave Vellante and we're going to dive into the intersection of high-performance computing, data and AI with Dr. Eng Lim Goh who's a Senior Vice President and CTO for AI at Hewlett Packard Enterprise. Dr. Goh, great to see you again. Welcome back to theCube. >> Hey, hello, Dave. Great to talk to you again. >> You might remember last year we talked a lot about swarm intelligence and how AI is evolving. Of course you hosted the Day 2 keynotes here at Discover. And you talked about thriving in the age of insights and how to craft a data-centric strategy and you addressed some of the biggest problems I think organizations face with data. And that's, you got to look, data is plentiful, but insights, they're harder to come by and you really dug into some great examples in retail, banking, and medicine and healthcare and media. But stepping back a little bit we'll zoom out on Discover '21, you know, what do you make of the events so far and some of your big takeaways? >> Hmm, well, you started with the insightful question. Data is everywhere then but we lack the insight. That's also part of the reason why that's a main reason why, Antonio on Day 1 focused and talked about that, the fact that we are in the now in the age of insight and how to thrive in this new age. What I then did on the Day 2 keynote following Antonio is to talk about the challenges that we need to overcome in order to thrive in this new age. >> So maybe we could talk a little bit about some of the things that you took away in terms of, I'm specifically interested in some of the barriers to achieving insights when you know customers are drowning in data. What do you hear from customers? What were your takeaway from some of the ones you talked about today? >> Very pertinent question, Dave. You know, the two challenges I spoke about how to, that we need to overcome in order to thrive in this new age, the first one is the current challenge. And that current challenge is, you know state of this, you know, barriers to insight, when we are awash with data. So that's a statement. How to overcome those barriers. One of the barriers to insight when we are awash in data, in the Day 2 keynote, I spoke about three main things, three main areas that receive from customers. The first one, the first barrier is with many of our customers, data is siloed. You know, like in a big corporation, you've got data siloed by sales, finance, engineering, manufacturing, and so on supply chain and so on. And there's a major effort ongoing in many corporations to build a Federation layer above all those silos so that when you build applications above they can be more intelligent. They can have access to all the different silos of data to get better intelligence and more intelligent applications built. So that was the first barrier we spoke about, you know, barriers to insight when we are awash with data. The second barrier is that we see amongst our customers is that data is raw and disperse when they are stored. And it's tough to get to value out of them. In that case I use the example of the May 6, 2010 event where the stock market dropped a trillion dollars in tens of minutes. We all know those who are financially attuned with, know about this incident. But that this is not the only incident. There are many of them out there. And for that particular May 6, event, you know it took a long time to get insight, months, yeah, before we, for months we had no insight as to what happened, why it happened. And there were many other incidences like this and the regulators were looking for that one rule that could mitigate many of these incidences. One of our customers decided to take the hard road to go with the tough data. Because data is raw and dispersed. So they went into all the different feeds of financial transaction information, took the tough, you know, took a tough road and analyze that data took a long time to assemble. And he discovered that there was quote stuffing. That people were sending a lot of trades in and then canceling them almost immediately. You have to manipulate the market. And why didn't we see it immediately? Well, the reason is the process reports that everybody sees had the rule in there that says all trades less than 100 shares don't need to report in there. And so what people did was sending a lot of less than 100 shares trades to fly under the radar to do this manipulation. So here is, here the second barrier. Data could be raw and disperse. Sometimes it's just have to take the hard road and to get insight. And this is one great example. And then the last barrier has to do with sometimes when you start a project to get insight, to get answers and insight, you realize that all the data's around you, but you don't seem to find the right ones to get what you need. You don't seem to get the right ones, yeah. Here we have three quick examples of customers. One was a great example where they were trying to build a language translator a machine language translator between two languages. But in order to do that they need to get hundreds of millions of word pairs of one language compare with the corresponding other hundreds of millions of them. They say, "Where I'm going to get all these word pairs?" Someone creative thought of a willing source and huge source, it was a United Nations. You see, so sometimes you think you don't have the right data with you, but there might be another source and a willing one that could give you that data. The second one has to do with, there was the, sometimes you may just have to generate that data. Interesting one. We had an autonomous car customer that collects all these data from their cars. Massive amounts of data, lots of sensors, collect lots of data. And, you know, but sometimes they don't have the data they need even after collection. For example, they may have collected the data with a car in fine weather and collected the car driving on this highway in rain and also in snow. But never had the opportunity to collect the car in hail because that's a rare occurrence. So instead of waiting for a time where the car can drive in hail, they build a simulation by having the car collected in snow and simulated hail. So these are some of the examples where we have customers working to overcome barriers. You have barriers that is associated with the fact, that data silo, if federated barriers associated with data that's tough to get at. They just took the hard road. And sometimes thirdly, you just have to be creative to get the right data you need. >> Wow, I tell you, I have about 100 questions based on what you just said. And as a great example, the flash crash in fact Michael Lewis wrote about this in his book, the "Flash Boys" and essentially. It was high frequency traders trying to front run the market and sending in small block trades trying to get sort of front ended. So that's, and they chalked it up to a glitch. Like you said, for months, nobody really knew what it was. So technology got us into this problem. Can I guess my question is can technology help us get get out of the problem? And that maybe is where AI fits in. >> Yes. Yes. In fact, a lot of analytics work went in to go back to the raw data that is highly dispersed from different sources, assemble them to see if you can find a material trend. You can see lots of trends. Like, no, we, if humans at things we tend to see patterns in clouds. So sometimes you need to apply statistical analysis, math to be sure that what the model is seeing is real. And that required work. That's one area. The second area is, you know, when this, there are times when you just need to go through that tough approach to find the answer. Now, the issue comes to mind now is that humans put in the rules to decide what goes into a report that everybody sees. And in this case before the change in the rules. By the way, after the discovery, the authorities changed the rules and all shares all trades of different, any sizes it has to be reported. Not, yeah. But the rule was applied to to say earlier that shares under 100, trades under 100 shares need not be reported. So sometimes you just have to understand that reports were decided by humans and for understandable reasons. I mean, they probably didn't, wanted for various reasons not to put everything in there so that people could still read it in a reasonable amount of time. But we need to understand that rules were being put in by humans for the reports we read. And as such there are times we just need to go back to the raw data. >> I want to ask you-- Or be it that it's going to be tough there. >> Yeah, so I want to ask you a question about AI as obviously it's in your title and it's something you know a lot about and I'm going to make a statement. You tell me if it's on point or off point. Seems that most of the AI going on in the enterprise is modeling data science applied to troves of data. But there's also a lot of AI going on in consumer, whether it's fingerprint technology or facial recognition or natural language processing. Will, to two-part question, will the consumer market, let's say as it has so often in the enterprise sort of inform us is sort of first part. And then will there be a shift from sort of modeling, if you will, to more, you mentioned autonomous vehicles more AI inferencing in real-time, especially with the Edge. I think you can help us understand that better. >> Yeah, this is a great question. There are three stages to just simplify, I mean, you know, it's probably more sophisticated than that, but let's just simplify there're three stages to building an AI system that ultimately can predict, make a prediction. Or to assist you in decision-making, have an outcome. So you start with the data, massive amounts of data that you have to decide what to feed the machine with. So you feed the machine with this massive chunk of data. And the machine starts to evolve a model based on all the data is seeing it starts to evolve. To a point that using a test set of data that you have separately kept a site that you know the answer for. Then you test the model, you know after you're trained it with all that data to see whether his prediction accuracy is high enough. And once you are satisfied with it, you then deploy the model to make the decision and that's the inference. So a lot of times depending on what we are focusing on. We in data science are we working hard on assembling the right data to feed the machine with? That's the data preparation organization work. And then after which you build your models you have to pick the right models for the decisions and prediction you wanted to make. You pick the right models and then you start feeding the data with it. Sometimes you pick one model and a prediction isn't that a robust, it is good, but then it is not consistent. Now what you do is you try another model. So sometimes you just keep trying different models until you get the right kind, yeah, that gives you a good robust decision-making and prediction. Now, after which, if it's tested well, Q8 you will then take that model and deploy it at the Edge, yeah. And then at the Edge is essentially just looking at new data applying it to the model that you have trained and then that model will give you a prediction or a decision. So it is these three stages, yeah. But more and more, your question reminds me that more and more people are thinking as the Edge become more and more powerful, can you also do learning at the Edge? That's the reason why we spoke about swarm learning the last time, learning at the Edge as a swarm. Because maybe individually they may not have enough power to do so, but as a swarm, they may. >> Is that learning from the Edge or learning at the Edge. In other words, is it-- >> Yes. >> Yeah, you don't understand my question, yeah. >> That's a great question. That's a great question. So answer is learning at the Edge, and also from the Edge, but the main goal, the goal is to learn at the Edge so that you don't have to move the data that Edge sees first back to the Cloud or the call to do the learning. Because that would be the reason, one of the main reasons why you want to learn at the Edge. So that you don't need to have to send all that data back and assemble it back from all the different Edge devices assemble it back to the Cloud side to do the learning. With swarm learning, you can learn it and keep the data at the Edge and learn at that point, yeah. >> And then maybe only selectively send the autonomous vehicle example you gave is great 'cause maybe they're, you know, there may be only persisting. They're not persisting data that is an inclement weather, or when a deer runs across the front and then maybe they do that and then they send that smaller data set back and maybe that's where it's modeling done but the rest can be done at the Edge. It's a new world that's coming to, let me ask you a question. Is there a limit to what data should be collected and how it should be collected? >> That's a great question again, yeah, well, today full of these insightful questions that actually touches on the second challenge. How do we, to in order to thrive in this new age of insight. The second challenge is our future challenge. What do we do for our future? And in there is the statement we make is we have to focus on collecting data strategically for the future of our enterprise. And within that, I talk about what to collect, and when to organize it when you collect, and then where will your data be going forward that you are collecting from? So what, when, and where. For the what data, for what data to collect that was the question you asked. It's a question that different industries have to ask themselves because it will vary. Let me give you the, you use the autonomous car example. Let me use that and you have this customer collecting massive amounts of data. You know, we talking about 10 petabytes a day from a fleet of their cars and these are not production autonomous cars. These are training autonomous cars, collecting data so they can train and eventually deploy a commercial cars. Also these data collection cars, they collect 10 as a fleet of them collect 10 petabytes a day. And then when it came to us, building a storage system to store all of that data they realize they don't want to afford to store all of it. Now here comes the dilemma. What should I, after I spent so much effort building all this cars and sensors and collecting data, I've now decide what to delete. That's a dilemma. Now in working with them on this process of trimming down what they collected. I'm constantly reminded of the 60s and 70s. To remind myself 60s and 70s, we call a large part of our DNA, junk DNA. Today we realized that a large part of that, what we call junk has function has valuable function. They are not genes but they regulate the function of genes. So what's junk in yesterday could be valuable today, or what's junk today could be valuable tomorrow. So there's this tension going on between you deciding not wanting to afford to store everything that you can get your hands on. But on the other hand, you know you worry, you ignore the wrong ones. You can see this tension in our customers. And then it depends on industry here. In healthcare they say, I have no choice. I want it all, why? One very insightful point brought up by one healthcare provider that really touched me was you know, we are not, we don't only care. Of course we care a lot. We care a lot about the people we are caring for. But we also care for the people we are not caring for. How do we find them? And therefore, they did not just need to collect data that they have with, from their patients they also need to reach out to outside data so that they can figure out who they are not caring for. So they want it all. So I asked them, "So what do you do with funding if you want it all?" They say they have no choice but they'll figure out a way to fund it and perhaps monetization of what they have now is the way to come around and fund that. Of course, they also come back to us, rightfully that you know, we have to then work out a way to to help them build a system. So that healthcare. And if you go to other industries like banking, they say they can afford to keep them all. But they are regulated same like healthcare. They are regulated as to privacy and such like. So many examples, different industries having different needs but different approaches to how, what they collect. But there is this constant tension between you perhaps deciding not wanting to fund all of that, all that you can store. But on the other hand you know, if you kind of don't want to afford it and decide not to store some, maybe those some become highly valuable in the future. You worry. >> Well, we can make some assumptions about the future, can't we? I mean we know there's going to be a lot more data than we've ever seen before, we know that. We know, well not withstanding supply constraints and things like NAND. We know the price of storage is going to continue to decline. We also know and not a lot of people are really talking about this but the processing power, everybody says, Moore's Law is dead. Okay, it's waning but the processing power when you combine the CPUs and NPUs, and GPUs and accelerators and so forth, actually is increasing. And so when you think about these use cases at the Edge you're going to have much more processing power. You're going to have cheaper storage and it's going to be less expensive processing. And so as an AI practitioner, what can you do with that? >> Yeah, it's a highly, again another insightful question that we touched on, on our keynote and that goes up to the why, I'll do the where. Where will your data be? We have one estimate that says that by next year, there will be 55 billion connected devices out there. 55 billion. What's the population of the world? Well, off the order of 10 billion, but this thing is 55 billion. And many of them, most of them can collect data. So what do you do? So the amount of data that's going to come in is going to way exceed our drop in storage costs our increasing compute power. So what's the answer? The answer must be knowing that we don't and even a drop in price and increase in bandwidth, it will overwhelm the 5G, it'll will overwhelm 5G, given the amount of 55 billion of them collecting. So the answer must be that there needs to be a balance between you needing to bring all that data from the 55 billion devices of the data back out to a central, as a bunch of central cost because you may not be able to afford to do that. Firstly bandwidth, even with 5G and as the, when you still be too expensive given the number of devices out there. You know given storage costs dropping it'll still be too expensive to try and install them all. So the answer must be to start at least to mitigate the problem to some leave most a lot of the data out there. And only send back the pertinent ones, as you said before. But then if you did that then, how are we going to do machine learning at the core and the Cloud side, if you don't have all the data you want rich data to train with. Sometimes you want to a mix of the positive type data, and the negative type data. So you can train the machine in a more balanced way. So the answer must be you eventually, as we move forward with these huge number of devices are at the Edge to do machine learning at the Edge. Today we don't even have power. The Edge typically is characterized by a lower energy capability and therefore, lower compute power. But soon, you know, even with low energy, they can do more with compute power, improving in energy efficiency. So learning at the Edge today we do inference at the Edge. So we data, model, deploy and you do inference at age. That's what we do today. But more and more, I believe given a massive amount of data at the Edge you have to have to start doing machine learning at the Edge. And if when you don't have enough power then you aggregate multiple devices' compute power into a swarm and learn as a swarm. >> Oh, interesting, so now of course, if I were sitting in a flyer flying the wall on HPE Board meeting I said, "Okay, HPE is a leading provider of compute." How do you take advantage that? I mean, we're going, I know it's future but you must be thinking about that and participating in those markets. I know today you are, you have, you know, Edge line and other products, but there's, it seems to me that it's not the general purpose that we've known in the past. It's a new type of specialized computing. How are you thinking about participating in that opportunity for your customers? >> The wall will have to have a balance. Where today the default, well, the more common mode is to collect the data from the Edge and train at some centralized location or number of centralized location. Going forward, given the proliferation of the Edge devices, we'll need a balance, we need both. We need capability at the Cloud side. And it has to be hybrid. And then we need capability on the Edge side. Yeah that we need to build systems that on one hand is Edge-adapted. Meaning they environmentally-adapted because the Edge differently are on it. A lot of times on the outside, they need to be packaging-adapted and also power-adapted. Because typically many of these devices are battery-powered. So you have to build systems that adapts to it. But at the same time, they must not be custom. That's my belief. They must be using standard processes and standard operating system so that they can run a rich set of applications. So yes, that's also the insightful for that. Antonio announced in 2018 for the next four years from 2018, $4 billion invested to strengthen our Edge portfolio our Edge product lines, Edge solutions. >> Dr. Goh, I could go on for hours with you. You're just such a great guest. Let's close. What are you most excited about in the future of certainly HPE, but the industry in general? >> Yeah, I think the excitement is the customers. The diversity of customers and the diversity in the way they have approached their different problems with data strategy. So the excitement is around data strategy. Just like, you know, the statement made for us was so, was profound. And Antonio said we are in the age of insight powered by data. That's the first line. The line that comes after that is as such we are becoming more and more data-centric with data the currency. Now the next step is even more profound. That is, you know, we are going as far as saying that data should not be treated as cost anymore, no. But instead, as an investment in a new asset class called data with value on our balance sheet. This is a step change in thinking that is going to change the way we look at data, the way we value it. So that's a statement. So this is the exciting thing, because for me a CTO of AI, a machine is only as intelligent as the data you feed it with. Data is a source of the machine learning to be intelligent. So that's why when the people start to value data and say that it is an investment when we collect it it is very positive for AI because an AI system gets intelligent, get more intelligence because it has huge amounts of data and a diversity of data. So it'd be great if the community values data. >> Well, are you certainly see it in the valuations of many companies these days? And I think increasingly you see it on the income statement, you know data products and people monetizing data services, and yeah, maybe eventually you'll see it in the balance sheet, I know. Doug Laney when he was at Gartner Group wrote a book about this and a lot of people are thinking about it. That's a big change, isn't it? Dr. Goh. >> Yeah, yeah, yeah. Your question is the process and methods in valuation. But I believe we'll get there. We need to get started and then we'll get there, I believe, yeah. >> Dr. Goh it's always my pleasure. >> And then the AI will benefit greatly from it. >> Oh yeah, no doubt. People will better understand how to align some of these technology investments. Dr. Goh, great to see you again. Thanks so much for coming back in theCube. It's been a real pleasure. >> Yes, a system is only as smart as the data you feed it with. (both chuckling) >> Well, excellent, we'll leave it there. Thank you for spending some time with us so keep it right there for more great interviews from HPE Discover '21. This is Dave Vellante for theCube, the leader in enterprise tech coverage. We'll be right back (upbeat music)

(upbeat music) >> Welcome back to HPE Discover 2021, theCUBE's virtual coverage, continuous coverage of HPE's Annual Customer Event. My name is Dave Vellante, and we're going to dive into the intersection of high-performance computing, data and AI with Doctor Eng Lim Goh, who's a Senior Vice President and CTO for AI at Hewlett Packard Enterprise. Doctor Goh, great to see you again. Welcome back to theCUBE. >> Hello, Dave, great to talk to you again. >> You might remember last year we talked a lot about Swarm intelligence and how AI is evolving. Of course, you hosted the Day 2 Keynotes here at Discover. And you talked about thriving in the age of insights, and how to craft a data-centric strategy. And you addressed some of the biggest problems, I think organizations face with data. That's, you've got a, data is plentiful, but insights, they're harder to come by. >> Yeah. >> And you really dug into some great examples in retail, banking, in medicine, healthcare and media. But stepping back a little bit we zoomed out on Discover '21. What do you make of the events so far and some of your big takeaways? >> Hmm, well, we started with the insightful question, right, yeah? Data is everywhere then, but we lack the insight. That's also part of the reason why, that's a main reason why Antonio on day one focused and talked about the fact that we are in the now in the age of insight, right? And how to try thrive in that age, in this new age? What I then did on a Day 2 Keynote following Antonio is to talk about the challenges that we need to overcome in order to thrive in this new age. >> So, maybe we could talk a little bit about some of the things that you took away in terms of, I'm specifically interested in some of the barriers to achieving insights. You know customers are drowning in data. What do you hear from customers? What were your takeaway from some of the ones you talked about today? >> Oh, very pertinent question, Dave. You know the two challenges I spoke about, that we need to overcome in order to thrive in this new age. The first one is the current challenge. And that current challenge is, you know, stated is now barriers to insight, when we are awash with data. So that's a statement on how do you overcome those barriers? What are the barriers to insight when we are awash in data? In the Day 2 Keynote, I spoke about three main things. Three main areas that we receive from customers. The first one, the first barrier is in many, with many of our customers, data is siloed, all right. You know, like in a big corporation, you've got data siloed by sales, finance, engineering, manufacturing and so on supply chain and so on. And there's a major effort ongoing in many corporations to build a federation layer above all those silos so that when you build applications above, they can be more intelligent. They can have access to all the different silos of data to get better intelligence and more intelligent applications built. So that was the first barrier we spoke about, you know? Barriers to insight when we are awash with data. The second barrier is that we see amongst our customers is that data is raw and disperse when they are stored. And you know, it's tough to get at, to tough to get a value out of them, right? And in that case, I use the example of, you know, the May 6, 2010 event where the stock market dropped a trillion dollars in terms of minutes. We all know those who are financially attuned with know about this incident but that this is not the only incident. There are many of them out there. And for that particular May 6 event, you know, it took a long time to get insight. Months, yeah, before we, for months we had no insight as to what happened. Why it happened? Right, and there were many other incidences like this and the regulators were looking for that one rule that could mitigate many of these incidences. One of our customers decided to take the hard road they go with the tough data, right? Because data is raw and dispersed. So they went into all the different feeds of financial transaction information, took the tough, you know, took a tough road. And analyze that data took a long time to assemble. And they discovered that there was caught stuffing, right? That people were sending a lot of trades in and then canceling them almost immediately. You have to manipulate the market. And why didn't we see it immediately? Well, the reason is the process reports that everybody sees, the rule in there that says, all trades less than a hundred shares don't need to report in there. And so what people did was sending a lot of less than a hundred shares trades to fly under the radar to do this manipulation. So here is the second barrier, right? Data could be raw and dispersed. Sometimes it's just have to take the hard road and to get insight. And this is one great example. And then the last barrier has to do with sometimes when you start a project to get insight, to get answers and insight, you realize that all the data's around you, but you don't seem to find the right ones to get what you need. You don't seem to get the right ones, yeah? Here we have three quick examples of customers. One was a great example, right? Where they were trying to build a language translator or machine language translator between two languages, right? By not do that, they need to get hundreds of millions of word pairs. You know of one language compare with the corresponding other. Hundreds of millions of them. They say, well, I'm going to get all these word pairs. Someone creative thought of a willing source and a huge, it was a United Nations. You see? So sometimes you think you don't have the right data with you, but there might be another source and a willing one that could give you that data, right? The second one has to do with, there was the sometimes you may just have to generate that data. Interesting one, we had an autonomous car customer that collects all these data from their their cars, right? Massive amounts of data, lots of sensors, collect lots of data. And, you know, but sometimes they don't have the data they need even after collection. For example, they may have collected the data with a car in fine weather and collected the car driving on this highway in rain and also in snow. But never had the opportunity to collect the car in hill because that's a rare occurrence. So instead of waiting for a time where the car can drive in hill, they build a simulation by having the car collected in snow and simulated him. So these are some of the examples where we have customers working to overcome barriers, right? You have barriers that is associated. In fact, that data silo, they federated it. Virus associated with data, that's tough to get at. They just took the hard road, right? And sometimes thirdly, you just have to be creative to get the right data you need. >> Wow! I tell you, I have about a hundred questions based on what you just said, you know? (Dave chuckles) And as a great example, the Flash Crash. In fact, Michael Lewis, wrote about this in his book, the Flash Boys. And essentially, right, it was high frequency traders trying to front run the market and sending into small block trades (Dave chuckles) trying to get sort of front ended. So that's, and they chalked it up to a glitch. Like you said, for months, nobody really knew what it was. So technology got us into this problem. (Dave chuckles) I guess my question is can technology help us get out of the problem? And that maybe is where AI fits in? >> Yes, yes. In fact, a lot of analytics work went in to go back to the raw data that is highly dispersed from different sources, right? Assembled them to see if you can find a material trend, right? You can see lots of trends, right? Like, no, we, if humans look at things that we tend to see patterns in Clouds, right? So sometimes you need to apply statistical analysis math to be sure that what the model is seeing is real, right? And that required, well, that's one area. The second area is you know, when this, there are times when you just need to go through that tough approach to find the answer. Now, the issue comes to mind now is that humans put in the rules to decide what goes into a report that everybody sees. Now, in this case, before the change in the rules, right? But by the way, after the discovery, the authorities changed the rules and all shares, all trades of different any sizes it has to be reported. >> Right. >> Right, yeah? But the rule was applied, you know, I say earlier that shares under a hundred, trades under a hundred shares need not be reported. So, sometimes you just have to understand that reports were decided by humans and for understandable reasons. I mean, they probably didn't wanted a various reasons not to put everything in there. So that people could still read it in a reasonable amount of time. But we need to understand that rules were being put in by humans for the reports we read. And as such, there are times we just need to go back to the raw data. >> I want to ask you... >> Oh, it could be, that it's going to be tough, yeah. >> Yeah, I want to ask you a question about AI as obviously it's in your title and it's something you know a lot about but. And I'm going to make a statement, you tell me if it's on point or off point. So seems that most of the AI going on in the enterprise is modeling data science applied to, you know, troves of data. But there's also a lot of AI going on in consumer. Whether it's, you know, fingerprint technology or facial recognition or natural language processing. Well, two part question will the consumer market, as it has so often in the enterprise sort of inform us is sort of first part. And then, there'll be a shift from sort of modeling if you will to more, you mentioned the autonomous vehicles, more AI inferencing in real time, especially with the Edge. Could you help us understand that better? >> Yeah, this is a great question, right? There are three stages to just simplify. I mean, you know, it's probably more sophisticated than that. But let's just simplify that three stages, right? To building an AI system that ultimately can predict, make a prediction, right? Or to assist you in decision-making. I have an outcome. So you start with the data, massive amounts of data that you have to decide what to feed the machine with. So you feed the machine with this massive chunk of data, and the machine starts to evolve a model based on all the data it's seeing. It starts to evolve, right? To a point that using a test set of data that you have separately kept aside that you know the answer for. Then you test the model, you know? After you've trained it with all that data to see whether its prediction accuracy is high enough. And once you are satisfied with it, you then deploy the model to make the decision. And that's the inference, right? So a lot of times, depending on what we are focusing on, we in data science are, are we working hard on assembling the right data to feed the machine with? That's the data preparation organization work. And then after which you build your models you have to pick the right models for the decisions and prediction you need to make. You pick the right models. And then you start feeding the data with it. Sometimes you pick one model and a prediction isn't that robust. It is good, but then it is not consistent, right? Now what you do is you try another model. So sometimes it gets keep trying different models until you get the right kind, yeah? That gives you a good robust decision-making and prediction. Now, after which, if it's tested well, QA, you will then take that model and deploy it at the Edge. Yeah, and then at the Edge is essentially just looking at new data, applying it to the model that you have trained. And then that model will give you a prediction or a decision, right? So it is these three stages, yeah. But more and more, your question reminds me that more and more people are thinking as the Edge become more and more powerful. Can you also do learning at the Edge? >> Right. >> That's the reason why we spoke about Swarm Learning the last time. Learning at the Edge as a Swarm, right? Because maybe individually, they may not have enough power to do so. But as a Swarm, they may. >> Is that learning from the Edge or learning at the Edge? In other words, is that... >> Yes. >> Yeah. You do understand my question. >> Yes. >> Yeah. (Dave chuckles) >> That's a great question. That's a great question, right? So the quick answer is learning at the Edge, right? And also from the Edge, but the main goal, right? The goal is to learn at the Edge so that you don't have to move the data that Edge sees first back to the Cloud or the Call to do the learning. Because that would be the reason, one of the main reasons why you want to learn at the Edge. Right? So that you don't need to have to send all that data back and assemble it back from all the different Edge devices. Assemble it back to the Cloud Site to do the learning, right? Some on you can learn it and keep the data at the Edge and learn at that point, yeah. >> And then maybe only selectively send. >> Yeah. >> The autonomous vehicle, example you gave is great. 'Cause maybe they're, you know, there may be only persisting. They're not persisting data that is an inclement weather, or when a deer runs across the front. And then maybe they do that and then they send that smaller data setback and maybe that's where it's modeling done but the rest can be done at the Edge. It's a new world that's coming through. Let me ask you a question. Is there a limit to what data should be collected and how it should be collected? >> That's a great question again, yeah. Well, today full of these insightful questions. (Dr. Eng chuckles) That actually touches on the the second challenge, right? How do we, in order to thrive in this new age of insight? The second challenge is our future challenge, right? What do we do for our future? And in there is the statement we make is we have to focus on collecting data strategically for the future of our enterprise. And within that, I talked about what to collect, right? When to organize it when you collect? And then where will your data be going forward that you are collecting from? So what, when, and where? For what data to collect? That was the question you asked, it's a question that different industries have to ask themselves because it will vary, right? Let me give you the, you use the autonomous car example. Let me use that. And we do have this customer collecting massive amounts of data. You know, we're talking about 10 petabytes a day from a fleet of their cars. And these are not production autonomous cars, right? These are training autonomous cars, collecting data so they can train and eventually deploy commercial cars, right? Also this data collection cars, they collect 10, as a fleet of them collect 10 petabytes a day. And then when they came to us, building a storage system you know, to store all of that data, they realized they don't want to afford to store all of it. Now here comes the dilemma, right? What should I, after I spent so much effort building all this cars and sensors and collecting data, I've now decide what to delete. That's a dilemma, right? Now in working with them on this process of trimming down what they collected, you know, I'm constantly reminded of the 60s and 70s, right? To remind myself 60s and 70s, we called a large part of our DNA, junk DNA. >> Yeah. (Dave chuckles) >> Ah! Today, we realized that a large part of that what we call junk has function as valuable function. They are not genes but they regulate the function of genes. You know? So what's junk in yesterday could be valuable today. Or what's junk today could be valuable tomorrow, right? So, there's this tension going on, right? Between you deciding not wanting to afford to store everything that you can get your hands on. But on the other hand, you worry, you ignore the wrong ones, right? You can see this tension in our customers, right? And then it depends on industry here, right? In healthcare they say, I have no choice. I want it all, right? Oh, one very insightful point brought up by one healthcare provider that really touched me was you know, we don't only care. Of course we care a lot. We care a lot about the people we are caring for, right? But who also care for the people we are not caring for? How do we find them? >> Uh-huh. >> Right, and that definitely, they did not just need to collect data that they have with from their patients. They also need to reach out, right? To outside data so that they can figure out who they are not caring for, right? So they want it all. So I asked them, so what do you do with funding if you want it all? They say they have no choice but to figure out a way to fund it and perhaps monetization of what they have now is the way to come around and fund that. Of course, they also come back to us rightfully, that you know we have to then work out a way to help them build a system, you know? So that's healthcare, right? And if you go to other industries like banking, they say they can afford to keep them all. >> Yeah. >> But they are regulated, seemed like healthcare, they are regulated as to privacy and such like. So many examples different industries having different needs but different approaches to what they collect. But there is this constant tension between you perhaps deciding not wanting to fund all of that, all that you can install, right? But on the other hand, you know if you kind of don't want to afford it and decide not to start some. Maybe those some become highly valuable in the future, right? (Dr. Eng chuckles) You worry. >> Well, we can make some assumptions about the future. Can't we? I mean, we know there's going to be a lot more data than we've ever seen before. We know that. We know, well, not withstanding supply constraints and things like NAND. We know the prices of storage is going to continue to decline. We also know and not a lot of people are really talking about this, but the processing power, but the says, Moore's law is dead. Okay, it's waning, but the processing power when you combine the CPUs and NPUs, and GPUs and accelerators and so forth actually is increasing. And so when you think about these use cases at the Edge you're going to have much more processing power. You're going to have cheaper storage and it's going to be less expensive processing. And so as an AI practitioner, what can you do with that? >> Yeah, it's a highly, again, another insightful question that we touched on our Keynote. And that goes up to the why, uh, to the where? Where will your data be? Right? We have one estimate that says that by next year there will be 55 billion connected devices out there, right? 55 billion, right? What's the population of the world? Well, of the other 10 billion? But this thing is 55 billion. (Dave chuckles) Right? And many of them, most of them can collect data. So what do you do? Right? So the amount of data that's going to come in, it's going to way exceed, right? Drop in storage costs are increasing compute power. >> Right. >> Right. So what's the answer, right? So the answer must be knowing that we don't, and even a drop in price and increase in bandwidth, it will overwhelm the, 5G, it will overwhelm 5G, right? Given the amount of 55 billion of them collecting. So the answer must be that there needs to be a balance between you needing to bring all of that data from the 55 billion devices of the data back to a central, as a bunch of central cost. Because you may not be able to afford to do that. Firstly bandwidth, even with 5G and as the, when you'll still be too expensive given the number of devices out there. You know given storage costs dropping is still be too expensive to try and install them all. So the answer must be to start, at least to mitigate from to, some leave most a lot of the data out there, right? And only send back the pertinent ones, as you said before. But then if you did that then how are we going to do machine learning at the Core and the Cloud Site, if you don't have all the data? You want rich data to train with, right? Sometimes you want to mix up the positive type data and the negative type data. So you can train the machine in a more balanced way. So the answer must be eventually, right? As we move forward with these huge number of devices all at the Edge to do machine learning at the Edge. Today we don't even have power, right? The Edge typically is characterized by a lower energy capability and therefore lower compute power. But soon, you know? Even with low energy, they can do more with compute power improving in energy efficiency, right? So learning at the Edge, today we do inference at the Edge. So we data, model, deploy and you do inference there is. That's what we do today. But more and more, I believe given a massive amount of data at the Edge, you have to start doing machine learning at the Edge. And when you don't have enough power then you aggregate multiple devices, compute power into a Swarm and learn as a Swarm, yeah. >> Oh, interesting. So now of course, if I were sitting and fly on the wall and the HPE board meeting I said, okay, HPE is a leading provider of compute. How do you take advantage of that? I mean, we're going, I know it's future but you must be thinking about that and participating in those markets. I know today you are, you have, you know, Edge line and other products. But there's, it seems to me that it's not the general purpose that we've known in the past. It's a new type of specialized computing. How are you thinking about participating in that opportunity for the customers? >> Hmm, the wall will have to have a balance, right? Where today the default, well, the more common mode is to collect the data from the Edge and train at some centralized location or number of centralized location. Going forward, given the proliferation of the Edge devices, we'll need a balance, we need both. We need capability at the Cloud Site, right? And it has to be hybrid. And then we need capability on the Edge side that we need to build systems that on one hand is an Edge adapter, right? Meaning they environmentally adapted because the Edge differently are on it, a lot of times on the outside. They need to be packaging adapted and also power adapted, right? Because typically many of these devices are battery powered. Right? So you have to build systems that adapts to it. But at the same time, they must not be custom. That's my belief. It must be using standard processes and standard operating system so that they can run a rich set of applications. So yes, that's also the insight for that Antonio announced in 2018. For the next four years from 2018, right? $4 billion invested to strengthen our Edge portfolio. >> Uh-huh. >> Edge product lines. >> Right. >> Uh-huh, Edge solutions. >> I could, Doctor Goh, I could go on for hours with you. You're just such a great guest. Let's close. What are you most excited about in the future of, certainly HPE, but the industry in general? >> Yeah, I think the excitement is the customers, right? The diversity of customers and the diversity in the way they have approached different problems of data strategy. So the excitement is around data strategy, right? Just like, you know, the statement made for us was so was profound, right? And Antonio said, we are in the age of insight powered by data. That's the first line, right? The line that comes after that is as such we are becoming more and more data centric with data that currency. Now the next step is even more profound. That is, you know, we are going as far as saying that, you know, data should not be treated as cost anymore. No, right? But instead as an investment in a new asset class called data with value on our balance sheet. This is a step change, right? Right, in thinking that is going to change the way we look at data, the way we value it. So that's a statement. (Dr. Eng chuckles) This is the exciting thing, because for me a CTO of AI, right? A machine is only as intelligent as the data you feed it with. Data is a source of the machine learning to be intelligent. Right? (Dr. Eng chuckles) So, that's why when the people start to value data, right? And say that it is an investment when we collect it it is very positive for AI. Because an AI system gets intelligent, get more intelligence because it has huge amounts of data and a diversity of data. >> Yeah. >> So it'd be great, if the community values data. >> Well, you certainly see it in the valuations of many companies these days. And I think increasingly you see it on the income statement. You know data products and people monetizing data services. And yeah, maybe eventually you'll see it in the balance sheet. I know Doug Laney, when he was at Gartner Group, wrote a book about this and a lot of people are thinking about it. That's a big change, isn't it? >> Yeah, yeah. >> Dr. Goh... (Dave chuckles) >> The question is the process and methods in valuation. Right? >> Yeah, right. >> But I believe we will get there. We need to get started. And then we'll get there. I believe, yeah. >> Doctor Goh, it's always my pleasure. >> And then the AI will benefit greatly from it. >> Oh, yeah, no doubt. People will better understand how to align, you know some of these technology investments. Dr. Goh, great to see you again. Thanks so much for coming back in theCUBE. It's been a real pleasure. >> Yes, a system is only as smart as the data you feed it with. (Dave chuckles) (Dr. Eng laughs) >> Excellent. We'll leave it there. Thank you for spending some time with us and keep it right there for more great interviews from HPE Discover 21. This is Dave Vellante for theCUBE, the leader in Enterprise Tech Coverage. We'll be right back. (upbeat music)

(upbeat music) >> Welcome back to the Quantcast Industry Summit on the demise of third-party cookies, The Cookie Conundrum, A Recipe for Success. We're here with Peter Day, the CTO, Quantcast and Shruti Koparkar, Head of Product Marketing Quancast. Thanks for coming on. Talk about the changing advertising landscape. >> Thanks for having us. >> Thank you for having us. >> So we've been hearing the story out to the big players, want to keep the data, make that centralized, control all the leverage, and then you've got the other end. You've got the open internet that still wants to be free and valuable for everyone. What's what are you guys doing to solve this problem? Because if cookies go away, what's going to happen there? How do people track things? You guys are in this business? First question, what is Quancast strategy to adapt to third-party cookies going away? What's going to be the answer? >> Yeah, so very rightly said, John. The mission, the Quancast mission is to champion of free and open internet. And with that in mind, our approach to this a world without third party cookies is really grounded in three fundamental things. First is industry standards. We think it's really important to participate and to work with organizations who are defining the standards that will guide the future of advertising. So with that in mind we've been participating with IAB Tech Lab, We've been part of their project, we are same thing with Prebid, who's kind of trying to figure out the pipes of identity the ID pipes of the future. And then also is W3C which is the World Wide Web Consortium. And our engineers and our engineering team are participating in their weekly meetings, trying to figure out what's happening with the browsers and keeping up with the progress there on things such as Google's FLoC. The second sort of thing is interoperability. As you've mentioned that a lots of different ID solutions that are emerging. You have UID 2.0, you have LiveRamp, you have Google's FLoC, and there will be more, there are more, and they will continue to be more. We really think it is important to build a platform that can ingest all of these signals. And so that's what we've done. The reason really is to meet our customers where they are at. Today our customers use multiple Data Management Platforms, DMPs. And that's why we support multiple of those. This is not going to be much different than that. We have to meet our customers where we are, or where they are at. And then finally, of course, which is at the very heart of who Quancast is, is innovation. As you can imagine being able to take all of these multiple signals in, including the IDs and the cohorts, but also others like contextual first party consent is becoming more and more important. And then there are many other signals like time, language, geolocation. So all of these signals can help us understand user behavior, intent and interests. In absence of third party cookies. However there's something to note about these. They're very raw, they're complex, they're messy, all of these different signals. They are changing all the time, their real time. And those incomplete in information isolation, just one of these signals can not help you build up true and complete picture. So what you really need is a technology like AI and Machine Learning, to really bring all of these signals together, combine them statistically, and get an understanding of user behavior intent and interest, and then act on it. Be it in terms of providing audience insights, or responding to bid requests and so on and so forth. So those are sort of the three fundamentals that our approach is grounded in which is industry standards, interoperability, and innovation. And you know, you have Peter here >> Yeah. who is the expert so you can dive much deeper into it. >> So Peter is CTO. You've got to tell us, how is this going to actually work? What are you guys doing from a technology standpoint to help with data-driven advertising and a third-party cookieless world? >> Well, we've been this is not a shock. You know, I think anyone who's been close to this space has known that the third party cookie has been reducing in quality in terms of its pervasiveness and its longevity for many years now. And the kind of death knell is really Google Chrome, making the changes that, they're going to be making. So we've been embarrassing in this space for many years and we've had to make a number of hugely diverse investments. So one of them is in how to, as a marketer how do I tell it my marketing still working in a world without (indistinct). The majority of marketers, completely relying on third party cookies today. It's tell them if their marketing is working or not. And so we've had to invest heavily and statistical techniques, which are closer to kind of echo metric models that marketers are used to have things like out of home advertising. It's going to be establishing whether their advertising is working or not in a digital environment. And actually this as with often the case in these kind of times of massive disruption, there's always opportunity to make things better. And we really think that's true. And you know, digital measurement is often mistaken precision for accuracy and there's a real opportunity to kind of see the wood for the trees if you'd like. And start to come up with better methods of measuring the effectiveness of advertising without third party cookies. And we've had to make countless other investments in areas like contextual modeling, and targeting that third-party cookies and connecting directly to publishers rather than going through this kind of loom escape that's going to tied together third party cookies. So I could, if I was to enumerate all the investments we've made I think it would be here till midnight, but we've had to make a number of investments over a number years. And that level investments only increasing at the moment. >> Peter, on that contextual, can you just double click on that and tell us more? >> Yeah, I mean, contextual it is, unfortunately when I think this is really poorly defined. It can mean everything from a publisher saying, Hey trust us this page is about SUV's, it's a what's possible now. And it's only really been possible the last couple of years which is to build statistical models of the entire internet based on the content that people are actually consuming. And this type of technology requires massive data processing capabilities, it's able to take advantage of the latest innovations in areas like natural language processing. And really gives computers, that kind of much deeper and richer understanding of the internet, which ultimately makes it possible to kind of organize the internet, in terms of the types of content of pages. So this type of technology has only been possible for the last few years. And, but we've been using contextual signals since our inception. Had always been massively predictive in terms of audience behaviors, in terms of where advertising is likely to work. And so we've been very fortunate to keep that investment going and take advantage of many of these innovations that are happening in academia and in kind of an adjacent areas >> On the AI and Machine Learning aspect. That seems to be a great differentiator in this day and age for getting the most out of the data. How is machine learning and AI factoring into your platform? >> I think it's how we've always operated, right from our inception. When we started as a measurement company. The way that we were giving our customers at the time we were just publishers, just the publisher side of our business. Insights into who their audience was, which was using Machine Learning techniques. And that's never really changed. The foundation of our platform has always been Machine Learning from before it was cool. A lot of our, kind of a lot of our co-teams have backgrounds in Machine Learning, and the PhDs in statistics and Machine Learning. And that really drives our decision-making. I mean, data is only useful if you can make sense of it and if you can organize it, and if you can take action on it, and to do that at this kind of scale it's absolutely necessary to use Machine Learning technology. >> So you mentioned contextual also, you know, in advertising we have everyone knows and that world that you got the contextual and behavioral dynamics. The behavior that's kind of generally can everyone's believing is happening. The consensus is undeniable is that, people are wanting to expect an environment where there's trust, there's truth, but also they want to be locked in. They don't want to get walled into a walled garden. Nobody wants to be in a wall garden. They want to be free to pop around and visit sites. It's more horizontal scalability than ever before yet. The bigger players are becoming walled garden vertical platforms. So with future of AI, the experience is going to come from this data. So the behaviors out there. How do you get >> Yeah. that contextual relevance and provide the horizontal scale that users expect? >> Yeah, I think it's a really good point and we're definitely at this kind of tipping point, we think in the broader industry. I think, you know, every publisher, right? We're really blessed to work with the biggest publishers in the world. All the way through to my mom's blog, right? So we get to hear the perspectives of the publishers at every scale. And they consistently tell us the same thing. Which is they want some more directly connect to consumers. They don't want to be tied into these walled gardens, which dictate how they must present their content. And in some cases what content they're allowed to present. And so, you know, our job as a company is to really provide level the playing field a little bit. Provide them the same capabilities they're only used to in the walled gardens, but let, give them more choice. In terms of how they structure their content, how they organize their content, how they organize their audiences, but make sure that they can fund that effectively. By making their audiences and their environments discoverable by marketers, measurable by marketers, and connect them as directly as possible to make that kind of ad funded economic model, as effective in the open internet as it is in social. And so a lot of the investments we've made over recent years have been really to kind of realize that vision, which is, it should be as easy for a marketer to be able to understand people on the open internet, as it is in social media. It should be as effective for them to reach people in that environment, is really high quality content as it is on Facebook. And so we've invested a lot of our R&D dollars in making that true. And we're now live with the Quantcast Platform which does exactly that. And as third party cookies go away, it only kind of exaggerate all kind of further emphasizes the need for direct connections between brands and publishers. And so we just want to build a technology that helps make that true, and gives the kind of technology to these marketers and publishers to connect, and to deliver great experiences without relying on these kind of walled gardens. >> Yeah. The direct to consumer, direct to audience is a new trend. You're seeing it everywhere. How do you guys support this new kind of signaling from for that's happening in these new world? How do you ingest the content, ingest this consent signaling? >> We were really fortunate to have an amazing an amazing R&D team. And, you know, we've had to do all sorts to make this, you know, to realize our vision. This has meant things like we, you know we have crawlers which stand the entire internet at this point, extract the content of the pages, and kind of make sense of it, and organize it. And organize it for publishers so that they can understand how their audiences overlap with potentially their competitors or collaborators, but more importantly, organize it for marketers. So they can understand what kind of high-impact opportunities are there for them there. So, you know, we've had to build a lot of technology. We've had to build analytics engines which can get answers back in seconds, so that, you know marketers and publishers can kind of interact with it with their own data and make sense of it and present it in a way that is compelling and then help them drive their strategy as well as their execution. We've had to invest in areas like consent management. Because we believe that a free and open internet is absolutely reliant on trust. And therefore we spend a lot of our time thinking about how do we make it easy for end-users to understand who has access to that data and easy friendly and users to be able to opt out. And as a result of that, we've now got the world's most widely adopted consent management platform. So it's hard to tackle one of these problems without tackling all of them. And we're fortunate enough to have had a large enough R&D budget over the last four or five years, make a number of investments, everything from consent and identity, through to contextual signals, through to measurement technologies, which really bring advertisers and publishers closer together. >> Great insight there. Shruti last word for you. What's the customer view here as you bring these new capabilities of the platform. What's what are you guys seeing as the highlight from a platform perspective? >> So the initial response that we've seen from our customers has been very encouraging. Both on the publisher side, as well as the marketer side. I think, you know, one of the things we hear quite a lot is you guys are at least putting forth a solution and action solution for us to test. Peter mentioned measurement. That really is where we started because you cannot optimize what you cannot measure. So that is where his team has started. And we have some measurement, very very initial capabilities still in alpha, but they are available in the platform for marketers to test out today. So the initial response has been very encouraging. People want to engage with us. Of course, our, you know, our fundamental value proposition which is that the Quantcast platform was never built to be reliant on third party data, these stale segments. Like we operate we've always operated on real time live data. The second thing is our premium publisher relationships. We have had the privilege of working like Peter served with some of the biggest publishers but we also have a very wide footprint. We have first party tags across over a hundred million plus web and mobile destinations. And, you know, as you must've heard like that sort of first party footprint, is going to come in really handy in a world without third party cookies. We are encouraging all of our customers, publishers and marketers to grow their first party data. And so that's something that's a strong point that customers love about us and lean into it quite a bit. So, yeah, the initial response has been great. Of course it doesn't hurt that we've made all these R&D investments. We can talk about consent, and, you know, I often say that consent it sounds simple, but it isn't, there's a lot of technology involved. But there's lots of legal work involved as it as well. We have a very strong legal team who has expertise built in. So yeah, a very good response initially. >> Democratization, everyone's a publisher, everyone's a media company. They have to think about being a platform. You guys provide that. So congratulations Peter, thanks for dropping the gems there. Shruti thanks for sharing the product highlights. Thanks for your time. >> Thank you. >> Okay, this is the Quancast Industry Summit on the demise of third-party cookies and what's next The Cookie Conundrum, the Recipe for success with Quancast I'm John Berger with theCUBE. Thanks for watching. (upbeat music)

>> Welcome to this IBM Watson Health client conversation. And we're probing the dynamics of the relationship between IBM and it's clients. We're going to look back at some of the challenges of 2020 and look forward to, you know, present year's priorities. We'll also touch on the future state of healthcare. My name is Dave Vellante. I'll be your host and I'm from theCUBE. And with me are Doctor Alex Towbin, who's Associate Chief Clinical Operations and Informatics at Cincinnati ChilDoctoren's Hospital and John Chrisman of course from IBM Watson health. Welcome gentlemen, Good to see you. Thanks for coming on. >> Thanks for having us. >> Yeah, thanks for having me. >> Yeah I know from talking to many clients around the world, of course virtually this past year, 11 months or so that relationships with technology partners they've been critical over during the pandemic to really help folks get through that. Not that we're through it yet but, we're still through the year now, there's I'm talking professionally and personally and Doctor Towbin, I wonder if you could please talk about 2020 and what role the IBM partnership played in helping Cincinnati children's, you know press on in the face of incredible challenges? >> Yeah, I think our story of 2020 really starts before the pandemic and we were fortunate to be able to plan a disaster and do disaster drill scenarios. And so, as we were going through those disaster drill scenarios, we were trying to build a solution that would enable us to be able to work if all of our systems were down and we worked with IBM Watson Health to design that solution to implement it, it involves using other solutions from our primary one. And we performed that disaster drill in the late January, early February timeframe of 2020. And while that drill had nothing to do with COVID it got us thinking about how to deal with a disaster, how to prepare for a disaster. And so we've just completed that and COVID was coming on the horizon. I'm starting to hear about it coming into the U.S for the first time. And we took that very seriously on our department. And so, because we had prepared for this this disaster drill had gone through the entire exercise and we built out different scenarios for what could happen with COVID what would be our worst case scenarios and how we would deal with them. And so we were able to then bring that to quickly down to two options on how our department and our hospital would handle COVID and deal with that within the radiology department and like many other sites that becomes options of working from home or working in a isolated way and an and an office scenario like where I'm sitting now and we planned out both scenarios and eventually made the decision. Our decision at that point was to work in our offices. We're fortunate to have private offices where we can retreat to and something like that. And so then our relationship with IBM was helpful and that we needed to secure more pieces of hardware. And so even though IBM is our PACS vendor and our enterprise imaging vendor, they also help us to secure the high resolution monitors that are needed. And we needed a large influx of those during the pandemic and IBM was able to help us to get those. >> Wow! So yeah you were able to sort of test your organization resilience before the pandemic. I mean, John, that's quite an accomplishment for last year. I'm sure there are many others. I wonder if one of you could pick it up from here and bring your perspectives into it and, you know maybe ask any questions that you would like to ask them. >> Yeah, sure, Doctor Towbin, that's great that we were able to help you with the hardware and procure things. So I'm just curious before the pandemic how many of the radiologists ever got to read from home, was that a luxury back then? And then post pandemic, are you guys going to shift to how many are on-site versus remote? >> Yeah, so we have a couple of scenarios. We've had talk about it both from our PACS perspective as well as from our VNA enterprise imaging perspective from PACS perspective we always designed our solution to be able to work from a home machine. Our machines, people would access that through a hospital-based VPN. So they would log in directly to VPN and then access the PACS that way. And that worked well. And many of our radiologists do that particularly when they're on call works best for our neuroradiologist who are on call a little bit more frequently. And so they do read from home in that scenario. With enterprise imaging and are used to the enterprise viewer and iConnect access. We always wanted that solution to work over the internet. And so it's set up securely through the internet but not through the VPN. And we have radiologists use that as a way to view studies from home, even not from home, so it can be over one of their mobile devices, such as an iPad and could be at least reviewing studies then. We, for the most part for our radiologist in the hospital that's why we made the decision to stay in the hospital. At COVID time, we have such a strong teaching mission in our department in such a commitment to the education of our trainees. We think that hospital being in the hospital is our best way to do that, it's so hard. We find to do it over something like zoom or other sharing screen-sharing technology. So we've stayed in and I think we'll continue to stay in. There will be some of those needs from a call perspective for example, reading from home, and that will continue. >> And then what's your success been with this with the technology and the efficiency of reading from home? Do you feel like you're just as efficient when you're at home versus onsite? >> The technology is okay. The, our challenges when we're reading from the PACS which is the preferred way to do it rather than the enterprise archive, the challenge is we have to use the PACS So we have to be connected through VPN which limits our bandwidth and that makes it a little bit slower to read. And also the dictation software is a little bit slower when we're doing it. So moving study to study that rapid turnover doesn't happen but we have other ways to make, to accelerate the workflow. We cashed studies through the worklist. So they're on the machine, they load a little bit more rapidly and that works pretty well. So not quite as fast, but not terrible. >> We appreciate your partnership. I know it's been going on 10 years. I think you guys have a policy that you have to look at the market again every 10 years. So what do you think of how the market's changed and how we've evolved with the VNA and with the zero footprint viewers? A lot of that wasn't available when you initially signed up with Amicas years ago, so. >> Yeah, we signed up so we've been on this platform and then, you know now the IBM family starting in 2010, so it's now now 11 years that we're, we've been on as this version of the PACS and about eight, seven or eight years from the iConnect platform. And through that, we've seen quite an evolution. We were one of the first Amicas clients to be on version six and one of the largest enterprises. And that went from, we had trouble at the launch of that product. We've worked very closely with Amicas then to merge. And now IBM from the development side, as well as the support side to have really what we think is a great product that works very well for us and drives our entire workflow all the operations of our department. And so we've really relished that relationship with now IBM. And it's been a very good one, and it's allowed us to do the things like having disaster drill planning that we talked about earlier as far as where I see the market I think PACS in particular is on the verge of the 3.0 version as a marketplace. So PACSS 1 one was about building the packs, I think, and and having electronic imaging digital imaging, PACS 2.0 is more of web-based technology, getting it out of those private networks within a radiology department. And so giving a little bit more to the masses and 3.0 is going to be more about incorporating machine learning. I really see that as the way the market's going to go and to where I think we're at the infancy of that part of the market now about how do you bring books in for machine learning algorithms to help to drive workflow or to drive some image interpretation or analysis, as far as enterprise imaging, we're on the cusp of a lot there as well. So we've been really driving deep with enterprise imaging leading nationally enterprise imaging and I have a role in the MSAM Enterprise Imaging Community. And through all of that work we've been trying to tackle works well from enterprise imaging point of view the challenges are outside of radiology, outside of cardiology and the places where we're trying to deal with medical photos, the photographs taken with a smart device or a digital camera of another type, and trying to have workflow that makes sense for providers not in those specialty to that don't have tools like a DICOM modality workloads store these giant million-dollar MRI scanners that do all the work for you, but dealing with off the shelf, consumer electronics. So making sure the workflow works for them, trying to tie reports in trying to standardize the language around it, so how do we tag photos correctly so that we can identify relevancy all of those things we're working through and are not yet standard within our, within the industry. And so we're doing a lot there and trying and seeing the products in the marketplace continuing to evolve around that on the viewer side, there's really been a big emergence as you mentioned about the zero footprint viewers or the enterprise viewer, allowing easy access easy viewing of images throughout the enterprise of all types of imaging through obtained in the enterprise and will eventually incorporate video pathology. The market is also trying to figure out if there can be one type of viewer that does them all that and so that type of universal viewer, a viewer that cardiologists can use the same as a radiologist the same as a dermatologist, same as a pathologist we're all I think a long way away from that. But that's the Marcus trying to figure those two things out. >> Yeah, I agree with you. I agree with your assessment. You talked about the non DICOM areas, and I know you've you've partnered with us, with ImageMover and you've got some mobile device capture taking place. And you're looking to expand that more to the enterprise. Are you also starting to use the XDS registry? That's part of the iConnect enterprise archive, or as well as wrapping things in DICOM, or are you going to stick with just wrapping things in DICOM? >> Yeah, so far we've been very bunched pro DICOM and using that throughout the enterprise. And we've always thought, or maybe we've evolved to think that there is going to be a role for XDS are I think our early concerns with XDS are the lack of other institutions using it. And so, even though it's designed for portability if no one else reads it, it's not portable. If no one else is using that. But as we move more and more into other specialties things like dermatology, ophthalmology, some of the labeling that's needed in those images and the uses, the secondary uses of those images for education, for publication, for dermatology workflow or ophthalmology workflow, needs to get back to that native file and the DICOM wrap may not make sense for them. And so we've been actively talking about switching towards XDS for some of the non DICOM, such as dermatology. We've not yet done that though. >> Given the era children's hospital has the impact on your patient load, then similar to what regular adult hospitals are, or have you guys had a pretty steady number of studies over the last year? >> In relay through the pandemic, we've had, it has been decreased, but children fortunately have not been as severely affected as adults. There is definitely disease in children and we see a fair amount of that. There are some unique things that happen in kids but that fortunately rare. So there's this severe inflammatory response that kids can get and can cause them to get very sick but it is quite rare. Our volumes are, I think I'm not I think our volumes are stable and our advanced imaging things like CT, MRI, nuclear medicine, they're really most decreased in radiography. And we see some weird patterns, inpatient volumes are relatively stable. So our single view chest x-rays, for example, have been stable. ER, visits are way down because people are either wearing masks, isolating or not wanting to come to the ER. So they're not getting sick with things like the flu or or even common colds or pneumonias. And so they're not coming into the ER as much. So our two view x-rays have dropped by like 30%. And so we were looking at this just yesterday. If you follow the graphs for the two we saw a dip of both around March, but essentially the one view chest were a straight line and the two view chest were a straight line and in March dropped 30 to 50% and then stayed at that lower level. Other x-rays are on the, stay at that low level side. >> Thanks, I know in 2021 we've got a big upgrade coming with you guys soon and you're going to stay in our standalone mode. I understand what the PACSS and not integrate deeply to the VNA. And so you'll have a couple more layers of storage there but can you talk about your excitement about going to 8.1 and what you're looking forward to based on your testimony. >> Yeah we're actually in, we're upgrading as we're talking which is interesting, but it's a good time for talking. I'm not doing that part of the work. And so our testing has worked well. I think we're, we are excited. We, you know, we've been on the product as I mentioned for over 10 years now. And for many of those years we were among the first, at each version. Now we're way behind. And we want to get back up to the latest and greatest and we want to stay cutting edge. There've been a lot of reasons why we haven't moved up to that level, but we do. We're very careful in our testing and we needed a version that would work for us. And there were things about previous versions that just didn't and as you mentioned, we're staying in that standalone mode. We very much want to be on the integrated mode in our future because enterprise imaging is so important and understanding how the comparisons fit in with the comparison in dermatology or chest wall deformity clinic, or other areas how those fit into the radiology story is important and it helped me as a radiologist be a better radiologist to see all those other pictures. So I want them there but we have to have the workflow, right. And so that's the part that we're still working towards and making sure that that fits so we will get there. It'll probably be in the next year or two to get to that immigrating mode. >> As you, look at the number of vendors you have I think you guys prefer to have less vendor partners than than more I know in the cardiology area you guys do some cardiology work. What has been the history or any, any look to the future of that related to enterprise imaging? Do you look to incorporate more of that into a singular solution? >> Cardiology is entirely part of our enterprise imaging solution. We all the cardiology amendments go to our vendor neutral archive on the iConnect platform. All of them are viewed across the enterprise using our enterprise viewer. They have their unique specialty viewer which is, you know, fine. I'm a believer that specialty, different specialties, deserve to have their specialty viewers to do theirs specialty reads. And at this point I don't think the universal viewer works or makes sense until we have that. And so all the cardiology images are there. They're all of our historical cardiology images are migrated and part of our enterprise solution. So they're part of the entire reference the challenge is they're just not all in PACSS. And so that's where, you know, an example, great example, why we need to get to this to the integrated mode to be able to see those. And the reason we didn't do that is the cardiology archive is so large to add a storage to the PACS archive. Didn't make sense if we knew we were going to be in an integrated mode eventually, and we didn't want to double our PACS storage and then get rid of it a couple of years later. >> So once you're on a new version of merge PACS and you're beyond this, what are your other goals in 2021? Are you looking to bring AI in? Are you using anybody else's AI currently? >> Yeah, we do have AI clinical it's phone age, so it's not not a ton of things but we've been using it clinically, fully integrated, it launches. When I open a study, when I opened a bone age study impacts it launches we have a bone age calculator as well that we've been using for almost two decades now. And so that we have to use that still but launching that automatically includes the patient's sex and birth date, which are keys for determining bone age, and all that information is there automatically. But at the same time, the images are sent to the machine learning algorithm. And in the background the machine determines a bone age that in the background it sends it straight to our dictation system and it's there when we opened the study. And so if I agree with that I signed the report and we're done. If I disagree, I copy it from my calculator and put it in until it takes just a couple of clicks. We are working on expanding. We've done a lot of research in artificial intelligence and the department. And so we've been things are sort of in the middle of translation of moving it from the research pure research realm to the clinical realm, something we're actively working on trying to get them in. Others are a little bit more difficult. >> That's the question on that John, Doctor, when you talk about injecting, you know machine intelligence into the equation. >> Yeah. >> What, how do you sort of value that? Does that give you automation? Does it improve your quality? Does it speed the outcome and maybe it's all of those but how do you sort of evaluate the impact to your organisation? >> I there's a lot of ways you can do it. And you touched on one of my favorite one of my favorite talking points, in a lot of what we've been doing and early machine learning is around image interpretation helping me as a radiologist to see a finding. Unfortunately, most of the things are fairly simple tasks that it's asking us to do. Like, is there a broken bone? Yes or no, I'm not trying to sound self-congratulatory or anything, but I'm really good at finding broken bones. I get, I've been doing it for a long time and, and radio, you know so machines doing that, they're going to perform as well as I can perform, you know, and that's the goal. Maybe they'll perform a little bit better maybe a little bit worse but we're talking tiny increments there they're really to me, not much value of that it's not something I would want. I don't value that at a time where I think machine learning can have real value around more on some of the things that you mentioned. So can it make me more efficient? Can it do the things that are so annoying that and they'd take, they're so tedious that they make me unhappy. A lot of little measurements for example are like that an example. So in a patient with cancer, we measure a little tumors everywhere and that's really important for their care, but it's tedious and so if a machine could do that in an automated way and I checked it that, you know, patient when because he or she can get that good quality care and I have a, you know, a workflow efficiency game. So that one's important. Another one that would be important is if the machine can see things I can't see. So I'm really good at finding fractures. I'm not really good at understanding what all the pixels mean and, you know in that same patient with cancer, oh what do all the pixels mean in that tumor? I know it's a tumor. I can see the tumor, I can say it's a tumor but sometimes those pixels have a lot of information in them and may give us prognosis, you know, say that this patient may, maybe this patient will do well with this specific type of chemotherapy or a specific or has a better prognosis with one with one drug compared to another. Those are things that we can't usually pick out. You know, it's beyond the level of that are I can perceive that one is really the cutting edge of machine learning. We're not there yet and then the other thing are things that, you know just the behind the scenes stuff that I don't necessarily need to be doing, or, you know so it's the non interpretive artificial intelligence. >> Dave: Right. >> And that's what I've been also trying to push. So an example of when the algorithms that we've been developing here we check airways. And this is a little bit historical in our department, but we want to make sure we're not missing a severe airway infection. That can be deadly, it's incredibly rare. Vaccines have made it go away completely but we still check airways. And so what happens is the technologist takes the x-ray. They come in to ask us if it's okay, we are interrupted from what we're doing. We open up the study, say yes or no. Okay, not okay, if it's not okay they go back, take another study. Then come back to us again and say, is it okay or not? And we repeat this a couple of times it takes them time that they don't need to spend and takes us time. And so we have, we've built an algorithm where the machine can check that and their machine is as good or a little bit worse than us, but give can give that feedback. >> Dave: Got it. >> The challenge is getting that feedback to the technologist quickly. And so that's, that's I think part for us to work on stuff. >> Thank you for that. So, John, we've probably got three or four minutes left. I'll let you bring it home and appreciate that Doctor Towbin >> I think one of the biggest impacts probably I knew this last year with the pandemic, Doctor Towbin is this, I know you're a big foodie. So having been to some good restaurants and dinners with the hot nurse in a house how's the pandemic affected you personally. And some of the things you like to do outside of work. >> Everything is shut down. And everything has changed. I have not left the house since March besides come to work and my family hasn't either. And so we're hardcore quarantining and staying you know, staying out and keeping it home. So we've not gone out to dinner or done much else. >> So its DoorDash and Uber Eats or just learned to cook at home. >> It's all cooking at home. We're fortunate, my wife loves to cook. My kids love to cook. I enjoy cooking, but I don't have the time as often. So we've done a lot of different are on our own experimenting. Maybe when the silver lining one of the things I've really relished about all this is all this time I get to spend with my family. And that closeness that we've been able to achieve because of being confined in our house the whole time. And so I've played get to play video games with my kids every night. We'd been on a big Fortnite Keck lately since it's been down making. So we've been playing that every night since we've watched movies a lot. And so as a family, we've, I it's something I'll look back fondly even though it's been a very difficult time but it's been an enjoyable time. >> I agree, I've enjoyed more family time this year as well, but final question is in 2021, beyond the PACS upgrade what are the top other two projects that you want to accomplish with us this year? And how can we help you? >> I think our big one is are the big projects are unexpanded enterprise imaging. And so we want to continue rolling out to other areas that will include eventually incorporating scopes, all the images from the operating room. We need to be able to get into pathology. I think the pathology is really going to be a long game. Unfortunately, I've been saying that already for 10 years and it's still probably another 10 years ago but we need to go. We can start with the gross pathology images all the pictures that we take for tumor boards and get those in before we start talking about whole slide scanning and getting in more of the more of the photographs in the institution. So we have a route ambulatory but we need inpatient and ER. >> All right one last question. What can IBM do to be a better partner for you guys? >> I think it's keep listening keep listening and keep innovating. And don't be afraid to be that innovative partner sort of thinking as the small company that startup, rather than the giant bohemoth that can sometimes happen with large companies, it's harder. It is fear to turn quickly, but being a nimble company and making quick decisions, quick innovations. >> Great, quick question. How would you grade IBM, your a tough grader? >> It depends on what I am a tough grader but it depends on what, you know as the overall corporate partnership? >> Yeah the relationship. >> I'd say it's A minus. >> Its pretty good. >> I think, I mean, I, we get a lot of love from IBM. I'm talking specifically in the imaging space. I not, maybe not, I don't know as much on the hardware side but we, yeah, we have a really good relationship. We feel like we're listened to and we're valued. >> All right, well guys, thanks so much. >> So even if it's not an A plus- >> Go ahead. >> I think there's some more to, you know, from the to keep innovating side there's little things that we just let you know we've been asking for that we don't always get but understand the company has to make business decisions not decisions on what's best for me. >> Of course got to hold that carrot out too. Well thanks guys, really appreciate your time. Great conversation. >> Yeah, thank you. >> All right and thank you for spending some time with us. You're watching client conversations with IBM Watson Health.

>> Welcome to this IBM Watson Health "Client Conversation." We're probing the dynamics of the relationships between IBM and its clients. And we're going to look back, we're going to explore the present situation and we're going to discuss the future state of healthcare. My name is Dave Vellante from theCUBE and with me are Dr. David Huelsman, who is a radiologist at TriHealth, which is a provider of healthcare in hospitals and John Kritzman who is with of course IBM Watson Health. Gentlemen welcome. Thanks so much for coming on. >> Thank you. >> Yeah, thanks for having us. >> Doctor let me say you're welcome. Let me start with you. As an analyst and a TV host in the tech industry, we often focus so much on the shiny new toy, the new widget, the new software. But when I talk to practitioners, almost to a person, they tell me that the relationship and trust are probably the most important elements of their success, in terms of a vendor relationship. And over the last year, we've relied on both personal and professional relationships to get us through some of the most challenging times any of us have ever seen. So, Dr. Huelsman, let me ask you, and thinking about the challenges you faced in 2020, what does partnership mean to you and how would you describe the relationship with IBM? >> Well, it is exactly the reason why when we started our journey on this enterprise imaging project at TriHealth, that we very early on made the decision We only wanted one vendor. We didn't want to do it piecemeal, like say get a vendor neutral archive from one organization, and the radiology viewer from another. We wanted to partner with the chosen vendor and develop that long-term relationship, where we learn from each other and we mutually benefit each other, in sort of not just have a transactional relationship, but that we share the same values. We share the same vision. And that's what stood out to us is Watson Health imagings vision, mirrored TriHealth's in what we were trying to achieve with our enterprise imaging project. >> You know, let me follow up with that if I could. A lot of times you hear the phrase, "Single throat to choke" and it's kind of a pejorative, right? It's a really negative term. And the way you just described that Dr. Huelsman is you were looking for a partnership. Yeah, sure. Maybe it was more manageable and maybe it was a sort of Singletree, but it was really about the partnership, going forward in a shared vision and really shared ownership of the outcome. Is that a fair characterization? >> Yeah, how about more positive is "One hand to shake." >> Wow, yeah, I love it. (chuckles) One hand to shake. I'm going to steal that line. That's good. I like it. Keep it positive. Okay, John, when you think about the past 12 months and I know you have history with TriHealth, and more recently have rejoined the account, but how would you kind of characterize that relationship and particularly anything you can add about the challenges of the 2020? What stands out to you? >> Yeah, I think going back to your one hand to shake or one vendor to hug all that's not allowed during COVID, but we're excited to be back working with you, I am in particular. And at the beginning of this sales process and RFP when you guys were looking for that vendor partner, we did talk to you about the journey, the journey with AI that we already had mature products on the vendor neutral archive side and all the product pieces that you were looking for. And I know you've recently went live over the last year and you've been working through, crawling through and learning to walk and starting to run, hopefully. And at some point we'll get to the end of the marathon, where you'll have all the AI pieces that you're looking for. But this journey has been eyeopening for all of us, from using consultants in the beginning, to developing different team members to help make you successful. So I think I've been tracking this from the outside looking in, and I'm happy to be back, more working direct with you this year to help ensure your longterm success. >> Yeah, that's great John. You have some history there. I'm going to probe that a little bit. So doctor, you talked about this enterprise imaging project. I presume that's part of, that's one of the vectors of this journey that you're on. What are you trying to accomplish in the sort of near term and midterm in 2021? John mentioned AI, is there a data element to this? Are there other, maybe more important pressing things? What are your main goals for 2021? >> Sure. Well, where we are, where we've started, the first step was getting all of our imaging stored consistently in the same place and in the same way. We had like many health system, as you grow, you acquire facilities, you acquire physician practices and they all have their own small packs system, different ways of storing the data. And so it becomes very unwieldy to be a large organization and try to provide a consistent manner of your physicians interacting with the data, with the imaging in the same way. And so it was a very large dissatisfier in our EMR to, oh if you wanted to see cardiovascular imaging, it's this tab. If you wanted to see radiology, it was this tab. If you wanted to see that, oh you got to go to the media tab. And so our big goal is, okay, let's get the enterprise archive. And so the Watson enterprise archive is to get all of our imaging stored in the same place, in the same way. And so that then our referring physicians and now with our patients as well, that you can view all the imaging, access it the same way and have the same tools. And so that's the initial step. And we're not even complete with that first step, that's where COVID and sort of diverting resources, but it's there, it's that foundation, it's there. And so currently we have the radiology, cardiology, orthopedics and just recently OB-GYN, all of those departments have their images stored on our Watson Enterprise Archive. So the ultimate goal was then any imaging, including not just what you typically think of radiology, but endoscopy and arthroscopy and those sort of images, or wound care images, in that any image, any picture in our organization will be stored on the archive. So that then when we have everything on that archive, it's easier to access consistently with the same tools. But it's also one of the large pieces of partnering with with Watson Health Imaging, is the whole cognitive solutions and AI piece. Is that, well now we're storing all the data in a consistent manner, you can access it in a consistent manner, well then we hope to analyze it in a consistent manner and to use machine learning, and the various protocols and algorithms that Watson Health Imaging develops, to employ those and to provide better care. >> Excellent, thank you for that. John, I wonder if you could add to that? I mean, you've probably heard this story before from other clients, as well as TriHealth, I call it EMR chaos. What can you add to this conversation? I'm particularly interested in what IBM Watson Health brings to the table. >> Sure, we've continued to work with TriHealth. And like we said earlier, you do have to walk before you can run. So a lot of this solution being put in place, was getting that archive stood up and getting all the images transferred out of the legacy systems. And I think that we're nearly done with that process. Doing some find audits, able to turn off some of the legacy systems. So the data is there for the easier to do modalities first, the radiology, the cardiology, the OB, as Dr. Huelsman mentioned and the ortho. And now it's really getting to the exciting point of really optimizing everything and then starting to bring in other ologies from the health system, trying to get everything in that single EMR view. So there was a lot of activity going on last year with optimizing the system, trying to fine tune hanging protocols, make the workflow for everybody, so that the systems are efficient. And I think we will continue on that road this year. We'll continue down further with other pieces of the solution that were not implemented yet. So there's some deeper image sharing pieces that are available. There are some pieces with mobile device image capture and video capture that can be deployed. So we look forward to working in 2021 on some of those areas, as well as the increased AI solutions. >> So Dr. Huelsman I wonder if you could double click on that. I mean if you're talking to IBM, what are the priorities that you have? What do you, what do you really need from Watson Health to get there? >> So I spoke with Daniel early last week, and sort of described it as now we have the foundation, we sort of have the skeleton and now it's time to put meat on the bones. And so what we're excited about is the upcoming patient synopsis would be the first piece of AI cognitive solutions that Watson Health Imaging provides. And it's sort of that partnership of we're not expecting it to be perfect, but is it better than we have today? There is no perfect solution, but does it improve our current workflow? And so we'll be very interested of when we go live with patients synopsis of does this help? Is this better than what we have today? And the focus then becomes partnering with Watson Health Imaging is how do we make it better for ourselves? How do we make it better for you? I think we're a large health organization and typically we're not an academic or heavy research institution, but we take care of a lot of patients. And if we can work together, I think we'll find solutions. It's really that triple aim of how to provide better care, at cheaper costs, with a better experience. And that's what we're all after. And what's your version of patient, the current version of patients synopsis, and okay does it work for us? Well, even if it does, how do we make it better? Or if it doesn't, how do we make it work? And I think if we work together, make it work for TriHealth, you can make it work at all your community-based health organizations. >> Yeah. So, John that brings me to, Dr. Huelsman mentioned a couple of things in terms of the outcomes. Lower costs, better patient experience, et cetera. I mean, generally for clients, how do you measure success? And then specifically with regard to TriHealth, what's that like? What's that part of the partnership? >> Yes, specifically with TriHealth, the measure of success will be when Dr. Huelsman is able to call and be a super reference for us, and have these tools working to his satisfaction. And when he's been able to give us great input from the customer side, to help improve the science side of it. So today he's able to launch his epic EMR in context and he has to dig through the data, looking for those valuable nuggets and with using natural language processing, when he has patients synopsis, that will all be done for him. He'll be able to pull up the study, a CT of the head for instance and he'll be able to get those nuggets of information using natural language processing that Watson services and get the valuable insights without spending five or 10 minutes interrogating the EMR. So we look forward to those benefits for him, from the data analytics side, but then we also look forward to in the future, delivering other AI for the imaging side, to help him find the slices of interest and the defects that are in that particular study. So whether that's with our partner AI solutions or as we bring care advisers to market. So we look forward to his input on those also. >> Can you comment on that Dr. Huelsman? I would imagine that you would be really looking forward to that vision that John just laid out, as well as other practitioners in your organization. Maybe you could talk about that, is that sort of within your reach? What can you tell us? >> Well, absolutely. That was sort of the shared vision and relationship that we hope for and sort of have that shared outlook is we have all this data, how do we analyze it to improve, provide better care cheaper? And there's no way to do that without you harnessing technology. And IBM has been on the cutting edge of technology for my lifetime. And so it's very exciting to have a partnership with WHI and IBM. There's a history, there's a depth. And so how do we work together to advance, because we want the same things. What impressed me was sure, radiology and AI has been in the news and been hyped and some think over-hyped, and what have you. Everyone's after that Holy grail. But it's that sense of you have the engineers that you talk to, but there is an understanding that don't design the system for the engineers, design it for the end user. Design it for the radiologist. Talk to the end user, because it can be the greatest tool in the world, but I can tell you as a radiologist, if it interrupts my workflow, if it interrupts my search pattern for looking at images, it doesn't help me and radiologists won't use it. And so just having a great algorithm won't help. It is how do you present it to the end user? How do I access it? How can I easily toggle on and off, or do I have to minimize and maximize, and log into a different system. We talked earlier is one throat to choke, or one vendor to hug, we only want one interface. Radiologists and users just want to look at their... They have the radiology viewer, they have their PACS, we look at it all day and you don't want to minimize that and bring up something else, you want to keep interacting with what you're used to. And the mouse buttons do the same thing, it's a mouse click away. And that's what the people at Watson Health Imaging that we've interacted with, they get it. They understand that's what a radiologist would want. They want to continue interacting with their PACS, not with a third vendor or another program or something else. >> I love that. That ton of outside in thinking, starting with the radiologist, back to the engineer, not the reverse. I think that's something that IBM, and I've been watching IBM for a long time, it's something that IBM has brought to the table with its deep industry expertise. I maybe have some other questions, but John I wanted to give you an opportunity. Is there anything that you would like to ask Dr. Huelsman that maybe I haven't touched on yet? >> Yeah. Being back on your account this year, what do you see as a success? What would you count as a success at the end of 2021, if we can deliver this year for you? >> The success would be say, at the end of the year, we've got the heavy hitters, all stored on the archive. Do we pick up all the little, we've got the low hanging fruit, now can we go after the line placement imaging and the arthroscopy and dioscomy, and all those smaller volume in pickups, that we truly get all of our imaging stored on that archive. And then the even larger piece is then do we start using the data on the archive with some cognitive solution? I would love to successfully implement, whether it's patient synopsis or one of the care advisors, that we start using sort of the analytics, the machine learning, some AI component that we successfully implement and maybe share good ideas with you. And sure we intend to go live with patient synopsis next month. I would love it by the end of the year, if the version that we're using patients synopsis and we find it helpful. And the version we use is better than what we went live with next month, because of feedback that we're able to give you. >> Great we looked forward to working with you on that. I guess, personally, with the pandemic in 2020, what have you become, I guess in 2020 that maybe you weren't a year ago before the pandemic, just out of curiosity? >> I'm not sure if we're anything different. A mantra that we've used in the department of radiology at TriHealth for a decade, "Improved service become more adaptable." And we're a service industry, so of course we want to improve service, but be adaptable, become more adaptable. And COVID certainly emphasize that need to be adaptable, to be flexible and the better tools we have. It was great early in the COVID when we had the shutdowns, we found ourselves, we have way more radiologists than we had studies that needed interpreted. So we were flexible all often and be home more. Well, the referring physicians don't know like, well is Dr. Huelsman working today? We don't expect them to look up our schedules. If I get a page that, Hey, can you take a look at this? It was great that at that time I didn't have a home workstation, but I had iConnect access. Before there was no way for me to access the images without getting on a VPN and logging on, it takes 10, 15 minutes before I'm able. Instead I could answer the phone, and I'm not going to say, "Oh, I'm sorry, I'm not at the hospital day, call this number someone else will help you." I have my iPad, go to ica.trihealth.com logged on, I'm looking at the images two minutes later. And so the ease of use, the flexibility, it helped us become adaptable. And I anticipate with we're upgrading the radiology viewer and the iConnect access next month as well, to try to educate our referring physicians, of sort of the image sharing capabilities within that next version of our viewer. Because telehealth has become like everywhere else. It's become much more important at TriHealth during this pandemic. And I think it will be a very big satisfier for both referring physicians and patients, that those image sharing capabilities, to be able to look at the same image, see the annotation that either the radiologist or the referring physician, oncologist, whoever is wanting to share images with the patient and the patient's family, to have multiple parties on at the same time. It will be very good. >> With the new tools that you have for working from home with your full workstation, are you as efficient reading at home? >> Yes. >> And having full access to the PACS as in-house? >> Absolutely. >> That's great to hear. Have you been able to take advantage of using any of the collaboration tools within iConnect, to collaborate with a referring physician, where he can see your pointer and you can see his, or is that something we need to get working? >> Hopefully if you ask me that a year from now, the answer will be yes. >> So does that exit a radiologist? Does that help a radiologist communicate with a referring physician? Or do you feel that that's going to be a- >> Absolutely. We still have our old school physicians that we love who come to the reading room, who come to the department of radiology and go over studies together. But it's dwindling, it's becoming fewer and fewer as certain individuals retire. And it's just different. But the more direct interaction we can have with referring physicians, the better information they can give us. And the more we're interacting directly, the better we are. And so I get it, they're busy, they don't want to, they may not be at the hospital. They're seeing patients at an outpatient clinic and a radiologist isn't even there, that's where that technology piece. This is how we live. We're an instantaneous society. We live through our phone and so great it's like a FaceTime capability. If you want to maintain those personal relationships, we're learning we can't rely on the orthopedist or whomever, whatever referring physician to stop by our reading room, our department. We need to make ourselves available to them and make it convenient. >> That market that you working in Cincinnati, we have a luxury of having quite a few customers with our iConnect solutions. There's been some talk between the multiple parties, of potentially being able to look across the other sites and using that common tool, but being able to query the other archives. Is that something that you'd in favor of supporting and think would add value so that the clinicians can see the longitudinal record? >> Yes And we already have that ability of we can view care everywhere in our EMR. So we don't have the images right away, but we can see other reports. Again, it's not convenient. It's not a click away, but it's two, three, four clicks away. But if I see, if it's one of my search patterns of I just worked the overnight shift last week and then you get something through the ER and there's no comparisons, and it's an abnormal chest CT. Well, I look in Care Everywhere. Oh, they had a chest CT at a different place in the city a year ago, and I can see the report. And so then at that time I can request, and it can take an hour or so, but look back and the images will be accessible to me. But so how do we improve on that? Is to make the images, that I don't have to wait an hour for the images. If we have image sharing among your organizations that can be much quicker, would be a big win. >> As you read in your new environment, do you swivel your chair and still read out of any other specialty systems, for any types of studies today? >> No, and that was a huge win. We used to have a separate viewing system for mammography and we were caught like there were dedicated viewing stations. And so even though we're a system, the radiologist working at this hospital, had to read the mammograms taken at that hospital. And one at the other hospital could only read the ones taken at that hospital. And you couldn't share the workload if it was heavy at one site and light at the other. Well, now it's all viewed through the radiology viewer if you merge PACS, in not just general radiology, but impressed. It has been so much better world that the workflow is so much better, that we can share the work list and be much more efficient. >> Do you feel that in your, your new world, that you're able to have less cherry picking between the group, I guess? Do you feel like there's less infighting or that the exams are being split up evenly through the work list? Or are you guys using some sort of assignment? >> No. And I'm curious with our next version of PACS, the next version of merge packs of 008. I forget which particular >> John: 008. >> It's 008, yeah. I know there's the feature of a smart work list to distribute the exams. Currently, we just have one. It's better than what we have before. It's one large list. We've subdivided, teased out some things that not all of the radiologist read of like MSK and cardiac and it makes it more convenient. But currently it is the radiologist choose what study they're going to open next. To me how I personally attack the list is I don't look at the list. Some radiologists can spend more time choosing what they're going to read next than they do reading. (chuckles) And so if you don't even look, and so the feature I love is just I don't want to take my eyes off my main viewer. And I don't want to swivel my chair. I don't want to turn my head to look at the list, I want everything right in front of me. And so currently the way you can use it is I never look at the list. I just use the keyboard shortcuts of, okay, well I'm done with that study. I mark it, there's one button I click on my mouse that marks it dictated, closes it and brings up the next study on the list. >> Hey guys, I got to jump in. We're running up against the clock, but John if you've got any final thoughts or Dr. Huelsman, please. >> Sure. Dr. Huelsman, I guess any homework for me? What are the top two or three things I can help you with in 2021 to be successful? >> Keep us informed of what you're working on, of what's available now. What's coming next, and how soon is it available? And you let us see those things? And we'll give you a feedback of hey, this is great. And we'll try to identify things, if you haven't thought of them, hey, this would be very helpful. >> Gents, great conversation. Gosh we could go on for another 45 minutes. And John you really have a great knowledge of the industry. And Dr. Huelsman, thanks so much for coming on. Appreciate it. >> Thank you. >> You're welcome >> And thanks for spending some time with us. You're watching "Client Conversations" with IBM Watson Health.