(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)

>>from the cube studios >>in Palo alto in >>boston connecting with thought leaders all around the world. This >>is a cute >>conversation. Hello welcome to the cubes coverage of HPD discovered 2021 virtual. I'm john for your host of the cube we're here with CUBA alumni. One of the original cube guests 2020 11 back in the day kurt president and chief architect of Hewlett Packard labs. He's also a Hewlett Packard enterprise fellow and vice president. Great to see you and you're in Vegas. I'm in Palo Alto. We've got a little virtual hybrid going on here. Thanks for spending time. >>Thanks john it's great to be back with you >>so much going on. I love to see you guys having this event kind of everyone in one spot. Good mojo. Great CHP, you know, back in the saddle again. I want to get your, take, your in the, in the, in the action right now on the lab side, which is great disruptive innovation is the theme. It's always been this year, more than ever coming out of the pandemic, people are looking for the future, looking to see the signs, they want to connect the dots. There's been some radical rethinking going on that you've been driving and in the labs, you hope you look back at last, take us through what's going on, what you're thinking, what's the, what's the big trends? >>Yeah, John So it's been interesting, you know, over the last 18 months, all of us had gone through about a decade's worth of advancement in decentralization, education, healthcare, our own work, what we're doing right now suddenly spread apart. Uh, and it got us thinking, you know, we think about that distributed mesh and as we, as we try and begin to return to normal and certainly think about all that we've lost, we want to move forward, we don't want to regress. And we started imagining, what does that world look like? And we think about the world of 20 2500 and 35 zeta bytes, 100 and 50 billion connected things out there. And it's the shape of the world has changed. That's where the data is going to be. And so we started thinking about what's it like to thrive in that kind of world. We had a global Defense research institute came to us, Nasa's that exact question. What's the edge? What do we need to prepare for for this age of insight? And it was kind of like when you had those exam questions and I was one of those kids who give you the final exam and if it's a really good question, suddenly everything clicked. I understood all the material because there was that really forcing question when they asked us that for me, it it solidified what I've been thinking about all the work we've done at labs over the last the last 10 years. And it's really about what does it take to survive and thrive. And for me it's three things. One is, success is going to go to whoever can reason over more information, who can gain the deepest insights from that information in time that matters and then can turn that insight into action at scale. So reason, insight and action. And it certainly was clear to me everything we've been trying to push for in labs, all those boundaries. We've been pushing all those conventions we've been defying are really trying to do that for, for our customers and our partners to bring in more information for them to understand, to be able to allow them to gain insight across departments across disciplines and then turn that insight into action at scale where scale is no longer one cloud or one company or one country, let alone one data center >>lot there. I love the dot I love that metadata and meta reasoning incites always been part of that. Um and you mentioned decentralization. Again, another big trend. I gotta ask you where is the big opportunity because a lot of people who are attending discover people watching are trying to ask what should they be thinking about. So what is that next big opportunity? How would you frame that and what should attendees look for coming out at HP discover. >>So one thing we're seeing is that this is actually a ubiquitous trend, whether we're talking about transportation or energy or communications, they all are trying to understand and how will they admit more of that data to make those real time decisions? Our expectation in the middle of this decade when we have the 125 petabytes, You know, 30% of that data will need real time action out of the edge where the speed of light is now material. And also we expect that at that point in time three out of four of those 185 petabytes, they'll never make it back to the data center. So understanding how we will allow that computation, that understanding to reach out to where the data is and then bringing in that's important. And then if we look at at those, all of those different areas, whether it's energy and transportation, communications, all that real time data, they all want to understand. And so I I think that as many people come to us virtually now, hopefully in person in the future when we have those conversations that labs, it's almost immediate takes a while for them and then they realize away that's me, this is my industry too, because they see that potential and suddenly where they see data, they see opportunity and they just want to know, okay, what does it take for me to turn that raw material into insight and then turn that insight into >>action, you know, storage compute never goes away, it gets more and more, you need more of it. This whole data and edge conversations really interesting. You know, we're living in that data centric, you know, everyone's gonna be a date a couple, okay. That we know that that's obvious. But I gotta ask you as you start to see machine learning, um cloud scale cloud operations, a new Edge and the new architecture is emerging and clients start to look at things like AI and they want to have more explain ability behind I hear that all the time. Can you explain it to me? Is there any kind of, what is it doing? Good as our biases, a good bad or you know, is really valuable expect experimental experiential. These are words are I'm hearing more and more of >>not so much a speeds >>and feeds game, but these are these are these are these are outcomes. So you got the core data, you've got a new architecture and you're hearing things like explainable ai experiential customer support, a new things happening, explain what this all means, >>You know, and it's it's interesting. We have just completed uh creating an Ai ethical framework for all of Hewlett Packard enterprise and whether we're talking about something that's internal improving a process, uh something that we sell our product or we're talking about a partnership where someone wants to build on top of our services and infrastructure, Build an AI system. We really wanted to encompass all of those. And so it was it was challenging actually took us about 18 months from that very first meeting for us to craft what are some principles for us to use to guide our our team members to give them that understanding. And what was interesting is we examined our principles of robustness of uh making sure they're human centric that they're reliable, that they are privacy preserving, that they are robust. We looked at that and then you look at where people want to apply these Ai today's AI and you start to realize there's a gap, there's actually areas where we have a great challenge, a human challenge and as interesting as possibly efficacious as today's A. I. S. R. We actually can't employ them with the confidence in the ethical position that we need to really pull that technology in. And what was interesting is that then became something that we were driving at labs. It began gave us a viewpoint into where there are gaps where, as you say, explica bility, you know, as fantastic as it is to talk into your mobile phone and have it translated into another one of hundreds of languages. I mean that is right out of Star trek and it's something we can all do. And frankly, it's, you know, we're expecting it now as efficacious as that is as we echo some other problems, it's not enough. We actually need to be explainable. We need to be able to audit these decisions. And so that's really what's informed now are trustworthy ai research and development program at Hewlett Packard Labs. Let's look at where we want to play. I I we look at what keeps us from doing it and then let's close the technology gap and it means some new things. It means new approaches. Sometimes we're going back back back to some of the very early ai um that things that we sort of left behind when suddenly the computational capability allowed us to enter into a machine learning and deep neural nets. Great applications, but it's not universally applicable. So that's where we are now. We're beginning to construct that second generation of AI systems where that explica bility where that trustworthiness and were more important that you said, understanding that data flow and the responsibility we have to those who created that data, especially when it's representing human information, that long term responsibility. What are the structures we need to support that ethically? >>That's great insight, Kirk, that's awesome stuff. And it reminds me of the old is new again, right? The cycles of innovation, you mentioned a I in the eighties, reminds me of dusting off and I was smiling because the notion of reasoning and natural language that's been around for a while, these other for a lot of Ai frame which have been around for a while But applied differently becomes interesting. The notion of Meta reasoning, I remember talking about that in 1998 around ontology and syntax and data analysis. I mean, again, well formed, you know, older ways to look at data. And so I gotta ask you, you know, you mentioned reasoning over information, getting the insights and having actions at scale. That doesn't sound like an R and D or labs issue. Right? I mean that that should be like in the market today. So I know you, there's stuff out there, what's different around the Hewlett Packard labs challenge because you guys, you guys are working on stuff that's kind of next gen, so why, what's next gen about reasoning moreover, information and getting insights? Because you know, there's a zillion startups out there that claim to be insights as a service, um, taking action outcomes >>and I think there were going to say a couple things. One is the technologies and the capabilities that God is this far. Uh, they're actually in an interesting position if we think of that twilight of moore's law is getting a little darker every day. Um, there's been such a tail wind behind us tremendous and we would have been foolish not to take advantage of it while it lasted, but as it now flattens out, we have to be realistic and say, you know what that ability to expect anticipate and then planned for a doubling and performance in the next 18 to 24 months because there's twice as many transistors in that square of silicon. We can't count on that anymore. We have to look now broader and it's not just one of these technology inflection points. There's so many we already mentioned ai it's voraciously vowing all this data at the same time. Now that data is all at the edge is no longer in the data center. I mean we may find ourselves laughing chuckling at the term itself data center. Remember when we sent it all the data? Because that's where the computers were. Well, that's 2020 thinking right, that's not even 2025. Thinking also security, that cyber threat of Nation State and criminal enterprises, all these things coming together and it's that confluence of discontinuities, that's what makes a loud problem. And the second piece is we don't just need to do it the way that we've been doing it because that's not necessarily sustainable. And if something is not sustainable is inherently inequitable because we can't afford to let everyone enjoy those benefits. So I think that's all those things, the technology confluence of technology, uh, disruptions and this desire to move to really sustainable, really inherently inequitable systems. That's what makes it a labs problem. >>I really think that's right on the money. And one of things I want to get your thoughts on, cause I know you have a unique historic view of the trajectory arc. Cloud computing that everyone's attention lift and shift cloud scale. Great cloud native. Now with hybrid and multi cloud clearly happening, all the cloud players were saying, oh, it's never gonna happen. All the data set is going to go away. Not really. The, the data center is just an edge big age. So you brought up the data center concept and you mentioned decentralization there, it's a distributed computing architecture, There is no line anymore between what's cloud and what's not the cloud is just the cloud and the data center is now a big fat edge and edges are smaller and bigger. Their nodes distribute computing now is the context. So this is not a new thing for Hewlett Packard enterprise. I mean you guys been doing distributed computing paradigms, supplying software and hardware and solutions Since I can remember since it was founded, what's new now, what do you say that folks are saying, what is HP doing for this new architecture? Because now an operating system is the word, the word that they want. They want to have an operating model, deV ops to have sex shops, all this is happening. What's the what's the state of the art from H. P. E. And how does the lab play into that vision? >>And it's so wonderful that you mentioned in our heritage because if you think about it was the first thing that Bill and they did, they made instruments of unparalleled value and quality for engineers and scientists. And the second thing they did was computerized that instrument control. And then they network them together and then they connect to the network measurement sensing systems to business computing. Right. And so that's really, that's exactly what we're talking about here. You know, and yesterday it was H. B. I. B. Cables. But today it is everything from an Aruba wireless gateway to a green Lake cloud that comes to you to now are cray exa scale supercomputing. And we wanted to look at that entire gamut and understand exactly what you said. How is today's modern developer who has been distinct in agile development in seven uh and devops and def sec ops. How can we make them as comfortable and confident deploying to any one of those systems or all of them in conjunction as confident as they've been deploying to a cloud. And I think that's really part of what we need to understand. And as you move out towards the edge things become interesting. A tiny amount of resources, the number of threats, physical and uh um cyber increased dramatically. It is no longer the healthy happy environment of that raised floor data center, It is actually out in the world but we have to because that's where the data is and so that's another piece of it that we're trying to bring with the labs are distributed systems lab trying to understand how do we make cloud native access every single bite everywhere from the tiniest little Edge embedded system, all the way up through that exa scale supercomputer, how do we admit all of that data to this entire generation and then the following subsequent generation, who will no longer understand what we were so worried about with things being in one place or another, they want to digest all the world's data regardless of where it is. >>You know, I was just having a conversation, you brought this up. Uh that's interesting around the history and the heritage, embedded systems is changing the whole hardware equations, changes the software driven model. Now, supply chain used to be constrained to software. Now you have a software supply chain, hardware, now you have software supply chain. So everything is happening in these kind of new use cases. And Edge is a great example where you want to have compute at the edge not having pulled back to some central location. So, again, advantage hp right, you've got more, you've got some solutions there. So all these like memory driven computing, something that you've worked on and been driving the machine product that we talked about when you guys launched a few years ago, um, looks like now a good R and D project, because all the discussions, I'm I'm hearing whether it's stuff in space or inside hybrid edges is I gotta have software running on an embedded system, I need security, I gotta have, you know, memory driven architecture is I gotta have data driven value in real time. This is new as a kind of a new shift, but you still need to run it. What's the update on the machine and the memory driven computing? And how does that connect the dots for this intelligent Edge? That's now super important in the hybrid equation. >>Yeah, it's fantastic you brought that up. You know, it's uh it's gratifying when you've been drawing pictures on your white board for 10 or 15 years and suddenly you see them printed uh and on the web and he's like, OK Yeah, you guys were there were there because we always knew it had to be bigger than us. And for a while you wonder, well is this the right direction? And then you get that gratification that you see it repeated. And I think one of the other elements that you said that was so important was talking about that supply chain uh and especially as we get towards these edge devices uh and the increasing cyber threat, you know, so much more about understanding the provenance of that supply chain and how we get beyond trust uh to prove. And in our case that proof is rooted in the silicon. Start with the silicon establish a silicon root of trust, something that can't be forged that that physically uncomfortable function in the silicon. And then build up that chain not of trust but a proof of measurable confidence. And then let's link that through the hardware through the data. And I think that's another element, understanding how that data is flowing in and we establish that that that provenance that's provable provenance and that also enables us to come back to that equitable question. How do we deal with all this data? Well, we want to make sure that everyone wants to buy in and that's why you need to be able to reward them. So being able to trace data into an AI model, trace it back out to its effect on society. All these are things that we're trying to understand the labs so that we can really establish this data economy and admit the day that we need to the problems that we have that really just are crying out for that solution bringing in that data, you just know where is the data, Where is the answer? Now I get to work with, I've worked for several years with the German center for your Degenerative Disease Research and I was teasing their director dr nakata. I said, you know, in a couple of years when you're getting that Nobel prize for medicine because you cracked Alzheimer's I want you to tell me how long was the answer hiding in plain sight because it was segregated across disciplines across geography and it was there. But we just didn't have that ability to view across the breath of the information and in a time that matters. And I think so much about what we're trying to do with the lab is that that's that reasoning moreover, more information, gaining insights in the time that matters and then it's all about action and that is driving that insight into the world regardless of whether it has to land in an exa scale supercomputer or tiny little edge device, we want today's application development teams to feel that degree of freedom to range over all of those that infrastructure and all of that data. >>You know, you bring up a great call out there. I want to just highlight that cause I thought that was awesome. The future breakthroughs are hiding in plain sight. It's the access to the people and the talent to solve the problems and the data that's stuck in the silos. You bring those together, you make that seamless and frictionless, then magic happens. That's that's really what we're talking about in this new world, isn't it? >>Absolutely, yeah. And it's one of those things that sometimes my kids as you know, why do you come in every day? And for me it is exactly that I think so many of the challenges we have are actually solvable if the right people knew the right information at the right time and that we all have that not again, not trust, but that proof that confidence, that measurable conference back to the instruments that that HP was always famous for. It was that precision and they all had that calibration tag. So you could measure your confidence in an HP instrument and the same. We want people to measure their confidence when data is flowing through Hewlett Packard Enterprise infrastructure. >>It's interesting to bring up the legacy because instrumentation network together, connecting to business systems. Hey, that sounds like the cloud observe ability, modern applications, instant action and actionable insights. I mean that's really the the same almost exact formula. >>Yeah, For me that's that, that the constant through line from the garage to right now is that ability to handle and connect people to the information that they need. >>Great, great to chat. You're always an inspiration and we could go for another hour talking about extra scale, green leg, all the other cool things going on at H P E. I got to ask you the final question, what are you most excited about for h B and his future and how and how can folks learn more to discover and what should they focus on? >>Uh so I think for me um what I love is that I imagine that world where the data you know today is out there at the edge and you know we have our Aruba team, we have our green Lake team, we have are consistent, you know, our core enterprise infrastructure business and now we also have all the way up through X scale compute when I think of that thriving business, that ability to bring in massive data analytics, machine learning and Ai and then stimulation and modeling. That's really what whether you're a scientist and engineer or an artist, you want to have that intersectionality. And I think we actually have this incredible, diverse set of resources to bring to bear to those problems that will span from edge to cloud, back to core and then to exit scale. So that's what really, that's why I find so exciting is all of the great uh innovators that we get to work with and the markets we get to participate in. And then for me it's also the fact it's all happening at Hewlett Packard Enterprise, which means we have a purpose. You know, if you ask, you know, when they did ask Dave Packer, Dave, why hp? And he said in 1960, we come together as a company because we can do something we could not do by ourselves and we make a contribution to society and I dare anyone to spend more than a couple of minutes with Antonio Neary and he won't remind you. And this is whether it is here to discover or in the halls at labs remind me our purpose, that Hewlett Packard Enterprise is to advance the way that people live and work. And for me that's that direct connection. So it's, it's the technology and then the purpose and that's really what I find so exciting about HPV. >>That's a great call out, Antonio deserves props. I love talking with him, he's the true Bill and Dave Bill. Hewlett Dave package spirit And I'll say that I've talked with him and one of the things that resident to me and resonates well is the citizenship and be interesting to see if Bill and Dave were alive today, that now it's a global citizenship. This is a huge part of the culture and I know it's still alive there at H P E. So, great call out there and props to Antonio and yourself and the team. Congratulations. Thanks for spending the time, appreciate it. >>Thank you john it's great to be with you again. >>Okay. Global labs. Global opportunities, radical. Rethinking this is what's happening within HP. Hewlett Packard Labs, Great, great contribution there from Kirk, have them on the cube and always fun to talk so much, so much to digest there. It's awesome. I'm john Kerry with the cube. Thanks for watching. >>Mm >>mhm Yeah.

from around the globe it's thecube with digital coverage of exascale day made possible by hewlett packard enterprise welcome everyone to the cube celebration of exascale day ben bennett is here he's an hpc strategist and evangelist at hewlett-packard enterprise ben welcome good to see you good to see you too son hey well let's evangelize exascale a little bit you know what's exciting you uh in regards to the coming of exoskilled computing um well there's a couple of things really uh for me historically i've worked in super computing for many years and i have seen the coming of several milestones from you know actually i'm old enough to remember gigaflops uh coming through and teraflops and petaflops exascale is has been harder than many of us anticipated many years ago the sheer amount of technology that has been required to deliver machines of this performance has been has been us utterly staggering but the exascale era brings with it real solutions it gives us opportunities to do things that we've not been able to do before if you look at some of the the most powerful computers around today they've they've really helped with um the pandemic kovid but we're still you know orders of magnitude away from being able to design drugs in situ test them in memory and release them to the public you know we still have lots and lots of lab work to do and exascale machines are going to help with that we are going to be able to to do more um which ultimately will will aid humanity and they used to be called the grand challenges and i still think of them as that i still think of these challenges for scientists that exascale class machines will be able to help but also i'm a realist is that in 10 20 30 years time you know i should be able to look back at this hopefully touch wood look back at it and look at much faster machines and say do you remember the days when we thought exascale was faster yeah well you mentioned the pandemic and you know the present united states was tweeting this morning that he was upset that you know the the fda in the u.s is not allowing the the vaccine to proceed as fast as you'd like it in fact it the fda is loosening some of its uh restrictions and i wonder if you know high performance computing in part is helping with the simulations and maybe predicting because a lot of this is about probabilities um and concerns is is is that work that is going on today or are you saying that that exascale actually you know would be what we need to accelerate that what's the role of hpc that you see today in regards to sort of solving for that vaccine and any other sort of pandemic related drugs so so first a disclaimer i am not a geneticist i am not a biochemist um my son is he tries to explain it to me and it tends to go in one ear and out the other um um i just merely build the machines he uses so we're sort of even on that front um if you read if you had read the press there was a lot of people offering up systems and computational resources for scientists a lot of the work that has been done understanding the mechanisms of covid19 um have been you know uncovered by the use of very very powerful computers would exascale have helped well clearly the faster the computers the more simulations we can do i think if you look back historically no vaccine has come to fruition as fast ever under modern rules okay admittedly the first vaccine was you know edward jenner sat quietly um you know smearing a few people and hoping it worked um i think we're slightly beyond that the fda has rules and regulations for a reason and we you don't have to go back far in our history to understand the nature of uh drugs that work for 99 of the population you know and i think exascale widely available exoscale and much faster computers are going to assist with that imagine having a genetic map of very large numbers of people on the earth and being able to test your drug against that breadth of person and you know that 99 of the time it works fine under fda rules you could never sell it you could never do that but if you're confident in your testing if you can demonstrate that you can keep the one percent away for whom that drug doesn't work bingo you now have a drug for the majority of the people and so many drugs that have so many benefits are not released and drugs are expensive because they fail at the last few moments you know the more testing you can do the more testing in memory the better it's going to be for everybody uh personally are we at a point where we still need human trials yes do we still need due diligence yes um we're not there yet exascale is you know it's coming it's not there yet yeah well to your point the faster the computer the more simulations and the higher the the chance that we're actually going to going to going to get it right and maybe compress that time to market but talk about some of the problems that you're working on uh and and the challenges for you know for example with the uk government and maybe maybe others that you can you can share with us help us understand kind of what you're hoping to accomplish so um within the united kingdom there was a report published um for the um for the uk research institute i think it's the uk research institute it might be epsrc however it's the body of people responsible for funding um science and there was a case a science case done for exascale i'm not a scientist um a lot of the work that was in this documentation said that a number of things that can be done today aren't good enough that we need to look further out we need to look at machines that will do much more there's been a program funded called asimov and this is a sort of a commercial problem that the uk government is working with rolls royce and they're trying to research how you build a full engine model and by full engine model i mean one that takes into account both the flow of gases through it and how those flow of gases and temperatures change the physical dynamics of the engine and of course as you change the physical dynamics of the engine you change the flow so you need a closely coupled model as air travel becomes more and more under the microscope we need to make sure that the air travel we do is as efficient as possible and currently there aren't supercomputers that have the performance one of the things i'm going to be doing as part of this sequence of conversations is i'm going to be having an in detailed uh sorry an in-depth but it will be very detailed an in-depth conversation with professor mark parsons from the edinburgh parallel computing center he's the director there and the dean of research at edinburgh university and i'm going to be talking to him about the azimoth program and and mark's experience as the person responsible for looking at exascale within the uk to try and determine what are the sort of science problems that we can solve as we move into the exoscale era and what that means for humanity what are the benefits for humans yeah and that's what i wanted to ask you about the the rolls-royce example that you gave it wasn't i if i understood it wasn't so much safety as it was you said efficiency and so that's that's what fuel consumption um it's it's partly fuel consumption it is of course safety there is a um there is a very specific test called an extreme event or the fan blade off what happens is they build an engine and they put it in a cowling and then they run the engine at full speed and then they literally explode uh they fire off a little explosive and they fire a fan belt uh a fan blade off to make sure that it doesn't go through the cowling and the reason they do that is there has been in the past uh a uh a failure of a fan blade and it came through the cowling and came into the aircraft depressurized the aircraft i think somebody was killed as a result of that and the aircraft went down i don't think it was a total loss one death being one too many but as a result you now have to build a jet engine instrument it balance the blades put an explosive in it and then blow the fan blade off now you only really want to do that once it's like car crash testing you want to build a model of the car you want to demonstrate with the dummy that it is safe you don't want to have to build lots of cars and keep going back to the drawing board so you do it in computers memory right we're okay with cars we have computational power to resolve to the level to determine whether or not the accident would hurt a human being still a long way to go to make them more efficient uh new materials how you can get away with lighter structures but we haven't got there with aircraft yet i mean we can build a simulation and we can do that and we can be pretty sure we're right um we still need to build an engine which costs in excess of 10 million dollars and blow the fan blade off it so okay so you're talking about some pretty complex simulations obviously what are some of the the barriers and and the breakthroughs that are kind of required you know to to do some of these things that you're talking about that exascale is going to enable i mean presumably there are obviously technical barriers but maybe you can shed some light on that well some of them are very prosaic so for example power exoscale machines consume a lot of power um so you have to be able to design systems that consume less power and that goes into making sure they're cooled efficiently if you use water can you reuse the water i mean the if you take a laptop and sit it on your lap and you type away for four hours you'll notice it gets quite warm um an exascale computer is going to generate a lot more heat several megawatts actually um and it sounds prosaic but it's actually very important to people you've got to make sure that the systems can be cooled and that we can power them yeah so there's that another issue is the software the software models how do you take a software model and distribute the data over many tens of thousands of nodes how do you do that efficiently if you look at you know gigaflop machines they had hundreds of nodes and each node had effectively a processor a core a thread of application we're looking at many many tens of thousands of nodes cores parallel threads running how do you make that efficient so is the software ready i think the majority of people will tell you that it's the software that's the problem not the hardware of course my friends in hardware would tell you ah software is easy it's the hardware that's the problem i think for the universities and the users the challenge is going to be the software i think um it's going to have to evolve you you're just you want to look at your machine and you just want to be able to dump work onto it easily we're not there yet not by a long stretch of the imagination yeah consequently you know we one of the things that we're doing is that we have a lot of centers of excellence is we will provide well i hate say the word provide we we sell super computers and once the machine has gone in we work very closely with the establishments create centers of excellence to get the best out of the machines to improve the software um and if a machine's expensive you want to get the most out of it that you can you don't just want to run a synthetic benchmark and say look i'm the fastest supercomputer on the planet you know your users who want access to it are the people that really decide how useful it is and the work they get out of it yeah the economics is definitely a factor in fact the fastest supercomputer in the planet but you can't if you can't afford to use it what good is it uh you mentioned power uh and then the flip side of that coin is of course cooling you can reduce the power consumption but but how challenging is it to cool these systems um it's an engineering problem yeah we we have you know uh data centers in iceland where it gets um you know it doesn't get too warm we have a big air cooled data center in in the united kingdom where it never gets above 30 degrees centigrade so if you put in water at 40 degrees centigrade and it comes out at 50 degrees centigrade you can cool it by just pumping it round the air you know just putting it outside the building because the building will you know never gets above 30 so it'll easily drop it back to 40 to enable you to put it back into the machine um right other ways to do it um you know is to take the heat and use it commercially there's a there's a lovely story of they take the hot water out of the supercomputer in the nordics um and then they pump it into a brewery to keep the mash tuns warm you know that's that's the sort of engineering i can get behind yeah indeed that's a great application talk a little bit more about your conversation with professor parsons maybe we could double click into that what are some of the things that you're going to you're going to probe there what are you hoping to learn so i think some of the things that that are going to be interesting to uncover is just the breadth of science that can be uh that could take advantage of exascale you know there are there are many things going on that uh that people hear about you know we people are interested in um you know the nobel prize they might have no idea what it means but the nobel prize for physics was awarded um to do with research into black holes you know fascinating and truly insightful physics um could it benefit from exascale i have no idea uh i i really don't um you know one of the most profound pieces of knowledge in in the last few hundred years has been the theory of relativity you know an austrian patent clerk wrote e equals m c squared on the back of an envelope and and voila i i don't believe any form of exascale computing would have helped him get there any faster right that's maybe flippant but i think the point is is that there are areas in terms of weather prediction climate prediction drug discovery um material knowledge engineering uh problems that are going to be unlocked with the use of exascale class systems we are going to be able to provide more tools more insight [Music] and that's the purpose of computing you know it's not that it's not the data that that comes out and it's the insight we get from it yeah i often say data is plentiful insights are not um ben you're a bit of an industry historian so i've got to ask you you mentioned you mentioned mentioned gigaflop gigaflops before which i think goes back to the early 1970s uh but the history actually the 80s is it the 80s okay well the history of computing goes back even before that you know yes i thought i thought seymour cray was you know kind of father of super computing but perhaps you have another point of view as to the origination of high performance computing [Music] oh yes this is um this is this is one for all my colleagues globally um you know arguably he says getting ready to be attacked from all sides arguably you know um computing uh the parallel work and the research done during the war by alan turing is the father of high performance computing i think one of the problems we have is that so much of that work was classified so much of that work was kept away from commercial people that commercial computing evolved without that knowledge i uh i have done in in in a previous life i have done some work for the british science museum and i have had the great pleasure in walking through the the british science museum archives um to look at how computing has evolved from things like the the pascaline from blaise pascal you know napier's bones the babbage's machines uh to to look all the way through the analog machines you know what conrad zeus was doing on a desktop um i think i think what's important is it doesn't matter where you are is that it is the problem that drives the technology and it's having the problems that requires the you know the human race to look at solutions and be these kicks started by you know the terrible problem that the us has with its nuclear stockpile stewardship now you've invented them how do you keep them safe originally done through the ascii program that's driven a lot of computational advances ultimately it's our quest for knowledge that drives these machines and i think as long as we are interested as long as we want to find things out there will always be advances in computing to meet that need yeah and you know it was a great conversation uh you're a brilliant guest i i love this this this talk and uh and of course as the saying goes success has many fathers so there's probably a few polish mathematicians that would stake a claim in the uh the original enigma project as well i think i think they drove the algorithm i think the problem is is that the work of tommy flowers is the person who took the algorithms and the work that um that was being done and actually had to build the poor machine he's the guy that actually had to sit there and go how do i turn this into a machine that does that and and so you know people always remember touring very few people remember tommy flowers who actually had to turn the great work um into a working machine yeah super computer team sport well ben it's great to have you on thanks so much for your perspectives best of luck with your conversation with professor parsons we'll be looking forward to that and uh and thanks so much for coming on thecube a complete pleasure thank you and thank you everybody for watching this is dave vellante we're celebrating exascale day you're watching the cube [Music]

>>From Miami beach, Florida. It's the queue covering a cryonics global cyber summit 2019 brought to you by Acronis. >>Okay. Welcome back everyone. This is the cubes coverage here at the Chronis global cyber summit 2019 I'm John furrier, host to the cube. We're Miami beach at the Fontainebleau hotel with a second day. Excited to have this next guest on Christian Garcia, senior vice president of finance and administration at the chauffeur housing ShipIt housing Institute of technology. Did they get it right? Almost right. housing. welcome back. Welcome to the cube. Good to see you. Good to see you. Thanks for having me here. This is a really cool story because you guys are doing something very entrepreneurial, right, with education, right. Okay. Inspired by the founder of a Chronis. Exactly as well. He's got. He's made a lot of money in his day, so he's doing some good things with it. Um, but this is an interesting opportunity for you to take a minute to explain what this Institute stands for. >>It's sit for short. >> Yeah, so sat actually as a name Schaffhausen Institute of technology. So we are actually starting up a university in Schaffhausen in Schaffhausen. These a beautiful tiny CD in Switzerland, 30 minutes or 30 minutes from the Zurich airport, which is the biggest airport in Switzerland, uh, close to Germany at the border with Germany. And uh, so that's kind of your, in the center of Europe and that's where we plan to have our main campus. Now let me tell you this story. How about the vision about target, his vision on these, on this project? Um, he, he said that, you know, uh, he needs to have skills in 10 to 15 years time that nowadays at the institutions that do not do not, do not bring, um, there is the need of computer scientists that are not enough computer scientists and we are having emergent technologies and these is something that provides us with tremendous opportunities, which we cannot even imagine nowadays what type of opportunities and to be on the forefront there. >>That's why we want to found these are, we have founded the Schaffhausen Institute of technology. >> Chef housing is a technology just for share. The day was just two months ago, couple months ago. It was two months ago where we, where we have started up the legal structure and now we are really laying the foundation. We have to find some that are kind of secured for for the next 12 to 18 months. And um, we are, you know, defining the strategic advisory board. We are setting up the curriculum for our students. And so it's everything up and running and to be defined. So risk is right at the creation present at creation. We are talking about this as a, this is the origination story. Exactly. Of the shelf house in Institute of technology. Exactly. What's the vision? >>I mean obviously getting skills for jobs that are our century, our time that's having been teaching in universities and before I get back. But is it about being open and what's the vision is just Switzerland is going to be global. Can you just share, what do you guys are thinking? >>Sure, absolutely. So basically what we are trying to do is to design a curriculum in um, computer science and physics because we think that computer science or present the software in physics represents the hardware. And these two things need to be combined in a entrepreneurial mindset or with an entrepreneurial mindset, which means that we also want to foster the transformation process and the anti entrepreneurship. Now, let me go back to the software path. Uh, our curriculum will cover, um, software engineering, cybersecurity. That's why we are here today. Uh, the curriculum we also cover, um, on the physics part. On the hardware part, we'll cover, uh, quantum technologies, uh, quantum physics and also new materials. Um, and these will be kind of the foundation that will build the curriculum for students, computer scientists to have physics and physics to have computer science in their curriculum so that at some point in time they can come together and to research together. >>This is the digital transformation that we're talking about. The, the intersection and the confluence of physical reality. A world that we live in, whether it's a baseball game or a soccer match to the digital culture, they're not mutually exclusive anymore and they're together. And then the impact is profound. I can only imagine. IOT, industrial, IOT, airplanes, cars, electricity, electronic batteries, all these things, correct. It's software and digital. And physical material. Exactly that you guys are thinking. >>Exactly. Exactly that and actually also considering the industry, talking to the industry, talking to chief information technology officers around the world to understand what they need are and what type of they believe of skills are needed in in 10 to 15 years time. And that's what we want to build up now to get >>well you guys car gotta go, you gotta go faster because there's jobs now. There's thousands of jobs right now in cybersecurity. There's thousands and thousands of jobs for provision and cloud computing. Amazon educate. We talked to them all the time. They just can't get the word out fast enough that Hey, if you're unemployed there's no excuse for being unemployed. Write down there's so many new jobs. But because someone didn't go to the linear school and exactly know go step by step over the years and now you can level up very quickly. Exactly for certification. But you guys are taking a much more bigger idea around real kind of masters level. Is that what it is? Undergraduate masters level? What's the level of, actually we, we, we are starting >>out with this university and we have already students that are at our or with our partner universities currently in Singapore with NUS. And we then move to Karnak and Molly here in the U S um, in order to have it, we'll do a degree. So that's a unique opportunity to already start up with some presence, uh, in, in education. And uh, you ultimately, they will be then acquired. So we hope by, by, by, by the industry and the were terrific. Elon Musk is in there somewhere innovating with who knows what's next out there and he's around. And next Sergei is out there too. A exactly. Exactly. So just look at our, at our home page, look at the curriculum, which we are currently defining now. Eh, that would be, that would be great on sit.org take me through how it works. I know you're just starting, but as you guys look at the world, I mean, first of all, I can see, I can see the attractiveness of a dual degree. >>Yeah. Because most kids get bored in college. They're freelancing anyway. They're learning on their own. I get that. But I can S so I want, so as you guys start building it out, what's going on? What's going, how's it work? What are you guys doing? You're recruiting tickets through the, the factory of work that needs to get done, if you will. What's the workflows look like? What's happening right now? So currently, I mean, we are talking about the university because we, we have students and we will have students and we weren't to have the best talents, uh, globally available. And that's why we are building institution that attracts those talents. And these is kind of the first priority to have, do I have the talents to get the tens to get students come to, to, to sit? And obviously the second part is he said, well, talking to the CEOs and Tito was in to understand what are the needs in 10 to 15 years as an outcome of this digital transformation. >>I mean, the world is computerized. Uh, as you just mentioned before, there are not enough computer scientists currently available. So four out of five companies in Switzerland direction also globally are lacking. Uh, of computer scientists and they understand, you know, at what the digital transformation means. And that's something that we really try to understand as well to build it up the curriculum. What's the timeline of starting with students? Is you right away? Do you have a location? Is there a building, I mean, give us a timeline. When did classes start? When you start bringing people in? Is it happening now? I mean, absolutely. So, so actually currently we are, we are hunting at, at uh, at some campus locations, looking at some campus locations, each a thousand where our main campus will be, will be located. Um, at the, at the, at the same time we are really building buildings structure. >>So we are appointing the strategic advisory board will be, we twill direct, eh, the curriculum of the university. Um, and, and which is represented already by, uh, very, um, great scientists. One of them, the president of the strategic advisory board being professor Dr. Noble selloff, which is a Nobel prize winner. And which actually brings in that, that new ma new material, um, science in our physics curriculum. So that's another thing that we are currently trying to do to build up that governance appropriate components. And third element that we are looking at is also to attract uh, industries and companies that sponsor the students. And that's actually an attractive ecosystem that we are trying to build up to combine science education and also entrepreneurship in business. In order to foster that, which means that we are looking at the campus, we are setting up a research center and I'm talking about two or three years down the line, the research center and then also a tech park where we can commercialize the innovation that the science green Springs in. >>So all in all we really aim to have a closed ecosystem and self sustaining ecosystem. Hopefully that we are going to establish. It's a really big idea. Congratulations. It's bold. It's and it's relevant. Absolutely. So I got to ask you the question, how do you finance all this? Who's paying for it? So tell us how do we get funded? It's very important. Otherwise we pull in, start up with such a tremendous pace. Uh, actually the vision is, is from Sergei Velo self, uh, founder and CEO of Acronis. Um, he, he's, Hey has actually secured the initial founding of the institution and now really we need to have more partners on board in order to make this self sustaining education edge educational system system as sustainable as you are going to be tuition base or scholarship based. Have you guys thought about that? Um, in terms of students it would be tuition-based ah, that's a classical classical model or at least at least in Switzerland and obviously to get the industry sponsoring students in order to also down the line employee them later on. >>That would be the idea situation. Nice vision for Sergei and nice gesture. But you've got to look at what his business is doing. They created a category called cyber protection. Extending the benefit to him is more candidates know physics edge. So why not? This is a great vision. Absolutely the win-win. Absolutely. And we all believe in that the entire, um, you know, stand up team believe in that vision. That's where we are here and building up this institution. Well when you need to go global will be in Silicon Valley and waiting for you guys to come there and collaborate with us there. I hope. I hope that because we want to compliment each other. As I mentioned, computer scientists, our need is globally and obviously also in the Silicon Valley and why not? I think the collaboration aspect is going to be a big part of the growth as you guys get >>settled in on the the first use case in Shevon housing. Exactly. You know, and get that built out, but I think with digital technologies, I think there'll be a great collaboration, bring some good talent in as faculty and advisors and exactly get the flywheel going except congratulations. Thanks for coming on. The key, the education game is changing with modernization of a global impact of technology for good. You're seeing the landscape of innovation hit education. This is another great example of it. Super proud. The interview. Thanks for coming on and sharing the insights. The world continues to evolve. Of course, the cube is, they're watching every turn. I'm John Feria here in Miami beach for the Crohn's global cyber summit. 2019 deck with more coverage after this short break.

you live from Las Vegas it's the cube covering you I pat forward America's 2019 brought to you by uipath welcome back everyone to the cubes live coverage of UI path forward here at the Bellagio in Las Vegas Nevada I'm your host Rebecca night co-hosting alongside Dave Volante we are joined by Gavin Jackson he is the senior vice president and managing director amia at uipath thanks so much for coming you are brand spanking new to brands thanking you AWS for four years yeah joined UI paths in September yeah I want to start this conversation by having you talk a little bit about what what appealed to you about UI path and what more do you want to make the leap after four years at AWS yeah so I had the privilege to be west of really having a really close proximity to enterprise customers and getting the opportunity to listen to what they really wanted when they were talking about their digital transformation journeys and as it turns out the sort of cloud first in the automation first eras if you will are operating models at to two sides of the same coin if you think about what the that the cloud proposition has been over the last number of years it's really been about sort of reducing or eliminating the undifferentiated heavy lifting so that builders can build and then that turned into an operating model principle and it became sort of cloud first it's the same thing for the automation world you know we are reducing and eliminating the undifferentiated heavy lifting of Tata a product of business processes and tasks and everything else whether they're complex tasks or simple tasks removing that so that builders can build and business people can innovate and given them the freedom to do what they need to do as business owners think about AWS we obviously follow them very closely yeah anybody but it strikes you didn't thank you such are filters yeah what's the analog so what I think we again I would say that we are we are providing tools so the builders could build but at the same time our our products that works across the entire business stack whether that is sort of automation first as an operating principle across all businesses or whether it's across a business persona whether it's a CFO or somebody in accounts or a salesperson or whatever might be we're building tools that take the mundane tasks away from those users so that they have the freedom to go and serve their customers or or innovate within finance or do the do the job that they really love doing and that's really important for the business it turns out there's not a lot of value and a lot of the work that people do every day so if we can remove some of that then innovation will have an exponential curve of progress and that's what we're focused on today yes yeah again there are similarities there so if I understand the you're shifting one date asked allowing people freeing them up to do so that they can have a strategic impact in their business yes yeah yeah I think it is so if you look at even the technology paradigms and how cloud and AWS evolved and then also the layer on how uipath is evolving in the same way so you have computing and compute power started really with the mainframe and went to distributed servers and then to virtual machines and then from virtual machines it went to hosted virtual machines in the cloud and then from then it went to containers and now we're in this world of server lists we're in the cloud right so effectively the logic lives in server lists and the infrastructure sort of disappears and that provides massive scale in the automation world you started off with big monolithic processes you then had sort of network processes with software and data in the middle of all of that networked RPA really came in as an early sort of tool to help automate a lot of that a lot of processes and now in the realms of sort of automation as a function where in the end like the end game really is where automation is the application and the the applications themselves the data sources the processes really disappear so that the best done analogy I can come up with a metaphor acting um up with is I'm a Marvel fan I'm a geeky kind of Marvel fan of my favorite character is his Iron Man or Tony Stark and more specifically the Jarvis AI so what's happening all the time with with Tony Stark in the Jarvis a is he's interacting with his AI user interface all the time and what's happening in the background is that Java she's working with probably you know a hundred different applications and a hundred different data sources and everything else and rather than having you know a human go and do what the integration work that robots are doing that for him and it's just coming back as a as an outcome yeah I'm gonna keep pushing on this yeah similarities and differences because where it seems to break down is where our PA is focusing on the citizen developer the the end-user I'm afraid of AWS I won't go near it I see that console I call it my techies hey you know AWS is you know you got to be you know pretty technical to actually leverage it at the same time I'm thinking well maybe not maybe my builders are building things that I can touch but help us square that circle yeah so I think you the world is trending towards as much automation as possible so if it can be automated or if you can reduce the the burden to get to innovation I think you know technology is moving that way even in coding I think the transit we're seeing whether it's AWS or anyone else is low to no code and so we we occupy a world within the RPA space or the intelligent automation space where we're providing tools for people that don't need a requirement or or a skill set to code and they can still manufacture a few world their own automations and particularly with a release that we're just announcing today which is Studio X it really kind of reduces the friction from a business user where's zero understanding of how to code to build their own automations whether it's kind of recording a process or just dragging and dropping different components into a process even like even I could do that and that's saying something I can tell you yes exactly yeah this idea of democratizing the the automation the building that you said yeah very much so what will this mean I mean what what does what does that bode for the future of how work gets done because that is at the core of what you're doing is typically understanding how and where work gets done or the bottlenecks where the challenges and how can our PA fix this so I think ultimately like a lot of technologies it's really about the the exponential curve of productivity and whether you're looking at a national level a global level a company level a human level every level productivity has declined really over the last number of years and technology hasn't done a great job to improve that and you can say that some technologies have done a good job again I'd use a TBS is a good job in terms of the proliferation or the how prolific you can get more code out and more more progress there but overall productivity has declined so our sort of view of the world is if you can democratize automation if you can use or add a digital workforce to your to your to your teams then you'll have an exponential curve of productivity which a human level is important company level is important a national level is important and probably at global level is important you know you guys might be right place right time as well yeah because I remember you know all the spending in the 80s said receive growth everywhere except the Nobel prize-winning economist Robert Solow yeah [Laughter] [Music] you guys are hitting it right at the right time yeah you be able to take credit for a lot of it but yeah your thoughts on that in terms of productivity depending yeah I think it is pent up I think that is where where we're at right now and it's ready to be unleashed and I think that these technologies are are the technologies that will unleash it I think really what's happened over the last number of decades probably is that the six trillion dollar IT industry they exist today has largely kind of increased productivity or performance of other technologies it hasn't really increased output so whether it's sort of you know the core networking when Cisco started core networking there was a big increase I would imagine in connectivity and outputs then the technologies that were laid on top of that maybe less so and it was just really kind of putting bad band-aids on problems so it was really technology solving technology problems rather than technology solving human output problems and so I think that this is now the most tangible technology category that really is turning technology into value and productivity for technology really unlocking a lot of value one of the things that your former boss Jeff Bezos said was bet on dreamy businesses that have unlimited upside these these dreamy businesses customers love them they grow to very large sizes they have strong returns on capital and they can endure for decades I wonder if you could put you iPad in that context of a dreamy business what does he know right I mean nobody right I mean so and this is one of the reasons I was attracted by the way to DUI path because I think I think that the robots themselves if you can just kind of look at the subcategory of the robot I think it's on a similar curve to how Gordon Moore was talking about the Intel microprocessor in 1965 and the exponential curve of progress I think we were on that similar curve so when I sort of project five years from now I just think that the amount the robots will be able to do the cognitive kind of capabilities it will be able to do are just phenomenal so and customers customers give us feedback all the time about to two things they love and they value what we do the value is important because it's very empirical for the first time they can actually deploy a technology and see almost an immediate return on their technology whether it's a point technology solving one process or a group of processes they can see an immediate empirical return the other thing that I like to measure I quite like is that they value it so they think they love it they love and value it so they love it meaning it actually induces an emotion so when you when you watch the robots in action and they watch something that has been holding your team back or there's been stifling productivity or whatever it is people get giddy about it it's quite fascinating to see comment about Gordon Moore and Ty that's a digital transformation when I think of digital transformation I think of data yeah what's the difference in a business in a digital business it's how they use data yeah they put data at the core and four years we march to the cadence of Moore's law and that's changed its that that's not what the innovation the engine is today it's it's machine intelligence it's data and it's cloud for scale where do you guys fit I mean obviously AI is a piece of that but but maybe you could add some color to where our PA fits in that equation so I think that's an important point because there's a lot of miscommunication I think about really what it means when you talk about digital transformation and what it means to be digitally transformed and really to see transformed you're really talking about a category of customers which are large more institutional enterprises and governments because they have something to transform what they're transforming into is more of a digital native sort of set of attributes more insurgent mindsets and these companies are to your point they're very data hungry they harvest as much data as they can from from value from data they're very customer centric they focus on the customer experience they use other people's resources oh the cloud being one great example of that and the missing point from what you said is they automate everything they've to meet it so part of the digital transformation journey is if it can be automated it will be automated and anything that's new will be born automated so let me ask a follow-up on that is there a cultural difference in amia versus what you're seeing in North America in terms of the receptivity to automation I mean there are certain parts of of Europe which are you know more protective of jobs do you see a cultural difference or are they kind of I mean we do see even some resistance here but when you talk to customers they're like no it's it's wonderful I love it what are you seeing in Europe so I don't I don't see much of a cultural difference there and I see don't I don't see yet I haven't seen any feedback yes Peres I'm very new still but I haven't seen anybody talk about really that this technology is a technology to take jobs out I think most people see this technology as a way of getting better performance out of humans you know pivoting them towards more so I would say like in some markets in my in my in my prior life in in many prior lives I would say that there's some countries like France for example that would have been a little bit more stayed within their approach to new technologies and adoption not so with regards to automation they see this as a real and game productivity increase thank you I think that's true for people who have tasted it yeah but I do think there's still some reticence in the ranks until they actually experience it that's why we'll talk to some customers about it they'll have bought a Thon's and just a yeah to educate people and what's possible to let them try to build their own robots and then people then the light bulbs go off that it's taking away the aggravations the frustrations the mundi the drudgery and then you said people get giddy about those things you don't have to do that yeah but then the question is also so so what creative things are you doing now so how are you spending your time what are you doing differently that makes your job more interesting more compelling yeah and and and I think that's the real question - so what is the okay yes receiving some money and people aren't having to do those mundane tasks but then what are what is the value add that the employees are now bringing to the table yeah so in actually sit and it takes made the right point as well in terms of the mechanism for doing that is the the part of the battle here is to spark the imagination just like anything really just let you like it back in the Amazon wild it's all of our spark in the imagination if you can if you can imagine it you can build it it's the same thing really with within our world now is figuring out with customers what think what tasks do they do that they hate doing either a user level or a downstream level what are the things that they really want to do that they need our help to harvest and so we do the same sort the same sort of things that we would have done with AWS where we did lots of hackathons and you bought lots of technology partners in with us and we would sort of building all of this we do exactly the same thing with the RP a space it's exactly the same this is really important because creativity is going to become an increasingly important because if productivity goes up it means you can do the same amount of work with less people so it is going to impact jobs and people are gonna have to be comfortable to get out of their comfort zone and become creative and find ways to apply these technologies to really advance but you know drive value to their organizations and actually I look at this as well as a long term technology whereas a long term technology is something that's important for my children I've three and they're still very young so twelve ten and six but eventually they will go into the workplace with these skills embedded they will just know the how you get work done is you have your robot do a whole load of tasks for you here and your your job is to build and to be creative and to harvest data and to manipulate data and and serve customers and focus on the customer experience that's really what it's all about the real brain works I've been a pleasure having you on the show at uipath thank you so much appreciate it i'm rebecca night for j4 day Volante please stay tuned for more from the cubes live coverage of uipath coming up in just a little bit

>> from Cambridge, Massachusetts. It's the Cube covering M I T. Chief data officer and information quality Symposium 2019. Brought to you by Silicon Angle Media. >> Welcome back >> to M I. T. Everybody watching the Cube, The leader in live tech coverage. My name is Dave Volonte here with Paul Guillen. My co host, Tom Davenport, is here is the president's distinguished professor at Babson College. Huebel? Um, good to see again, Tom. Thanks for coming on. Glad to be here. So, yeah, this is, uh let's see. The 13th annual M I t. Cdo lucky. >> Yeah, sure. As this year. Our seventh. I >> think so. Really? Maybe we'll offset. So you gave a talk earlier? She would be afraid of the machines, Or should we embrace them? I think we should embrace them, because so far, they are not capable of replacing us. I mean, you know, when we hit the singularity, which I'm not sure we'll ever happen, But it's certainly not going happen anytime soon. We'll have a different answer. But now good at small, narrow task. Not so good at doing a lot of the things that we do. So I think we're fine. Although as I said in my talk, I have some survey data suggesting that large U. S. Corporations, their senior executives, a substantial number of them more than half would liketo automate as many jobs as possible. They say. So that's a little scary. But unfortunately for us human something, it's gonna be >> a while before they succeed. Way had a case last year where McDonald's employees were agitating for increasing the minimum wage and tThe e management used the threat of wrote of robotics sizing, hamburger making process, which can be done right to thio. Get them to back down. Are you think we're going to Seymour of four that were maybe a eyes used as a threat? >> Well, I haven't heard too many other examples. I think for those highly structured, relatively low level task, it's quite possible, particularly if if we do end up raising the minimum wage beyond a point where it's economical, pay humans to do the work. Um, but I would like to think that, you know, if we gave humans the opportunity, they could do Maur than they're doing now in many cases, and one of the things I was saying is that I think companies are. Generally, there's some exceptions, but most companies they're not starting to retrain their workers. Amazon recently announced they're going to spend 700,000,000 to retrain their workers to do things that a I and robots can't. But that's pretty rare. Certainly that level of commitment is very rare. So I think it's time for the companies to start stepping up and saying, How can we develop a better combination of humans and machines? >> The work by, you know, brain Nelson and McAfee, which is a little dated now. But it definitely suggests that there's some things to be concerned about. Of course, ultimately there prescription was one of an optimist and education, and yeah, on and so forth. But you know, the key point there is the machines have always replace humans, but now, in terms of cognitive functions, but you see it everywhere you drive to the airport. Now it's Elektronik billboards. It's not some person putting up the kiosks, etcetera, but you know, is you know, you've you've used >> the term, you know, paid the cow path. We don't want to protect the past from the future. All right, so, to >> your point, retraining education I mean, that's the opportunity here, isn't it? And the potential is enormous. Well, and, you know, let's face it, we haven't had much in the way of productivity improvements in the U. S. Or any other advanced economy lately. So we need some guests, you know, replacement of humans by machines. But my argument has always been You can handle innovation better. You can avoid sort of race to the bottom at automation sometimes leads to, if you think creatively about humans and machines working as colleagues. In many cases, you remember in the PC boom, I forget it with a Fed chairman was it might have been, Greenspan said, You can see progress everywhere except in the product. That was an M. I. T. Professor Robert Solow. >> OK, right, and then >> won the Nobel Prize. But then, shortly thereafter, there was a huge productivity boom. So I mean is there may be a pent up Well, God knows. I mean, um, everybody's wondering. We've been spending literally trillions on I t. And you would think that it would have led toe productivity, But you know, certain things like social media, I think reduced productivity in the workplace and you know, we're all chatting and talking and slacking and sewing all over the place. Maybe that's is not conducive to getting work done. It depends what you >> do with that social media here in our business. It's actually it's phenomenal to see political coverage these days, which is almost entirely consist of reprinting politicians. Tweets >> Exactly. I guess it's made life easier for for them all people reporters sitting in the White House waiting for a press conference. They're not >> doing well. There are many reporters left. Where do you see in your consulting work your academic work? Where do you see a I being used most effectively in organizations right now? And where do you think that's gonna be three years from now? >> Well, I mean, the general category of activity of use case is the sort of someone's calling boring I. It's data integration. One thing that's being discussed a lot of this conference, it's connecting your invoices to your contracts to see Did we actually get the stuff that we contracted for its ah, doing a little bit better job of identifying fraud and doing it faster so all of those things are quite feasible. They're just not that exciting. What we're not seeing are curing cancer, creating fully autonomous vehicles. You know, the really aggressive moonshots that we've been trying for a while just haven't succeeded at what if we kind of expand a I is gonna The rumor, trawlers. New cool stuff that's coming out. So considering all these new checks with detective Aye, aye, Blockchain new security approaches. When do you think that machines will be able to make better diagnoses than doctors? Well, I think you know, in a very narrow sense in some cases, that could do it now. But the thing is, first of all, take a radiologist, which is one of the doctors I think most at risk from this because they don't typically meet with patients and they spend a lot of time looking at images. It turns out that the lab experiments that say you know, these air better than human radiologist say I tend to be very narrow, and what one lab does is different from another lab. So it's just it's gonna take a very long time to make it into, you know, production deployment in the physician's office. We'll probably have to have some regulatory approval of it. You know, the lab research is great. It's just getting it into day to day. Reality is the problem. Okay, So staying in this context of digital a sort of umbrella topic, do you think large retail stores roll largely disappeared? >> Uh, >> some sectors more than others for things that you don't need toe, touch and feel, And soon before you're to them. Certainly even that obviously, it's happening more and more on commerce. What people are saying will disappear. Next is the human at the point of sale. And we've been talking about that for a while. In In grocery, Not so not achieve so much yet in the U. S. Amazon Go is a really interesting experiment where every time I go in there, I tried to shoplift. I took a while, and now they have 12 stores. It's not huge yet, but I think if you're in one of those jobs that a substantial chunk of it is automata ble, then you really want to start looking around thinking, What else can I do to add value to these machines? Do you think traditional banks will lose control of the payment system? Uh, No, I don't because the Finn techs that you see thus far keep getting bought by traditional bank. So my guess is that people will want that certainty. And you know, the funny thing about Blockchain way say in principle it's more secure because it's spread across a lot of different ledgers. But people keep hacking into Bitcoin, so it makes you wonder. I think Blockchain is gonna take longer than way thought as well. So, you know, in my latest book, which is called the Aye Aye Advantage, I start out talking by about Tamara's Law, This guy Roy Amara, who was a futurist, not nearly as well known as Moore's Law. But it said, You know, for every new technology, we tend to overestimate its impact in the short run and underestimated Long, long Ryan. And so I think a I will end up doing great things. We may have sort of tuned it out of the time. It actually happens way finally have autonomous vehicles. We've been talking about it for 50 years. Last one. So one of the Democratic candidates of the 75 Democratic ended last night mentioned the chief manufacturing officer Well, do you see that automation will actually swing the pendulum and bring back manufacturing to the U. S. I think it could if we were really aggressive about using digital technologies in manufacturing, doing three D manufacturing doing, um, digital twins of every device and so on. But we are not being as aggressive as we ought to be. And manufacturing companies have been kind of slow. And, um, I think somewhat delinquent and embracing these things. So they're gonna think, lose the ability to compete. We have to really go at it in a big way to >> bring it. Bring it all back. Just we've got an election coming up. There are a lot of concern following the last election about the potential of a I chatbots Twitter chat bots, deep fakes, technologies that obscure or alter reality. Are you worried about what's coming in the next year? And that that >> could never happen? Paul. We could never see anything deep fakes I'm quite worried about. We don't seem. I know there's some organizations working on how we would certify, you know, an image as being really But we're not there yet. My guess is, certainly by the time the election happens, we're going to have all sorts of political candidates saying things that they never really said through deep fakes and image manipulation. Scary? What do you think about the call to break up? Big check. What's your position on that? I think that sell a self inflicted wound. You know, we just saw, for example, that the automobile manufacturers decided to get together. Even though the federal government isn't asking for better mileage, they said, We'll do it. We'll work with you in union of states that are more advanced. If Big Tak had said, we're gonna work together to develop standards of ethical behavior and privacy and data and so on, they could've prevented some of this unless they change their attitude really quickly. I've seen some of it sales force. People are talking about the need for data standard data protection standards, I must say, change quickly. I think they're going to get legislation imposed and maybe get broken up. It's gonna take awhile. Depends on the next administration, but they're not being smart >> about it. You look it. I'm sure you see a lot of demos of advanced A I type technology over the last year, what is really impressed you. >> You know, I think the biggest advances have clearly been in image recognition looking the other day. It's a big problem with that is you need a lot of label data. It's one of the reasons why Google was able to identify cat photos on the Internet is we had a lot of labeled cat images and the Image net open source database. But the ability to start generating images to do synthetic label data, I think, could really make a big difference in how rapidly image recognition works. >> What even synthetic? I'm sorry >> where we would actually create. We wouldn't have to have somebody go around taking pictures of cats. We create a bunch of different cat photos, label them as cat photos have variations in them, you know, unless we have a lot of variation and images. That's one of the reasons why we can't use autonomous vehicles yet because images differ in the rain and the snow. And so we're gonna have to have synthetic snow synthetic rain to identify those images. So, you know, the GPU chip still realizes that's a pedestrian walking across there, even though it's kind of buzzed up right now. Just a little bit of various ation. The image can throw off the recognition altogether. Tom. Hey, thanks so much for coming in. The Cube is great to see you. We gotta go play Catch. You're welcome. Keep right. Everybody will be back from M I t CDO I Q In Cambridge, Massachusetts. Stable, aren't they? Paul Gillis, You're watching the Cube?

(energetic, playful techno music) [Voiceover] - Live from Las Vegas, it's the CUBE covering AWS re:Invent 2018. Brought to you by Amazon web Services, Intel, and their ecosystem partners. >> We are live here at the Sands Expo, one of eight venues that are actually here for AWS re:Invent this week as we continue our three day coverage here with you Tuesday, Wednesday, and Thursday as well, live from Las Vegas here on the CUBE. Along with Justin Warren, I'm John Walls, and we're now joined by Phil Tee, who is the CEO and co-founder of Moodsoft. Phil, good to see you this afternoon. >> Great to be with you, thank you for inviting me along. >> You bet, nice to have you. In fact, I'm going to be the only guy without a charming accent on this set as a matter of fact. A UK and an Aussie here. Your world's data, right? And it kind of reminds me of the old movie Jaws when Robert says we're going to need a bigger boat, right? AI ops, is that your bigger boat? Is that how you're handling this world of data? >> I think that's exactly spot on and one of the things we observe at Moodsoft with our customers is just this crazy complexity that they have to deal with. I mean, we cover everything from large financials, telephone companies, e-commerce businesses, and the drive to adopt agile and cloud and software defined in the the enterprise has driven complexity to the point where the poor old human brain is just out of luck, right? The unaided "I'll figure it out by myself" approach, dead in the water, and you've got to use this artificial intelligence approach precisely as your "bigger boat" to go catch that shark. >> Right. So tell us, there's a lot of hype around AI, and machine learning, and all of these different buzzwords are getting thrown around. Dial us in a little bit. Explain what you mean by AI in the context you're talking about things with Moodsoft. >> So, I think that's very perceptive. There's a tweet going around at the moment which describes the difference between machine learning and AI as, if it's written in Python it's probably machine learning, if it's on a Powerpoint deck it's probably AI. Which is kind of funny, but to the point. And there is a ton of hype going around artificial intelligence. There are some purists who claim there is no such thing as AI and everything we talk about today is really machine learning or data driven algorithms. And there's some truth in that. Really, what we mean by AI is the full panoply of both feature detection, you know, looking for patterns that are not obvious to the human eye all the way through to deep learning, neural nets, convolutional neural nets, where you are training a system to recognize features of the data as representative of something underlying that you're hunting for. So in the case of AI ops it's looking for the cause of, or looking for the presence of, a potential service-impacting outage in the data that we monitor, in the events. But one thing it's not going to do is, it's not going to unplug itself from the internet and come and kill you anytime soon. It's really quite benign and very useful to our customers with what they deal with. >> So, to that point, because you have so much data, and it seems like, I hate to say, most of it isn't needed or most of it isn't of value, but a lot of it isn't, if not most. How do you then decern, how do you assess value and assign value to what really is important and then, put it to use today, when you're getting so much more information than you were even a year ago? >> So just to put a little bit of context on the amount of data, way back before the cloud and virtualization, a typical enterprise, a high event rate would have been 100, 200, events a second. Nowadays, in an average customer of ours, you add a zero or two to that rate, maybe even three. And it's one of the reasons why the legacy systems really struggle with that data. So, job one is, if you accept, and I certainly do, that most of that data is junk. Most of it is inconsequential. You've got to have an algorithmic way of getting rid of that. You know, the old-fashioned way was creating lists of "ignore it because it's a certain severity", "ignore it because it comes from this list of hosts", you know, the whole listing approach. What we do is we use information science. So we can measure the semantic content, and the informational content, of an event to work out whether it's telling us something of import. And we use that technique with great effectiveness to eliminate as much as ninety, ninety-five, percent of the inbound data as effectively affecting nothing. So that narrows the data lake, you feel, down to a point where we can process it in real time through much more computer-intensive AI algorithms to kind of get that high-quality indication of an instance or a potential instance. >> A lot of machine learning and AI is based on learning from history, so, "we've seen all this stuff before and we know what that means", or, even encouraging the machines to go and look at the historical data and then pull out the details as you said. Even things that a human might miss, you'll look at that data and then learn new things. How does that work when we're doing all of this innovation? When there's all of this change and novelty coming in, how does the AI system cope with that kind of environment? >> So, you have to have a dual approach. I mean, I guess everyone's familiar with Mikolas Nassim Taleb's book, The Black Swans. He was trying to explain why it is that you can get a bunch of Nobel Prize winners in a room to design a hedge fund and it can still go bankrupt in the blink of and eye, like the long-term capital management. And the truth of the matter is, yes, an awful lot of the techniques that are supervised and based upon a training set are vulnerable to the "unknown unknowns" to misquote Donald Rumsfeld, and that's why we use a combination of unsupervised feature detection and supervised learning. The unsupervised feature detection just knows something as an unusual, highly correlated, pattern or feature in the data and needs no prior understanding of what's going on. Now, interestingly, there are some hybrid techniques now. You may have heard of something called transfer learning, which is the idea that you partially train a neural net on some kind of standard corpus. It'd be like the stuff that you already know and adapting that sort of partially trained net to something that is literally very, very, very adapted to the system that it's monitoring, it does that very quickly rather than having to wait for a certain critical amount of data before the net is converged. And so those sort of techniques, which we also experiment with at Moodsoft, I think are going to be interesting directions for us in the future with our platform, but there's maybe a hundred PhDs a week given out in AI and machine learning these days. It's definitely getting a lot of focus and there's a ton of innovation that's coming down the line. One thing that we're particularly committed to is shortening the distance between when something's invented and when we can get it into our customer's hands. >> There's usually quite a lag, historically, it's about ten years before someone discovers something and then it actually makes it into the business world so if we could shorten that cycle that would be quite useful. >> We know an academic called professor Maggie Bowden who's just getting ready to retire and she was one of the original authors of the neural net papers in the 1960's, so that kind of gives you an idea of the lag, it could be many, many, decades and it's a shame because the truth of the matter is the pressure on all the people coming to a show like this that want to benefit from the public cloud, new ways of thinking about the application development toolchain, they don't have time to wait around for that innovation to come to them. We've got to drive it a lot faster and, certainly, we view that as one of our missions at Moodsoft, as being passionately involved and sort of shortening that gap between innovation and a production implementation as something really cool. >> So what have you seen at the show so far that you think you want to take to your customers and say, "oh, actually, this is happening and you need to get on to this now"? >> One thing I've observed here is, I guess if we would've been here two years ago, nobody was talking about AI ops. I mean essentially the entirety of how people looked at the cloud was "same old stuff, just lives somewhere different, we can use all of the old techniques". You walk around here, there's a bunch of startups, more established companies, recognizing that a new approach is necessary and my sense of it is that this market which, I mean, let's be honest, we were pretty lonely in it two or three years ago, is starting to feel like it's a little more populated and that's goodness, we're very happy about that, so that is definitely a take away. You know, to go to customers and say "this is no longer bleeding edge, it's simply leading edge". >> Not just a gap in the market, there is actually a market in that gap. >> You're the bigger boat. >> Well, we hope so. >> Phil, thanks for being with us. We appreciate your time here on the CUBE and once again, have a great show and we do thank you for your time, sir. >> Thank you very much indeed. Great talking to you both. >> Phil Tee from Moodsoft joining us here on the CUBE. We're at AWS re:Invent and we're at the Sands, and we're in Las Vegas. (energetic, playful techno music)

>> Live from Toronto Canada, it's The Cube, covering Global Cloud and Blockchain Summit 2018, brought to you by The Cube. >> Hello, everyone welcome back to The Cube's live coverage here in Toronto for the first Global Cloud and Blockchain Summit in conjunction with the Blockchain futurist happening this week it's run. I'm John Fourier, my cohost Dave Vellante, we're here with Cube alumni, Bradley Rotter, pioneer Blockchain investor, seasoned pro was there in the early days as an investor in hedge funds, continuing to understand the impacts of cryptocurrency, and its impact for investors, and long on many of the crypto. Made some great predictions on The Cube last time at Polycon in the Bahamas. Bradley, great to see you, welcome back. >> Thank you, good to see both of you. >> Good to have you back. >> So I want to just get this out there because you have an interesting background, you're in the cutting edge, on the front lines, but you also have a history. You were early before the hedge fund craze, as a pioneer than. >> Yeah. >> Talk about that and than how it connects to today, and see if you see some similarities, talk about that. >> I actually had begun trading commodity futures contracts when I was 15. I grew up on a farm in Iowa, which is a small state in the Midwest. >> I've heard of it. >> And I was in charge of >> Was it a test market? (laughing) >> I was in charge of hedging our one corn contract so I learned learned the mechanisms of the market. It was great experience. I traded commodities all the way through college. I got to go to West Point as undergrad. And I raced back to Chicago as soon as I could to go to the University of Chicago because that's where commodities were trading. So I'd go to night school at night at the University of Chicago and listen to Nobel laureates talk about the official market theory and during the day I was trading on the floor of the the Chicago Board of Trade and the Chicago Mercantile Exchange. Grown men yelling, kicking, screaming, shoving and spitting, it was fabulous. (laughing) >> Sounds like Blockchain today. (laughing) >> So is that what the dynamic is, obviously we've seen the revolution, certainly of capital formation, capital deployment, efficiency, liquidity all those things are happening, how does that connect today? What's your vision of today's market? Obviously lost thirty billion dollars in value over the past 24 hours as of today and we've taken a little bit of a haircut, significant haircut, since you came on The Cube, and you actually were first to predict around February, was a February? >> February, yeah. >> You kind of called the market at that time, so props to that, >> Yup. >> Hope you're on the right >> Thank you. >> side of those shorts >> Thank you. >> But what's going on? What is happening in the capital markets, liquidity, why are the prices dropping? What's the shift? So just a recap, at the time in February, you said look I'm on short term bear, on Bitcoin, and may be other crypto because all the money that's been made. the people who made it didn't think they had to pay taxes. And now they're realizing, and you were right on. You said up and up through sort of tax season it's going to be soft and then it's going to come back and it's exactly what happened. Now it's flipped again, so your thoughts? >> So my epiphany was I woke up in the middle of the night and said oh my God, I've been to this rodeo before. I was trading utility tokens twenty years ago when they were called something else, IRUs, do you remember that term? IRU was the indefeasible right to use a strand of fiber, and as the internet started kicking off people were crazy about laying bandwidth. Firms like Global Crossing we're laying cable all over the ocean floors and they laid too much cable and the cable became dark, the fiber became dark, and firms like Global Crossing, Enron, Enron went under really as a result of that miss allocation. And so it occurred to me these utility tokens now are very similar in characteristic except to produce a utility token you don't have to rent a boat and lay cable on the ocean floor in order to produce one of these utility tokens, that everybody's buying, I mean it takes literally minutes to produce a token. So in a nutshell it's too many damn tokens. It was like the peak of the internet, which we were all involved in. It occurred to me then in January of 2000 the market was demanding internet shares and the market was really good at producing internet shares, too many of them, and it went down. So I think we're in a similar situation with cryptocurrency, the Wall Street did come in, there were a hundred plus hedge funds of all shapes and sizes scrambling and buying crypto in the fall of last year. It's kind of like Napoleon's reason for attacking Russia, seemed like a good idea at the time. (laughing) And so we're now in a corrective phase but literally there's been too many tokens. There are so many tokens that we as humans can't even deal with that. >> And the outlook, what's the outlook for you? I mean, I'll see there's some systemic things going to be flushed out, but you long on certain areas? What do you what do you see as a bright light at the end of the tunnel or sort right in front of you? What's happening from a market that you're excited about? >> At a macro scale I think it's apparent that the internet deserves its own currency, of course it does and there will be an internet currency. The trick is which currency shall that be? Bitcoin was was a brilliant construct, the the inventor of Bitcoin should get a Nobel Prize, and I hope she does. (laughing) >> 'Cause Satoshi is female, everyone knows that. (laughing) >> I got that from you actually. (laughing) But it may not be Bitcoin and that's why we have to be a little sanguine here. You know, people got a little bit too optimistic, Bitcoin's going to a hundred grand, no it's going to five hundred grand. I mean, those are all red flags based on my experience of trading on the floor and investing in hedge funds. Bitcoin, I think I'm disappointed in Bitcoins adoption, you know it's still very difficult to use Bitcoin and I was hoping by now that that would be a different scenario but it really isn't. Very few people use Bitcoin in their daily lives. I do, I've been paying my son his allowance for years in Bitcoin. Son of a bitch is rich now. (laughing) >> Damn, so on terms of like the long game, you seeing the developers adopted a theory and that was classic, you know the decentralized applications. We're here at a Cloud Blockchain kind of convergence conference where developers mattered on the Cloud. You saw a great developer, stakeholders with Amazon, Cloud native, certainly there's a lot of developers trying to make things easier, faster, smarter, with crypto. >> Yup. >> So, but all at the same time it's hard for developers. Hearing things like EOS coming on, trying to get developers. So there's a race for developer adoption, this is a major factor in some of the success and price drops too. Your thoughts on, you know the impact, has that changed anything? I mean, the Ethereum at the lowest it's been all year. >> Yup. Yeah well, that was that was fairly predictable and I've talked about that at number of talks I've given. There's only one thing that all of these ICOs have had in common, they're long Ethereum. They own Ethereum, and many of those projects, even out the the few ICO projects that I've selectively been advising I begged them to do once they raised their money in Ethereum is to convert it into cash. I said you're not in the Ethereum business, you're in whatever business that you're in. Many of them ported on to that stake, again caught up in the excitement about the the potential price appreciation but they lost track of what business they were really in. They were speculating in Ethereum. Yeah, I said they might as well been speculating in Apple stock. >> They could have done better then Ethereum. >> Much better. >> Too much supply, too many damn tokens, and they're easy to make. That's the issue. >> Yeah. >> And you've got lots of people making them. When one of the first guys I met in this space was Vitalik Buterin, he was 18 at the time and I remember meeting him I thought, this is one of the smartest guys I've ever met. It was a really fun meeting. I remember when the meeting ended and I walked away I was about 35 feet away and he LinkedIn with me. Which I thought was cute. >> That's awesome, talk about what you're investing-- >> But, now there's probably a thousand Vitalik Buterin's in the space. Many of them are at this conference. >> And a lot of people have plans. >> Super smart, great ideas, and boom, token. >> And they're producing new tokens. They're all better improved, they're borrowing the best attributes of each but we've got too many damn tokens. It's hard for us humans to be able to keep track of that. It's almost like requiring a complicated new browser download for every website you went to. We just can't do that. >> Is the analog, you remember the dot com days, you referred to it earlier, there was quality, and the quality lasted, sustained, you know, the Amazon's, the eBay's, the PayPal's, etc, are there analogs in this market, in your view, can you sniff out the sort of quality? >> There are definitely analogs, I think, but I think one of the greatest metrics that we can we can look at is that utility token being utilized? Not many of them are being utilized. I was giving a talk last month, 350 people in the audience, and I said show of hands, how many people have used a utility token this year? One hand went up. I go, Ethereum? Ethereum. Will we be using utility tokens in the future? Of course we will but it's going to have to get a whole lot easier for us humans to be able to deal with them, and understand them, and not lose them, that's the big issue. This is just as much a cybersecurity play as it is a digital currency play. >> Elaborate on that, that thought, why is more cyber security playing? >> Well, I've had an extensive background in cyber security as an investor, my mantra since 9/11 has been to invest in catalyze companies that impact the security of the homeland. A wide variety of security plays but primarily, cyber security. It occurred to me that the most valuable data in the world used to be in the Pentagon. That's no longer the case. Two reasons basically, one, the data has already been stolen. (laughing) Not funny. Two, if you steal the plans for the next generation F39 Joint Strike Force fighter, good for you, there's only two buyers. (laughing) The most valuable data in the world today, as we sit here, is a Bitcoin private key, and they're coming for them. Prominent Bitcoin holders are being hunted, kidnapped, extorted, I mean it's a rather extraordinary thing. So the cybersecurity aspect of if all of our assets are going to be digitized you better damn well keep those keys secure and so that's why I've been focused on the cybersecurity aspect. Rivets, one of the ICOs that I invested in is developing software that turns on the power of the hardware TPM, trusted execution environment, that's already on your phone. It's a place to hold keys in hardware. So that becomes fundamentally important in holding your keys. >> I mean certainly we heard stories about kidnapping that private key, I mean still how do you protect that? That's a good question, that's a really interesting question. Is it like consensus, do you have multiple people involved, do you get beaten up until you hand over your private key? >> It's been happening. It's been happening. >> What about the security token versus utility tokens? A lot of tokens now, so there's yeah, too many tokens on the utility side, but now there's a surge towards security tokens, and Greg Bettinger wrote this morning that the market has changed over and the investor side's looking more and more like traditional in structures and companies, raising money. So security token has been a, I think relief for some people in the US for sure around investing in structures they understand. Is that a real dynamic or is that going to sustain itself? How do you see security tokens? >> And we heard in the panel this morning, you were in there, where they were predicting the future of the valuation of the security tokens by the end of the year doubling, tripling, what ever it was, but what are your thoughts? >> I think security tokens are going to be the next big thing, they have so many advantages to what we now regard as share certificates. My most exciting project is that I'm heavily involved in is a project called the Entanglement Institute. That's going to, in the process of issuing security infrastructure tokens, so our idea is a public-private partnership with the US government to build the first mega quantum computing center in Newport, Rhode Island. Now the private part of the public-private partnership by the issuance of tokens you have tremendous advantages to the way securities are issued now, transparency, liquidity. Infrastructure investments are not very liquid, and if they were made more liquid more people would buy them. It occurred to me it would have been a really good idea if grandpa would have invested in the Hoover Dam. Didn't have the chance. We think that there's a substantial demand of US citizens that would love to invest in our own country and would do so if it were more liquid, if it was more transparent, if the costs were less of issuing those tokens. >> More efficient, yeah. >> So you see that as a potential way to fund public infrastructure build-outs? >> It will be helpful if infrastructure is financed in the future. >> How do you see the structure on the streets, this comes up all the time, there's different answers to this. There's not like there's one, we've seen multiple but I'm putting a security token, what am i securing against, cash flow, equity, right to convert to utility tokens? So we're starting to see a variety of mechanisms, 'cause you have to investor a security outcome. >> Yeah, so as an investor, what do you look for? >> Well, I think it's almost limitless of what these smart securities, you know can be capable of, for example one of the things that were that we're talking with various parts of the government is thinking about the tax credit. The tax credit that have been talked about at the Trump administration, that could be really changed on its head if you were able to use smart securities, if you will. Who says that the tax credit for a certain project has to be the same as all other projects? The president has promised a 1.5 trillion dollar infrastructure investment program and so far he's only 1.5 trillion away from the goal. It hasn't started yet. Wilbur Ross when, in the transition team, I had seen the white paper that he had written, was suggesting an 82% tax credit for infrastructure investment. I'm going 82%, oh my God, I've never. It's an unfathomable number. If it were 82% it would be the strongest fiscal stimulus of your lifetime and it's a crazy number, it's too big. And then I started thinking about it, maybe an 82% tax credit is warranted for a critical infrastructure as important as quantum computing or cyber security. >> Cyber security. >> Exactly, very good point, and maybe the tax credit is 15% for another bridge over the Mississippi River. We already got those. So a smart infrastructure token would allow the Larry Kudlow to turn the dial and allow economic incentive to differ based on the importance of the project. >> The value of the project. >> That is a big idea. >> That is a big idea. >> That is what we're working on. >> That is a big idea, that is a smart contract, smart securities that have allocations, and efficiencies, and incentives that aren't perverse or generic. >> It aligns with the value of the society he needs, right. Talk about quantum computing more, the potential, why quantum, what attracted you to quantum? What do you see as the future of quantum computing? >> You know, you don't you don't have to own very much Bitcoin before what wakes you up in the middle of the night is quantum computing. It's a hundred million times faster than computing as we know it today. The reason that I'm involved in this project, I believe it's a matter of national security that we form a national initiative to gain quantum supremacy, or I call it data supremacy. And right now we're lagging, the Chinese have focused on this acutely and are actually ahead, I believe of the United States. And it's going to take a national initiative, it's going to take a Manhattan Project, and that's that's really what Entanglement Institute is, is a current day Manhattan Project partnering with government and three-letter agencies, private industry, we have to hunt as a pack and focus on this or we're going to be left behind. >> And that's where that's based out of. >> Newport, Rhode Island. >> And so you got some DC presence in there too? >> Yes lots of DC presence, this is being called Quantum summer in Washington DC. Many are crediting the Entanglement Institute for that because they've been up and down the halls of Congress and DOD and other-- >> Love to introduce you to Bob Picciano, Cube alumni who heads up quantum computing for IBM, would be a great connection. They're doing trying to work their, great chips to building, open that up. Bradley thanks for coming on and sharing your perspective. Always great to see you, impeccable vision, you've got a great vision. I love the big ideas, smart securities, it's coming, that is, I think very clear. >> Thank you for sharing. >> Thank you. The Cube coverage here live in Toronto. The Cube, I'm John Furrier, Dave Vellante, more live coverage, day one of three days of wall-to-wall coverage of the Blockchain futurist conference. This is the first global Cloud Blockchain Summit here kicking off the whole week. Stay with us for more after this short break.

>> Announcer: Live from Madrid, Spain, it's the Cube, covering HPE Discover Madrid 2017, brought to you by Hewlett Packard Enterprise. >> Welcome back to Madrid, Spain everybody, this is the Cube, the leader in live tech coverage. We're here covering HPE Discover 2017. I'm Dave Vellante with my cohost for the week, Peter Burris, Randy Meyer is back, he's the vice president and general manager Synergy and Mission Critical Solutions at Hewlett Packard Enterprise and Paul Shellerd is here, the director of the Center for Theoretical Cosmology at Cambridge University, thank you very much for coming on the Cube. >> It's a pleasure. >> Good to see you again. >> Yeah good to be back for the second time this week. I think that's, day stay outlets play too. >> Talking about computing meets the cosmos. >> Well it's exciting, yesterday we talked about Superdome Flex that we announced, we talked about it in the commercial space, where it's taking HANA and Orcale databases to the next level but there's a whole different side to what you can do with in memory compute. It's all in this high performance computing space. You think about the problems people want to solve in fluid dynamics, in forecasting, in all sorts of analytics problems, high performance compute, one of the things it does is it generates massive amounts of data that people then want to do things with. They want to compare that data to what their model said, okay can I run that against, they want to take that data and visualize it, okay how do I go do that. The more you can do that in memory, it means it's just faster to deal with because you're not going and writing this stuff off the disk, you're not moving it to another cluster back and forth, so we're seeing this burgeoning, the HPC guys would call it fat nodes, where you want to put lots of memory and eliminate the IO to go make their jobs easier and Professor Shallard will talk about a lot of that in terms of what they're doing at the Cosmos Institute, but this is a trend, you don't have to be a university. We're seeing this inside of oil and gas companies, aerospace engineering companies, anybody that's solving these complex computational problems that have an analytical element to whether it's comparative model, visualize, do something with that once you've done that. >> Paul, explain more about what it is you do. >> Well in the Cosmos Group, of which I'm the head, we're interested in two things, cosmology, which is trying to understand where the universe comes from, the whole big bang and then we're interested in black holes, particularly their collisions which produce gravitational waves, so they're the two main areas, relativity and cosmology. >> That's a big topic. I don't even know where to start, I just want to know okay what have you learned and can you summarize it for a lay person, where are you today, what can you share with us that we can understand? >> What we do is we take our mathematical models and we make predictions about the real universe and so we try and compare those to the latest observational data. We're in a particularly exciting period of time at the moment because of a flood of new data about the universe and about black holes and in the last two years, gravitational waves were discovered, there's a Nobel prize this year so lots of things are happening. It's a very data driven science so we have to try and keep up with this flood of new data which is getting larger and larger and also with new types of data, because suddenly gravitational waves are the latest thing to look at. >> What are the sources of data and new sources of data that you're tapping? >> Well, in cosmology we're mainly interested in the cosmic microwave background. >> Peter: Yeah the sources of data are the cosmos. >> Yeah right, so this is relic radiation left over from the big bang fireball, it's like a photograph of the universe, a blueprint and then also in the distribution of galaxies, so 3D maps of the universe and we've only, we're in a new age of exploration, we've only got a tiny fraction of the universe mapped so far and we're trying to extract new information about the origin of the universe from that data. In relativity, we've got these gravitational waves, these ripples in space time, they're traversing across the universe, they're essentially earthquakes in the universe and they're sound waves or seismic waves that propagate to us from these very violent events. >> I want to take you to the gravitational waves because in many respects, it's an example of a lot of what's here in action. Here's what I mean, that the experiment and correct me if I'm wrong, but it's basically, you create a, have two lasers perpendicular to each other, shooting a signal about two or three miles in that direction and it is the most precise experiment ever undertaken because what you're doing is you're measuring the time it takes for one laser versus another laser and that time is a function of the slight stretching that comes from the gravitational rays. That is an unbelievable example of edge computing, where you have just the tolerances to do that, that's not something you can send back to the cloud, you gotta do a lot of the compute right there, right? >> That's right, yes so a gravitational wave comes by and you shrink one way and you stretch the other. >> Peter: It distorts the space time. >> Yeah you become thinner and these tiny, tiny changes are what's measured and nobody expected gravitational waves to be discovered in 2015, we all thought, oh another five years, another five years, they've always been saying, we'll discover them, we'll discover them, but it happened. >> And since then, it's been used two or three times to discover new types of things and there's now a whole, I'm sure this is very centric to what you're doing, there's now a whole concept of gravitational information, can in fact becomes an entirely new branch of cosmology, have I got that right? >> Yeah you have, it's called multimessenger astronomy now because you don't just see the universe in electromagnetic waves, in light, you hear the universe. This is qualitatively different, it's sound waves coming across the universe and so combining these two, the latest event was where they heard the event first, then they turned their telescope and they saw it. So much information came out of that, even information about cosmology, because these signals are traveling hundreds of billions of light years across to us, we're getting a picture of the whole universe as they propagate all that way, so we're able to measure the expansion rate of the universe from that point. >> The techniques for the observational, the technology for observation, what is that, how has that evolved? >> Well you've got the wrong guy here. I'm from the theory group, we're doing the predictions and these guys with their incredible technology, are seeing the data, seeing and it's imagined, the whole point is you've gotta get the predictions and then you've gotta look in the data for a needle in the haystack which is this signature of these black holes colliding. >> You think about that, I have a model, I'm looking for the needle in the haystack, that's a different way to describe an in memory analytic search pattern recognition problem, that's really what it is. This is the world's largest pattern recognition problem. >> Most precise, and literally. >> And that's an observation that confirms your theory right? >> Confirms the theory, maybe it was your theory. >> I'm actually a cosmologist, so in my group we have relativists who are actively working on the black hole collisions and making predictions about this stuff. >> But they're dampening vibration from passing trucks and these things and correcting it, it's unbelievable. But coming back to the technology, the technology is, one of the reasons why this becomes so exciting and becomes practical is because for the first time, the technology has gotten to the point where you can assume that the problem you're trying to solve, that you're focused on and you don't have to translate it in technology terms, so talk a little bit about, because in many respects, that's where business is. Business wants to be able to focus on the problem and how to think the problem differently and have the technology to just respond. They don't want to have to start with the technology and then imagine what they can do with it. >> I think from our point of view, it's a very fast moving field, things are changing, new data's coming in. The data's getting bigger and bigger because instruments are getting packed tighter and tighter, there's more information, so we've got a computational problem as well, so we've got to get more computational power but there's new types of data, like suddenly there's gravitational waves. There's new types of analysis that we want to do so we want to be able to look at this data in a very flexible way and ingest it and explore new ideas more quickly because things are happening so fast, so that's why we've adopted this in memory paradigm for a number of years now and the latest incarnation of this is the HP Superdome flex and that's a shared memory system, so you can just pull in all your data and explore it without carefully programming how the memory is distributed around. We find this is very easy for our users to develop data analytic pipelines to develop their new theoretical models and to compare the two on the single system. It's also very easy for new users to use. You don't have to be an advanced programmer to get going, you can just stay with the science in a sense. >> You gotta have a PhD in Physics to do great in Physics, you don't have to have a PhD in Physics and technology. >> That's right, yeah it's a very flexible program. A flexible architecture with which to program so you can more or less take your laptop pipeline, develop your pipeline on a laptop, take it to the Superdome and then scale it up to these huge memory problems. >> And get it done fast and you can iterate. >> You know these are the most brilliant scientists in the world, bar none, I made the analogy the other day. >> Oh, thanks. >> You're supposed to say aw, chucks. >> Peter: Aw, chucks. >> Present company excepted. >> Oh yeah, that's right. >> I made the analogy of, imagine I.M. Pei or Frank Lloyd Wright or someone had to be their own general contractor, right? No, they're brilliant at designing architectures and imagining things that no one else could imagine and then they had people to go do that. This allows the people to focus on the brilliance of the science without having to go become the expert programmer, we see that in business too. Parallel programming techniques are difficult, spoken like an old tandem guy, parallelism is hard but to the extent that you can free yourself up and focus on the problem and not have to mess around with that, it makes life easier. Some problems parallelize well, but a lot of them don't need to be and you can allow the data to shine, you can allow the science to shine. >> Is it correct that the barrier in your ability to reach a conclusion or make a discovery is the ability to find that needle in a haystack or maybe there are many, but. >> Well, if you're talking about obstacles to progress, I would say computational power isn't the obstacle, it's developing the software pipelines and it's the human personnel, the smart people writing the codes that can look for the needle in the haystack who have the efficient algorithms to do that and if they're cobbled by having to think very hard about the hardware and the architecture they're working with and how they've parallelized the problem, our philosophy is much more that you solve the problem, you validate it, it can be quite inefficient if you like, but as long as it's a working program that gets you to where you want, then your second stage you worry about making it efficient, putting it on accelerators, putting it on GPUs, making it go really fast and that's, for many years now we've bought these very flexible shared memory or in memory is the new word for it, in memory architectures which allow new users, graduate students to come straight in without a Master's degree in high performance computing, they can start to tackle problems straight away. >> It's interesting, we hear the same, you talk about it at the outer reaches of the universe, I hear it at the inner reaches of the universe from the life sciences companies, we want to map the genome and we want to understand the interaction of various drug combinations with that genetic structure to say can I tune exactly a vaccine or a drug or something else for that patient's genetic makeup to improve medical outcomes? The same kind of problem, I want to have all this data that I have to run against a complex genome sequence to find the one that gets me to the answer. From the macro to the micro, we hear this problem in all different sorts of languages. >> One of the things we have our clients, mainly in business asking us all the time, is with each, let me step back, as analysts, not the smartest people in the world, as you'll attest I'm sure for real, as analysts, we like to talk about change and we always talked about mainframe being replaced by minicomputer being replaced by this or that. I like to talk in terms of the problems that computing's been able to take on, it's been able to take on increasingly complex, challenging, more difficult problems as a consequence of the advance of technology, very much like you're saying, the advance of technology allows us to focus increasingly on the problem. What kinds of problems do you think physicists are gonna be able to attack in the next five years or so as we think about the combination of increasingly powerful computing and an increasingly simple approach to use it? >> I think the simplification you're indicating here is really going to more memory. Holding your whole workload in memory, so that you, one of the biggest bottlenecks we find is ingesting the data and then writing it out, but if you can do everything at once, then that's the key element, so one of the things we've been working on a great deal is in situ visualization for example, so that you see the black holes coming together and you see that you've set the right parameters, they haven't missed each other or something's gone wrong with your simulation, so that you do the post-processing at the same time, you never need the intermediate data products, so larger and larger memory and the computational power that balances with that large memory. It's all very well to get a fat node, but you don't have the computational power to use all those terrabytes, so that's why this in memory architecture of the Superdome Flex is much more balanced between the two. What are the problems that we're looking forward to in terms of physics? Well, in cosmology we're looking for these hints about the origin of the universe and we've made a lot of progress analyzing the Plank satellite data about the cosmic microwave background. We're honing in on theories of inflation, which is where all the structure in the universe comes from, from Heisenberg's uncertainty principle, rapid period of expansion just like inflation in the financial markets in the very early universe, okay and so we're trying to identify can we distinguish between different types and are they gonna tell us whether the universe comes from a higher dimensional theory, ten dimensions, gets reduced to three plus one or lots of clues like that, we're looking for statistical fingerprints of these different models. In gravitational waves of course, this whole new area, we think of the cosmic microwave background as a photograph of the early universe, well in fact gravitational waves look right back to the earliest moment, fractions of a nanosecond after the big bang and so it may be that the answers, the clues that we're looking for come from gravitational waves and of course there's so much in astrophysics that we'll learn about compact objects, about neutron stars, about the most energetic events there are in the whole universe. >> I never thought about the idea, because cosmic radiation background goes back what, about 300,000 years if that's right. >> Yeah that's right, you're very well informed, 400,000 years because 300 is. >> Not that well informed. >> 370,000. >> I never thought about the idea of gravitational waves as being noise from the big bang and you make sense with that. >> Well with the cosmic microwave background, we're actually looking for a primordial signal from the big bang, from inflation, so it's yeah. Well anyway, what were you gonna say Randy? >> No, I just, it's amazing the frontiers we're heading down, it's kind of an honor to be able to enable some of these things, I've spent 30 years in the technology business and heard customers tell me you transformed by business or you helped me save costs, you helped me enter a new market. Never before in 30 plus years of being in this business have I had somebody tell me the things that you're providing are helping me understand the origins of the universe. It's an honor to be affiliated with you guys. >> Oh no, the honor's mine Randy, you're producing the hardware, the tools that allow us to do this work. >> Well now the honor's ours for coming onto the Cube. >> That's right, how do we learn more about your work and your discoveries, inclusions. >> In terms of looking at. >> Are there popular authors we could read other than Stephen Hawking? >> Well, read Stephen's books, they're very good, he's got a new one called A Briefer History of Time so it's more accessible than the Brief History of Time. >> So your website is. >> Yeah our website is ctc.cam.ac.uk, the center for theoretical cosmology and we've got some popular pages there, we've got some news stories about the latest things that have happened like the HP partnership that we're developing and some nice videos about the work that we're doing actually, very nice videos of that. >> Certainly, there were several videos run here this week that if people haven't seen them, go out, they're available on Youtube, they're available at your website, they're on Stephen's Facebook page also I think. >> Can you share that website again? >> Well, actually you can get the beautiful videos of Stephen and the rest of his group on the Discover website, is that right? >> I believe so. >> So that's at HP Discover website, but your website is? >> Is ctc.cam.ac.uk and we're just about to upload those videos ourselves. >> Can I make a marketing suggestion. >> Yeah. >> Simplify that. >> Ctc.cam.ac.uk. >> Yeah right, thank you. >> We gotta get the Cube at one of these conferences, one of these physics conferences and talk about gravitational waves. >> Bone up a little bit, you're kind of embarrassing us here, 100,000 years off. >> He's better informed than you are. >> You didn't need to remind me sir. Thanks very much for coming on the Cube, great pleasure having you today. >> Thank you. >> Keep it right there everybody, Mr. Universe and I will be back after this short break. (upbeat techno music)

(hopeful futuristic music) >> Hey welcome back everybody, Jeff Frick here with theCUBE. We are really excited to have this cube conversation here in the Palo Alto studio with a real close friend of theCUBE, and repeat alumni, Leslie Berlin. I want to get her official title; she's the historian for the Silicon Valley archive at Stanford. Last time we talked to Leslie, she had just come out with a book about Robert Noyce, and the man behind the microchip. If you haven't seen that, go check it out. But now she's got a new book, it's called "Troublemakers," which is a really appropriate title. And it's really about kind of the next phase of Silicon Valley growth, and it's hitting bookstores. I'm sure you can buy it wherever you can buy any other book, and we're excited to have you on Leslie, great to see you again. >> So good to see you Jeff. >> Absolutely, so the last book you wrote was really just about Noyce, and obviously, Intel, very specific in, you know, the silicon in Silicon Valley obviously. >> Right yeah. >> This is a much, kind of broader history with again just great characters. I mean, it's a tech history book, but it's really a character novel; I love it. >> Well thanks, yeah; I mean, I really wanted to find people. They had to meet a few criteria. They had to be interesting, they had to be important, they had to be, in my book, a little unknown; and most important, they had to be super-duper interesting. >> Jeff Frick: Yeah. >> And what I love about this generation is I look at Noyce's generation of innovators, who sort of working in the... Are getting their start in the 60s. And they really kind of set the tone for the valley in a lot of ways, but the valley at that point was still just all about chips. And then you have this new generation show up in the 70s, and they come up with the personal computer, they come up with video games. They sort of launch the venture capital industry in the way we know it now. Biotech, the internet gets started via the ARPANET, and they kind of set the tone for where we are today around the world in this modern, sort of tech infused, life that we live. >> Right, right, and it's interesting to me, because there's so many things that kind of define what Silicon Valley is. And of course, people are trying to replicate it all over the place, all over the world. But really, a lot of those kind of attributes were started by this class of entrepreneurs. Like just venture capital, the whole concept of having kind of a high risk, high return, small carve out from an institution, to put in a tech venture with basically a PowerPoint and some faith was a brand new concept back in the day. >> Leslie Berlin: Yeah, and no PowerPoint even. >> Well that's right, no PowerPoint, which is probably a good thing. >> You're right, because we're talking about the 1970s. I mean, what's so, really was very surprising to me about this book, and really important for understanding early venture capital, is that now a lot of venture capitalists are professional investors. But these venture capitalists pretty much to a man, and they were all men at that point, they were all operating guys, all of them. They worked at Fairchild, they worked at Intel, they worked at HP; and that was really part of the value that they brought to these propositions was they had money, yes, but they also had done this before. >> Jeff Frick: Right. >> And that was really, really important. >> Right, another concept that kind of comes out, and I think we've seen it time and time again is kind of this partnership of kind of the crazy super enthusiastic visionary that maybe is hard to work with and drives everybody nuts, and then always kind of has the other person, again, generally a guy in this time still a lot, who's kind of the doer. And it was really the Bushnell-Alcorn story around Atari that really brought that home where you had this guy way out front of the curve but you have to have the person behind who's actually building the vision in real material. >> Yeah, I mean I think something that's really important to understand, and this is something that I was really trying to bring out in the book, is that we usually only have room in our stories for one person in the spotlight when innovation is a team sport. And so, the kind of relationship that you're talking about with Nolan Bushnell, who started Atari, and Al Alcorn who was the first engineer there, it's a great example of that. And Nolan is exactly this very out there person, big curly hair, talkative, outgoing guy. After Atari he starts Chuck E. Cheese, which kind of tells you everything you need to know about someone who's dreaming up Chuck E. Cheese, super creative, super out there, super fun oriented. And you have working with him, Al Alcorn, who's a very straight laced for the time, by which I mean, he tried LSD but only once. (cumulative laughing) Engineer, and I think that what's important to understand is how much they needed each other, because the stories are so often only about the exuberant out front guy. To understand that those are just dreams, they are not reality without these other people. And how important, I mean, Al Alcorn told me look, "I couldn't have done this without Nolan, "kind of constantly pushing me." >> Right, right. >> And then in the Apple example, you actually see a third really important person, which to me was possibly the most exciting part of everything I discovered, which was the importance of the guy named Mike Markkula. Because in Jobs you had the visionary, and in Woz you had the engineer, but the two of them together, they had an idea, they had a great product, the Apple II, but they didn't have a company. And when Mike Markkula shows up at the garage, you know, Steve Jobs is 21 years old. >> Jeff Frick: Right. >> He has had 17 months of business experience in his life, and it's all his attack for Atari, actually. And so how that company became a business is due to Mike Markkula, this very quiet guy, very, very ambitious guy. He talked them up from a thousand stock options at Intel to 20,000 stock options at Intel when he got there, just before the IPO, which is how he could then turn around and help finance >> Jeff Frick: Right. >> The birth of Apple. And he pulled into Apple all of the chip people that he had worked with, and that is really what turned Apple into a company. So you had the visionary, you had the tech guy, you also needed a business person. >> But it's funny though because in that story of his visit to the garage he's specifically taken by the engineering elegance of the board >> Leslie Berlin: Right. >> That Woz put together, which I thought was really neat. So yeah, he's a successful business man. Yes he was bringing a lot of kind of business acumen value to the opportunity, but what struck him, and he specifically talks about what chips he used, how he planned for the power supply. Just very elegant engineering stuff that touched him, and he could recognize that they were so far ahead of the curve. And I think that's such another interesting point is that things that we so take for granted like mice, and UI, and UX. I mean the Atari example, for them to even think of actually building it that would operate with a television was just, I mean you might as well go to Venus, forget Mars, I mean that was such a crazy idea. >> Yeah, I mean I think Al ran to Walgreens or something like that and just sort of picked out the closest t.v. to figure out how he could build what turned out to be Pong, the first super successful video game. And I mean, if you look also at another story I tell is about Xerox Park; and specifically about a guy named Bob Taylor, who, I know I keep saying, "Oh this might be my favorite part." But Bob Taylor is another incredible story. This is the guy who convinced DARPA to start, it was then called ARPA, to start the ARPANET, which became the internet in a lot of ways. And then he goes on and he starts the computer sciences lab at Xerox Park. And that is the lab that Steve Jobs comes to in 1979, and for the first time sees a GUI, sees a mouse, sees Windows. And this is... The history behind that, and these people all working together, these very sophisticated Ph.D. engineers were all working together under the guidance of Bob Taylor, a Texan with a drawl and a Master's Degree in Psychology. So what it takes to lead, I think, is a really interesting question that gets raised in this book. >> So another great personality, Sandra Kurtzig. >> Yeah. >> I had to look to see if she's still alive. She's still alive. >> Leslie Berlin: Yeah. >> I'd love to get her in some time, we'll have to arrange for that next time, but her story is pretty fascinating, because she's a woman, and we still have big women issues in the tech industry, and this is years ago, but she was aggressive, she was a fantastic sales person, and she could code. And what was really interesting is she started her own software company. The whole concept of software kind of separated from hardware was completely alien. She couldn't even convince the HP guys to let her have access to a machine to write basically an NRP system that would add a ton of value to these big, expensive machines that they were selling. >> Yeah, you know what's interesting, she was able to get access to the machine. And HP, this is not a well known part of HP's history, is how important it was in helping launch little bitty companies in the valley. It was a wonderful sort of... Benefited all these small companies. But she had to go and read to them the definition of what an OEM was to make an argument that I am adding value to your machines by putting software on it. And software was such an unknown concept. A, people who heard she was selling software thought she was selling lingerie. And B, Larry Ellison tells a hilarious story of going to talk to venture capitalists about... When he's trying to start Oracle, he had co-founders, which I'm not sure everybody knows. And he and his co-founders were going to try to start Oracle, and these venture capitalists would, he said, not only throw him out of the office for such a crazy idea, but their secretaries would double check that he hadn't stolen the copy of Business Week off the table because what kind of nut job are we talking to here? >> Software. >> Yeah, where as now, I mean when you think about it, this is software valley. >> Right, right, it's software, even, world. There's so many great stories, again, "Troublemakers" just go out and get it wherever you buy a book. The whole recombinant DNA story and the birth of Genentech, A, is interesting, but I think the more kind of unique twist was the guy at Stanford, who really took it upon himself to take the commercialization of academic, generated, basic research to a whole 'nother level that had never been done. I guess it was like a sleepy little something in Manhattan they would send some paper to, but this guy took it to a whole 'nother level. >> Oh yeah, I mean before Niels showed up, Niels Reimers, he I believe that Stanford had made something like $3,000 off of the IP from its professors and students in the previous decades, and Niels said "There had to be a better way to do this." And he's the person who decided, we ought to be able to patent recombinant DNA. And one of the stories that's very, very interesting is what a cultural shift that required, whereas engineers had always thought in terms of, "How can this be practical?" For biologists this was seen as really an unpleasant thing to be doing, don't think about that we're about basic research. So in addition to having to convince all sorts of government agencies and the University of California system, which co-patented this, to make it possible, just almost on a paperwork level... >> Right. >> He had to convince the scientists themselves. And it was not a foregone conclusion, and a lot of people think that what kept the two named co-inventors of recombinant DNA, Stan Cohen and Herb Boyer, from winning the Nobel Prize is that they were seen as having benefited from the work of others, but having claimed all the credit, which is not, A, isn't fair, and B, both of those men had worried about that from the very beginning and kept saying, "We need to make sure that this includes everyone." >> Right. >> But that's not just the origins of the biotech industry in the valley, the entire landscape of how universities get their ideas to the public was transformed, and that whole story, there are these ideas that used to be in university labs, used to be locked up in the DOD, like you know, the ARPANET. And this is the time when those ideas start making their way out in a significant way. >> But it's this elegant dance, because it's basic research, and they want it to benefit all, but then you commercialize it, right? And then it's benefiting the few. But if you don't commercialize it and it doesn't get out, you really don't benefit very many. So they really had to walk this fine line to kind of serve both masters. >> Absolutely, and I mean it was even more complicated than that, because researchers didn't have to pay for it, it was... The thing that's amazing to me is that we look back at these people and say, "Oh these are trailblazers." And when I talked to them, because something that was really exciting about this book was that I got to talk to every one of the primary characters, you talk to them, and they say, "I was just putting one foot in front of the other." It's only when you sort of look behind them years later that you see, "Oh my God, they forged a completely new trail." But here it was just, "No I need to get to here, "and now I need to get to here." And that's what helped them get through. That's why I start the book with the quote from Raiders of the Lost Ark where Sallah asks Indy, you know basically, how are you going to stop, "Stop that car." And he says, "How are you going to do it Indy?" And Indy says, "I don't know "I'm making it up as I go along." And that really could almost be a theme in a lot of cases here that they knew where they needed to get to, and they just had to make it up to get there. >> Yeah, and there's a whole 'nother tranche on the Genentech story; they couldn't get all of the financing, so they actually used outsourcing, you know, so that whole kind of approach to business, which was really new and innovative. But we're running out of time, and I wanted to follow up on the last comment that you made. As a historian, you know, you are so fortunate or smart to pick your field that you can talk to the individual. So, I think you said, you've been doing interviews for five or six years for this book, it's 100 pages of notes in the back, don't miss the notes. >> But also don't think the book's too long. >> No, it's a good book, it's an easy read. But as you reflect on these individuals and these personalities, so there's obviously the stories you spent a lot of time writing about, but I'm wondering if there's some things that you see over and over again that just impress you. Is there a pattern, or is it just, as you said, just people working hard, putting one step in front of the other, and taking those risks that in hindsight are so big? >> I would say, I would point to a few things. I'd point to audacity; there really is a certain kind of adventurousness, at an almost unimaginable level, and persistence. I would also point to a third feature at that time that I think was really important, which was for a purpose that was creative. You know, I mean there was the notion, I think the metaphor of pioneering is much more what they were doing then what we would necessarily... Today we would call it disruption, and I think there's a difference there. And their vision was creative, I think of them as rebels with a cause. >> Right, right; is disruption the right... Is disruption, is that the right way that we should be thinking about it today or are just kind of backfilling the disruption after the fact that it happens do you think? >> I don't know, I mean I've given this a lot of thought, because I actually think, well, you know, the valley at this point, two-thirds of the people who are working in the tech industry in the valley were born outside of this country right now, actually 76 percent of the women. >> Jeff Frick: 76 percent? Wow. >> 76 percent of the women, I think it's age 25 to 44 working in tech were born outside of the United States. Okay, so the pioneering metaphor, that's just not the right metaphor anymore. The disruptive metaphor has a lot of the same concepts, but it has, it sounds to me more like blowing things up, and doesn't really thing so far as to, "Okay, what comes next?" >> Jeff Frick: Right, right. >> And I think we have to be sure that we continue to do that. >> Right, well because clearly, I mean, the Facebooks are the classic example where, you know, when he built that thing at Harvard, it was not to build a new platform that was going to have the power to disrupt global elections. You're trying to get dates, right? I mean, it was pretty simple. >> Right. >> Simple concept and yet, as you said, by putting one foot in front of the other as things roll out, he gets smart people, they see opportunities and take advantage of it, it becomes a much different thing, as has Google, as has Amazon. >> That's the way it goes, that's exactly... I mean, and you look back at the chip industry. These guys just didn't want to work for a boss they didn't like, and they wanted to build a transistor. And 20 years later a huge portion of the U.S. economy rests on the decisions they're making and the choices. And so I think this has been a continuous story in Silicon Valley. People start with a cool, small idea and it just grows so fast among them and around them with other people contributing, some people they wish didn't contribute, okay then what comes next? >> Jeff Frick: Right, right. >> That's what we figure out now. >> All right, audacity, creativity and persistence. Did I get it? >> And a goal. >> And a goal, and a goal. Pong, I mean was a great goal. (cumulative laughing) All right, so Leslie, thanks for taking a few minutes. Congratulations on the book; go out, get the book, you will not be disappointed. And of course, the Bob Noyce book is awesome as well, so... >> Thanks. >> Thanks for taking a few minutes and congratulations. >> Thank you so much Jeff. >> All right this is Leslie Berlin, I'm Jeff Frick, you're watching theCUBE. See you next time, thanks for watching. (electronic music)

>> Narrator: Live from San Francisco, it's the CUBE. Covering Samsung Developer Conference 2017, brought to you by Samsung. >> Okay, welcome back everyone. We are here live with the CUBE coverage where Cloud Native and the SmartThings Conference from Samsung Developer Conference. I'm John Furrier, the founder, the co-founder of SiliconANGLE Media. Co-host of the cube here with Redg Snodgrass, who's the chairman of ReadWrite and ReadWrite Labs. >> Hello everybody. >> Also been an entrepreneur, he's done the Wearable World events, done a lot of things in tech, riding the waves. You seen them, a lot of action going on, Redg. Want to get your the thoughts as we wrap up day one of two days of wall-to-wall coverage of the cubes, Samsung Developer Conference, a lot going on. You know Samsung, they're trying to play their best hand that possible. Obviously, they're not going to come out and say, We're not really ready for primetime, for the cloud. But the reality is, they're not ready for primetime for the cloud and IoT. However, huge strides in positioning, messaging, and the self awareness of their stove pipes. They are series of stove pipes that they've recognized, We've got to make this a 2.0 Bixby that crosses across all of Samsung, open up IoT. >> Redg: Which I thought was great. >> Open ecosystem, everything else, to me, is a work in progress, kind of, cover the, hide the ball, a little bit, I mean, what's your thoughts? Do you agree or what's your reaction? >> Oh man, I was on a panel earlier today. And somebody was like, oh, this is great. And I wanted to go back to, back when we did the open API service with Alcatel-Lucent, when we roll out all this stuff for the telcos. I mean, it's just, it's a lot of hype, initially. But what I do like about it is it seems like there's a dogged commitment to creating all the different documentations necessary and bringing that in, I mean, if they really put the full marketing weight behind it, this could get really interesting really fast. I mean, they own almost every device in your home already. >> Well, I said the word hide-the-ball. Maybe I should take a step back and not be too harsh. What I mean by that is, they're not hiding the ball on purpose, I think they're, by design, and I think Greg mentioned this earlier. Greg Narain said, they're doing it by design. And I think that that's a good call. SmarterThings is a good positioning because it highlights multiple devices and connecting it together. I think if they played the data card and the cloud too much, they would've overplayed their hand, and it's not needed. I mean, do you think it's needed? I mean, I don't think it's needed. >> Well, one of the biggest problems with IoT right now is that you have multiple different silos creating data. And then all those data silos have to figure out how to come together and talk about it. I mean, it seems like they're taking a step out, and saying, hey, we want to build that solution. Which is great, I'm more interested in the orchestration between different OSs, like, how are they really going to do that? Because it, we talked a lot about, when you build one of these ecosystems, you're really just building an economy. And the more open that you let your economy, right, the more business models come in, the more people that can be there. And so, if we were to start thinking about these OSs as real economies, like what do you need to have economy work? >> Well, I think this is why, we were talking earlier, I think that you had a good point. I think that validates what I'm thinking out loud here, which is, why play the data card. They don't need to because it's still open-book. They still got to figure it out, and that's not a bad thing. They play with their best hand, which is the consumer hand. >> Redg: It's consumerism is where they're at >> The devices are awesome, the screen on the phones are phenomenal, they got TVs. They got a little bit of a family hub going on with the living room, kitchen thing, with the refrigerators. That's IoT, they got healthcare because it's a device issue. So they're working their way from the consumer edge into the industrial edge. Now, if you're in the IT world, you have security problems. So most people that we talk to, at the humans, they say, hey John, my plate is full, I got to staff up my DevOps and my application developers. I got to unbolt security from my IT department, make that report to the board as a profit center now. And I got all this machine learning and Cloud Ops, and you want me to do what? Like, instrument my entire factory with this IoT thing? So people are holding the brakes. >> Well, I mean think about it. Every day, right, you're confronted with another executive that has like fallen on a sword of a major security hack, a major security issue. And so, as an executive of a major like business unit, with a technology group in front of you, you're sitting there making all these decisions every day. And it used to, you used to come and say, okay, we're going to make decisions every eight, nine months. And you have this big waterfall thing in front of you. And you know that, from your vendors, that. >> John: It's predictable >> Everything was predictable, and now it's like, oh man, I got to get into this Google Glass stuff, and I've got, no, now it's wearables, and wearables, that doesn't work, I need my IoT infrastructure stuff. And so we're moving the court, you know, away from all these CIO, CTOs consistently of what they need to think about next. >> It's interesting, if you look at the stack, go back to the old 80s OSI model, you got the lower level stack, middleware, and then application stacks. If you follow the data, and the networks, and the packets, how it moves, you can almost see the trends, batch versus real time. And I think what we've seen in the big data world, in data sciences, which can be analytics, obviously specialty industry. But the role of data and realtime, self-driving cars, really highlights this really huge wave coming, which is how that people dealt with data and software, the relationship between software and data was different. You store it in a database, build the database, call the database, get the data out, load it in, slow, monolithic, siloed. But now you have data that you need in really low latency at any given time, in any different app, from any different database, in less than a millisecond how do you do that. >> Well, think of it. >> John: That takes intelligence. >> About two years ago, I had a great conversation with a big packet moving company that managed most of the packet movement for most of the internet. And we were talking about, what does it look like per person in the US in the next like three or four years? And it could be up to a petabyte a day at a per person. Now that sounds awesome because if you look at all the different like videos we watch, it's like, oh, that's great, really cool flying car. You know, connecting windows, no one's really doing the math on that. And if it's a petabyte per day per person, like in the US a year even, or you know. I could see models where it could be a month. Think about what that does to the network load. We just don't have the math to be able, you know, possibility to handle that. >> This is why the decentralization with Blockchain is interesting. Even though Blockchain is hyped up, I think it's fundamental to the internet, as this Dr. Wong from Alibaba, who told me that last week. He said it was like a TCP/IP, I agree with him because you have distributed computers, which we know about. We've been there, done that, but now you got decentralized and distributed, two different concepts at the same time. That's a fundamental paradigm shift. >> Well, I mean it's just, so, I mean, you got to. >> It's intoxicating to think about what that disrupts. >> No, no, I love it, I mean, honestly, I've fallen in love with narrow band networks the last week. For some reason, I'm the weirdest person on the planet. Because it's such a solution for security. It's such a solution for a lot of this back calling and data that we're going to have. It'll be interesting to look at, but when you think about the pure math on this. >> John: Are you back calling data or are you back calling compute? >> Oh, well it's so. >> That's a different conversation. The trend is, don't move the data. Throw the compute at it because compute is, this is an architectural renaissance happening, people are re-imagining. >> How many, how many startup. >> In global infrastructure. >> Execs can even like draw architecture? Right, with all the lame startups, I mean, when was the last time you saw like somebody pitch. When they came to pitch, it's like, let me talk about my architecture. >> John: That should be the first slide. >> It should be the slide that you talk about as an executive and everything, I don't see. >> If he can't get on the whiteboard. >> Startups deliver architecture. >> If you can't get on the whiteboard and lay out an architecture on fundamentally the core engine of your technology, you shouldn't get funded. >> Well, so that is a major issue that's happening right now because I do think that we have this group think where we've disallowed a lot of R&D thinking. We don't do longterm R&D before we get a product to market. And now, like all. >> John: Sometimes you can't. Sometimes you have to sprint out and put a stake in the ground and iterate. >> Think about all of the connected device product. How do you test the connected device product to scale? Right, I mean the iPhone, you know Samsung, everybody has all these devices out there, they're getting this data, it's coming in they can actually iterate on that product and make decisions, right? >> Well, that brings up a good point. We saw this at the Cube at VMWorld. For the first time we heard people grumbling in the hallways like, you know, I love the ENC tries, but they just haven't tested this use case. And the use case was a new workload that had unique characteristics. In this case they needed low latency. It was an edge device, so it was mandatory to have no latency with all this was trickling data in. But in this case, they had set up their virtual SAN in a tiered basis. And they needed a certain hardware configuration with vSAN. And they've never tested the hardware stack with the software stack. So it's just one of those things that the hardware vendor just never imagined, you can't QA the unknown. So this is where I'd see Samsung doing things like in-chip and seeing what Intel's doing with some of their FPGA stuff. You can see that these infrastructure guys got to bring that DevOps concept to the consumer world. >> Redg: Oh, it's going to be so hard. >> Which is programming the. >> Redg: So hard. >> The hardware at will. >> Yeah, well. >> John: Like the cloud DevOps ethos. What do you think of that? >> Yeah, no, no, no, look, I mean, I'm such a big fan of being able to get your product in people's hands, to be able to see the use cases, develop them out and push that forward. You know, big corporations can do that. You have 10 iterations of almost every iPhone right now, with thousands of engineers iterating on it. So when you look at like the competitor, which is your device right now, versus every other piece of IoT technology that isn't been perfected or anything. Our biggest issue is we're driven by the success of the smartphone for every other piece of technology today. And that's, that makes it hard to drive adoption for any other devices. >> So I get your thoughts on this, 'cause we wrap up day one. Obviously, let's talk about the developers that they're targeting, okay. >> Okay. >> The Samsung developers that they're targeting is the same kind of developers that Apple's targeting. Let's just call it out, however, you see voice-activated touch, you're seeing the services tools, now they're bringing in an IoT. You're not hearing Apple talk about IoT. This is unique, you got Google onstage, wink, wink, hey, everybody we're here, we're Google, Android, coming together. What is in the mind of the developer in the Samsung ecosystem right now, what's your take on it, what's the psychology of that developer? >> I built an app at one point in time. It was dating app a long time ago, right, with some other guys, they built it, I was just the mouth. It's called Scout and we were on the Simian platform, and the iPhone, and we were on web, we were on mobile web. And in the iPhone app store, all with one engineer. And it was really hard because we had real-time chat. It was just so much crazy things. At the end of the day, what always matters is, again, you're building economies, you're not building fun playgrounds or anything else like that. And if your economy is, your platform is the easiest to use, it has the capabilities and advantages that are the norm, right, you'll win. Bass Diffusion is great it's this guy out here, he won a Nobel prize, but what Bass Diffusion says, in order for you to win in a market, you need two things, imitation and innovation. Imitation, for instance, in TVs, is your TV black and white, is it color. As things move up, innovation eventually overtakes, and always becomes innovation. So when you look at like what's needed in market, the platform that is the easiest to use, the platform that has the most capable imitative qualities, it's just very easy for you to push things to market universally from OS to OS, along with certain pieces of innovation around business models, certain API capabilities that may make it easier for them to deliver revenues. If those are the things that are delivered, that we see pushed out, a good blend of imitation and innovation, the win. It's that person that actually can deliver it. >> Well, we're seeing gaming in entertainment really driving change, Netflix earnings just came out. They blew it away again, you're seeing the cord cutters are clearly there. >> So much for Disney, right? >> E-commerce, yeah, I mean, Amazon's still got to make some moves too, even though they were still winning. No one's really falling out of the chair for Prime. I mean, no, I don't know a lot of people who rigorously turn on Prime, they shop on Prime, but not necessarily watching any entertainment. So I'm a little critical of Amazon on that. But, then again, but Amazon's doing the right thing. Netflix, Amazon, YouTube, you're seeing a culture of digital entertainment shifting. E-commerce is shifting, and now you got web services. I think Amazon encapsulates, in my mind, a great strategy, retail and services, but if you extend that out to the rest of the world, voice-activated apps, you can blend in commerce entertainment, you can replicate Amazon. I mean, they could replicate everything out there in the open. >> Amazon is so good at understanding where they fit in the stack and then, pushing the edge case further and further and further along. They're really brilliant, versus like VMware that's like, oh man, we can make apps, no problem. They went to make apps, and it didn't work out so well, they're great with VMs, so. >> John: They're great with operators in the enterprise, not so much with DevOps. >> No, no, no, no, and it's. >> They got pivotal for that now. Michael Dell bought everyone up. >> Yeah, exactly. It's understanding where you fit in the stack and being able to take advantage of it strategically. I mean, like I said, I think Samsung's positioned really well, I mean, I wouldn't have come and hung out with everybody if I was like, ah, I'm going to be bored all day. There's a lot of really exciting things. >> We got a lot of eye candy, no doubt about it. I love their TVs, love their screens. The new Samsung phone, is spectacular, you what I mean. >> I'm pretty ecstatic. >> It was the first phone that wanted me to get transferred off my iPhone. And I ended up getting the little junior Samsung here, but. >> Oh no, well it'll be interesting as they start to connect their platform together as all a lot of these other developers start pushing the pieces of their strategy together. Remember, it's like whenever you throw a strategy out here like this, it's like you have a big puzzle with a lot of empty pieces. >> I mean, the question I have for you is, let's just close out the segment. What do you think, what area should Samsung really be doubling down on or peddling faster, I should say. What should be developing faster? Is it the open APIs, is it the cloud? And they got to get the open ecosystem going, in my opinion. That's my take, what do you think they should be working on the most right now? >> Yeah, I mean like look, cloud is going to be really, really, there's a lot of competitors out in cloud. There's a lot of multiple, there's a lot of choices, right. Where I've seen them like really do well, I'll go back to the fact that I firmly believe that Google never really monetized the Android that Samsung did that a lot better. And so, by looking at the different points in the market, where they're good, I mean, their ecosystem is solid. I mean, yes, I mean it seems like the sexy thing is Apple, but I've talked to several developers, and I know where they make their money, and they do a strong amount of revenue, if not equivalent to where the iPhone is, at least from what I've heard so far. >> The android market share it's not shabby at all. >> Not, so. >> Damn good. >> So they've, they've been able to do this, like, from that, taken that Android stack, applying that imitation and innovation on top of it, fascinatingly so, I wouldn't count them out for this. And I'm pretty encouraged to see all the other aspects, but I like the ecosystem built out too. >> Redg Snodgrass, ReadWrite Labs, quick plug for you. What's going on in your world? Got some recent activities happening, please share update. >> So, yeah it's great, so we just launched our IOT revolution event series where we look at the atomic unit of different markets. And what that means is, we find the real buyers and sellers, a lot like what Debbie Lann, who I love, did. And we look at the buyers and sellers together, along with the top series A startups, all around newsworthy issues. And so, whatever it's like, is it hacking and Russia. You know, then we'll get cybersecurity experts up, and we'll talk about those issues from an executive point of view. And that's the thing that's making me most excited because I get to have all these conversations with people. It will be on video, onstage, November 13th, is the first one, it's a private event, but we'll work out anybody. >> Where's it going to be? >> It'll be in San Francisco, around 100 Broadway. So it's kind of a quiet thing, but I'd love for everybody to come if you're interested. >> It's a quiet thing but I want everyone to come. It was, not going there, too many people are going. >> It's like my parties, right? >> It's like a Yogi Berra. Well, thanks for coming out, appreciate, wrapping up day one of coverage The Cube. This is Samsung Developer Conference 2017. Hashtag SDC2017, that's what they're calling it. Lot of great guests today go to YouTube.com/siliconangle for all the great footage. And also check the Twitter sphere, lot of photos. And shout-out to Vanessa, out there has like helped us set everything up. Appreciate it and great to the team. That's day one wrap up, thanks for watching. (upbeat music)

>> Announcer: Live, from Orlando, Florida, it's the Cube covering Grace Hopper's celebration of women in computing brought to you by Silicon Angle Media >> Welcome back to the Cube's coverage of the Grace Hopper conference here in Orlando, Florida I'm your host Rebecca Knight, along with my co host Jeff Frick. We're joined by Megan Smith. We're very excited to have you on the show. >> It's good to be here >> She is the third US CTO and also the CEO of a new company, Shift7.co, so thanks so much for joining us. >> Thanks for having me, it's great to be here. It's so fun to be at Hopper, >> Rebecca: It is, it is! >> It's cool, it's the Grace Hopper celebration, because we're trying to celebrate women in computing, and we're what, at 18 thousand people now, >> The biggest ever, >> Plus I think, 6 thousand people joining on the livestream, which is great. >> Before the cameras were rolling, we were talking about your role as the 3rd US CTO, and just talking about getting more technology into government to help leaders work together, and move faster. Tell us a little about this initiative. >> What's so great, is it's not partisan, fixing the government and making it work better, so all the work that we were doing continues. What we were able to put in place, during the Obama administration, and continues to Trump, were things like, the CT office. Having technical people, so I worked at Google, people work at Amazon, Facebook, Twitter, these companies who have that background, to join in on policy conversations, one, to join in on capacity building the government, so data sciences and tech and, let's have our services be as great as Amazon, or as Twitter, or Oracle, and not be sort of retro, really serve the American people. And then also, helping the American people in general, with capacity building, things like computer science for all. So that was an initiative that continues to get all of our children to have coding at school. That all children, you couldn't graduate from high school without having had some experience on learning of coding Coding is a 21st century fluency, it's a skill we all need, Like freshman biology. You want to know some biology, you want to know some coding, you want to know how to write, so making sure they have is tech-up, which was a program we started to help train Americans, there's six hundred thousand jobs open, in the United States, and they pay 50% more than the average American salary. The companies are starving. How do we rapidly get more Americans into these jobs? It turns out that people have, of course, created these fabulous code boot camps, you can train in three months for these jobs, some of them are paid, some times they pay you, all different kinds, some are online, some are offline, they're all over the country. So we're able to get more people to consider, a job like that, culturally they think, Well I don't, why would I, I don't know how to do that. Well you can, this is a fun and interesting and exciting career, you can do digital marketing, you can do user interface design. You can get involved in front end or back end coding, product management, all those things, sales. And so, how do you pull lots more Americans in, get our companies fueled so we have really the economic opportunity, and they're all over the country. Location wise, and topic wise. So we did tech hour now, and a tech jobs tour, which is not what we did in government, but we continue some of that work. >> This weird dichotomy, because on one end, people are worried about tech taking jobs, on the other hand, there's a ton of open tech jobs. And there's this transition period, that's difficult, obviously for people that didn't grow up, but one of the keynote speakers today, told a really heartening story, that she didn't get into it until the day she had to leave her abusive husband, and now she is a coder >> That's Doctor Sue Black, who was just given the Order of the British Empire, I mean, she is an incredible computer scientist. Yes, she escaped an abusive marriage with three small children, in her early 20s, I think. Ended up moving into public housing, and dealing with three children only being the school from 9 until 3, and eventually getting her PhD in computer science, and really, she started Techmoms now, she continues to do research in other things, but she's really trying to use her story, and her organizing capacity, to have more people realize this isn't hard like figuring out gravity waves that won the Nobel prize. This is hard like writing a hard essay, so we all can learn to write an essay. It takes some mastery work, you don't learn it in kindergarten but by the time you're in 7th, 8th, 9th, 10th, 12th grade, you can do it. >> It's not rocket science. >> Right, so coding is like that. >> The other piece you said that's very interesting, is the consumerization of IT. We've seen it at enterprise, a huge trend. But, now I expect everything that's on my phone, when I interact with Facebook or Amazon, or whatever, to be in all the applications, so, as you said, that's influencing government, and the way they have to deliver services, and I would imagine, too, with kind of the next wave of kids coming in, graduating, going into public service, they certainly have that expectation, right? They've been working on their phone forever of course it should be on the phone. >> And so we want everybody in our country fluent in computer science and coding at a basic level, like again, like freshman biology or takin' chemistry in high school, or taking writing. So that everyone could realize this is not rocket science we could have these kinds of capabilities as part of our services, from Housing and Urban Development, from the Department of Education. You know, a lot of us use our phones to get places, you know, on our maps, and so that's actually data coming from the US Geological survey, if you're looking at the weather, you're looking at NOAA's satellites, this is open government data. We were able to open over two hundred thousand data sets, from all over government, not private data, but public data, that you could make an entire app store, or Google play set of products on top of that. Government wouldn't have to pay for that, it just packages up the API as well. A really good example of that, is the US census team. There's nothing more big data than census, they have all of our information from a data perspective, and so they did opportunity.census.gov, and they said to various agencies, let us help you bridge these data sets into something that someone could build on top of, like we're seeing from the courses sector, we saw wonderful things like, Housing and Urban Development said, okay, our challenges are housing affordability, mobility, these are the challenges instead of having HUD make an app for Americans to come to, they just can explain what their problem is, what data sets, and then engage extraordinary companies, like airbnb, Redfins, Zillow, these fabulous tech companies, who can make instead a product for 100% of the Americans, rather than only wealthy or middle class Americans, and so they did things like, opportunity score, and airbnb helping you figuring out, if I rent a room in my house I can make my rent more affordable, very creative apps, that we can see, same thing for the Department of Ed or Department of Labor, and as the data gets out there, and as apps come, also the opportunity for data science and machine learning. What if, as much as we serve ads to ourselves, in these algorithms, what if we use the algorithms to help Americans find a job that they would love? You know, job matching, and these kinds of opportunities. of the problems in the world, and helping government get more fluent at that. And the way to do that is not so much, jam the government You have to do this, but find terrific talent like we see at Hopper, and have them cycle into the government, to be co-leaders just like a surgeon general would come. >> Are you facing recruitment challenges in that same way though? In the sense that technology is having a hard enough time recruiting and retaining women, but the government, too, is that seen as enough of an employer of choice for young talented, bright ambitious, young women? >> I'm not in government now, but when we were in there, we found a very interesting thing. Alex Mcgovern, who had been the general counsel of Twitter who was Stephanie's CTO with me and led a lot of our tech quals we called TQ like tech IQ in policy, together with economists and lawyers and others have if we're going to decide net neutrality, let's include everyone, including computer scientists, and we're going to sue bridge and open source, So we talked about that, and on the way going in Mcgovern, he said, wouldn't it be cool if, just like when you look at a lawyer's resume, you might see that they clerk and they served their county through clerking and through the judicial system, as well as being a private lawyer, they were a public defender, that's a pretty normal thing to see on a legal resume. If you looked at medical, you might see them going into NIH or doing some research, if you looked at a scientist, they might have gone to, done some NSF work or others. But for the tech crew, there is of course amazing technical people in NASA, NAH and the Department of Energy, and there's great IT teams, but there's not this thing that the Silicon Valley crew resume would say, oh, yeah, I served my country. So that's why, under President Obama, we were able to create these incredible programs. The Presidential Innovation Fellows, which was a one year term of service, The United States Digital Service, which is a three months to a two year term of service in the VA. What's more amazing if you build Amazon, than to go as a second act and help our veterans? It's an incredible honor, to the point of, will they come? Yes, that's what we were hoping, could we have that be a normal thing, and yes it's become a normal thing. And the Trump administration continues it. The 18F team is in the general services administration, they're on 18th and F so they have a code name. But that particular team is located around the country, not only in DC but in San Francisco, in Chicago, and others. So you see this tech sector flowing now into the government on a regular basis, and we welcome more peoples. The government is who shows up to help, so we need the tech sector to show up cause we've got a lot of money as a country, but if we're not effectively using it we're not serving the American people and foster children, veterans, elders, others need the services that they deserve and we have the money, so let's make it happen the way the tech sector is delivering Amazon packages or searches. >> What is your feeling, this is not your first Grace Hopper obviously, but what is your feeling about this conference, and advice that you would give to young women who are here, maybe for their first or second time, in terms of getting the most their time here? >> You know, I think the main thing is, it's a celebration, that's fun and you can walk up to anyone, so just talk to everyone. I've been talking to a million people on the floor, fabulous. Students are here, more senior technical leaders are here. We've been running speed mentoring, we're running a program called the Tech Jobs Tour, it's at Techjobstour.com, it's a #Americanshiring, and we've been going to 50 different cities and so we're running a version of that, and we do speed mentoring, so come to the speed mentoring sessions, it's a five minute pop, talk to someone about what you're tryin' to do. Lot's of programs like that, get into the sessions, come to the keynotes which are so inspiring, and Melinda Gates was amazing today, Dr. Fefe Lee was incredible, just across aboard, Dr Sue Black was here, I thought it was great today, actually, just to reflect on Melinda's keynote, I think this might have been the first time, I was talking to her, that she's really talked about her own technical experience >> That struck me, too! As a coder, starting in computer science. I didn't really understand that she had really started very early, with Apple 3 and the story of her dad >> And her love of her Apple 3, right! and really high school coding, which is so important for young people in high school and middle school, even younger. The Muscogee Creek Tribe, in Oklahoma, is teaching robotics in head start, so we can start in preschool. Just make it fun, and interesting. They're funny, they don't do battle bots, because you don't really want to teach 3 and 4 year olds to fight, so instead they have collaborative robots. >> Robots who work together Age appropriate. >> Well Megan Smith, this has been so fun talking to you, thanks so much for coming on our show. >> Thanks for having me. >> We will have more from the Grace Hopper Conference just after this, I'm Rebecca Knight for Jeff Frick (music)

>> Announcer: Live from Orlando, Florida, it's theCUBE! Covering Grace Hopper Celebration of Women in Computing. Brought to you buy SiliconANGLE Media. >> Welcome back to theCUBE's coverage of the Grace Hopper conference here in Orlando, Florida. I'm your host, Rebecca Knight, along with my cohost, Jeff Frick. We're joined by Dr. Deborah Berebichez. She is the chief data scientist at Metis, which is owned by Kaplan. Thanks so much for joining us. >> Thank you, Rebecca. Thanks for inviting me, too. >> You have had such an interesting and varied professional career. You were even a host of a lot of different science-oriented television programs. You work on initiatives to get young women into technology. But one of the things that is most impressive is that you were the first Mexican woman to ever earn her PhD in physics-- >> Deborah: In physics, at Stanford. >> From Stanford University. What an accomplishment. But talk a little bit about your path to Stanford. Tell our viewers a little bit more about your trajectory. >> It's definitely a convoluted, and not a typical path. I grew up in Mexico City in a conservative community that discouraged girls and young women from pursuing a career in the hard sciences. I was told from a very young age that physics was for geniuses, and that I had better pick a more feminine path, like communications or something else, which were great careers, but they were not the right ones for a very inquisitive mind like mine. When I confessed to my mom in high school that I loved physics and math, she said, "Don't tell the boys, "because you'll intimidate them, "and you may not be able to get married." >> Rebecca: Nonsense! >> Actually, it's funny, because that kind of overt bias is sometimes easier to combat than the one that more women experience, which is a more subtle bias. You know, that the media tells us that some things are for boys and for women. So, in my case, it was very open, and so it almost gave me more courage to try to fight against it. Anyways, so, it came time to pick what career, what BA to do in college, and I was told by the advisors in school that philosophy was a more feminine and acceptable path, but it also asked a lot of questions about the universe. So, I enrolled in a local college in Mexico City to study philosophy, but the more I tried to stifle my love for physics and math, the more that inner voice was screaming, "This is your path. "You have to do it, you have to study physics." Just like a lot of kids do their rebellious things behind their parents' back, I would go and rent from the library books about obscure physicists like Tycho Brahe, this Danish astronomer who was locked up in a tower, and I was thinking, I'll be just like him, kind of antisocial, nobody will like me, but at least I'll have my data, my numbers, to keep me company. >> Rebecca: This was your teenage rebellion, is reading about brooding philosophers? >> Well, there other-- >> Okay. >> In the middle of my BA in philosophy in Mexico, I decided to apply to universities in the US to give it a chance, and give myself the opportunity to pursue both BAs, physics and philosophy. I was very fortunate to get a full scholarship to attend Brandeis University, and I say that because, in Mexico, universities are about eight times less expensive than in the US, so I could have not afford to go anywhere else. While at Brandeis, I took the courage to take a very general course in astronomy. Very little math, introductory course, and there I met the teaching assistant, who was a graduate student by the name of Roopesh. He was from India. Roopesh and I became good friends, and he told me that I wasn't the typical student that just wanted to get an A in the class and do the homework well, that my curiosity had no end. That I would ask questions about quantum mechanics and statistical mechanics, and I wanted to know everything about the universe and nature. So, one time, we were walking in Harvard Square, and I realized that I was the only one who could make my dream of becoming a physicist happen. With teary eyes, I told Roopesh, "I don't want to die without trying. "I just don't want to die without trying to do physics." He called his advisor on a payphone. He was the head of the graduate student committee, so he called me to Brandeis. He handed me a book called Div, Grad and Curl, Vector Calculus in Three Dimensions. For me, it was an alien language. He said to me, "There's a problem, "because the BA in physics takes four years, "and your scholarship is only for two years. "But guess what, someone else has done this at Brandeis. "His name is Ed Witten. "Do you know who he is? "He switched from history to physics." I said, "You're kidding. "Ed Witten is a very famous physicist, "the father of string theory. "Clearly, there's no way I could pull this off." He says to me, "I give you two months this summer. "If, by the end of the summer, "you pass a test on this one book, "I'll let you skip through "the first two years of the physics major "so you can complete the BA in only two years." Roopesh decided to mentor me and tutor me 10 hours a day for eight weeks. I tell the story of Roopesh because I always wanted to pay him back. He said to me, when he was growing up in India, in Darjeeling, there was an old man who would teach him and his sisters the tabla, the musical instrument, English, and math. And when they wanted to pay him back, the old man said, "No, the only way you could ever pay me back "is if you do this with someone else in the world." That's how my mission in life started, to inspire, encourage, and help other, especially women, but minorities who, like myself, want a career in STEM, but for some reason, whether it be financial or social, feel that they cannot achieve their dreams. >> Great story. >> Yeah, wow! Incredible! >> And then, you asked about Stanford. So, then I went back to Mexico, and I was doing a Master's in theoretical physics, and I was again told by my community, "Okay, you've got it over with. "Stay here, get married and stay as part of the community." But I was still more hungry for knowledge, and to do more physics. I was very late in the application cycle, and I decided to apply to schools. I went to my Mexican advisor's office, and I said, "You know, I'm going to leave again. "I'd like to go to the US where I can pursue experiments. "I wrote to a couple of professors." He says, "Who did you write to?" I say, "Well, there's one particularly interesting one, "Steve Chu at Stanford." His jaw dropped. He said, "Steve Chu?" I said, "Yes, why?" He said, "Do you realize he just won the Nobel Prize "a couple of months ago?" And Steve Chu later became Secretary of Energy in the US. I was so fortunate that he received my email with interest, invited me to work directly with him at Stanford. That's how my career started. >> It's such a good mix of fortuitousness, serendipity, but also doggedness on your part, so, really, there's a lot going on. >> Don't be shy, is my-- >> This gets to our final question, really, which is, what's your advice for the younger versions of you? >> The first thing is that it was not all easy for me. There was a lot of failure along the way. My first advice is, the people who get to the end of the line and succeed in life are not the ones that simply persevere and get everything right. They're the ones that keep getting up and succeeding step after step. It's the courage to get to the end and persevere even when failure exists. The second piece of advice, especially for parents out there, is when your kids ask questions about the world and nature, don't just give them the answer. Go through the pleasure of finding things out, as Feynman would say. Especially with computing. Computers are a tool, a magnificent tool. But they're just a tool to another goal, which is to gain insights about the world. It's more important to be a critical thinker and a thought leader, rather than just focus on being proficient at coding. >> You had the element of humor, you had the element of storytelling, you had the element of everyday things in the way, 'cause you're obviously a super smart lady to accomplish these things. Not everybody's so super smart, so you've created a style in which you can help those that aren't maybe necessarily PhDs from Stanford to gain interest, to become interested, to kind of hook 'em into this interesting world that you're so passionate about. >> Yeah, thank you. I try to do it through my TV show that I cohost with The Science Channel called Outrageous Acts of Science, which serves exactly that purpose, to get people interested in the fact that science and STEM is behind everyday life. It's not just some complicated equation in a board. It's what we go through every day, and if you just gain the joy of discovering those concepts, you're set. >> Great. Well, Deborah, thank you so much for joining us. It's been so much fun talking to you. >> Thank you. I loved being here. >> I'm Rebecca Knight for Jeff Frick. We will have more from Grace Hopper just after this. (fast techno music)