(upbeat music) >> Okay, we're back. We're here in the main stage in Cloud City. I'm John Furrier and Dave Vellante. Normally, we're over there on theCUBE set, but here we've got a special presentation. We'll talk about Totogi and the new CEO of Totogi is Danielle, who is also the CEO of TelcoDR, Digital Revolution. Great to see you. And of course, Robin Langley, we interviewed you in theCUBE, CTO of Totogi. This is a main stage conversation because this is the big news. >> Yeah. >> You guys launched there with a hundred million dollar investment. We covered that news a couple weeks ago and you as the CEO. What's the story. Tell us what is happening with Totogi? Why such a big focus? What's the big push? >> Yeah, I'm really excited about Totogi because I really think this team is working to build public cloud tools for Telco the right way. It's everything I've been talking about. I talked about it yesterday in my keynote and this is really the execution of that vision. So, I'm super excited about that. A couple of days ago, Rob and I were talking about the charging system, but there's another product that Totogi introduced to the world and that's the webscale BSS system. So I think we're going to talk about that today. It's going to be great. >> Let's get into actually the charging system, which was great processing here. What is this focus? What is BSS about with cloud? How does the public cloud innovation change the game with this? >> Well, a little bit like charging. I mean, there are maybe, you know, a hundred plus BSS systems out there, why does the world need yet another BSS? And I think one thing is we're coupling up with public cloud, which gives it that webscale element. Right? We can have a platform. Never do another upgrade again, which I think is really exciting. But I think the really key thing that we're working on is we're building on top of an open API standard. And a lot of vendors talk about their APIs, why is this different? These are standards developed by TM forum, right? It's an independent body in our industry. They've been working on these, sorry, open APIs, and all the different vendors signed a manifesto that say, "I pledge. I pledge to support the open API", but if you look at the leaderboard and everyone is Sub10, Sub5, right? And so it's kind of like, going through the actions and not falling, you know, saying it, but not following it up and we're doing it. >> Wow, so... >> Yeah. >> Dave: Robin, you guys just popped up on the leaderboard. You went from a standing start to, I think more than 10. >> Yeah. >> I don't think that's ever been done before, has it? >> No, so we were out there. We published 12 APIs and we've got a quote from, you know, TM forums saying, essentially I've never seen anyone move so fast and to publish. And it's our intent to publish, you know, 50 plus, all of their APIs by the end of the year. >> So, how were you able to do that? I mean, like, were you holding them back? Just kind of dumping them on one day? This is the nature of the new business, isn't it? >> Yeah, absolutely and then you think about BSS. It's just, you know, been known for years to be a spaghetti of, you know, applications, you know, disparate data, data being duplicated, systems not talking to each other, lots of different interface types. And it was crying out to be just, you know, sold properly in the cloud. And the public cloud is perfect for this. You know, we can build a model and start, rather than looking at the applications first, you know, let's look at the model, the unified model and build on those open APIs and then start to, you know, allow people to come in and create an ecosystem of applications all using that same model. >> If you don't mind me asking you, if you can explain. 'Cause we talked before we weren't on camera, but we talked about the cloud and you were explaining to me how this is perfect for the challenges that you guys are trying to solve. What about the public cloud dynamic or innovation component that you guys are leveraging? Take us through a little bit on that, because I think that's a big story here that's under the covers is... >> Yeah. >> What you're capable of doing here. Do you mind explaining? >> Yeah, no, absolutely. So the cloud gives us this true scalability across everything. You know, we can scale to billions of records. So we can hook in, you know, to suck in data from, you know, our on-premise systems anywhere. We have, you know, a product called Devflow, so we used to do that. And it can really allow us to bring that data in, scale-out, use standard term cloud innovations, like Lambda functions and AWS, you know, DynamoDB, and present that, you know, through that open API. So we can use, you know graphQL, you know, present that with rest on top. And so you can then build on top of that. You can take any low code, no code application building tool you like, put that on top and then start building your own ecosystem. You can build inventory systems, CRM, anything you like. >> Well one thing that's really interesting about these projects is they usually take months, years to deploy, right? And what we're doing is we're providing, almost BSS as a service, right? It's an API layer that anyone can go to. Maybe you need to use it for five minutes, five months, five years, right? With the open standard and your own developers can learn how to use this text stack and code to it doesn't require us. And so we're really trying to get away from being an SI, you know, systems integrator or heavy services revenue, and instead build the product that enables the telcos to use their own people, to build the applications that they, they know what they want, and so, here you go. >> It's a platform. >> Yeah. >> It's a platform. >> So, how do you connect to systems on the ground? Like what's the modern approach to doing that? >> Yeah, go for it. >> Yeah so, telcos have, you know, a huge amount of data on premise. They have difficulties you can get to it. So, as I mentioned before, we had this Devflows product and it has connectors. We have like 30 plus connectors to all the standard sort of, billing systems, CRM systems, you know, we can hook into things like Salesforce. And we can create either, you know, couple of a real-time interface in there, or we can start to suck data into the cloud and then make it available. So, if they want to start with a nice, easy step and just build slowly, we can just hook in and pull that information out. If there may be, you know, an attribute that you want to, you know, use in some of that application, you can easily get to it. And then, you know, over time you start to build your data into the cloud and then you've got the scale, you know, and all the innovations of that brings with it. >> So is Devflow an on-ramp, if you will, for the public cloud, is that the way you were thinking about it? >> Yeah. >> Yeah. Yeah, I mean, I call it the slurper. (group chuckles) Right. I mean, these telcos have, like Robin was saying, spaghetti systems that have been, you know, customized and connected and integrated. I mean, it is a jungle out there of data. They're not going to be able to move this in one step. We just think of like a pile of spaghetti, like the whole bowl. >> Overcooked spaghetti. >> Right overcooked, the whole bowl comes out and it's really hard to just pull out one noodle and the rest is there and what are you going to do? And so the slurper, right, Devflows, allows you to select which data you want to pull out. It could be one time, you could have it sync. You don't have to do the whole thing and it doesn't disrupt the production environment that's on-premise. But now you're starting to move your data into the public cloud and then like Robin was saying, you can throw it up against quick sites. You can throw it up against different Amazon services. You can create new applications. And so it's not this like, you know, big bang kind of approach. You can start to do it in pieces and I think that's what the industry needs. >> I'm talking about this the other day, when we're talk about charging. What a lot of vendors will do is they'll put a wrapper around it, containerize it and then shove it into the public cloud and say, "Okay". >> Check mark. >> Yeah a checkbox. And it affects how they price, if they price the same way. But we talked a lot about pricing the other day, really pricing like cloud, consumption pricing. How are you pricing in this case? >> Same with the charging system. The BSS system is paid by the use, paid by the API call. So, really excited to introduce yet, again, a free tier. We think we're doing 500 million API calls per month for free. We think this is great for a smaller telco where like, you're experimenting and just getting to know the system and before you like, go all in and buy. And I think that API pricing is going to go right at the heart of some of these vendors that love to charge by the subscriber or a perpetual license agreement, right? They're not quite moving as a service. And so, yeah. >> Are you saying, they're going to be disruptive in the pricing in terms of lower cost or more, consumable. >> And I think it's also an easier on ramp, right? It's easier to start paying by the use and experimenting. And it's really easy, just like I was talking about with charging, where you're going to get the same great product that you would sell to a tier one at a price that you can afford. And now those smaller two or three guys aren't having to make a trade off between great technology, but I'm paying through the nose or sacrifice on the tech, but I can afford it. And so, I think you're going to see this ecosystem of people starting to learn how to code and think in this way. Telcos have already decided that they want to adopt the TM forum, open APIs. They're on all the RFPs. Do you support it? Everyone says they support it, but we don't see anyone really doing it. They're not on the leaderboard. >> And there's transparency, because you're pricing by API call, right? Versus the spaghetti, you guys call it, the hairball of what am I paying for? >> Right, you're getting, all of this. It's by the subscriber. It's millions and millions of dollars. Oh, and you know, you're going to need to buy a bunch of consulting revenue to make it all work and talk to each other. Pay up, right? And that's what we're living in today. And I'm taking us to the, you know, public cloud future by the API. >> This is the big cloud revolution. It's unbundling has been a really big part of the consumption of technology paid by the usage, get in, get some value, get some data, understand what it is, double down on it, iterate. >> Put it up with different services that are available that we don't have, but Amazon uses, right? They have call centers up there, they have ML that you may want to use like, start using it, start coding, start learning about the AWS tech stack. >> So is it available now? >> Yeah. >> Yeah. No, it's available now. We've already published the swagger for the BSS APIs. So, you know, they can come on board, they can go to access to all the API straight away and start using it. They can load up their favorite REST clients and then start developing. >> So you got a dozen APIs today. Where are we headed? What can we expect? >> All by the end of the year. There's over 50 APIs. You know, the number one guy on the board is at like 22, 21, 22 APIs covered. We'll be 50 plus by the end of the year. And we're just going to blow doors. >> The API economy has come to telco. >> Yeah, I mean, it's really BSS' Lego pieces, right. Assembling these different components and really opening it up. And I think there's been a lot of power by the vendors to keep it locked down, keep it close. Yes, we have an API, but you got to use our people to do it. Here's the hundreds of thousands or millions of dollars that you're going to pay us and keep us in business, and fat and happy, and I'm coming right in on the low end. Right, dropping that price, opening it up. I think telcos are going to love it. >> Well, Mike, you said too, you'll allow the smaller telcos to have the same, actually, better capabilities than the larger telcos, right? Maybe the stack's not as mature or whatever, but they'll get there and they'll get there with a simpler, easier to understand pricing model and way, way faster. >> Yeah. >> All right and that's where the disruption comes. >> And I Think this is where AWS has really done well as a hyper scaler against their competition, is that they've really gotten to market very quickly with their services. Maybe they're not perfect, but they ship 'em. And they get them out there and they get people using them. They use them internally and they get them out. And I think this is where maybe some of the other hyperscalers, they hold them back and they wait until they're a little bit more mature. And AWS is one because they've been fast. And I want to sort of copy that feat. >> I think your idea of subscriber love in your keynote, and I think applies here because Amazon web services has done such a great job of working backwards from the customer. So they'd ship it fast on used cases that they know have been proven through customer interactions. >> Yep. >> They don't just make up new features. And then they iterate. They go, "Okay". >> Start simple, grow on that, learn from the market. What are people using? What are they not using? Iterate, iterate, iterate. >> Okay, so with that in mind, working backwards from your customer, how do you see the feature set evolving for this functionality? How do you see it evolving as a product? >> Yeah, I mean, I think all of the BSS systems today have been designed with manual people on the other side of the screen, right? And we've seen chat bots take off, we've seen, you know, using chat as support. I think we need to start getting into more automation right? Which is really going to change up telco, right? They have thousands of customer support agents and you're like, "Dude, I just want a SIM, that's all I need". >> Yeah. >> Just like, where do I push a button and send an Uber to my house and drop it off or eSim. And so, speeding up business, empowering the subscriber. We know how to interact, we just went through COVID where we learned about different apps that overnight, you can like order all of your groceries and order all of your food and there it is, and it was contactless and... >> It's funny, you said future of work, which we love that term, "work". Workloads, work force, you got all these kind of new dynamics going on with cloud enablement and the changes is radical. And the value is there. There's value opportunities. >> I mean like, you know, where are the ARVR applications, right? Where your agent pops. I saw the demo. There's a strife in Austin and they're going to kill me 'cause I can't remember their name. But they had a little on your mobile phone, a little holographic customer support. Like, "How can I help you"? Right. And I'm like, "Where's that", like, imagine you're like, ATT, you're not like on the phone for like an hour and a half trying to like, figure out what's wrong. And it's like, you know, it knows what's wrong. It understands my needs and so, no one's working on that. We're still working on, keyboards. >> Right, that and chat bot is a great example because it's all AI, and where's the best AI? It's in the cloud because that's where the data is. That's where the best of modeling has been. (chuckles) >> I think your point, it's the scale of data. >> Absolutely. >> And machine learning and AI needs a lot of data points to get really good. I mean, I'm old, I'm 50. I graduated in 1993. I took an AI class from Niels Nielsen, like the godfather of AI, right? Okay, like that AI, even 10 years ago AI, it's just moving so quickly and it's now super affordable. >> Well, I really want to thank you guys for coming up and sharing that knowledge and insight, congratulations on the product and open APIs. Love open API's open source with some new revolution. Danielle and Robin. Thank you so much. >> Thanks so much. >> Thank you. >> Thank you. >> Congratulations. Thank you everyone for coming. (crowd applauding) (people whooping) Okay, back to you in the studio at Cloud City.

>>from around the globe. It's the Q. With digital coverage >>of exa scale day made possible by Hewlett Packard Enterprise. We're back at the celebration of Exa Scale Day. This is Dave Volant, and I'm pleased to welcome to great guests Brian Dance Berries Here. Here's what The ISS Program Science office at the Johnson Space Center. And Dr Mark Fernandez is back. He's the Americas HPC technology officer at Hewlett Packard Enterprise. Gentlemen, welcome. >>Thank you. Yeah, >>well, thanks for coming on. And, Mark, Good to see you again. And, Brian, I wonder if we could start with you and talk a little bit about your role. A T. I s s program Science office as a scientist. What's happening these days? What are you working on? >>Well, it's been my privilege the last few years to be working in the, uh, research integration area of of the space station office. And that's where we're looking at all of the different sponsors NASA, the other international partners, all the sponsors within NASA, and, uh, prioritizing what research gets to go up to station. What research gets conducted in that regard. And to give you a feel for the magnitude of the task, but we're coming up now on November 2nd for the 20th anniversary of continuous human presence on station. So we've been a space faring society now for coming up on 20 years, and I would like to point out because, you know, as an old guy myself, it impresses me. That's, you know, that's 25% of the US population. Everybody under the age of 20 has never had a moment when they were alive and we didn't have people living and working in space. So Okay, I got off on a tangent there. We'll move on in that 20 years we've done 3000 experiments on station and the station has really made ah, miraculously sort of evolution from, ah, basic platform, what is now really fully functioning national lab up there with, um, commercially run research facilities all the time. I think you can think of it as the world's largest satellite bus. We have, you know, four or five instruments looking down, measuring all kinds of things in the atmosphere during Earth observation data, looking out, doing astrophysics, research, measuring cosmic rays, X ray observatory, all kinds of things, plus inside the station you've got racks and racks of experiments going on typically scores, you know, if not more than 50 experiments going on at any one time. So, you know, the topic of this event is really important. Doesn't NASA, you know, data transmission Up and down, all of the cameras going on on on station the experiments. Um, you know, one of one of those astrophysics observatory's you know, it has collected over 15 billion um uh, impact data of cosmic rays. And so the massive amounts of data that that needs to be collected and transferred for all of these experiments to go on really hits to the core. And I'm glad I'm able toe be here and and speak with you today on this. This topic. >>Well, thank you for that, Bryan. A baby boomer, right? Grew up with the national pride of the moon landing. And of course, we've we've seen we saw the space shuttle. We've seen international collaboration, and it's just always been something, you know, part of our lives. So thank you for the great work that you guys were doing their mark. You and I had a great discussion about exa scale and kind of what it means for society and some of the innovations that we could maybe expect over the coming years. Now I wonder if you could talk about some of the collaboration between what you guys were doing and Brian's team. >>Uh, yeah, so yes, indeed. Thank you for having me early. Appreciate it. That was a great introduction. Brian, Uh, I'm the principal investigator on Space Born computer, too. And as the two implies, where there was one before it. And so we worked with Bryant and his team extensively over the past few years again high performance computing on board the International Space Station. Brian mentioned the thousands of experiments that have been done to date and that there are currently 50 orm or going on at any one time. And those experiments collect data. And up until recently, you've had to transmit that data down to Earth for processing. And that's a significant amount of bandwidth. Yeah, so with baseball and computer to we're inviting hello developers and others to take advantage of that onboard computational capability you mentioned exa scale. We plan to get the extra scale next year. We're currently in the era that's called PETA scale on. We've been in the past scale era since 2000 and seven, so it's taken us a while to make it that next lead. Well, 10 years after Earth had a PETA scale system in 2017 were able to put ah teraflop system on the International space station to prove that we could do a trillion calculations a second in space. That's where the data is originating. That's where it might be best to process it. So we want to be able to take those capabilities with us. And with H. P. E. Acting as a wonderful partner with Brian and NASA and the space station, we think we're able to do that for many of these experiments. >>It's mind boggling you were talking about. I was talking about the moon landing earlier and the limited power of computing power. Now we've got, you know, water, cool supercomputers in space. I'm interested. I'd love to explore this notion of private industry developing space capable computers. I think it's an interesting model where you have computer companies can repurpose technology that they're selling obviously greater scale for space exploration and apply that supercomputing technology instead of having government fund, proprietary purpose built systems that air. Essentially, you use case, if you will. So, Brian, what are the benefits of that model? The perhaps you wouldn't achieve with governments or maybe contractors, you know, kind of building these proprietary systems. >>Well, first of all, you know, any any tool, your using any, any new technology that has, you know, multiple users is going to mature quicker. You're gonna have, you know, greater features, greater capabilities, you know, not even talking about computers. Anything you're doing. So moving from, you know, governor government is a single, um, you know, user to off the shelf type products gives you that opportunity to have things that have been proven, have the technology is fully matured. Now, what had to happen is we had to mature the space station so that we had a platform where we could test these things and make sure they're gonna work in the high radiation environments, you know, And they're gonna be reliable, because first, you've got to make sure that that safety and reliability or taken care of so that that's that's why in the space program you're gonna you're gonna be behind the times in terms of the computing power of the equipment up there because, first of all and foremost, you needed to make sure that it was reliable and say, Now, my undergraduate degree was in aerospace engineering and what we care about is aerospace engineers is how heavy is it, how big and bulky is it because you know it z expensive? You know, every pound I once visited Gulfstream Aerospace, and they would pay their employees $1000 that they could come up with a way saving £1 in building that aircraft. That means you have more capacity for flying. It's on the orders of magnitude. More important to do that when you're taking payloads to space. So you know, particularly with space born computer, the opportunity there to use software and and check the reliability that way, Uh, without having to make the computer, you know, radiation resistance, if you will, with heavy, you know, bulky, um, packaging to protect it from that radiation is a really important thing, and it's gonna be a huge advantage moving forward as we go to the moon and on to Mars. >>Yeah, that's interesting. I mean, your point about cots commercial off the shelf technology. I mean, that's something that obviously governments have wanted to leverage for a long, long time for many, many decades. But but But Mark the issue was always the is. Brian was just saying the very stringent and difficult requirements of space. Well, you're obviously with space Born one. You got to the point where you had visibility of the economics made sense. It made commercial sense for companies like Hewlett Packard Enterprise. And now we've sort of closed that gap to the point where you're sort of now on that innovation curve. What if you could talk about that a little bit? >>Yeah, absolutely. Brian has some excellent points, you know, he said, anything we do today and requires computers, and that's absolutely correct. So I tell people that when you go to the moon and when you go to the Mars, you probably want to go with the iPhone 10 or 11 and not a flip phone. So before space born was sent up, you went with 2000 early two thousands computing technology there which, like you said many of the people born today weren't even around when the space station began and has been occupied so they don't even know how to program or use that type of computing. Power was based on one. We sent the exact same products that we were shipping to customers today, so they are current state of the art, and we had a mandate. Don't touch the hardware, have all the protection that you can via software. So that's what we've done. We've got several philosophical ways to do that. We've implemented those in software. They've been successful improving in the space for one, and now it's space born to. We're going to begin the experiments so that the rest of the community so that the rest of the community can figure out that it is economically viable, and it will accelerate their research and progress in space. I'm most excited about that. Every venture into space as Brian mentioned will require some computational capability, and HP has figured out that the economics air there we need to bring the customers through space ball into in order for them to learn that we are reliable but current state of the art, and that we could benefit them and all of humanity. >>Guys, I wanna ask you kind of a two part question. And, Brian, I'll start with you and it z somewhat philosophical. Uh, I mean, my understanding was and I want to say this was probably around the time of the Bush administration w two on and maybe certainly before that, but as technology progress, there was a debate about all right, Should we put our resource is on moon because of the proximity to Earth? Or should we, you know, go where no man has gone before and or woman and get to Mars? Where What's the thinking today, Brian? On that? That balance between Moon and Mars? >>Well, you know, our plans today are are to get back to the moon by 2024. That's the Artemus program. Uh, it's exciting. It makes sense from, you know, an engineering standpoint. You take, you know, you take baby steps as you continue to move forward. And so you have that opportunity, um, to to learn while you're still, you know, relatively close to home. You can get there in days, not months. If you're going to Mars, for example, toe have everything line up properly. You're looking at a multi year mission you know, it may take you nine months to get there. Then you have to wait for the Earth and Mars to get back in the right position to come back on that same kind of trajectory. So you have toe be there for more than a year before you can turn around and come back. So, you know, he was talking about the computing power. You know, right now that the beautiful thing about the space station is, it's right there. It's it's orbiting above us. It's only 250 miles away. Uh, so you can test out all of these technologies. You can rely on the ground to keep track of systems. There's not that much of a delay in terms of telemetry coming back. But as you get to the moon and then definitely is, you get get out to Mars. You know, there are enough minutes delay out there that you've got to take the computing power with you. You've got to take everything you need to be able to make those decisions you need to make because there's not time to, um, you know, get that information back on the ground, get back get it back to Earth, have people analyze the situation and then tell you what the next step is to do. That may be too late. So you've got to think the computing power with you. >>So extra scale bring some new possibilities. Both both for, you know, the moon and Mars. I know Space Born one did some simulations relative. Tomorrow we'll talk about that. But But, Brian, what are the things that you hope to get out of excess scale computing that maybe you couldn't do with previous generations? >>Well, you know, you know, market on a key point. You know, bandwidth up and down is, of course, always a limitation. In the more computing data analysis you can do on site, the more efficient you could be with parsing out that that bandwidth and to give you ah, feel for just that kind of think about those those observatory's earth observing and an astronomical I was talking about collecting data. Think about the hours of video that are being recorded daily as the astronauts work on various things to document what they're doing. They many of the biological experiments, one of the key key pieces of data that's coming back. Is that video of the the microbes growing or the plants growing or whatever fluid physics experiments going on? We do a lot of colloids research, which is suspended particles inside ah liquid. And that, of course, high speed video. Is he Thio doing that kind of research? Right now? We've got something called the I s s experience going on in there, which is basically recording and will eventually put out a syriza of basically a movie on virtual reality recording. That kind of data is so huge when you have a 360 degree camera up there recording all of that data, great virtual reality, they There's still a lot of times bringing that back on higher hard drives when the space six vehicles come back to the Earth. That's a lot of data going on. We recorded videos all the time, tremendous amount of bandwidth going on. And as you get to the moon and as you get further out, you can a man imagine how much more limiting that bandwidth it. >>Yeah, We used to joke in the old mainframe days that the fastest way to get data from point a to Point B was called C Tam, the Chevy truck access method. Just load >>up a >>truck, whatever it was, tapes or hard drive. So eso and mark, of course space born to was coming on. Spaceport one really was a pilot, but it proved that the commercial computers could actually work for long durations in space, and the economics were feasible. Thinking about, you know, future missions and space born to What are you hoping to accomplish? >>I'm hoping to bring. I'm hoping to bring that success from space born one to the rest of the community with space born to so that they can realize they can do. They're processing at the edge. The purpose of exploration is insight, not data collection. So all of these experiments begin with data collection. Whether that's videos or samples are mold growing, etcetera, collecting that data, we must process it to turn it into information and insight. And the faster we can do that, the faster we get. Our results and the better things are. I often talk Thio College in high school and sometimes grammar school students about this need to process at the edge and how the communication issues can prevent you from doing that. For example, many of us remember the communications with the moon. The moon is about 250,000 miles away, if I remember correctly, and the speed of light is 186,000 miles a second. So even if the speed of light it takes more than a second for the communications to get to the moon and back. So I can remember being stressed out when Houston will to make a statement. And we were wondering if the astronauts could answer Well, they answered as soon as possible. But that 1 to 2 second delay that was natural was what drove us crazy, which made us nervous. We were worried about them in the success of the mission. So Mars is millions of miles away. So flip it around. If you're a Mars explorer and you look out the window and there's a big red cloud coming at you that looks like a tornado and you might want to do some Mars dust storm modeling right then and there to figure out what's the safest thing to do. You don't have the time literally get that back to earth have been processing and get you the answer back. You've got to take those computational capabilities with you. And we're hoping that of these 52 thousands of experiments that are on board, the SS can show that in order to better accomplish their missions on the moon. And Omar, >>I'm so glad you brought that up because I was gonna ask you guys in the commercial world everybody talks about real time. Of course, we talk about the real time edge and AI influencing and and the time value of data I was gonna ask, you know, the real time, Nous, How do you handle that? I think Mark, you just answered that. But at the same time, people will say, you know, the commercial would like, for instance, in advertising. You know, the joke the best. It's not kind of a joke, but the best minds of our generation tryingto get people to click on ads. And it's somewhat true, unfortunately, but at any rate, the value of data diminishes over time. I would imagine in space exploration where where you're dealing and things like light years, that actually there's quite a bit of value in the historical data. But, Mark, you just You just gave a great example of where you need real time, compute capabilities on the ground. But but But, Brian, I wonder if I could ask you the value of this historic historical data, as you just described collecting so much data. Are you? Do you see that the value of that data actually persists over time, you could go back with better modeling and better a i and computing and actually learn from all that data. What are your thoughts on that, Brian? >>Definitely. I think the answer is yes to that. And, you know, as part of the evolution from from basically a platform to a station, we're also learning to make use of the experiments in the data that we have there. NASA has set up. Um, you know, unopened data access sites for some of our physical science experiments that taking place there and and gene lab for looking at some of the biological genomic experiments that have gone on. And I've seen papers already beginning to be generated not from the original experimenters and principal investigators, but from that data set that has been collected. And, you know, when you're sending something up to space and it to the space station and volume for cargo is so limited, you want to get the most you can out of that. So you you want to be is efficient as possible. And one of the ways you do that is you collect. You take these earth observing, uh, instruments. Then you take that data. And, sure, the principal investigators air using it for the key thing that they designed it for. But if that data is available, others will come along and make use of it in different ways. >>Yeah, So I wanna remind the audience and these these these air supercomputers, the space born computers, they're they're solar powered, obviously, and and they're mounted overhead, right? Is that is that correct? >>Yeah. Yes. Space borne computer was mounted in the overhead. I jokingly say that as soon as someone could figure out how to get a data center in orbit, they will have a 50 per cent denser data station that we could have down here instead of two robes side by side. You can also have one overhead on. The power is free. If you can drive it off a solar, and the cooling is free because it's pretty cold out there in space, so it's gonna be very efficient. Uh, space borne computer is the most energy efficient computer in existence. Uh, free electricity and free cooling. And now we're offering free cycles through all the experimenters on goal >>Eso Space born one exceeded its mission timeframe. You were able to run as it was mentioned before some simulations for future Mars missions. And, um and you talked a little bit about what you want to get out of, uh, space born to. I mean, are there other, like, wish list items, bucket bucket list items that people are talking about? >>Yeah, two of them. And these air kind of hypothetical. And Brian kind of alluded to them. Uh, one is having the data on board. So an example that halo developers talk to us about is Hey, I'm on Mars and I see this mold growing on my potatoes. That's not good. So let me let me sample that mold, do a gene sequencing, and then I've got stored all the historical data on space borne computer of all the bad molds out there and let me do a comparison right then and there before I have dinner with my fried potato. So that's that's one. That's very interesting. A second one closely related to it is we have offered up the storage on space borne computer to for all of your raw data that we process. So, Mr Scientist, if if you need the raw data and you need it now, of course, you can have it sent down. But if you don't let us just hold it there as long as they have space. And when we returned to Earth like you mentioned, Patrick will ship that solid state disk back to them so they could have a new person, but again, reserving that network bandwidth, uh, keeping all that raw data available for the entire duration of the mission so that it may have value later on. >>Great. Thank you for that. I want to end on just sort of talking about come back to the collaboration between I S s National Labs and Hewlett Packard Enterprise, and you've got your inviting project ideas using space Bourne to during the upcoming mission. Maybe you could talk about what that's about, and we have A We have a graphic we're gonna put up on DSM information that you can you can access. But please, mark share with us what you're planning there. >>So again, the collaboration has been outstanding. There. There's been a mention off How much savings is, uh, if you can reduce the weight by a pound. Well, our partners ice s national lab and NASA have taken on that cost of delivering baseball in computer to the international space station as part of their collaboration and powering and cooling us and giving us the technical support in return on our side, we're offering up space borne computer to for all the onboard experiments and all those that think they might be wanting doing experiments on space born on the S s in the future to take advantage of that. So we're very, very excited about that. >>Yeah, and you could go toe just email space born at hp dot com on just float some ideas. I'm sure at some point there'll be a website so you can email them or you can email me david dot volonte at at silicon angle dot com and I'll shoot you that that email one or that website once we get it. But, Brian, I wanna end with you. You've been so gracious with your time. Uh, yeah. Give us your final thoughts on on exa scale. Maybe how you're celebrating exa scale day? I was joking with Mark. Maybe we got a special exa scale drink for 10. 18 but, uh, what's your final thoughts, Brian? >>Uh, I'm going to digress just a little bit. I think I think I have a unique perspective to celebrate eggs a scale day because as an undergraduate student, I was interning at Langley Research Center in the wind tunnels and the wind tunnel. I was then, um, they they were very excited that they had a new state of the art giant room size computer to take that data we way worked on unsteady, um, aerodynamic forces. So you need a lot of computation, and you need to be ableto take data at a high bandwidth. To be able to do that, they'd always, you know, run their their wind tunnel for four or five hours. Almost the whole shift. Like that data and maybe a week later, been ableto look at the data to decide if they got what they were looking for? Well, at the time in the in the early eighties, this is definitely the before times that I got there. They had they had that computer in place. Yes, it was a punchcard computer. It was the one time in my life I got to put my hands on the punch cards and was told not to drop them there. Any trouble if I did that. But I was able thio immediately after, uh, actually, during their run, take that data, reduce it down, grabbed my colored pencils and graph paper and graph out coefficient lift coefficient of drag. Other things that they were measuring. Take it back to them. And they were so excited to have data two hours after they had taken it analyzed and looked at it just pickled them. Think that they could make decisions now on what they wanted to do for their next run. Well, we've come a long way since then. You know, extra scale day really, really emphasizes that point, you know? So it really brings it home to me. Yeah. >>Please, no, please carry on. >>Well, I was just gonna say, you know, you talked about the opportunities that that space borne computer provides and and Mark mentioned our colleagues at the I S s national lab. You know, um, the space station has been declared a national laboratory, and so about half of the, uh, capabilities we have for doing research is a portion to the national lab so that commercial entities so that HP can can do these sorts of projects and universities can access station and and other government agencies. And then NASA can focus in on those things we want to do purely to push our exploration programs. So the opportunities to take advantage of that are there marks opening up the door for a lot of opportunities. But others can just Google S s national laboratory and find some information on how to get in the way. Mark did originally using s national lab to maybe get a good experiment up there. >>Well, it's just astounding to see the progress that this industry is made when you go back and look, you know, the early days of supercomputing to imagine that they actually can be space born is just tremendous. Not only the impacts that it can have on Space six exploration, but also society in general. Mark Wayne talked about that. Guys, thanks so much for coming on the Cube and celebrating Exa scale day and helping expand the community. Great work. And, uh, thank you very much for all that you guys dio >>Thank you very much for having me on and everybody out there. Let's get the XO scale as quick as we can. Appreciate everything you all are >>doing. Let's do it. >>I've got a I've got a similar story. Humanity saw the first trillion calculations per second. Like I said in 1997. And it was over 100 racks of computer equipment. Well, space borne one is less than fourth of Iraq in only 20 years. So I'm gonna be celebrating exa scale day in anticipation off exa scale computers on earth and soon following within the national lab that exists in 20 plus years And being on Mars. >>That's awesome. That mark. Thank you for that. And and thank you for watching everybody. We're celebrating Exa scale day with the community. The supercomputing community on the Cube Right back

>>Fly from San Francisco. It's the cube covering RSA conference 2020 San Francisco brought to you by Silicon angled medias >>live in. Welcome to the cube coverage here in San Francisco at Moscone hall for RSA 2020 I'm John furrier, host of the cube. We're here breaking down all the actions in cyber security. I'll say three days of wall-to-wall cube coverage. You got two great guests here, experts in the cybersecurity enterprise security space. Over 25 years. We've got two gurus and experts. We've got Bob Mindell, executive vice president of North America cyber practice for cap Gemini and Joe McMahon, head of North America cyber strategy, even a practitioner in the intelligence community. Langley, you've been in the business for 25 years. You've seen the waves guys, welcome to the cube. Thank you John. Thanks for having us. So first let's just take a step back. A cyber certainly on the number one agenda kind of already kind of broken out of it in terms of status, board level conversation, every CSO, risk management and a lot of moving parts. >>Now, cyber is not just a segment in the industry. It is the industry. Bob, this is a big part of business challenge today. What's your view? What was going on? So John has a great point. It's actually a business challenge and that's one of the reasons why it's now the top challenge. It's been a tech challenge for a long time. It wasn't always a business challenge for you as was still considered an it challenge and once it started impacting business and got into a board level discussion, it's now top of mind as a business challenge and how it can really impact the business continuity. Joe is talking before we came on camera about you know CEOs can have good days here and there and bad days then but sees us all have bad days all the time because there's so much, it's so hard. You're on the operations side. >>You see a day to day in the trenches as well as the strategy. This is really an operations operationalizing model. As new technology comes out, the challenge is operationalizing them for not only a business benefit but business risk management. It's like changing an airplane engine out at 35,000 feet. It's really hard. What are you seeing as the core challenge? This is not easy. It's a really complex industry. I mean, you take the word cybersecurity, right? Ready? Cybersecurity conference. I see technology, I see a multitude of different challenges that are trying to be solved. It means something different to everybody, and that's part of the problem is it's a really broad ecosystem that we're in. If you meet one person that says, I know all of cyber, they're lying, right? It's just like saying, I know active directory and GRC and I know DNS and I know how to, how to code, right? >>Those people don't exist and cyber is a little bit the same way. So for me, it's just recognizing the intricacies. It's figuring out the complexities, how people processing technology really fit together and it's an operation. It is an ongoing, and during operation, this isn't a program that you can run. You run it for a year, you install and you're done. There's ebbs and flows. You talked about the CISOs and the bad days. There's wins and there's losses. Yeah. And I think part of that is just having the conversation with businesses. Just like in it, you have bad days and good days wins and losses. It's the same thing in cybersecurity and we've got to set that expectation. Yeah, you didn't bring up a good point. I've been saying this on the cube and we've been having conversations around this. It used to be security as part of it, right? >>But now that it's part of the business, the things that you're mentioning around people, process, technology, the class, that kind of transformational formula, it is business issues, organizational behavior. Not everyone's an expert specialism versus generalists. So this is like not just a secure thing, it's the business model of a company is changing. So that's clear. There's no doubt. And then you've got the completion of the cloud coming, public cloud, hybrid multi-cloud. Bob, this is a number one architectural challenge. So outside of the blocking and tackling basics, right, there's now the future business is at risk. What does cap Gemini do? And because you guys are well known, great brand, helping companies be successful, how do you guys go to customers and say, Hey, here's what you do. What's the, what's the cap Gemini story? >>So the cat termini stories is really about increasing your cybersecurity maturity, right? As Joe said, starting out at the basics. If you look at a lot of the breaches that have occurred today have occurred because we got away from the basics and the fundamentals, right? Shiny new ball syndrome. Really. Exactly exasperates that getting away from the basics. So the technology is an enabler, but it's not the be all and end all right, go into the cloud is absolutely a major issue. That's increasing the perimeter, right? We've gone through multiple ways as we talked about, right? So now cloud is is another way, cloud, mobile, social. How do you deal with those from on prem, off prem. But ultimately it's about increasing your cyber cyber security maturity and using the cloud as just increasing the perimeter, right? So you need to, you really need to understand, you have your first line defense and then your maturity is in place. Whether the data resides in your organization, in the cloud, on a mobile device, in a social media, you're responsible for it all. And if you don't have the basics, then you're, you're really, and you guys bring a playbook, is that what you guys come in and do? Correct. Correct. Right. So our goal is to coordinate people, process technology and leverage playbooks, leverage the run books that we had been using for many years. >>I want to get down to you on this one because of what happens when you take that to the, into the practitioner mode or at implementation. Customers want the best technology possible. They go for the shiny new choice. Bob just laid out. There's also risks too because it may or may not be big. So you've got to balance out. I got to get an edge technically because the perimeters becoming huge surface area now or some say has gone. Now you've got edge, just all one big exposed environment, surface area for vulnerabilities is massive. So I need better tech. How do you balance and obtain the best tech and making sure it works and it's in production and secure. So there's a couple of things, right, and this is not, it's not just our, and you'll hear it from other people that have been around a long time, but a lot of organizations that we see have built themselves so that their cybersecurity organization is supporting all these tools that we see. >>That's the wrong way to do it. The tools should support the mission of the organization, right? If my mission is to defend my enterprise, there are certain things that I need to do, right? There's questions I need to be able to ask and get answers to. There's data I need visibility into. There's protections and controls I need to be able to implement. If I can lay those out in some coordinated strategic fashion and say, here's all the things I'm trying to accomplish, here's who's going to do it. Here's my really good team, here's my skilled resources, here's my workflows, my processes, all that type of stuff. Then I can go find the right technology to put into that. And I can actually measure if that technology is effective in supporting my mission. But too often we start with the technology and then we hammer against it and we run into CISOs and they say, I bought all this stuff and it's not working and come hell yeah. >>And that's backing into it the wrong. So I've heard from CSOs, I'd like they buying all these tools. It's like a tool shed. Don't be the fool with the wrong tool as they I say. But that brings up the question of, okay, as you guys go to customers, what are some of the main pain points or issues that they're trying to overcome that that are opportunities that you guys are helping with? Uh, on the business side and on the technical side, what are some of the things? So on the business side, you know, one is depending on their level of maturity and the maturity of the organization and the board of directors and their belief in, in how they need to help fund this. We can start there. We can start by helping draw out the threat landscape within that organization where they are maturity-wise and where they need to go and help them craft that message to the board of directors and get executive sponsorship from the board down in order to take them from baby, a very immature organization or you know, a reactive organization to an adaptive organization, right. >>And really become defenders. So from a business perspective, we can help them there. From the technology perspective, Joe, uh, you know, or an implementation perspective. I think, you know, it's been a really interesting road like being in this a long time, you know, late two thousands when nation States were first really starting to become a thing. All the industries we were talking to, every customer is like, I want to be the best in my industry. I want to be the shining example. And boards in leadership were throwing money at it and everybody was on this really aggressive path to get there. The conversation is shifted a little bit with a lot of the leadership we talked to. It's, I just want to be good enough, maybe a little bit better than good enough, but my, my objective anymore is it to leave the industry. Cause that's really expensive and there's only one of those. >>My objective is to complete my mission maybe a little bit above and beyond, but I need the right size and right. So we spent a lot of time helping organizations, I would say optimize, right? It's what is the right level of people, what is the right amount of resources, what's the right spend, what's the right investment, the right allocation of technology and mix of everything, right? And sometimes it's finding the right partner. Sometimes it's doing certain things in house. It's, there's no one way to solve this problem, but you've got to go look at the business challenges. Look at the operational realities of the customer, their budgets, all those, their geographies mattered, right? Some places it's easy to hire talent. Some places it's not so easy to hire talent. And that's a good point, right? Some organizations, >>they just need to understand what does good look like and we can, we have so many years of experience. We have so many customers use skates is we've been there and we've done that. We can bring the band and show them this is what good looks like and this is sustainable >>of what good looks like. I want to get your reactions to, I was talking to Keith Alexander, general Keith Alexander, a former cyber command had last night and we were talking about officers, his defense and that kind of reaction. How the Sony hack was was just was just, they just went after him as an example. Everyone knows about that hack, but he really was getting at the idea of human efficiency, the human equation, which is if you have someone working on something that here, but their counterpart might be working on it maybe from a different company or in the same company, they're redundant. So there's a lot of burnout, a lot of people putting out fires. So reactive is clearly, I see as a big trend that the conversation's shifting towards let's be proactive, let's get more efficient in the collaboration as well as the technology. What you, how do you guys react to that? What's your view on that statement? So >>people is the number one issue, in my opinion. In this space, there's a shortage of people. The people that are in it are working very long hours. They're burnt out. So we constantly need to be training and bringing more people into the industry. Then there's the scenario around information sharing, right? Threat information sharing, and then what levels are you comfortable with as an organization to share that information? How can you share best practices? So that's where the ice sacks come into play. That's also where us as a practitioner and we have communities, we have customers, we bring them together to really information, share, share, best practice. It's in all of our best interests. We all have the same goal and the goal is to protect our assets, especially in the United States. We have to protect our assets. So we need, the good thing is that it's a pretty open community in that regards and sharing the information, training people, getting people more mature in their people, process technology, how they can go execute it. >>Yeah. What's your take on the whole human equation piece? Right? So sharing day, you probably heard a word and the word goes back to where I came from, from my heritage as well, but I'm sure general Alexander used the word mission at some point, right? So to me, that's the single biggest rallying point for all of the people in this. If you're in this for the right reasons, it's because you care about the mission. The mission is to defend us. Stop the bad guys from doing days, right? Whether you're defending the government, whether you're defending a commercial enterprise, whether you're defending the general public, right? Whatever the case is, if you're concerned, you know, if you believe in the mission, if you're committed to the mission, that's where the energy comes from. You know, there's a lot of, there's a lot of talk about the skill gap and the talent gap and all of those types of things. >>To me, it's more of a mindset issue than anything. Right? The skill sets can be taught. They can be picked up over time. I was a philosophy major. All right? Somehow I ended up here. I have no idea how, um, but it's because I cared about the mission and everybody has a part to play. If you build that peer network, uh, both at an individual level and at an organizational and a company level, that's really important in this. Nobody's, nobody's an expert at everything. Like we said, you brought a philosophy. I think one of the things I have observed in interviewing and talking to people is that the world's changed so much that you almost need those fresh perspectives because the problems are new problems, statements, technology is just a part of the problem set back to the culture. The customer problem, Bob, is that they got to get all this work done. >>And so what are some of the use cases that you guys are working on that that is a low hanging fruit in the industry or our customer base? How do you guys engage with customers? So our target market is fortune 500 global 1000 so the biggest of the big enterprises in the world, right? And because of that, we've seen a lot of a complex environments, multinational companies as our customers. Right? We don't go at it from a pure vertical base scenario or a vertical base solution. We believe that horizontal cybersecurity can it be applied to most verticals. Right. And there's some tweaking along the way. Like in financial services, there's regulars and FFIC that you need to be sure you adapt to. But for the most part the fundamentals are applicable. All right. With that said, you know, large multinational manufacturing organization, right? They have a major challenge in that they have manufacturing sites all over the world. >>They building something that is, you know, unique. It has significant IP to it, but it's not secure. Historically they would have said, well, nobody's really gonna just deal steal what we do because it's really not differentiated in the world, but it is differentiated and it's a large corporation making a lot of money. Unfortunately ransomware, that'd be a photographer. Ransomware immediately, right? Like exact down their operations and their network, right? So their network goes down. They can have, they can, they can not have zero downtown and their manufacturing plants around the world. So for us, we're implementing solutions and it's an SLA for them is less than six seconds downtime by two that help secure these global manufacturing environment. That's classic naive when they are it. Oh wow. We've got to think about security on a much broader level. I guess the question I have for you guys, Joe, you talk about when do you guys get called in? >>I mean what's your main value proposition that you guys, cause you guys got a broad view of the industry, that expertise. Why do, why are customers calling you guys and what do you guys deliver? They need something that actually works, right? It's, it's you mentioned earlier, I think when we were talking how important experiences, right? And it's, Bob said it too, having been there, done that I think is really important. The fact that we're not chasing hype, we're not selling widgets. That we have an idea of what good looks like and we can help an organization kind of, you know, navigate that path to get there is really important. So, uh, you know, one of our other customers, large logistics company, been operating for a very long time. You know, very, very mature in terms of their, it operations, those types of things. But they've also grown through merger and acquisition. >>That's a challenge, uh, cause you're taking on somebody else's problem set and they just realize, simply put that their existing security operations wasn't meeting their needs. So we didn't come in and do anything fancy necessarily. It's put a strategic plan in place, figure out where they are today, what are the gaps, what do they need to do to overcome those gaps? Let's go look at their daily operations, their concept of operations, their mission, their vision, all of that stuff down to the individual analysts. Like we talked about the mindset and skillset. But then frankly it's putting in the hard work, right? And nobody wants to put in the heart. I don't want to say nobody wants to put in the hard work. That's fun. There's a lot of words that's gets done I guess by the questions that you guys getting called in on from CSOs chief and Mason security officers. >>Guess who calls you? So usually we're in talking to the Cisco, right? We're having the strategic level conversation with the Cisco because the Cisco either has come in new or has been there. They may have had a breach. Then whatever that compelling event may be, they've come to the realization that they're not where they need to be from a maturity perspective and their cyber defense needs revamping. So that's our opportunity for us to help them really increase the maturity and help them become defenders. Guys, great for the insight. Thanks for coming on the cube. Really appreciate you sharing the insights. Guys. Give a quick plug for what you guys are doing. Cap Gemini, you guys are growing. What do you guys look to do? What are some of the things that's going on? Give the company plug. Thanks Sean show. It's been a very interesting journey. >>You know this business started out from Lockheed Martin to Leidos cyber. We were acquired by cap Gemini a year ago last week. It's a very exciting time. We're growing the business significantly. We have huge growth targets for 2020 and beyond, right? We're now over 800 practitioners in North America, over 2,500 practitioners globally, and we believe that we have some very unique differentiated skill sets that can help large enterprises increase their maturity and capabilities plug there. Yeah, I mean, look, nothing makes us happier than getting wins when we're working with an organization and we get to watch a mid level analyst brief the so that they just found this particular attack and Oh by the way, because we're mature and we're effective, that we were able to stop it and prevent any impact to the company. That's what makes me proud. That's what makes it so it makes it fun. >>Final question. We got a lot of CSOs in our community. They're watching. What's the pitch to the CSO? Why, why you guys, we'd love to come in to understand what are their goals, how can we help them, but ultimately where do they believe they think they are and where do they need to go and we can help them walk that journey. Whether it's six months, a year, three years, five years. We can take them along that journey and increase the cyber defense maturity. Joe, speak to the CSO. What are they getting? They're getting confidence. They're getting execution. They're getting commitment to delivery. They're getting basically a, a partner in this whole engagement. We're not a vendor. We're not a service provider. We are a partner. A trusted partner. Yeah, partnerships is key. Building out in real time. A lot new threats. Got to be on offense and defense going on. A lot of new tech to deal with. I mean, it's a board level for a long time. Guys, thanks for coming on. Cap Gemini here inside the cube, bringing their practices, cybersecurity, years of experience with big growth targets. Check them out. I'm John with the cube. Thanks for watching.

>> From Boston, Massachusetts, it's The Cube, covering LiveWorx '18. Brought to you by PTC. >> Welcome back to Boston, everybody. This is the LiveWorx show, hosted by PTC, and you're watching The Cube, the leader in live tech coverage. I'm Dave Vellante with my co-host, Stu Miniman, covering IoT, Blockchain, AI, the Edge, the Cloud, all kinds of crazy stuff going on. Kevin Ashton is here. He's the inventor of the term, IoT, and the creator of the Wemo Home Automation platform. You may be familiar with that, the Smart Plugs. He's also the co-founder and CEO of Zensi, which is a clean tech startup. Kevin, thank you for coming on The Cube. >> Thank you for having me. >> You're very welcome. So, impressions of LiveWorx so far? >> Oh wow! I've been to a few of these and this is the biggest one so far, I think. I mean, it's day one and the place is hopping. It's like, it's really good energy here. It's hard to believe it's a Monday. >> Well, it's interesting right? You mean, you bring a ton of stayed manufacturing world together with this, sort of, technology world and gives us this interesting cocktail. >> I think the manufacturing world was stayed in the 1900s but in the 21st century, it's kind of the thing to be doing. Yeah, and this... I guess this is, you're right. This is not what people think of when they think of manufacturing, but this is really what it looks like now. It's a digital, energetic, young, exciting, innovative space. >> Very hip. And a lot of virtual reality, augmented reality. Okay, so this term IoT, you're accredited, you're the Wikipedia. Look up Kevin, you'll see that you're accredited with inventing, creating that term. Where did it come from? >> Oh! So, IoT is the Internet of Things. And back in 1990s, I was a Junior Manager at Proctor & Gamble, consumer goods company. And we were having trouble keeping some products on the shelves, in the store, and I had this idea of putting this new technology called RFID tags. Little microchips, into all Proctor products. Gamble makes like two billion products a year or something and putting it into all of them and connecting it to this other new thing called the internet, so we'd know where our stuff was. And, yeah the challenge I faced as a young executive with a crazy idea was how to explain that to senior management. And these were guys who, in those days, they didn't even do email. You send them an email, they'd like have their secretary print it out and then hand write a reply. It would come back to you in the internal mail. I'm really not kidding. And I want to put chips in everything. Well the good news was, about 1998, they'd heard of the internet, and they'd heard that the internet was a thing you were supposed to be doing. They didn't know what it was. So I literally retitled my PowerPoint presentation, which was previously called Smart Packaging, to find a way to get the word Internet in. And the way I did it was I wrote, Internet of Things. And I got my money and I founded a research center with Proctor & Gamble's money at MIT, just up the road here. And basically took the PowerPoint presentation with me, all over the world, to convince other people to get on board. And somehow, the name stuck. So that's the story. >> Yeah, it's fascinating. I remember back. I mean, RFID was a big deal. We've been through, you know-- I studied Mechanical Engineering. So manufacturing, you saw the promise of it, but like the internet, back in the 90s, it was like, "This seems really cool. "What are you going to do with it?" >> Exactly, and it kind of worked. Now it's everywhere. But, yeah, you're exactly right. >> When you think back to those times and where we are in IoT, which I think, most of us still say, we're still relatively early in IoT, industrial internet. What you hear when people talk about it, does it still harken back to some of the things you thought? What's different, what's the same? >> So some of the big picture stuff is very much the same, I think. We had this, the fundamental idea behind the MIT research, behind the Internet of Things was, get computers to gather the relevant information. If we can do that, now we have this whole, powerful new paradigm in computing. Coz it's not about keyboards anymore, and in places like manufacturing, I mean Proctor & Gamble is a manufacturing company, they make things and they sell them. The problem in manufacturing is keyboards just don't scale as an information capture technology. You can't sit in a warehouse and type everything you have. And something goes out the door and type it again. And so, you know, in the 90s, barcodes came and then we realized that we could do much better. And that was the Internet of Things. So that big picture, wouldn't it be great if we knew wherever things was, automatically? That's come true and at times, a million, right? Some of the technologies that are doing it are very unexpected. Like in the 1990s, we were very excited about RFID, partly because vision technology, you know, cameras connected to computers, was not working at all. It looked very unpromising, with people been trying for decades to do machine vision. And it didn't work. And now it does, and so a lot of things, we thought we needed RFID for, we can now do with vision, as an example. Now, the reason vision works, by the way, is an interesting one, and I think is important for the future of Internet of Things, vision works because suddenly we had digital cameras connected to networks, mainly in smartphones, that we're enable to create this vast dataset, that could then be used to train their algorithms, right? So what is was, I've scanned in a 100 images in my lab at MIT and I'm trying to write an algorithm, machine vision was very hard to do. When you've got hundreds of, millions of images available to you easily because phones and digital cameras are uploading all the time, then suddenly you can make the software sing and dance. So, a lot of the analytical stuff we've already seen in machine vision, we'll start to see in manufacturing, supply chain, for example, as the data accumulates. >> If you go back to that time, when you were doing that PowerPoint, which was probably less than a megabyte, when you saved it, did you have any inkling of the data explosion and were you even able to envision how data models would change to accommodate, did you realize at the time that the data model, the data pipeline, the ability to store all this distributed data would have to change? Were you not thinking that way? >> It's interesting because I was the craziest guy in the room. When I came to internet bandwidth and storage ability, I was thinking in, maybe I was thinking in gigabytes, when everyone else was thinking in kilobytes, right? But I was wrong. I wasn't too crazy, I was not crazy enough. I wouldn't, quick to quote, quite go so far as to call it a regret, but my lesson for life, the next generation of innovators coming up, is you actually can't let, kind of, the average opinion in the room limit how extreme your views are. Because if it seems to make sense to you, that's all that matters, right? So, I didn't envision it, is the answer to your question, even though, I was envisioning stuff, that seemed crazy to a lot of other people. I wasn't the only crazy one, but I was one of the few. And so, we underestimated, even in our wildest dreams, we underestimated the bandwidth and memory innovation, and so we've seen in the last 25 years. >> And, I don't know. Stu, you're a technologist, I'm not, but based on what you see today, do you feel like, the technology infrastructure is there to support these great visions, or do we have to completely add quantum computing or blockchain? Are we at the doorstep, or are we decades away? >> Oh, were at the doorstep. I mean, I think the interesting thing is, a lot of Internet of Things stuff, in particular, is invisible for number of reasons, right? It's invisible because, you know, the sensors and chips are embedded in things and you don't see them, that's one. I mean, there is a billion more RFID tags made in the world, than smartphones every year. But you don't see them. You see the smartphone, someone's always looking at their smartphone. So you don't realize that's there. So that's one reason, but, I mean, the other reason is, the Internet of Things is happening places and in companies that don't have open doors and windows, they're not on the high street, right? They are, it's warehouses, it's factories, it's behind the scenes. These companies, they have no reason to talk about what they are doing because it's a trade secret or it's you know, just not something people want to write about or read about, right? So, I just gave a talk here, and one of the examples I gave was a company who'd, Heidelberger. Heidelberger makes 60% of the offset printing presses in the world. They're one of the first Internet of Things pioneers. Most people haven't heard of them, most people don't see offset printers everyday. So the hundreds of sensors they have in their hundreds of printing presses, completely invisible to most of us, right? So, it's definitely here, now. You know, will the infrastructure continue to improve? Yes. Will we see things that are unimaginable today, 20 years from today? Yes. But I don't see any massive limitations now in what the Internet of Things can become. >> We just have a quick question, your use case for that offset printing, is it predictive maintenance, or is it optimization (crosstalk). >> It is initially like, it was in 1990s, when the customer calls and says, "My printing press isn't working, help", instead of sending the guide and look at the diagnostics, have the diagnostics get sent to the guide, that was the first thing, but then gradually, that evolves to realtime monitoring, predictive maintenance, your machine seems to be less efficient than the average of all the machines. May be we can help you optimize. Now that's the other thing about all Internet of Things applications. You start with one sensor telling you one thing for one reason, and it works, you add two, and you find four things you can do and you add three, and you find nine things you can do, and the next thing you know, you're an Internet of Things company. You never meant to be. But yeah, that's how it goes. It's a little bit like viral or addictive. >> Well, it's interesting to see the reemergence, new ascendancy of PTC. I mean, heres a company in 2003, who was, you know, bouncing along the ocean's floor, and then the confluence of all this trends, some acquisitions and all of a sudden, they're like, the hot new kid on the block. >> Some of that's smart management, by the way. >> Yeah, no doubt. >> And, I don't work for PTC but navigating the change is important and I want to say, all of the other things I just talked about in my talk, but, you know, we think about these tools that companies like PTC make as design tools. But they're very quickly transitioning to mass production tools, right? So it used be, you imagined a thing on your screen and you made a blueprint of it. Somebody made it in the shop. And then it was, you didn't make it in a shop, you had a 3D printer. And you could make a little model of it and show management. Everyone was very excited about that. Well, you know, what's happening now, what will happen more is that design on the screen will be plugged right in to the production line and you push a button and you make a million. Or your customer will go to a website, tweak it a little bit, make it a different color or different shape or something, and you'll make one, on your production line that makes a million. So, there's this seamless transition happening from imagining things using software, to actually manufacturing them using software, which is very much the core of what Internet of Things is about and it's a really exciting part of the current wave of the industrial revolution. >> Yeah, so Kevin, you wrote a book which follows some of those themes, I believe, it's How to Fly A Horse. I've read plenty of books where it talks about people think that innovation is, you know, some guy sitting under a tree, it hits him in the head and he does things. But we know that, first of all, almost everybody is building on you know, the shoulders of those before us. Talk a little bit about creativity, innovation. >> Okay. Sure. >> Your thoughts on that. >> So, I have an undergraduate degree in Scandinavian studies, okay? I studied Ibsen in 19th century Norwegian, at university. And then I went to Proctor & Gamble and I did marketing for color cosmetics. And then the next thing that happened to me was I'm at MIT, right? I'm an Executive Director of this prestigious lab at MIT. And I did this at the same time that the Harry Potter books were becoming popular, right? So I already felt like, oh my God! I've gone to wizard school but nobody realizes that I'm not a wizard. I was scared of getting found out, right? I didn't feel like a wizard because anything I managed to create was like the 1000th thing I did after 999 mistakes. You know, I was like banging my head against the wall. And I didn't know what I was doing. And occasionally, I got lucky, and I was like, oh they're going to figure out, that I'm not like them, right? I don't have the magic. And actually what happened to me at MIT over four years, I figured out nobody had the magic. There is no magic, right? There were those of us who believed this story about geniuses and magic, and there were other people who were just getting on with creating and the people at MIT were the second group. So, that was my revelation that I wasn't an imposter, I was doing things the way everybody I'd ever heard of, did them. And so, I did some startups and then I wanted to write a book, like kind of correcting the record, I guess. Because it's frustrating to me, like now, I'm called the inventor of the Internet of Things. I'm not the inventor of the Internet of Things. I wrote three words on a PowerPoint slide, I'm one of a hundred thousand people that all chipped away at this problem. And probably my chips were not as big as a lot of other people's, right? So, it was really important to me to talk about that, coz I meet so many people who want to create something, but if it doesn't happen instantly, or they don't have the brilliant idea in the shower, you know, they think they must be bad at it. And the reality is all creating is a series of steps. And as I was writing the book, I researched, you know, famous stories like Newton, and then less famous stories like the African slave kid who discovered how to farm vanilla, right? And found that everybody was doing it the same way, and in every discipline. It doesn't matter if it's Kandinsky painting a painting, or some scientist curing cancer. Everybody is struggling. They're struggling to be heard, they're struggling to be understood, they're struggling to figure out what to do next. But the ones who succeed, just keep going. I mean, and the title, How To Fly A Horse is because of the Wright brothers. Coz that's how they characterized the problem they were trying to solve and there are classic example of, I mean, literally, everybody else was jumping off mountains wit wings on their back, and dying, and the Wright brothers took this gradual, step by step approach, and they were the ones who solved the problem, how to fly. >> There was no money, and no resources, and Samuel Pierpont Langley gave up. >> Yeah, exactly. The Wright brothers were bicycle guys and they just figured out how to convert what they knew into something else. So that's how you create. I mean, we're surrounded by people who know how to do that. That's the story of How To Fly A Horse. >> So what do we make of, like a Steve Jobs. Is he an anomaly, or is he just surrounded by people who, was he just surrounded by people who knew how to create? >> I talk about Steve Jobs in the book, actually, and yeah, I think the interesting thing about Jobs is defining characteristic, as I see it. And yeah, I followed the story of Apple since I was a kid, one of the first news I ever saw was an Apple. Jobs was never satisfied. He always believed things could be made better. And he was laser focused on trying to make them better, sometimes to the detriment of the people around him, but that focus on making things better, enabled him, yes, to surround himself with people who were good at doing what they did, but also then driving them to achieve things. I mean, interesting about Apple now is, Apple are sadly becoming, kind of, just another computer company now, without somebody there, who is not-- I mean, he's stand up on stage and say I've made this great thing, but what was going on in his head often was, but I wish that curve was slightly different or I wish, on the next one, I'm going to fix this problem, right? And so the minute you get satisfied with, oh, we're making billions of dollars, everything's great, that's when your innovation starts to plummet, right? So that was, I think to me, Jobs was a classic example of an innovator, because he just kept going. He kept wanting to make things better. >> Persistence. Alright, we got to go. Thank you so much. >> Thank you guys. >> For coming on The Cube. >> Great to see you. >> Great to meet you, Kevin. Alright, keep it right there buddy. Stu and I will be back with our next guest. This is The Cube. We're live from LiveWorx at Boston and we'll be right back.

>> [Narrator] Live from New York, it's theCube, covering Machine Learning Everywhere. Build your ladder to AI, brought to you by IBM. >> Welcome back here to New York City as we continue at IBM's Machine Learning Everywhere, build your ladder to AI, along with Dave Vellante, I'm John Walls. It is now a great honor of ours to have I think probably and arguably the greatest chess player of all time, Garry Kasparov now joins us. He's currently the chairman of the Human Rights Foundation, political activist in Russia as well some time ago. Thank you for joining us, we really appreciate the time, sir. >> Thank you for inviting me. >> We've been looking forward to this. Let's just, if you would, set the stage for us. Artificial Intelligence obviously quite a hot topic. The maybe not conflict, the complementary nature of human intelligence. There are people on both sides of the camp. But you see them as being very complementary to one another. >> I think that's natural development in this industry that will bring together humans and machines. Because this collaboration will produce the best results. Our abilities are complementary. The humans will bring creativity and intuition and other typical human qualities like human judgment and strategic vision while machines will add calculation, memory, and many other abilities that they have been acquiring quickly. >> So there's room for both, right? >> Yes, I think it's inevitable because no machine will ever reach 100% perfection. Machines will be coming closer and closer, 90%, 92, 94, 95. But there's still room for humans because at the end of the day even with this massive power you have guide it. You have to evaluate the results and at the end of the day the machine will never understand when it reaches the territory of diminishing returns. It's very important for humans actually to identify. So what is the task? I think it's a mistake that is made by many pundits that they automatically transfer the machine's expertise for the closed systems into the open-ended systems. Because in every closed system, whether it's the game of chess, the game of gall, video games like daughter, or anything else where humans already define the parameters of the problem, machines will perform phenomenally. But if it's an open-ended system then machine will never identify what is the sort of the right question to be asked. >> Don't hate me for this question, but it's been reported, now I don't know if it's true or not, that at one point you said that you would never lose to a machine. My question is how capable can we make machines? First of all, is that true? Did you maybe underestimate the power of computers? How capable to you think we can actually make machines? >> Look, in the 80s when the question was asked I was much more optimistic because we saw very little at that time from machines that could make me, world champion at the time, worry about machines' capability of defeating me in the real chess game. I underestimated the pace it was developing. I could see something was happening, was cooking, but I thought it would take longer for machines to catch up. As I said in my talk here is that we should simply recognize the fact that everything we do while knowing how we do that, machines will do better. Any particular task that human perform, machine will eventually surpass us. >> What I love about your story, I was telling you off-camera about when we had Erik Brynjolfsson and Andrew McAfee on, you're the opposite of Samuel P. Langley to me. You know who Samuel P. Langley is? >> No, please. >> Samuel P. Langley, do you know who Samuel P. Langley is? He was the gentleman that, you guys will love this, that the government paid. I think it was $50,000 at the time, to create a flying machine. But the Wright Brothers beat him to it, so what did Samuel P. Langley do after the Wright Brothers succeeded? He quit. But after you lost to the machine you said you know what? I can beat the machine with other humans, and created what is now the best chess player in the world, is my understanding. It's not a machine, but it's a combination of machines and humans. Is that accurate? >> Yes, in chess actually, we could demonstrate how the collaboration can work. Now in many areas people rely on the lessons that have been revealed, learned from what I call advanced chess. That in this team, human plus machine, the most important element of success is not the strengths of the human expert. It's not the speed of the machine, but it's a process. It's an interface, so how you actually make them work together. In the future I think that will be the key of success because we have very powerful machine, those AIs, intelligent algorithms. All of them will require very special treatment. That's why also I use this analogy with the right fuel for Ferrari. We will have expert operators, I call them the shepherds, that will have to know exactly what are the requirements of this machine or that machine, or that group of algorithms to guarantee that we'll be able by our human input to compensate for their deficiencies. Not the other way around. >> What let you to that response? Was it your competitiveness? Was it your vision of machines and humans working together? >> I thought I could last longer as the undefeated world champion. Ironically, 1997 when you just look at the game and the quality of the game and try to evaluate the Deep Blue real strengths, I think I was objective, I was stronger. Because today you can analyze these games with much more powerful computers. I mean any chess app on your laptop. I mean you cannot really compare with Deep Blue. That's natural progress. But as I said, it's not about solving the game, it's not about objective strengths. It's about your ability to actually perform at the board. I just realized while we could compete with machines for few more years, and that's great, it did take place. I played two more matches in 2003 with German program. Not as publicized as IBM match. Both ended as a tie and I think they were probably stronger than Deep Blue, but I knew it would just be over, maybe a decade. How can we make chess relevant? For me it was very natural. I could see this immense power of calculations, brute force. On the other side I could see us having qualities that machines will never acquire. How about bringing together and using chess as a laboratory to find the most productive ways for human-machine collaboration? >> What was the difference in, I guess, processing power basically, or processing capabilities? You played the match, this is 1997. You played the match on standard time controls which allow you or a player a certain amount of time. How much time did Deep Blue, did the machine take? Or did it take its full time to make considerations as opposed to what you exercised? >> Well it's the standard time control. I think you should explain to your audience at that time it was seven hours game. It's what we call classical chess. We have rapid chess that is under one hour. Then you have blitz chess which is five to ten minutes. That was a normal time control. It's worth mentioning that other computers they were beating human players, myself included, in blitz chess. In the very fast chess. We still thought that more time was more time we could have sort of a bigger comfort zone just to contemplate the machine's plans and actually to create real problems that machine would not be able to solve. Again, more time helps humans but at the end of the day it's still about your ability not to crack under pressure because there's so many things that could take you off your balance, and machine doesn't care about it. At the end of the day machine has a steady hand, and steady hand wins. >> Emotion doesn't come into play. >> It's not about apps and strength, but it's about guaranteeing that it will play at a certain level for the entire game. While human game maybe at one point it could go a bit higher. But at the end of the day when you look at average it's still lower. I played many world championship matches and I analyze the games, games played at the highest level. I can tell you that even the best games played by humans at the highest level, they include not necessarily big mistakes, but inaccuracies that are irrelevant when humans facing humans because I make a mistake, tiny mistake, then I can expect you to return the favor. Against the machine it's just that's it. Humans cannot play at the same level throughout the whole game. The concentration, the vigilance are now required when humans face humans. Psychologically when you have a strong machine, machine's good enough to play with a steady hand, the game's over. >> I want to point out too, just so we get the record straight for people who might not be intimately familiar with your record, you were ranked number one in the world from 1986 to 2005 for all but three months. Three months, that's three decades. >> Two decades. >> Well 80s, 90s, and naughts, I'll give you that. (laughing) That's unheard of, that's phenomenal. >> Just going back to your previous question about why I just look for some new form of chess. It's one of the key lessons I learned from my childhood thanks to my mother who spent her live just helping me to become who I am, who I was after my father died when I was seven. It's about always trying to make the difference. It's not just about winning, it's about making a difference. It led me to kind of a new motto in my professional life. That is it's all about my own quality of the game. As long as I'm challenging my own excellence I will never be short of opponents. For me the defeat was just a kick, a push. So let's come up with something new. Let's find a new challenge. Let's find a way to turn this defeat, the lessons from this defeat into something more practical. >> Love it, I mean I think in your book I think, was it John Henry, the famous example. (all men speaking at once) >> He won, but he lost. >> Motivation wasn't competition, it was advancing society and creativity, so I love it. Another thing I just want, a quick aside, you mentioned performing under pressure. I think it was in the 1980s, it might have been in the opening of your book. You talked about playing multiple computers. >> [Garry] Yeah, in 1985. >> In 1985 and you were winning all of them. There was one close match, but the computer's name was Kasparov and you said I've got to beat this one because people will think that it's rigged or I'm getting paid to do this. So well done. >> It's I always mention this exhibition I played in 1985 against 32 chess-playing computers because it's not the importance of this event was not just I won all the games, but nobody was surprised. I have to admit that the fact that I could win all the games against these 32 chess-playing computers they're only chess-playing machine so they did nothing else. Probably boosted my confidence that I would never be defeated even by more powerful machines. >> Well I love it, that's why I asked the question how far can we take machines? We don't know, like you said. >> Why should we bother? I see so many new challenges that we will be able to take and challenges that we abandoned like space exploration or deep ocean exploration because they were too risky. We couldn't actually calculate all the odds. Great, now we have AI. It's all about increasing our risk because we could actually measure against this phenomenal power of AI that will help us to find the right pass. >> I want to follow up on some other commentary. Brynjolfsson and McAfee basically put forth the premise, look machines have always replaced humans. But this is the first time in history that they have replaced humans in the terms of cognitive tasks. They also posited look, there's no question that it's affecting jobs. But they put forth the prescription which I think as an optimist you would agree with, that it's about finding new opportunities. It's about bringing creativity in, complementing the machines and creating new value. As an optimist, I presume you would agree with that. >> Absolutely, I'm always saying jobs do not disappear, they evolve. It's an inevitable part of the technological progress. We come up with new ideas and every disruptive technology destroys some industries but creates new jobs. So basically we see jobs shifting from one industry to another. Like from agriculture, manufacture, from manufacture to other sectors, cognitive tasks. But now there will be something else. I think the market will change, the job market will change quite dramatically. Again I believe that we will have to look for riskier jobs. We will have to start doing things that we abandoned 30, 40 years ago because we thought they were too risky. >> Back to the book you were talking about, deep thinking or machine learning, or machine intelligence ends and human intelligence begins, you talked about courage. We need fail safes in place, but you also need that human element of courage like you said, to accept risk and take risk. >> Now it probably will be easier, but also as I said the machine's wheel will force a lot of talent actually to move into other areas that were not as attractive because there were other opportunities. There's so many what I call raw cognitive tasks that are still financially attractive. I hope and I will close many loops. We'll see talent moving into areas where we just have to open new horizons. I think it's very important just to remember it's the technological progress especially when you're talking about disruptive technology. It's more about unintended consequences. The fly to the moon was just psychologically it's important, the Space Race, the Cold War. But it was about also GPS, about so many side effects that in the 60s were not yet appreciated but eventually created the world we have now. I don't know what the consequences of us flying to Mars. Maybe something will happen, one of the asteroids will just find sort of a new substance that will replace fossil fuel. What I know, it will happen because when you look at the human history there's all this great exploration. They ended up with unintended consequences as the main result. Not what was originally planned as the number one goal. >> We've been talking about where innovation comes from today. It's a combination of a by-product out there. A combination of data plus being able to apply artificial intelligence. And of course there's cloud economics as well. Essentially, well is that reasonable? I think about something you said, I believe, in the past that you didn't have the advantage of seeing Deep Blue's moves, but it had the advantage of studying your moves. You didn't have all the data, it had the data. How does data fit into the future? >> Data is vital, data is fuel. That's why I think we need to find some of the most effective ways of collaboration between humans and machines. Machines can mine the data. For instance, it's a breakthrough in instantly mining data and human language. Now we could see even more effective tools to help us to mine the data. But at the end of the day it's why are we doing that? What's the purpose? What does matter to us, so why do we want to mine this data? Why do we want to do here and not there? It seems at first sight that the human responsibilities are shrinking. I think it's the opposite. We don't have to move too much but by the tiny shift, just you know percentage of a degree of an angle could actually make huge difference when this bullet reaches the target. The same with AI. More power actually offers opportunities to start just making tiny adjustments that could have massive consequences. >> Open up a big, that's why you like augmented intelligence. >> I think artificial is sci-fi. >> What's artificial about it, I don't understand. >> Artificial, it's an easy sell because it's sci-fi. But augmented is what it is because our intelligent machines are making us smarter. Same way as the technology in the past made us stronger and faster. >> It's not artificial horsepower. >> It's created from something. >> Exactly, it's created from something. Even if the machines can adjust their own code, fine. It still will be confined within the parameters of the tasks. They cannot go beyond that because again they can only answer questions. They can only give you answers. We provide the questions so it's very important to recognize that it is we will be in the leading role. That's why I use the term shepherds. >> How do you spend your time these days? You're obviously writing, you're speaking. >> Writing, speaking, traveling around the world because I have to show up at many conferences. The AI now is a very hot topic. Also as you mentioned I'm the Chairman of Human Rights Foundation. My responsibilities to help people who are just dissidents around the world who are fighting for their principles and for freedom. Our organization runs the largest dissident gathering in the world. It's called the Freedom Forum. We have the tenth anniversary, tenth event this May. >> It has been a pleasure. Garry Kasparov, live on theCube. Back with more from New York City right after this. (lively instrumental music)