>>from around the globe. It's the Q with digital coverage of exa scale day made possible by Hewlett Packard Enterprise. Welcome, everyone to the Cube celebration of Exa Scale Day. Shaheen Khan is here. He's the founding partner, an analyst at Orion X And, among other things, he is the co host of Radio free HPC Shaheen. Welcome. Thanks for coming on. >>Thanks for being here, Dave. Great to be here. How are you >>doing? Well, thanks. Crazy with doing these things, Cove in remote interviews. I wish we were face to face at us at a supercomputer show, but, hey, this thing is working. We can still have great conversations. And And I love talking to analysts like you because you bring an independent perspective. You're very wide observation space. So So let me, Like many analysts, you probably have sort of a mental model or a market model that you look at. So maybe talk about your your work, how you look at the market, and we could get into some of the mega trends that you see >>very well. Very well. Let me just quickly set the scene. We fundamentally track the megatrends of the Information Age And, of course, because we're in the information age, digital transformation falls out of that. And the megatrends that drive that in our mind is Ayotte, because that's the fountain of data five G. Because that's how it's gonna get communicated ai and HBC because that's how we're gonna make sense of it Blockchain and Cryptocurrencies because that's how it's gonna get transacted on. That's how value is going to get transferred from the place took place and then finally, quantum computing, because that exemplifies how things are gonna get accelerated. >>So let me ask you So I spent a lot of time, but I D. C and I had the pleasure of of the High Performance computing group reported into me. I wasn't an HPC analyst, but over time you listen to those guys, you learning. And as I recall, it was HPC was everywhere, and it sounds like we're still seeing that trend where, whether it was, you know, the Internet itself were certainly big data, you know, coming into play. Uh, you know, defense, obviously. But is your background mawr HPC or so that these other technologies that you're talking about it sounds like it's your high performance computing expert market watcher. And then you see it permeating into all these trends. Is that a fair statement? >>That's a fair statement. I did grow up in HPC. My first job out of school was working for an IBM fellow doing payroll processing in the old days on and and And it went from there, I worked for Cray Research. I worked for floating point systems, so I grew up in HPC. But then, over time, uh, we had experiences outside of HPC. So for a number of years, I had to go do commercial enterprise computing and learn about transaction processing and business intelligence and, you know, data warehousing and things like that, and then e commerce and then Web technology. So over time it's sort of expanded. But HPC is a like a bug. You get it and you can't get rid of because it's just so inspiring. So supercomputing has always been my home, so to say >>well and so the reason I ask is I wanted to touch on a little history of the industry is there was kind of a renaissance in many, many years ago, and you had all these startups you had Kendall Square Research Danny Hillis thinking machines. You had convex trying to make many supercomputers. And it was just this This is, you know, tons of money flowing in and and then, you know, things kind of consolidate a little bit and, uh, things got very, very specialized. And then with the big data craze, you know, we've seen HPC really at the heart of all that. So what's your take on on the ebb and flow of the HPC business and how it's evolved? >>Well, HBC was always trying to make sense of the world, was trying to make sense of nature. And of course, as much as we do know about nature, there's a lot we don't know about nature and problems in nature are you can classify those problems into basically linear and nonlinear problems. The linear ones are easy. They've already been solved. The nonlinear wants. Some of them are easy. Many of them are hard, the nonlinear, hard, chaotic. All of those problems are the ones that you really need to solve. The closer you get. So HBC was basically marching along trying to solve these things. It had a whole process, you know, with the scientific method going way back to Galileo, the experimentation that was part of it. And then between theory, you got to look at the experiment and the data. You kind of theorize things. And then you experimented to prove the theories and then simulation and using the computers to validate some things eventually became a third pillar of off science. On you had theory, experiment and simulation. So all of that was going on until the rest of the world, thanks to digitization, started needing some of those same techniques. Why? Because you've got too much data. Simply, there's too much data to ship to the cloud. There's too much data to, uh, make sense of without math and science. So now enterprise computing problems are starting to look like scientific problems. Enterprise data centers are starting to look like national lab data centers, and there is that sort of a convergence that has been taking place gradually, really over the past 34 decades. And it's starting to look really, really now >>interesting, I want I want to ask you about. I was like to talk to analysts about, you know, competition. The competitive landscape is the competition in HPC. Is it between vendors or countries? >>Well, this is a very interesting thing you're saying, because our other thesis is that we are moving a little bit beyond geopolitics to techno politics. And there are now, uh, imperatives at the political level that are driving some of these decisions. Obviously, five G is very visible as as as a piece of technology that is now in the middle of political discussions. Covert 19 as you mentioned itself, is a challenge that is a global challenge that needs to be solved at that level. Ai, who has access to how much data and what sort of algorithms. And it turns out as we all know that for a I, you need a lot more data than you thought. You do so suddenly. Data superiority is more important perhaps than even. It can lead to information superiority. So, yeah, that's really all happening. But the actors, of course, continue to be the vendors that are the embodiment of the algorithms and the data and the systems and infrastructure that feed the applications. So to say >>so let's get into some of these mega trends, and maybe I'll ask you some Colombo questions and weaken geek out a little bit. Let's start with a you know, again, it was one of this when I started the industry. It's all it was a i expert systems. It was all the rage. And then we should have had this long ai winter, even though, you know, the technology never went away. But But there were at least two things that happened. You had all this data on then the cost of computing. You know, declines came down so so rapidly over the years. So now a eyes back, we're seeing all kinds of applications getting infused into virtually every part of our lives. People trying to advertise to us, etcetera. Eso So talk about the intersection of AI and HPC. What are you seeing there? >>Yeah, definitely. Like you said, I has a long history. I mean, you know, it came out of MIT Media Lab and the AI Lab that they had back then and it was really, as you mentioned, all focused on expert systems. It was about logical processing. It was a lot of if then else. And then it morphed into search. How do I search for the right answer, you know, needle in the haystack. But then, at some point, it became computational. Neural nets are not a new idea. I remember you know, we had we had a We had a researcher in our lab who was doing neural networks, you know, years ago. And he was just saying how he was running out of computational power and we couldn't. We were wondering, you know what? What's taking all this difficult, You know, time. And it turns out that it is computational. So when deep neural nets showed up about a decade ago, arm or it finally started working and it was a confluence of a few things. Thalib rhythms were there, the data sets were there, and the technology was there in the form of GPS and accelerators that finally made distractible. So you really could say, as in I do say that a I was kind of languishing for decades before HPC Technologies reignited it. And when you look at deep learning, which is really the only part of a I that has been prominent and has made all this stuff work, it's all HPC. It's all matrix algebra. It's all signal processing algorithms. are computational. The infrastructure is similar to H B. C. The skill set that you need is the skill set of HPC. I see a lot of interest in HBC talent right now in part motivated by a I >>mhm awesome. Thank you on. Then I wanna talk about Blockchain and I can't talk about Blockchain without talking about crypto you've written. You've written about that? I think, you know, obviously supercomputers play a role. I think you had written that 50 of the top crypto supercomputers actually reside in in China A lot of times the vendor community doesn't like to talk about crypto because you know that you know the fraud and everything else. But it's one of the more interesting use cases is actually the primary use case for Blockchain even though Blockchain has so much other potential. But what do you see in Blockchain? The potential of that technology And maybe we can work in a little crypto talk as well. >>Yeah, I think 11 simple way to think of Blockchain is in terms off so called permission and permission less the permission block chains or when everybody kind of knows everybody and you don't really get to participate without people knowing who you are and as a result, have some basis to trust your behavior and your transactions. So things are a lot calmer. It's a lot easier. You don't really need all the supercomputing activity. Whereas for AI the assertion was that intelligence is computer herbal. And with some of these exa scale technologies, we're trying to, you know, we're getting to that point for permission. Less Blockchain. The assertion is that trust is computer ble and, it turns out for trust to be computer ble. It's really computational intensive because you want to provide an incentive based such that good actors are rewarded and back actors. Bad actors are punished, and it is worth their while to actually put all their effort towards good behavior. And that's really what you see, embodied in like a Bitcoin system where the chain has been safe over the many years. It's been no attacks, no breeches. Now people have lost money because they forgot the password or some other. You know, custody of the accounts have not been trustable, but the chain itself has managed to produce that, So that's an example of computational intensity yielding trust. So that suddenly becomes really interesting intelligence trust. What else is computer ble that we could do if we if we had enough power? >>Well, that's really interesting the way you described it, essentially the the confluence of crypto graphics software engineering and, uh, game theory, Really? Where the bad actors air Incentive Thio mined Bitcoin versus rip people off because it's because because there are lives better eso eso so that so So Okay, so make it make the connection. I mean, you sort of did. But But I want to better understand the connection between, you know, supercomputing and HPC and Blockchain. We know we get a crypto for sure, like in mind a Bitcoin which gets harder and harder and harder. Um and you mentioned there's other things that we can potentially compute on trust. Like what? What else? What do you thinking there? >>Well, I think that, you know, the next big thing that we are really seeing is in communication. And it turns out, as I was saying earlier, that these highly computational intensive algorithms and models show up in all sorts of places like, you know, in five g communication, there's something called the memo multi and multi out and to optimally manage that traffic such that you know exactly what beam it's going to and worth Antenna is coming from that turns out to be a non trivial, you know, partial differential equation. So next thing you know, you've got HPC in there as and he didn't expect it because there's so much data to be sent, you really have to do some data reduction and data processing almost at the point of inception, if not at the point of aggregation. So that has led to edge computing and edge data centers. And that, too, is now. People want some level of computational capability at that place like you're building a microcontroller, which traditionally would just be a, you know, small, low power, low cost thing. And people want victor instructions. There. People want matrix algebra there because it makes sense to process the data before you have to ship it. So HPCs cropping up really everywhere. And then finally, when you're trying to accelerate things that obviously GP use have been a great example of that mixed signal technologies air coming to do analog and digital at the same time, quantum technologies coming so you could do the you know, the usual analysts to buy to where you have analog, digital, classical quantum and then see which, you know, with what lies where all of that is coming. And all of that is essentially resting on HBC. >>That's interesting. I didn't realize that HBC had that position in five G with multi and multi out. That's great example and then I o t. I want to ask you about that because there's a lot of discussion about real time influencing AI influencing at the edge on you're seeing sort of new computing architectures, potentially emerging, uh, video. The acquisition of arm Perhaps, you know, amore efficient way, maybe a lower cost way of doing specialized computing at the edge it, But it sounds like you're envisioning, actually, supercomputing at the edge. Of course, we've talked to Dr Mark Fernandez about space born computers. That's like the ultimate edge you got. You have supercomputers hanging on the ceiling of the International space station, but But how far away are we from this sort of edge? Maybe not. Space is an extreme example, but you think factories and windmills and all kinds of edge examples where supercomputing is is playing a local role. >>Well, I think initially you're going to see it on base stations, Antenna towers, where you're aggregating data from a large number of endpoints and sensors that are gathering the data, maybe do some level of local processing and then ship it to the local antenna because it's no more than 100 m away sort of a thing. But there is enough there that that thing can now do the processing and do some level of learning and decide what data to ship back to the cloud and what data to get rid of and what data to just hold. Or now those edge data centers sitting on top of an antenna. They could have a half a dozen GPS in them. They're pretty powerful things. They could have, you know, one they could have to, but but it could be depending on what you do. A good a good case study. There is like surveillance cameras. You don't really need to ship every image back to the cloud. And if you ever need it, the guy who needs it is gonna be on the scene, not back at the cloud. So there is really no sense in sending it, Not certainly not every frame. So maybe you can do some processing and send an image every five seconds or every 10 seconds, and that way you can have a record of it. But you've reduced your bandwidth by orders of magnitude. So things like that are happening. And toe make sense of all of that is to recognize when things changed. Did somebody come into the scene or is it just you know that you know, they became night, So that's sort of a decision. Cannot be automated and fundamentally what is making it happen? It may not be supercomputing exa scale class, but it's definitely HPCs, definitely numerically oriented technologies. >>Shane, what do you see happening in chip architectures? Because, you see, you know the classical intel they're trying to put as much function on the real estate as possible. We've seen the emergence of alternative processors, particularly, uh, GP use. But even if f b g A s, I mentioned the arm acquisition, so you're seeing these alternative processors really gain momentum and you're seeing data processing units emerge and kind of interesting trends going on there. What do you see? And what's the relationship to HPC? >>Well, I think a few things are going on there. Of course, one is, uh, essentially the end of Moore's law, where you cannot make the cycle time be any faster, so you have to do architectural adjustments. And then if you have a killer app that lends itself to large volume, you can build silicon. That is especially good for that now. Graphics and gaming was an example of that, and people said, Oh my God, I've got all these cores in there. Why can't I use it for computation? So everybody got busy making it 64 bit capable and some grass capability, And then people say, Oh, I know I can use that for a I And you know, now you move it to a I say, Well, I don't really need 64 but maybe I can do it in 32 or 16. So now you do it for that, and then tens, of course, come about. And so there's that sort of a progression of architecture, er trumping, basically cycle time. That's one thing. The second thing is scale out and decentralization and distributed computing. And that means that the inter communication and intra communication among all these notes now becomes an issue big enough issue that maybe it makes sense to go to a DPU. Maybe it makes sense to go do some level of, you know, edge data centers like we were talking about on then. The third thing, really is that in many of these cases you have data streaming. What is really coming from I o t, especially an edge, is that data is streaming and when data streaming suddenly new architectures like F B G. A s become really interesting and and and hold promise. So I do see, I do see FPG's becoming more prominent just for that reason, but then finally got a program all of these things on. That's really a difficulty, because what happens now is that you need to get three different ecosystems together mobile programming, embedded programming and cloud programming. And those are really three different developer types. You can't hire somebody who's good at all three. I mean, maybe you can, but not many. So all of that is challenges that are driving this this this this industry, >>you kind of referred to this distributed network and a lot of people you know, they refer to this. The next generation cloud is this hyper distributed system. When you include the edge and multiple clouds that etcetera space, maybe that's too extreme. But to your point, at least I inferred there's a There's an issue of Leighton. See, there's the speed of light s So what? What? What is the implication then for HBC? Does that mean I have tow Have all the data in one place? Can I move the compute to the data architecturally, What are you seeing there? >>Well, you fundamentally want to optimize when to move data and when to move, Compute. Right. So is it better to move data to compute? Or is it better to bring compute to data and under what conditions? And the dancer is gonna be different for different use cases. It's like, really, is it worth my while to make the trip, get my processing done and then come back? Or should I just developed processing capability right here? Moving data is really expensive and relatively speaking. It has become even more expensive, while the price of everything has dropped down its price has dropped less than than than like processing. So it is now starting to make sense to do a lot of local processing because processing is cheap and moving data is expensive Deep Use an example of that, Uh, you know, we call this in C two processing like, you know, let's not move data. If you don't have to accept that we live in the age of big data, so data is huge and wants to be moved. And that optimization, I think, is part of what you're what you're referring to. >>Yeah, So a couple examples might be autonomous vehicles. You gotta have to make decisions in real time. You can't send data back to the cloud flip side of that is we talk about space borne computers. You're collecting all this data You can at some point. You know, maybe it's a year or two after the lived out its purpose. You ship that data back and a bunch of disk drives or flash drives, and then load it up into some kind of HPC system and then have at it and then you doom or modeling and learn from that data corpus, right? I mean those air, >>right? Exactly. Exactly. Yeah. I mean, you know, driverless vehicles is a great example, because it is obviously coming fast and furious, no pun intended. And also, it dovetails nicely with the smart city, which dovetails nicely with I o. T. Because it is in an urban area. Mostly, you can afford to have a lot of antenna, so you can give it the five g density that you want. And it requires the Layton sees. There's a notion of how about if my fleet could communicate with each other. What if the car in front of me could let me know what it sees, That sort of a thing. So, you know, vehicle fleets is going to be in a non opportunity. All of that can bring all of what we talked about. 21 place. >>Well, that's interesting. Okay, so yeah, the fleets talking to each other. So kind of a Byzantine fault. Tolerance. That problem that you talk about that z kind of cool. I wanna I wanna sort of clothes on quantum. It's hard to get your head around. Sometimes You see the demonstrations of quantum. It's not a one or zero. It could be both. And you go, What? How did come that being so? And And of course, there it's not stable. Uh, looks like it's quite a ways off, but the potential is enormous. It's of course, it's scary because we think all of our, you know, passwords are already, you know, not secure. And every password we know it's gonna get broken. But give us the give us the quantum 101 And let's talk about what the implications. >>All right, very well. So first off, we don't need to worry about our passwords quite yet. That that that's that's still ways off. It is true that analgesic DM came up that showed how quantum computers can fact arise numbers relatively fast and prime factory ization is at the core of a lot of cryptology algorithms. So if you can fact arise, you know, if you get you know, number 21 you say, Well, that's three times seven, and those three, you know, three and seven or prime numbers. Uh, that's an example of a problem that has been solved with quantum computing, but if you have an actual number, would like, you know, 2000 digits in it. That's really harder to do. It's impossible to do for existing computers and even for quantum computers. Ways off, however. So as you mentioned, cubits can be somewhere between zero and one, and you're trying to create cubits Now there are many different ways of building cubits. You can do trapped ions, trapped ion trapped atoms, photons, uh, sometimes with super cool, sometimes not super cool. But fundamentally, you're trying to get these quantum level elements or particles into a superimposed entanglement state. And there are different ways of doing that, which is why quantum computers out there are pursuing a lot of different ways. The whole somebody said it's really nice that quantum computing is simultaneously overhyped and underestimated on. And that is that is true because there's a lot of effort that is like ways off. On the other hand, it is so exciting that you don't want to miss out if it's going to get somewhere. So it is rapidly progressing, and it has now morphed into three different segments. Quantum computing, quantum communication and quantum sensing. Quantum sensing is when you can measure really precise my new things because when you perturb them the quantum effects can allow you to measure them. Quantum communication is working its way, especially in financial services, initially with quantum key distribution, where the key to your cryptography is sent in a quantum way. And the data sent a traditional way that our efforts to do quantum Internet, where you actually have a quantum photon going down the fiber optic lines and Brookhaven National Labs just now demonstrated a couple of weeks ago going pretty much across the, you know, Long Island and, like 87 miles or something. So it's really coming, and and fundamentally, it's going to be brand new algorithms. >>So these examples that you're giving these air all in the lab right there lab projects are actually >>some of them are in the lab projects. Some of them are out there. Of course, even traditional WiFi has benefited from quantum computing or quantum analysis and, you know, algorithms. But some of them are really like quantum key distribution. If you're a bank in New York City, you very well could go to a company and by quantum key distribution services and ship it across the you know, the waters to New Jersey on that is happening right now. Some researchers in China and Austria showed a quantum connection from, like somewhere in China, to Vienna, even as far away as that. When you then put the satellite and the nano satellites and you know, the bent pipe networks that are being talked about out there, that brings another flavor to it. So, yes, some of it is like real. Some of it is still kind of in the last. >>How about I said I would end the quantum? I just e wanna ask you mentioned earlier that sort of the geopolitical battles that are going on, who's who are the ones to watch in the Who? The horses on the track, obviously United States, China, Japan. Still pretty prominent. How is that shaping up in your >>view? Well, without a doubt, it's the US is to lose because it's got the density and the breadth and depth of all the technologies across the board. On the other hand, information age is a new eyes. Their revolution information revolution is is not trivial. And when revolutions happen, unpredictable things happen, so you gotta get it right and and one of the things that these technologies enforce one of these. These revolutions enforce is not just kind of technological and social and governance, but also culture, right? The example I give is that if you're a farmer, it takes you maybe a couple of seasons before you realize that you better get up at the crack of dawn and you better do it in this particular season. You're gonna starve six months later. So you do that to three years in a row. A culture has now been enforced on you because that's how it needs. And then when you go to industrialization, you realize that Gosh, I need these factories. And then, you know I need workers. And then next thing you know, you got 9 to 5 jobs and you didn't have that before. You don't have a command and control system. You had it in military, but not in business. And and some of those cultural shifts take place on and change. So I think the winner is going to be whoever shows the most agility in terms off cultural norms and governance and and and pursuit of actual knowledge and not being distracted by what you think. But what actually happens and Gosh, I think these exa scale technologies can make the difference. >>Shaheen Khan. Great cast. Thank you so much for joining us to celebrate the extra scale day, which is, uh, on 10. 18 on dso. Really? Appreciate your insights. >>Likewise. Thank you so much. >>All right. Thank you for watching. Keep it right there. We'll be back with our next guest right here in the Cube. We're celebrating Exa scale day right back.

>> (upbeat music) Live from Boston Massachusetts. It's theCube! Covering Red Hat Summit 2017. Brought to you by Red Hat. >> It is day three of the Red Hat Summit, here in Boston Massachusetts. I'm Rebecca Knight. Along with Stu Miniman. We are wrapping up this conference Stu. We just had the final keynote of the morning. Before the cameras were rolling, you were teasing me a little bit that you have more scoop on the AWS deal. I'm interested to hear what you learned. >> (Stu) Yeah, Rebecca. First of all, may the fourth be with you. >> (Rebecca) Well, thank you. Of course, yes. And also with you. >> (Stu) Always. >> Yeah. (giggles) >> (Stu) So, day three of the keynote. They started out with a little bit of fun. They gave out some "May The Fourth Be With You" t-shirts. They had a little Star Wars duel that I was Periscoping this morning. So, love their geeking out. I've got my Millennium Falcon cuff links on. >> (Rebecca) You're into it. >> I saw a bunch of guys wearing t-shirts >> (Rebecca) Princess Leia was walking around! >> Princess Leia was walking around. There were storm troopers there. >> (Rebecca) Which is a little sad to see, but yes. >> (Stu) Uh, yeah. Carrie Fisher. >> Yes. >> Absolutely, but the Amazon stuff. Sure, I think this is the biggest news coming out of the show. I've said this a number of times. And we're still kind of teasing out exactly what it is. Cause, partially really this is still being built out. There's not going to be shipping until later this year. So things like how pricing works. We're still going to get there. But there's some people that were like "Oh wait!' "Open shift can be in AWS, that's great!" "But then I can do AWS services on premises." Well, what that doesn't mean, of course is that I don't have everything that Amazon does packaged up into a nice little container. We understand how computer coding works. And even with open-source and how we can make things server-less. And it's not like I can take everything that everybody says and shove it in my data center. It's just not feasible. What that means though, is it is the same applications that I can run. It's running in OpenShift. And really, there's the hooks and the API's to make sure that I can leverage services that are used in AWS. Of course, from my standpoint I'm like "OK!" So, tell me a little bit about how what latency there's going to be between those services. But it will be well understood as we build these what it's going to be use for. Certain use cases. We already talked to Optim. I was really excited about how they could do this for their environment. So, it's something we expect to be talking about throughout the rest of the year. And by the time we get to AWS Reinvent the week after Thanksgiving, I expect we'll have a lot more detail. So, looking forward to that. >> (Rebecca) And it will be rolled out too. So we'll have a really good sense of how it's working in the marketplace. >> (Stu) Absolutely. >> So other thoughts on the key note. I mean, one of the things that really struck me was talking about open-source. The history of open-source. It started because of a need to license existing technologies in a cheaper way. But then, really, the point that was made is that open-source taught tech how to collaborate. And then tech taught the world how to collaborate. Because it really was the model for what we're seeing with crowdsourcing solutions to problems facing education, climate change, the developing world. So I think that that is really something that Red Hat has done really well. In terms of highlighting how open-source is attacking many of the worlds most pressing problems. >> (Stu) Yeah, Rebecca I agree. We talked with Jim Whitehurst and watched him in the keynotes in previous days. And talked about communities and innovation and how that works. And in a lot of tech conferences it's like "Okay, what are the business outcomes?" And here it's, "Well, how are we helping the greater good?" "How are we helping education?" It was great to see kids that are coding and doing some cool things. And they're like, "Oh yeah, I've done Java and all these other things." And the Red Hat guys were like, "Hey >> (Rebecca) We're hiring. Yeah. (giggles) >> can we go hire this seventh grader?" Had the open-source hardware initiative that they were talking about. And how they can do that. Everything from healthcare to get a device that used to be $10,000 to be able to put together the genome. Is I can buy it on Amazon for What was it? Like six seven hundred dollars and put it together myself. So, open-source and hardware are something we've been keeping an eye on. We've been at the Open Compute Project event. Which Facebook launched. But, these other initiatives. They had.... It was funny, she said like, "There's the internet of things." And they have the thing called "The Thing" that you can tie into other pieces. There was another one that weaved this into fabric. And we can sensor and do that. We know healthcare, of course. Lot's of open-source initiatives. So, lots of places where open-source communities and projects are helping proliferate and make greater good and make the world a greater place. Flattening the world in many cases too. So, it was exciting to see. >> And the woman from the Open-Source Association. She made this great point. And she wasn't trying to be flip. But she said one of our questions is: Are you emotionally ready to be part of this community? And I thought that that was so interesting because it is such a different perspective. Particularly from the product side. Where, "This is my IP. This is our idea. This is our lifeblood. And this is how we're going to make money." But this idea of, No. You need to be willing to share. You need to be willing to be copied. And this is about how we build ideas and build the next great things. >> (Stu) Yeah, if you look at the history of the internet, there was always. Right, is this something I have to share information? Or do we build collaboration? You know, back to the old bulletin board days. Through the homebrew computing clubs. Some of the great progress that we've made in technology and then technology enabling beyond have been because we can work in a group. We can work... Build on what everyone else has done. And that's always how science is done. And open-source is just trying to take us to the next level. >> Right. Right. Right. And in terms of one of the last... One of the last things that they featured in the keynote was what's going on at the MIT media lab. Changing the face of agriculture. And how they are coding climate. And how they are coding plant nutrition. And really this is just going to have such a big change in how we consume food and where food is grown. The nutrients we derive from fruit. I was really blown away by the fact that the average apple we eat in the grocery store has been around for 14 months. Ew, ew! (laughs) So, I mean, I'm just exciting what they're doing. >> Yeah, absolutely right. If we can help make sure people get clean water. Make sure people have availability of food. Shorten those cycles. >> (Rebecca) Right, right. Exactly. >> The amount of information, data. The whole Farm to Table Initiative. A lot of times data is involved in that. >> (Rebecca) Yeah. It's not necessarily just the stuff that you know, grown on the roof next door. Or in the farm a block away. I looked at a local food chain that's everywhere is like Chipotle. You know? >> (Rebecca) Right. >> They use data to be able to work with local farmers. Get what they can. Try to help change some of the culture pieces to bring that in. And then they ended up the keynote talking more about innovation award winners. You and I have had the chance to interview a bunch of them. It's a program I really like. And talking to some of the Red Hatters there actually was some focus to work with... Talk to governments. Talk to a lot of internationals. Because when they started the program a few years ago. It started out very U.S.-centric. So, they said "Yeah." It was a little bit coincidence that this year it's all international. Except for RackSpace. But, we should be blind when we think about who has great ideas and good innovation. And at this conference, I bumped into a lot of people internationally. Talked to a few people coming back from the Red Sox game. And it was like, "How was it?" And they were like, "Well, I got a hotdog and I understood this. But that whole ball and thing flying around, I don't get it." And things like that. >> So, they're learning about code but also baseball. So this is >> (Stu) Yeah, what's your take on the global community that you've seen at the show this week? >> (Rebecca) Well, as you've said, there are representatives from 70 countries here. So this really does feel like the United Nations of open-source. I think what is fascinating is that we're here in the states. And so we think about these hotbeds of technological innovation. We're here in Boston. Of course there's Silicon Valley. Then there are North Carolina, where Red Hat's based. Atlanta, Austin, Seattle, of course. So all these places where we see so much innovation and technological progress taking place here in the states. And so, it can be easy to forget that there are also pockets all over Europe. All over South America. In Africa, doing cool things with technology. And I think that that is also ... When we get back to one of the sub themes of this conference... I mean, it's not a sub theme. It is the theme. About how we work today. How we share ideas. How we collaborate. And how we manage and inspire people to do their best work. I think that that is what I'd like to dig into a little today. If we can. And see how it is different in these various countries. >> Yeah, and this show, what I like is when its 13th year of the show, it started out going to a few locations. Now it's very stable. Next year, they'll be back in San Francisco. The year after, they'll be back here in Boston. They've go the new Boston office opening up within walking distance of where we are. Here GE is opening up their big building. I just heard there's lots of startups when I've been walking around the area. Every time I come down to the Sea Port District. It's like, "Wow, look at all the tech." It's like, Log Me In is right down the road. There's this hot little storage company called Wasabi. That's like two blocks away. Really excited but, one last thing back on the international piece. Next week's OpenStack Summit. I'll be here, doing theCube. And some of the feedback I've been getting this week It's like, "Look, the misperception on an OpenStack." One of the reasons why people are like, "Oh, the project's floundering. And it's not doing great, is because the two big use case. One, the telecommunication space. Which is a small segment of the global population. And two, it's gaining a lot of traction in Europe and in Asia. Whereas, in North America public cloud has kind of pushed it aside a little bit. So, unfortunately the global tech press tends to be very much, "Oh wait, if it's seventy-five percent adoption in North America, that's what we expect. If its seventy-five percent overseas, it's not happening. So (giggles) it's kind of interesting. >> (Rebecca) Right. And that myopia is really a problem because these are the trends that are shaping our future. >> (Stu) Yeah, yeah. >> So today, I'm also going to be talking to the Women In Tech winners. That very exciting. One of the women was talking about how she got her idea. Or really, her idea became more formulated, more crystallized, at the Grace Hopper Conference. We, of course, have a great partnership with the Grace Hopper Conference. So, I'm excited to talk to her more about that today too. >> (Stu) Yeah, good lineup. We have few more partners. Another customer EasiER AG who did the keynote yesterday. Looking forward to digging in. Kind of wrapping up all of this. And Rebecca it's been fun doing it with you this week. >> And I'm with you. And may the force... May the fourth be with you. >> And with you. >> (giggles) Thank you, we'll have more today later. From the Red Hat Summit. Here in Boston, I'm Rebecca Knight for Stu Miniman. (upbeat music)

[Music] live from cambridge massachusetts extracting the signal from the noise it's the cube covering the MIT chief data officer and information quality symposium now your host dave Volante and paul Gillett hi buddy welcome back to Cambridge Massachusetts we're at MIT Paul Gillan and myself are here for two days and we're really pleased to have sandy Pentland on he's the director of MIT Media labs entrepreneurship program just coming off a keynote mr. Alex sandy Pentland Spellman thanks for coming with you how'd you get that name sandy was that the color you know my dad was named Alex too so I had to get the diminutive so Alexander turns into Zander or Sasha or sandy ah excellent so man it's stuck so we learned from your keynote today that like your mom said hey if every other kid jumps off the bridge do you and the answer should be yes why is that well if your other friends or presumably as rational as you and have same sort of values as you and if they're doing something that looks crazy they must have a piece of information you don't like maybe Godzilla is coming bridges come and it really is time to get off but and so so while it's used as a metaphor for doing the irrational things it's actually shows that using your social context can be most rational because it's a way of getting information that you don't otherwise have so you broke down your talk to chief data officers and new types of analysis smarter organizations smarter networks and then really interesting new new architecture if we could sort of break those down sure you talked about sort of networks not individual nodes as really should be the focus to understand behavior can you unpack that a little well it's a little bit like the bridge or metaphor you know a lot of what we learn a lot of our behavior comes from watching other people we're not even conscious of it but you know if everybody else starts you know wearing a certain sort of shoe or or you know acting in a certain or using a phrase in business like all these new sort of buzz phrases like oh you have to - because it's to fit in it means something it's it's part of being hyper formants and being part of your group but that's not in data analytics today today what they look at is just your personal properties not what you're exposed to and the group that you're part of so they would look at the guy on the bridge and they say he's not going to jump because he doesn't have that information but on the other hand if all of other people who like him are making a different decision he probably is going to jump and your research has been you dig into organizations and you've found the relationship between productivity and this type of analysis has been pretty substantial very substantive offenses a ssin and outside of the organization dealing with customers so people focus on things like personality history various sort of training things like that what we find is compared to the pattern of interaction with other people so who do you talk to when and what situations those other factors are tiny they're often a whole order of magnitude less important than just do you talk to all the people in your group do you talk outside of your group do if you violate the org chart and talk to other people if you do you're almost certainly one of the high productivity high innovation people so what what impact does this have or were the implications of this on organizations which historically have been have been highly Madonn hierarchies reporting structures all of these institutions that we evolved in the post-world War two ERA is this working against their productivity well what they did is is they set some simple rules in that they could deal with and wrap their head around but what we find is that those simple rules are exactly the opposite of what you need for innovation and because really what they're doing is they're enforcing silos they're enforcing atomization of the work and everybody talks about we need to be more fluid we need to be more innovative we need to be able to move faster and what that requires is better communication habits and so what we find when we measure the communication habits is that that's exactly right better communication habits lead to more innovative organizations what's really amazing is almost no organization does it so people don't know does everybody talk to everybody in this group do they talk outside of the group there's no graphic there's no visualization and when you give a group a visualization of their pattern of organization of communication they change it and they become more innovative they become more productive I'm sure you're familiar with holacracy this idea that of doing away with with organizational boundaries and sort of do titles and sure everybody talks to everyone is that in your view a better way to structure an organization think that's too extreme but it's headed in the right direction I mean so what we're talking first of all people try to do this without any data so you know everybody's the same well everybody really isn't the same and how would you know if you're behaving as well as the same as other people or I mean there's no data so so what I'm suggesting is something that's sort of halfway between the two yeah you can have leaders you can have organization in there but you also have to have good flow of ideas and what that means is you have to make talking outside your org chart a value it's something you're rewarded for it means that including everybody in the loop in your organization is something you ought to be rewarded for and of course that requires data so the sorts of things we do with peoples we make displays could just be piece of paper that shows the patterns of communication and we give it to everybody and you know what people actually know what to do with it when you give it to them they say well gee you know this group of people is all talking to each other but they're not talking to that group maybe they ought to talk to each other it's that simple but in the lack of data you can't feel so you instrumented people essentially with let's badges and you could measure conversations at the watercooler yeah they're their frequency their duration not the content not the content just that's the activity just is it happening right and is it happening between groups just just people from this group go to that other groups water cooler stuff like that and that actually is enough to really make a substantial difference in the corporation and you gave an example of you were able to predict trending stories on Twitter better than the internal mechanism and Twitter did I understand that Kerina so what we've done by studying organizations like this and coming up with these sort of rules of how people behave so the notion that people learn from each other and that it's the patterns of communication that matter you can encode that along with machine learning and suddenly you get something that looks like machine learning but in many ways it's more powerful and more reliable and so we have a spin-out called Endor and what that does is it lets your average guy who can use a spreadsheet do something that's really competitive with the best machine learning groups in the world and that's pretty exciting because everybody has these reams of data but what they don't have is a whole bunch of PhDs who can study it for six months and and come up with a machine learning algorithm to do it they have a bunch of guys that are smart know the business but they don't know the machine learning so it endured doesn't supply something like a spreadsheet to be able to allow the normal guy to do as good as the machine learning guys there's a lot of focus right now on anticipating predicting customer behavior better a lot of us been focused on on individuals understanding individuals better is that wrongheaded I mean should marketers be looking more at this group theory and treating customers more as buckets of similar behaviors it's not it's not buckets but treating people as individuals is is a mistake because while people do have individual preferences most of those preferences are learned from other people it's keeping up with the Jones it's fitting in its it's learning what the best practice is so you can predict people better from the company they keep than you can from their demographics always virtually every single time you can do better from the company they keep than from the standard sort of data so what that means is when you do analysis you need to look at the relationships between people and at one level it's sort of obvious you analyze somebody personally without knowing something about their relationships right about you know the type of things they do the places they go those are important but they're usually not in the data and what I find is I do this with a lot of big organizations and what I find is you look at their data analytics it's all based on individuals and it's not based on the context to those individuals absolutely I want to ask you further about that because when I think of the surveys that I fill out they're always about my personal preference Yahoo I want to do I can't remember ever filling out a survey that asked me about what my peer group does are you saying that those are the questions we should be asking yeah exactly right and of course you want to get data about that you want to know if if you go to these locations all the time to go to that restaurant you go to this sort of entertainment who else goes there what are they by what's trending in your group because it's not the general population and these not necessary people I know but they're people I identify with Yammer haps that's why I go to certain restaurants not because my friends go there but because people who I aspire to be like yeah there yeah and and the other way around you go there and you say well gosh these other people are like me because they go here too and I see that they're you know wearing different sort of clothes or they're by or the simplest thing you go to restaurants you see other people all buying the mushi yes maybe I should try the mushi I usually don't like it but seems to work well and this is I like this restaurant and everybody else who comes here likes it so I'll try it right it's that simple so it's important to point out we're talking about the predictive analytics Capas they're probably people watching might say this Sandi's crazy we mean we don't want it personalized we want to personalize the customer experience still I'm presuming sure but when we're talking about predictive analytics you're saying the the community the peer group is a much better predictor than the individual that's right yeah okay so I want to come back to the the org chart these are you saying that org charts shouldn't necessarily change but the incentives should or your previous thing to do is you have an org chart but the incentives that are across the entire organization is good communication within the box you're in and good communication outside of the box and to put those incentives in place you need to have data you need to be able to have some way of estimating does everybody talk to each other do they talk to the rest of the organization and there's a variety of ways you can do that we do it with little badges we do it by analyzing phone call data email is not so good because email is not really a social relationship it's just this this little formal thing you do often but by using things like the badges like the phone calls surveys for that matter right you can give people feedback about are they communicating in the right way are they communicating with other parts of the organization and by visualizing that to people they'll begin to do the right thing you had this notion of network tuning oh you don't want an insufficiently diverse network but you don't want a network that's too dense you might find the sweet spot in the middle desert how do you actually implement that that tuning well the first thing is is you have to measure okay you have to know how dense is the social interaction the communication pattern because if you don't know that there's nothing to - right and then what you want to ask is you want to ask the signal property of something being two dances the same ideas go around then around and around so you look at the graph that you get from this data and you ask you know this Joe talked to Bob talk to Mary talk to Joe talk to you know is it full of cycles like that and if it's too full of cycles then that's a problem right because it's the same people talking to each other same ideas going around and there's some nice mathematical formulas for major in it they're sort of hard to put into English but it has to do with if you look at the flow of ideas are you getting a sufficiently diverse set of ideas coming to you or is it just the same people all talking to each other so are you sort of cut off from the rest of the world in your book social physics you talk about rewards and incentives isms and one of the things that struck me as you say that that rewards that people are actually more motivated by rewards for others than for themselves correct me if I'm wrong if paraphrasing you wrong there but but there's but but rewarding the group or or doing something good for somebody else is actually a powerful incentive is it is that the true the case well you said it almost right so so if you want to change behavior these social incentives are more powerful than financial incentives so if you have everybody in a group let's say and people are rewarded by the behavior of the other people in the group what will they do well they'll talk to the other people about doing the right thing because their reward my reward depends on your behavior so I'm gonna talk to you about it okay and your reward depends on it you'll talk and I don't know so what we're doing is we're creating much more communication around this problem and social pressure because you know if you don't do it you're screwing me and and you know I may not be a big thing but you're gonna think twice about that whereas some small financial award usually it's not such a big thing for people so if you think people talk a lot about you know persona persona marketing when I first met John Fourier he had this idea of affinity rank which was his version of you know peer group PageRank hmm do you do you hear a lot about you know get a lot of questions about persona persona marketing and and what does your research show in terms of how we should be appealing to that persona so sorry good questions about that some time and I don't know what he really originally intended but the way people often imply it is very static you have a particular persona that's fixed for all parts of your life well that's not true I mean you could be a baseball coach for your kid and a banker during the day and a member of a church and those are three different personas and what defines those personas it's the group that you're interacting with it's it's the the people you learn with and try and fit in so your persona is a variable thing and the thing that's the key to it is what are the groups that you're you're interacting with so if I analyzed your groups of interactions I'd see three different clusters I'd see the baseball one I'd say the banking one I'd see the church group one and then I would know that you have three personas and I could tell which one you're in typically by seeing who you're spending time with right now is the risk of applying this idea of behaviors influenced by groups is there the risk of falling over into profiling and essentially treating people anticipating behaviors based upon characteristics that may not be indicative of how any individual might act back credit alcoholics as you example right I don't get a job because people like people who are similar to me tend to be alcoholics let's say this is different though so this is not people who are similar to you if you hang out with alcoholics all the time then they're really eyes are good on that you're an alcoholic it may not be yes and there is a risk of over identifying or or extrapolating but it's different than people like me I mean if you go to the you know the dingy bars were beers or a buck and everybody gets wasted and you do that repeatedly you're talking about behaviors rather than characteristics behaviors rather than characteristics right I mean you know if you drink a lot maybe you drink a lot so we have a question from the crowd so it says real time makes persona very difficult yeah so it was come back to furriers premise was I was Twitter data you know such is changing very rapidly so are there social platforms that you see that can inform in real time to help us sort of get a better understanding of persona and affinity group affinity well there are data sources that do that right so first as if I look at telephone data or credit card data even for that matter sure this geo-located I can ask but what sort of people buy here or what sort of people are in this bar or restaurant and I can look at their demographics and where they go to I showed an example of that in San Francisco using data from San Francisco so there is this data which means that any app that's interested in it that has sufficient breadth and although sufficient adoption can do these sorts of analyses can you give an example of how you're working with the many organizations now I'm sure you can't name them but can you give an example of how you're applying these principles practically now whether it's in law enforcement or in consumer marketing how are you putting these to work well there's a bunch of different things that that go together with this view of you know it's the flow of ideas that's the important thing not the demographics so talk about behavior change and we're working with a small country to change their traffic safety by enrolling people in small groups where you know the benefit I get for driving right depends on your safety and we're good buddies we know that that's how you sign up sign up with your buddies and what that means is I'm going to talk to you about your driving if you're driving in a dangerous way and that we've seen in small experiments is a lot more effective than giving you points on your driver's license or discount on your insurance the social relationships so so that's an example another example is we're beginning a project to look at unemployment and what we see is is that people have a hard time getting re-employed don't have diverse enough social networks and it sounds kind of common sense but they don't physically get out enough compared to the people that do get jobs so what's the obvious thing well you encouraged them to get out more you make it easier for them to get out more so those are some examples when you talk about health care what you can do is you can say well look you know I don't know particular things you're doing but based on the behavior that you show right and the behavior of the people you hang with you may be at much higher risk of diabetes and it's not any particular behavior this is the way medical stuff is always pitched is you know it's this behavior that beer every combination of things all right and so you're not really aware that you're doing anything bad but if all your buds are at risk of it then you probably are too because you're probably doing a lot of the same sort of behaviors and medicine is a place where people are willing to give up some of the privacy because the consequences are so important so we're looking at people who are interested in personalized medicine and are willing to you know share their data about where they go and what they spend time doing in order to get statistics back from the people they spend time with about what are the risk factors they pick up from the people around them and the behaviors they engage in um your message this to the cdos today was you know you were sort of joking you're measuring that right and a lot of times they weren't a lot of the non-intuitive things your research has found so I wanna talk about the data and access to the data and how the CBO can you know affect change in their organization a lot of the data lives in silos I mean if they certainly think of social data Facebook LinkedIn yeah Twitter you mentioned credit card data is that a problem or is data becoming more accessible through api's or is it still just sort of a battle to get that data architecture running well it's a it's a battle and in fact actually it's a political and very passionate battle and it revolves around who controls the data and privacy is a big part of that so one of the messages is that to be able to get really ditch data sources you have to engage with the customer a lot so people are more than willing our research we've set up you know entire cities where we've changed the rules and we've found that people are more than willing to volunteer very detailed personal data under two conditions one is they have to know that it's safe so you're not reselling it you're handling it in a secure way it's not going to get out in some way and the other is that they get value for it and they can see the value so it's not spreading out and they're part of the discussion so you know you want more personalized medicine people are willing to share right because it's important to them or for their family you know if you want to share we're willing to share very personal stuff about their kids they would never do that but if it results in the kid getting a better education more opportunity yeah they're absolutely willing so that leads to a great segue into enigma yeah you talked about enigma as a potential security layer for the internet but also potential privacy yeah solution so talk about enigma where it's at yeah what it is where it's at and how it potentially could permeate yeah so we've been building architectures and working with this sort of problem this conundrum basically datas and silos people feel paranoid and probably correctly about their data leaky now companies don't have access to data don't know what to do with it and a lot of it has to do with safe sharing another aspect of this problem is cybersecurity you're getting increasing the amount of attacks done stuff bad for companies bad for people it's just going to get worse and we actually know what the answers to these things are the answers our data is encrypted all the time everywhere you do the computation on encrypted data you never transmit it you never unencrypted it to be able to do things we also know that in terms of control of the data is possible to build fairly simple permission mechanisms so that you know the computer just won't share it in the wrong places and if it does you know skyrockets go up and the cop scum you can build systems like that today but the part that's never been able never allowed that to happen is you need to keep track of a lot of things in a way that's not hackable you need to know that somebody doesn't just short-circuit it or take it out the back and what's interesting is the mechanisms that are in Bitcoin give you exactly that power so you whatever you feel about Bitcoin you know it's speculative bubble or whatever the blockchain which is part of it is this open ledger that is unhackable and and has the following characteristics that's amazing it's called trustless what that means is you can work with a bunch of crooks and still know that the ledger that you're keeping is correct because it doesn't require trusting people to work with them it's something where everybody has to agree to be able to get things and it works it works in Bitcoin at scale over the whole world and so what we've done is adapted that technology to be able to build a system called enigma which takes data in an encrypted form computes on it in an encrypted form transmits it according to the person's permissions and only that way in an encrypted form and you know it provides this layer of security and privacy that we've never had before there have been some projects that come close to this but know we're pretty excited about this and and what I think you're going to see is you're going to see some of the big financial institutions trying to use it among themselves some of the big logistics some of the big medical things trying to use it in in hotspots where they have real problems but the hope is is that it gets spread among the general population so it becomes quite literally the privacy and security level that doesn't have Warren Buffett might be right that it might fail as a currency but the technology has really inspired some new innovations that's right so so it's essentially a distributed it's not a walled garden it's a distributed black box that's what you're describing you never exposed the data that's right you don't need a trusted third party that's getting attacked that's right nobody has to stamp that this is correct because the moment you do that first of all other people are controlling you and the second thing is is there a point of attack so it gets rid of that trusted third party centralization makes it distributed you can have again a bunch of bad actors in the system it doesn't hurt it's peer-to-peer where you have to have 51% of the people being bad before things really go bad how do you solve the problem of performing calculations on encrypted data because they're classic techniques actually it's been known for over 20 years how to do that but there are two pieces missing one piece is it wasn't efficient it scaled really poorly and what we did is came up with a way of solving that by making it essentially multi scale so it's it's a distributed solution for this that brings the cost down to something that's linear in the number of elements which is a real change and the second is keeping track of all of the stuff in a way that's secure it's fine to have an addition that's secure you know but if that isn't better than a whole system that secure it doesn't do you any good and so that's where the blockchain comes in it gives you this accounting mechanism for knowing which computations are being done who has access to them what the keys are things like that so Google glass was sort of incubated in MIT Media labs and well before yeah my group you go right in your group and yeah it didn't take off me because it's just not cool it looks kind of goofy but now enigma has a lot of potential solving a huge problem are you can open-source it what do you yeah it's an open-source system we hope to get more people involved in it and right now we're looking for some test beds to show how well it works and make sure that all the things are dotted and crossed and so forth and where can people learn more about it oh go to a nygma dot media dot mit.edu all right sandy we're way over our time so obviously you were interesting so thanks keep right there buddy Paul and I we right back with our next guest we're live from see this is the cube right back [Music]