>>We are back in the final stretch at Supercomputing 22 here in Dallas. I'm your host Paul Gillum with my co-host Dave Nicholson, and we've been talking to so many smart people this week. It just, it makes, boggles my mind are next guest. J Poso is the HPC and AI technology strategist at Dell. Jay also has a PhD in astronomy from the University of Texas. And I'm guessing you were up watching the Artemis launch the other night? >>I, I wasn't. I really should have been, but, but I wasn't, I was in full super computing conference mode. So that means discussions at, you know, various venues with people into the wee hours. >>How did you make the transition from a PhD in astronomy to an HPC expert? >>It was actually really straightforward. I did theoretical astrophysics and I was modeling what white dwarfs look like when they create matter and then explode as type one A super Novi, which is a class of stars that blow up. And it's a very important class because they blow up almost exactly the same way. So if you know how bright they are physically, not just how bright they appear in the sky, but if you can determine from first principles how bright they're, then you have a standard ruler for the universe when they go off in a galaxy, you know how far the galaxy is about how faint it is. So to model these though, you had to understand equations of physics, including electron degeneracy pressure, as well as normal fluid dynamics kinds of of things. And so you were solving for an explosive burning front, ripping through something. And that required a supercomputer to have anywhere close to the fat fidelity to get a reasonable answer and, and hopefully some understanding. >>So I've always said electrons are degenerate. I've always said it and I, and I mentioned to Paul earlier, I said, finally we're gonna get a guest to consort through this whole dark energy dark matter thing for us. We'll do that after, after, after the segment. >>That's a whole different, >>So, well I guess super computing being a natural tool that you would use. What is, what do you do in your role as a strategist? >>So I'm in the product management team. I spend a lot of time talking to customers about what they want to do next. HPC customers are always trying to be maximally productive of what they've got, but always wanting to know what's coming next. Because if you think about it, we can't simulate the entire human body cell for cell on any supercomputer day. We can simulate parts of it, cell for cell or the whole body with macroscopic physics, but not at the, you know, atomic level, the entire organism. So we're always trying to build more powerful computers to solve larger problems with more fidelity and less approximations in it. And so I help people try to understand which technologies for their next system might give them the best advance in capabilities for their simulation work, their data analytics work, their AI work, et cetera. Another part of it is talking to our great technology partner ecosystem and learning about which technologies they have. Cause it feeds the first thing, right? So understanding what's coming, and Dell has a, we're very proud of our large partner ecosystem. We embrace many different partners in that with different capabilities. So understanding those helps understand what your future systems might be. That those are two of the major roles in it. Strategic customers and strategic technologies. >>So you've had four days to wander the, this massive floor here and lots of startups, lots of established companies doing interesting things. What have you seen this week that really excites you? >>So I'm gonna tell you a dirty little secret here. If you are working for someone who makes super computers, you don't get as much time to wander the floor as you would think because you get lots of meetings with people who really want to understand in an NDA way, not just in the public way that's on the floor, but what's, what are you not telling us on the floor? What's coming next? And so I've been in a large number of customer meetings as well as being on the floor. And while I can't obviously share the everything, that's a non-disclosure topic in those, some things that we're hearing a lot about, people are really concerned with power because they see the TDP on the roadmaps for all the silicon providers going way up. And so people with power comes heat as waste. And so that means cooling. >>So power and cooling has been a big topic here. Obviously accelerators are, are increasing in importance in hpc not just for AI calculations, but now also for simulation calculations. And we are very proud of the three new accelerator platforms we launched here at the show that are coming out in a quarter or so. Those are two of the big topics we've seen. You know, there's, as you walk the floor here, you see lots of interesting storage vendors. HPC community's been do doing storage the same way for roughly 20 years. But now we see a lot of interesting players in that space. We have some great things in storage now and some great things that, you know, are coming in a year or two as well. So it's, it's interesting to see that diversity of that space. And then there's always the fun, exciting topics like quantum computing. We unveiled our first hybrid classical quantum computing system here with I on Q and I can't say what the future holds in this, in this format, but I can say we believe strongly in the future of quantum computing and that this, that future will be integrated with the kind of classical computing infrastructure that we make and that will help make quantum computing more powerful downstream. >>Well, let's go down that rabbit hole because, oh boy, boy, quantum computing has been talked about for a long time. There was a lot of excitement about it four or five years ago, some of the major vendors were announcing quantum computers in the cloud. Excitement has kind of died down. We don't see a lot of activity around, no, not a lot of talk around commercial quantum computers, yet you're deep into this. How close are we to have having a true quantum computer or is it a, is it a hybrid? More >>Likely? So there are probably more than 20 and I think close to 40 companies trying different approaches to make quantum computers. So, you know, Microsoft's pursuing a topol topological approach, do a photonics based approach. I, on Q and i on trap approach. These are all different ways of trying to leverage the quantum properties of nature. We know the properties exist, we use 'em in other technologies. We know the physics, but trying the engineering is very difficult. It's very difficult. I mean, just like it was difficult at one point to split the atom. It's very difficult to build technologies that leverage quantum properties of nature in a consistent and reliable and durable way, right? So I, you know, I wouldn't wanna make a prediction, but I will tell you I'm an optimist. I believe that when a tremendous capability with, with tremendous monetary gain potential lines up with another incentive, national security engineering seems to evolve faster when those things line up, when there's plenty of investment and plenty of incentive things happen. >>So I think a lot of my, my friends in the office of the CTO at Dell Technologies, when they're really leading this effort for us, you know, they would say a few to several years probably I'm an optimist, so I believe that, you know, I, I believe that we will sell some of the solution we announced here in the next year for people that are trying to get their feet wet with quantum. And I believe we'll be selling multiple quantum hybrid classical Dell quantum computing systems multiple a year in a year or two. And then of course you hope it goes to tens and hundreds of, you know, by the end of the decade >>When people talk about, I'm talking about people writ large, super leaders in supercomputing, I would say Dell's name doesn't come up in conversations I have. What would you like them to know that they don't know? >>You know, I, I hope that's not true, but I, I, I guess I understand it. We are so good at making the products from which people make clusters that we're number one in servers, we're number one in enterprise storage. We're number one in so many areas of enterprise technology that I, I think in some ways being number one in those things detracts a little bit from a subset of the market that is a solution subset as opposed to a product subset. But, you know, depending on which analyst you talk to and how they count, we're number one or number two in the world in supercomputing revenue. We don't always do the biggest splashy systems. We do the, the frontier system at t, the HPC five system at ENI in Europe. That's the largest academic supercomputer in the world and the largest industrial super >>That's based the world on Dell. Dell >>On Dell hardware. Yep. But we, I think our vision is really that we want to help more people use HPC to solve more problems than any vendor in the world. And those problems are various scales. So we are really concerned about the more we're democratizing HPC to make it easier for more people to get in at any scale that their budget and workloads require, we're optimizing it to make sure that it's not just some parts they're getting, that they are validated to work together with maximum scalability and performance. And we have a great HPC and AI innovation lab that does this engineering work. Cuz you know, one of the myths is, oh, I can just go buy a bunch of servers from company X and a network from company Y and a storage system from company Z and then it'll all work as an equivalent cluster. Right? Not true. It'll probably work, but it won't be the highest performance, highest scalability, highest reliability. So we spend a lot of time optimizing and then we are doing things to try to advance the state of HPC as well. What our future systems look like in the second half of this decade might be very different than what they look like right. Now. >>You mentioned a great example of a limitation that we're running up against right now. You mentioned an entire human body as a, as a, as an organism >>Or any large system that you try to model at the atomic level, but it's a huge macro system, >>Right? So will we be able to reach milestones where we can get our arms entirely around something like an entire human organism with simply quantitative advances as opposed to qualitative advances? Right now, as an example, let's just, let's go down to the basics from a Dell perspective. You're in a season where microprocessor vendors are coming out with next gen stuff and those next NextGen microprocessors, GPUs and CPUs are gonna be plugged into NextGen motherboards, PCI e gen five, gen six coming faster memory, bigger memory, faster networking, whether it's NS or InfiniBand storage controllers, all bigger, better, faster, stronger. And I suspect that systems like Frontera, I don't know, but I suspect that a lot of the systems that are out there are not on necessarily what we would think of as current generation technology, but maybe they're n minus one as a practical matter. So, >>But yeah, I mean they have a lifetime, so Exactly. >>The >>Lifetime is longer than the evolution. >>That's the normal technologies. Yeah. So, so what some people miss is this is, this is the reality that when, when we move forward with the latest things that are being talked about here, it's often a two generation move for an individual, for an individual organization. Yep. >>So now some organizations will have multiple systems and they, the system's leapfrog and technology generations, even if one is their real large system, their next one might be newer technology, but smaller, the next one might be a larger one with newer technology and such. Yeah. So the, the biggest super computing sites are, are often running more than one HPC system that have been specifically designed with the latest technologies and, and designed and configured for maybe a different subset of their >>Workloads. Yeah. So, so the, the, to go back to kinda the, the core question, in your opinion, do we need that qualitative leap to something like quantum computing in order to get to the point, or is it simply a question of scale and power at the, at the, at the individual node level to get us to the point where we can in fact gain insight from a digital model of an entire human body, not just looking at a, not, not just looking at an at, at an organ. And to your point, it's not just about human body, any system that we would characterize as being chaotic today, so a weather system, whatever. Do you, are there any milestones that you're thinking of where you're like, wow, you know, I have, I, I understand everything that's going on, and I think we're, we're a year away. We're a, we're, we're a, we're a compute generation away from being able to gain insight out of systems that right now we can't simply because of scale. It's a very, very long question that I just asked you, but I think I, but hopefully, hopefully you're tracking it. What, what are your, what are your thoughts? What are these, what are these inflection points that we, that you've, in your mind? >>So I, I'll I'll start simple. Remember when we used to buy laptops and we worried about what gigahertz the clock speed was Exactly. Everybody knew the gigahertz of it, right? There's some tasks at which we're so good at making the hardware that now the primary issues are how great is the screen? How light is it, what's the battery life like, et cetera. Because for the set of applications on there, we we have enough compute power. We don't, you don't really need your laptop. Most people don't need their laptop to have twice as powerful a processor that actually rather up twice the battery life on it or whatnot, right? We make great laptops. We, we design for all of those, configure those parameters now. And what, you know, we, we see some customers want more of x, somewhat more of y but the, the general point is that the amazing progress in, in microprocessors, it's sufficient for most of the workloads at that level. Now let's go to HPC level or scientific and technical level. And when it needs hpc, if you're trying to model the orbit of the moon around the earth, you don't really need a super computer for that. You can get a highly accurate model on a, on a workstation, on a server, no problem. It won't even really make it break a sweat. >>I had to do it with a slide rule >>That, >>That >>Might make you break a sweat. Yeah. But to do it with a, you know, a single body orbiting with another body, I say orbiting around, but we both know it's really, they're, they're both ordering the center of mass. It's just that if one is much larger, it seems like one's going entirely around the other. So that's, that's not a super computing problem. What about the stars in a galaxy trying to understand how galaxies form spiral arms and how they spur star formation. Right now you're talking a hundred billion stars plus a massive amount of inter stellar medium in there. So can you solve that on that server? Absolutely not. Not even close. Can you solve it on the largest super computer in the world today? Yes and no. You can solve it with approximations on the largest super computer in the world today. But there's a lot of approximations that go into even that. >>The good news is the simulations produce things that we see through our great telescopes. So we know the approximations are sufficient to get good fidelity, but until you really are doing direct numerical simulation of every particle, right? Right. Which is impossible to do. You need a computer as big as the universe to do that. But the approximations and the science in the science as well as the known parts of the science are good enough to give fidelity. So, and answer your question, there's tremendous number of problem scales. There are problems in every field of science and study that exceed the der direct numerical simulation capabilities of systems today. And so we always want more computing power. It's not macho flops, it's real, we need it, we need exo flops and we will need zeta flops beyond that and yada flops beyond that. But an increasing number of problems will be solved as we keep working to solve problems that are farther out there. So in terms of qualitative steps, I do think technologies like quantum computing, to be clear as part of a hybrid classical quantum system, because they're really just accelerators for certain kinds of algorithms, not for general purpose algorithms. I do think advances like that are gonna be necessary to solve some of the very hardest problem. It's easy to actually formulate an optimization problem that is absolutely intractable by the larger systems in the world today, but quantum systems happen to be in theory when they're big and stable enough, great at that kind of problem. >>I, that should be understood. Quantum is not a cure all for absolutely. For the, for the shortage of computing power. It's very good for certain, certain >>Problems. And as you said at this super computing, we see some quantum, but it's a little bit quieter than I probably expected. I think we're in a period now of everybody saying, okay, there's been a lot of buzz. We know it's gonna be real, but let's calm down a little bit and figure out what the right solutions are. And I'm very proud that we offered one of those >>At the show. We, we have barely scratched the surface of what we could talk about as we get into intergalactic space, but unfortunately we only have so many minutes and, and we're out of them. Oh, >>I'm >>J Poso, HPC and AI technology strategist at Dell. Thanks for a fascinating conversation. >>Thanks for having me. Happy to do it anytime. >>We'll be back with our last interview of Supercomputing 22 in Dallas. This is Paul Gillen with Dave Nicholson. Stay with us.

(upbeat music) >> Welcome back, everyone, to theCUBE's coverage here live on the floor at AWS re:MARS 2022. I'm John Furrier, host for theCUBE. Great event, machine learning, automation, robotics, space, that's MARS. It's part of the re-series of events, re:Invent's the big event at the end of the year, re:Inforce, security, re:MARS, really intersection of the future of space, industrial, automation, which is very heavily DevOps machine learning, of course, machine learning, which is AI. We have Luis Ceze here, who's the CEO co-founder of OctoML. Welcome to theCUBE. >> Thank you very much for having me in the show, John. >> So we've been following you guys. You guys are a growing startup funded by Madrona Venture Capital, one of your backers. You guys are here at the show. This is a, I would say small show relative what it's going to be, but a lot of robotics, a lot of space, a lot of industrial kind of edge, but machine learning is the centerpiece of this trend. You guys are in the middle of it. Tell us your story. >> Absolutely, yeah. So our mission is to make machine learning sustainable and accessible to everyone. So I say sustainable because it means we're going to make it faster and more efficient. You know, use less human effort, and accessible to everyone, accessible to as many developers as possible, and also accessible in any device. So, we started from an open source project that began at University of Washington, where I'm a professor there. And several of the co-founders were PhD students there. We started with this open source project called Apache TVM that had actually contributions and collaborations from Amazon and a bunch of other big tech companies. And that allows you to get a machine learning model and run on any hardware, like run on CPUs, GPUs, various GPUs, accelerators, and so on. It was the kernel of our company and the project's been around for about six years or so. Company is about three years old. And we grew from Apache TVM into a whole platform that essentially supports any model on any hardware cloud and edge. >> So is the thesis that, when it first started, that you want to be agnostic on platform? >> Agnostic on hardware, that's right. >> Hardware, hardware. >> Yeah. >> What was it like back then? What kind of hardware were you talking about back then? Cause a lot's changed, certainly on the silicon side. >> Luis: Absolutely, yeah. >> So take me through the journey, 'cause I could see the progression. I'm connecting the dots here. >> So once upon a time, yeah, no... (both chuckling) >> I walked in the snow with my bare feet. >> You have to be careful because if you wake up the professor in me, then you're going to be here for two hours, you know. >> Fast forward. >> The average version here is that, clearly machine learning has shown to actually solve real interesting, high value problems. And where machine learning runs in the end, it becomes code that runs on different hardware, right? And when we started Apache TVM, which stands for tensor virtual machine, at that time it was just beginning to start using GPUs for machine learning, we already saw that, with a bunch of machine learning models popping up and CPUs and GPU's starting to be used for machine learning, it was clear that it come opportunity to run on everywhere. >> And GPU's were coming fast. >> GPUs were coming and huge diversity of CPUs, of GPU's and accelerators now, and the ecosystem and the system software that maps models to hardware is still very fragmented today. So hardware vendors have their own specific stacks. So Nvidia has its own software stack, and so does Intel, AMD. And honestly, I mean, I hope I'm not being, you know, too controversial here to say that it kind of of looks like the mainframe era. We had tight coupling between hardware and software. You know, if you bought IBM hardware, you had to buy IBM OS and IBM database, IBM applications, it all tightly coupled. And if you want to use IBM software, you had to buy IBM hardware. So that's kind of like what machine learning systems look like today. If you buy a certain big name GPU, you've got to use their software. Even if you use their software, which is pretty good, you have to buy their GPUs, right? So, but you know, we wanted to help peel away the model and the software infrastructure from the hardware to give people choice, ability to run the models where it best suit them. Right? So that includes picking the best instance in the cloud, that's going to give you the right, you know, cost properties, performance properties, or might want to run it on the edge. You might run it on an accelerator. >> What year was that roughly, when you were going this? >> We started that project in 2015, 2016 >> Yeah. So that was pre-conventional wisdom. I think TensorFlow wasn't even around yet. >> Luis: No, it wasn't. >> It was, I'm thinking like 2017 or so. >> Luis: Right. So that was the beginning of, okay, this is opportunity. AWS, I don't think they had released some of the nitro stuff that the Hamilton was working on. So, they were already kind of going that way. It's kind of like converging. >> Luis: Yeah. >> The space was happening, exploding. >> Right. And the way that was dealt with, and to this day, you know, to a large extent as well is by backing machine learning models with a bunch of hardware specific libraries. And we were some of the first ones to say, like, know what, let's take a compilation approach, take a model and compile it to very efficient code for that specific hardware. And what underpins all of that is using machine learning for machine learning code optimization. Right? But it was way back when. We can talk about where we are today. >> No, let's fast forward. >> That's the beginning of the open source project. >> But that was a fundamental belief, worldview there. I mean, you have a world real view that was logical when you compare to the mainframe, but not obvious to the machine learning community. Okay, good call, check. Now let's fast forward, okay. Evolution, we'll go through the speed of the years. More chips are coming, you got GPUs, and seeing what's going on in AWS. Wow! Now it's booming. Now I got unlimited processors, I got silicon on chips, I got, everywhere >> Yeah. And what's interesting is that the ecosystem got even more complex, in fact. Because now you have, there's a cross product between machine learning models, frameworks like TensorFlow, PyTorch, Keras, and like that and so on, and then hardware targets. So how do you navigate that? What we want here, our vision is to say, folks should focus, people should focus on making the machine learning models do what they want to do that solves a value, like solves a problem of high value to them. Right? So another deployment should be completely automatic. Today, it's very, very manual to a large extent. So once you're serious about deploying machine learning model, you got a good understanding where you're going to deploy it, how you're going to deploy it, and then, you know, pick out the right libraries and compilers, and we automated the whole thing in our platform. This is why you see the tagline, the booth is right there, like bringing DevOps agility for machine learning, because our mission is to make that fully transparent. >> Well, I think that, first of all, I use that line here, cause I'm looking at it here on live on camera. People can't see, but it's like, I use it on a couple couple of my interviews because the word agility is very interesting because that's kind of the test on any kind of approach these days. Agility could be, and I talked to the robotics guys, just having their product be more agile. I talked to Pepsi here just before you came on, they had this large scale data environment because they built an architecture, but that fostered agility. So again, this is an architectural concept, it's a systems' view of agility being the output, and removing dependencies, which I think what you guys were trying to do. >> Only part of what we do. Right? So agility means a bunch of things. First, you know-- >> Yeah explain. >> Today it takes a couple months to get a model from, when the model's ready, to production, why not turn that in two hours. Agile, literally, physically agile, in terms of walk off time. Right? And then the other thing is give you flexibility to choose where your model should run. So, in our deployment, between the demo and the platform expansion that we announced yesterday, you know, we give the ability of getting your model and, you know, get it compiled, get it optimized for any instance in the cloud and automatically move it around. Today, that's not the case. You have to pick one instance and that's what you do. And then you might auto scale with that one instance. So we give the agility of actually running and scaling the model the way you want, and the way it gives you the right SLAs. >> Yeah, I think Swami was mentioning that, not specifically that use case for you, but that use case generally, that scale being moving things around, making them faster, not having to do that integration work. >> Scale, and run the models where they need to run. Like some day you want to have a large scale deployment in the cloud. You're going to have models in the edge for various reasons because speed of light is limited. We cannot make lights faster. So, you know, got to have some, that's a physics there you cannot change. There's privacy reasons. You want to keep data locally, not send it around to run the model locally. So anyways, and giving the flexibility. >> Let me jump in real quick. I want to ask this specific question because you made me think of something. So we're just having a data mesh conversation. And one of the comments that's come out of a few of these data as code conversations is data's the product now. So if you can move data to the edge, which everyone's talking about, you know, why move data if you don't have to, but I can move a machine learning algorithm to the edge. Cause it's costly to move data. I can move computer, everyone knows that. But now I can move machine learning to anywhere else and not worry about integrating on the fly. So the model is the code. >> It is the product. >> Yeah. And since you said, the model is the code, okay, now we're talking even more here. So machine learning models today are not treated as code, by the way. So do not have any of the typical properties of code that you can, whenever you write a piece of code, you run a code, you don't know, you don't even think what is a CPU, we don't think where it runs, what kind of CPU it runs, what kind of instance it runs. But with machine learning model, you do. So what we are doing and created this fully transparent automated way of allowing you to treat your machine learning models if you were a regular function that you call and then a function could run anywhere. >> Yeah. >> Right. >> That's why-- >> That's better. >> Bringing DevOps agility-- >> That's better. >> Yeah. And you can use existing-- >> That's better, because I can run it on the Artemis too, in space. >> You could, yeah. >> If they have the hardware. (both laugh) >> And that allows you to run your existing, continue to use your existing DevOps infrastructure and your existing people. >> So I have to ask you, cause since you're a professor, this is like a masterclass on theCube. Thank you for coming on. Professor. (Luis laughing) I'm a hardware guy. I'm building hardware for Boston Dynamics, Spot, the dog, that's the diversity in hardware, it's tends to be purpose driven. I got a spaceship, I'm going to have hardware on there. >> Luis: Right. >> It's generally viewed in the community here, that everyone I talk to and other communities, open source is going to drive all software. That's a check. But the scale and integration is super important. And they're also recognizing that hardware is really about the software. And they even said on stage, here. Hardware is not about the hardware, it's about the software. So if you believe that to be true, then your model checks all the boxes. Are people getting this? >> I think they're starting to. Here is why, right. A lot of companies that were hardware first, that thought about software too late, aren't making it. Right? There's a large number of hardware companies, AI chip companies that aren't making it. Probably some of them that won't make it, unfortunately just because they started thinking about software too late. I'm so glad to see a lot of the early, I hope I'm not just doing our own horn here, but Apache TVM, the infrastructure that we built to map models to different hardware, it's very flexible. So we see a lot of emerging chip companies like SiMa.ai's been doing fantastic work, and they use Apache TVM to map algorithms to their hardware. And there's a bunch of others that are also using Apache TVM. That's because you have, you know, an opening infrastructure that keeps it up to date with all the machine learning frameworks and models and allows you to extend to the chips that you want. So these companies pay attention that early, gives them a much higher fighting chance, I'd say. >> Well, first of all, not only are you backable by the VCs cause you have pedigree, you're a professor, you're smart, and you get good recruiting-- >> Luis: I don't know about the smart part. >> And you get good recruiting for PhDs out of University of Washington, which is not too shabby computer science department. But they want to make money. The VCs want to make money. >> Right. >> So you have to make money. So what's the pitch? What's the business model? >> Yeah. Absolutely. >> Share us what you're thinking there. >> Yeah. The value of using our solution is shorter time to value for your model from months to hours. Second, you shrink operator, op-packs, because you don't need a specialized expensive team. Talk about expensive, expensive engineers who can understand machine learning hardware and software engineering to deploy models. You don't need those teams if you use this automated solution, right? Then you reduce that. And also, in the process of actually getting a model and getting specialized to the hardware, making hardware aware, we're talking about a very significant performance improvement that leads to lower cost of deployment in the cloud. We're talking about very significant reduction in costs in cloud deployment. And also enabling new applications on the edge that weren't possible before. It creates, you know, latent value opportunities. Right? So, that's the high level value pitch. But how do we make money? Well, we charge for access to the platform. Right? >> Usage. Consumption. >> Yeah, and value based. Yeah, so it's consumption and value based. So depends on the scale of the deployment. If you're going to deploy machine learning model at a larger scale, chances are that it produces a lot of value. So then we'll capture some of that value in our pricing scale. >> So, you have direct sales force then to work those deals. >> Exactly. >> Got it. How many customers do you have? Just curious. >> So we started, the SaaS platform just launched now. So we started onboarding customers. We've been building this for a while. We have a bunch of, you know, partners that we can talk about openly, like, you know, revenue generating partners, that's fair to say. We work closely with Qualcomm to enable Snapdragon on TVM and hence our platform. We're close with AMD as well, enabling AMD hardware on the platform. We've been working closely with two hyperscaler cloud providers that-- >> I wonder who they are. >> I don't know who they are, right. >> Both start with the letter A. >> And they're both here, right. What is that? >> They both start with the letter A. >> Oh, that's right. >> I won't give it away. (laughing) >> Don't give it away. >> One has three, one has four. (both laugh) >> I'm guessing, by the way. >> Then we have customers in the, actually, early customers have been using the platform from the beginning in the consumer electronics space, in Japan, you know, self driving car technology, as well. As well as some AI first companies that actually, whose core value, the core business come from AI models. >> So, serious, serious customers. They got deep tech chops. They're integrating, they see this as a strategic part of their architecture. >> That's what I call AI native, exactly. But now there's, we have several enterprise customers in line now, we've been talking to. Of course, because now we launched the platform, now we started onboarding and exploring how we're going to serve it to these customers. But it's pretty clear that our technology can solve a lot of other pain points right now. And we're going to work with them as early customers to go and refine them. >> So, do you sell to the little guys, like us? Will we be customers if we wanted to be? >> You could, absolutely, yeah. >> What we have to do, have machine learning folks on staff? >> So, here's what you're going to have to do. Since you can see the booth, others can't. No, but they can certainly, you can try our demo. >> OctoML. >> And you should look at the transparent AI app that's compiled and optimized with our flow, and deployed and built with our flow. That allows you to get your image and do style transfer. You know, you can get you and a pineapple and see how you look like with a pineapple texture. >> We got a lot of transcript and video data. >> Right. Yeah. Right, exactly. So, you can use that. Then there's a very clear-- >> But I could use it. You're not blocking me from using it. Everyone's, it's pretty much democratized. >> You can try the demo, and then you can request access to the platform. >> But you get a lot of more serious deeper customers. But you can serve anybody, what you're saying. >> Luis: We can serve anybody, yeah. >> All right, so what's the vision going forward? Let me ask this. When did people start getting the epiphany of removing the machine learning from the hardware? Was it recently, a couple years ago? >> Well, on the research side, we helped start that trend a while ago. I don't need to repeat that. But I think the vision that's important here, I want the audience here to take away is that, there's a lot of progress being made in creating machine learning models. So, there's fantastic tools to deal with training data, and creating the models, and so on. And now there's a bunch of models that can solve real problems there. The question is, how do you very easily integrate that into your intelligent applications? Madrona Venture Group has been very vocal and investing heavily in intelligent applications both and user applications as well as enablers. So we say an enable of that because it's so easy to use our flow to get a model integrated into your application. Now, any regular software developer can integrate that. And that's just the beginning, right? Because, you know, now we have CI/CD integration to keep your models up to date, to continue to integrate, and then there's more downstream support for other features that you normally have in regular software development. >> I've been thinking about this for a long, long, time. And I think this whole code, no one thinks about code. Like, I write code, I'm deploying it. I think this idea of machine learning as code independent of other dependencies is really amazing. It's so obvious now that you say it. What's the choices now? Let's just say that, I buy it, I love it, I'm using it. Now what do I got to do if I want to deploy it? Do I have to pick processors? Are there verified platforms that you support? Is there a short list? Is there every piece of hardware? >> We actually can help you. I hope we're not saying we can do everything in the world here, but we can help you with that. So, here's how. When you have them all in the platform you can actually see how this model runs on any instance of any cloud, by the way. So we support all the three major cloud providers. And then you can make decisions. For example, if you care about latency, your model has to run on, at most 50 milliseconds, because you're going to have interactivity. And then, after that, you don't care if it's faster. All you care is that, is it going to run cheap enough. So we can help you navigate. And also going to make it automatic. >> It's like tire kicking in the dealer showroom. >> Right. >> You can test everything out, you can see the simulation. Are they simulations, or are they real tests? >> Oh, no, we run all in real hardware. So, we have, as I said, we support any instances of any of the major clouds. We actually run on the cloud. But we also support a select number of edge devices today, like ARMs and Nvidia Jetsons. And we have the OctoML cloud, which is a bunch of racks with a bunch Raspberry Pis and Nvidia Jetsons, and very soon, a bunch of mobile phones there too that can actually run the real hardware, and validate it, and test it out, so you can see that your model runs performant and economically enough in the cloud. And it can run on the edge devices-- >> You're a machine learning as a service. Would that be an accurate? >> That's part of it, because we're not doing the machine learning model itself. You come with a model and we make it deployable and make it ready to deploy. So, here's why it's important. Let me try. There's a large number of really interesting companies that do API models, as in API as a service. You have an NLP model, you have computer vision models, where you call an API and then point in the cloud. You send an image and you got a description, for example. But it is using a third party. Now, if you want to have your model on your infrastructure but having the same convenience as an API you can use our service. So, today, chances are that, if you have a model that you know that you want to do, there might not be an API for it, we actually automatically create the API for you. >> Okay, so that's why I get the DevOps agility for machine learning is a better description. Cause it's not, you're not providing the service. You're providing the service of deploying it like DevOps infrastructure as code. You're now ML as code. >> It's your model, your API, your infrastructure, but all of the convenience of having it ready to go, fully automatic, hands off. >> Cause I think what's interesting about this is that it brings the craftsmanship back to machine learning. Cause it's a craft. I mean, let's face it. >> Yeah. I want human brains, which are very precious resources, to focus on building those models, that is going to solve business problems. I don't want these very smart human brains figuring out how to scrub this into actually getting run the right way. This should be automatic. That's why we use machine learning, for machine learning to solve that. >> Here's an idea for you. We should write a book called, The Lean Machine Learning. Cause the lean startup was all about DevOps. >> Luis: We call machine leaning. No, that's not it going to work. (laughs) >> Remember when iteration was the big mantra. Oh, yeah, iterate. You know, that was from DevOps. >> Yeah, that's right. >> This code allowed for standing up stuff fast, double down, we all know the history, what it turned out. That was a good value for developers. >> I could really agree. If you don't mind me building on that point. You know, something we see as OctoML, but we also see at Madrona as well. Seeing that there's a trend towards best in breed for each one of the stages of getting a model deployed. From the data aspect of creating the data, and then to the model creation aspect, to the model deployment, and even model monitoring. Right? We develop integrations with all the major pieces of the ecosystem, such that you can integrate, say with model monitoring to go and monitor how a model is doing. Just like you monitor how code is doing in deployment in the cloud. >> It's evolution. I think it's a great step. And again, I love the analogy to the mainstream. I lived during those days. I remember the monolithic propriety, and then, you know, OSI model kind of blew it. But that OSI stack never went full stack, and it only stopped at TCP/IP. So, I think the same thing's going on here. You see some scalability around it to try to uncouple it, free it. >> Absolutely. And sustainability and accessibility to make it run faster and make it run on any deice that you want by any developer. So, that's the tagline. >> Luis Ceze, thanks for coming on. Professor. >> Thank you. >> I didn't know you were a professor. That's great to have you on. It was a masterclass in DevOps agility for machine learning. Thanks for coming on. Appreciate it. >> Thank you very much. Thank you. >> Congratulations, again. All right. OctoML here on theCube. Really important. Uncoupling the machine learning from the hardware specifically. That's only going to make space faster and safer, and more reliable. And that's where the whole theme of re:MARS is. Let's see how they fit in. I'm John for theCube. Thanks for watching. More coverage after this short break. >> Luis: Thank you. (gentle music)

(upbeat music) >> Welcome back, everyone. It's theCUBE's coverage here in Las Vegas. I'm John Furrier, your host of theCUBE with re:MARS. Amazon re:MARS stands for machine learning, automation, robotics, and space. Lot of great content, accomplishment. AI meets meets robotics and space, industrial IoT, all things data. And we've got two great guests here to unpack the AI side of it. Manoj Suvarna, Managing Director at AI Ecosystem at Deloitte and Arte Merritt, Conversational AI Lead at AWS. Manoj, it's great to see you CUBE alumni. Art, welcome to theCUBE. >> Thanks for having me. I appreciate it. >> So AI's the big theme. Actually, the big disconnect in the industry has been the industrial OT versus IT, and that's happening. Now you've got space and robotics meets what we know is machine learning and AI which we've been covering. This is the confluence of the new IoT market. >> It absolutely is. >> What's your opinion on that? >> Yeah, so actually it's taking IoT beyond the art of possible. One area that we have been working very closely with AWS. We're strategic alliance with them. And for the past six years, we have been investing a lot in transformations. Transformation as it relate to the cloud, transformation as it relate to data modernization. The new edge is essentially on AI and machine learning. And just this week, we announced a new solution which is more focused around enhancing contact center intelligence. So think about the edge of the contact center, where we all have experiences around dealing with customer service and how to really take that to the next level, challenges that clients are facing in every part of that business. So clearly. >> Well, Conversational AI is a good topic. Talk about the relationship with Deloitte and Amazon for a second around AI because you guys have some great projects going on right now. That's well ahead of the curve on solving the scale problem 'cause there's a scale and problem, practical problem and then scale. What's the relationship with Amazon and Deloitte? >> We have a great alliance and relationship. Deloitte brings that expertise to help folks build high quality, highly effective conversational AI and enterprises are implementing these solutions to really try to improve the overall customer experience. So they want to help agents improve productivity, gain insights into the reasons why folks are calling but it's really to provide that better user experience being available 24/7 on channels users prefer to interact. And the solutions that Deloitte is building are highly advanced, super exciting. Like when we show demos of them to potential customers, the eyes light up and they want those solutions. >> John: Give an example when their eyes light up. What are you showing there? >> One solution, it's called multimodal interfaces. So what this is, is when you're call into like a voice IVR, Deloitte's solution will send the folks say a mobile app or a website. So the person can interact with both the phone touching on the screen and the voice and it's all kept in sync. So imagine you call the doctor's office or say I was calling a airline and I want to change my flight or sorry, change the seat. If they were to say, seat 20D is available. Well, I don't know what that means, but if you see the map while you're talking, you can say, oh, 20D is the aisle. I'm going to select that. So Deloitte's doing those kind of experiences. It's incredible. >> Manoj, this is where the magic comes into play when you bring data together and you have integration like this. Asynchronously or synchronously, it's all coming together. You have different platforms, phone, voice, silo databases potentially, the old way. Now, the new ways integrating. What makes it all work? What's the key to success? >> Yeah, it's certainly not a trivial feat. Bringing together all of these ecosystems of relationships, technologies all put together. We cannot do it alone. This is where we partner with AWS with some of our other partners like Salesforce and OneReach and really trying to bring a symphony of some of these solutions to bear. When you think about, going back to the example of contact center, the challenges that the pandemic posed in the last couple of years was the fact that who's a humongous rise in volume of number of calls. You can imagine people calling in asking for all kinds of different things, whether it's airlines whether it is doctor's office and retail. And then couple with that is the fact that there's the labor shortage. And how do you train agents to get them to be productive enough to be able to address hundreds or thousands of these calls? And so that's where we have been starting to, we have invested in those solutions bringing those technologies together to address real client problems, not just slideware but actual production environments. And that's where we launched this solution called TrueServe as of this week, which is really a multimodal solution that is built with preconceived notions of technologies and libraries where we can then be industry agnostic and be able to deliver those experiences to our clients based on whatever vertical or industry they're in. >> Take me through the client's engagement here because I can imagine they want to get a practical solution. They're going to want to have it up and running, not like a just a chatbot, but like they completely integrated system. What's the challenge and what's the outcome first set of milestones that you see that they do first? Do they just get the data together? Are they deploying a software solution? What's the use cases? >> There's a couple different use cases. We see there's the self-service component that we're talking about with the chatbots or voice IVR solutions. There's also use cases for helping the agents, so real-time agent assist. So you call into a contact center, it's transcribed in real time, run through some sort of knowledge base to give the agents possible answers to help the user out, tying in, say the Salesforce data, CRM data, to know more about the user. Like if I was to call the airline, it's going to say, "Are you calling about your flight to San Francisco tomorrow?" It knows who I am. It leverages that stuff. And then the key piece is the analytics knowing why folks are calling, not just your metrics around, length of calls or deflections, but what were the reasons people were calling in because you can use that data to improve your underlying products or services. These are the things that enterprise are looking for and this is where someone like Deloitte comes in, brings that expertise, speeds up the time to market and really helps the customers. >> Manoj, what was the solution you mentioned that you guys announced? >> Yeah, so this is called Deloitte TrueServe. And essentially, it's a combination of multiple different solutions combinations from AWS, from Salesforce, from OneReach. All put together with our joint engineering and really delivering that capability. Enhancing on that is the analytics component, which is really critical, especially because when you think about the average contact center, less than 10% of the data gets analyzed today, and how do you then extract value out of that data and be able to deliver business outcomes. >> I was just talking to some of the other day about Zoom. Everyone records their zoom meetings, and no one watches them. I mean, who's going to wade through that. Call center is even more high volume. We're talking about massive data. And so will you guys automate that? Do you go through every single piece of data, every call and bring it down? Is that how it works? >> Go ahead. >> There's just some of the things you can do. Analyze the calls for common themes, like figuring out like topic modeling, what are the reasons people are calling in. Summarizing that stuff so you can see what those underlying issues are. And so that could be, like I was mentioning, improving the product or service. It could also be for helping train the agents. So here's how to answer that question. And it could even be reinforcing positive experiences maybe an agent had a particular great call and that could be a reference for other folks. >> Yeah, and also during the conversation, when you think about within 60 to 90 seconds, how do you identify the intonation, the sentiments of the client customer calling in and be able to respond in real time for the challenges that they might be facing and the ability to authenticate the customer at the same time be able to respond to them. I think that is the advancements that we are seeing in the market. >> I think also your point about the data having residual values also excellent because this is a long tail of value in this data, like for predictions and stuff. So NASA was just on before you guys came on, talking about the Artemis project and all the missions and they have to run massive amounts of simulations. And this is where I've kind of seen the dots connect here. You can run with AI, run all the heavy lifting without human touching it to get that first ingestion or analysis, and then iterating on the data based upon what else happens. >> Manoj: Absolutely. >> This is now the new normal, right? Is this? >> It is. And it's transverse towards across multiple domains. So the example we gave you was around Conversational AI. We're now looking at that for doing predictive analytics. Those are some examples that we are doing jointly with AWS SageMaker. We are working on things like computer vision with some of the capabilities and what computer vision has to offer. And so when you think about the continuum of possibilities of what we can bring together from a tools, technology, services perspective, really the sky is the limit in terms of delivering these real experiences to our clients. >> So take me through a customer. Pretending I'm a customer, I get it. I got to do this. It's a competitive advantage. What are the outcomes that they are envisioning? What are some of the patterns you're seeing with customers? What outcomes are they expecting and what kind of high level upside you see them envisioning coming out of the data? >> So when you think about the CxOs today and the board, a lot of them are thinking about, okay, how do you build more efficiency in those system? How do you enable a technology or solution for them to not only increase their top line but as well as their bottom line? How do you enhance the customer experience, which in this case is spot on because when you think about, when customers go repeat to a vendor, it's based on quality, it's based on price. Customer experience is now topping that where your first experience, whether it's through a chat or a virtual assistant or a phone call is going to determine the longevity of that customer with you as a vendor. And so clearly, when you think about how clients are becoming AI fuel, this is where we are bringing in new technologies, new solutions to really push the art to the limit and the art of possible. >> You got a playbook too to do this? >> Yeah, yeah, absolutely. We have done that. And in fact, we are now taking that to the next level up. So something that I've mentioned about this before, which is how do you trust an AI system as it's building up. >> Hold on, I need to plug in. >> Yeah, absolutely. >> I put this here for a reason to remind me. No, but also trust is a big thing. Just put that trustworthy. This is an AI ethics question. >> Arte: It's a big. >> Let's get into it. This is huge. Data's data. Data can be biased from coming in >> Part of it, there are concerns you have to look at the bias in the data. It's also how you communicate through these automated channels, being empathetic, building trust with the customer, being concise in the answers and being accessible to all sorts of different folks and how they might communicate. So it's definitely a big area. >> I mean, you think about just normal life. We all lived situations where we got a text message from a friend or someone close to us where, what the hell, what are you saying? And they had no contextual bad feelings about it or, well, there's misunderstandings 'cause the context isn't there 'cause you're rapid fire them on the subway. I'm riding my bike. I stop and text, okay, I'm okay. Church response could mean I'm busy or I'm angry. Like this is now what you said about empathy. This is now a new dynamic in here. >> Oh, the empathy is huge, especially if you're say a financial institution or building that trust with folks and being empathetic. If someone's reaching out to a contact center, there's a good chance they're upset about something. So you have to take that. >> John: Calm them down first. >> Yeah, and not being like false like platitude kind of things, like really being empathetic, being inclusive in the language. Those are things that you have conversation designers and linguistics folks that really look into that. That's why having domain expertise from folks like Deloitte come in to help with that. 'Cause maybe if you're just building the chat on your own, you might not think of those things. But the folks with the domain expertise will say like, Hey, this is how you script it. It's the power of words, getting that message across clearly. >> The linguistics matter? >> Yeah, yeah. >> It does. >> By vertical too, I mean, you could pick any the tribe, whatever orientation and age, demographics, genders. >> All of those things that we take for granted as a human. When you think about trust, when you think about bias, when you think about ethics, it just gets amplified. Because now you're dealing with millions and millions of data points that may or may not be the right direction in terms of somebody's calling in depending on what age group they're in. Some questions might not be relevant for that age group. Now a human can determine that, but a bot cannot. And so how do you make sure that when you look at this data coming in, how do you build models that are ethically aware of the contextual algorithms and the alignment with it and also enabling that experience to be much enhanced than taking it backwards, and that's really. >> I can imagine it getting better with as people get scaled up a bit 'cause then you're going to have to start having AI to watch the AI at some point, as they say. Where are we in the progress in the industry right now? Because I know there's been a lot of news stories around, ethics and AI and bias and it's a moving train actually, but still problems are going to be solved. Are we at the tipping point yet? Are we still walking in before we crawl or crawling before we walk? I should say, I mean, where are we? >> I think we are in between a crawling or walk phase. And the reason for that is because it varies depending on whether you're regulated industry or unregulated. In the regulated industry, there are compliance regulations requirements, whether it's government whether it's banking, financial institutions where they have to meet Sarbanes-Oxley and all kinds of compliance requirements, whereas an unregulated industry like retail and consumer, it is anybody's gain. And so the reality of it is that there is more of an awareness now. And that's one of the reasons why we've been promoting this jointly with AWS. We have a framework that we have established where there are multiple pillars of trust, bias, privacy, and security that companies and organizations need to think about. Our data scientists, ML engineers need to be familiar with it, but because while they're super great in terms of model building and development, when it comes to the business, when it comes to the client or a customer, it is super important for them to trust this platform, this algorithm. And that is where we are trying to build that momentum, bring that awareness. One of my colleagues has written this book "Trustworthy AI". We're trying to take the message out to the market to say, there is a framework. We can help you get there. And certainly that's what we are doing. >> Just call Deloitte up and you're going to take care of them. >> Manoj: Yeah. >> On the Amazon side, Amazon Web Services. I always interview Swami every year at re:Invent and he always get the updates. He's been bullish on this for a long time on this Conversational AI. What's the update on the AWS side? Where are you guys at? What's the current trends that you're riding? What wave are you riding right now? >> So some of the trends we see in customer interest, there's a couple of things. One is the multimodal interfaces we we're just chatting about where the voice IVA is synced with like a web or mobile experience, so you take that full advantage of the device. The other is adding additional AI into the Conversational AI. So one example is a customer that included intelligent document processing as part of the chatbot. So instead of typing your name and address, take a photo of your driver's license. It was an insurance onboarding chatbot, so you could take a photo of your existing insurance policy. It'll extract that information to build the new insurance policy. So folks get excited about that. And the third area we see interest is what's called multi-bot orchestration. And this is where you can have one main chatbot. Marshall user across different sub-chatbots based on the use case persona or even language. So those things get people really excited and then AWS is launching all sorts of new features. I don't know which one is coming out. >> I know something's coming out tomorrow. He's right at corner. He's big smile on his face. He wouldn't tell me. It's good. >> We have for folks like the closer alliance relationships, we we're able to get previews. So there a preview of all the new stuff. And I don't know what I could, it's pretty exciting stuff. >> You get in trouble if you spill the beans here. Don't, be careful. I'll watch you. We'll talk off camera. All exciting stuff. >> Yeah, yeah. I think the orchestrator bot is interesting. Having the ability to orchestrate across different contextual datasets is interesting. >> One of the areas where it's particularly interesting is in financial services. Imagine a bank could have consumer accounts, merchant accounts, investment banking accounts. So if you were to chat with the chatbot and say I want to open account, well, which account do you mean? And so it's able to figure out that context to navigate folks to those sub-chatbots behind the scenes. And so it's pretty interesting style. >> Awesome. Manoj while we're here, take a minute to quickly give a plug for Deloitte. What your program's about? What customers should expect if they work with you guys on this project? Give a quick commercial for Deloitte. >> Yeah, no, absolutely. I mean, Deloitte has been continuing to lead the AI field organization effort across our client base. If you think about all the Fortune 100, Fortune 500, Fortune 2000 clients, we certainly have them where they are in advanced stages of multiple deployments for AI. And we look at it all the way from strategy to implementation to operational models. So clients don't have to do it alone. And we are continuing to build our ecosystem of relationships, partnerships like the alliances that we have with AWS, building the ecosystem of relationships with other emerging startups, to your point about how do you continue to innovate and bring those technologies to your clients in a trustworthy environment so that we can deliver it in production scale. That is essentially what we're driving. >> Well, Arte, there's a great conversation and the AI will take over from here as we end the segment. I see a a bot coming on theCUBE later and there might be CUBE be replaced with robots. >> Right, right, right, exactly. >> I'm John Furrier, calling from Palo Alto. >> Someday, CUBE bot. >> You can just say, Alexa do my demo for me or whatever it is. >> Or digital twin for John. >> We're going to have a robot on earlier do a CUBE interview and that's Dave Vellante. He'd just pipe his voice in and be fun. Well, thanks for coming on, great conversation. >> Thank you. Thanks for having us. >> CUBE coverage here at re:MARS in Las Vegas. Back to the event circle. We're back in the line. Got re:Inforce and don't forget re:Invent at the end of the year. CUBE coverage of this exciting show here. Machine learning, automation, robotics, space. That's MARS, it's re:MARS. I'm John Furrier. Thanks for watching. (gentle music)

(upbeat music) >> Okay, welcome back everyone. CUBE's coverage of re:MARS, here in Las Vegas, in person. I'm John Furrier, host of theCUBE. This is the analyst panel wrap up analysis of the keynote, the show, past one and a half days. We got two great guests here. We got Andy Thurai, Vice President, Principal Consultant, Constellation Research. Larry Carvalho, Principal Consultant at RobustCloud LLC. Congratulations going out on your own. >> Thank you. >> Andy, great to see you. >> Great to see you as well. >> Guys, thanks for coming out. So this is the session where we break down and analyze, you guys are analysts, industry analysts, you go to all the shows, we see each other. You guys are analyzing the landscape. What does this show mean to you guys? 'Cause this is not obvious to the normal tech follower. The insiders see the confluence of robotics, space, automation and machine learning. Obviously, it's IoTs, industrials, it's a bunch of things. But there's some dots to connect. Let's start with you, Larry. What do you see here happening at this show? >> So you got to see how Amazon started, right? When AWS started. When AWS started, it primarily took the compute storage, networking of Amazon.com and put it as a cloud service, as a service, and started selling the heck out of it. This is a stage later now that Amazon.com has done a lot of physical activity, and using AIML and the robotics, et cetera, it's now the second phase of innovation, which is beyond digital transformation of back office processes, to the transformation of physical processes where people are now actually delivering remotely and it's an amazing area. >> So back office's IT data center kind of vibe. >> Yeah. >> You're saying front end, industrial life. >> Yes. >> Life as we know it. >> Right, right. I mean, I just stopped at a booth here and they have something that helps anybody who's stuck in the house who cannot move around. But with Alexa, order some water to bring them wherever they are in the house where they're stuck in their bed. But look at the innovation that's going on there right at the edge. So I think those are... >> John: And you got the Lunar, got the sex appeal of the space, Lunar Outpost interview, >> Yes. >> those guys. They got Rover on Mars. They're going to have be colonizing the moon. >> Yes. >> I made a joke, I'm like, "Well, I left a part back on earth, I'll be right back." (Larry and Andy laugh) >> You can't drive back to the office. So a lot of challenges. Andy, what's your take of the show? Take us your analysis. What's the vibe, what's your analysis so far? >> It's a great show. So, as Larry was saying, one of the thing was that when Amazon started, right? So they were more about cloud computing. So, which means is they try to commoditize more of data center components or compute components. So that was working really well for what I call it as a compute economy, right? >> John: Mm hmm. >> And I call the newer economy as more of a AIML-based data economy. So when you move from a compute economy into a data economy, there are things that come into the forefront that never existed before, never popular before. Things like your AIML model creation, model training, model movement, model influencing, all of the above, right? And then of course the robotics has come long way since then. And then some of what they do at the store, or the charging, the whole nine yards. So, the whole concept of all of these components, when you put them on re:Invent, such a big show, it was getting lost. So that's why they don't have it for a couple of years. They had it one year. And now all of a sudden they woke up and say, "You know what? We got to do this!" >> John: Yeah. >> To bring out this critical components that we have, that's ripe, mature for the world to next component. So that's why- I think they're pretty good stuff. And some of the robotics things I saw in there, like one of them I posted on my Twitter, it's about the robot dog, sniffing out the robot rover, which I thought was pretty hilarious. (All laugh) >> Yeah, this is the thing. You're seeing like the pandemic put everything on hold on the last re:Mars, and then the whole world was upside down. But a lot of stuff pulled forward. You saw the call center stuff booming. You saw the Zoomification of our workplace. And I think a lot of people got to the realization that this hybrid, steady-state's here. And so, okay. That settles that. But the digital transformation of actually physical work? >> Andy: Yeah. >> Location, the walk in and out store right over here we've seen that's the ghost store in Seattle. We've all been there. In fact, I was kind of challenged, try to steal something. I'm like, okay- (Larry laughs) I'm pulling all my best New Jersey moves on everyone. You know? >> Andy: You'll get charged for it. >> I couldn't get away with it. Two double packs, drop it, it's smart as hell. Can't beat the system. But, you bring that to where the AI machine learning, and the robotics meet, robots. I mean, we had robots here on theCUBE. So, I think this robotics piece is a huge IoT, 'cause we've been covering industrial IoT for how many years, guys? And you could know what's going on there. Huge cyber threats. >> Mm hmm. >> Huge challenges, old antiquated OT technology. So I see a confluence in the collision between that OT getting decimated, to your point. And so, do you guys see that? I mean, am I just kind of seeing mirage? >> I don't see it'll get decimated, it'll get replaced with a newer- >> John: Dave would call me out on that. (Larry laughs) >> Decimated- >> Microsoft's going to get killed. >> I think it's going to have to be reworked. And just right now, you want do anything in a shop floor, you have to have a physical wire connected to it. Now you think about 5G coming in, and without a wire, you get minute details, you get low latency, high bandwidth. And the possibilities are endless at the edge. And I think with AWS, they got Outposts, they got Snowcone. >> John: There's a threat to them at the edge. Outpost is not doing well. You talk to anyone out there, it's like, you can't find success stories. >> Larry: Yeah. >> I'm going to get hammered by Amazon people, "Oh, what're you're saying that?" You know, EKS for example, with serverless is kicking ass too. So, I mean I'm not saying Outpost was wrong answer, it was a right at the time, what, four years ago that came out? >> Yeah. >> Okay, so, but that doesn't mean it's just theirs. You got Dell Technologies want some edge action. >> Yeah. >> So does HPE. >> Yes. >> So you got a competitive edge situation. >> I agree with that and I think that's definitely not Amazon's strong point, but like everything, they try to make it easy to use. >> John: Yeah. >> You know, you look at the AIML and they got Canvas. So Canvas says, hey, anybody can do AIML. If they can do that for the physical robotic processes, or even like with Outpost and Snowcone, that'll be good. I don't think they're there yet, and they don't have the presence in the market, >> John: Yeah. >> like HPE and, >> John: Well, let me ask you guys this question, because I think this brings up the next point. Will the best technology win or will the best solution win? Because if cloud's a platform and all software's open source, which you can make those assumptions, you then say, hey, they got this killer robotics thing going on with Artemis and Moonshot, they're trying to colonize the moon, but oh, they discovered a killer way to solve a big problem. Does something fall out of this kind of re:Mars environment, that cracks the code and radically changes and disrupts the IoT game? That's my open question. I don't know the answer. I'd love to get your take on what might be possible, what wild card's out there around, disrupting the edge. >> So one thing I see the way, so when IoT came into the world of play, it's when you're digitizing the physical world, it's IoT that does digitalization part of that actually, right? >> But then it has its own set of problems. >> John: Yeah. >> You're talking about you installing sensor everywhere, right? And not only installing your own sensor, but also you're installing competitor sensors. So in a given square feet how many sensors can you accommodate? So there are physical limitations on liabilities of bandwidth and networking all of that. >> John: And integration. >> As well. >> John: Your point. >> Right? So when that became an issue, this is where I was talking to the robotic guys here, a couple of companies, and one of the use cases they were talking about, which I thought was pretty cool, is, rather than going the sensor route, you go the robot route. So if you have either a factor that you want to map out, you put as many sensors on your robot, whatever that is, and then you make it go around, map the whole thing, and then you also do a surveillance in the whole nine yards. So, you can either have a fixed sensors or you can have moving sensors. So you can have three or four robots. So initially, when I was asking them about the price of it, when they were saying about a hundred thousand dollars, I was like, "Who would buy that?" (John and Larry laugh) >> When they then explained that, this is the use case, oh, that makes sense, because if you had to install, entire factory floor sensors, you're talking about millions of dollars. >> John: Yeah. >> But if you do the moveable sensors in this way, it's a lot cheaper. >> John: Yeah, yeah. >> So it's based on your use case, what are your use cases? What are you trying to achieve? >> The general purpose is over. >> Yeah. >> Which you're getting at, and that the enablement, this is again, this is the cloud scale open question- >> Yep. >> it's, okay, the differentiations isn't going to be open source software. That's open. >> It's going to be in the, how you configure it. >> Yes. >> What workflows you might have, the data streams. >> I think, John, you're bringing up a very good point about general purpose versus special purpose. Yesterday Zoox was on the stage and when they talked about their vehicle, it's made just for self-driving. You walk around in Vegas, over here, you see a bunch of old fashioned cars, whether they're Ford or GM- >> and they put all these devices around it, but you're still driving the same car. >> John: Yeah, exactly. >> You can retrofit those, but I don't think that kind of IoT is going to work. But if you redo the whole thing, we are going to see a significant change in how IoT delivers value all the way from the industrial to home, to healthcare, mining, agriculture, it's going to have to redo. I'll go back to the OT question. There are some OT guys, I know Rockwell and Siemens, some of them are innovating faster. The ones who innovate faster to keep up with the IT side, as well as the MLAI model are going to be the winners on that one. >> John: Yeah, I agree. Andy, your thoughts on manufacturing, you brought up the sensor thing. Robotics ultimately is, end of the day, an opportunity there. Obviously machine learning, we know what that does. As we move into these more autonomous builds, what does that look like? And is Amazon positioned well there? Obviously they have big manufacturers. Some are saying that they might want to get out of that business too, that Jassy's evaluating that some are saying. So, where does this all lead for that robotics manufacturing lifestyle, walk in, grab my food? 'Cause it's all robotics and AI at the end of the day, I got sensors, I got cameras, I got non-humans moving heavy lifting stuff, fixing the moon will be done by robots, not humans. So it's all coming. What's your analysis? >> Well, so, the point about robotics is on how far it has come, it is unbelievable, right? Couple of examples. One was that I was just talking to somebody, was explaining to them, to see that robot dog over there at the Boston Dynamics one- >> John: Yeah. >> climbing up and down the stairs. >> Larry: Yeah. >> That's more like the dinosaur movie opening the doors scene. (John and Larry laugh) It's like that for me, because the coordinated things, it is able to go walk up and down, that's unbelievable. But okay, it does that, and then there was also another video which is going on viral on the internet. This guy kicks the dog, robot dog, and then it falls down and it gets back up, and the sentiment that people were feeling for the dog, (Larry laughs) >> you can't, it's a robot, but people, it just comes at that level- >> John: Empathy, for a non-human. >> Yeah. >> But you see him, hey you, get off my lawn, you know? It's like, where are we? >> It has come to that level that people are able to kind of not look at that as a robot, but as more like a functioning, almost like a pet-level, human-level being. >> John: Yeah. >> And you saw that the human-like walking robot there as well. But to an extent, in my view, they are all still in an experimentation, innovation phase. It doesn't made it in the industrial terms yet. >> John: Yeah, not yet, it's coming. >> But, the problem- >> John: It's coming fast. That's what I'm trying to figure out is where you guys see Amazon and the industry relative to what from the fantasy coming reality- >> Right. >> of space in Mars, which is, it's intoxicating, let's face it. People love this. The nerds are all here. The geeks are all here. It's a celebration. James Hamilton's here- >> Yep. >> trying to get him on theCUBE. And he's here as a civilian. Jeff Barr, same thing. I'm here, not for Amazon, I bought a ticket. No, you didn't buy a ticket. (Larry laughs) >> I'm going to check on that. But, he's geeking out. >> Yeah. >> They're there because they want to be here. >> Yeah. >> Not because they have to work here. >> Well, I mean, the thing is, the innovation velocity has increased, because, in the past, remember, the smaller companies couldn't innovate because they don't have the platform. Now Compute is a platform available at the scale you want, AI is available at the scale. Every one of them is available at the scale you want. So if you have an idea, it's easy to innovate. The innovation velocity is high. But where I see most of the companies failing, whether startup or big company, is that you don't find the appropriate use case to solve, and then don't sell it to the right people to buy that. So if you don't find the right use case or don't sell the right value proposition to the actual buyer, >> John: Mm hmm. >> then why are you here? What are you doing? (John laughs) I mean, you're not just an invention, >> John: Eh, yeah. >> like a telephone kind of thing. >> Now, let's get into next talk track. I want to get your thoughts on the experience here at re:Mars. Obviously AWS and the Amazon people kind of combined effort between their teams. The event team does a great job. I thought the event, personally, was first class. The coffee didn't come in late today, I was complaining about that, (Larry laughs) >> people complaining out there, at CUBE reviews. But world class, high bar on the quality of the event. But you guys were involved in the analyst program. You've been through the walkthrough, some of the briefings. I couldn't do that 'cause I'm doing theCUBE interviews. What would you guys learn? What were some of the key walkaways, impressions? Amazon's putting all new teams together, seems on the analyst relations. >> Larry: Yeah. >> They got their mojo booming. They got three shows now, re:Mars, re:inforce, re:invent. >> Andy: Yeah. >> Which will be at theCUBE at all three. Now we got that coverage going, what's it like? What was the experience like? Did you feel it was good? Where do they need to improve? How would you grade the Amazon team? >> I think they did a great job over here in just bringing all the physical elements of the show. Even on the stage, where they had robots in there. It made it real and it's not just fake stuff. And every, or most of the booths out there are actually having- >> John: High quality demos. >> high quality demos. (John laughs) >> John: Not vaporware. >> Yeah, exactly. Not vaporware. >> John: I won't say the name of the company. (all laugh) >> And even the sessions were very good. They went through details. One thing that stood out, which is good, and I cover Low Code/No Code, and Low Code/No Code goes across everything. You know, you got DevOps No Low-Code Low-Code. You got AI Low Code/No Code. You got application development Low Code/No Code. What they have done with AI with Low Code/No Code is very powerful with Canvas. And I think that has really grown the adoption of AI. Because you don't have to go and train people what to do. And then, people are just saying, Hey, let me kick the tires, let me use it. Let me try it. >> John: It's going to be very interesting to see how Amazon, on that point, handles this, AWS handles this data tsunami. It's cause of Snowflake. Snowflake especially running the table >> Larry: Yeah. >> on the old Hadoop world. I think Dave had a great analysis with other colleagues last week at Snowflake Summit. But still, just scratching the surface. >> Larry: Yeah. >> The question is, how shared that ecosystem, how will that morph? 'Cause right now you've got Data Bricks, you've got Snowflake and a handful of others. Teradata's got some new chops going on there and a bunch of other folks. Some are going to win and lose in this downturn, but still, the scale that's needed is massive. >> So you got data growing so much, you were talking earlier about the growth of data and they were talking about the growth. That is a big pie and the pie can be shared by a lot of folks. I don't think- >> John: And snowflake pays AWS, remember that? >> Right, I get it. (John laughs) >> I get it. But they got very unique capabilities, just like Netflix has very unique capabilities. >> John: Yeah. >> They also pay AWS. >> John: Yeah. >> Right? But they're competing on prime. So I really think the cooperation is going to be there. >> John: Yeah. >> The pie is so big >> John: Yeah. >> that there's not going to be losers, but everybody could be winners. >> John: I'd be interested to follow up with you guys after next time we have an event together, we'll get you back on and figure out how do you measure this transitions? You went to IDC, so they had all kinds of ways to measure shipments. >> Larry: Yep. >> Even Gartner had fumbled for years, the Magic Quadrant on IaaS and PaaS when they had the market share. (Larry laughs) And then they finally bundled PaaS and IaaS together after years of my suggesting, thank you very much Gartner. (Larry laughs) But that just performs as the landscape changes so does the scoreboard. >> Yep. >> Right so, how do you measure who's winning and who's losing? How can we be critical of Amazon so they can get better? I mean, Andy Jassy always said to me, and Adam Salassi same way, we want to hear how bad we're doing so we can get better. >> Yeah. >> So they're open-minded to feedback. I mean, not (beep) posting on them, but they're open to critical feedback. What do you guys, what feedback would you give Amazon? Are they winning? I see them number one clearly over Azure, by miles. And even though Azure's kicking ass and taking names, getting back in the game, Microsoft's still behind, by a long ways, in some areas. >> Andy: Yes. In some ways. >> So, the scoreboard's changing. What's your thoughts on that? >> So, look, I mean, at the end of the day, when it comes to compute, right, Amazon is a clear winner. I mean, there are others who are catching up to it, but still, they are the established leader. And it comes with its own advantages because when you're trying to do innovation, when you're trying to do anything else, whether it's a data collection, we were talking about the data sensors, the amount of data they are collecting, whether it's the store, that self-serving store or other innovation projects, what they have going on. The storage compute and process of that requires a ton of compute. And they have that advantage with them. And, as I mentioned in my last article, one of my articles, when it comes to AIML and data programs, there is a rich and there is a poor. And the rich always gets richer because they, they have one leg up already. >> John: Yeah. >> I mean the amount of model training they have done, the billion or trillion dollar trillion parametrization, fine tuning of the model training and everything. They could do it faster. >> John: Yeah. >> Which means they have a leg up to begin with. So unless you are given an opportunity as a smaller, mid-size company to compete at them at the same level, you're going to start at the negative level to begin with. You have a lot of catch up to do. So, the other thing about Amazon is that they, when it comes to a lot of areas, they admit that they have to improve in certain areas and they're open and willing and listen to the people. >> Where are you, let's get critical. Let's do some critical analysis. Where does Amazon Websters need to get better? In your opinion, what criticism would you, in a constructive way, share? >> I think on the open source side, they need to be more proactive in, they are already, but they got to get even better than what they are. They got to engage with the community. They got to be able to talk on the open source side, hey, what are we doing? Maybe on the hardware side, can they do some open-sourcing of that? They got graviton. They got a lot of stuff. Will they be able to share the wealth with other folks, other than just being on an Amazon site, on the edge with their partners. >> John: Got it. >> If they can now take that, like you said, compute with what they have with a very end-to-end solution, the full stack. And if they can extend it, that's going to be really beneficial for them. >> Awesome. Andy, final word here. >> So one area where I think they could improve, which would be a game changer would be, right now, if you look at all of their solutions, if you look at the way they suggest implementation, the innovations, everything that comes out, comes out across very techy-oriented. The persona is very techy-oriented. Very rarely their solutions are built to the business audience or to the decision makers. So if I'm, say, an analyst, if I want to build, a business analyst rather, if I want to build a model, and then I want to deploy that or do some sort of application, mobile application, or what have you, it's a little bit hard. It's more techy-oriented. >> John: Yeah, yeah. >> So, if they could appeal or build a higher level abstraction of how to build and deploy applications for business users, or even build something industry specific, that's where a lot of the legacy companies succeeded. >> John: Yeah. >> Go after manufacturing specific or education. >> Well, we coined the term 'Supercloud' last re:Invent, and that's what we see. And Jerry Chen at Greylock calls it Castles in the Cloud, you can create these moats >> Yep. >> on top of the CapEx >> Yep. >> of Amazon. >> Exactly. >> And ride their back. >> Yep. >> And the difference in what you're paying and what you're charging, if you're good, like a Snowflake or a Mongo. I mean, Mongo's, they're just as big as Snow, if not bigger on Amazon than Snowflake is. 'Cause they use a lot of compute. No one turns off their database. (John laughs) >> Snowflake a little bit different, a little nuanced point, but, this is the new thing. You see Goldman Sachs, you got Capital One. They're building their own kind of, I call them sub clouds, but Dave Vellante says it's a Supercloud. And that essentially is the model. And then once you have a Supercloud, you say, great, I'm going to make sure it works on Azure and Google. >> Andy: Yep. >> And Alibaba if I have to. So, we're kind of seeing a playbook. >> Andy: Mm hmm. >> But you can't get it wrong 'cause it scales. >> Larry: Yeah, yeah. >> You can't scale the wrong answer. >> Andy: Yeah. >> So that seems to be what I'm watching is, who gets it right? Product market fit. Then if they roll it out to the cloud, then it becomes a Supercloud, and that's pure product market fit. So I think that's something that I've seen some people trying to figure out. And then, are you a supplier to the Superclouds? Like a Dell? Or you become an enabler? >> Andy: Yeah. >> You know, what's Dell Technologies do? >> Larry: Yeah. >> I mean, how do the box movers compete? >> Larry: I, the whole thing is now hybrid and you're going to have to see just, you said. (Larry laughs) >> John: Hybrid's a steady-state. I don't need to. >> Andy: I mean, >> By the way we're (indistinct), we can't get the chips, cause Broadcom and Apple bought 'em all. (Larry laughs) I mean there's a huge chip problem going on. >> Yes. I agree. >> Right now. >> I agree. >> I mean all these problems when you attract to a much higher level, a lot of those problems go away because you don't care about what they're using underlying as long as you deliver my solution. >> Larry: Yes. >> Yeah, it could be significantly, a little bit faster than what it used to be. But at the end of the day, are you solving my specific use case? >> John: Yeah. >> Then I'm willing to wait a little bit longer. >> John: Yeah. Time's on our side and now they're getting the right answers. Larry, Andy, thanks for coming on. This great analyst session turned into more of a podcast vibe, but you know what? (Larry laughs) To chill here at re:Mars, thanks for coming on, and we unpacked a lot. Thanks for sharing. >> Both: Thank you. >> Appreciate it. We'll get you back on. We'll get you in the rotation. We'll take it virtual. Do a panel. Do a panel, do some panels around this. >> Larry: Absolutely. >> Andy: Oh this not virtual, this physical. >> No we're live right now! (all laugh) We get back to Palo Alto. You guys are influencers. Thanks for coming on. You guys are moving the market, congratulations. Take a minute, quick minute each to plug any work you're doing for the people watching. Larry, what are you working on? Andy? You go after Larry, what you're working on. >> Yeah. So since I started my company, RobustCloud, since I left IDC about a year ago, I'm focused on edge computing, cloud-native technologies, and Low Code/No Code. And basically I help companies put their business value together. >> All right, Andy, what are you working on? >> I do a lot of work on the AIML areas. Particularly, last few of my reports are in the AI Ops incident management and ML Ops areas of how to generally improve your operations. >> John: Got it, yeah. >> In other words, how do you use the AIML to improve your IT operations? How do you use IT Ops to improve your AIML efficiency? So those are the- >> John: The real hardcore business transformation. >> Yep. >> All right. Guys, thanks so much for coming on the analyst session. We do keynote review, breaking down re:Mars after day two. We got a full day tomorrow. I'm John Furrier with theCUBE. See you next time. (pleasant music)

(upbeat music) >> Welcome back. Day two of theCUBE's coverage of re:MARS in Las Vegas. Amazon re:MARS, it's part of the Re Series they call it at Amazon. re:Invent is their big show, re:Inforce is a security show, re:MARS is the new emerging machine learning automation, robotics, and space. The confluence of machine learning powering a new industrial age and inflection point. I'm John Furrier, host of theCUBE. We're here to break it down for another wall to wall coverage. We've got a great guest here, CUBE alumni from our AWS startup showcase, Krishna Gade, founder and CEO of fiddler.ai. Welcome back to theCUBE. Good to see you. >> Great to see you, John. >> In person. We did the remote one before. >> Absolutely, great to be here, and I always love to be part of these interviews and love to talk more about what we're doing. >> Well, you guys have a lot of good street cred, a lot of good word of mouth around the quality of your product, the work you're doing. I know a lot of folks that I admire and trust in the AI machine learning area say great things about you. A lot going on, you guys are growing companies. So you're kind of like a startup on a rocket ship, getting ready to go, pun intended here at the space event. What's going on with you guys? You're here. Machine learning is the centerpiece of it. Swami gave the keynote here at day two and it really is an inflection point. Machine learning is now ready, it's scaling, and some of the examples that they were showing with the workloads and the data sets that they're tapping into, you know, you've got CodeWhisperer, which they announced, you've got trust and bias now being addressed, we're hitting a level, a new level in ML, ML operations, ML modeling, ML workloads for developers. >> Yep, yep, absolutely. You know, I think machine learning now has become an operational software, right? Like you know a lot of companies are investing millions and billions of dollars and creating teams to operationalize machine learning based products. And that's the exciting part. I think the thing that that is very exciting for us is like we are helping those teams to observe how those machine learning applications are working so that they can build trust into it. Because I believe as Swami was alluding to this today, without actually building trust into AI, it's really hard to actually have your business users use it in their business workflows. And that's where we are excited about bringing their trust and visibility factor into machine learning. >> You know, a lot of us all know what you guys are doing here in the ecosystem of AWS. And now extending here, take a minute to explain what Fiddler is doing for the folks that are in the space, that are in discovery mode, trying to understand who's got what, because like Swami said on stage, it's a full-time job to keep up on all the machine learning activities and tool sets and platforms. Take a minute to explain what Fiddler's doing, then we can get into some, some good questions. >> Absolutely. As the enterprise is taking on operationalization of machine learning models, one of the key problems that they run into is lack of visibility into how those models perform. You know, for example, let's say if I'm a bank, I'm trying to introduce credit risk scoring models using machine learning. You know, how do I know when my model is rejecting someone's loan? You know, when my model is accepting someone's loan? And why is it doing it? And I think this is basically what makes machine learning a complex thing to implement and operationalize. Without this visibility, you cannot build trust and actually use it in your business. With Fiddler, what we provide is we actually open up this black box and we help our customers to really understand how those models work. You know, for example, how is my model doing? Is it accurately working or not? You know, why is it actually rejecting someone's loan application? We provide these both fine grain as well as coarse grain insights. So our customers can actually deploy machine learning in a safe and trustworthy manner. >> Who is your customer? Who you're targeting? What persona is it, the data engineer, is it data science, is it the CSO, is it all the above? >> Yeah, our customer is the data scientist and the machine learning engineer, right? And we usually talk to teams that have a few models running in production, that's basically our sweet spot, where they're trying to look for a single pane of glass to see like what models are running in their production, how they're performing, how they're affecting their business metrics. So we typically engage with like head of data science or head of machine learning that has a few machine learning engineers and data scientists. >> Okay, so those people that are watching, you're into this, you can go check it out. It's good to learn. I want to get your thoughts on some trends that I see emerging, and I want to get your reaction to those. Number one, we're seeing the cloud scale now and integration a big part of things. So the time to value was brought up on stage today, Swami kind of mentioned time to value, showed some benchmark where they got four hours, some other teams were doing eight weeks. Where are we on the progression of value, time to value, and on the scale side. Can you scope that for me? >> I mean, it depends, right? You know, depending upon the company. So for example, when we work with banks, for them to time to operationalize a model can take months actually, because of all the regulatory procedures that they have to go through. You know, they have to get the models reviewed by model validators, model risk management teams, and then they audit those models, they have to then ship those models and constantly monitor them. So it's a very long process for them. And even for non-regulated sectors, if you do not have the right tools and processes in place, operationalizing machine learning models can take a long time. You know, with tools like Fiddler, what we are enabling is we are basically compressing that life cycle. We are helping them automate like model monitoring and explainability so that they can actually ship models more faster. Like you get like velocity in terms of shipping models. For example, one of the growing fintech companies that started with us last year started with six models in production, now they're running about 36 models in production. So it's within a year, they were able to like grow like 10x. So that is basically what we are trying to do. >> At other things, we at re:MARS, so first of all, you got a great product and a lot of markets that grow onto, but here you got space. I mean, anyone who's coming out of college or university PhD program, and if they're into aero, they're going to be here, right? This is where they are. Now you have a new core companies with machine learning, not just the engineering that you see in the space or aerospace area, you have a new engineering. Now I go back to the old days where my parents, there was Fortran, you used Fortran was Lingua Franca to manage the equipment. Little throwback to the old school. But now machine learning is companion, first class citizen, to the hardware. And in fact, and some will say more important. >> Yep, I mean, machine learning model is the new software artifact. It is going into production in a big way. And I think it has two different things that compare to traditional software. Number one, unlike traditional software, it's a black box. You cannot read up a machine learning model score and see why it's making those predictions. Number two, it's a stochastic entity. What that means is it's predictive power can wane over time. So it needs to be constantly monitored and then constantly refreshed so that it's actually working in tech. So those are the two main things you need to take care. And if you can do that, then machine learning can give you a huge amount of ROI. >> There is some practitioner kind of like craft to it. >> Correct. >> As you said, you got to know when to refresh, what data sets to bring in, which to stay away from, certainly when you get to the bias, but I'll get to that in a second. My next question is really along the lines of software. So if you believe that open source will dominate the software business, which I do, I mean, most people won't argue. I think you would agree with that, right? Open source is driving everything. If everything's open source, where's the differentiation coming from? So if I'm a startup entrepreneur or I'm a project manager working on the next Artemis mission, I got to open source. Okay, there's definitely security issues here. I don't want to talk about shift left right now, but like, okay, open source is everything. Where's the differentiation, where do I have the proprietary edge? >> It's a great question, right? So I used to work in tech companies before Fiddler. You know, when I used to work at Facebook, we would build everything in house. We would not even use a lot of open source software. So there are companies like that that build everything in house. And then I also worked at companies like Twitter and Pinterest, which are actually used a lot of open source, right? So now, like the thing is, it depends on the maturity of the organization. So if you're a Facebook or a Google, you can build a lot of things in house. Then if you're like a modern tech company, you would probably leverage open source, but there are lots of other companies in the world that still don't have the talent pool to actually build, take things from open source and productionize it. And that's where the opportunity for startups comes in so that we can commercialize these things, create a great enterprise experience, so actually operationalize things for them so that they don't have to do it in house for them. And that's the advantage working with startups. >> I don't want to get all operating systems with you on theory here on the stage here, but I will have to ask you the next question, which I totally agree with you, by the way, that's the way to go. There's not a lot of people out there that are peaked. And that's just statistical and it'll get better. Data engineering is really narrow. That is like the SRE of data. That's a new role emerging. Okay, all the things are happening. So if open source is there, integration is a huge deal. And you start to see the rise of a lot of MSPs, managed service providers. I run Kubernetes clusters, I do this, that, and the other thing. So what's your reaction to the growth of the integration side of the business and this role of new services coming from third parties? >> Yeah, absolutely. I think one of the big challenges for a chief data officer or someone like a CTO is how do they devise this infrastructure architecture and with components, either homegrown components or open source components or some vendor components, and how do they integrate? You know, when I used to run data engineering at Pinterest, we had to devise a data architecture combining all of these things and create something that actually flows very nicely, right? >> If you didn't do it right, it would break. >> Absolutely. And this is why it's important for us, like at Fiddler, to really make sure that Fiddler can integrate to all varies of ML platforms. Today, a lot of our customers use machine learning, build machine learning models on SageMaker. So Fiddler nicely integrate with SageMaker so that data, they get a seamless experience to monitor their models. >> Yeah, I mean, this might not be the right words for it, but I think data engineering as a service is really what I see you guys doing, as well other things, you're providing all that. >> And ML engineering as a service. >> ML engineering as a- Well it's hard. I mean, it's like the hard stuff. >> Yeah, yeah. >> Hear, hear. But that has to enable. So you as a business entrepreneur, you have to create a multiple of value proposition to your customers. What's your vision on that? What is that value? It has to be a multiple, at least 5 to 10. >> I mean, the value is simple, right? You know, if you have to operationize machine learning, you need visibility into how these things work. You know, if you're CTO or like chief data officer is asking how is my model working and how is it affecting my business? You need to be able to show them a dashboard, how it's working, right? And so like a data scientist today struggles to do this. They have to manually generate a report, manually do this analysis. What Fiddler is doing them is basically reducing their work so that they can automate these things and they can still focus on the core aspect of model building and data preparation and this boring aspect of monitoring the model and creating reports around the models is automated for them. >> Yeah, you guys got a great business. I think it's a lot of great future there and it's only going to get bigger. Again, the TAM's going to expand as the growth rising tide comes in. I want to ask you on while we're on that topic of rising tides, Dave Malik and I, since re:Invent last year have been kind of kicked down around this term that we made up called supercloud. And supercloud was a word that came out of these clouds that were not Amazon hyperscalers. So Snowflake, Buildman Sachs, Capital One, you name it, they're building massive proprietary value on top of the CapEx of Amazon. Jerry Chen at Greylock calls it castles in the cloud. You can create these moats. >> Yeah, right. >> So this is a phenomenon, right? And you land on one, and then you go to the others. So the strategies, everyone goes to Amazon first, and then hits Azure and GCP. That then creates this kind of multicloud so, okay, so super cloud's kind of happening, it's a thing. Charles Fitzgerald will disagree, he's a platformer, he says he's against the term. I get why, but he's off base a little. We can't wait to debate him on that. So superclouds are happening, but now what do I do about multicloud, because now I understand multicloud, I have this on that cloud, integrating across clouds is a very difficult thing. >> Krishna: Right, right, right. >> If I'm Snowflake or whatever, hey, I'll go to Azure, more TAM expansion, more market. But are people actually working together? Are we there yet? Where it's like, okay, I'm going to re-operationalize this code base over here. >> I mean, the reality of it, enterprise wants optionality, right? I think they don't want to be locked in into one particular cloud vendor on one particular software. And therefore you actually have in a situation where you have a multicloud scenario where they want to have some workloads in Amazon, some workloads in Azure. And this is an opportunity for startups like us because we are cloud agnostic. We can monitor models wherever you have. So this is where a lot of our customers, they have some of their models are running in their data centers and some of their models running in Amazon. And so we can provide a universal single pan of glass, right? So we can basically connect all of those data and actually showcase. I think this is an opportunity for startups to combine the data streams come from various different clouds and give them a single pain of experience. That way, the sort of the where is your data, where are my models running, which cloud are there, is all abstracted out from the customer. Because at the end of the day, enterprises will want optionality. And we are in this multicloud. >> Yeah, I mean, this reminds me of the interoperability days back when I was growing into the business. Everything was interoperability and OSI and the standards came out, but what's your opinion on openness, okay? There's a kneejerk reaction right now in the market to go silo on your data for governance or whatever reasons, but yet machine learning gurus and experts will say, "Hey, you want to horizon horizontal scalability and have the best machine learning models, you've got to have access to data and fast in real time or near real time." And the antithesis is siloing. >> Krishna: Right, right, right. >> So what's the solution? Customers control the data plane and have a control plane that's... What do customers do? It's a big challenge. >> Yeah, absolutely. I think there are multiple different architectures of ML, right, you know? We've seen like where vendors like us used to deploy completely on-prem, right? And they still do it, we still do it in some customers. And then you had this managed cloud experience where you just abstract out the entire operations from the customer. And then now you have this hybrid experience where you split the control plane and data plane. So you preserve the privacy of the customer from the data perspective, but you still control the infrastructure, right? I don't think there's a right answer. It depends on the product that you're trying to solve. You know, Databricks is able to solve this control plane, data plane split really well. I've seen some other tools that have not done this really well. So I think it all depends upon- >> What about Snowflake? I think they a- >> Sorry, correct. They have a managed cloud service, right? So predominantly that's their business. So I think it all depends on what is your go to market? You know, which customers you're talking to? You know, what's your product architecture look like? You know, from Fiddler's perspective today, we actually have chosen, we either go completely on-prem or we basically provide a managed cloud service and that's actually simpler for us instead of splitting- >> John: So it's customer choice. >> Exactly. >> That's your position. >> Exactly. >> Whoever you want to use Fiddler, go on-prem, no problem, or cloud. >> Correct, or cloud, yeah. >> You'll deploy and you'll work across whatever observability space you want to. >> That's right, that's right. >> Okay, yeah. So that's the big challenge, all right. What's the big observation from your standpoint? You've been on the hyperscaler side, your journey, Facebook, Pinterest, so back then you built everything, because no one else had software for you, but now everybody wants to be a hyperscaler, but there's a huge CapEx advantage. What should someone do? If you're a big enterprise, obviously I could be a big insurance, I could be financial services, oil and gas, whatever vertical, I want a supercloud, what do I do? >> I think like the biggest advantage enterprise today have is they have a plethora of tools. You know, when I used to work on machine learning way back in Microsoft on Bing Search, we had to build everything. You know, from like training platforms, deployment platforms, experimentation platforms. You know, how do we monitor those models? You know, everything has to be homegrown, right? A lot of open source also did not exist at the time. Today, the enterprise has this advantage, they're sitting on this gold mine of tools. You know, obviously there's probably a little bit of tool fatigue as well. You know, which tools to select? >> There's plenty of tools available. >> Exactly, right? And then there's like services available for you. So now you need to make like smarter choices to cobble together this, to create like a workflow for your engineers. And you can really get started quite fast, and actually get on par with some of these modern tech companies. And that is the advantage that a lot of enterprises see. >> If you were going to be the CTO or CEO of a big transformation, knowing what you know, 'cause you just brought up the killer point about why it's such a great time right now, you got platform as a service and the tooling essentially reset everything. So if you're going to throw everything out and start fresh, you're basically brewing the system architecture. It's a complete reset. That's doable. How fast do you think you could do that for say a large enterprise? >> See, I think if you set aside the organization processes and whatever kind of comes in the friction, from a technology perspective, it's pretty fast, right? You can devise a data architecture today with like tools like Kafka, Snowflake and Redshift, and you can actually devise a data architecture very clearly right from day one and actually implement it at scale. And then once you have accumulated enough data and you can extract more value from it, you can go and implement your MLOps workflow as well on top of it. And I think this is where tools like Fiddler can help as well. So I would start with looking at data, do we have centralization of data? Do we have like governance around data? Do we have analytics around data? And then kind of get into machine learning operations. >> Krishna, always great to have you on theCUBE. You're great masterclass guest. Obviously great success in your company. Been there, done that, and doing it again. I got to ask you, since you just brought that up about the whole reset, what is the superhero persona right now? Because it used to be the full stack developer, you know? And then it's like, then I call them, it didn't go over very well in theCUBE, the half stack developer, because nobody wants to be a half stack anything, a half sounds bad, worse than full. But cloud is essentially half a stack. I mean, you got infrastructure, you got tools. Now you're talking about a persona that's going to reset, look at tools, make selections, build an architecture, build an operating environment, distributed computing operating. Who is that person? What's that persona look like? >> I mean, I think the superhero persona today is ML engineering. I'm usually surprised how much is put on an ML engineer to do actually these days. You know, when I entered the industry as a software engineer, I had three or four things in my job to do, I write code, I test it, I deploy it, I'm done. Like today as an ML engineer, I need to worry about my data. How do I collect it? I need to clean the data, I need to train my models, I need to experiment with what it is, and to deploy them, I need to make sure that they're working once they're deployed. >> Now you got to do all the DevOps behind it. >> And all the DevOps behind it. And so I'm like working halftime as a data scientist, halftime as a software engineer, halftime as like a DevOps cloud. >> Cloud architect. >> It's like a heroic job. And I think this is why this is why obviously these jobs are like now really hard jobs and people want to be more and more machine learning >> And they get paid. >> engineering. >> Commensurate with the- >> And they're paid commensurately as well. And this is where I think an opportunity for tools like Fiddler exists as well because we can help those ML engineers do their jobs better. >> Thanks for coming on theCUBE. Great to see you. We're here at re:MARS. And great to see you again. And congratulations on being on the AWS startup showcase that we're in year two, episode four, coming up. We'll have to have you back on. Krishna, great to see you. Thanks for coming on. Okay, This is theCUBE's coverage here at re:MARS. I'm John Furrier, bringing all the signal from all the noise here. Not a lot of noise at this event, it's very small, very intimate, a little bit different, but all on point with space, machine learning, robotics, the future of industrial. We'll back with more coverage after the short break. >> Man: Thank you John. (upbeat music)

from around the globe it's thecube covering space and cyber security symposium 2020 hosted by cal poly hello and welcome to thecube's coverage we're here hosting with cal poly an amazing event space in the intersection of cyber security this session is defending satellite and space infrastructure from cyber threats got two great guests we've got major general john shaw combined four space component commander u.s space command and vandenberg air force base in california and roland cuello who's the ceo of maverick space systems gentlemen thank you for spending the time to come on to this session for the cal poly space and cyber security symposium appreciate it absolutely um guys defending satellites and space infrastructure is the new domain obviously it's a war warfighting domain it's also the future of the world and this is an important topic because we rely on space now for our everyday life and it's becoming more and more critical everyone knows how their phones work and gps just small examples of all the impacts i'd like to discuss with this hour this topic with you guys so if we can have you guys do an opening statement general if you can start with your opening statement we'll take it from there thanks john and greetings from vandenberg air force base we are just down the road from cal poly here on the central coast of california and uh very proud to be part of this uh effort and part of the partnership that we have with with cal poly on a number of fronts um i should uh so in in my job here i actually uh have two hats that i wear and it's i think worth talking briefly about those to set the context for our discussion you know we had two major organizational events within our department of defense with regard to space last year in 2019 and probably the one that made the most headlines was the stand-up of the united states space force that happened uh december 20th last year and again momentous the first new branch in our military since 1947 uh and uh it is a it's just over nine months old now as we're making this recording uh and already we're seeing a lot of change uh with regard to how we're approaching uh organizing training and equipping on a service side or space capabilities and so i uh in that with regard to the space force the hat i wear there is commander of space operations command that was what was once 14th air force when we were still part of the air force here at vandenberg and in that role i'm responsible for the operational capabilities that we bring to the joint warfighter and to the world from a space perspective didn't make quite as many headlines but another major change that happened last year was the uh the reincarnation i guess i would say of united states space command and that is a combatant command it's how our department of defense organizes to actually conduct warfighting operations um most people are more familiar perhaps with uh central command centcom or northern command northcom or even strategic command stratcom well now we have a space com we actually had one from 1985 until 2002 and then stood it down in the wake of the 9 11 attacks and a reorganization of homeland security but we've now stood up a separate command again operationally to conduct joint space operations and in that organization i wear a hat as a component commander and that's the combined force-based component command uh working with other all the additional capabilities that other services bring as well as our allies that combined in that title means that uh i under certain circumstances i would lead an allied effort uh in space operations and so it's actually a terrific job to have here on the central coast of california uh both working the uh how we bring space capabilities to the fight on the space force side and then how we actually operate those capabilities it's a point of joint in support of joint warfighters around the world um and and national security interests so that's the context now what el i i also should mention you kind of alluded to john you're beginning that we're kind of in a change situation than we were a number of years ago and that space we now see space as a warfighting domain for most of my career going back a little ways most of my my focus in my jobs was making sure i could bring space capabilities to those that needed them bringing gps to that special operations uh soldier on the ground somewhere in the world bringing satellite communications for our nuclear command and control bringing those capabilities for other uses but i didn't have to worry in most of my career about actually defending those space capabilities themselves well now we do we've actually gone to a point where we're are being threatened in space we now are treating it more like any other domain normalizing in that regard as a warfighting domain and so we're going through some relatively emergent efforts to protect and defend our capabilities in space to to design our capabilities to be defended and perhaps most of all to train our people for this new mission set so it's a very exciting time and i know we'll get into it but you can't get very far into talking about all these space capabilities and how we want to protect and defend them and how we're going to continue their ability to deliver to warfighters around the globe without talking about cyber because they fit together very closely so anyway thanks for the chance to be here today and i look forward to the discussion general thank you so much for that opening statement and i would just say that not only is it historic with the space force it's super exciting because it opens up so much more challenges and opportunities for to do more and to do things differently so i appreciate that statement roland your opening statement your your job is to put stuff in space faster cheaper smaller better your opening statement please um yes um thank you john um and yes you know to um general shaw's point you know with with the space domain and the need to protect it now um is incredibly important and i hope that we are more of a help um than a thorn in your side um in terms of you know building satellites smaller faster cheaper um you know and um definitely looking forward to this discussion and you know figuring out ways where um the entire space domain can work together you know from industry to to us government even to the academic environment as well so first would like to say and preface this by saying i am not a cyber security expert um we you know we build satellites um and uh we launch them into orbit um but we are by no means you know cyber security experts and that's why um you know we like to partner with organizations like the california cyber security institute because they help us you know navigate these requirements um so um so i'm the ceo of um of maverick space systems we are a small aerospace business in san luis obispo california and we provide small satellite hardware and service solutions to a wide range of customers all the way from the academic environment to the us government and everything in between we support customers through an entire you know program life cycle from mission architecture and formulation all the way to getting these customer satellites in orbit and so what we try to do is um provide hardware and services that basically make it easier for customers to get their satellites into orbit and to operate so whether it be reducing mass or volume um creating greater launch opportunities or providing um the infrastructure and the technology um to help those innovations you know mature in orbit you know that's you know that's what we do our team has experienced over the last 20 years working with small satellites and definitely fortunate to be part of the team that invented the cubesat standard by cal poly and stanford uh back in 2000 and so you know we are in you know vandenberg's backyard um we came from cal poly san luis obispo um and you know our um our hearts are fond you know of this area and working with the local community um a lot of that success um that we have had is directly attributable um to the experiences that we learned as students um working on satellite programs from our professors and mentors um you know that's you know all you know thanks to cal poly so just wanted to tell a quick story so you know back in 2000 just imagine a small group of undergraduate students you know myself included with the daunting task of launching multiple satellites from five different countries on a russian launch vehicle um you know many of us were only 18 or 19 not even at the legal age to drink yet um but as you know essentially teenagers we're managing million dollar budgets um and we're coordinating groups um from around the world um and we knew that we knew what we needed to accomplish um yet we didn't really know um what we were doing when we first started um the university was extremely supportive um and you know that's the cal poly learn by doing philosophy um i remember you know the first time we had a meeting with our university chief legal counsel and we were discussing the need to to register with the state department for itar nobody really knew what itar was back then um and you know discussing this with the chief legal counsel um you know she was asking what is itar um and we essentially had to explain you know this is um launching satellites as part of the um the u.s munitions list and essentially we have a similar situation you know exporting munitions um you know we are in similar categories um you know as you know as weapons um and so you know after that initial shock um everybody jumped in you know both feet forward um the university um you know our head legal counsel professors mentors and the students um you know knew we needed to tackle this problem um because you know the the need was there um to launch these small satellites and um you know the the reason you know this is important to capture the entire spectrum of users of the community um is that the technology and the you know innovation of the small satellite industry occurs at all levels you know so we have academia commercial national governments we even have high schools and middle schools getting involved and you know building satellite hardware um and the thing is you know the the importance of cyber security is incredibly important because it touches all of these programs and it touches you know people um at a very young age um and so you know we hope to have a conversation today um to figure out you know how do we um create an environment where we allow these programs to thrive but we also you know protect and you know keep their data safe as well thank you very much roland appreciate that uh story too as well thanks for your opening statement gentlemen i mean i love this topic because defending the assets in space is is as obvious um you look at it but there's a bigger picture going on in our world right now and generally you kind of pointed out the historic nature of space force and how it's changing already operationally training skills tools all that stuff is revolving you know in the tech world that i live in you know change the world is a topic they use that's thrown around a lot you can change the world a lot of young people we have just other panels on this where we're talking about how to motivate young people changing the world is what it's all about with technology for the better evolution is just an extension of another domain in this case space is just an extension of other domains similar things are happening but it's different there's a huge opportunity to change the world so it's faster there's an expanded commercial landscape out there certainly government space systems are moving and changing how do we address the importance of cyber security in space general we'll start with you because this is real it's exciting if you're a young person there's touch points of things to jump into tech building hardware to changing laws and and everything in between is an opportunity and it's exciting and it's truly a chance to change the world how does the commercial government space systems teams address the importance of cyber security so john i think it starts with with the realization that as i like to say that cyber and space are bffs uh there's nothing that we do on the cutting edge of space that isn't heavy reliant heavily reliant on the cutting edge of cyber and frankly there's probably nothing on the cutting edge of cyber that doesn't have a space application and when you realize that you see how how closely those are intertwined as we need to move forward at at speed it becomes fundamental to to the to answering your question let me give a couple examples we one of the biggest challenges i have on a daily basis is understanding what's going on in the space domain those on the on the on the surface of the planet talk about tyranny of distance across the oceans across large land masses and i talk about the tyranny of volume and you know right now we're looking out as far as the lunar sphere there's activity that's extending out to the out there we expect nasa to be conducting uh perhaps uh human operations in the lunar environment in the next few years so it extends out that far when you do the math that's a huge volume how do you do that how do you understand what's happening in real time in within that volume it is a big data problem by the very definition of that that kind of effort to that kind of challenge and to do it successfully in the years ahead it's going to require many many sensors and the fusion of data of all kinds to present a picture and then analytics and predictive analytics that are going to deliver an idea of what's going on in the space arena and that's just if people are not up to mischief once you have threats introduced into that environment it is even more challenging so i'd say it's a big data problem that we'll be enjoying uh tackling in the years ahead a second example is you know we if i if i had to if we had to take a vote of what were the most uh amazing robots that have ever been designed by humans i think that spacecraft would have to be up there on the list whether it's the nasa spacecraft that explore other planets or the ones that we or gps satellites that that amazingly uh provide a wonderful service to the entire globe uh and beyond they are amazing technological machines that's not going to stop i mean all the work that roland talked about at the at the even even that we're doing it at the kind of the microsoft level is is putting cutting-edge technology into smaller packages you can to get some sort of capability out of that as we expand our activities further and further into space for national security purposes or for exploration or commercial or civil the the cutting edge technologies of uh artificial intelligence uh and machine to machine engagements and machine learning are going to be part of that design work moving forward um and then there's the threat piece as we try to as we operate these these capabilities how these constellations grow that's going to be done via networks and as i've already pointed out space is a warfighting domain that means those networks will come under attack we expect that they will and that may happen early on in a conflict it may happen during peace time in the same way that we see cyber attacks all the time everywhere in many sectors of of activity and so by painting that picture you kind of get you we start to see how it's intertwined at the very very base most basic level the cutting edge of cyber and cutting edge of space with that then comes the need to any cutting edge cyber security capability that we have is naturally going to be needed as we develop space capabilities and we're going to have to bake that in from the very beginning we haven't done that in the past as well as we should but moving forward from this point on it will be an essential ingredient that we work into all of our new capability roland we're talking about now critical infrastructure we're talking about new capabilities being addressed really fast so it's kind of chaotic now there's threats so it's not as easy as just having capabilities because you've got to deal with the threats the general just pointed out but now you've got critical infrastructure which then will enable other things down down the line how do you protect it how do we address this how do you see this being addressed from a security standpoint because you know malware these techniques can be mapped in as extended into into space and takeovers wartime peacetime these things are all going to be under threat that's pretty well understood i think people kind of get that how do we address it what's your what's your take yeah you know absolutely and you know i couldn't agree more with general shaw you know with cyber security and space being so intertwined um and you know i think with fast and rapid innovation um comes you know the opportunity for threats especially um if you have bad actors um that you know want to cause harm and so you know as a technology innovator and you're pushing the bounds um you kind of have a common goal of um you know doing the best you can um and you know pushing the technology balance making it smaller faster cheaper um but a lot of times what entrepreneurs and you know small businesses and supply chains um are doing and don't realize it is a lot of these components are dual use right i mean you could have a very benign commercial application but then a small you know modification to it and turn it into a military application and if you do have these bad actors they can exploit that and so you know i think the the big thing is um creating a organization that is you know non-biased that just wants to kind of level the playing field for everybody to create a set standard for cyber security in space i think you know one group that would be perfect for that you know is um cci um you know they understand both the cybersecurity side of things and they also have you know at cal poly um you know the the small satellite group um and you know just having kind of a a clearinghouse or um an agency where um can provide information that is free um you know you don't need a membership for and to be able to kind of collect that but also you know reach out to the entire value chain you know for a mission and um making them aware um of you know what potential capabilities are and then how it might um be you know potentially used as a weapon um and you know keeping them informed because i think you know the the vast majority of people in the space industry just want to do the right thing and so how do we get that information free flowing to you know to the us government so that they can take that information create assessments and be able to not necessarily um stop threats from occurring presently but identify them long before that they would ever even happen um yeah that's you know general i want to i want to follow up on that real quick before we go to the next talk track critical infrastructure um you mentioned you know across the oceans long distance volume you know when you look at the physical world you know you had you know power grids here united states you had geography you had perimeters uh the notion of a perimeter and the moat this is and then you had digital comes in then you have we saw software open up and essentially take down this idea of a perimeter and from a defense standpoint and that everything changed and we had to fortify those critical assets uh in the u.s space increases the same problem statement significantly because it's you can't just have a perimeter you can't have a moat it's open it's everywhere like what digital's done and that's why we've seen a slurge of cyber in the past two decades attacks with software so this isn't going to go away you need the critical infrastructure you're putting it up there you're formulating it and you've got to protect it how do you view that because it's going to be an ongoing problem statement what's the current thinking yeah i i think my sense is a mindset that you can build a a firewall or a defense or some other uh system that isn't dynamic in his own right is probably not heading in the right direction i think cyber security in the future whether it's for our space systems or for other critical infrastructure is going to be a dynamic fight that happens at a machine-to-machine um a speed and dynamic um i don't think it's too far off where we will have uh machines writing their own code in real time to fight off attacks that are coming at them and by the way the offense will probably be doing the same kind of thing and so i i guess i would not want to think that the answer is something that you just build it and you leave it alone and it's good enough it's probably going to be a constantly evolving capability constantly reacting to new threats and staying ahead of those threats that's the kind of use case just to kind of you know as you were kind of anecdotal example is the exciting new software opportunities for computer science majors i mean i tell my young kids and everyone man it's more exciting now i wish i was 18 again it's so so exciting with ai bro i want to get your thoughts we were joking on another panel with the dod around space and the importance of it obviously and we're going to have that here and then we had a joke it's like oh software's defined everything it says software's everything ai and and i said well here in the united states companies had data centers and they went to the cloud and they said you can't do break fix it's hard to do break fix in space you can't just send a tech up i get that today but soon maybe robotics the general mentions robotics technologies and referencing some of the accomplishments fixing things is almost impossible in space but maybe form factors might get better certainly software will play a role what's your thoughts on that that landscape yeah absolutely you know for for software in orbit um you know there's there's a push for you know software-defined radios um to basically go from hardware to software um and you know that's that that's a critical link um if you can infiltrate that and a small satellite has propulsion on board you could you know take control of that satellite and cause a lot of havoc and so you know creating standards and you know that kind of um initial threshold of security um you know for let's say you know these radios you know communications and making that um available um to the entire supply chain to the satellite builders um and operators you know is incredibly key and you know that's again one of the initiatives that um that cci is um is tackling right now as well general i want to get your thoughts on best practices around cyber security um state of the art today uh and then some guiding principles and kind of how the if you shoot the trajectory forward what what might happen uh around um supply chain there's been many stories where oh we outsourced the chips and there's a little chip sitting in a thing and it's built by someone else in china and the software is written from someone in europe and the united states assembles it it gets shipped and it's it's corrupt and it has some cyber crime making i'm oversimplifying the the statement but this is what when you have space systems that involve intellectual property uh from multiple partners whether it's from software to creation and then deployment you get supply chain tiers what are some of the best practices that you see involving that don't stunt the innovation but continues to innovate but people can operate safely what's your thoughts yeah so on supply chain i think i think the symposium here is going to get to hear from lieutenant general jt thompson uh from space missile system center down in los angeles and and uh he's a he's just down the road from us there uh on the coast um and his team is is the one that we look to really focus on as he acquires and develop again bake in cyber security from the beginning and knowing where the components are coming from and and properly assessing those as you as you put together your space systems is a key uh piece of what his team is focused on so i expect we'll hear him talk about that when it talks to i think she asked the question a little more deeply about how do the best practices in terms of how we now develop moving forward well another way that we don't do it right is if we take a long time to build something and then you know general general jt thompson's folks take a while to build something and then they hand it over to to to me and my team to operate and then they go hands-free and and then and then that's you know that's what i have for for years to operate until the next thing comes along that's a little old school what we're going to have to do moving forward with our space capabilities and with the cyber piece baked in is continually developing new capability sets as we go we actually have partnership between general thompson's team and mine here at vandenberg on our ops floor or our combined space operations center that are actually working in real time together better tools that we can use to understand what's going on the space environment to better command and control our capabilities anywhere from military satellite communications to space domain awareness sensors and such and so and we're developing those capabilities in real time it's a dev and and with the security pieces so devsecops is we're practicing that in in real time i think that is probably the standard today that we're trying to live up to as we continue to evolve but it has to be done again in close partnership all the time it's not a sequential industrial age process while i'm on the subject of partnerships so general thompson's and team and mine have good partnerships it's part partnerships across the board are going to be another way that we are successful and that uh it means with with academia in some of the relationships that we have here with cal poly it's with the commercial sector in ways that we haven't done before the old style business was to work with just a few large um companies that had a lot of space experience well we need we need a lot of kinds of different experience and technologies now in order to really field good space capabilities and i expect we'll see more and more non-traditional companies being part of and and organizations being part of that partnership that will work going forward i mentioned at the beginning that um uh allies are important to us so everything that uh that role and i've been talking about i think you have to extrapolate out to allied partnerships right it doesn't help me uh as a combined force component commander which is again one of my jobs it doesn't help me if the united states capabilities are cyber secure but i'm trying to integrate them with capabilities from an ally that are not cyber secure so that partnership has to be dynamic and continually evolving together so again close partnering continually developing together from the acquisition to the operational sectors with as many um different sectors of our economy uh as possible are the ingredients to success general i'd love to just follow up real quick i was having just a quick reminder for a conversation i had with last year with general keith alexander who was does a lot of cyber security work and he was talking about the need to share faster and the new school is you got to share faster and to get the data you mentioned observability earlier you need to see what everything's out there he's a real passionate person around getting the data getting it fast and having trusted partners so that's not it's kind of evolving as i mean sharing is a well-known practice but with cyber it's sensitive data potentially so there's a trust relationship there's now a new ecosystem that's new for uh government how do you view all that and your thoughts on that trend of the sharing piece of it on cyber so it's i don't know if it's necessarily new but it's at a scale that we've never seen before and by the way it's vastly more complicated and complex when you overlay from a national security perspective classification of data and information at various levels and then that is again complicated by the fact you have different sharing relationships with different actors whether it's commercial academic or allies so it gets very very uh a complex web very quickly um so that's part of the challenge we're working through how can we how can we effectively share information at multiple classification levels with multiple partners in an optimal fashion it is certainly not optimal today it's it's very difficult even with maybe one industry partner for me to be able to talk about data at an unclassified level and then various other levels of classification to have the traditional networks in place to do that i could see a solution in the future where our cyber security is good enough that maybe i only really need one network and the information that is allowed to flow to the players within the right security environment um to uh to make that all happen as quickly as possible so you've actually uh john you've hit on yet another big challenge that we have is um is evolving our networks to properly share with the right people at the right uh clearance levels as at speed of war which is what we're going to need yeah and i wanted to call that out because this is an opportunity again this discussion here at cal poly and around the world is for new capabilities and new people to solve the problems and um it's again it's super exciting if you you know you're geeking out on this it's if you have a tech degree or you're interested in changing the world there's so many new things that could be applied right now roland will get your thoughts on this because one of the things in the tech trends we're seeing this is a massive shift all the theaters of the tech industry are are changing rapidly at the same time okay and it affects policy law but also deep tech the startup communities are super important in all this too we can't forget them obviously the big trusted players that are partnering certainly on these initiatives but your story about being in the dorm room now you got the boardroom and now you got everything in between you have startups out there that want to and can contribute and you know what's an itar i mean i got all these acronym certifications is there a community motion to bring startups in in a safe way but also give them a ability to contribute because you look at open source that proved everyone wrong on software that's happening now with this now open network concept the general is kind of alluding to which is it's a changing landscape your thoughts i know you're passionate about this yeah absolutely you know and i think um you know as general shaw mentioned you know we need to get information out there faster more timely and to the right people um and involving not only just stakeholders in the us but um internationally as well you know and as entrepreneurs um you know we have this very lofty vision or goal uh to change the world and um oftentimes um you know entrepreneurs including myself you know we put our heads down and we just run as fast as we can and we don't necessarily always kind of take a breath and take a step back and kind of look at what we're doing and how it's touching um you know other folks and in terms of a community i don't know of any formal community out there it's mostly ad hoc and you know these ad hoc communities are folks who let's say have you know was was a student working on a satellite um you know in college and they love that entrepreneurial spirit and so they said well i'm gonna start my own company and so you know a lot of the these ad hoc networks are just from relationships um that are that have been built over the last two decades um you know from from colleagues that you know at the university um i do think formalizing this and creating um kind of a you know clearinghouse to to handle all of this is incredibly important yeah um yeah there's gonna be a lot of entrepreneurial activity no doubt i mean just i mean there's too many things to work on and not enough time so i mean this brings up the question though while we're on this topic um you got the remote work with covid everyone's working remotely we're doing this remote um interview rather than being on stage works changing how people work and engage certainly physical will come back but if you looked at historically the space industry and the talent you know they're all clustered around the bases and there's always been these areas where you're you're a space person you're kind of working there and there's jobs there and if you were cyber you were 10 in other areas over the past decade there's been a cross-pollination of talent and location as you see the intersection of space general start with you you know first of all central coast is a great place to live i know that's where you guys live but you can start to bring together these two cultures sometimes they're you know not the same maybe they're getting better we know they're being integrated so general can you just share your thoughts because this is uh one of those topics that everyone's talking about but no one's actually kind of addressed directly um yeah john i i think so i think i want to answer this by talking about where i think the space force is going because i think if there was ever an opportunity or inflection point in our department of defense to sort of change culture and and try to bring in non-traditional kinds of thinking and and really kind of change uh maybe uh some of the ways that the department of defense has does things that are probably archaic space force is an inflection point for that uh general raymond our our chief of space operations has said publicly for a while now he wants the us space force to be the first truly digital service and uh you know what we what we mean by that is you know we want the folks that are in the space force to be the ones that are the first adopters or the early adopters of of technology um to be the ones most fluent in the cutting edge technological developments on space and cyber and and other um other sectors of the of of the of the economy that are technologically focused uh and i think there's some can that can generate some excitement i think and it means that we probably end up recruiting people into the space force that are not from the traditional recruiting areas that the rest of the department of defense looks to and i think it allows us to bring in a diversity of thought and diversity of perspective and a new kind of motivation um into the service that i think is frankly is is really exciting so if you put together everything i mentioned about how space and cyber are going to be best friends forever and i think there's always been an excitement in them you know from the very beginning in the american psyche about space you start to put all these ingredients together and i think you see where i'm going with this that really changed that cultural uh mindset that you were describing it's an exciting time for sure and again changing the world and this is what you're seeing today people do want to change world they want a modern world that's changing roy look at your thoughts on this i was having an interview a few years back with a tech entrepreneur um techie and we were joking we were just kind of riffing and we and i said everything that's on star trek will be invented and we're almost there actually if you think about it except for the transporter room you got video you got communicators so you know not to bring in the star trek reference with space force this is digital and you start thinking about some of the important trends it's going to be up and down the stack from hardware to software to user experience everything your thoughts and reaction yeah abs absolutely and so you know what we're seeing is um timeline timelines shrinking dramatically um because of the barrier to entry for you know um new entrants and you know even your existing aerospace companies is incredibly low right so if you take um previously where you had a technology on the ground and you wanted it in orbit it would take years because you would test it on the ground you would verify that it can operate in space in a space environment and then you would go ahead and launch it and you know we're talking tens if not hundreds of millions of dollars to do that now um we've cut that down from years to months when you have a prototype on the ground and you want to get it launched you don't necessarily care if it fails on orbit the first time because you're getting valuable data back and so you know we're seeing technology being developed you know for the first time on the ground and in orbit in a matter of a few months um and the whole kind of process um you know that that we're doing as a small business is you know trying to enable that and so allowing these entrepreneurs and small small companies to to get their technology in orbit at a price that is sometimes even cheaper than you know testing on the ground you know this is a great point i think this is really an important point to call out because we mentioned partnerships earlier the economics and the business model of space is doable i mean you do a mission study you get paid for that you have technology you can get stuff up up quickly and there's a cost structure there and again the alternative was waterfall planning years and millions now the form factors are different now again there may be different payloads involved but you can standardize payloads you got robotic arms all this is all available this brings up the congestion problem this is going to be on the top of mind the generals of course but you got the proliferation okay of these constellation systems you have more and more tech vectors i mean essentially that's malware i mean that's a probe you throw something up in space that could cause some interference maybe a takeover general this is the this is the real elephant in the room the threat matrix from new stuff and new configurations so general how does the proliferation of constellation systems change the threat matrix so i i think the uh you know i guess i'm gonna i'm gonna be a little more optimistic john than i think you pitched that i'm actually excited about these uh new mega constellations in leo um i'm excited about the the growing number of actors that are that are going into space for various reasons and why is that it's because we're starting to realize a new economic engine uh for the nation and for human society so the question is so so i think we want that to happen right when uh um when uh we could go to almost any any other domain in history and and and you know there when when air traffic air air travel started to become much much more commonplace with many kinds of uh actors from from private pilots flying their small planes all the way up to large airliners uh you know there there was a problem with congestion there was a problem about um challenges about uh behavior and are we gonna be able to manage this and yes we did and it was for the great benefit of society i could probably look to the maritime domain for similar kinds of things and so this is actually exciting about space we are just going to have to find the ways as a society and it's not just the department of defense it's going to be civil it's going to be international find the mechanisms to encourage this continued investment in the space domain i do think the space force uh will play a role in in providing security in the space environment as we venture further out as as economic opportunities emerge uh wherever they are um in the in the lunar earth lunar system or even within the solar system space force is going to play a role in that but i'm actually really excited about the those possibilities hey by the way i got to say you made me think of this when you talked about star trek and and and space force and our technologies i remember when i was younger watching the the next generation series i thought one of the coolest things because being a musician in my in my spare time i thought one of the coolest things was when um commander riker would walk into his quarters and and say computer play soft jazz and there would just be the computer would just play music you know and this was an age when you know we had we had hard uh um uh media right like how will that that is awesome man i can't wait for the 23rd century when i can do that and where we are today is is so incredible on those lines the things that i can ask alexa or siri to play um well that's the thing everything that's on star trek think about it almost invented i mean you got the computers you got the only thing really is the holograms are starting to come in you got now the transporter room now that's physics we'll work on that right right so there's a there is this uh a balance between physics and imagination but uh we have not exhausted either well um personally everyone that knows me knows i'm a huge star trek fan all the series of course i'm an original purist but at that level but this is about economic incentive as well roland i want to get your thoughts because you know the gloom and doom you got to think about the the bad stuff to make it good if i if i put my glass half full on the table there's economic incentives just like the example of the plane and the air traffic there's there's actors that are more actors that are incented to have a secure system what's your thoughts to general's comments around the optimism and and the potential threat matrix that needs to be managed absolutely so and you know one of the things that we've seen over the years um as you know we build these small satellites is a lot of the technology you know that the general is talking about um you know voice recognition miniaturized chips and sensors um started on the ground and i mean you know you have you know your iphone um that about 15 years ago before the first iphone came out um you know we were building small satellites in the lab and we were looking at cutting-edge state-of-the-art magnetometers and sensors um that we were putting in our satellites back then we didn't know if they were going to work and then um a few years later as these students graduate they go off and they go out to under you know other industries and so um some of the technology that was first kind of put in these cubesats in the early 2000s you know kind of ended up in the first generation iphone smartphones um and so being able to take that technology rapidly you know incorporate that into space and vice versa gives you an incredible economic advantage because um not only are your costs going down um because you know you're mass producing you know these types of terrestrial technologies um but then you can also um you know increase you know revenue and profit um you know by by having you know smaller and cheaper systems general let's talk about that for real quickly it's a good point i want to just shift it into the playbook i mean everyone talks about playbooks for management for tech for startups for success i mean one of the playbooks that's clear from in history is investment in r d around military and or innovation that has a long view spurs innovation commercially i mean just there's a huge many decades of history that shows that hey we got to start thinking about these these challenges and you know next you know it's in an iphone this is history this is not like a one-off and now with space force you get you're driving you're driving the main engine of innovation to be all digital you know we we riff about star trek which is fun but the reality is you're going to be on the front lines of some really new cool mind-blowing things could you share your thoughts on how you sell that people who write the checks or recruit more talent well so i first i totally agree with your thesis that the that you know national security well could probably go back an awful long way hundreds to thousands of years that security matters tend to drive an awful lot of innovation and creativity because um you know i think the the probably the two things that drive drive people the most are probably an opportunity to make money uh but only by beating that out are trying to stay alive um and uh and so i don't think that's going to go away and i do think that space force can play a role um as it pursues uh security uh structures you know within the space domain to further encourage economic investment and to protect our space capabilities for national security purposes are going to be at the cutting edge this isn't the first time um i think we can point back to the origins of the internet really started in the department of defense and with a partnership i should add with academia that's how the internet got started that was the creativity in order to to meet some needs there cryptography has its roots in security but we use it uh in in national security but now we use it in for economic reasons and meant and a host of other kinds of reasons and then space itself right i mean we still look back to uh apollo era as an inspiration for so many things that inspired people to to either begin careers in in technical areas or in space and and so on so i think i think in that same spirit you're absolutely right i guess i'm totally agreeing with your thesis the space force uh will be and a uh will have a positive inspirational influence in that way and we need to to realize that so when we are asking for when we're looking for how we need to meet capability needs we need to spread that net very far look for the most creative solutions and partner early and often with those that that can that can work on those when you're on the new frontier you've got to have a team sport it's a team effort you mentioned the internet just anecdotally i'm old enough to remember this because i remember the days that was going on and said the government if the policy decisions that the u.s made at that time was to let it go a little bit invisible hand they didn't try to commercialize it too fast and but there was some policy work that was done that had a direct effect to the innovation versus take it over and next you know it's out of control so i think you know i think this this just a cross-disciplinary skill set becomes a big thing where you need to have more people involved and that's one of the big themes of this symposium so it's a great point thank you for sharing that roland your thoughts on this because you know you got policy decisions we all want to run faster we want to be more innovative but you got to have some ops view now mostly ops people want things very tight very buttoned up secure the innovators want to go faster it's the yin and yang that's that's the world we live in how's it all balanced in your mind yeah um you know one of the things um that may not be apparently obvious is that you know the us government and department of um of defense is one of the biggest investors in technology in the aerospace sector um you know they're not the traditional venture capitalists but they're the ones that are driving technology innovation because there's funding um you know and when companies see that the us governments is interested in something businesses will will re-vector um you know to provide that capability and in the i would say the more recent years we've had a huge influx of private equity venture capital um coming into the markets to kind of help augment um you know the government investment and i think having a good partnership and a relationship with these private equity venture capitalists and the us government is incredibly important because the two sides you know can can help collaborate and kind of see a common goal but then also too on um you know the other side is you know there's that human element um and as general shaw was saying it's like not you know not only do companies you know obviously want to thrive and do really well some companies just want to stay alive um to see their technology kind of you know grow into what they've always dreamed of and you know oftentimes entrepreneurs um are put in a very difficult position because they have to make payroll they have to you know keep the lights on and so sometimes they'll take investment um from places where they may normally would not have you know from potentially foreign investment that could potentially you know cause issues with you know the you know the us supply chain well my final question is the best i wanted to say for last because i love the idea of human space flight i'd love to be on mars i'm not sure i'll be able to make it someday but how do you guys see the possible impacts of cyber security on expanding human space flight operations i mean general this is your wheelhouse this is urine command putting humans in space and certainly robots will be there because they're easy to go because they're not human but humans in space i mean you're starting to see the momentum the discussion uh people are are scratching that itch what's your take on that how do we see making this more possible well i i think we will see we will see uh commercial space tourism uh in the future i'm not sure how wide and large a scale it will become but we'll we will see that and um part of uh i think the mission of the space force is going to be probably to again do what we're doing today is have really good awareness of what's going on the domain to uh to to to ensure that that is done safely and i think a lot of what we do today will end up in civil organizations to do space traffic management and safety uh in in that uh arena um and uh um it is only a matter of time uh before we see um humans going even beyond the you know nasa has their plan the the artemis program to get back to the moon and the gateway initiative to establish a a space station there and that's going to be an exploration initiative but it is only a matter of time before we have um private citizens or private corporations putting people in space and not only for tourism but for economic activity and so it'll be really exciting to watch it would be really exciting and space force will be a part of it general roland i want to thank you for your valuable time to come on this symposium i really appreciate it final uh comment i'd love to you to spend a minute to share your personal thoughts on the importance of cyber security to space and we'll close it out we'll start with you roland yeah so i think that the biggest thing um i would like to try to get out of this you know from my own personal perspective is um creating that environment that allows um you know the the aerospace supply chain small businesses you know like ourselves be able to meet all the requirements um to protect um and safeguard our data but also um create a way that you know we can still thrive and it won't stifle innovation um you know i'm looking forward um to comments and questions um you know from the audience um to really kind of help um you know you know basically drive to that next step general final thoughts the importance of cyber security to space i'll just i'll go back to how i started i think john and say that space and cyber are forever intertwined they're bffs and whoever has my job 50 years from now or 100 years from now i predict they're going to be saying the exact same thing cyber and space are are intertwined for good we will always need the cutting edge cyber security capabilities that we develop as a nation or as a as a society to protect our space capabilities and our cyber capabilities are going to need space capabilities in the future as well general john shaw thank you very much roland cleo thank you very much for your great insight thank you to cal poly for putting this together i want to shout out to the team over there we couldn't be in person but we're doing a virtual remote event i'm john furrier with thecube and siliconangle here in silicon valley thanks for watching

>> Announcer: From around the globe, it's "theCUBE" covering Space and Cybersecurity Symposium 2020 hosted by Cal Poly. >> I want to welcome to theCUBE's coverage, we're here hosting with Cal Poly an amazing event, space and the intersection of cyber security. This session is Defending Satellite and Space Infrastructure from Cyber Threats. We've got two great guests. We've got Major General John Shaw of combined force space component commander, U.S. space command at Vandenberg Air Force Base in California and Roland Coelho, who's the CEO of Maverick Space Systems. Gentlemen, thank you for spending the time to come on to this session for the Cal Poly Space and Cybersecurity Symposium. Appreciate it. >> Absolutely. >> Guys defending satellites and space infrastructure is the new domain, obviously it's a war-fighting domain. It's also the future of the world. And this is an important topic because we rely on space now for our everyday life and it's becoming more and more critical. Everyone knows how their phones work and GPS, just small examples of all the impacts. I'd like to discuss with this hour, this topic with you guys. So if we can have you guys do an opening statement. General if you can start with your opening statement, we'll take it from there. >> Thanks John and greetings from Vandenberg Air Force Base. We are just down the road from Cal Poly here on the central coast of California, and very proud to be part of this effort and part of the partnership that we have with Cal Poly on a number of fronts. In my job here, I actually have two hats that I wear and it's I think, worth talking briefly about those to set the context for our discussion. You know, we had two major organizational events within our Department of Defense with regard to space last year in 2019. And probably the one that made the most headlines was the standup of the United States Space Force. That happened December 20th, last year, and again momentous, the first new branch in our military since 1947. And it's just over nine months old now, as we're makin' this recording. And already we're seein' a lot of change with regard to how we are approaching organizing, training, and equipping on a service side for space capabilities. And so, with regard to the Space Force, the hat I wear there is Commander of Space Operations Command. That was what was once 14th Air Force, when we were still part of the Air Force here at Vandenberg. And in that role, I'm responsible for the operational capabilities that we bring to the joint warfighter and to the world from a space perspective. Didn't make quite as many headlines, but another major change that happened last year was the reincarnation, I guess I would say, of United States Space Command. And that is a combatant command. It's how our Department of Defense organizes to actually conduct war-fighting operations. Most people are more familiar perhaps with Central Command, CENTCOM or Northern Command, NORTHCOM, or even Strategic Command, STRATCOM. Well, now we have a SPACECOM. We actually had one from 1985 until 2002, and then stood it down in the wake of the 9/11 attacks and a reorganization of Homeland Security. But we've now stood up a separate command again operationally, to conduct joint space operations. And in that organization, I wear a hat as a component commander, and that's the combined force-based component command working with other, all the additional capabilities that other services bring, as well as our allies. The combined in that title means that under certain circumstances, I would lead in an allied effort in space operations. And so it's actually a terrific job to have here on the central coast of California. Both working how we bring space capabilities to the fight on the Space Force side, and then how we actually operate those capabilities in support of joint warfighters around the world and national security interests. So that's the context. Now what also I should mention and you kind of alluded to John at your beginning, we're kind of in a changed situation than we were a number of years ago, in that we now see space as a war-fighting domain. For most of my career, goin' back a little ways, most of my focus in my jobs was making sure I could bring space capabilities to those that needed them. Bringing GPS to that special operations soldier on the ground somewhere in the world, bringing satellite communications for our nuclear command and control, bringing those capabilities for other uses. But I didn't have to worry in most of my career, about actually defending those space capabilities themselves. Well, now we do. We've actually gone to a point where we're are being threatened in space. We now are treating it more like any other domain, normalizing in that regard as a war-fighting domain. And so we're going through some relatively emergent efforts to protect and defend our capabilities in space, to design our capabilities to be defended, and perhaps most of all, to train our people for this new mission set. So it's a very exciting time, and I know we'll get into it, but you can't get very far into talking about all these space capabilities and how we want to protect and defend them and how we're going to continue their ability to deliver to warfighters around the globe, without talking about cyber, because they fit together very closely. So anyway, thanks for the chance to be here today. And I look forward to the discussion. >> General, thank you so much for that opening statement. And I would just say that not only is it historic with the Space Force, it's super exciting because it opens up so much more challenges and opportunities to do more and to do things differently. So I appreciate that statement. Roland in your opening statement. Your job is to put stuff in space, faster, cheaper, smaller, better, your opening statement, please. >> Yes, thank you, John. And yes, to General Shaw's point with the space domain and the need to protect it now is incredibly important. And I hope that we are more of a help than a thorn in your side in terms of building satellites smaller, faster, cheaper. Definitely looking forward to this discussion and figuring out ways where the entire space domain can work together, from industry to U.S. government, even to the academic environment as well. So first, I would like to say, and preface this by saying, I am not a cybersecurity expert. We build satellites and we launch them into orbit, but we are by no means cybersecurity experts. And that's why we like to partner with organizations like the California Cybersecurity Institute because they help us navigate these requirements. So I'm the CEO of Maverick Space Systems. We are a small aerospace business in San Luis Obispo, California. And we provide small satellite hardware and service solutions to a wide range of customers. All the way from the academic environment to the U.S. government and everything in between. We support customers through an entire program life cycle, from mission architecture and formulation, all the way to getting these customer satellites in orbit. And so what we try to do is provide hardware and services that basically make it easier for customers to get their satellites into orbit and to operate. So whether it be reducing mass or volume, creating greater launch opportunities, or providing the infrastructure and the technology to help those innovations mature in orbit, that's what we do. Our team has experience over the last 20 years, working with small satellites. And I'm definitely fortunate to be part of the team that invented the CubeSat standard by Cal Poly and Stanford back in 2000. And so, we are in VandenBerg's backyard. We came from Cal Poly San Luis Obispo and our hearts are fond of this area, and working with the local community. A lot of that success that we have had is directly attributable to the experiences that we learned as students, working on satellite programs from our professors and mentors. And that's all thanks to Cal Poly. So just wanted to tell a quick story. So back in 2000, just imagine a small group of undergraduate students, myself included, with the daunting task of launching multiple satellites from five different countries on a Russian launch vehicle. Many of us were only 18 or 19, not even at the legal age to drink yet, but as essentially teenagers we were managing million-dollar budgets. And we were coordinating groups from around the world. And we knew what we needed to accomplish, yet we didn't really know what we were doing when we first started. The university was extremely supportive and that's the Cal Poly learn-by-doing philosophy. I remember the first time we had a meeting with our university chief legal counsel, and we were discussing the need to register with the State Department for ITAR. Nobody really knew what ITAR was back then. And discussing this with the chief legal counsel, she was asking, "What is ITAR?" And we essentially had to explain, this is, launching satellites is part of the U.S. munitions list. And essentially we had a similar situation exporting munitions. We are in similar categories as weapons. And so, after that initial shock, everybody jumped in both feet forward, the university, our head legal counsel, professors, mentors, and the students knew we needed to tackle this problem because the need was there to launch these small satellites. And the reason this is important to capture the entire spectrum of users of the community, is that the technology and the innovation of the small satellite industry occurs at all levels, so we have academia, commercial, national governments. We even have high schools and middle schools getting involved and building satellite hardware. And the thing is the importance of cybersecurity is incredibly important because it touches all of these programs and it touches people at a very young age. And so, we hope to have a conversation today to figure out how do we create an environment where we allow these programs to thrive, but we also protect and keep their data safe as well. >> Thank you very much Roland. Appreciate that a story too as well. Thanks for your opening statement. Gentlemen, I mean I love this topic because defending the assets in space is obvious, if you look at it. But there's a bigger picture going on in our world right now. And general, you kind of pointed out the historic nature of Space Force and how it's changing already, operationally, training, skills, tools, all that stuff is evolving. You know in the tech world that I live in, change the world is a topic they use, gets thrown around a lot, you can change the world. A lot of young people, and we have other panels on this where we're talkin' about how to motivate young people, changing the world is what it's all about technology, for the better. Evolution is just an extension of another domain. In this case, space is just an extension of other domains, similar things are happening, but it's different. There's huge opportunity to change the world, so it's faster. There's an expanded commercial landscape out there. Certainly government space systems are moving and changing. How do we address the importance of cybersecurity in space? General, we'll start with you because this is real, it's exciting. If you're a young person, there's touch points of things to jump into, tech, building hardware, to changing laws, and everything in between is an opportunity, and it's exciting. And it is truly a chance to change the world. How does the commercial government space systems teams, address the importance of cybersecurity? >> So, John, I think it starts with the realization that as I like to say, that cyber and space are BFFs. There's nothing that we do on the cutting edge of space that isn't heavily reliant on the cutting edge of cyber. And frankly, there's probably nothing on the cutting edge of cyber that doesn't have a space application. And when you realize that and you see how closely those are intertwined as we need to move forward at speed, it becomes fundamental to answering your question. Let me give a couple examples. One of the biggest challenges I have on a daily basis is understanding what's going on in the space domain. Those on the surface of the planet talk about tyranny of distance across the oceans or across large land masses. And I talk about the tyranny of volume. And right now, we're looking out as far as the lunar sphere. There's activity that's extending out there. We expect NASA to be conducting perhaps human operations in the lunar environment in the next few years. So it extends out that far. When you do the math that's a huge volume. How do you do that? How do you understand what's happening in real time within that volume? It is a big data problem by the very definition of that kind of effort and that kind of challenge. And to do it successfully in the years ahead, it's going to require many, many sensors and the fusion of data of all kinds, to present a picture and then analytics and predictive analytics that are going to deliver an idea of what's going on in the space arena. And that's just if people are not up to mischief. Once you have threats introduced into that environment, it is even more challenging. So I'd say it's a big data problem that we'll enjoy tackling in the years ahead. Now, a second example is, if we had to take a vote of what were the most amazing robots that have ever been designed by humans, I think that spacecraft would have to be up there on the list. Whether it's the NASA spacecraft that explore other planets, or GPS satellites that amazingly provide a wonderful service to the entire globe and beyond. They are amazing technological machines. That's not going to stop. I mean, all the work that Roland talked about, even that we're doin' at the kind of the microsat level is putting cutting-edge technology into small a package as you can to get some sort of capability out of that. As we expand our activities further and further into space for national security purposes, or for exploration or commercial or civil, the cutting-edge technologies of artificial intelligence and machine-to-machine engagements and machine learning are going to be part of that design work moving forward. And then there's the threat piece. As we operate these capabilities, as these constellations grow, that's going to be done via networks. And as I've already pointed out space is a war-fighting domain. That means those networks will come under attack. We expect that they will and that may happen early on in a conflict. It may happen during peace time in the same way that we see cyber attacks all the time, everywhere in many sectors of activity. And so by painting that picture, we start to see how it's intertwined at the very, very most basic level, the cutting edge of cyber and cutting edge of space. With that then comes the need to, any cutting edge cybersecurity capability that we have is naturally going to be needed as we develop space capabilities. And we're going to have to bake that in from the very beginning. We haven't done that in the past as well as we should, but moving forward from this point on, it will be an essential ingredient that we work into all of our capability. >> Roland, we're talkin' about now, critical infrastructure. We're talkin' about new capabilities being addressed really fast. So, it's kind of chaotic now there's threats. So it's not as easy as just having capabilities, 'cause you've got to deal with the threats the general just pointed out. But now you've got critical infrastructure, which then will enable other things down the line. How do you protect it? How do we address this? How do you see this being addressed from a security standpoint? Because malware, these techniques can be mapped in, extended into space and takeovers, wartime, peace time, these things are all going to be under threat. That's pretty well understood, and I think people kind of get that. How do we address it? What's your take? >> Yeah, yeah, absolutely. And I couldn't agree more with General Shaw, with cybersecurity and space being so intertwined. And, I think with fast and rapid innovation comes the opportunity for threats, especially if you have bad actors that want to cause harm. And so, as a technology innovator and you're pushing the bounds, you kind of have a common goal of doing the best you can, and pushing the technology bounds, making it smaller, faster, cheaper. But a lot of times what entrepreneurs and small businesses and supply chains are doing, and don't realize it, is a lot of these components are dual use. I mean, you could have a very benign commercial application, but then a small modification to it, can turn it into a military application. And if you do have these bad actors, they can exploit that. And so, I think that the big thing is creating a organization that is non-biased, that just wants to kind of level the playing field for everybody to create a set standard for cybersecurity in space. I think one group that would be perfect for that is CCI. They understand both the cybersecurity side of things, and they also have at Cal Poly the small satellite group. And just having kind of a clearing house or an agency where can provide information that is free, you don't need a membership for. And to be able to kind of collect that, but also reach out to the entire value chain for a mission, and making them aware of what potential capabilities are and then how it might be potentially used as a weapon. And keeping them informed, because I think the vast majority of people in the space industry just want to do the right thing. And so, how do we get that information free flowing to the U.S. government so that they can take that information, create assessments, and be able to, not necessarily stop threats from occurring presently, but identify them long before that they would ever even happen. Yeah, that's- >> General, I want to follow up on that real quick before we move to the next top track. Critical infrastructure you mentioned, across the oceans long distance, volume. When you look at the physical world, you had power grids here in the United States, you had geography, you had perimeters, the notion of a perimeter and a moat, and then you had digital comes in. Then you have, we saw software open up, and essentially take down this idea of a perimeter, and from a defense standpoint, and everything changed. And we have to fortify those critical assets in the U.S. Space increases the same problem statement significantly, because you can't just have a perimeter, you can't have a moat, it's open, it's everywhere. Like what digital's done, and that's why we've seen a surge of cyber in the past two decades, attacks with software. So, this isn't going to go away. You need the critical infrastructure, you're putting it up there, you're formulating it, and you got to protect it. How do you view that? Because it's going to be an ongoing problem statement. What's the current thinking? >> Yeah, I think my sense is that a mindset that you can build a firewall, or a defense, or some other system that isn't dynamic in its own right, is probably not headed in the right direction. I think cybersecurity in the future, whether it's for space systems, or for other critical infrastructure is going to be a dynamic fight that happens at a machine-to-machine speed and dynamic. I don't think that it's too far off where we will have machines writing their own code in real time to fight off attacks that are coming at them. And by the way, the offense will probably be doing the same kind of thing. And so, I guess I would not want to think that the answer is something that you just build it and you leave it alone and it's good enough. It's probably going to be a constantly-evolving capability, constantly reacting to new threats and staying ahead of those threats. >> That's the kind of use case, you know as you were, kind of anecdotal example is the exciting new software opportunities for computer science majors. I mean, I tell my young kids and everyone, man it's more exciting now. I wish I was 18 again, it's so exciting with AI. Roland, I want to get your thoughts. We were joking on another panel with the DoD around space and the importance of it obviously, and we're going to have that here. And then we had a joke. It's like, oh software's defined everything. Software's everything, AI. And I said, "Well here in the United States, companies had data centers and then they went to the cloud." And then he said, "You can do break, fix, it's hard to do break, fix in space. You can't just send a tech up." I get that today, but soon maybe robotics. The general mentions robotics technologies, in referencing some of the accomplishments. Fixing things is almost impossible in space. But maybe form factors might get better. Certainly software will play a role. What's your thoughts on that landscape? >> Yeah, absolutely. You know, for software in orbit, there's a push for software-defined radios to basically go from hardware to software. And that's a critical link. If you can infiltrate that and a small satellite has propulsion on board, you could take control of that satellite and cause a lot of havoc. And so, creating standards and that kind of initial threshold of security, for let's say these radios, or communications and making that available to the entire supply chain, to the satellite builders, and operators is incredibly key. And that's again, one of the initiatives that CCI is tackling right now as well. >> General, I want to get your thoughts on best practices around cybersecurity, state-of-the-art today, and then some guiding principles, and kind of how the, if you shoot the trajectory forward, what might happen around supply chain? There's been many stories where, we outsource the chips and there's a little chip sittin' in a thing and it's built by someone else in China, and the software is written from someone in Europe, and the United States assembles it, it gets shipped and it's corrupt, and it has some cyber, I'm making it up, I'm oversimplifying the statement. But this is what when you have space systems that involve intellectual property from multiple partners, whether it's from software to creation and then deployment. You got supply chain tiers. What are some of best practices that you see involving, that don't stunt the innovation, but continues to innovate, but people can operate safely. What's your thoughts? >> Yeah, so on supply chain, I think the symposium here is going to get to hear from General JT Thompson from space and missile system center down in Los Angeles, and he's just down the road from us there on the coast. And his team is the one that we look to to really focus on, as he fires and develops to again bake in cybersecurity from the beginning and knowing where the components are coming from, and properly assessing those as you put together your space systems, is a key piece of what his team is focused on. So I expect, we'll hear him talk about that. When it talks to, I think, so you asked the question a little more deeply about how do the best practices in terms of how we now develop moving forward. Well, another way that we don't do it right, is if we take a long time to build something and then General JT Thompson's folks take a while to build something, and then they hand it over to me, and my team operate and then they go hands free. And then that's what I have for years to operate until the next thing comes along. That's a little old school. What we're going to have to do moving forward with our space capabilities, and with the cyber piece baked in is continually developing new capability sets as we go. We actually have partnership between General Thompson's team and mine here at Vandenberg on our ops floor, or our combined space operation center, that are actually working in real time together, better tools that we can use to understand what's going on in the space environment to better command and control our capabilities anywhere from military satellite communications, to space domain awareness, sensors, and such. And we're developing those capabilities in real time. And with the security pieces. So DevSecOps is we're practicing that in real time. I think that is probably the standard today that we're trying to live up to as we continue to evolve. But it has to be done again, in close partnership all the time. It's not a sequential, industrial-age process. While I'm on the subject of partnerships. So, General Thompson's team and mine have good partnerships. It's partnerships across the board are going to be another way that we are successful. And that it means with academia and some of the relationships that we have here with Cal Poly. It's with the commercial sector in ways that we haven't done before. The old style business was to work with just a few large companies that had a lot of space experience. Well, we need a lot of kinds of different experience and technologies now in order to really field good space capabilities. And I expect we'll see more and more non-traditional companies being part of, and organizations, being part of that partnership that will work goin' forward. I mentioned at the beginning that allies are important to us. So everything that Roland and I have been talking about I think you have to extrapolate out to allied partnerships. It doesn't help me as a combined force component commander, which is again, one of my jobs. It doesn't help me if the United States capabilities are cybersecure, but I'm tryin' to integrate them with capabilities from an ally that are not cybersecure. So that partnership has to be dynamic and continually evolving together. So again, close partnering, continually developing together from the acquisition to the operational sectors, with as many different sectors of our economy as possible, are the ingredients to success. >> General, I'd love to just follow up real quick. I was having just a quick reminder for a conversation I had with last year with General Keith Alexander, who does a lot of cybersecurity work, and he was talking about the need to share faster. And the new school is you got to share faster to get the data, you mentioned observability earlier, you need to see what everything's out there. He's a real passionate person around getting the data, getting it fast and having trusted partners. So that's not, it's kind of evolving as, I mean, sharing's a well known practice, but with cyber it's sensitive data potentially. So there's a trust relationship. There's now a new ecosystem. That's new for government. How do you view all that and your thoughts on that trend of the sharing piece of it on cyber? >> So, I don't know if it's necessarily new, but it's at a scale that we've never seen before. And by the way, it's vastly more complicated and complex when you overlay from a national security perspective, classification of data and information at various levels. And then that is again complicated by the fact you have different sharing relationships with different actors, whether it's commercial, academic, or allies. So it gets very, very complex web very quickly. So that's part of the challenge we're workin' through. How can we effectively share information at multiple classification levels with multiple partners in an optimal fashion? It is certainly not optimal today. It's very difficult, even with maybe one industry partner for me to be able to talk about data at an unclassified level, and then various other levels of classification to have the traditional networks in place to do that. I could see a solution in the future where our cybersecurity is good enough that maybe I only really need one network and the information that is allowed to flow to the players within the right security environment to make that all happen as quickly as possible. So you've actually, John you've hit on yet another big challenge that we have, is evolving our networks to properly share, with the right people, at the right clearance levels at the speed of war, which is what we're going to need. >> Yeah, and I wanted to call that out because this is an opportunity, again, this discussion here at Cal Poly and around the world is for new capabilities and new people to solve the problems. It's again, it's super exciting if you're geeking out on this. If you have a tech degree or you're interested in changin' the world, there's so many new things that could be applied right now. Roland, I want to get your thoughts on this, because one of the things in the tech trends we're seeing, and this is a massive shift, all the theaters of the tech industry are changing rapidly at the same time. And it affects policy law, but also deep tech. The startup communities are super important in all this too. We can't forget them. Obviously, the big trusted players that are partnering certainly on these initiatives, but your story about being in the dorm room. Now you've got the boardroom and now you got everything in between. You have startups out there that want to and can contribute. You know, what's an ITAR? I mean, I got all these acronym certifications. Is there a community motion to bring startups in, in a safe way, but also give them ability to contribute? Because you look at open source, that proved everyone wrong on software. That's happening now with this now open network concept, the general was kind of alluding to. Which is, it's a changing landscape. Your thoughts, I know you're passionate about this. >> Yeah, absolutely. And I think as General Shaw mentioned, we need to get information out there faster, more timely and to the right people, and involving not only just stakeholders in the U.S., but internationally as well. And as entrepreneurs, we have this very lofty vision or goal to change the world. And oftentimes, entrepreneurs, including myself, we put our heads down and we just run as fast as we can. And we don't necessarily always kind of take a breath and take a step back and kind of look at what we're doing and how it's touching other folks. And in terms of a community, I don't know of any formal community out there, it's mostly ad hoc. And, these ad hoc communities are folks who let's say was a student working on a satellite in college. And they loved that entrepreneurial spirit. And so they said, "Well, I'm going to start my own company." And so, a lot of these ad hoc networks are just from relationships that have been built over the last two decades from colleagues at the university. I do think formalizing this and creating kind of a clearing house to handle all of this is incredibly important. >> And there's going to be a lot of entrepreneurial activity, no doubt, I mean there's too many things to work on and not enough time. I mean this brings up the question that I'm going to, while we're on this topic, you got the remote work with COVID, everyone's workin' remotely, we're doin' this remote interview rather than being on stage. Work's changing, how people work and engage. Certainly physical will come back. But if you looked at historically the space industry and the talent, they're all clustered around the bases. And there's always been these areas where you're a space person. You kind of work in there and the job's there. And if you were cyber, you were generally in other areas. Over the past decade, there's been a cross-pollination of talent and location. As you see the intersection of space, general we'll start with you, first of all, central coast is a great place to live. I know that's where you guys live. But you can start to bring together these two cultures. Sometimes they're not the same. Maybe they're getting better. We know they're being integrated. So general, can you just share your thoughts because this is one of those topics that everyone's talkin' about, but no one's actually kind of addressed directly. >> Yeah, John, I think so. I think I want to answer this by talkin' about where I think the Space Force is going. Because I think if there was ever an opportunity or an inflection point in our Department of Defense to sort of change culture and try to bring in non-traditional kinds of thinking and really kind of change maybe some of the ways that the Department of Defense does things that are probably archaic, Space Force is an inflection point for that. General Raymond, our Chief of Space Operations, has said publicly for awhile now, he wants the U.S. Space Force to be the first truly digital service. And what we mean by that is we want the folks that are in the Space Force to be the ones that are the first adopters, the early adopters of technology. To be the ones most fluent in the cutting edge, technologic developments on space and cyber and other sectors of the economy that are technologically focused. And I think there's some, that can generate some excitement, I think. And it means that we'll probably ended up recruiting people into the Space Force that are not from the traditional recruiting areas that the rest of the Department of Defense looks to. And I think it allows us to bring in a diversity of thought and diversity of perspective and a new kind of motivation into the service, that I think is frankly really exciting. So if you put together everything I mentioned about how space and cyber are going to be best friends forever. And I think there's always been an excitement from the very beginning in the American psyche about space. You start to put all these ingredients together, and I think you see where I'm goin' with this. That this is a chance to really change that cultural mindset that you were describing. >> It's an exciting time for sure. And again, changing the world. And this is what you're seeing today. People do want to change the world. They want a modern world that's changing. Roland, I'll get your thoughts on this. I was having an interview a few years back with a technology entrepreneur, a techie, and we were joking, we were just kind of riffing. And I said, "Everything that's on "Star Trek" will be invented." And we're almost there actually, if you think about it, except for the transporter room. You got video, you got communicators. So, not to bring in the "Star Trek" reference with Space Force, this is digital. And you start thinking about some of the important trends, it's going to be up and down the stack, from hardware to software, to user experience, everything. Your thoughts and reaction. >> Yeah, absolutely. And so, what we're seeing is timelines shrinking dramatically because of the barrier to entry for new entrants and even your existing aerospace companies is incredibly low, right? So if you take previously where you had a technology on the ground and you wanted it in orbit, it would take years. Because you would test it on the ground. You would verify that it can operate in a space environment. And then you would go ahead and launch it. And we're talking tens, if not hundreds of millions of dollars to do that. Now, we've cut that down from years to months. When you have a prototype on the ground and you want to get it launched, you don't necessarily care if it fails on orbit the first time, because you're getting valuable data back. And so, we're seeing technology being developed for the first time on the ground and in orbit in a matter of a few months. And the whole kind of process that we're doing as a small business is trying to enable that. And so, allowing these entrepreneurs and small companies to get their technology in orbit at a price that is sometimes even cheaper than testing on the ground. >> You know this is a great point. I think this is really an important point to call out because we mentioned partnerships earlier, the economics and the business model of space is doable. I mean, you do a mission study. You get paid for that. You have technology that you get stuff up quickly, and there's a cost structure there. And again, the alternative was waterfall planning, years and millions. Now the form factors are doing, now, again, there may be different payloads involved, but you can standardize payloads. You've got robotic arms. This is all available. This brings up the congestion problem. This is going to be on the top of mind of the generals of course, but you've got the proliferation of these constellation systems. You're going to have more and more tech vectors. I mean, essentially that's malware. I mean, that's a probe. You throw something up in space that could cause some interference. Maybe a takeover. General, this is the real elephant in the room, the threat matrix from new stuff and new configurations. So general, how does the proliferation of constellation systems change the threat matrix? >> So I think the, you know I guess I'm going to be a little more optimistic John than I think you pitched that. I'm actually excited about these new mega constellations in LEO. I'm excited about the growing number of actors that are going into space for various reasons. And why is that? It's because we're starting to realize a new economic engine for the nation and for human society. So the question is, so I think we want that to happen. When we could go to almost any other domain in history and when air travel started to become much, much more commonplace with many kinds of actors from private pilots flying their small planes, all the way up to large airliners, there was a problem with congestion. There was a problem about, challenges about behavior, and are we going to be able to manage this? And yes we did. And it was for the great benefit of society. I could probably look to the maritime domain for similar kinds of things. And so this is actually exciting about space. We are just going to have to find the ways as a society, and it's not just the Department of Defense, it's going to be civil, it's going to be international, find the mechanisms to encourage this continued investment in the space domain. I do think that Space Force will play a role in providing security in the space environment, as we venture further out, as economic opportunities emerge, wherever they are in the lunar, Earth, lunar system, or even within the solar system. Space Force is going to play a role in that. But I'm actually really excited about those possibilities. Hey, by the way, I got to say, you made me think of this when you talked about "Star Trek" and Space Force and our technologies, I remember when I was younger watchin' the Next Generation series. I thought one of the coolest things, 'cause bein' a musician in my spare time, I thought one of the coolest things was when Commander Riker would walk into his quarters and say, "Computer play soft jazz." And there would just be, the computer would just play music. And this was an age when we had hard media. Like how will that, that is awesome. Man, I can't wait for the 23rd century when I can do that. And where we are today is so incredible on those lines. The things that I can ask Alexa or Siri to play. >> Well that's the thing, everything that's on "Star Trek," think about it, it's almost invented. I mean, you got the computers, you got, the only thing really is, holograms are startin' to come in, you got, now the transporter room. Now that's physics. We'll work on that. >> So there is this balance between physics and imagination, but we have not exhausted either. >> Well, firstly, everyone that knows me knows I'm a huge "Star Trek" fan, all the series. Of course, I'm an original purist, but at that level. But this is about economic incentive as well. Roland, I want to get your thoughts, 'cause the gloom and doom, we got to think about the bad stuff to make it good. If I put my glass half full on the table, this economic incentives, just like the example of the plane and the air traffic. There's more actors that are incented to have a secure system. What's your thoughts to general's comments around the optimism and the potential threat matrix that needs to be managed. >> Absolutely, so one of the things that we've seen over the years, as we build these small satellites is a lot of that technology that the General's talking about, voice recognition, miniaturized chips, and sensors, started on the ground. And I mean, you have your iPhone, that, about 15 years ago before the first iPhone came out, we were building small satellites in the lab and we were looking at cutting-edge, state-of-the-art magnetometers and sensors that we were putting in our satellites back then. We didn't know if they were going to work. And then a few years later, as these students graduate, they go off and they go out to other industries. And so, some of the technology that was first kind of put in these CubeSats in the early 2000s, kind of ended up in the first generation iPhone, smartphones. And so being able to take that technology, rapidly incorporate that into space and vice versa gives you an incredible economic advantage. Because not only are your costs going down because you're mass producing these types of terrestrial technologies, but then you can also increase revenue and profit by having smaller and cheaper systems. >> General, let's talk about that real quickly, that's a good point, I want to just shift it into the playbook. I mean, everyone talks about playbooks for management, for tech, for startups, for success. I mean, one of the playbooks that's clear from your history is investment in R&D around military and/or innovation that has a long view, spurs innovation, commercially. I mean, just there's a huge, many decades of history that shows that, hey we got to start thinking about these challenges. And next thing you know it's in an iPhone. This is history, this is not like a one off. And now with Space Force you're driving the main engine of innovation to be all digital. You know, we riff about "Star Trek" which is fun, the reality is you're going to be on the front lines of some really new, cool, mind-blowing things. Could you share your thoughts on how you sell that to the people who write the checks or recruit more talent? >> First, I totally agree with your thesis that national security, well, could probably go back an awful long way, hundreds to thousands of years, that security matters tend to drive an awful lot of innovation and creativity. You know I think probably the two things that drive people the most are probably an opportunity to make money, but beating that out are trying to stay alive. And so, I don't think that's going to go away. And I do think that Space Force can play a role as it pursues security structures, within the space domain to further encourage economic investment and to protect our space capabilities for national security purposes, are going to be at the cutting edge. This isn't the first time. I think we can point back to the origins of the internet, really started in the Department of Defense, with a partnership I should add, with academia. That's how the internet got started. That was the creativity in order to meet some needs there. Cryptography has its roots in security, in national security, but now we use it for economic reasons and a host of other kinds of reasons. And then space itself, I mean, we still look back to Apollo era as an inspiration for so many things that inspired people to either begin careers in technical areas or in space and so on. So I think in that same spirit, you're absolutely right. I guess I'm totally agreeing with your thesis. The Space Force will have a positive, inspirational influence in that way. And we need to realize that. So when we are asking for, when we're looking for how we need to meet capability needs, we need to spread that net very far, look for the most creative solutions and partner early and often with those that can work on those. >> When you're on the new frontier, you got to have a team sport, it's a team effort. And you mentioned the internet, just anecdotally I'm old enough to remember this 'cause I remember the days that it was goin' on, is that the policy decisions that the U.S. made at that time was to let it go a little bit invisible hand. They didn't try to commercialize it too fast. But there was some policy work that was done, that had a direct effect to the innovation. Versus take it over, and the next thing you know it's out of control. So I think there's this cross-disciplinary skillset becomes a big thing where you need to have more people involved. And that's one of the big themes of this symposium. So it's a great point. Thank you for sharing that. Roland, your thoughts on this because you got policy decisions. We all want to run faster. We want to be more innovative, but you got to have some ops view. Now, most of the ops view people want things very tight, very buttoned up, secure. The innovators want to go faster. It's the ying and yang. That's the world we live in. How's it all balance in your mind? >> Yeah, one of the things that may not be apparently obvious is that the U.S. government and Department of Defense is one of the biggest investors in technology in the aerospace sector. They're not the traditional venture capitalists, but they're the ones that are driving technology innovation because there's funding. And when companies see that the U.S. government is interested in something, businesses will revector to provide that capability. And, I would say the more recent years, we've had a huge influx of private equity, venture capital coming into the markets to kind of help augment the government investment. And I think having a good partnership and a relationship with these private equity, venture capitalists and the U.S. government is incredibly important because the two sides can help collaborate and kind of see a common goal. But then also too, on the other side there's that human element. And as General Shaw was saying, not only do companies obviously want to thrive and do really well, some companies just want to stay alive to see their technology kind of grow into what they've always dreamed of. And oftentimes entrepreneurs are put in a very difficult position because they have to make payroll, they have to keep the lights on. And so, sometimes they'll take investment from places where they may normally would not have, from potentially foreign investment that could potentially cause issues with the U.S. supply chain. >> Well, my final question is the best I wanted to save for last, because I love the idea of human space flight. I'd love to be on Mars. I'm not sure I'm able to make it someday, but how do you guys see the possible impacts of cybersecurity on expanding human space flight operations? I mean, general, this is your wheelhouse. This is your in command, putting humans in space and certainly robots will be there because they're easy to go 'cause they're not human. But humans in space. I mean, you startin' to see the momentum, the discussion, people are scratchin' that itch. What's your take on that? How do we see makin' this more possible? >> Well, I think we will see commercial space tourism in the future. I'm not sure how wide and large a scale it will become, but we will see that. And part of the, I think the mission of the Space Force is going to be probably to again, do what we're doin' today is have really good awareness of what's going on in the domain to ensure that that is done safely. And I think a lot of what we do today will end up in civil organizations to do space traffic management and safety in that arena. And, it is only a matter of time before we see humans going, even beyond the, NASA has their plan, the Artemis program to get back to the moon and the gateway initiative to establish a space station there. And that's going to be a NASA exploration initiative. But it is only a matter of time before we have private citizens or private corporations putting people in space and not only for tourism, but for economic activity. And so it'll be really exciting to watch. It'll be really exciting and Space Force will be a part of it. >> General, Roland, I want to thank you for your valuable time to come on this symposium. Really appreciate it. Final comment, I'd love you to spend a minute to share your personal thoughts on the importance of cybersecurity to space and we'll close it out. We'll start with you Roland. >> Yeah, so I think the biggest thing I would like to try to get out of this from my own personal perspective is creating that environment that allows the aerospace supply chain, small businesses like ourselves, be able to meet all the requirements to protect and safeguard our data, but also create a way that we can still thrive and it won't stifle innovation. I'm looking forward to comments and questions, from the audience to really kind of help, basically drive to that next step. >> General final thoughts, the importance of cybersecurity to space. >> I'll go back to how I started I think John and say that space and cyber are forever intertwined, they're BFFs. And whoever has my job 50 years from now, or a hundred years from now, I predict they're going to be sayin' the exact same thing. Cyber and space are intertwined for good. We will always need the cutting edge, cybersecurity capabilities that we develop as a nation or as a society to protect our space capabilities. And our cyber capabilities are going to need space capabilities in the future as well. >> General John Shaw, thank you very much. Roland Coelho, thank you very much for your great insight. Thank you to Cal Poly for puttin' this together. I want to shout out to the team over there. We couldn't be in-person, but we're doing a virtual remote event. I'm John Furrier with "theCUBE" and SiliconANGLE here in Silicon Valley, thanks for watching. (upbeat music)