>>Welcome back everyone to Supercloud 22. This is the Cube's live presentation streaming out virtually our inaugural event, kind of a pilot I'm John Furo of the cube with Dave ante. Got a great panel here to discuss the enablers and blockers question mark for superclouds. We got, we got kit Culbert, CTO of VMware basketball, Gor CEO platform nine, and has Pani who is the CEO of RA systems. We got a mix of the big leader, VMware and the upstart companies growing into the same space, all cloud native friends of the cube. Great to see you guys. Thanks for coming on. Thank >>You. >>Start. All right. So there's no debate cloud native is booming. We see that clearly Kubernetes became a unifying force. It's an ops layer kind of almost like a kind of a midline between dev and ops DevSecOps is happening at scale. What are the blockers and what are the enablers for super cloud? What do we need? Let's see what do get your take? >>Sure. So UN I spoke about this a little bit in, at New York summit, the big trend I'm seeing, and it's, it's a blocker that's being sort of taken care of by enterprises, which is, you know, until very recently, Kubernetes was effectively a project that NA would take on. They'd try things out, they'd go to the cloud, they'd spin things up. And then the next team would come and they'd do the same things. And there was no consistency. There was no ization, it's a mess, right? It's all over the place. Some things are moving fast. Some things are not going fast and this is not how enterprises do business, right? That's not how things work. Traditionally enterprises have had it organizations that create standards, right? So those it organizations now kind of are starting to think like a platform organization. So centrally come up with the right framework for all application teams to consume infrastructure, modern infrastructure. So I'm not using the word Kubernetes here because Kubernetes is an enabler. We are a Kubernetes company, obviously, but it's about modern applications, modern infrastructure. So stepping back and thinking about it as to how an enterprise will do this across the board is the right answer. And I'm seeing this happen in a pretty significant way across all the large enterprises I talked to. >>That's why you've had a great career. And we talked before you came on Opia you did a turnaround there, we, you even go back to the old days of the web web 1.0 and early software. You've seen the movie before. >>Yes. >>You know, complexity is not solved way more complexity. This is kind of the old enterprise way. And they don't want that. They've seen the benefits of self-service. They see architecture and standards as being an enabler. Where are we in here in the market? Is, are we positioned in your opinion for customers to get the value of a super cloud? >>Absolutely. So if you think about, first of all, I think the topic of cloud native developers and app developers picking containers and Kubernetes, that's a done deal, right? That has already happened. So every cloud native developer is already using these tools. Now, I think as has been discussed today in you, in the earlier sessions, is, are the operations and infrastructure catching up or they're lagging behind, right? As more and more developers are using multi-cloud technologies, enterprises are creating a choice, I think operations and what we also strongly believe that's actually part of the name of our company is, is a platform. The platform of which a company uses to transform itself to be cloud native is the big opportunity. I don't think it's a blocker, but it's a huge opportunity. And I think this is where, you know, as you can't stop developers from developing on different clouds, private, public, multi edge, that's gonna happen. Innovation is gonna continue. But then how does the infrastructure in the platform make it seamless? Right? And almost treat all these different clouds as a single pan super cloud platform. That's I think is the >>Opportunity. So we in a platform more than with other companies, or is there one unified platform called cloud native? We know customers been buying tools from security they're they got so many tools in, in their tools shed, so to speak. What is that platform? I mean, is it more unique, fragmentation? Is it unified? >>I mean, if you think about it, a couple of it's a combination of tools that are stitched together to reach a purpose, right? So if you think about, you know, APIs continued APIs that's been discussed earlier today, I think that's, that should be standardized. The other thing is always on monitoring because I think that's a very key aspect. Once you build it, then as the enterprises are using it, the always on monitoring becomes. So I think it's a combination of capabilities that are stitched together to enable the acceleration for companies to become cloud native. >>I, I have a thought on a blocker. None of you guys are gonna like it. Oh, maybe you can come. Maybe some of you guys probably won't but comment, but maybe John will. I think AWS is a blocker to Supercloud cuz they, they don't want those cross cloud service. It's like they, they, for years they wouldn't even say multicloud. The first time I heard it was in Boston three weeks ago, I actually heard it. So Hey, you see, >>You know, I'm gonna disagree with that. Okay. >>But, but okay, go ahead. All >>So we'll get their reaction. So my, we just heard from the last panel that the security should be leading the consortium. Yeah. Because they're, they're not the enemy they're actually, >>Maybe they should be >>Well back in the old web days, when standards were driving things, you had a common enemy, proprietary NASAs, proprietary networking stack. So the evil empire was at and T that's owned Unix. If you remember, they copyright that. >>So you think they're greasing the skids for, >>I think Supercloud, I think the hyperscalers could cuz they're driving the CapEx, they're providing the value. So in my opinion, Amazon and Azure, whoever does the right thing first can win every, maybe >>This is how Google could catch up >>It. It could be a, it could be a Slingshot move. It could, you know, boomerang, someone to the front of the line or extend. Amazon's already huge lead. So if I'm AWS, if I'm Adam Slosky and I'm talking to Andy Jassy, he says, how am I gonna differentiate myself? I'd say, I'm gonna come in and own multicloud. I'm gonna own Supercloud we are the Supercloud and you work with AWS's primitives in a way that makes services work. I would go for that. I'd be like, okay, show me more. What do you >>Think? I, I, I don't think think any one company is going to be a super cloud because I think yes, there is going to be a lot of workloads on public clouds, but there's a huge amount of workloads at the enterprise at the edge at the store. I think those will continue for various reasons, whether it's data, sovereignty regulations. So I think it's going to be a combination. Everybody's not gonna go to one, you know, cloud, it's going to be an amalgamation. >>Okay. But I I've argued that snowflake is a form of a super data cloud and a very specific use case, you know, Aviatrix is trying to be a network, you know, layer and you know, sneak in a security, let me on and on, on a lot of small you get, you get super cloud stove pipes, but, but nonetheless you're, you're still abstracting. I mean, we've this industry attractions, right? >>Well this, this concept I completely agree with, right? This idea that, so, so one of the, my is that right now enterprises buy 500 different technologies and they have to become PhDs in 500 different things. It's just never gonna happen skills issue, which is no way. Right. So what's gonna happen is all of these providers are gonna essentially become managed service providers. Cloud is in manifestation of that. Snowflake is a ation data breaks is a manifestation of that. Right? So in our general industry, there's gonna be a handful of platforms. Right. And they're gonna work across these clouds. Amazon may have one too. Right? Look, they, they, they, for the longest time sort of ignored OnPrem, but now they have something called SSA, which runs on Preem. Right. Why, why would they bother? Because, well, obviously there's a lot of money to be made in a data center as well. >>So I, my sense is they get it completely understand and appreciate that there's other things outside of Amazon. But in terms of what Bosco was talking about, my sense is, you know, these multiple platforms will come about. And to the point we were making earlier about standardization and I, I mean, is it gonna be one company or is it gonna be standards that everybody will else will adopt? There's a topic that the three of us have talked about before, which is this vCenter for Kubernetes. Right. And all due respect to kit. Right. My sense is that there there's gonna be multiple companies that are gonna start working towards a vCenter for Kubernetes. And it is right. I mean, that's how I've, I mean, I've been thinking about this before and a half years, including >>VMware. >>Yeah. And you know, and we, we should compare notes. Right. But what's gonna happen is there was a, there was a distinct advantage VMware had back in the day because ESX was their product. Right. And that was a standard right now. What's the ESX in the new it's sort of Kubernetes, right. I mean, it's on bare metal for the most part or whatever VMs. So that's a standard, that's got standardized APIs, the things around it are standardized APIs. So what is the unfair advantage that one company has other than execution? >>Nothing. Well also composability if you over rotate on Kubernetes, for example, and not take advantage of say C two, for instance. Totally, >>Totally. >>It's a mix and match. >>Yeah. But I think, I think if you get too focused on Kubernetes, it's a means to an end. Yeah. But at the end of the day, it it's a mean to end end. And I think all these tools, there's a lot of standardization happening that's gonna happen. Right. And no one vendor is gonna control that. Right. It's it's going to be, it's gonna continue. I think how you bring these together and orchestrate right. And manage the service. Because I think that if you think about the lack of skills to keep up with the operations and platforms is one of the largest inhibitors right now for enterprises to move as fast as they want to become cloud native. >>And you have the shiny new toy problem kit where people just go and grab it. You know, Keith Townsend has a, as a quote, he says, look, we essentially move at the speed of the CIO or else we're going too fast or too slow. So, so the, to, to the point about the new toy now I've got new skills. >>Yep. Well, so this has been a really good discussion. And I think so there's a couple of things, right. Going back to the, the paper that we wrote, right. How we have these different sort of layers of multi-cloud services or, or categories of multi-cloud services. And it's exactly to capture some of the ex different examples you just mentioned. And yeah, the challenge is that each of them by themselves are a little bit of an island today. Like you don't have that extra level of integration. And so what the platform teams typically do is try to add that extra glue to make the experience more seamless for the, the, the, you know, developers at that company. And so like, you know, for instance, things like identity. So the nice thing about going to a single public cloud is that there's one, usually one identity system for everything. And that's great. All the different services roles are, you know, are back all that. Stuff's all centralized, but you don't have that when you're going across many different multicloud services. So what does that look like? So I think there's some of these different crosscutting concerns that we need to look at how we standardize on as an industry. And that's, again, one of the things >>You felt that part. And I think, I think also the other key thing is yes, you can always say I'll put everything in one world, world garden and I'm done. Yeah. Okay. But that's not the reality because at some point you need, the flexibility and cost comes into play and flexibility to move comes into play. And I think that is a key factor. Yep. Right. >>Yeah. And so like, so then the question is, what degrees of freedom do you give yourself there? And I think that's the architectural question is how you, how do you design it? What sort of abstractions do you leverage? And I think that goes back to some of our discussion before, which is, do you directly go on top of a native cloud service or do you use a multi-cloud service? >>But I think it's a combination of, I don't think it's either or no, it's not, it's not an either or you have to have the ability to choose a public cloud or do it private. Yeah. At the same time you don't change. It's like a common dictionary, right. You're not gonna change every time the accent changes, you know? So that's, >>So here's a question for you guys. So what has to happen for super clouds, be existing assume that AWS and Azure and Google, aren't gonna sit still assume that maybe they normalize into some sort of swim lane or position that they have to rationalize. What, assuming they're not gonna sit still, what has to happen for super clouds to, to actually work >>Well? Well, I think, you know, really quick going back to the platform team point, I would say that the platform teams at various companies, and we got one at VMware two, they're creating a rudimentary form of a super cloud. Right. Cause they, you know, absolutely like if, if they are supporting multiple clouds, like all the things they're stitching together and all that work, that is a super cloud. The problem is that there's not really a standard approach or architecture or reusable things to enable that. I think that's really what's missing. >>Yeah. But I think the key here is standard us reusable. Because for example, we have customers who are in doesn't matter where they are, some of their loads are in public cloud. Some are in private, some are at the edge, but they're still using the same platform. Yeah. Right. So it is a standard open source based technology. So it is standard. There's no lock in for them from an infrastructure point of view. Yep. And it gives them the flexibility because certain apps, you wanna put it on the public cloud, certain apps, you do not, you need the, I mean, for example, some of the AI, I think earlier discussion that was going on about chips and AI and ML workloads. I mean, think about moving all of that to a public cloud, to, and I think a lot of machine learning and AI applications are going to happen where the data is getting created at the edge. Yeah. At the edge >>Public cloud. It's not gonna happen cloud. It's gonna be real time in, >>It's gonna the end time. And so therefore you have to decide based on your workload, what are you gonna move all the way to a public cloud? And what are you need to do to make business decisions at this spot where the data is created? >>That's a huge disruptor potentially to Supercloud. This is a whole new architecture that emerges at the edge with a whole new set of economics. I >>Think the edge is gonna be like massively disruptive. >>I think it's gonna think about, if you think about the edge, go beyond just the classic definition of edge. Think about branches in stores, retail stores. Yeah. Right. I mean, you cannot shut down retail store because you lost connectivity to the network or something you still have to serve your company >>Edge is a disruptive enabler. I think it's gonna change potentially change the position of the players in the business. Whoever embraces the edge. >>Yeah. Maybe going back to the question that you had asked before, which is what is, what is a framework for a super cloud? So you said something that is important, which is your team's burning one. Yeah. I met that team. Actually. They seem to be very sharp guys. >>They're they're mine. They're my are great. They're awesome. >>We got a deal going on here. Yeah. >>I tried. We have >>It. >>So this is the interesting part, right? So I will pause it that the super cloud of the future will be a company that owns zero servers and no network. >>Okay. >>That's gonna happen. Okay. So I just kind of it's >>Full point you >>Made before I made that point just about the public cloud, just so Mr. >>Yeah, yeah, yeah, yeah. No, that really interesting. Not >>We that, so I've thought about this a long time that in my opinion, and I've, I'm, I'm sure I've said this to you, John, that, you know, the one company that I've always believed has the best shot at doing this well is actually VMware because that's the one company that's, you know, that there's, there's no, you know, infrastructure back haul. Right. You know, that you're carrying, but, but in terms of thinking and getting there, you know, being, being a company that can do it is not the same as being the company who has done it. That's a, there's a distance, but >>I have to defend that now because hyperscalers are not gonna be able to super cloud. They're not now it's hype. See, agreed, great point. Public clouds will be part of the super cloud. Yeah, totally. But they will not, the hyperscalers are not building super clouds. Totally. They're blocking it. Right. Yeah. >>They're enabling it. >>We agree on >>No, they're enabling >>Because it's, it's not in there to their advantage. Right. Look, the, the snowflake example you gave is the pivotal example in this conversation. Yep. Right. Why does snowflake exist at all when Redshift exists and all these other things exist because they provide value that is beyond a single clouds purview. Right. And at that point, just step back from our platforms and what we sell. Forget about that for a minute. Right. It's it's about, look, I think, I think this, we are, this market is early, we're out early, right. 10 years from now, what will a company look like? That actually solves a superly problem they're gonna solve for yeah. Kubernetes, whatever. Right. But they're gonna solve for truly modern applications. >>Yeah. They're gonna refactor application that has new economics new value, right. >>At that point, this idea of edge and cloud, forget about it. Right. This is all distribution issues, right. It doesn't really matter. Is it retail or not? Yeah, absolutely. These are places, but, but the way, the right way to think about this is not about edge versus cloud, right? This is about an app. Sometimes it needs to run in one location and it's good enough. Sometimes it needs to run in 10,000 locations and, and it's a distribution issue. I've always believed there's this idea of edge versus cloud. This is BS, right? Because it, it is a cloud over a different size. Sure. But, but I'm making a slightly different point. Sure. Which is, it's a distribution problem. Right. If you step back and think about distribution, my app could run in Azure or AWS or in a retail store, in a branch or whatever. Right. >>And once that is done, the question is, how am I in, in making all this happen? There was a point made in the prior conversation, in the, in the session about a database kind of popping up in the place where I needed to run. Okay. Nobody does that today, by the way. Right. At least truly well right about that, sir, that will come. Right? Yeah. But when that comes, my application is a conglomerate of compute data. I don't know a, a service bus and network and all these things and they will all kind of pop together. That company does not exist >>Today. Well, we'll, we will be documenting which we have more time. We're gonna document it. We have to unfortunately stop this panel because it's awesome. We can go for another hour. Sure. Let's bring you guys back, but that's it. The super cloud of the future will look like something and we're gonna debate it. And speaking of snowflake, we have the co-founder here next to sit down with us to talk about what he thinks about this super cloud. He, he probably heard the comment, come back more coverage. This break with the co-founder of snowflake after the short break. >>Do thank you.

>>Hello, welcome back to the cubes coverage of Monaco, crypto summit presented by digital bits. It's a conference where a lot of the people using digital bits and the industry coming together around the future of crypto in the applicates got a great guest garage, rod cot, founder, and CEO of an innovative company. Love this co I love this company, Luke mogul, Rob, thanks for coming on the queue. Appreciate it. Oh, >>Thank you for having >>Us. Yeah. So I checked out what you guys are doing. You've got the sports metaverse angle going on with super valuable, cuz sports is super entertaining. Uh, people are engaged. There's huge fan base, huge online now, digital convergence going on with the physical, you know, you see all kinds of sports betting going on now everything's going digital. There's a whole nother consumer experience going on with sports and the game is still the same on the, on the field or so to, or the court. That's correct. Yeah. Now it's going to digital take a minute to explain what you guys are working on. >>Yeah, so yes, we are building out a sports ERs where we are bringing athletes, whether they're NBA stars, NFL stars, w N B a many of those athletes into meows giving them the ownership of the entire, um, meows commerce along with gameplay. So that's something from our perspective, this, uh, this is something that we're focused on. We're building out stadiums. Athletes can own stadiums. Athlete can create their own training centers, media hubs. Um, and imagine Lisa, Leslie for example, is building out a woman leadership sports academy, right? We have Michael Cooper building out defensive academy. So those are all the brands. We have 174 NBA w N B stars. And, um, and we are building out this, >>The brand is the brand, is the platform that's correct. That's the trend we're seeing. And it's, it's also an extension of their reach in community. So there's, they can convert their star power and athlete with owner's approval. If they probably write it on to the contracts, he, they can imagine all the complications, but they bring that online and extend that energy and brand equity yep. To fans and social network. Yeah. >>And many of these athletes are tremendous successful in their web two careers, right? Yeah. Um, some are current athletes, some are former athletes, but they have built such a brand persona where people are following them on Instagram. For example, Carlos Boozer. He has like almost 6 million followers between Twitter and Instagram and those kind of brands are looking or how do I give back to the community? How do I engage with my community and web three? And especially with our platform, we are giving that power back to the players. >>So you guys got some big names booers on there. You mentioned Carlos Boozer. You mentioned that Lisa, Leslie others among others, Michael Cooper throw back to the old Lakers, uh, magic. Johnson's kind actually here in crypto. We just saw him in the lobbies and in dinner and the other night, um, at Nobu, um, you got a lot of NBA support. Take a take, take, even explain how you're working this angle. Uh, you got some great traction, uh, momentum. Um, you got great pedigree, riot games in your career. Uh, you kind of get the world, the tech world, the media world, as it comes together. What's the secret sauce here? Is it the NBA relationship combination of the team explained >>It's really focusing on what, uh, we are building on me was focusing on players first, right? So players are literally, we call our platform as, uh, owned by the players, made for the players. Uh, and engagement is really all done through the players, right? So that's our key sauce. And when we worked out with NBA, we, we are part of the NBA BPA acceleration program for 2022 that is funded by a six Z, uh, and, and many others. Um, and our partnership with league is very critical. So it's not only partnered with player association partnered with leagues, whether it's NBA, w N B a NFL. So those are the venues. And this becomes almost a program, especially for athletes to really generate this lifetime engagement and royalty model because some of this famous athletes really want to give back to the communities. So like for example, I use Lisa Leslie a lot, but Lisa, Leslie really wants to empower women leadership, leadership, and really help, um, women in sports, for example. Right? So those are the angles that, um, that really people are excited about. >>Well, for the people watching that might not understand some of the ins and outs of sports and, and rod, your background in your team, it's interesting. The sports teams have been on the big day to train for many, many years. You look at all the stadiums. Now they've got mobile devices, they got wifi under the chairs. They use data and technology to manage the team. Mm-hmm, <affirmative> manage the stadium and venue and operations suppliers, whatnot. And then also the fans. So you, they, they got about a decade or so experience already in the digital world. This is not new to the, to the sports world. Yeah. So you guys come to the table kind of at a good time. >>Yeah. Especially the defi of the sports, right? So there's a defi of the finance, but this is the really, uh, a, a decentralization of the sports is something that there's a lot of traction. And there are many companies that are really focusing on that. Our focus obviously is players first, right? How do we give power to the players? Uh, and those are really driving the entire engagement. And also the brands >>How's the NBA feel about this because, you know, you got the NBA and you get the team, you got the owners. I mean, the democratization of the players, which I love by the way that angle kind of brings their power. Now's the new kind of balance of power. How is the NBA handling this? What's some of the conversations you've had with the, the organization. >>Yeah. So obviously there are a lot of things that, uh, people have to be careful about, right? They have existing contracts, existing, digital media rights. Um, so that's something that, uh, we have to be very tactful when we are working with NBA and NPA, uh, on what we can say, we cannot say. So that is obviously they have a lot of existing multimillion or billion dollar contracts that they cannot void with the web because the evolution of web three, >>You know, I love, uh, riffing on the notion of contract compliance when there's major structural change happening. Remember back in baseball, back in the days before the internet, the franchise rights was geographic territory. Mm-hmm <affirmative> well, if you're the New York Yankees, you're doing great. If you're Milwaukee, you're not doing too good, but then comes the internet. That's good. That's no geography. There's no boundaries. That's good. So you're gonna have stadiums have virtual Bo. So again, how do they keep up with the contracts? Yeah. I mean, this is gonna be a fundamental issue. >>That's >>Good. Good. And I think if they don't move, the players are gonna fill that void. >>That's correct. Yeah. And especially with this, this an IL deal, right. That happened for the players, uh, especially college athletes. So we are in process of onboarding 1.5 million college athletes. Uh, and those athletes are looking for not only paying for the tuition for the colleges, but also for engagement and generating this early on, uh, >>More okay. Rod, we're gonna make a prediction here in the cube, 20, 20 we're in Monaco, all the NBA, NHL, the teams they're gonna be run by player Dows. Yeah. What do you think? A very good prediction. Yeah. Very good prediction. Yeah. I mean you, I mean, that's a joke, I'm joking aside. I mean, it's kind of connecting the dots, but you know, whether that happens or not, what this means is if this continues to go down this road, that's correct. Get the players collectively could come together. Yeah. And flip the script. >>Yeah. And that's the entire decentralization, right. So it's like the web three has really disrupted this industry as you know. Um, and, and I know your community knows that too. >>Of course, course we do. We love it. >>Something from sports perspective, we are very excited. >>Well, I love it. Love talking. Let's get to the, to the weeds here on the product, under the hood, tell about the roadmap, obviously NFTs are involved. That's kind of sexy right now. I get the digital asset model on there. Uh, but there's a lot more under the coverage. You gotta have a platform, you gotta have the big data and then ultimately align into connecting other systems together. How do you view the tech roadmap and the product roadmap? What's your vision? >>Yeah. So the, the one thing that you had to be T full, uh, as a company, whether it's LUT, mogul or any other startup, is you have to be really part of the ecosystem. So the reason why we are here at Monaco is that we obviously are looking at partnership with digital bits, um, and those kind of partnership, whether it's fourth centric, centric are very critical for the ecosystem in the community to grow. Um, and that's one thing you cannot build a, another, uh, isolated metaverse right? So that's one thing. Many companies have done it, but obviously not. >>It's a wall garden doesn't work. >>Exactly. So you have to be more open platform. So one things that we did early on in our platform, we have open APIs and SDKs where not only you as an athlete can bring in your, uh, other eCommerce or web, uh, NFTs or anything you want, but you can also bring in other real estate properties. So when we are building out this metaverse, you start with real estate, then you build out obviously stadiums and arenas and academies training academies, but then athletes can bring their, uh, web commerce, right. Where it's NFT wearables shoe line. So >>Not an ecosystem on top of Luke Mo. So you're like, I'm almost like you think about a platform as a service and a cloud computing paradigm. Yeah. Look different, not decentralized, but similarly enabling others, do the heavy lifting on their behalf. Yeah. Is that right? >>So that's correct. Yes. So we are calling ourself as the sports platform as a service, right. So we want to add the word sports because we, uh, in, in many contexts, right. When you're building metaverse, you can get distracted with them, especially we are in Los Angeles. Right. >>Can I get a luxury box for the cube and some of the metaverse islands and the stadiums you're doing? >>We, we are working >>On it. We're >>Definitely working on, especially the, uh, Los Angeles, uh, stadium. Yeah. >>Well, we're looking for some hosts, anyone out there looking for some hosts, uh, for the metaverse bring your avatar. You can host the cube, bro. Thanks for coming on the cube. Really appreciate. What's the, what's next for you guys, obviously, continuing to build momentum. You got your playful, how many people on the team what's going on, give a plug for the company. What are you looking for share with the audience, some of the, some of your goals. Yeah. >>So, uh, the main thing we're looking for is really, um, from a brand perspective, if you are looking at buying properties, this would be an amazing time to buy virtual sports stadium. Um, so we are, obviously we have 175 stadiums in roadmap right now. We started with Los Angeles. Then we are in San Francisco, New York, Qatar, Dubai. So all those sports stadiums, whether they're basketball, football, soccer are all the properties. And, uh, from a community perspective, if you want to get an early access, we are all about giving back to the community. Uh, so you can buy it at a much better presale price right now. Uh, so that's one, the second thing is that if you have any innovative ideas or a player that you want to integrate into, we have an very open platform from a community engagement perspective. If you have something unique from a land sale perspective yeah. Or the NFD perspective plug, contact us at, at Raj lumo.com. >>And I'm assuming virtual team, you in LA area where where's your home. >>So, yeah, so I live in Malibu, um, and our office is in Santa Monica. We have an office in India. Uh, we have few developers also in Europe. So, uh, and then we are team of 34 people right now >>Looking to hire some folks >>We are looking for, what >>Are you, what are you looking for? >>So, uh, we are looking for a passionate sports, uh, fanatics. >>It's a lot, not hard to find. Yeah. >><laugh> who knows how to also code. Right? So from blockchain perspective, we are, uh, chain agnostic. Uh, but obviously right now we are building on polygon, but we are chain agnostic. So if you have any blockchain development experience, uh, that's something we, we are looking for. Yeah. >>RA, thanks for coming out. Luke Mo check him out. I'm John furry with the cube here in Monaco for the mono crypto summit presented by digital bits. We got all the action, a lot of great guests going on, stay with us for more coverage. Um, John furrier, thanks for watching.

>>And welcome to the DockerCon cube cover here on the main stage. So HIRA RA development manager at J Frogg. Welcome to the cube. You guys have been on many times, uh, with J Frogg on the cube, great product you guys are doing great. Congratulations on all the six. Thanks for coming on the cube. >>Thank you. Thank you for having >>Me. So I'm really interested in talking about the supply chain, uh, package management, supply chain, and software workflow, huge discussion. This is one of the hottest issues that's being solved on by, with, with in DevOps and DevSecOps in, in the planet. It's all over the, all over the news, a real challenge, open source, growing so fast and so successful with cloud scale and with automation, as you guys know, you gotta ha you gotta know what's trusted, so you gotta build trust into the, the product itself. So developers don't have to do all the rework. Everyone kind of knows this right now, and this is a key solve problem you guys are solving. So I gotta ask you, what is the package management issue? Why is it such an important topic when you're talking about security? >>Yeah. Uh, so if you look at, uh, look at how software is built today, about 80 to 90% of that is open source. And currently the way we, the way we pull those open source libraries, we just, we just have blind trust in, in repositories that are central, and we rely on whatever mechanism they have built to, to establish that trust, uh, with the developer who is building it. And from, from our experience, uh, we have learned that that is not sufficient, uh, that is not sufficient to tell us that that particular developer built that end product and, uh, whatever code that they build is actually coming out in the end product. So we need, we need something to bridge that gap. We need, we need a trustworthy mechanism there to bridge that gap. And there are, there are a few other, uh, elements to it. >>Um, all these center depositories are prone to, uh, single point of failures. And, you know, in, we have all experience what happens when one of those goes down and how it stops production and how it, how it stops just software, uh, development, right? And we, what we are working on is how do we build a system where we, we can actually have, uh, liquid software as a reality and just continue to build software, regardless of all these systems of being live all the time, uh, and also have a, an implicit, uh, way of mechanism to trust, uh, what is coming out of those systems? >>You know, we've talked with you guys in the past about the building blocks of software and what flows through the pipelines, all that stuff's part of what is automated these days and, and, and important. And what I gotta ask you because security these days is like, don't trust anything, you know, um, here it's, you're, you're trusting software to be in essence verified. I'm simplifying, obviously. So I gotta ask you what is being done to solve this problem, because states change, you know, you got data, you got software injections, and you got, we got containers and Kubernetes right here, helping all this is on the table now, but what is currently being done to solve the problem? Cause it's really hard. >>Yeah, it is. It is a really hard problem. And currently, right, when we develop software, we have a team, uh, which, which we work with and we trust whatever is coming out of the team. And we have, we have a, um, what do you call certified, uh, pro production mechanism to build that software and actually release it to our customers. And when it is done in house, it is easy because we are, we control all the pieces. Now what happens when, when we are doing this with open source, we don't have that chain. We need that chain, which is independent. We just independent of where the software was, you know, produced versus where it is going to be used. We need a way to have Providence of how it was built, which parts actually went in, uh, making, uh, making the end product. Uh, and, and what are the things that we see are, are, are, uh, continuing, uh, uh, continuing evidences that this software can be used. So if there is a vulnerability that is discovered now, that is discovered, and it is released in some database, and we need to do corrective action to say that this vulnerability associated with this version, and there is no, there's no automated mechanism. So we are working on an automated mechanism where, where you can run a command, which will tell you what has happened with this piece of, uh, software, this version of it, and whether it is production worthy or not. >>It's a great goal. I gotta say, but I'll tell you, I can guarantee there's gonna be a ton of skeptics on this security people. Oh, no, I don't. I doubt it's always a back door. Um, what's the relationship with Docker? How do you guys see this evolving? Obviously it's a super important mission. Um, it's not a trend that's gonna go away. Supply chain software is here to stay. Um, it's not gonna go away. And we saw this in hardware and everyone kind of knows kind of what happens when you see these vulnerabilities. Um, you gotta have trusted software, right? This is gonna be continuing what's the relationship with DockerCon? What are you guys doing with dock and here at DockerCon? >>So we, when we actually started working on this project, uh, both Docker and, uh, J frog had had similar ideas in mind of how, how do we make this, uh, this trust mechanism available to anyone, uh, who wants it, whether they're, whether they're in interacting with dock hub or, or regardless of that, right. And how do we actually make it a mechanism, uh, that just, uh, uh, that just provides this kind of, uh, this kind of trust, uh, without, without the developer having to do something. Uh, so what we worked with, uh, with Docker is actually integrating, um, integrating our solution so that anywhere there, uh, there is, uh, Docker being used currently, uh, people don't have to change those, uh, those behaviors or change those code, uh, those code lines, uh, right. Uh, because changing hand, uh, changing this a single line of code in hundreds of systems, hundreds of CI systems is gonna be really hard. Uh, and we wanted to build a seamless integration between Docker and the solution that we are building, uh, so that, so that you can continue to do Docker pro and dock push and, but get, uh, get all the benefits of the supply chain security solution that we have. >>Okay. So let's step back for a minute and let's discuss about the pro what is the project and where's the commercial J Frogg Docker intersect take that, break that apart, just step out the project for us. What's the intended goals. What is the project? Where is it? How do people get involved and how does that intersect with the commercial interest of JRO and Docker? >>Yeah. Yeah. My favorite topic to talk about. So the, the project is called Peria, uh, Peria is, uh, is an open source project. It is, it is an effort that started with JRO and, and Docker, but by no means limited to just JRO and dock contributing, we already have five companies contributing. Uh, we are actually building a working product, uh, which will demo during, uh, during our, uh, our talk. And there is more to come there's more to come. It is being built iteratively, and, and the solution is basically to provide a decentralized mechanism, uh, similar to similar to how, how you, uh, do things with GI, so that you have, you have the, uh, the packages that you are using available at your nearest peer. Uh, there is also going to be a multi load build verification mechanism, uh, and all of the information about the packages that you're going to use will be available on a Providence log. >>So you can always query that and find out what is the latest state of affairs, what ES were discovered and make, make quick decisions. And you don't have to react after the fact after it has been in the news for a while. Uh, so you can react to your customer's needs, um, uh, as quick as they happen. And we feel that the, our emphasis on open source is key here because, uh, given our experience, you know, 80 to 90% of software that is packaged, contains open source, and there is no way currently, which we, uh, or no engineering mechanisms currently that give us that, uh, that confidence that we, whatever we are building and whatever we are dependencies we are pulling is actually worthwhile putting it into production. >>I mean, you really, it's a great service. I mean, you think about like all that's coming out, open source, open source become very social, too. People are starting projects just to code and get, get in the, in the community and hang out, uh, and just get in the fray and just do stuff. And then you see venture capitals coming in funding those projects, it's a new economic system as well, not just code, so I can see this pipeline beautifully up for scale. How do people get involved with this project? Cause again, my, my questions all gonna be around integration, how frictionless it is. That's gonna be the challenge. You mentioned that, so I can see people getting involved. What's what's how do people join? What do they do? What can they do here at Docker con? >>Yeah. Uh, so we have a website, Percy, I P yr S I a.io, and you'll find all kinds of information there. Uh, we have a GI presence. Uh, we have community meetings that are open to public. We are all, we are all doing this under the, uh, under the umbrella limits foundation. We had a boots scrap project within Linux foundation. Uh, so people who have interest in, in all these areas can come in, just, just attend those meetings, uh, add, uh, you know, add comments or just attend our stand up. So we are running it like a, like a agile from, uh, process. We are doing stand up, we are doing retrospectives and we are, we are doing planning and, and we are, we are iteratively building this. So what you'll see at Dr. Conn is, is just a, a little bit of a teaser of what we have built so far and what you, what you can expect to, uh, see in, in future such events. >>So thanks for coming on the queue. We've got 30 seconds left, put a quick plug in for the swamp up, coming up. >>Yeah. Uh, so we, we will talk a lot more about Peria and our open source efforts and how we would like you all to collaborate. We'll be at swamp up, uh, in San Diego on May 26th, uh, May 24th to 26th. Uh, so hope to see you there, hope to discuss more about Peria and, and see what he will do with, uh, with this project. Thank you. >>All right. Thanks for coming on the back to the main stage. I'm John cube. Thanks for watching. >>Thank >>You.

>>Okay. And welcome back to the cubes live coverage here in San Francisco, California for 80 us summit 2022 Amazon web services summit 2020 New York city is coming up in the summer will be there. Check us out the cube.net. Our next guest here is Jim long. The CEO of dig also known as local. BTV a very interesting AWS customer doing some really progressive things around video and, uh, challenging the status quo in code cutting and all kinds of broadcast models. Jim, welcome to the cube. Great to see you. >>Thank you, John. Great to be here. Okay. >>So first of all, before we get into some of the disrupt option, take a minute to explain what is dig and local BTV. >>Uh, dig is all about, uh, providing, uh, edge video networking for broadcast television, basically modernizing local television and hopefully extending it to hyper local content like high schools and community government and community channels and things like that. So essentially free bringing, using the internet as an antenna to bring broadcast television to your phone, your laptop you're connected TVs. >>So if I understand it correctly, if I UN and I look at the, the materials of your site, you basically go into each market, Metro areas like New York Philly bay area, grab the tee signal out of the air. >>Yep. >>Local TV, and then open that up to everyone. Who's got, um, an >>Correct. And, uh, what, we've, where we're essentially building a hybrid network with AWS. Uh, I like to say we got all the smart and account stuff, you know, in the cloud at AWS. And we have all the dumb, fast stuff in the actual TV market. We have servers and transcoding there we work with, uh, of course, um, uh, AWS on that centrally as well. But basically that hybrid cloud allows us to be the fastest simplest and lowest cost way to get a local video. Any type could be an antenna or an IP stream to a local house. So we're, so are the local pickup and delivery people. We're not building a brand, we're not building content. We're delivering the local content to the local views. You >>Like the pipes. >>We are, we're essentially an infrastructure company. Um, we're right at that wonderful intersection of the, uh, the infrastructure and the content where I always like to play. >>I like, I love the store. I think the cost of that nature, how you're using Amazon, it's really impressive. Um, what are some of the cool things you're doing on AWS that you think's notable? >>Well, of course the, the standard issue stuff where you want to store all your data in the cloud. Right? So we, uh, and we use a quick site to, to get to that. And obviously we're using S3 and we're using media tailor, which we really like, which is cuz we first actual company on the planet. I believe that's inserting digital ads, impression based ads into local broadcast streams. So that's, that's fun because the advertisers, they like the fact that they could still do traditional TV buys and they could spice it up with digital impressions based, but ads on us. Yeah. And, and we're adding to it a real fun thing called clip it, which is user clipping. It's an app that's been running on AWS for years. It's had over half a million plays in social media. Yeah. We're combining those together and, and AWS makes it very simple to do that. >>Well, I've been using your app on my Firestick and uh, download local BTV on the app store. Um, I gotta say the calendar's awesome. And the performance is 10 times better than, than some of the other streaming apps because the other performance they crash all the time. The calendar's weird. So congratulations. Clearly you're running the cloud technology. I gotta ask you what's going on in the market? Netflix missed their earnings. The stock was down big time. Um, obviously competition what's up going on with Netflix? >>Well, what's, it's a big shift. >>What does it mean for the streaming market? >>Well, what it means is, is, is a consumer choice. It's really the golden age of consumer choice. Uh, originally back when I was a kid, it was all antenna TV. We didn't even have DBRS right. And then, uh, the cable companies and the satellite companies, the phone companies came in and took over and all of a sudden everyone started paying for TV for just linear TV. Right? And then the next thing, you know, streaming comes around, uh, Netflix shows up for, for VOD or, or SVOD, they call it cuz it's payt TV and uh, and the whole, uh, that ecosystem starts to melt down. And now you have a consumer choice market where you can pay, pay for VAD or pay for, for linear. And everyone does linear and everyone does VAD or you can use free TV. Now we correctly guessed that free TV was gonna have a huge comeback. You know, know what is it about free even obviously gen Z smarter than us boomers. They love free too. Uh, targeted advertising makes the ads less, uh, painful or less of a distraction. Uh, so we knew that free ad supported TV was gonna happen. Lots of stuff happened. And then, then the, uh, major media companies started doing their own subscription apps. Right? They're all cool. >>We like paramount plus >>Paramount plus Disney pluses, PN peacock, uh, time Warner's doing something. I mean, it's all cool, but you know, people only have so much of a big pocketbook. So what it's doing is pay TV has now become much more complicated, but also you, you know, you gotta trade off. So you saw it with Netflix, right? Yeah. Netflix is suffering from there's too much pay TV. So where are you gonna put your money on Comcast? On YouTube TV paramount plus Netflix. >>Yeah. I mean, I love the free thing. I gotta bring up something. I wanna get your reaction to a company called low cast went under, they got sued out of their deal. They were the free TV. Are you guys have issues like them? What's the cast most people don't know got was, was >>Doing same. So we started before low cast and we're uh, what we would call a permissions based system, legal system. The broadcast Mar industry, uh, is, uh, is the wild wild west. I mean, I like to say antenna TV is a direct to consumer. The antenna is a direct to consumer device and it's controlled by the channel. People it's not controlled by a platform like Comcast, right? It's not controlled by a stick. >>When you say channel, do you mean like CBS or >>Yeah, CBS or the local Korean religious cooking channel or, uh, Spanish channels or local independent to television, which is really a national treasure for us. The United States really should be making sure that local content, local channels, uh, do well local businesses, you know, with targeted advertising, Janes nail salon can, can now advertise just in San Jose and not the entire San Francisco TV market. Um, so you ha you have, have all that going on and we recognize, you know, that, that local content, but you have to have permission from the channel stuff. It's not easy because you got channels on stations. You have syndicators, it's hard to keep track of. And sometimes you, you, uh, you, you know, you have to shift things around, but, uh, low cast, uh, like another kind before it just went hog wild, illegal, trying to use a loophole, uh, didn't quite work out for 'em and, uh, >>You see, they have put out of business by the networks, the names, the big names. Yes. Content people, >>Correct. I mean the big, the big guys, but I mean, because they weren't following the rules, um, >>The rules, meaning license, the content, right. >>Well correct. Or yes, >>Basically they, they were stealing the content in the eyes of the, >>Well, there is, there is, it is a little of, a bit of a gray area between the FCC and the copyright laws that Congress made. So, um, there are people certainly out there that think there is a path there, low cast, didn't find it. We're not trying to find it. Uh, we just want to get all the free TV, uh, the bottom line. And you've seen fast channels explode recently, Pluto, uh, Samsung TV. >>And what does that all mean? >>Well, what it means is people love free TV and the best free TV out there is your local TV. So putting that on the internet and those comp, but the media companies, they have trouble with this new stuff. What's, >>What's your >>They're overthinking it. What's >>Some of this CBS, NBC, all these big guys. >>Well, those guys have a little less trouble than the people that actually, uh, they're affiliates, right? So there's 210 TV markets and the, uh, your major networks, you know, they have their own stations. And in a bit, you know, in about 39% of the population, which is about 15 to 20, is it >>Cultural or is a system system problem? >>No, it's a, it's a problem of all the, the media companies are just having trouble moving towards the new technology and, and they're, I think they're siloing it. >>So why not? You gonna let 'em die. Are you trying to do deals with em? >>Oh no, no, absolutely. For us, if we don't make money, unless stations make money, we want local TV to, to flourish. It is local TV is Neilson, just report yesterday, you know, uh, that, uh, local TV is growing. We're taking advantage of that. And I think the station groups are having a little trouble realizing that they have the original, fast channels before Pluto, before Tubi did it in movies. And, and, and what >>Are people understanding in the, in the industry? I know NA's coming up a show. Yeah, >>That's right. >>National associated of broadcasters. What's going on in that industry right now. And you're, if you get to put it down the top three problems that are opportunities to be solved, what would they be? >>Well, I think, you know, I think the, the, the, the last, the, the best one that's left is what we're doing. I have to say it, uh, I think it's worth billions. >>You free TV over the air free and stream >>O TV. Oh yeah. Over the air TV that also works with the internet, right. Public internet connected to public television stations so that everybody, including homeless people, et cetera, that, you know, they don't have a TV, they don't have an antenna, they can't afford comp. They got an >>IPhone though. >>They an iPhone. For sure. And, and so it's, it's, uh, it's a wonderful thing. It's, you know, our national broadcasting and I don't think the station groups or the major networks are taking advantage of it they're as much as they should. Yeah. And, and I don't think, you know, obviously NBC and CBS with their new apps, they're sort of done with that. They did mergers, they got, they got the virtual pay guys. I mean, YouTube TV off the ground, the only thing left is suck another shitload of good, uh, eyeballs and, and advertising. >>Well, I mean, yeah, I think that, that, and what you said earlier around subscription fatigue, I mean, nobody wants to have 20 subscriptions. >>Well, that brings up a whole new other war. That's going on that, thank goodness. We're not part of it's the platforms versus the cable companies. Right. Versus whatever. Right. Everyone's trying to be your open garden or your closed garden. They're trying to get your subscriptions in bundle self bundling it's. But I mean, it's wonderful for consumers, if you can navigate through it. Uh, we wanna, we think we'll have one of the gems in any of that everyone's want local TV. And so we'll supply that we're already doing that. We're supplying it to a couple companies, uh, free cast as a company, uh, app, a universal streaming, you know, manager, your all, all your, uh, streaming, a streaming aggregation, put your paid stuff in, put your free stuff in. They do that. And, and as, as does Roku try trying to do that fire TV, Xfinity's trying to do it. So it's all, it's a new war for the platform and hopefully we'll be on everyone. >>Well, you've been in this industry for a long time, you know, the streaming market, you know, the TV market. Um, so it's, it's good. I think it's a new battle, the shift's happening. Um, what should people know about dig local? BTV what are some of your goals for the next year or two? What are you trying to do? >>Well, what we're really trying to do is make sure that local, uh, local television thrives so that it can support wider communities. It could support hyper local content. So if you're, if you're, and we love the old paradigm and channel change, right? Forget, you know, every other app has all these boxes going by on different rows and stuff. And, and yeah, you can search and find stuff, but there's nothing like just changing channels, whether a commercial's on or, or you, you wanna see what else is on. You know, you're gonna go from local television and maybe all of a sudden, you'll see the local high school play over on another part of the, of the spectrum. And, and what we're trying to do is get those communities together. And the local high school people come over and find the local, you know, uh, Spanish, uh, Nova channel or something like that. >>So local is the new hot. >>It is. Absolutely. And by the way, it's where this high CPMs are gonna go. And the more targeted you get >>Ad revenue, >>I mean, that's for us is, is, is our number one, re we have a number of revenue streams, but targeted ads are really great for local, right? And, and so we're, we're gonna make an announce. We've >>Lost that we've lost that local, I've seen local things that local Palo Alto paper, for instance, just shut down this local sports high school coverage, our youth sports, because they don't budget, right? There's no TV community channels, like some Comcast throwaway channel. Um, we lost, we, we lo we're losing >>Local. No, I think that's a real national shame. And so I think if we can strengthen local television, I think it'll strengthen all local media. So we expect to help local radio and local newspapers. That's a bigger part of the vision. Uh, but I it's gonna happen. There's >>An education angle here too. >>There is an education angle because the bottom line is you can use linear television as a way to augment. Uh, we have a really exciting project going on in New York, uh, uh, with, uh, some of the housing, uh, projects, uh, in Harlem and, and, and the Bronx, uh, their I idea is to have the, the homework channel and they can, and literally when you have a, and both swiping and everything you can have, I mean, literally you can have a hundred schools that, that have things well, >>We know zoom schooling sucks. I mean, that didn't work. So I think you're gonna see a lot of augmentation, right. >>Amazon. >>I was just talking to some people here, AI training, machine learning, training, all here could be online in linear format. >>Yeah. And exactly. And then I think about the linear format is it's discovery television, and you can also, um, you know, you can also record it. Yeah. Right. If you see a program and you want to record it, you sit >>Record. So final minute we have left. I want to just get your thoughts on this one thing and, and ask your question. Are you looking for content? Are you, I outreach at the content providers who, >>Well, we're, we're PRI our primary mission is to get more channel local channels on which really means station groups and independence. We have a number, I mean, basically 50% of the channels in any market. When we move into it are like, this is a no-brainer. I want more eyeballs. We're Nielsen, uh, RA, uh, rated mean we support. And so we, >>How many markets are you in right now? >>We're in 21 now. And we hope to be in, uh, over 50 by the end of the year, covering more than half the United States. >>So, all right, Jim, thanks for coming on the queue. Really appreciate it. >>My pleasure. Good luck >>Recognition. Very disruptive disrupting media, um, combination of over the air TV, local with I internet. Obviously we love that with a cube. We want a cube channel anywhere possible. I'm John furry host of the queue here at AWS summit. Highing all the big trends and technologies in cloud and media back with more coverage after this short break,

>>Well, welcome back to the cubes coverage of AWS reinvent 2021. We're here joined by Ian buck, general manager and vice president of accelerated computing at Nvidia I'm. John Ford, your host of the QB. And thanks for coming on. So in video, obviously, great brand congratulates on all your continued success. Everyone who has does anything in graphics knows the GPU's are hot and you guys get great brand great success in the company, but AI and machine learning was seeing the trend significantly being powered by the GPU's and other systems. So it's a key part of everything. So what's the trends that you're seeing, uh, in ML and AI, that's accelerating computing to the cloud. Yeah, >>I mean, AI is kind of drape bragging breakthroughs innovations across so many segments, so many different use cases. We see it showing up with things like credit card, fraud prevention and product and content recommendations. Really it's the new engine behind search engines is AI. Uh, people are applying AI to things like, um, meeting transcriptions, uh, virtual calls like this using AI to actually capture what was said. Um, and that gets applied in person to person interactions. We also see it in intelligence systems assistance for a contact center, automation or chat bots, uh, medical imaging, um, and intelligence stores and warehouses and everywhere. It's really, it's really amazing what AI has been demonstrated, what it can do. And, uh, it's new use cases are showing up all the time. >>Yeah. I'd love to get your thoughts on, on how the world's evolved just in the past few years, along with cloud, and certainly the pandemics proven it. You had this whole kind of full stack mindset initially, and now you're seeing more of a horizontal scale, but yet enabling this vertical specialization in applications. I mean, you mentioned some of those apps, the new enablers, this kind of the horizontal play with enablement for specialization, with data, this is a huge shift that's going on. It's been happening. What's your reaction to that? >>Yeah, it's the innovations on two fronts. There's a horizontal front, which is basically the different kinds of neural networks or AIS as well as machine learning techniques that are, um, just being invented by researchers for, uh, and the community at large, including Amazon. Um, you know, it started with these convolutional neural networks, which are great for image processing, but as it expanded more recently into, uh, recurrent neural networks, transformer models, which are great for language and language and understanding, and then the new hot topic graph neural networks, where the actual graph now is trained as a, as a neural network, you have this underpinning of great AI technologies that are being adventure around the world in videos role is try to productize that and provide a platform for people to do that innovation and then take the next step and innovate vertically. Um, take it, take it and apply it to two particular field, um, like medical, like healthcare and medical imaging applying AI, so that radiologists can have an AI assistant with them and highlight different parts of the scan. >>Then maybe troublesome worrying, or requires more investigation, um, using it for robotics, building virtual worlds, where robots can be trained in a virtual environment, their AI being constantly trained, reinforced, and learn how to do certain activities and techniques. So that the first time it's ever downloaded into a real robot, it works right out of the box, um, to do, to activate that we co we are creating different vertical solutions, vertical stacks for products that talk the languages of those businesses, of those users, uh, in medical imaging, it's processing medical data, which is obviously a very complicated large format data, often three-dimensional boxes in robotics. It's building combining both our graphics and simulation technologies, along with the, you know, the AI training capabilities and different capabilities in order to run in real time. Those are, >>Yeah. I mean, it's just so cutting edge. It's so relevant. I mean, I think one of the things you mentioned about the neural networks, specifically, the graph neural networks, I mean, we saw, I mean, just to go back to the late two thousands, you know, how unstructured data or object store created, a lot of people realize that the value out of that now you've got graph graph value, you got graph network effect, you've got all kinds of new patterns. You guys have this notion of graph neural networks. Um, that's, that's, that's out there. What is, what is a graph neural network and what does it actually mean for deep learning and an AI perspective? >>Yeah, we have a graph is exactly what it sounds like. You have points that are connected to each other, that established relationships and the example of amazon.com. You might have buyers, distributors, sellers, um, and all of them are buying or recommending or selling different products. And they're represented in a graph if I buy something from you and from you, I'm connected to those end points and likewise more deeply across a supply chain or warehouse or other buyers and sellers across the network. What's new right now is that those connections now can be treated and trained like a neural network, understanding the relationship. How strong is that connection between that buyer and seller or that distributor and supplier, and then build up a network that figure out and understand patterns across them. For example, what products I may like. Cause I have this connection in my graph, what other products may meet those requirements, or also identifying things like fraud when, when patterns and buying patterns don't match, what a graph neural networks should say would be the typical kind of graph connectivity, the different kind of weights and connections between the two captured by the frequency half I buy things or how I rate them or give them stars as she used cases, uh, this application graph neural networks, which is basically capturing the connections of all things with all people, especially in the world of e-commerce, it's very exciting to a new application, but applying AI to optimizing business, to reducing fraud and letting us, you know, get access to the products that we want, the products that they have, our recommendations be things that, that excited us and want us to buy things >>Great setup for the real conversation that's going on here at re-invent, which is new kinds of workloads are changing. The game. People are refactoring their business with not just replatform, but actually using this to identify value and see cloud scale allows you to have the compute power to, you know, look at a note on an arc and actually code that. It's all, it's all science, all computer science, all at scale. So with that, that brings up the whole AWS relationship. Can you tell us how you're working with AWS before? >>Yeah. 80 of us has been a great partner and one of the first cloud providers to ever provide GPS the cloud, uh, we most more recently we've announced two new instances, uh, the instance, which is based on the RA 10 G GPU, which has it was supports the Nvidia RTX technology or rendering technology, uh, for real-time Ray tracing and graphics and game streaming is their highest performance graphics, enhanced replicate without allows for those high performance graphics applications to be directly hosted in the cloud. And of course runs everything else as well, including our AI has access to our AI technology runs all of our AI stacks. We also announced with AWS, the G 5g instance, this is exciting because it's the first, uh, graviton or ARM-based processor connected to a GPU and successful in the cloud. Um, this makes, uh, the focus here is Android gaming and machine learning and France. And we're excited to see the advancements that Amazon is making and AWS is making with arm and the cloud. And we're glad to be part of that journey. >>Well, congratulations. I remember I was just watching my interview with James Hamilton from AWS 2013 and 2014. He was getting, he was teasing this out, that they're going to build their own, get in there and build their own connections, take that latency down and do other things. This is kind of the harvest of all that. As you start looking at these new new interfaces and the new servers, new technology that you guys are doing, you're enabling applications. What does, what do you see this enabling as this, as this new capability comes out, new speed, more, more performance, but also now it's enabling more capabilities so that new workloads can be realized. What would you say to folks who want to ask that question? >>Well, so first off I think arm is here to stay and you can see the growth and explosion of my arm, uh, led of course, by grab a tiny to be. I spend many others, uh, and by bringing all of NVIDIA's rendering graphics, machine learning and AI technologies to arm, we can help bring that innovation. That arm allows that open innovation because there's an open architecture to the entire ecosystem. Uh, we can help bring it forward, uh, to the state of the art in AI machine learning, the graphics. Um, we all have our software that we released is both supportive, both on x86 and an army equally, um, and including all of our AI stacks. So most notably for inference the deployment of AI models. We have our, the Nvidia Triton inference server. Uh, this is the, our inference serving software where after he was trained to model, he wanted to play it at scale on any CPU or GPU instance, um, for that matter. So we support both CPS and GPS with Triton. Um, it's natively integrated with SageMaker and provides the benefit of all those performance optimizations all the time. Uh, things like, uh, features like dynamic batching. It supports all the different AI frameworks from PI torch to TensorFlow, even a generalized Python code. Um, we're activating how activating the arm ecosystem as well as bringing all those AI new AI use cases and all those different performance levels, uh, with our partnership with AWS and all the different clouds. >>And you got to making it really easy for people to use, use the technology that brings up the next kind of question I want to ask you. I mean, a lot of people are really going in jumping in the big time into this. They're adopting AI. Either they're moving in from prototype to production. There's always some gaps, whether it's knowledge, skills, gaps, or whatever, but people are accelerating into the AI and leaning into it hard. What advancements have is Nvidia made to make it more accessible, um, for people to move faster through the, through the system, through the process? >>Yeah, it's one of the biggest challenges. The other promise of AI, all the publications that are coming all the way research now, how can you make it more accessible or easier to use by more people rather than just being an AI researcher, which is, uh, uh, obviously a very challenging and interesting field, but not one that's directly in the business. Nvidia is trying to write a full stack approach to AI. So as we make, uh, discover or see these AI technologies come available, we produce SDKs to help activate them or connect them with developers around the world. Uh, we have over 150 different STKs at this point, certain industries from gaming to design, to life sciences, to earth scientist. We even have stuff to help simulate quantum computing. Um, and of course all the, all the work we're doing with AI, 5g and robotics. So, uh, we actually just introduced about 65 new updates just this past month on all those SDKs. Uh, some of the newer stuff that's really exciting is the large language models. Uh, people are building some amazing AI. That's capable of understanding the Corpus of like human understanding, these language models that are trained on literally the continent of the internet to provide general purpose or open domain chatbots. So the customer is going to have a new kind of experience with a computer or the cloud. Uh, we're offering large language, uh, those large language models, as well as AI frameworks to help companies take advantage of this new kind of technology. >>You know, each and every time I do an interview with Nvidia or talk about Nvidia my kids and their friends, they first thing they said, you get me a good graphics card. Hey, I want the best thing in their rig. Obviously the gaming market's hot and known for that, but I mean, but there's a huge software team behind Nvidia. This is a well-known your CEO is always talking about on his keynotes, you're in the software business. And then you had, do have hardware. You were integrating with graviton and other things. So, but it's a software practices, software. This is all about software. Could you share kind of more about how Nvidia culture and their cloud culture and specifically around the scale? I mean, you, you hit every, every use case. So what's the software culture there at Nvidia, >>And it is actually a bigger, we have more software people than hardware people, people don't often realize this. Uh, and in fact that it's because of we create, uh, the, the, it just starts with the chip, obviously building great Silicon is necessary to provide that level of innovation, but as it expanded dramatically from then, from there, uh, not just the Silicon and the GPU, but the server designs themselves, we actually do entire server designs ourselves to help build out this infrastructure. We consume it and use it ourselves and build our own supercomputers to use AI, to improve our products. And then all that software that we build on top, we make it available. As I mentioned before, uh, as containers on our, uh, NGC container store container registry, which is accessible for me to bus, um, to connect to those vertical markets, instead of just opening up the hardware and none of the ecosystem in develop on it, they can with a low-level and programmatic stacks that we provide with Kuda. We believe that those vertical stacks are the ways we can help accelerate and advance AI. And that's why we make as well, >>Ram a little software is so much easier. I want to get that plug for, I think it's worth noting that you guys are, are heavy hardcore, especially on the AI side. And it's worth calling out, uh, getting back to the customers who are bridging that gap and getting out there, what are the metrics they should consider as they're deploying AI? What are success metrics? What does success look like? Can you share any insight into what they should be thinking about and looking at how they're doing? >>Yeah. Um, for training, it's all about time to solution. Um, it's not the hardware that that's the cost, it's the opportunity that AI can provide your business and many, and the productivity of those data scientists, which are developing, which are not easy to come by. So, uh, what we hear from customers is they need a fast time to solution to allow people to prototype very quickly, to train a model to convergence, to get into production quickly, and of course, move on to the next or continue to refine it often. So in training is time to solution for inference. It's about our, your ability to deploy at scale. Often people need to have real time requirements. They want to run in a certain amount of latency, a certain amount of time. And typically most companies don't have a single AI model. They have a collection of them. They want, they want to run for a single service or across multiple services. That's where you can aggregate some of your infrastructure leveraging the trading infant server. I mentioned before can actually run multiple models on a single GPU saving costs, optimizing for efficiency yet still meeting the requirements for latency and the real time experience so that your customers have a good, a good interaction with the AI. >>Awesome. Great. Let's get into, uh, the customer examples. You guys have obviously great customers. Can you share some of the use cases, examples with customers, notable customers? >>Yeah. I want one great part about working in videos as a technology company. You see, you get to engage with such amazing customers across many verticals. Uh, some of the ones that are pretty exciting right now, Netflix is using the G4 instances to CLA um, to do a video effects and animation content. And, you know, from anywhere in the world, in the cloud, uh, as a cloud creation content platform, uh, we work in the energy field that Siemens energy is actually using AI combined with, um, uh, simulation to do predictive maintenance on their energy plants, um, and, and, uh, doing preventing or optimizing onsite inspection activities and eliminating downtime, which is saving a lot of money for the engine industry. Uh, we have worked with Oxford university, uh, which is Oxford university actually has over two, over 20 million artifacts and specimens and collections across its gardens and museums and libraries. They're actually using convenient GPS and Amazon to do enhance image recognition, to classify all these things, which would take literally years with, um, uh, going through manually each of these artifacts using AI, we can click and quickly catalog all of them and connect them with their users. Um, great stories across graphics, about cross industries across research that, uh, it's just so exciting to see what people are doing with our technology together with, >>And thank you so much for coming on the cube. I really appreciate Greg, a lot of great content there. We probably going to go another hour, all the great stuff going on in the video, any closing remarks you want to share as we wrap this last minute up >>Now, the, um, really what Nvidia is about as accelerating cloud computing, whether it be AI, machine learning, graphics, or headphones, community simulation, and AWS was one of the first with this in the beginning, and they continue to bring out great instances to help connect, uh, the cloud and accelerated computing with all the different opportunities integrations with with SageMaker really Ks and ECS. Uh, the new instances with G five and G 5g, very excited to see all the work that we're doing together. >>Ian buck, general manager, and vice president of accelerated computing. I mean, how can you not love that title? We want more, more power, more faster, come on. More computing. No, one's going to complain with more computing know, thanks for coming on. Thank you. Appreciate it. I'm John Farrell hosted the cube. You're watching Amazon coverage reinvent 2021. Thanks for watching.

>> live from New York. It's the Q covering AWS Global Summit 2019 brought to you by Amazon Web service, is >> welcome back here in New York City on stew Minimum. My co host is Corey Quinn. In the keynote this morning, Warner Vogel's made some new announcements what they're doing and also brought out a couple of customers who are local and really thrilled and excited to have on the program the C i O and E V P from Finn Ra here in New York City. Steve Randall, thanks so much for joining us. You're welcome. Thank you. All right, so, you know, quite impressive. You know when when I say one of those misunderstood words out there to talk about scale and you talk about speed and you know, you were you know, I'm taking so many notes in your keynote this 1 500,000 compute note. Seven terabytes worth of new data daily with half a trillion validation checks per day, some pretty impressive scale, and therefore, you know, it's I t is not the organ that kind of sits in the basement, and the business doesn't think about it business and I t need to be in lobster. So, you know, I think most people are familiar with in Rome. But maybe give us the kind of bumper sticker as Thio What dinner is today and you know, the >> the organization. Yeah, I started it Fender and 2013. I thought I was gonna come into a typical regulator, which is, as you alluded to technologies, kind of in the basement. Not very important, not strategic. And I realized very quickly two things. Number one, The team was absolutely talented. A lot of the people that we've got on her team came from start ups and other technology companies. Atypical financial service is and the second thing is we had a major big data challenge on our hands. And so the decision to go to the cloud S I started in March 2013. By July of that year, I was already having dialogue with our board of directors about having to go to the cloud in orderto handle the data. >> Yeah, so you know, big data was supposed to be that bit flip that turned that. Oh, my God. I have so much data to Oh, yea, I can monetize and do things with their data. So give us a little bit of that, That data journey And what? That that you talk about the flywheel? The fact that you've got inside Finneran. >> Yeah. So we knew that we needed the way were running at that time on data warehouse appliances from E, M. C. And IBM. And which a data warehouse appliance. You go back 10 15 years. That was where big data was running. But those machines are vertically scalable, and when you hit the top of the scale, then you've got to buy another bigger one, which might not be available. So public cloud computing is all about horizontal scale at commodity prices to things that those those data data warehouse appliance didn't have. They were vertical and proprietary, inexpensive. And so the key thing was to come up to select the cloud vendor between Google, IBM, You know, the usual suspects and architect our applications properly so that we wouldn't be overly vendor dependent on the cloud provider and locked in if you will, and that we could have flexibility to use commodity software. So we standardized in conjunction with our move to the public cloud on open source software, which we continue today. So no proprietary software for the most part running in the cloud. And we were just very smart about architect ing our systems at that point in time to make sure that those opportunities prevailed. And the other thing I would say, this kind of the secret of our success Is it because we were such early adopters we were in the financial service industry and a regulator toe boots that we had engineering access to the cloud providers and the big, big date open source software vendors. So we actually had the engineers from eight of us and other firms coming in to help us learn how to do it, to do it right. And that's been part of our culture ever since. >> One thing that was, I guess a very welcome surprise is normally these keynotes tend to fall into almost reductive tropes where first, we're gonna have some Twitter for pet style start up talking about all the higher level stuff they're doing, and then we're gonna have a large, more serious company. Come in and talk about how we moved of'em from our data center into the cloud gay Everyone clap instead, there was it was very clear. You're using higher level, much higher level service is on top of the cloud provider. It's not just running the M somewhere else in the same way you would on premise. Was that a transitional step that you went through or did you effectively when you went all in, start leveraging those higher service is >> okay. It's a great question. And ah, differentiator for us versus a lot. A lot of the large organizations with a legacy footprint that would not be practical to rewrite. We had outsourced I t entirely in the nineties E T s and it was brought back in source in in house early in this decade. And so we had kind of a fresh, fresh environment. Fresh people, no legacy, really other than the data warehouse appliances. So we had a spring a springboard to rewrite our abs in an agile way to be fully cloud enabled. So we work with eight of us. We work with Cloudera. We work with port works with all the key vendors at that time and space to figure out how to write Ah wraps so they could take most advantage of what the cloud was offering at that time. And that continues to prevail today. >> That that's a great point because, you know so often it's that journey to cloud. But it's that application modernization, that journey. Right. So bring us in little inside there is. You know how it is. You know, what expertise did Finn Ra have there? I mean, you don't want to be building applications. It is the open stuff source. The things wasn't mature enough. How much did they have toe help work, you know, Would you call it? You know, collaboration? >> Yeah. The first year was hard because I would have, you know, every high performance database vendor, and I see a number of them here today. I'm sure they're paddling their AWS version now, but they had a a private, proprietary database version. They're saying if you want to handle the volumes that you're seeing and predicting you really need a proprietary, they wouldn't call it proprietary. But it was essentially ah, very unique solution point solution that would cause vendor dependency. And so and then and then my architects internally, we're saying, No way, Wanna go open source because that's where the innovation and evolution is gonna be fastest. And we're not gonna have vendor Lock in that decision that that took about a year to solidify. But once we went that way, we never looked back. So from that standpoint, that was a good bad, and it made sense. The other element of your question is, how How much of this did we do on our own, rely on vendors again? The kind of dirty little secret of our beginnings here is that we ll average the engineer, you know, So typically a firm would get the sales staff, right. We got the engineers we insisted on in orderto have them teach our engineers how to do these re architectures to do it right. Um and we use that because we're in the financial service industry as a regulator, right? So they viewed us as a reference herbal account that would be very valuable in their portfolio. So in many regards, that was way scratch each other's back. But ultimately, the point isn't that their engineers trained our engineers who trained other engineers. And so when I when I did the, uh um keynote at the reinvented 2016 sixteen one of my pillars of our success was way didn't rely overly on vendors. In the end, we trained 2016 1 5 to 600 of our own staff on how to do cloud architectures correctly. >> I think at this point it's very clear that you're something of an extreme outlier in that you integrate by the nature of what you do with very large financial institutions. And these historically have not been firms that have embraced the cloud with speed and enthusiasm that Fenner has. Have you found yourself as you're going in this all in on the cloud approach that you're having trouble getting some of those other larger financial firms to meet you there, or is that not really been a concern based upon fenders position with an ecosystem? >> Um, I would say that five years ago, very rare, I would say, You know, we've had a I made a conscious effort to be very loud in the process of conferences about our journey because it has helped us track talent. People are coming to work for us as a senior financial service. The regulator that wouldn't have considered it five years ago, and they're doing it because they want to be part of this experience that we're having, but it's a byproduct of being loud, and the press means that a lot of firms are saying, Well, look what Fender is doing in the cloud Let's go talk to them So we've had probably at this 50.200 firms that have come defender toe learn from our experience. We've got this two hour presentation that kind of goes through all the aspects of how to do it right, what, what to avoid, etcetera, etcetera. And, um, you know, I would say now the company's air coming into us almost universally believe it's the right direction. They're having trouble, whether it's political issues, technology dat, you name it for making the mo mentum that we've made. But unlike 45 years ago, all of them recognize that it's it's the direction to go. That's almost undisputed at this point. And you're opening comment. Yeah, we're very much an outlier. We've moved 97 plus percent of our APS 99 plus percent of our data. We are I mean, the only thing that hasn't really been moved to the cloud at this point our conscious decisions, because those applications that are gonna die on the vine in the data center or they don't make sense to move to the cloud for whatever reason. >> Okay, You've got almost all your data in the cloud and you're using open source technology. Is Cory said if I was listening to a traditional financial service company, you know, they're telling me all the reasons that for governance and compliance that they're not going to do it. So you know, why do you feel safe putting your your data in the cloud? >> Uh, well, we've looked at it. So, um, I spent my first year of Finn run 2013 early, 2014 but mostly 2013. Convincing our board of directors that moving our most critical applications to the public cloud was going to be no worse from the information security standpoint than what we're doing in our private data centers. That presentation ultimately made it to other regulators, major firms on the street industry, lobbyist groups like sifma nephi. AP got a lot of air time, and it basically made the point using logic and reasoning, that going to the cloud and doing it right not doing it wrong, but doing it right is at least is secure from a physical logical standpoint is what we were previously doing. And then we went down that route. I got the board approval in 2015. We started looking at it and realizing, Wait a minute, what we're doing here encrypting everything, using micro segmentation, we would never. And I aren't doing this in our private data center. It's more secure. And at that point in time, a lot of the analysts in our industry, like Gardner Forrester, started coming out with papers that basically said, Hey, wait a minute, this perception the cloud is not as safe is on Prem. That's wrong. And now we look at it like I can't imagine doing what we're doing now in a private data center. There's no scale. It's not a secure, etcetera, etcetera. >> And to some extent, when you're dealing with banks and start a perspective now and they say, Oh, we don't necessarily trust the cloud. Well, that's interesting. Your regulator does. In other cases, some tax authorities do. You provided tremendous value just by being as public as you have been that really starts taking the wind out of the sails of the old fear uncertainty and doubt. Arguments around cloud. >> Yeah, I mean, doubts around. It's not secure. I don't have control over it. If you do it right, those are those are manageable risks, I would argue. In some cases, you've got more risk not doing it. But I will caution everything needs to be on the condition that you've got to do it right. Sloppy migration in the cloud could make you less secure. So there there are principles that need to be followed as part of >> this. So Steve doing it right. You haven't been sitting still. One of the things that really caught my attention in the keynote was you said the last four years you've done three re architectures and what I want. Understand? You said each time you got a better price performance, you know, you do think so. How do you make sure you do it right? Yet have flexibility both in an architect standpoint, and, you know, don't you have to do a three year reserves intense for some of these? How do you make sure you have the flexibility to be able to take advantage of you? Said the innovation in automation. >> Yeah. Keep moving forward with. That's Ah, that's a deep technical question. So I'm gonna answer it simply and say that we've architected the software and hardware stack such. There's not a lot of co dependency between them, and that's natural. I t. One on one principle, but it's easier to do in the cloud, particularly within AWS, who kind of covers the whole stacks. You're not going to different vendors that aren't integrated. That helps a lot. But you also have architect it, right? And then once you do that and then you automate your software development life cycle process, it makes switching out anyone component of that stack pretty easy to do and highly automated, in some cases completely automated. And so when new service is our new versions of products, new classes of machines become available. We just slip him in, and the term I use this morning mark to market with Moore's Law. That's what we aspire to do to have the highest levels of price performance achievable at the time that it's made available. That wasn't possible previously because you would go by ah hardware kit and then you'd appreciate it for five years on your books at the end of those five years, it would get kind of have scale and reliability problems. And then you go spend tens of millions of dollars on a new kit and the whole cycle would start over again. That's not the case here. >> Machine learning something you've been dipping into. Tell us the impact, what that has and what you see. Going forward. >> It's early, but we're big believers in machine learning. And there's a lot of applications for at Venera in our various investigatory and regulatory functions. Um, again, it's early, but I'm a big believer that the that the computer stored scale, commodity costs in the public cloud could be tapped into and lever it to make Aye aye and machine learning. Achieve what everybody has been talking about it, hoping to achieve the last several decades. We're using it specifically right now in our surveillance is for market manipulation and fraud. So fraudsters coming in and manipulating prices in the stock market to take advantage of trading early days but very promising in terms of what it's delivered so far. >> Steve want to give you the final word. You know, your thank you. First of all for being vocal on this. It sounds like there's a lot of ways for people to understand and see. You know what Fenner has done and really be a you know, an early indicator. So, you know, give us a little bit. Look forward, you know what more? Where's Finn Ra going next on their journey. And what do you want to see more from, You know, Amazon and the ecosystem around them to make your life in life, your peers better. >> Yes. So some of the kind of challenges that Amazon is working with us and partnering Assan is getting Ah Maur, automated into regional fell over our our industries a little bit queasy about having everything run with a relatively tight proximity in the East Coast region. And while we replicate our data to the to the other East region, we think AIM or co production environment, like we have across the availability zones within the East, would be looked upon with Maur advocacy of that architecture. From a regulatory standpoint, that would be one another. One would be, um, one of the big objections to moving to a public cloud vendor like Amazon is the vendor dependency and so making sure that we're not overly technically dependent on them is something that I think is a shared responsibility. The view that you could go and run a single application across multiple cloud vendors. I don't think anybody has been able to successfully do that because of the differences between providers. You could run one application in one vendor and another application in another vendor. That's fine, but that doesn't really achieve the vendor dependency question and then going forward for Finn or I mean, riel beauty is if you architected your applications right without really doing any work at all, you're going to continuously get the benefits of price performance as they go forward. You're not kind of locked into a status quo, So even without doing much of any new work on our applications, we're gonna continue to get the benefits. That's probably outside of the elastic, massive scale that we take advantage of. That's probably the biggest benefit of this whole journey. >> Well, Steve Randall really appreciate >> it. >> Thank you so much for sharing the journey of All right for Cory cleanups to minimum back with lots more here from eight Summit in New York City. Thanks for watching the cue

>> live from Washington D. C. It's the Cube covering a ws public sector summit by Amazon Web services. >> Hello, everyone. Welcome back to the cubes Live coverage of a ws Public Sector summit here in our nation's capital. I'm your host, Rebecca Knight. We're joined by Raghu Rahman. He is the director of Fin Row, the Financial Industry Regulatory Authority. Thank you so much for coming on the Cube >> fighter back. Good afternoon, but happy to be here. >> So we're angry. This is the 10th annual public sector. Somebody should have said so Tell us a little bit about Finn Ra and what you do. They're >> sure Fender itself is the financial industry Regulatory authority way our private sector, not for profit institutions. Our mission is investor protection on market integrity. Way our member funded on DH. We have a member driven board board of directors and we engage in ensuring that all the stock market operations in the U. S. Capital markets play with rules. So that's the essence of who we are. >> And all of those stakeholders have a vested interest in making sure their rivals are also playing bythe. So you're here giving a presentation on fraud detection, using machine learning and artificial intelligence. That's right. What was So what were you saying? >> So, Brenda, we have a very deliberate technology strategy on We constantly keep pace with technology in order to affect our business in the best possible way, way. Always are looking for a means to get more efficient and more effective and use our funding for the best possible business value so to that, and wear completely in the cloud for a lot off our market regulation operations. All the applications are in the clouds. We, in fact, we were one of the early adopters of the cloud. From that perspective, all of our big data operations were fully operational in the cloud by 2016 itself. That was itself a two year project that we started in 40 14 then from 2016 were being working with machine language on recently. Over the past six months or so, we've been working with neural networks. So this was an opportunity for us to share what? Where we have bean, where we're coming from, where we're going with the intent that whatever we do by way of principles can be adopted by any other enterprise. We're looking to share our journey on to encourage others to adopt technology. That's really what why we do this >> and I want to dig into the presentation a little bit. But can you just set the scene for our viewers about what kinds of how big a problem fraud is with these financial institutions and how much money is on the table here? >> Well, I don't want to get you to the actual dollar figures, because each dimension off it comes up with a different aspect to it. Waken say that in full in federal, we have a full caseload year after year, decade after decade that end up with multiple millions of dollars worth of fines just on the civil cases alone. And then there are, of course, multibillion dollar worth problems that we read in the media cases going as far back as Bernie Madoff. Case is going through the different banking systems so that our various kinds of fraud across the different financial sectors, of course, we're focused on the capital markets alone. We don't do anything with regard to banking or things of that nature, But even in our own case, we franchise composed of nearly 33 100 people on all of us, engaging the fulltime task of ensuring that markets are fair for the investors on for the other participants, it's a big deal. >> So in your in your presentation, you told the story of two of your colleagues who are facing different kinds of challenges to sort to make your story come alive. Tell our viewers a little bit about about their challenges. >> We spoke about Brad, who is an expert. He's an absolute wizard when it comes to market regulation, and he's being doing this for a long time on DH What I shared with the members of the audience earlier today. Wass He can probably look ATT market, even data on probably tell you what the broker had for breakfast. >> That >> scary good on. We also shared the story about Jamie, who is in the member supervision division offender, a wicked, smart and extensive experience. So these are the kind of dedicated people that we have a fender on guy took up to Rhea life use cases sort of questions that they face. So in the case of Brad, it is always a question of Hey, we're good. But how do we get better? What is the unknown unknown there? The volume of transactions in the market keeps going up. How do we then end up with a situation where we can do effective surveillance in the market on detect the behaviors that are not off interest that are not for doctor? That might be even. Don't write manipulated. How do we make sure that way? Got it all, so to speak? That's Brad's thing. >> That idea about these? No, these unknown nun note Because we know we have no no known unknowns with the unknown unknowns are even scarier. >> Exactly. They are, and we want to shed light on that for ourselves and make sure that the markets are really fair for everybody to operate him. That is where use of the latest technologies helps us get better and better at it. To reduce the number of unknown unknowns to shed light on the entirety of market activities on toe, perform effective surveillance. So that was a just off our conversation today. How we have gotten better in the past 45 years, how machine language machine learning based technologies have helped us how artificial intelligence that we started working with specifically, neural networks have started helping us even further. >> Okay, okay. And then Jamie had a problem, too. >> In Jimmy's case. Member supervision, if you will. The problem is off a different context and character. They're still volumes of data. We still receive more than 1,000,000 individual pieces of document every year that we work with. But in her case, the important aspect of it is that it is unstructured data. It makes sense to humans. It is in plain English, but the machines, it's really difficult. So over the past two years, way have created an entirely new text analytics platform on that helps us parts through hundreds of thousands of different documents. Those could come from e mails it to come from war documents, spreadsheets, evenhanded and documents. We can go through all of those extract meaningful information, automatically summarized them, even have measures off confidence that the machine will imprint upon it to say how confident I am. I that this is off relevance to you. It will imprint that. And then it represented Jamie for her toe. Use her judgment and expertise to make a final call. One thing that we are really conscious about is way. Don't let algorithms completely take everything through. We always have a human. So we think of a I as really assistive intelligence on. We bring that to a fact for our business, >> and I think that that's a really key there, too, for the for the employees is to know that this is this is this's taking away some of their more manual, more boring tests and actually freeing them up to do the more creative, analytical problem solving >> you hit you. I think you hit that nail right on the head. All the tedious work the machine bus on. Then it leaves humans to do like you said, Absolutely the creative, the inter toe on the final judgment call. I think that's a great system. >> How much to these solutions cost way >> generally are not pricing these things individually, however overall, one of the things that we did with the cloud was actually reduce our overall cost ofthe technology. So from that perspective, we don't look at Costas, the primary driver, although many times these things do end up costing less than the prior system that we would be in. However, the benefits that offer to our clientele, the benefit that it offers to our business, to the people that are investors in the stock market, that is tremendous, and that has a lot of value for us. >> So what is next for Finneran? I mean, this is This is a really moment for so many industries in terms of the the rise of cyber threats, the end and fraud being such a huge problem. Privacy thes air the financial services industry more than, I guess maybe is equal to healthcare. This's really sensitive stuff we're talking about here. What what are some of the things that you have on the horizon? What are some of the things that you're hearing from your members? >> So all of our members treat data security really, really special on really carefully on wear, very deliberate and very conscious about how we treat the data that is interested to us way have to obligations. One is to treat it securely. The other is to extract appropriate insights from it because that's the purpose of why we're being interested with the data. Wait, take both of those dimensions very seriously. Way have an entire infrastructure organization. It's composed off experts in the field way, headed by a chief information security officer with a large team that looks at multi layered security right from the application defending itself all the way to perimeter security. We go off that we have extensive identity and access management systems. We also have an extensive program to combat insider tracks. So this type of multi layer security is what helps us keep the data secure. >> And >> every day we do notice that there are additional track factors that get exposed. So we keep ourselves on the edge in terms ofthe working with all the vendors that we partner with in working with the latest technologies to protect our data as an example, all of our data in the cloud is completely encrypted with high encryption, and it is encrypted both at rest. I'm during flight so that even in the rare case that someone has access to something is gibberish. So that's the intent of the encryption himself. So that is the extent to which we take things very seriously. >> I want to ask you to, but the technology backlash that we're seeing so much and you're you live here so you really know about the climate that does that technology industries, air facing for so long. They were our national treasure and they still are considered it all in a lot of ways. The Amazons, the Googles, the facebooks of the world. But now we have a presidential candidates calling for the break up of big tech and and they And there's been a real souring on the part of the public of concerns about privacy. How What are your thoughts? What are you seeing? What are you hearing on the ground here in D. C? >> With specifically with regard to where we operate from Infanta? We've tried not to access or use any data. That is not for regulatory purpose. Wear Very careful about it. Way don't sprawl across and crawl across social media just on a general fishing expedition. We try not to do that. All of the data that we take in store on operate technology upon we are entitled to use it for by policy are my rules are my regulation for the specific purpose off our regulator activities. We take that very seriously. We try not to access data outside off what we have need for on. So we limit ourselves to the context and that, if you look at, is really what the public is trying to tell us, don't take our data and use it in ways that we did not really authorize you to do. So So the other thing is that franchise on our profit, not for not for profit institutions. We really have absolutely no interest beyond regulatory capability to use the data. We absolutely shut it down for any other use way are not so that way. We are very clear about what our mission is. Where we use our data, why we use it and stop. >> Great. Well, Raghu, thank you so much for coming on the Cube. It's been a pleasure talking to you. >> Thank you. Thank >> you. I'm Rebecca Knight. Please stay tuned for more of the cubes. Live coverage of the es W s public Sector summit here in Washington. D c. Stay tuned. >> Oh,

>> Welcome to the latest edition of the MIT Sloan Expert Series. I'm your host, Rebecca Knight. Our topic today is racial bias in the sharing economy, how Uber and Lyft are failing black passengers, and what to do about it. Here to talk about that is Chris Knittel. He is a professor of Applied Economics here at MIT Sloan, and he's also the co-author of a study that shows how Uber and Lyft drivers discriminate based on a passenger's skin color. Thanks so much for joining us. >> Oh, it's great to be here. >> Before we begin, I want to remind our viewers that we will be taking your questions live on social media. Please use the hashtag MITSloanExpert to pose your questions on Twitter. Chris, let's get started. >> Chris: Sure. So there is a lot of research that shows how difficult it is to hail a cab, particularly for black people. Uber and Lyft were supposed to represent a more egalitarian travel option, but you didn't find that. >> That's right, so what we found in two experiments that we ran, and one in Seattle, and one in Boston, is that Uber and Lyft drivers were discriminating based on race. >> Rebecca: We've already seen, actually some evidence of racial discrimination in the sharing economy, not just with ride sharing apps. >> Sure, so there's evidence for Airbnb. And what's interesting about Airbnb actually, is that discrimination is two-sided. So not only do white renters of properties not want to rent to black rentees, but white renters do not stay at a home of a black home owner. >> Did your findings and the findings of that other research you just talked about, does it make you discouraged? >> Partly, I was an optimist. We went into this, at least I went into this hoping that we wouldn't find discrimination, but one thing that has helped, or at least shined a more positive light, is that there are ways that we can do better in this sector. >> You've talked about this study, which you undertook with colleagues from the University of Washington and Stanford, shows the power of the experiment. Can you talk a little bit about what you mean by that? >> Sure, what we did was actually run two randomized control trials. Just like you would test whether a blood pressure medication works, so you would have a control group that wouldn't get the medication, and a treatment group that would. We did the same thing where we sent out in Seattle both black and white RAs that hailed Uber and Lyft rides, and we randomized whether or not it was a black RA calling the ride or a white RA that particular time, and they all drove the same exact route at the same exact times of the day. >> So what did you find? Let's talk about first, what you found in Seattle. >> Sure, so in Seattle, we measured how long it took for a ride to be accepted, and also, how long it took, once it was accepted, for the driver to show up and pick up the passenger. And what we found is, if you're a black research assistant, that in hailing an Uber ride, it took 30 percent longer for a ride to be accepted, and also 30 percent longer for the driver to show up and pick you up. >> 30 percent seems substantial. >> Well, for the time it takes to accept the ride, we're talking seconds, but for the time it takes for a passenger to actually be picked up, it's over a minute longer. And I'll mention also for Lyft, we found a 30 percent increase in the amount of time it took to be accepted, but there was no statistically significant impact on how long it took for the driver to actually show up. >> So, the thing about the minute difference, that can be material, particularly if you're trying to catch a cab, an Uber or a Lyft for a job interview or to get to the airport. >> Yeah, this is introspection, but I always seem to be late, so even a minute can be very costly. >> I hear you, I hear you. So why do you think there was the difference between Lyft and Uber? >> What's interesting, and we learned this while we were doing the experiment, a Lyft driver sees the name of the passenger before they accept the ride, whereas an Uber driver only sees the name after they've accepted. So in order for an Uber driver to discriminate, they have to first accept the ride, and then see the name and then cancel, whereas a Lyft driver can just pass it up right away. So it turns out because of that, the Lyft platform is more easily capable of handling discrimination because it pushed it to another driver faster than the Uber platform. >> I want to come back to that, but I want to say also, that difference caused you to change the way you did the experiment in Boston. >> In Boston, a couple differences. One is that we sent out RAs with two cell phones actually. So each RA had an Uber and Lyft account under a stereotypically white sounding name, and then also an Uber and Lyft account under a stereotypically black sounding name. That was one difference, and then also, what we measured in Boston that we didn't measure in Seattle, is cancellations. So an Uber driver accepts the ride, and then cancels on the RA. >> Let's go back to the stereotypically black sounding name verses white sounding name. You're an economist, how did you determine what those names are? >> We relied on another published paper that actually looked at birth records from the 1970s in Boston, and the birth records tell you not only the name, but also the race of the baby. So they found names that actually 100 percent of the time were African American or 100 percent of the time were not African American. So we relied on those names. >> And the names were... >> So you could imagine Jamal for example, compared to Jerry. >> Alright, Ayisha and Alison. >> Chris: Sure. >> So what was your headline finding in Boston? >> In Boston, what we found is, if you were a black male calling an Uber ride, that you were canceled upon more than twice as often as if you were a white male. >> And what about Lyft? >> For Lyft, there is no cancellation effect, and that's not because there's no discrimination, it's just that they don't have to accept and then cancel the ride, they can just pass up the ride completely. It's actually a nice little experiment within the experiment, we shouldn't find an effect of names on cancellations for Lyft and in fact, we don't. >> And also, the driver network is much thicker in Boston than in Seattle. >> So in Boston, although we found this cancellation effect, we didn't find that it has a measurable impact on how long you wait. And this is somewhat speculation, but we speculate that that's because the driver network is so much more dense in Boston that, although you were canceled upon, there's so many only drivers nearby, that it doesn't lead to a longer wait time. >> How do you think what you found compares to hailing traditional cabs? We started our conversation talking about the vast body of research that shows how difficult it is for black people to hail cabs. >> Yeah, we are quick to point out that we are not at all saying that Uber and Lyft are worse than traditional, status quo system, and we want to definitely make that clear. In fact, in Seattle, we had our same research assistants stand at the busiest corners and hail cabs. What we found there is, if you were a black research assistant, the first cab passed you 80 percent of the time. But if you were a white research assistant, it only passed you 20 percent of the time. So just like the previous literature has found, we found discrimination with the status quo system as well. >> You've talked to the companies about you findings, what has the response been? >> That's been actually heartening. Both companies reached out to us very quickly, and we've had continued conversations with them, and we're actually trying to design followup studies to minimize the amount of discrimination that's occurring for both Uber and Lyft. >> But those are off the record and... >> Right, we're not talking specifics, but what I can say is that the companies understand this research and they definitely want to do better. >> In fact, the companies both have issued statements about this, the first one is from Lyft, "we are extremely proud of the positive impact..." Uber has also responded. So let's talk about solutions to this. What do you and your colleagues who undertook this research suggest? >> We've been brainstorming, we don't know for sure if we have the silver bullet, but a few things could change, for example, you could imagine Uber and Lyft getting rid of names completely. We realize that has a trade off in the sense that it's nice to know the name of the driver... >> Rebecca: Sure, you can strike up a conversation... >> It makes it more social, it makes it more personal, more peer to peer if you will. But it would eliminate the type of discrimination that we uncovered. Another potential change is to delay when you give the name to the driver, so that the driver has to commit more to the ride than he or she previously had to. And that may increase the costs of discrimination. >> So that would be changing the software? >> Right, so you could imagine now, like I said, with Lyft that you see the name right away. Maybe you wait until they're 30 seconds away from the passenger before you give them the name. >> What about the dawn of the age of autonomous vehicles? Might that have an impact? We already know that Uber is experimenting with driverless cars in Pittsburgh and Arizona. >> That would obviously solve it, so that would take the human element out of things, and it's important to point out that these are the drivers that are deciding to discriminate. So provided you didn't write the autonomous vehicle software to discriminate, you would know for sure that that car is not going to discriminate. >> What about a driver education campaign? Do you think that would make a difference? I'm reminded of an essay written by Doug Glanville, who is an ESPN commentator and former pro ball player. He writes, on talking about his experience being denied service by an Uber driver, "the driver had concluded I was a threat, "either because I was dangerous myself, "or because I would direct him to a bad neighborhood, "or give him a lower tip, either way, "given the circumstances, it was hard "to attribute his refusal to anything other than my race. "Shortly after we walked away, I saw the driver assisting "another passenger who was white." >> We all hope that information helps, and eliminates discrimination. It's certainly possible that Uber and Lyft could have a full information campaign, where they show the tip rates for different ethnicities, they show the bad ride probabilities for different ethnicities, and my hope is that once the drivers learn that there aren't differences across ethnicities, that the drivers would internalize that, and stop discriminating. >> Policy, Senator Al Franken has weighed in on this, urging Uber and Lyft to address your research. Do you think that there could be policies too? Does government have a role to play? >> Potentially, but what I'll say again is, that Uber and Lyft, I think have all the incentive in the world to fix this, and that they seem to be taking active steps to fixing this. So what could policy makers do? They can, obviously it's already outlawed. They could come down and potentially fine the companies if there's more evidence of discrimination. But I would at least allow the companies some time to internalize this research, and respond to it, and see how effective they can be. >> Many, many think tanks and government advocacy groups have weighed in too. The MIT Sloan Expert Series recently sat down with Eva Millona of the Massachusetts Immigrant and Refugee Coalition. She will talk about this research in the context of immigration, let's roll that clip. >> We're an advocacy organization, and we represent the interest of foreign born, and our mission is to promote and enhance immigrant and refugee integration. Anecdotally, yes, I would say that the research, and given the impressive sample of the research really leads to a sad belief that discrimination is still out there, and there is a lot that needs to be done across sectors to really address these issues. We are really privileged to live in such a fantastic commonwealth with the right leadership and all sectors together, really making our commonwealth a welcoming place. And I do want to highlight the fantastic role of our Attorney General for standing up for our values, but Massachusetts is one state, and it could be an example, but the concern is nation wide. Given a very divisive campaign, and also not just a campaign, but also, what is currently happening at the national level that the current administration is really rejecting this welcoming effort, and the values of our country as a country, who welcomes immigrants. All sectors need to be involved in an effort to really make our society a better one for everyone. And it's going to take political leadership to really set the right tone, send the right message, and really look into the integration, and the welcoming of the newcomers as an investment in our future of our nation. Uber and Lyft have an opportunity here to provide leadership and come up with promotion of policies that integrate the newcomers, or that are welcoming to the newcomers, provide education and training, and train their people. And as troubling as the result of this research are, we like to believe that this is the attitude of the drivers, but not really what the corporate represents, so we see an opportunity for the corporate to really step in and work and promote policies of integration, policies of improvement and betterment for the whole of society and provide an example. Let me say thank you to Professor Knittle for his leadership and MIT for always being a leader, and looking into these issues. But if we can go deeper into A, the size, B, the geography, but also looking into a wider range of all communities that are represented. Looking into the Latino community, looking into the Arab communities in other parts of the nation in a more rigorous, more deep and larger size of research will be very helpful in terms of promoting better policies and integration for everybody who chooses America to be their home. >> That was Eva Millona of the Massechusetts Immigrant and Refugee Advocacy Coalition. Chris, are you confident this problem can in fact be remedied? >> I think we can do better, for sure. And I would say we need more studies like what we just preformed to see how widespread it is. We only studied two cities, we also haven't looked at all at how the driver's race impacts the discrimination. >> Now we're going to turn to you, questions from our viewers. Questions have already been coming in this morning and overnight, lots of great ones. Please use the hashtag MITSloanExpert to pose your question. The first one comes from Justin Wang, who is an MIT Sloan MBA student. He asks, "what policies can sharing economy startups "implement to reduce racial bias?" >> Well, I would say the first thing is to be aware of this. I think Uber and Lyft and Airbnb potentially were caught off guard with the amount of discrimination that was taking place. So the research that we preformed, and the research on Airbnb gives new startups a head start on designing their platforms. >> Just knowing that this is an issue. >> Knowing it's an issue, and potentially designing their platforms to think of ways to limit the amount of discrimination. >> Another question, did you look at gender bias? Do you have any indication that drivers discriminate based on gender? >> We did look at gender bias. The experiments weren't set up to necessarily nail that, but one thing that we found, for example in Boston, is that there is some evidence that women drivers were taken on longer trips. Again, both the male and the female RAs are going from the same point A to the same point B. >> Rebecca: That was a controlled part of the setting. >> That was the controlled part of the experiment. And we found evidence that women passengers were taken on longer trips and in fact, one of our RAs commented that she remembers going through the same intersection three times before she finally said something to the driver. >> And you think... So you didn't necessarily study this as part of it, but do you have any speculation, conjecture about why this was happening? >> Well, there's two potential motives. One is a financial motive that, by taking the passenger on a longer drive. They potentially get a higher fare. But I've heard anecdotal evidence that a more social motive might also be at play. For example, I have a colleague here at Sloan, who's told me that she's been asked out on dates three times while taking Uber and Lyft rides. >> So drivers taking the opportunity to flirt a little bit. >> Chris: Sure. >> Another question, can you comment on the hashtag DeleteUber campaign? This of course, is about the backlash against Uber responding that it was intending to profit from President Trump's executive order, the banning immigrants and refugees from certain countries from entering the United States. Uber maintains that its intentions were misunderstood, but it didn't stop the hashtag DeleteUber campaign. >> Yeah, I haven't followed that super closely, but to me it seems like Uber's getting a bit of a bad rap. One potential reason why they allowed Uber drivers to continue working is that, maybe they wanted to bring protesters to the airports to protest. So from that perspective, actually having Uber and Lyft still in business would be beneficial. >> Another question, did your study take into account the race of the drivers themselves? >> We actually we not allowed to. So any time you do a randomized control trial in the field like this, you have to go through a campus committee that approves or disapproves the research, and they were worried that if we collected information on the driver, that potentially, Uber and Lyft could go back into their records and find the drivers that discriminate, and then have penalties assigned to those drivers. >> So it just wouldn't be allowed to... >> At least in this first phase, yeah. They didn't want us to collect those data. >> Last question, we have time for one more. Why aren't there more experiments in the field of applies economics like this one? That's a good question. >> That's a great question, and in fact, I think many of us are trying to push experiments as much as possible. My other line of research is actually in energy and climate change research, and we've been- >> Rebecca: You like the hot topic. (lauhging) >> We've been designing a bunch of experiments to look at how information impacts consumers' choices in terms of what cars to buy, how it impacts their use of electricity at home. And experiments, randomized control trials actually started in developmental economics, where MIT has actually pioneered their use. And again, it's the best way to actually test, the most rigorous way to test whether intervention actually has an effect because you have both the controlled group and the treatment group. >> So why aren't they done more often? >> Well, it's tough, often you need to find a third party, for example, we didn't need a third party in the sense that we could just send RAs out with Uber and Lyft. But if we wanted to do anything with the drivers, for example, an information campaign, or if we wanted to change the platform at all, we would've needed Uber and Lyft to partner with us, and that can sometimes be difficult to do. And also experiments, let's be honest, are pretty expensive. >> Expensive because, you obviously weren't partnered with Uber and Lyft for this one, but... >> Right, but we had research assistants take 1500 Uber and Lyft rides, so we had to pay for each of those rides, and we also had to give them an hourly rate for their time. >> Well, Chris Knittle, thank you so much. This has been great talking to you, and you've given us a lot to think about. >> It's been fun, thanks for having me. >> And thank you for joining us on this edition of the MIT Sloan Expert Series. We hope to see you again soon.