>>Hey everyone. Welcome back to Chicago. The Cube is excited to be live on day two of Ansible Fest, 2022. Lisa Martin and John Fur. You're here having some great conversations, a lot of cube alumni, a lot of wisdom from the Ansible community coming at you on this program this week. You know, John, we've been, we've been hearing stories about the power and the capabilities and the collective wisdom of the Ansible community. You can feel it here. Yeah, there's no doubt about that. It's, Ansible is nothing, as Stephanie Chair said yesterday, if not a community and the significant contributions that it makes over and over again, or it's fuel. >>I mean the power of the community is what drives Ansible is gonna drive the future of, I think, cloud in our next generation modern application environment. And it's collective intelligence. It's a production system at the end of the day. And I think these guys have harnessed it. So it should be a really great segment to talk about all the contributor work that's been done. So I'm looking forward to it. >>We've got two great alumni here to talk about the contributor work, how you can get involved. Please welcome back to the cube. Carol Chen, principal community architect at Red Hat. Adam Miller joins us as well, fresh from the keynote stage senior principal software engineer at Red Hat. Guys, great to have you on the cube. Great to be here. Yeah, thank you. So we, we talked, we enjoyed your keynotes, Adam, and what you were talking about on stage, the Ansible contributor summit. That's, you guys have been doing what, this is the seven you've had seven so far in just a couple of years. >>Well, we had seven virtual contributor summits. >>Seven virtual. This is the first Monday was the first in person in. >>First in person since the pandemic and actually the 15th contributor summit overall >>15th overall. Talk about the contributor summits, what the contributors are able to do and the influence that it's having on Ansible Red Hat and what people are able to do with cloud. At the Edge automation. Yeah. >>So our community contributors have always had ways to influence and contribute to the project. But the contributor Summit is really a place where we can get people together, preferably in the same place so that we can, you know, have a really great dynamic conversations and interactions. But we also want to make sure that we don't leave out people who have been constantly online joining us. So this year we are so happy to be here in Chicago in person. We've had about 60 to 70 here joining us. And at first I thought maybe we'll have one third of the attendees joining online because about 30 to 40 people signed up to join online. But in the end, we have more than 100 per people watching our live stream. So that's more than half of the attendees overall, were joining us online. So that really shows where, you know, the contributors are interested in participating for >>Develop. Right. Yeah, it's been, it's been interesting. It's been since 2019, since the in-person Ansible Fest in Atlanta. Now we're in Chicago, we had the pandemic. Couple interesting observations from our side that I wanna get your reaction to Adam Carol. And that is one Ansible's relevance has grown significantly since then. Just from a cloud growth standpoint, developer open source standpoint, and how people work and collaborate has changed. So your contributor based in your community is getting more powerful in scope, in my opinion. Like in, as they become, have the keys to the kingdom in the, in their respective worlds as it gets bigger and larger. So the personas are changing, the makeup of the community's changing and also how you guys collaborate is changing. Can you share your, what's going on with those two dynamics? Cause I think that power dynamic is, is looking really good. How are you guys handling >>That? Yeah, so I mean, I, I had the opportunity to represent the community on stage yesterday as part of the keynote and talk to this point specifically is one of the things that we've seen is the project has had the opportunity to kind of grow and evolve. There's been certain elements that have had to kind of decompose from a technology perspective. We actually had to kind of break it apart and change the architecture a little bit and move things into what are called Ansible collections, which, you know, folks here are very familiar with No One Love. And we've seen a lot of community work in the form of working groups coalesce around those organically. However, they've done so in kind of different ways. They, they pick tools and collaboration platforms that are popular to their subject matter expertise audience and things like that. So we find ourselves in a place where kind of the, the community itself had more or less segmented naturally in a way. And we needed to find ways to, you know, kind of ke that >>Fragmentation by demographics or by expertise or both as >>A Mostly, mostly expertise. Yeah. And so there was a open source technology called Matrix. It is a open source, standardized, federated messaging platform that we're able to use to start to bridge back some of those communities that have kind of broken off and, and made their their own home elsewhere on the internet. So now we're able to, for example, the right, the docs organization, they had a, a group of people who was very interested in contributing to the Ansible documentation, but they'd already self-organized on Discord. And what was interesting there is the existing team for the Ansible documentation, they were already on internet Relay Chat, also known as irc. And Matrix allowed us to actually bring those two together and bridge that into the other matrix cha chat channels that we had. So now we're able to have people from all over the world in different areas and different platforms, coalesce and, and cross. It's like a festival cross pollinate. Yeah. >>And you're meeting the contributors exactly where they are and where they want to be, where they're comfortable. >>Yes. Yeah, we always say we, we reach out to where they are. So, >>And, and, and much in the way that Ansible has the capability to reach out to things in their own way, Right. And allow that subject matter expertise to, you know, cause the technology has the potential and possibility and capability to talk to anything over any protocol. We're able to do, you know, kind of the same thing with Matrix, allowing us to bridge into any chat platform that it has support for bridging and, and we're able to bring a lot of people >>Together. Yeah. And how's that, how's the feedback been on that so far? >>I, I think it has been very positive. For example, I want to highlight that the technical writers that we have contributing via Discord is actually a group from Nigeria. And Dave also participated in the contributor summit online virtually joining us in, in, you know, on the matrix platform. So that, that bridge that really helps to bring together people from different geographical regions and also different topics and arenas like that. >>So what were some of the outcomes of the contributor summit? The, the first in person in a while, great. That you guys were able to do seven virtually during the pandemic. That's hard. It's hard to get people together. You, there's so much greatness and innovation that comes when we're all together in person that just can't replicate by video. You can do a lot. Right. But talk about some of the outcomes from Monday. What were some of the feedback? What were some of the contributions that you think are really going to impact the community? >>I think for a lot of us, myself included, the fact that we are in person and meeting people face to face, it helps to really build the connections. And when we do talk about contribution, the connection is so important that you understand, well this person a little bit about their background, what they've done for the SPO project and or just generally what, what they're interested in that builds the rapport and connection that helps, you know, further, further collaboration in the future. Because maybe on that day we did not have any, you know, co contributions or anything, but the fact that we had a chance to sit together in the same place to discuss things and share new ideas, roadmaps is really the, the kind of a big step to the future for our community. Yes, >>Yes. And in a lot of ways we often online the project has various elements that are able to function asynchronously. So we work very well globally across many time zones. And now we were able to get a lot of people in the same place at the same time, synchronously in the same time zone. And then we had breakout sessions where the subject matter, you know, working groups were able to kind of go and focus on things that maybe have been taking a little while to discuss in, in that asynchronous form of communication and do it synchronously and, you know, be in the same room and work on things. It's been, it's been fantastic >>Developers there, like they, they take to asynchronous like fish to water. It's not a problem. But I do want to ask if there's any observations that you guys have had now that we're kind of coming out of that one way, but the pandemic, but the world's changed. It's hybrid, hybrid work environment, steady state. So we see that. Any observations on your end on what's new that you observed that people are gravitating to? Is there a pattern of styles is or same old self-governing, or what's new? What do you see that's coming out of the pandemic that might be a norm? >>I I think that even though people are excited to get back in person, there are, things have changed, like you said, and we have to be more aware of, there are people who think that not be in person, it's okay. And that's how they want to do it. And we have to make sure that they, they are included. So we, we did want to make a high priority for online participation in this event. And like I said, even though only 4 30, 40 people signed up to join us online initially, so that it was what we were expecting, but in the end, more than 100 people were watching us and, and joining participation in >>Actually on demand consumption be good too, >>Right? Yeah. So, you know, I think going forward that is probably the trend. And as, as much as we, we love being in person, we, we want this to continue that we, we take care of people who are, has been constantly participating online and contributing you >>Meaning again, meaning folks where they are, but also allowing the, the, those members that want to get together to, to collaborate in person. I can only imagine the innovation that's gonna come even from having part of the back, Right. >>And, and not to continue to harp on the matrix point, but it, it's been very cool because Matrix has the ability to do live video sessions using open source another to open source technology called jy. So we're able to actually use the same place that we normally find ourselves, you know, congregating and collaborating for the project itself in an asynchronous and, you know, somewhat synchronous way to also host these types of things that are, are now hybrid that used to be, you know, all one way or all the other. Yeah. And it's been, it's >>Been incredible. Integration is, the integration is have been fascinating to watch how you guys do that. And also, you know, with q we've been virtual too. It's like, it's like people don't want another microsite, but they want a more of a festival vibe, a hub, right? Like a place to kind of check in and have choice, not get absolutely jammed into a, you know, forum or, you know, or whatever. Hey, if you wanna be on Discord, be on Discord, right? Why >>Not? And we still, you know, we do still have our asynchronous forms of >>Work through >>Our get GitHub. We have our projects, we have our issues, we have our, you know, wiki, we have various elements there that everybody can continue to collaborate on. And it's all been, it's all been very good. >>Speaking of festivals, octoberfest that's going on, not to be confused with Octoberfest, that was last month. Talk about how the Ansible project and the Ansible community is involved in Octoberfest. Give us the dates, Carol. So >>YesTo Fest is a annual thing in October. So October Octoberfest, I think it's organized by Digital Ocean for the past eight or nine years. And it's really a, a way to kind of encourage people to contribute to open source projects. So it's not anal specific, but we as an Ansible project encourage people to take this opportunity to, you know, a lot of them doing their first contributions during this event. And when, when we first announced, we're participating in Octoberfest within the first four days of October, which is over a weekend actually. We've had 24 contributions, it, 24 issues fixed, which is like amazing, like, you know, just the interest and the, the momentum that we had. And so far until I just checked with my teammates this morning that we've had about 35 contributions so far during the month, which is, and I'm sorry, I forgot to mention this is only for Ansible documentation. So yeah, specifically. And, and that's also one thing we want to highlight, that contributions don't just come in code in, you know, kind of software side, but really there's many ways to contribute and documentation is such a, a great way for first time users, first time contributors to get involved. So it's really amazing to see these contributions from all over the world. And also partly thanks to the technical writers in Nigeria kind of promoting and sharing this initiative. And it's just great to see the, the results from that. Can >>You double click on the different ways of contribution? You mentioned a couple documentation being one, code being the other, but what is the breadth of opportunities that the contributors have to contribute to the project? >>Oh, there's, there's so many. So I actually take care more of outreach efforts in the community. So I helped to organize events and meetups from around the world. And now that we're slowly coming out of the pandemic, I've seen more and more in person meetups. I was just talking to someone from Minneapolis, they're trying to get, get people back together again. They have people in Singapore, in Netherlands from pretty much, you know, all corners of the globe wanting to form not just for the Ansible project, but the local kind of connection with the re people in the region, sometimes in their own language, in their local languages to really work together on the project and just, >>You know, you to create a global Yeah. Network, right? I mean it's like Ansible Global. >>Exactly. >>Create local subnets not to get all networking, >>Right? >>Yeah. >>Yeah. One, one quick thing I want to touch on Theto Fest. I think it's a great opportunity for existing contributors to mentor cause many people like to help bring in new contributors and this is kind of a focal point to be able to focus on that. And then to, to the the other point we, you know, it, it's been, it's been extremely powerful to see as we return these sub communities pop up and, and kind of work with themselves, so on different ways to contribute. So code is kind of the one that gets the most attention. I think documentation I think is a unsung hero, highly important, great way. The logistical component, which is invaluable because it allows us to continue with our adoption and evangelization and things like that. So specifically adoption and evangelize. Evangelization is another place that contributors can join and actually spawn a local meetup and then connect in with the existing community and try to, you know, help increase the network, create a new subject. Yeah. >>Yeah. Network affects huge. And I think the thing that you brought up about reuse is, is part of that whole things get documented properly. The leverage that comes out of that just feeds into the system that flywheel. Absolutely. I mean it's, that's how communities are supposed to work, right? Yep. Yes. >>That's what I was just gonna comment on is the flywheel effect that it's clearly present and very palpable. Thank you so much for joining John, me on the program, talking about the contributors summit, the ways of contribution, the impacts that are being made so far, what Octoberfest is already delivering. And we're, we still have about 10 days or so left in October, so there's still more time for contributors to get involved. We thank you so much for your insights and your time. Thank >>You. Thank you so much for having us. >>Our pleasure. For our guests and John Purer, I'm Lisa Martin. You're watching The Cube Live from Chicago, day two of our coverage of Red Hat Ansible Summit 22. We will see you right n after this short break with our next guest.

(gentle music) >> Hello from Chicago, Lisa Martin here at AnsibleFest 2022 with John Furrier. John, it's great to be here. The transformation of enterprise and industry through automation. This is not only the 10th anniversary of Ansible, this was the first in-person AnsibleFest since 2019. >> It's awesome, it's awesome, Lisa, and I want to welcome everyone to our live performance here in Chicago. We were remote for two years, 2019 in Atlanta. AnsibleFest, part of Red Hat now, Red Hat part of IBM. So much has happened in the past couple years and I think one of the things that we're going to cover this week here in Chicago, is the evolution of Ansible, where it fits into the new cloud-native ecosystems emerging, and also, kind of, what it means for developers and operators. And we're going to see a lot of that here at AnsibleFest with wall-to-wall coverage, keynote just happened. Very interesting to see, you know, Ansible stayed true to their knitting, as you say, you know. What do they do? No big announcements. Some big community news. But humble. >> Very humble. Very humble, but also very excited. All the keynotes did a great job of addressing that community, and being grateful to the community for, really, the evolution that we see at Ansible and now 10 years later. They were talking a lot about smoothing operations for the developers, democratizing automation across the organization. They talked a little bit about that skills gap. I wanted to get your opinion, 'cause as we know there's, they talked about it from a demand perspective, there's over 300,000 open positions on LinkedIn for Ansible skills. So a lot of opportunity there, a lot of opportunity for them to help democratize automation across organizations. >> Yeah, I mean, I think the big theme last year we heard, "Three things, top three things at AnsibleFest 2021, Animation, Automation, Automation." Again, this year the same theme, "Automate everywhere" is what they're talking about. But I think you're right, there's a cultural shift where the entire cloud ecosystems kind of spun to the doorstep of what Ansible's ecosystem stood for for many years in the decade, which is configuration, running things at scale. That notion is now persistent across all the enterprise. And I think the key takeaway from the keynote, in my opinion, is that configuration and automation around devices and infrastructure stuff is an enterprise architecture now, it's not just a, kind of a corner case, or a specific use case, it's going to be native across the entire enterprise architecture. And that's why we heard a lot of cultural shift conversations. And that is where the people who are running the Ansible stuff, they're going to be the keys to having the keys to the kingdom. And I think you're going to see a lot more of this automation at scale. I love the introduction of ops-as-code, that's a little piggyback off of infrastructure-as-code and infrastructure-as-configuration. They're saying operations now is the new software model and it's like ops dev, not dev ops. So it's really interesting to see how the operator is now a very big important role in the next level of cloud native. And it's really exciting because this is kind of what we've been reporting on theCUBE, for over 12 years. So, watching Ansible grow organically into a powerhouse community is very interesting. To see how they operationalize this, you know, going forward. >> Well the operator's becoming really pivotal catalysts in this next way, that you've been covering for 12 years. You know, if we think about some of the challenges and the barriers to adopting automation that organizations have had, one of them has been skills, staff rather. The other has been, "Hey, we need to really determine which processes to automate, that's actually going to give us the most ROI, most bang for our buck." They talked a little bit about that today, but that's still something that Ansible is working with its customers and the community to help sort of demystify. >> Yeah and I think that they were front and center around, "You on the room," people in the room, "you make this happen." They're very much, it's not a top-down corporate thing, Ansible staying true to their roots as I mentioned. But the thing about skills gap is interesting, you heard Kaete Piccirilli talking about, "Level Up how your organization automates, push your people, expand your scope." So the theme is, the power is in the hands of this community to essentially be the new enterprise architecture for operations. At the same time that feeds the trend around, we're seeing this accelerated cloud-native developer we're seeing, we're going to be at KubeCon next week, that cloud-native developer, they want to go faster, they want self-service. So you're seeing higher velocity cloud-native development putting pressure on the ops teams to level up, so the theme kind of connects for me. I think Red Hat has got it right here, with Ansible, that the theme is shifting to ops better get their act together, to level up and to the velocity of what the developers are expecting. At the same time, giving them the freedom to be using infrastructure-as-code, infrastructure-as-configuration, and ultimately, ops-as-code. To me, I think this is like the evolution of how infrastructure-as-code, which is the nirvana of DevOps, now is ops-as-code. Which means, if that's true, ops becomes much more invisible, if you will, which is what developers want. >> And we're going to be breaking down ops-as-code today, no doubt, in our conversations with some of the great Ansible community folks and partners and leaders that we have on, as well as tomorrow in our full two days of coverage. You talked about cultural shift, we talk about that a lot John, it's challenging, but one of the things I think that was very palpable this morning, is the power of the Ansible community. Not just those folks that are here with us in Chicago, but all the folks watching virtually online. >> Yeah. >> Truly help drive that cultural shift that is needed for organizations to really be able to streamline cloud ops. >> Yeah and I think Adam Miller who came on, I thought his portion was excellent, around community. He talked about, you know, the 10 years, put a little exclamation point on that. Managing the communications within the community. He actually brought up IRC and Slack and then, "We have Discord." And they introduced a new standard for communications it's called Matrix, which is open-source based. And even in their decision making, their principles around open source stay true. Again, they checked the box there, I thought that was really cool. The other thing that, within the meat of the product, the automation platform, Matt Jones was talking about the scale, the managing at scale, is one thing. But the thing that I think that hit, jumped out at me, was that this trusted automation messaging was really huge. Signing, having signatures, that really hits the supply chain that we've been talking about, and we're going to talk about it next week at KubeCon, the software supply chain is trusting the code. And I think as you have automation, it's a really big part of the new platform. So, I thought that was really the meat on the bone there. >> That was a very strong theme, was the trust this morning. You know, another thing that was important was Walter Bentley, who's coming on, I believe, later today, talked about how organizations really need to think about the value that automation can deliver to the business and then develop an automation strategy, really thinking at it strategically rather than what a lot of folks have done. And they've put automation in sort of in silos and pockets. He's really talking about, how can you actually make it strategic across the organization and make sure that you really fully see and understand and can articulate the value to the business, from a competitive advantage perspective, that it's going to deliver. >> Yeah, and Stefanie Chiras who's coming on too, she mentioned a lot about the multi-cloud, multi-environment layer, how Ansible can sit across all the environments and then still support the cloud-native through what she called "an automation loop". That's going to be really talking to what we're seeing as multi-cloud or super-cloud, next-gen cloud, where Ansible's role of automating isn't just corner case in the enterprise. Again, if it's an enterprise-wide architecture, it will be a centerpiece of multi-cloud, multiple capabilities. Whether that's compatibility services or, you know, stuff running best of breed on different clouds. 'Cause, obviously Amazon was on stage here, they're talking about this, big Ansible supporter. So, we've got Google supporting Ansible, so you got the multiple clouds and even VM-Ware environments. So, Ansible sits across all this. And so, I think the big opportunity that I'm seeing come out of this, is that if Ansible is in this position, this could be a catalyst for them to be the multi-cloud hybrid architecture for configuration and operations, and I think, the edge is going to be a really interesting conversation. We have a lot of guests coming on, I'll talk about that. But, I think running distributed workloads across multiple clouds in multiple environments, that's a killer app and we'll see if they can pull it off. We're going to be drilling everyone on that topic today, so I'm looking forward to it. >> We're going to be dissecting that. I like how you paint that picture of Ansible really as the nucleus of that hybrid cloud strategy. You know, so many organizations are living in a hybrid cloud world for many reasons, but for Ansible to be able to be that catalyst. And question for you, if we think about that, when we talk about multi-cloud strategically or organically or whatnot, where is automation moving in terms of the customer conversation? We know Ansible's really focused on smoothing the developer experience, but where is automation going, in your vision, up the C-suite stack? >> Well, multi-cloud is a C-suite message and they love to hear that, but you talk to anyone who's in the trenches, they hate multi-cloud. It's more complexity and there's a lot of issues around latency. So what you're seeing is, you're starting to see an evolution of more about compatibility and interoperability. And this is kind of classic enterprise abstraction layers when you start getting into these inflection points, as things get better, so it gets sometimes more complex. So I think Ansible's notion of simplicity and ease of use, could be the catalyst for this abstraction layer between clouds. So it's all about reducing the complexities, because at the end of the day, if you want to do something on multiple clouds, whether that's run common services across, that's not making it simpler. You got to, it's going to be harder before it gets easier. So, if that makes any sense. So doing multi-cloud sounds great on paper, but it's really hard and that's why no one's really doing it. So you're going to start to see multi-cloud, what we call super-cloud, which is more capabilities on one cloud. And then having them still differentiate the idea that some standard's going to emerge, is complete fantasy. I think you're going to, we still need more innovation. Amazon does a great job, Microsoft's coming up on number two position as well, the clouds still need to differentiate. But that doesn't change Ansible's position. They can still be that shim layer or bolt-on, to whatever clouds do best. If you run 'em on machine learning on Google, that's cool. You want to use Amazon for this? How do you make those work? That's a hard problem. And, again, that's where automation ends up. >> And with that context, do you think that Ansible has the capability of helping to dial down some of the complexity that's in this hybrid multi-cloud world? >> Yeah, I mean, I think the thing about what's going on great here, that's unique in the history of the computer industry, is open source is so powerful and it continues to power away with growth. So, more code is coming. So, software supply chain is a big issue, we heard that with the trusted thing, but also now, how people buy now is different. You can actually try stuff out on open source and then go to Red Hat, Ansible, and say, "Hey, I'm going to get some support." So there's a lot of community collective intelligence involved in decision making, not just coding, but buyer selection and consumption. So the entire paradigm of purchasing software and using it, has completely changed. So, that puts Ansible in a leading position because they got a great community, and now you've got open source continuing to thrive away. So, if you're a customer, you don't need the big enterprise sales pitch you can just try the code, if you like it, then you go with Ansible. So it's really kind of set up nicely, this cloud market, for companies like Ansible, because they have the community and they got the software, it's open and it is what it is, it's transparent, everything's above board. >> Yeah, you know, you talk about the community, you mentioned Matrix earlier, and one of the things that was also quite resonant during the keynote this morning was the power of collaboration and how incredibly important that is to them, to stay native to their open-source roots, as you you said. But also really go to where the customers are. And they talked about that with respect to Matrix and Discord and that was an interesting, this is the community reaching out to really kind of grow upon itself. >> Well, being someone who's used all those tools, even IRC 'cause I'm old, all the old folks use IRC. Then the, kind of, Gen X'ers use, and the millennials use Slack. Discord, the way they mentioned Discord, it's so true. If you're a gamer, you're younger, you're using Discord. Now, Matrix is new, they're trying to introduce an open source, 'cause remember they don't control Discord and they don't control Slack. So Slack's Salesforce now, and Discord is probably going to try to get bought by Microsoft, but still, it's not open. So Matrix is their open-sourced chat service. And I thought that was interesting and I think, that got my attention, because that went against the principles of users that like Slack. So, it'd be great. I mean if Matrix, if that takes off, then that's going to be a case study of going against-the-grain on the best-of-breed package software like Slack or Discord. But I think the demographic shift is interesting. Discord is for younger generations, let's see how Matrix will do. And the uptake wasn't that big. Only been around for a couple months, we've seen almost 5,000 members. But, you know, not a failure. >> Right. >> But not a home run either. >> Right. Well we'll have to see how that progresses- >> Yeah, we'll see how that plays out. >> as all of the generations in the workforce today try to work together and collaborate. You know, if we think about some of the things that we're going to talk about today and tomorrow, business outcomes, increasing business agility, being able to ensure compliance, with security and regulatory requirements, which are only proliferating, really also helping organizations to optimize those costs and be as competitive as they possibly can. So I'm excited to dissect the announcements that came out today, some of the things that we're going to hear today and tomorrow, and really get a great view of the automation infrastructure marketplace and what's going on. >> Yeah, it's going to be great. Infrastructure-as-code, infrastructure-as-config, operations-as-code, it's all leading to, you know, distributed computing edge. It's hybrid. >> Yep. All right John- >> Yeah. >> looking forward to two days of wall-to-wall CUBE coverage with you, coming to you live from Chicago, at the first AnsibleFest in person, since 2019. Lisa Martin and John Furrier with you here all day today and tomorrow, stick around, our first guest joins us. We're going to dissect ops-as-code, stick around. (gentle music)

>> From the theCUBE studios in Palo Alto in Boston, bringing you data driven insights from theCUBE and ETR. This is "Breaking Analysis" with Dave Vellante. >> Welcome to this week's Wikibon, theCUBE's insights powered by ETR. As we exited the isolation economy last year, supercloud is a term that we introduced to describe something new that was happening in the world of cloud. In this Breaking Analysis, we address the 10 most frequently asked questions we get around supercloud. Okay, let's review these frequently asked questions on supercloud that we're going to try to answer today. Look at an industry that's full of hype and buzzwords. Why the hell does anyone need a new term? Aren't hyperscalers building out superclouds? We'll try to answer why the term supercloud connotes something different from hyperscale clouds. And we'll talk about the problems that superclouds solve specifically. And we'll further define the critical aspects of a supercloud architecture. We often get asked, isn't this just multi-cloud? Well, we don't think so, and we'll explain why in this Breaking Analysis. Now in an earlier episode, we introduced the notion of super PaaS. Well, isn't a plain vanilla PaaS already a super PaaS? Again, we don't think so, and we'll explain why. Who will actually build and who are the players currently building superclouds? What workloads and services will run on superclouds? And 8-A or number nine, what are some examples that we can share of supercloud? And finally, we'll answer what you can expect next from us on supercloud? Okay, let's get started. Why do we need another buzzword? Well, late last year, ahead of re:Invent, we were inspired by a post from Jerry Chen called "Castles in the Cloud." Now in that blog post, he introduced the idea that there were sub-markets emerging in cloud that presented opportunities for investors and entrepreneurs that the cloud wasn't going to suck the hyperscalers. Weren't going to suck all the value out of the industry. And so we introduced this notion of supercloud to describe what we saw as a value layer emerging above the hyperscalers CAPEX gift, we sometimes call it. Now it turns out, that we weren't the only ones using the term as both Cornell and MIT have used the phrase in somewhat similar, but different contexts. The point is something new was happening in the AWS and other ecosystems. It was more than IaaS and PaaS, and wasn't just SaaS running in the cloud. It was a new architecture that integrates infrastructure, platform and software as services to solve new problems that the cloud vendors in our view, weren't addressing by themselves. It seemed to us that the ecosystem was pursuing opportunities across clouds that went beyond conventional implementations of multi-cloud. And we felt there was a structural change going on at the industry level, the supercloud, metaphorically was highlighting. So that's the background on why we felt a new catch phrase was warranted, love it or hate it. It's memorable and it's what we chose. Now to that last point about structural industry transformation. Andy Rappaport is sometimes and often credited with identifying the shift from the vertically integrated IBM mainframe era to the fragmented PC microprocesor-based era in his HBR article in 1991. In fact, it was David Moschella, who at the time was an IDC Analyst who first introduced the concept in 1987, four years before Rappaport's article was published. Moschella saw that it was clear that Intel, Microsoft, Seagate and others would replace the system vendors, and put that forth in a graphic that looked similar to the first two on this chart. We don't have to review the shift from IBM as the center of the industry to Wintel, that's well understood. What isn't as well known or accepted is what Moschella put out in his 2018 book called "Seeing Digital" which introduced the idea of "The Matrix" that's shown on the right hand side of this chart. Moschella posited that new services were emerging built on top of the internet and hyperscale clouds that would integrate other innovations and would define the next era of computing. He used the term Matrix because the conceptual depiction included not only horizontal technology rose like the cloud and the internet, but for the first time included connected industry verticals, the columns in this chart. Moschella pointed out that whereas historically, industry verticals had a closed value chain or stack and ecosystem of R&D, and production, and manufacturing, and distribution. And if you were in that industry, the expertise within that vertical generally stayed within that vertical and was critical to success. But because of digital and data, for the first time, companies were able to traverse industries, jump across industries and compete because data enabled them to do that. Examples, Amazon and content, payments, groceries, Apple, and payments, and content, and so forth. There are many examples. Data was now this unifying enabler and this marked a change in the structure of the technology landscape. And supercloud is meant to imply more than running in hyperscale clouds, rather it's the combination of multiple technologies enabled by CloudScale with new industry participants from those verticals, financial services and healthcare, manufacturing, energy, media, and virtually all in any industry. Kind of an extension of every company is a software company. Basically, every company now has the opportunity to build their own cloud or supercloud. And we'll come back to that. Let's first address what's different about superclouds relative to hyperscale clouds? You know, this one's pretty straightforward and obvious, I think. Hyperscale clouds, they're walled gardens where they want your data in their cloud and they want to keep you there. Sure, every cloud player realizes that not all data will go to their particular cloud so they're meeting customers where their data lives with initiatives like Amazon Outposts and Azure Arc, and Google Anthos. But at the end of the day, the more homogeneous they can make their environments, the better control, security, cost, and performance they can deliver. The more complex the environment, the more difficult it is to deliver on their brand promises. And of course, the lesser margin that's left for them to capture. Will the hyperscalers get more serious about cross-cloud services? Maybe, but they have plenty of work to do within their own clouds and within enabling their own ecosystems. They had a long way to go a lot of runway. So let's talk about specifically, what problems superclouds solve? We've all seen the stats from IDC or Gartner, or whomever the customers on average use more than one cloud. You know, two clouds, three clouds, five clouds, 20 clouds. And we know these clouds operate in disconnected silos for the most part. And that's a problem because each cloud requires different skills because the development environment is different as is the operating environment. They have different APIs, different primitives, and different management tools that are optimized for each respective hyperscale cloud. Their functions and value props don't extend to their competitors' clouds for the most part. Why would they? As a result, there's friction when moving between different clouds. It's hard to share data, it's hard to move work. It's hard to secure and govern data. It's hard to enforce organizational edicts and policies across these clouds, and on-prem. Supercloud is an architecture designed to create a single environment that enables management of workloads and data across clouds in an effort to take out complexity, accelerate application development, streamline operations and share data safely, irrespective of location. It's pretty straightforward, but non-trivial, which is why I always ask a company's CEO and executives if stock buybacks and dividends will yield as much return as building out superclouds that solve really specific and hard problems, and create differential value. Okay, let's dig a bit more into the architectural aspects of supercloud. In other words, what are the salient attributes of supercloud? So first and foremost, a supercloud runs a set of specific services designed to solve a unique problem and it can do so in more than one cloud. Superclouds leverage the underlying cloud native tooling of a hyperscale cloud, but they're optimized for a specific objective that aligns with the problem that they're trying to solve. For example, supercloud might be optimized for lowest cost or lowest latency, or sharing data, or governing, or securing that data, or higher performance for networking, for example. But the point is, the collection of services that is being delivered is focused on a unique value proposition that is not being delivered by the hyperscalers across clouds. A supercloud abstracts the underlying and siloed primitives of the native PaaS layer from the hyperscale cloud and then using its own specific platform as a service tooling, creates a common experience across clouds for developers and users. And it does so in a most efficient manner, meaning it has the metadata knowledge and management capabilities that can optimize for latency, bandwidth, or recovery, or data sovereignty, or whatever unique value that supercloud is delivering for the specific use case in their domain. And a supercloud comprises a super PaaS capability that allows ecosystem partners through APIs to add incremental value on top of the supercloud platform to fill gaps, accelerate features, and of course innovate. The services can be infrastructure-related, they could be application services, they could be data services, security services, user services, et cetera, designed and packaged to bring unique value to customers. Again, that hyperscalers are not delivering across clouds or on-premises. Okay, so another common question we get is, isn't that just multi-cloud? And what we'd say to that is yes, but no. You can call it multi-cloud 2.0, if you want, if you want to use it, it's kind of a commonly used rubric. But as Dell's Chuck Whitten proclaimed at Dell Technologies World this year, multi-cloud by design, is different than multi-cloud by default. Meaning to date, multi-cloud has largely been a symptom of what we've called multi-vendor or of M&A, you buy a company and they happen to use Google Cloud, and so you bring it in. And when you look at most so-called, multi-cloud implementations, you see things like an on-prem stack, which is wrapped in a container and hosted on a specific cloud or increasingly a technology vendor has done the work of building a cloud native version of their stack and running it on a specific cloud. But historically, it's been a unique experience within each cloud with virtually no connection between the cloud silos. Supercloud sets out to build incremental value across clouds and above hyperscale CAPEX that goes beyond cloud compatibility within each cloud. So if you want to call it multi-cloud 2.0, that's fine, but we chose to call it supercloud. Okay, so at this point you may be asking, well isn't PaaS already a version of supercloud? And again, we would say no, that supercloud and its corresponding superPaaS layer which is a prerequisite, gives the freedom to store, process and manage, and secure, and connect islands of data across a continuum with a common experience across clouds. And the services offered are specific to that supercloud and will vary by each offering. Your OpenShift, for example, can be used to construct a superPaaS, but in and of itself, isn't a superPaaS, it's generic. A superPaaS might be developed to support, for instance, ultra low latency database work. It would unlikely again, taking the OpenShift example, it's unlikely that off-the-shelf OpenShift would be used to develop such a low latency superPaaS layer for ultra low latency database work. The point is supercloud and its inherent superPaaS will be optimized to solve specific problems like that low latency example for distributed databases or fast backup and recovery for data protection, and ransomware, or data sharing, or data governance. Highly specific use cases that the supercloud is designed to solve for. Okay, another question we often get is who has a supercloud today and who's building a supercloud, and who are the contenders? Well, most companies that consider themselves cloud players will, we believe, be building or are building superclouds. Here's a common ETR graphic that we like to show with Net Score or spending momentum on the Y axis and overlap or pervasiveness in the ETR surveys on the X axis. And we've randomly chosen a number of players that we think are in the supercloud mix, and we've included the hyperscalers because they are enablers. Now remember, this is a spectrum of maturity it's a maturity model and we've added some of those industry players that we see building superclouds like CapitalOne, Goldman Sachs, Walmart. This is in deference to Moschella's observation around The Matrix and the industry structural changes that are going on. This goes back to every company, being a software company and rather than pattern match an outdated SaaS model, we see new industry structures emerging where software and data, and tools, specific to an industry will lead the next wave of innovation and bring in new value that traditional technology companies aren't going to solve, and the hyperscalers aren't going to solve. You know, we've talked a lot about Snowflake's data cloud as an example of supercloud. After being at Snowflake Summit, we're more convinced than ever that they're headed in this direction. VMware is clearly going after cross-cloud services you know, perhaps creating a new category. Basically, every large company we see either pursuing supercloud initiatives or thinking about it. Dell showed project Alpine at Dell Tech World, that's a supercloud. Snowflake introducing a new application development capability based on their superPaaS, our term of course, they don't use the phrase. Mongo, Couchbase, Nutanix, Pure Storage, Veeam, CrowdStrike, Okta, Zscaler. Yeah, all of those guys. Yes, Cisco and HPE. Even though on theCUBE at HPE Discover, Fidelma Russo said on theCUBE, she wasn't a fan of cloaking mechanisms, but then we talked to HPE's Head of Storage Services, Omer Asad is clearly headed in the direction that we would consider supercloud. Again, those cross-cloud services, of course, their emphasis is connecting as well on-prem. That single experience, which traditionally has not existed with multi-cloud or hybrid. And we're seeing the emergence of companies, smaller companies like Aviatrix and Starburst, and Clumio and others that are building versions of superclouds that solve for a specific problem for their customers. Even ISVs like Adobe, ADP, we've talked to UiPath. They seem to be looking at new ways to go beyond the SaaS model and add value within their cloud ecosystem specifically, around data as part of their and their customers digital transformations. So yeah, pretty much every tech vendor with any size or momentum and new industry players are coming out of hiding, and competing. Building superclouds that look a lot like Moschella's Matrix, with machine intelligence and blockchains, and virtual realities, and gaming, all enabled by the internet and hyperscale cloud CAPEX. So it's moving fast and it's the future in our opinion. So don't get too caught up in the past or you'll be left behind. Okay, what about examples? We've given a number in the past, but let's try to be a little bit more specific. Here are a few we've selected and we're going to answer the two questions in one section here. What workloads and services will run in superclouds and what are some examples? Let's start with analytics. Our favorite example is Snowflake, it's one of the furthest along with its data cloud, in our view. It's a supercloud optimized for data sharing and governance, query performance, and security, and ecosystem enablement. When you do things inside of that data cloud, what we call a super data cloud. Again, our term, not theirs. You can do things that you could not do in a single cloud. You can't do this with Redshift, You can't do this with SQL server and they're bringing new data types now with merging analytics or at least accommodate analytics and transaction type data, and bringing open source tooling with things like Apache Iceberg. And so it ticks the boxes we laid out earlier. I would say that a company like Databricks is also in that mix doing it, coming at it from a data science perspective, trying to create that consistent experience for data scientists and data engineering across clouds. Converge databases, running transaction and analytic workloads is another example. Take a look at what Couchbase is doing with Capella and how it's enabling stretching the cloud to the edge with ARM-based platforms and optimizing for low latency across clouds, and even out to the edge. Document database workloads, look at MongoDB, a very developer-friendly platform that with the Atlas is moving toward a supercloud model running document databases very, very efficiently. How about general purpose workloads? This is where VMware comes into to play. Very clearly, there's a need to create a common operating environment across clouds and on-prem, and out to the edge. And I say VMware is hard at work on that. Managing and moving workloads, and balancing workloads, and being able to recover very quickly across clouds for everyday applications. Network routing, take a look at what Aviatrix is doing across clouds, industry workloads. We see CapitalOne, it announced its cost optimization platform for Snowflake, piggybacking on Snowflake supercloud or super data cloud. And in our view, it's very clearly going to go after other markets is going to test it out with Snowflake, running, optimizing on AWS and it's going to expand to other clouds as Snowflake's business and those other clouds grows. Walmart working with Microsoft to create an on-premed Azure experience that's seamless. Yes, that counts, on-prem counts. If you can create that seamless and continuous experience, identical experience from on-prem to a hyperscale cloud, we would include that as a supercloud. You know, we've written about what Goldman is doing. Again, connecting its on-prem data and software tooling, and other capabilities to AWS for scale. And we can bet dollars to donuts that Oracle will be building a supercloud in healthcare with its Cerner acquisition. Supercloud is everywhere you look. So I'm sorry, naysayers it's happening all around us. So what's next? Well, with all the industry buzz and debate about the future, John Furrier and I, have decided to host an event in Palo Alto, we're motivated and inspired to further this conversation. And we welcome all points of view, positive, negative, multi-cloud, supercloud, hypercloud, all welcome. So theCUBE on Supercloud is coming on August 9th, out of our Palo Alto studios, we'll be running a live program on the topic. We've reached out to a number of industry participants, VMware, Snowflake, Confluent, Sky High Security, Gee Rittenhouse's new company, HashiCorp, CloudFlare. We've hit up Red Hat and we expect many of these folks will be in our studios on August 9th. And we've invited a number of industry participants as well that we're excited to have on. From industry, from financial services, from healthcare, from retail, we're inviting analysts, thought leaders, investors. We're going to have more detail in the coming weeks, but for now, if you're interested, please reach out to me or John with how you think you can advance the discussion and we'll see if we can fit you in. So mark your calendars, stay tuned for more information. Okay, that's it for today. Thanks to Alex Myerson who handles production and manages the podcast for Breaking Analysis. And I want to thank Kristen Martin and Cheryl Knight, they help get the word out on social and in our newsletters. And Rob Hof is our editor in chief over at SiliconANGLE, who does a lot of editing and appreciate you posting on SiliconANGLE, Rob. Thanks to all of you. Remember, all these episodes are available as podcasts wherever you listen. All you got to do is search Breaking Analysis podcast. It publish each week on wikibon.com and siliconangle.com. You can email me directly at david.vellante@siliconangle.com or DM me @DVellante, or comment on my LinkedIn post. And please do check out ETR.ai for the best survey data. And the enterprise tech business will be at AWS NYC Summit next Tuesday, July 12th. So if you're there, please do stop by and say hello to theCUBE, it's at the Javits Center. This is Dave Vellante for theCUBE insights powered by ETR. Thanks for watching. And we'll see you next time on "Breaking Analysis." (bright music)

>>from around the >>globe it's the cube with >>coverage of Kublai >>Khan and Cloud Native Con Europe 2020 >>one virtual >>brought to you by red hat. The cloud native computing foundation and ecosystem partners. Welcome back to the cubes coverage of Kublai khan 21 cloud native gone 21 virtual. I'm john for your host of the cube. We're here with a great segment of an entrepreneur also the creator and maintainer of fluent bit Eduardo Silva who's now the founder of Palihapitiya was a startup. Going to commercialize and have an enterprise grade fluent D influence bit Eduardo. Great to have you on. Thanks for coming on the cube >>during the place for having me here. So I'm pretty happy to share the news about the crew and whenever you want, >>exciting trends, exciting trends happening with C N C f koo Kahne cloud native cloud native a lot of data, a lot of management, a lot of logging, a lot of observe ability, a lot of end user um contributions and enterprise adoption. So let's get into it first by give us a quick update on fluent D anything upcoming to highlight. >>Yeah, well fluent is actually turning two years old right now. So it's the more metric project that we have a lot of management and processing in the market. And we're really happy to see that the sides are project that was started 10 years ago, its adoption. You can see continues growing ecosystem from a planning perspective and companies adopting the technology that that is really great. So it's very overwhelming and actually really happy to take this project and continue working with companies, individuals and and right now what is the position where we are now with through And these are part of the Roma is like one of the things that people is facing not because of the tool because people have every time there has more data, more Metro services the system are scaling up is like about performance, right? And performance is critical if you're slowing down data processing actually you're not getting the data at the right time where you need it right. Nobody's people needs real time query is real time analysis. So from a security perspective we're going to focus a lot on everything that is about performance I would say for this year and maybe the other one, I would say that we won't see many new futures around fluently itself as as a project so we'll be mostly about back texting and performance improvements. >>Yeah, I definitely want to dig in with you on the data and logging challenges around kubernetes especially with and to end workflows and there's the different environments that sits in the middle of. But first before we get there, just take a minute to explain for the folks um not that savvy with fluent bit. What is fluent bit real quick, explain what it is. >>Okay, so I will start with a quick story about this, so when we started flowing the, we envision that at some point I'm talking about six years ago, right, all this IOT train or embedded or h will be available and for that you we got back to heavy right? If you have a constraint environment or you want to process data in a more faster way without all the capabilities at that time we say that he might not be suitable for that. So the thing is okay and it was not longer like a single software piece right? We want to say through in this an ecosystem, right? And as part of the ecosystem we have sck where people can connect applications fluid the but also we say we need like a flu Indie but that could be lightweight and faster. Burundi is reading ruby right? And the critical part in C. But since it's written ruby of course there's some process calls on how do you process the data and how much you can scale? Right. So we said if you're going to dig into embedded or small constrained environment, let's write a similar solution. But in C language so we can optimize a memory, can optimize scenario and all this kind of um needs will be will will be effective, right? And we started to spread called fluent bed and through a bit it's like a nowadays like a lightweight version of Wendy, it has started for the Marilyn knows, but after a few years people from the cloud space, I'm talking about containers, kubernetes, they started to ask for more futures for flowing it because they wanted they have influence, but also they wanted to have flowing better than because of it was lively and nowadays we can see that what fluent established the market and true indeed, we're getting around $2 million dollars every single day. So nowadays the attraction of the break is incredible. And it's mostly used to um want to collect logs from the files from system be and for most of coordinated environment disabled, process all this information on a pen, meta data and solve all the problem of how do I collect my data? How do I make sure that the data has the right context meta data and I'm able to deliver this data. So a central place like a job provider or any kind of storage. >>That's great. And I love the fact that's written C, which kind of gives the, I'll say it more performance on the code. Less overhead, get deeper closer um and people No, no, see it's high performance, quick, quick stats. So how old is the project through a bit, What version are you on? >>Uh, a little bit. It's, I'm not sure it is turning six or seven this year, 96. It's been around >>for a while. >>Yeah, yeah. We just released this this week, one at 73 right. We have done more than 100 releases actually really settled two and it's pretty past sometimes we have releases every 23 weeks. So the operation, the club medical system is quite fast. People once and more future more fixes and they don't want to wait for a couple of months for the next release. They wanted to have the continue image right away to test it out and actually sends away as a project. We worked with most of providers like AWS Microsoft actor google cloud platform, the demon for this fixes and improvements are in a weekly basis. >>You guys got a lot of props, I was checking around on the internet, you guys are getting strong um, reviews on logging for kubernetes with the couple releases ago, you had higher performance improvements for google AWS logged in postgres equal and other environments. Um but the question that I'm getting and I'm hearing from folks is, you know, I have end to end workflows and they've been steady. They've been strong. But as more data comes in and more services are connecting to it from network protocols, two Other cloud services, the complexity of what was once a straight straightforward workflow and to end is impacted by this new data. How do you guys address that? How would you speak to that use case? >>Well, for for us data we have taken approaches. Data for us is agnostic on the way that it comes from but that it comes from and the format that comes from for for example, if you talk about the common uses case that we have now is like data come from different formats. Every single developer use the all looking format come from different channels, TCP file system or another services. So it is very, very different. How do we get this data? And that is a big challenge. Right? How do we take data from different sources, different format and you try to unify this internal and then if you're going to talk for example to less exert let's say you Jason you're going to talk to africa, they have their own binary protocol. So we are kind of the backbone that takes all the data transfer data and try to adapt to the destination expected payload from a technical perspective. Yeah, is really challenging. Is really challenging also that Nowadays, so two years ago people was finding processing, I don't know 500,000 messages per second, But nowadays they won 10, 20 40,000. So prime architecture perspective Yeah, there are many challenges and and I think that the teamwork from the maintaining this and with companies has provided a lot of value, a lot of value. And I think that the biggest proof here is that the adoption like adoption and big adoption, you have more banks reported more enhancement requests. All right. So if I get >>this right, you got different sources of data collection issues. If you look on the front end and then you got some secret sauce with bit fluent, I mean uh inside the kubernetes clusters um and then you deliver it to multiple services and databases and cloud services. That that right. Is that the key? The key value is that is that the key value proposition? Did I get that right with fluent bit? >>Mhm. Yeah, I would say most of the technical implementation when the of the value of the technical implementation, I would say that is towards being the vendor neutral. Right? So when you come, when you go to the market and you go to the talk to bank institution hospital form and if the company right, most of them are facing this concept of bender looking right, they use a Bender database but you have to get married. So they're tooling, right? And I'm not going to mention any inventor name. Right? Actually it's very fun. Well for example, the business model, this company that start with S and ends with swung right? For example is you pay as much money so you pay as much money compared to the data that you ingested. But the default tools in just the whole data. But in reality if you go to the enterprise they say yeah. I mean just in all my data into Splunk or X provider right? But from 100 that I'm interesting, which I'm paying for, I'm just using this service to query at least 20 of the data. So why I mean just in this 80 extra I didn't get it right. That's why I want to send and this is real use case there's this language is really good for where is analyzed the data But they said yeah, 80 of my data is just a five data. I will need it maybe in a couple of months just I want to send it to Amazon history or any kind of other a archive service. So users, the value that says is that I want to have a mentor neutral pipeline which me as a user, I went to this side work went to send data, went to send it and also I can come to my bills. Right? And I think that is the biggest value. So you can go to the market. They will find maybe other tools for logging or tools for Matrix because there's a ton of them. But I think that none of them can say we are gender neutral. Not all of them can offer this flexibility to the use, right? So from a technical language performance but from an end user is being the neutrality. >>Okay. So I have to ask you then here in the C n C F projects that are going on and the community around um um fluent bit, you have to have those kinds of enhancements integrations, for instance, for not only performance improvement, but extensive bility. So enterprises there, they want everything right. They make things very >>complicated. They're very >>complicated infrastructure. So if they want some policy they want to have data ingestion policies or take advantage of no vendor lock in, how is the community responding? How did what's your vision for helping companies now? You've got your new venture and you got the open source project, How does this evolve? How do you see this evolving eduardo? Because there is a need for use cases that don't need all the data, but you need all the data to get some of the data. Right. So it's a you have a new new >>paradigm of >>coding and you want to be dynamic and relevant. What's the how do you see this evolving? >>Yeah. Actually going to give you some spoilers. Right. So some years before report. Yeah. So users has this a lot of they have a lot of problems how to collect the data processing data and send the data. We just told them right, Performance is a continuous improvement, Right? Because you have always more data, more formats, that's fine. But one critical thing that people say, hey, you say, hey, I want to put my business logic in the pipeline. So think about this if you have to embed we are the platform for data. Right? But we also provide capabilities to do data processing because you can grab the data or you can do custom modifications over the data. One thing that we did like a year two years ago is we added this kind of stream processing capabilities, can you taste equal for Kaka? But we have our own sequel engine influence them. So when the data is flowing without having any data banks, any index or anything, we can do data aggregation. You can, you can put some business logic on it and says for all the data that matches this pattern, stand it to a different destination, otherwise send it to caracas plan or elastic. So we have, this is what we have now. Extreme processing capabilities. Now what is the spoiler and what we're going next. Right now there are two major areas. One of them is distributed. Extreme processing right? The capabilities to put this intelligence on the age, on the age I'm referring to for example, a cooper needs note right or constrained environment, right? Communities on the age is something that is going on. There are many companies using that approach but they want to put some intelligence and data processing where the data is being generated. Because there is one problem when you have more data and you want to create the data, you have to wait and to centralize all the data in the database for your service. And there's a legend see right, millions sometimes hours because data needs to be in Mexico. But what about it? To have 100 of notes, but each one is already right, influenced it. Why you don't run the queries there. That is one of the features that we have. And well now talking from the challenges from spoil perspectives, people says, okay, I love this pipeline. I noticed Lambert has a political architecture but the language see it's not my thing, right? I don't want to go and see. Nobody likes see that we are honest about that. And there are many mass words about security or not just nothing, which is true, right? It's really easy to mess up things and see. Right? So, and we said, okay, so now our next level, it's like we're going to provide this year the ability to write your own plug ins in Western webassembly. So with the web is simply interface. You can run your own pregnancy goal, rust or any kind of weapon sending support language and translate that implementation to native. Wasn't that fluent that will understand. So C as a language won't be with one being longer uploaded for you as a developer. As a company that wants to put more business logic into the bike. Well that is one of the things that are coming up and really we already have some docs but they're not ready to show. So maybe we can expect something for us at the end of this year. >>Great stuff by the way, from a c standpoint us, old timers like me used to program and see, and not a lot of C courses being taught, but if you do know see it's very valuable. But again, to your point, the developers are are focused on coding the apps, not so much the underlying. So I think that's that's key. I will like to ask you one final question of water before we wrap up, how do you deploy fluid bid? What's the is it is that you're putting it inside the cluster? Is there is that scripts, What's the what's the architecture real quick? Give us a quick overview of the architecture. >>Okay, so that it's not just for a classroom, you can run it on any machine. Windows, Linux, IBM Yeah, and that doesn't need to be a kubernetes. Classic. Right? When we created to invade Copernicus was quite new at the same time. So if you talk about kubernetes deploys as a demon set at the moment is pretty much a part that runs on every note like an agent. Right? Uh, all you can run necessarily on any kind of machine. Oh and one thing before we were, I just need to mention something that from the spoil it. But because it's just getting, we're having many news these days. Is that fluently used to be mostly for logging right? And influence the specifically project. We've got many people from years ago saying, you know what? I'm losing my agent for logging to a bed but I have my agents for metrics and sometimes this is quite heavy to have multiple agents on your age. So now flowing bed is extending the capabilities to deal with native metrics. Right. The first version will be available about this week in cuba come right. We will be able to process host matrix for application metrics and send them to permit use with open matrix format in a native way. So we extended the political system to be a better citizen with open metrics and in the future also with open telemetry, which is a hot thing that is coming up on this month. >>Everyone loves metrics. That's super important. Having the data Is really, really important as day two operations and get all this stuff is happening. I wanna thank you for coming on and sharing the update and congratulations on. The new venture will keep following you and look good for the big launch but fluent bit looking good. Congratulations. Thanks for coming on. >>Thank you so much help governments. >>Okay this is the cubes coverage of Kublai khan 21 cloud Native Con 21 virtual soon we'll be back in real life at the events extracting the signal from the noise. Thanks for watching. Yeah.

>>Yeah, yeah. >>Welcome back. I'm so excited to introduce our next session empowerment through inclusion, reimagining society and technology. This is a topic that's personally very near and dear to my heart. Did you know that there's only 2% of Latinas in technology as a Latina? I know that there's so much more we could do collectively to improve these gaps and diversity. I thought spot diversity is considered a critical element across all levels of the organization. The data shows countless times. A diverse and inclusive workforce ultimately drives innovation better performance and keeps your employees happier. That's why we're passionate about contributing to this conversation and also partnering with organizations that share our mission of improving diversity across our communities. Last beyond, we hosted the session during a breakfast and we packed the whole room. This year, we're bringing the conversation to the forefront to emphasize the importance of diversity and data and share the positive ramifications that it has for your organization. Joining us for this session are thought spots Chief Data Strategy Officer Cindy Housing and Ruhollah Benjamin, associate professor of African American Studies at Princeton University. Thank you, Paola. So many >>of you have journeyed with me for years now on our efforts to improve diversity and inclusion in the data and analytic space. And >>I would say >>over time we cautiously started commiserating, eventually sharing best practices to make ourselves and our companies better. And I do consider it a milestone. Last year, as Paola mentioned that half the room was filled with our male allies. But I remember one of our Panelists, Natalie Longhurst from Vodafone, suggesting that we move it from a side hallway conversation, early morning breakfast to the main stage. And I >>think it was >>Bill Zang from a I G in Japan. Who said Yes, please. Everyone else agreed, but more than a main stage topic, I want to ask you to think about inclusion beyond your role beyond your company toe. How Data and analytics can be used to impact inclusion and equity for the society as a whole. Are we using data to reveal patterns or to perpetuate problems leading Tobias at scale? You are the experts, the change agents, the leaders that can prevent this. I am thrilled to introduce you to the leading authority on this topic, Rou Ha Benjamin, associate professor of African studies at Princeton University and author of Multiple Books. The Latest Race After Technology. Rou ha Welcome. >>Thank you. Thank you so much for having me. I'm thrilled to be in conversation with you today, and I thought I would just kick things off with some opening reflections on this really important session theme. And then we could jump into discussion. So I'd like us to as a starting point, um, wrestle with these buzzwords, empowerment and inclusion so that we can have them be more than kind of big platitudes and really have them reflected in our workplace cultures and the things that we design in the technologies that we put out into the world. And so to do that, I think we have to move beyond techno determinism, and I'll explain what that means in just a minute. Techno determinism comes in two forms. The first, on your left is the idea that technology automation, um, all of these emerging trends are going to harm us, are going to necessarily harm humanity. They're going to take all the jobs they're going to remove human agency. This is what we might call the techno dystopian version of the story and this is what Hollywood loves to sell us in the form of movies like The Matrix or Terminator. The other version on your right is the techno utopian story that technologies automation. The robots as a shorthand, are going to save humanity. They're gonna make everything more efficient, more equitable. And in this case, on the surface, he seemed like opposing narratives right there, telling us different stories. At least they have different endpoints. But when you pull back the screen and look a little bit more closely, you see that they share an underlying logic that technology is in the driver's seat and that human beings that social society can just respond to what's happening. But we don't really have a say in what technologies air designed and so to move beyond techno determinism the notion that technology is in the driver's seat. We have to put the human agents and agencies back into the story, the protagonists, and think carefully about what the human desires worldviews, values, assumptions are that animate the production of technology. And so we have to put the humans behind the screen back into view. And so that's a very first step and when we do that, we see, as was already mentioned, that it's a very homogeneous group right now in terms of who gets the power and the resource is to produce the digital and physical infrastructure that everyone else has to live with. And so, as a first step, we need to think about how to create more participation of those who are working behind the scenes to design technology now to dig a little more a deeper into this, I want to offer a kind of low tech example before we get to the more hi tech ones. So what you see in front of you here is a simple park bench public bench. It's located in Berkeley, California, which is where I went to graduate school and on this particular visit I was living in Boston, and so I was back in California. It was February. It was freezing where I was coming from, and so I wanted to take a few minutes in between meetings to just lay out in the sun and soak in some vitamin D, and I quickly realized, actually, I couldn't lay down on this bench because of the way it had been designed with these arm rests at intermittent intervals. And so here I thought. Okay, the the armrest have, ah functional reason why they're there. I mean, you could literally rest your elbows there or, um, you know, it can create a little bit of privacy of someone sitting there that you don't know. When I was nine months pregnant, it could help me get up and down or for the elderly, the same thing. So it has a lot of functional reasons, but I also thought about the fact that it prevents people who are homeless from sleeping on the bench. And this is the Bay area that we were talking about where, in fact, the tech boom has gone hand in hand with a housing crisis. Those things have grown in tandem. So innovation has grown within equity because we haven't thought carefully about how to address the social context in which technology grows and blossoms. And so I thought, Okay, this crisis is growing in this area, and so perhaps this is a deliberate attempt to make sure that people don't sleep on the benches by the way that they're designed and where the where they're implemented and So this is what we might call structural inequity. By the way something is designed. It has certain effects that exclude or harm different people. And so it may not necessarily be the intense, but that's the effect. And I did a little digging, and I found, in fact, it's a global phenomenon, this thing that architects called hostile architecture. Er, I found single occupancy benches in Helsinki, so only one booty at a time no laying down there. I found caged benches in France. And in this particular town. What's interesting here is that the mayor put these benches out in this little shopping plaza, and within 24 hours the people in the town rallied together and had them removed. So we see here that just because we have, uh, discriminatory design in our public space doesn't mean we have to live with it. We can actually work together to ensure that our public space reflects our better values. But I think my favorite example of all is the meter bench. In this case, this bench is designed with spikes in them, and to get the spikes to retreat into the bench, you have to feed the meter you have to put some coins in, and I think it buys you about 15 or 20 minutes. Then the spikes come back up. And so you'll be happy to know that in this case, this was designed by a German artists to get people to think critically about issues of design, not just the design of physical space but the design of all kinds of things, public policies. And so we can think about how our public life in general is metered, that it serves those that can pay the price and others are excluded or harm, whether we're talking about education or health care. And the meter bench also presents something interesting. For those of us who care about technology, it creates a technical fix for a social problem. In fact, it started out his art. But some municipalities in different parts of the world have actually adopted this in their public spaces in their parks in order to deter so called lawyers from using that space. And so, by a technical fix, we mean something that creates a short term effect, right. It gets people who may want to sleep on it out of sight. They're unable to use it, but it doesn't address the underlying problems that create that need to sleep outside in the first place. And so, in addition to techno determinism, we have to think critically about technical fixes that don't address the underlying issues that technology is meant to solve. And so this is part of a broader issue of discriminatory design, and we can apply the bench metaphor to all kinds of things that we work with or that we create. And the question we really have to continuously ask ourselves is, What values are we building in to the physical and digital infrastructures around us? What are the spikes that we may unwittingly put into place? Or perhaps we didn't create the spikes. Perhaps we started a new job or a new position, and someone hands us something. This is the way things have always been done. So we inherit the spike bench. What is our responsibility when we noticed that it's creating these kinds of harms or exclusions or technical fixes that are bypassing the underlying problem? What is our responsibility? All of this came to a head in the context of financial technologies. I don't know how many of you remember these high profile cases of tech insiders and CEOs who applied for Apple, the Apple card and, in one case, a husband and wife applied and the husband, the husband received a much higher limit almost 20 times the limit as his wife, even though they shared bank accounts, they lived in Common Law State. And so the question. There was not only the fact that the husband was receiving a much better interest rate and the limit, but also that there was no mechanism for the individuals involved to dispute what was happening. They didn't even know what the factors were that they were being judged that was creating this form of discrimination. So in terms of financial technologies, it's not simply the outcome that's the issue. Or that could be discriminatory, but the process that black boxes, all of the decision making that makes it so that consumers and the general public have no way to question it. No way to understand how they're being judged adversely, and so it's the process not only the product that we have to care a lot about. And so the case of the apple cart is part of a much broader phenomenon of, um, racist and sexist robots. This is how the headlines framed it a few years ago, and I was so interested in this framing because there was a first wave of stories that seemed to be shocked at the prospect that technology is not neutral. Then there was a second wave of stories that seemed less surprised. Well, of course, technology inherits its creator's biases. And now I think we've entered a phase of attempts to override and address the default settings of so called racist and sexist robots, for better or worse. And here robots is just a kind of shorthand, that the way people are talking about automation and emerging technologies more broadly. And so as I was encountering these headlines, I was thinking about how these air, not problems simply brought on by machine learning or AI. They're not all brand new, and so I wanted to contribute to the conversation, a kind of larger context and a longer history for us to think carefully about the social dimensions of technology. And so I developed a concept called the New Jim Code, which plays on the phrase Jim Crow, which is the way that the regime of white supremacy and inequality in this country was defined in a previous era, and I wanted us to think about how that legacy continues to haunt the present, how we might be coding bias into emerging technologies and the danger being that we imagine those technologies to be objective. And so this gives us a language to be able to name this phenomenon so that we can address it and change it under this larger umbrella of the new Jim Code are four distinct ways that this phenomenon takes shape from the more obvious engineered inequity. Those were the kinds of inequalities tech mediated inequalities that we can generally see coming. They're kind of obvious. But then we go down the line and we see it becomes harder to detect. It's happening in our own backyards. It's happening around us, and we don't really have a view into the black box, and so it becomes more insidious. And so in the remaining couple minutes, I'm just just going to give you a taste of the last three of these, and then a move towards conclusion that we can start chatting. So when it comes to default discrimination. This is the way that social inequalities become embedded in emerging technologies because designers of these technologies aren't thinking carefully about history and sociology. Ah, great example of this came Thio headlines last fall when it was found that widely used healthcare algorithm affecting millions of patients, um, was discriminating against black patients. And so what's especially important to note here is that this algorithm healthcare algorithm does not explicitly take note of race. That is to say, it is race neutral by using cost to predict healthcare needs. This digital triaging system unwittingly reproduces health disparities because, on average, black people have incurred fewer costs for a variety of reasons, including structural inequality. So in my review of this study by Obermeyer and colleagues, I want to draw attention to how indifference to social reality can be even more harmful than malicious intent. It doesn't have to be the intent of the designers to create this effect, and so we have to look carefully at how indifference is operating and how race neutrality can be a deadly force. When we move on to the next iteration of the new Jim code coded exposure, there's attention because on the one hand, you see this image where the darker skin individual is not being detected by the facial recognition system, right on the camera or on the computer. And so coated exposure names this tension between wanting to be seen and included and recognized, whether it's in facial recognition or in recommendation systems or in tailored advertising. But the opposite of that, the tension is with when you're over included. When you're surveiled when you're to centered. And so we should note that it's not simply in being left out, that's the problem. But it's in being included in harmful ways. And so I want us to think carefully about the rhetoric of inclusion and understand that inclusion is not simply an end point. It's a process, and it is possible to include people in harmful processes. And so we want to ensure that the process is not harmful for it to really be effective. The last iteration of the new Jim Code. That means the the most insidious, let's say, is technologies that are touted as helping US address bias, so they're not simply including people, but they're actively working to address bias. And so in this case, There are a lot of different companies that are using AI to hire, create hiring software and hiring algorithms, including this one higher view. And the idea is that there there's a lot that AI can keep track of that human beings might miss. And so so the software can make data driven talent decisions. After all, the problem of employment discrimination is widespread and well documented. So the logic goes, Wouldn't this be even more reason to outsource decisions to AI? Well, let's think about this carefully. And this is the look of the idea of techno benevolence trying to do good without fully reckoning with what? How technology can reproduce inequalities. So some colleagues of mine at Princeton, um, tested a natural learning processing algorithm and was looking to see whether it exhibited the same, um, tendencies that psychologists have documented among humans. E. And what they found was that in fact, the algorithm associating black names with negative words and white names with pleasant sounding words. And so this particular audit builds on a classic study done around 2003, before all of the emerging technologies were on the scene where two University of Chicago economists sent out thousands of resumes to employers in Boston and Chicago, and all they did was change the names on those resumes. All of the other work history education were the same, and then they waited to see who would get called back. And the applicants, the fictional applicants with white sounding names received 50% more callbacks than the black applicants. So if you're presented with that study, you might be tempted to say, Well, let's let technology handle it since humans are so biased. But my colleagues here in computer science found that this natural language processing algorithm actually reproduced those same associations with black and white names. So, too, with gender coded words and names Amazon learned a couple years ago when its own hiring algorithm was found discriminating against women. Nevertheless, it should be clear by now why technical fixes that claim to bypass human biases are so desirable. If Onley there was a way to slay centuries of racist and sexist demons with a social justice box beyond desirable, more like magical, magical for employers, perhaps looking to streamline the grueling work of recruitment but a curse from any jobseekers, as this headline puts it, your next interview could be with a racist spot, bringing us back to that problem space we started with just a few minutes ago. So it's worth noting that job seekers are already developing ways to subvert the system by trading answers to employers test and creating fake applications as informal audits of their own. In terms of a more collective response, there's a federation of European Trade unions call you and I Global that's developed a charter of digital rights for work, others that touches on automated and a I based decisions to be included in bargaining agreements. And so this is one of many efforts to change their ecosystem to change the context in which technology is being deployed to ensure more protections and more rights for everyday people in the US There's the algorithmic accountability bill that's been presented, and it's one effort to create some more protections around this ubiquity of automated decisions, and I think we should all be calling from more public accountability when it comes to the widespread use of automated decisions. Another development that keeps me somewhat hopeful is that tech workers themselves are increasingly speaking out against the most egregious forms of corporate collusion with state sanctioned racism. And to get a taste of that, I encourage you to check out the hashtag Tech won't build it. Among other statements that they have made and walking out and petitioning their companies. Who one group said, as the people who build the technologies that Microsoft profits from, we refuse to be complicit in terms of education, which is my own ground zero. Um, it's a place where we can we can grow a more historically and socially literate approach to tech design. And this is just one, um, resource that you all can download, Um, by developed by some wonderful colleagues at the Data and Society Research Institute in New York and the goal of this interventionist threefold to develop an intellectual understanding of how structural racism operates and algorithms, social media platforms and technologies, not yet developed and emotional intelligence concerning how to resolve racially stressful situations within organizations, and a commitment to take action to reduce harms to communities of color. And so as a final way to think about why these things are so important, I want to offer a couple last provocations. The first is for us to think a new about what actually is deep learning when it comes to computation. I want to suggest that computational depth when it comes to a I systems without historical or social depth, is actually superficial learning. And so we need to have a much more interdisciplinary, integrated approach to knowledge production and to observing and understanding patterns that don't simply rely on one discipline in order to map reality. The last provocation is this. If, as I suggested at the start, inequity is woven into the very fabric of our society, it's built into the design of our. Our policies are physical infrastructures and now even our digital infrastructures. That means that each twist, coil and code is a chance for us toe. We've new patterns, practices and politics. The vastness of the problems that we're up against will be their undoing. Once we accept that we're pattern makers. So what does that look like? It looks like refusing color blindness as an anecdote to tech media discrimination rather than refusing to see difference. Let's take stock of how the training data and the models that we're creating have these built in decisions from the past that have often been discriminatory. It means actually thinking about the underside of inclusion, which can be targeting. And how do we create a more participatory rather than predatory form of inclusion? And ultimately, it also means owning our own power in these systems so that we can change the patterns of the past. If we're if we inherit a spiked bench, that doesn't mean that we need to continue using it. We can work together to design more just and equitable technologies. So with that, I look forward to our conversation. >>Thank you, Ruth. Ha. That was I expected it to be amazing, as I have been devouring your book in the last few weeks. So I knew that would be impactful. I know we will never think about park benches again. How it's art. And you laid down the gauntlet. Oh, my goodness. That tech won't build it. Well, I would say if the thoughts about team has any saying that we absolutely will build it and will continue toe educate ourselves. So you made a few points that it doesn't matter if it was intentional or not. So unintentional has as big an impact. Um, how do we address that does it just start with awareness building or how do we address that? >>Yeah, so it's important. I mean, it's important. I have good intentions. And so, by saying that intentions are not the end, all be all. It doesn't mean that we're throwing intentions out. But it is saying that there's so many things that happened in the world, happened unwittingly without someone sitting down to to make it good or bad. And so this goes on both ends. The analogy that I often use is if I'm parked outside and I see someone, you know breaking into my car, I don't run out there and say Now, do you feel Do you feel in your heart that you're a thief? Do you intend to be a thief? I don't go and grill their identity or their intention. Thio harm me, but I look at the effect of their actions, and so in terms of art, the teams that we work on, I think one of the things that we can do again is to have a range of perspectives around the table that can think ahead like chess, about how things might play out, but also once we've sort of created something and it's, you know, it's entered into, you know, the world. We need to have, ah, regular audits and check ins to see when it's going off track just because we intended to do good and set it out when it goes sideways, we need mechanisms, formal mechanisms that actually are built into the process that can get it back on track or even remove it entirely if we find And we see that with different products, right that get re called. And so we need that to be formalized rather than putting the burden on the people that are using these things toe have to raise the awareness or have to come to us like with the apple card, Right? To say this thing is not fair. Why don't we have that built into the process to begin with? >>Yeah, so a couple things. So my dad used to say the road to hell is paved with good intentions, so that's >>yes on. In fact, in the book, I say the road to hell is paved with technical fixes. So they're me and your dad are on the same page, >>and I I love your point about bringing different perspectives. And I often say this is why diversity is not just about business benefits. It's your best recipe for for identifying the early biases in the data sets in the way we build things. And yet it's such a thorny problem to address bringing new people in from tech. So in the absence of that, what do we do? Is it the outside review boards? Or do you think regulation is the best bet as you mentioned a >>few? Yeah, yeah, we need really need a combination of things. I mean, we need So on the one hand, we need something like a do no harm, um, ethos. So with that we see in medicine so that it becomes part of the fabric and the culture of organizations that that those values, the social values, have equal or more weight than the other kinds of economic imperatives. Right. So we have toe have a reckoning in house, but we can't leave it to people who are designing and have a vested interest in getting things to market to regulate themselves. We also need independent accountability. So we need a combination of this and going back just to your point about just thinking about like, the diversity on teams. One really cautionary example comes to mind from last fall, when Google's New Pixel four phone was about to come out and it had a kind of facial recognition component to it that you could open the phone and they had been following this research that shows that facial recognition systems don't work as well on darker skin individuals, right? And so they wanted Thio get a head start. They wanted to prevent that, right? So they had good intentions. They didn't want their phone toe block out darker skin, you know, users from from using it. And so what they did was they were trying to diversify their training data so that the system would work better and they hired contract workers, and they told these contract workers to engage black people, tell them to use the phone play with, you know, some kind of app, take a selfie so that their faces would populate that the training set, But they didn't. They did not tell the people what their faces were gonna be used for, so they withheld some information. They didn't tell them. It was being used for the spatial recognition system, and the contract workers went to the media and said Something's not right. Why are we being told? Withhold information? And in fact, they told them, going back to the park bench example. To give people who are homeless $5 gift cards to play with the phone and get their images in this. And so this all came to light and Google withdrew this research and this process because it was so in line with a long history of using marginalized, most vulnerable people and populations to make technologies better when those technologies are likely going toe, harm them in terms of surveillance and other things. And so I think I bring this up here to go back to our question of how the composition of teams might help address this. I think often about who is in that room making that decision about sending, creating this process of the contract workers and who the selfies and so on. Perhaps it was a racially homogeneous group where people didn't want really sensitive to how this could be experienced or seen, but maybe it was a diverse, racially diverse group and perhaps the history of harm when it comes to science and technology. Maybe they didn't have that disciplinary knowledge. And so it could also be a function of what people knew in the room, how they could do that chest in their head and think how this is gonna play out. It's not gonna play out very well. And the last thing is that maybe there was disciplinary diversity. Maybe there was racial ethnic diversity, but maybe the workplace culture made it to those people. Didn't feel like they could speak up right so you could have all the diversity in the world. But if you don't create a context in which people who have those insights feel like they can speak up and be respected and heard, then you're basically sitting on a reservoir of resource is and you're not tapping into it to ensure T to do right by your company. And so it's one of those cautionary tales I think that we can all learn from to try to create an environment where we can elicit those insights from our team and our and our coworkers, >>your point about the culture. This is really inclusion very different from just diversity and thought. Eso I like to end on a hopeful note. A prescriptive note. You have some of the most influential data and analytics leaders and experts attending virtually here. So if you imagine the way we use data and housing is a great example, mortgage lending has not been equitable for African Americans in particular. But if you imagine the right way to use data, what is the future hold when we've gotten better at this? More aware >>of this? Thank you for that question on DSO. You know, there's a few things that come to mind for me one. And I think mortgage environment is really the perfect sort of context in which to think through the the both. The problem where the solutions may lie. One of the most powerful ways I see data being used by different organizations and groups is to shine a light on the past and ongoing inequities. And so oftentimes, when people see the bias, let's say when it came to like the the hiring algorithm or the language out, they see the names associated with negative or positive words that tends toe have, ah, bigger impact because they think well, Wow, The technology is reflecting these biases. It really must be true. Never mind that people might have been raising the issues in other ways before. But I think one of the most powerful ways we can use data and technology is as a mirror onto existing forms of inequality That then can motivate us to try to address those things. The caution is that we cannot just address those once we come to grips with the problem, the solution is not simply going to be a technical solution. And so we have to understand both the promise of data and the limits of data. So when it comes to, let's say, a software program, let's say Ah, hiring algorithm that now is trained toe look for diversity as opposed to homogeneity and say I get hired through one of those algorithms in a new workplace. I can get through the door and be hired. But if nothing else about that workplace has changed and on a day to day basis I'm still experiencing microaggressions. I'm still experiencing all kinds of issues. Then that technology just gave me access to ah harmful environment, you see, and so this is the idea that we can't simply expect the technology to solve all of our problems. We have to do the hard work. And so I would encourage everyone listening to both except the promise of these tools, but really crucially, um, Thio, understand that the rial kinds of changes that we need to make are gonna be messy. They're not gonna be quick fixes. If you think about how long it took our society to create the kinds of inequities that that we now it lived with, we should expect to do our part, do the work and pass the baton. We're not going to magically like Fairy does create a wonderful algorithm that's gonna help us bypass these issues. It can expose them. But then it's up to us to actually do the hard work of changing our social relations are changing the culture of not just our workplaces but our schools. Our healthcare systems are neighborhoods so that they reflect our better values. >>Yeah. Ha. So beautifully said I think all of us are willing to do the hard work. And I like your point about using it is a mirror and thought spot. We like to say a fact driven world is a better world. It can give us that transparency. So on behalf of everyone, thank you so much for your passion for your hard work and for talking to us. >>Thank you, Cindy. Thank you so much for inviting me. Hey, I live back to you. >>Thank you, Cindy and rou ha. For this fascinating exploration of our society and technology, we're just about ready to move on to our final session of the day. So make sure to tune in for this customer case study session with executives from Sienna and Accenture on driving digital transformation with certain AI.

>>Thank you. Thank you so much for having me. I'm thrilled to be in conversation with you today. And I thought I would just kick things off with some opening reflections on this really important session theme, and then we can jump into discussion. So I'd like us to, as a starting point, um, wrestle with these buzz words, empowerment and inclusion so that we can, um, have them be more than kind of big platitudes and really have them reflected in our workplace cultures and the things that we design and the technologies that we put out into the world. And so to do that, I think we have to move beyond techno determinism and I'll explain what that means in just a minute. And techno determinism comes in two forms. The first on your left is the idea that technology automate. Um, all of these emerging trends are going to harm us are going to necessarily, um, harm humanity. >>They're going to take all the jobs they're going to remove human agency. This is what we might call the techno dystopian version of the story. And this is what Hollywood loves to sell us in the form of movies like the matrix or Terminator. The other version on your right is the techno utopian story that technologies automation, the robots, as a shorthand are going to save humanity. They're going to make everything more efficient, more equitable. And in this case, on the surface, they seem like opposing narratives, right? They're telling us different stories. At least they have different endpoints, but when you pull back the screen and look a little bit more closely, you see that they share an underlying logic, that technology is in the driver's seat and that human beings, that social society can just respond to what's happening. But we don't, I really have a say in what technologies are designed. >>And so to move beyond techno determinism, the notion that technology is in the driver's seat, we have to put the human agents and agencies back into the story protagonists and think carefully about what the human desires, worldviews values assumptions are that animate the production of technology. We have to put the humans behind the screen back into view. And so that's a very first step in when we do that. We see as was already mentioned that it's a very homogenous group right now in terms of who gets the power and the resources to produce the digital and physical infrastructure that everyone else has to live with. And so, as a first step, we need to think about how to, to create more participation of those who are working behind the scenes to design technology. Now, to dig a little more deeper into this, I want to offer a kind of low tech example before we get to the more high tech ones. >>So what you see in front of you here is a simple park bench public it's located in Berkeley, California, which is where I went to graduate school. And on this one particular visit, I was living in Boston. And so I was back in California, it was February, it was freezing where I was coming from. And so I wanted to take a few minutes in between meetings to just lay out in the sun and soak in some vitamin D. And I quickly realized actually I couldn't lay down on the bench because of the way it had been designed with these arm rests at intermittent intervals. And so here I thought, okay, th th the armrests have a functional reason why they're there. I mean, you could literally rest your elbows there, or, um, you know, it can create a little bit of privacy of someone sitting there that you don't know. >>Um, when I was nine months pregnant, it could help me get up and down or for the elderly the same thing. So it has a lot of functional reasons, but I also thought about the fact that it prevents people who are, are homeless from sleeping on the bench. And this is the Bay area that we're talking about, where in fact, the tech boom has gone hand in hand with a housing crisis. Those things have grown in tandem. So innovation has grown with inequity because we have, I haven't thought carefully about how to address the social context in which technology grows and blossoms. And so I thought, okay, this crisis is growing in this area. And so perhaps this is a deliberate attempt to make sure that people don't sleep on the benches by the way that they're designed and where the, where they're implemented. And so this is what we might call structural inequity, by the way something is designed. >>It has certain yeah. Affects that exclude or harm different people. And so it may not necessarily be the intent, but that's the effect. And I did a little digging and I found, in fact, it's a global phenomenon, this thing that architect next call, hostile architecture around single occupancy, benches and Helsinki. So only one booty at a time, no Nolan down there. I've found caged benches in France. Yeah. And in this particular town, what's interesting here is that the mayor put these benches out in this little shopping Plaza and within 24 hours, the people in the town rally together and have them removed. So we see here that just because we, we have a discriminatory design in our public space, doesn't mean we have to live with it. We can actually work together to ensure that our public space reflects our better values. But I think my favorite example of all is the metered bench. >>And then this case, this bench is designed with spikes in them and to get the spikes to retreat into the bench, you have to feed the meter. You have to put some coins in, and I think it buys you about 15, 20 minutes, then the spikes come back up. And so you will be happy to know that in this case, uh, this was designed by a German artist to get people to think critically about issues of design, not the design of physical space, but the design of all kinds of things, public policies. And so we can think about how our public life in general is metered, that it serves those that can pay the price and others are excluded or harmed. Whether we're talking about education or healthcare. And the meter bench also presents something interesting for those of us who care about technology, it creates a technical fix for a social problem. >>In fact, it started out as art, but some municipalities in different parts of the world have actually adopted this in their public spaces, in their parks in order to deter so-called loiters from using that space. And so by a technical fix, we mean something that creates a short-term effect, right? It gets people who may want to sleep on it out of sight. They're unable to use it, but it doesn't address the underlying problems that create that need to sleep outside of the first place. And so, in addition to techno determinism, we have to think critically about technical fixes, that don't address the underlying issues that the tech tech technology is meant to solve. And so this is part of a broader issue of discriminatory design, and we can apply the bench metaphor to all kinds of things that we work with, or that we create. >>And the question we really have to continuously ask ourselves is what values are we building in to the physical and digital infrastructures around us? What are the spikes that we may unwittingly put into place? Or perhaps we didn't create the spikes. Perhaps we started a new job or a new position, and someone hands us something, this is the way things have always been done. So we inherit the spiked bench. What is our responsibility? When we notice that it's creating these kinds of harms or exclusions or technical fixes that are bypassing the underlying problem, what is our responsibility? All of this came to a head in the context of financial technologies. I don't know how many of you remember these high profile cases of tech insiders and CEOs who applied for apples, >>The Apple card. And in one case, a husband and wife applied, and the husband, the husband received a much higher limit, almost 20 times the limit as his, >>His wife, even though they shared bank accounts, they lived in common law state. Yeah. >>And so the question there was not only the fact that >>The husband was receiving a much better rate and a high and a better >>The interest rate and the limit, but also that there was no mechanism for the individuals involved to dispute what was happening. They didn't even know how, what the factors were that they were being judged that was creating this form of discrimination. So >>In terms of financial technologies, it's not simply the outcome, that's the issue, or that can be discriminatory, >>But the process that black box is all of the decision-making that makes it so that consumers and the general public have no way to question it, no way to understand how they're being judged adversely. And so it's the process, not only the product that we have to care a lot about. And so the case of the Apple card is part of a much broader phenomenon >>Of, um, races >>And sexist robots. This is how the headlines framed it a few years ago. And I was so interested in this framing because there was a first wave of stories that seemed to be shocked at the prospect, that technology is not neutral. Then there was a second wave of stories that seemed less surprised. Well, of course, technology inherits its creators biases. And now I think we've entered a phase of attempts to override and address the default settings of so-called racist and sexist robots for better or worse than here. Robots is just a kind of shorthand that the way that people are talking about automation and emerging technologies more broadly. And so, as I was encountering these headlines, I was thinking about how these are not problems simply brought on by machine learning or AI. They're not all brand new. And so I wanted to contribute to the conversation, a kind of larger context and a longer history for us to think carefully about the social dimensions of technology. And so I developed a concept called the new Jim code, >>Which plays on the phrase, >>Jim Crow, which is the way that the regime of white supremacy and inequality in this country was defined in a previous era. And I wanted us to think about how that legacy continues to haunt the present, how we might be coding bias into emerging technologies and the danger being that we imagine those technologies to be objective. And so this gives us a language to be able to name this phenomenon so that we can address it and change it under this larger umbrella of the new Jim code are four distinct ways that this phenomenon takes shape from the more obvious engineered inequity. Those are the kinds of inequalities tech mediated in the qualities that we can generally see coming. They're kind of obvious, but then we go down the line and we see it becomes harder to detect it's happening in our own backyards, it's happening around us. And we don't really have a view into the black box. And so it becomes more insidious. And so in the remaining couple of minutes, I'm just, just going to give you a taste of the last three of these, and then a move towards conclusion. Then we can start chatting. So when it comes to default discrimination, this is the way that social inequalities >>Become embedded in emerging technologies because designers of these technologies, aren't thinking carefully about history and sociology. A great example of this, uh, came to, um, uh, the headlines last fall when it was found that widely used healthcare algorithm, effecting millions of patients, um, was discriminating against black patients. And so what's especially important to note here is that this algorithm, healthcare algorithm does not explicitly take note of race. That is to say it is race neutral by using cost to predict healthcare needs this digital triaging system unwittingly reproduces health disparities, because on average, black people have incurred fewer costs for a variety of reasons, including structural inequality. So in my review of this study, by Obermeyer and colleagues, I want to draw attention to how indifference to social reality can be even more harmful than malicious intent. It doesn't have to be the intent of the designers to create this effect. >>And so we have to look carefully at how indifference is operating and how race neutrality can be a deadly force. When we move on to the next iteration of the new Jim code, coded exposure, there's a tension because on the one hand, you see this image where the darker skin individual is not being detected by the facial recognition system, right on the camera, on the computer. And so coded exposure names, this tension between wanting to be seen and included and recognized whether it's in facial recognition or in recommendation systems or in tailored advertising. But the opposite of that, the tension is with when you're over, it >>Included when you're surveilled, when you're >>Too centered. And so we should note that it's not simply in being left out, that's the problem, but it's in being included in harmful ways. And so I want us to think carefully about the rhetoric of inclusion and understand that inclusion is not simply an end point, it's a process, and it is possible to include people in harmful processes. And so we want to ensure that the process is not harmful for it to really be effective. The last iteration of the new Jim code. That means the, the most insidious let's say is technologies that are touted as helping us address bias. So they're not simply including people, but they're actively working to address bias. And so in this case, there are a lot of different companies that are using AI to hire, uh, create hiring, um, software and hiring algorithms, including this one higher view. >>And the idea is that there there's a lot that, um, AI can keep track of that human beings might miss. And so, so the software can make data-driven talent decisions after all the problem of employment discrimination is widespread and well-documented, so the logic goes, wouldn't this be even more reason to outsource decisions to AI? Well, let's think about this carefully. And this is the idea of techno benevolence, trying to do good without fully reckoning with what, how technology can reproduce inequalities. So some colleagues of mine at Princeton, um, tested a natural learning processing algorithm and was looking to see whether it exhibited the same, um, tendencies that psychologists have documented among humans. And what they found was that in fact, the algorithm associated black names with negative words and white names with pleasant sounding words. And so this particular audit builds on a classic study done around 2003 before all of the emerging technologies were on the scene where two university of Chicago economists sent out thousands of resumes to employers in Boston and Chicago. >>And all they did was change the names on those resumes. All of the other work history education were the same. And then they waited to see who would get called back and the applicants, the fictional applicants with white sounding names received 50% more callbacks than the, the black applicants. So if you're presented with that study, you might be tempted to say, well, let's let technology handle it since humans are so biased. But my colleagues here in computer science found that this natural language processing algorithm actually reproduced those same associations with black and white names. So two with gender coded words and names as Amazon learned a couple years ago, when its own hiring algorithm was found discriminating against women, nevertheless, it should be clear by now why technical fixes that claim to bypass human biases are so desirable. If only there was a way to slay centuries of racist and sexist demons with a social justice bot beyond desirable, more like magical, magical for employers, perhaps looking to streamline the grueling work of recruitment, but a curse from any job seekers as this headline puts it. >>Your next interview could be with a racist bot, bringing us back to that problem space. We started with just a few minutes ago. So it's worth noting that job seekers are already developing ways to subvert the system by trading answers to employers tests and creating fake applications as informal audits of their own. In terms of a more collective response. There's a Federation of European trade unions call you and I global that's developed a charter of digital rights for workers that touches on automated and AI based decisions to be included in bargaining agreements. And so this is one of many efforts to change the ecosystem, to change the context in which technology is being deployed to ensure more protections and more rights for everyday people in the U S there's the algorithmic accountability bill that's been presented. And it's one effort to create some more protections around this ubiquity of automated decisions. >>And I think we should all be calling for more public accountability when it comes to the widespread use of automated decisions. Another development that keeps me somewhat hopeful is that tech workers themselves are increasingly speaking out against the most egregious forms of corporate collusion with state sanctioned racism. And to get a taste of that, I encourage you to check out the hashtag tech, won't build it among other statements that they've made and walking out and petitioning their companies. One group said as the, at Google at Microsoft wrote as the people who build the technologies that Microsoft profits from, we refuse to be complicit in terms of education, which is my own ground zero. Um, it's a place where we can, we can grow a more historically and socially literate approach to tech design. And this is just one resource that you all can download, um, by developed by some wonderful colleagues at the data and society research Institute in New York. >>And the, the goal of this intervention is threefold to develop an intellectual understanding of how structural racism operates and algorithms, social media platforms and technologies not yet developed and emotional intelligence concerning how to resolve racially stressful situations within organizations and a commitment to take action, to reduce harms to communities of color. And so as a final way to think about why these things are so important, I want to offer, uh, a couple last provocations. The first is pressed to think a new about what actually is deep learning when it comes to computation. I want to suggest that computational depth when it comes to AI systems without historical or social depth is actually superficial learning. And so we need to have a much more interdisciplinary, integrated approach to knowledge production and to observing and understanding patterns that don't simply rely on one discipline in order to map reality. >>The last provocation is this. If as I suggested at the start in the inequity is woven into the very fabric of our society. It's built into the design of our, our policies, our physical infrastructures, and now even our digital infrastructures. That means that each twist coil and code is a chance for us to weave new patterns, practices, and politics. The vastness of the problems that we're up against will be their undoing. Once we, that we are pattern makers. So what does that look like? It looks like refusing colorblindness as an anecdote to tech media discrimination, rather than refusing to see difference. Let's take stock of how the training data and the models that we're creating. Have these built in decisions from the past that have often been discriminatory. It means actually thinking about the underside of inclusion, which can be targeting and how do we create a more participatory rather than predatory form of inclusion. And ultimately it also means owning our own power in these systems so that we can change the patterns of the past. If we're, if we inherit a spiked bench, that doesn't mean that we need to continue using it. We can work together to design more, just an equitable technologies. So with that, I look forward to our conversation.

>>from San Francisco. It's the queue covering our essay conference. 2020. San Francisco Brought to you by Silicon Angle Media's >>Welcome Back Around Here at the Cube. Coverage for our conference. Mosconi, South Floor. Bring you all the action day one of three days of cube coverage where the security game is changing, the big players are making big announcements. The market's changing from on premise to cloud. Then hybrid Multi cloud was seeing that wave coming. A great guest here. Barr, our VP of product strategy and co founder of the Mystery, was acquired by Palo Alto Networks. Worries employed now, Rishi. Thanks for coming on. Thank you. Absolutely happy to be here. So, first of all, great journey for your company. Closed a year ago. Half a 1,000,000,000. Roughly give or take 60. Congratulations. Thank you. Big accomplishments. You guys were taken out right in the growth phase. Now at Palo Alto Networks, which we've been following, you know, very careful. You got a new CMO over there, Jean English? No, we're very well. We're very bullish on Palo Alto. Even though that the on premise transitions happening cloud. You guys are well positioned. How's things going things are going fantastic. We're investing a lot in the next Gen security business across the board, as mentioned Prisma Cloud is big business. And then on the other side, which is what I'm part of the cortex family focused on the Security operations center and the efficiencies That's fantastic and, ah, lot off product innovations, investment and the customer pull from an operations perspective. So very excited. You guys had a big announcement on Monday, and then yesterday was the earnings, which really kind of points to the trend that we're seeing, which is the wave to the cloud, which you're well positioned for this transition going on. I want to get to the news first. Then we get into some of the macro industry questions you guys announced the X ore, which is redefining orchestration. Yes. What is this about? What's this news about? Tell us. >> So this news is about Mr was acquired about a year ago as well. This is taking that Mr Platform and expanding it on, expanding it to include a very core piece, which is Intel management. If you look at a traditional saw, what has happened is soccer teams have had the same dead and over the last few years acquired a sword platform such as a mystery security orchestration, automation and response platform. But the Edge Intel team has always been still separate the threat Intel feeds that came in with separate. With this, we are expanding the power of automation and applying doc to the threat intelligence as well. That is, thread intelligence, current state of the art right now. So the current state of the art of threat intelligence is are the larger organizations typically subscribe to a lot of faith, feeds open source feeds and aggregate them. But the challenge is to aggregate them the sit in a repository and nobody knows what to do with them. So the operationalization of those feeds is completely missing. >> So basically, that is going to have data pile. Corpus is sitting there. No one touches it, and then everyone has to. It's a heavy lift. It's a heavy lift, and nobody knows. Cisco sees the value coming out of it. How do you proactively hunt using those? How do you put them to protecting proactively to explain cortex X, or what is it? And what's the value? So the cortex X or as a platform. There are four core pieces, three off which for the core tenants of the misto since the big one is automation and orchestration. So today we roughly integrate with close to 400 different products security and I t products. Why are the FBI on let customers build these work flows come out of the box with close to 80 or 90 different workloads. The idea of these workloads is being able to connect to one product for the data go to another taken action there Automation, orchestration builds a visual book second s case management and this is very critical, right? I mean, if you look at the process side of security, we have never focused as an industry and the process and the human side of security. So how do you make sure every security alert on the process the case management escalation sl A's are all managed. So that's a second piece off cortex. Third collaboration. One of the core tenants of Mr Waas. We heard from customers that analysts do not talk to each other effectively on when they do. Nobody captures that knowledge. So the misto has an inbuilt boardroom which now Cortex X or has the collaboration war room on that is now available to be able to chat among analysts. But not only that charged with the board take actions. The fourth piece, which is the new expanded platform, is the personal management to be able to now use the power of orchestration, automation collaboration, all for threat intelligence feeds as well. Not only the alerts >> so and so you're adding in the threat. Intelligence feeds, yes. So is that visualize ai on the machine Learning on that? How is that being process in real time? How does that on demand work for that fills. So the biggest piece is applying the automation and intelligence to automatically score that on being able to customize the scoring the customer's needs. Customized confidence score perfect. And once you have the high fidelity indicators automatically go block them as an example. If you get a very high fidelity IOC from FBI that this particular domain is the militias domain, you would want to block that in. Your firewall is executed immediately, and that is not happening today. That is the core, and that's because of the constraint is I don't know the data the way we don't know the data and it's manual. Some human needs to review it. Some human needs to go just not being surfaced, just not. So let's get back into some of the human piece. I love the collaboration piece. One of things that I hear all the time in my cube interviews across all the hundreds of events we go to is the human component you mentioned. Yes, people have burnt out. I mean, like the security guys. I mean, the joke was CIOs have good days once in a while, CSOs don't have any good days, and it's kind of a job board pejorative to that. But that's the reality. Is that it works? Yes. We actually okay, if you have another job. Talking of jokes, we have this. Which is what do you call and overwork security analyst. A security analyst, because every one of them >>is over word. >>So this is a huge thing. So, like the ai and some of the predictive analytics trend Is tourist personalization towards the analyst Exactly. This is a trend that we're seeing. What's your view on this? What? You're absolutely We're seeing that trend which is How do you make sure analyst gets to see the data they're supposed to see at the right time? Right. So there's one aspect is what do you bring up to the analyst? What is relevant and you bring it up at the right time to be able to use it. Respond with that. So that comes in one from an ML perspective and machine learning. And our cortex. XDR suite of products actually does a fantastic job of bringing very rich data to the analyst at the right time. And then the second is, can we help analyst respond to it? Can we take the repetitive work away from them with a playbook approach? And that's what the cortex platform brings to that. I love to riff on some future scenarios kind of. I won't say sci fi, but I got to roll a little bit of a future to me. I think security has to get to like a multi player gaming environment because imagine like a first person shooter game, you know where or a collaborative game where it's fun. Because once you start that collaboration, yes, then you're gonna have some are oi around. I saw that already. Don't waste your time or you get to know people. So sharing has been a big part? Yes. How soon do you think we're gonna get to an environment where I won't say like gaming? But that notion of a headset on I got some data. I know you are your reputation. I think your armor, you're you're certifications. Metaphorically putting. I think way have a lot of these aspects and I think it's a very critical point. You mentioned right one of the things which we call the virtual war room and like sex or I was pointing out the fact that you can have analysts sit in front of a collaboration war room not only charge for the appears but charged with a boat to go take care of. This is equivalent to remember that matrix movie plugging and says, you know how to fly this helicopter data and now I do. That's exactly what it is. I think we need to point move to a point where, no matter what the security tool is what your endpoint is, you should not have to learn every endpoint every time the normalization off, running those commands via the collaboration War Room should be dead. I would say we're starting to see in some of the customers are topics or they're using the collaboration war room to run those commands intractably, I would say, though, there's a big challenge. Security vendors do not do a good job normalizing that data, and that is where we're trying to reach you. First of all, you get the award for bringing up a matrix quote in The Cube interview. So props to that. So you have blue teams. Red teams picked the pill. I mean, people are people picking their teams. You know what's what's going on. How do you see the whole Red Team Blue team thing happening? I think that's a really good stuff happening. In my opinion, John, what's going on is right now so far, if you see if I go back three years our adversaries were are committing. Then we started to see this trend off red teaming automation with beach automation and bunch of companies starting to >>do that >>with Cortex X or on similar products, we're starting to now automate the blue team side of things, which is how do you automatically respond how do you protect yourself? How do you put the response framework back there? I think the next day and I'm starting to see is these things coming together into a unified platform where the blue team and the team are part of the same umbrella. They're sharing the data. They're sharing the information on the threat Intel chair. So I see we are a very, very good part. Of course, the adversities. I'm not gonna sit idle like you said about the Dev ops mindset. Heavens, notion of knowledge coming your way and having sharing packages all baked out for you. She doesn't do the heavy lifting. That's really the problem. The data is a problem. So much demand so much off it. And you don't know what is good and what is not. Great. Great conversation again. The Matrix reference about your journey. You've been an entrepreneur and sold. You had a great exit again. Politics is world class blue chip company in the industry public going through a transition. What's it like from an entrepreneur now to the big company? What's the opportunity is amazing. I think journey has been very quick. One. We saw some crazy growth with the misto on. Even after the acquisition, it's been incredibly fast pace. It's very interesting lot of one of the doctors like, Hey, you must be no resting is like, No, the journey is amazing. I think he s Polito Networks fundamentally believe that. We need to know where it really, really fast to keep the adversaries out on. But that's been the journey. Um, and we have accelerated, in fact, some of our product plans that we hard as a start up on delivering much faster. So the journey has been incredible, and we have been seeing that growth Will they picked you guys write up? There's no vesting interesting going on when you guys were on the uphill on the upslope growth and certainly relevance for Palo Alto. So clearly, you know, you haven't fun. People vested arrest when they checked out, You guys look like you're doing good. So I got to ask you the question that when you started, what was the original mission? Where is it now? I mean this Is there any deviation? What's been the kind? Of course you know, this is very, very relevant questions. It's very interesting. Right after the acquisition, we went and looked at a pitch deck, which we presented overseas in mid 2015. Believe it or not, the mission has not changed, not changing iron. It had the same competent off. How do you make the life off a security person? A security analyst? Easy. It's all the same mission by automating more by applying AI and learning to help them further by letting them collaborate. All the aspects off case management process, collaboration, automation. It's not changed. That's actually very powerful, because if you're on the same mission, of course you're adding more and more capabilities. But we're still on the same path on going on that. So every company's got their own little nuanced. Moore's Law for Intel. What made you guys successful was that the culture of Dev ops? It sounds like you guys had a certain either it was cut in grain. I think I would say, by the way, making things easy. But you got to do it. You got to stay the course. What was that? I think that's a fundamental cultural feature. Yeah, there's one thing really stand by, and I actually tweeted about a few weeks ago, this which is every idea, is as good as good as its execution. So there's two things between really focus on which is customer focused on. We were really, really portable about customer needs to get the product needs to use the product, customer focus and execution. As we heard the customers loud and clear, every small better. And that's what we also did. You guys have this agile mindset as well, absolutely agile mindset and the development that comes with the customer focus because way kind of these micro payments customer wants this like, why do they want this? What is the end goal? Attributed learner. Move on to make a decision making line was on Web services Way debate argue align! Go Then go. And then once you said we see great success story again Startup right out of the gate 2015. Acquire a couple years later, conventions you and your team and looking forward to seeing your next Palo Alto Networks event. Or thanks for coming on. Great insight here on the cube coverage. I'm John Furrier here on the ground floor of our S e commerce on Mosconi getting all the signal extracting it from the noise here on the Cube. Thanks for watching. >>Yeah, yeah,

(upbeat music) >> From the Silicon Valley Media Office in Boston Massachusetts, it's theCUBE. Now here's your host, Dave Vellante. >> Hello everyone, and welcome to this exclusive CUBE conversation. Here's the setup. The storage industry has been drowning in complexity for years. Companies like Pure Storage and Nutanix, you know they reached escape velocity last decade, primarily because they really understood well how to deliver great products, that were simpler to use. But as we enter the 2020's, virtually every player in the storage business is trying to simplify it's portfolio. And the mandate is coming from customers, that are under huge pressure to operationalize and bring to market their major digital initiatives. They simply can't spend time managing infrastructure that the way they used to. They have to reallocate resources up the stack, so to speak to more strategic efforts. Now, as you know post the acquisition of EMC by Dell, we have followed closely, and been reporting on their efforts to manage the simplification of the storage portfolio under the leadership of Jeff Clark. IBM is one of those leading companies, along with Dell EMC, NetApp, and HPE that are under tremendous pressure to continue to simplify their respective portfolios. IBM as a company, has declared the dawn of a new era. They call it Chapter II of Digital and AI. Whereas, the company claims it's all about scaling and moving from experimentation to transformation. Chapter II, I will tell you unquestionably is not about humans managing complex storage infrastructure. Under the leadership of General Manager, Ed Walsh, the companies storage division has aligned with this Chapter II vision, and theCUBE has been able to secure an exclusive interview with Ed, who joins me today. Great to see you my friend. >> Thanks very much for having me. >> So, you're very welcome. And you heard my narrative. How did we get here? How did the industry get so complex? >> I like the way you kicked it off, because I think you nailed it. It's just how the storage industry has always been. And there was a reason for it twenty years ago, but it's almost, it's run its course, and I could tell you what were now seeing, but everyone there's always a difference between high end solutions sets, and low end solution sets. In fact their different, there's custom silicon on the high end. So think about EMC Matrix in the day, it was the ultimate custom hardware and software combination. And then the low end storage, well it didn't have any of that. And then there's a mid tier. But we actually, everything is based upon it. So you think about the right availability, the right price port, feature function, availability features. It made sense that you had to have that unique thing. So, what's happened is, we're all doing sustaining innovation. So we're all coming out with the next high end array for you. EMC's next one is Hashtag, Next Generation storage, right, mid-range. So they're going to redo their midrange. And then low end, but they never come together, and this is where the complexity is, you're nailing it. So no one is a high end or a low end shop, they basically use it all, but what they're having to do, is they have to manage and understand each one of those platforms. How to maintain it, it's kind of specialized. How to report on it, how to automate, each the automation requirements are different, but different API to actually automate it. Now the minute you say, now help me modernize that and bring me to a hybrid multi-cloud, now you're doing kind of a complex thing over multiple ways, and against different platforms, which are all completely different. And the key thing is, in the past it made sense to a have high end silicon with high end software, and it made sense. And different low end, and basically, because of some of the innovation we've driven, no longer do you have to do that. There's one platform that allows you to have one platform to meet those different requirements, and dramatically simplify what you're doing for enterprises. >> So, we're going to talk a little bit more about what you guys are announcing. But how do you know when you get there, to this land of simple? >> One it's hard to get there, we can talk about that too. But it's a, when a client, so we just had a call this morning with our board advisor for storage, our division. And they're kind of the bigs of the bigs. Up on the need, more on the high end side, just so you know the sample size. But literally, in the discussion we were talking about the platform simplification, how do you get to hybrid cloud, what we're going to do with the cyber incident response type of capabilities have resiliency. And literally in the call they are already emailing their team, saying we need to do something more strategic, we need to do that, we need to look at this holistically. They love the simplicity. Everything we just went through, they can't do anymore. Especially in Chapter II, it's about modernizing your existing mission critical enterprises, and then put them in the context of Hybrid multi-cloud. That's hard, you can't do it with all these different platforms, so they're looking for, let me spend less. Like you said, to get my team to do up-stack things, they definitely don't want to be managing different disparate storage organizations. They want to move forward and use that freed up resource to do other things, so. When I see big companies literally jumping at it, and giving the example. You know I want to talk about the cyber resiliency thing, I've had four of those this week. That's exactly what we need to have done, so it's just, I haven't had a conversation yet that clients aren't actually excited about this, and it's actually pretty straightforward. >> So I'll give you the benefit of the doubt, and again we'll get there, but assuming your there. Why do you think it took you so long? You kind of mentioned it's hard. >> So, transformations are never easy, and typically whoever is the transformation engine, gets shot in the back of the head, right. So it's really hard to get teams to do something different. So imagine every platform, EMC has nine now, right. So it is through acquisition of others, you have VP's, you know. VP of development, offering and maybe sales, and then you have whole teams, where you have founders you've acquired. So you have real people, that they love their platform, and there's no way they're going to give it up. They always come up with the next generation, and how it's going to solve all ills, but it's a people transformation. How do you get we're going to take three and say, hey, it's one platform. Now to do that it's a operational transformation challenge. It's actually driving the strategy, you don't do it in matter of a week, there's development to make sure that you can actually meet all the different use cases, that will take you literally years to do, and have a new platform. But, I think it's just hard to do. Now, anyone that's going to do that, let's say you know EMC or HP wants to do it. They're going to have to do the same thing we did, which is going to take them years of development. But also, it's managing that transition and the people involved, or the founders you've acquired, or it just it's amazing. In fact, it's the most wonderful part of my job is dealing with people, but it can frustrate you. >> So we've seen this over the years, look at NetApp, right with waffle, it was one size fits all for years, but they just couldn't cover all markets. And then they were faced with TAM expansion, of course now the portfolio expands. Do you think -- >> And now they have three and -- >> And David Scott at HPE, Storage VP at the time used to talk about how complex EMC's portfolio was, and you see HPE has to expand the portfolio. >> We all did, including IBM. >> Do you think Pure will have to face the same sort of -- >> We are seeing Pure with three, right. And that's without the file, so I'm just talking about what we do for physical, virtual, and container workloads and cloud. If you start going to what we're going to scale up to object we all have our own there too. And I'm not even counting the three to get to that. So you see Pure doing the exact the same thing, because they are trying to expand their TAM. And you have to do some basic innovation to have a platform actually meet the requirements, of the high end requirements, the mid range, and the entry level requirements. It's not just saying, I'm going to have one, you're actually have to do a lot of development to do it. >> All right, let's get to the news. What are you guys announcing? >> So basically, we're announcing a brand new, a dramatic simplification of our distributed storage. So, everything for non-Z. If you're doing physical boxes, bare metal, Linux. You're doing virtual environments, VMware environments, hyper-V, Power VM, or if you're doing container workloads or into the cloud. Our platforms are now one. One software, one API to manage. But we're going to actually, we're going to do simplification without compromise. We're going to give you want you need. You're going to need an entry level packaging, midrange and high end, but it's going to be one software allows you to meet every single price requirement and functionality. And we'll be able to do some surprises on the upside for what we're bringing out to you, because we believe in value in automation. We can up the value we bring to our clients, but also dramatically take out the cost complexity. But one thing we're getting rid of, is saying the need, the requirement to have a different hardware software platform for high end, midrange and low end. It's one hardware and software platform that gets you across all those. And that's where you get a dramatic simplification. >> So same OS? >> Same OS? >> Normally, you'd do, you'd optimize the code for the high end, midrange and low end. Why are you able to address all three with one OS? How are you able to do that? >> It took us three and half years, it was actually, I will talk about a couple innovation pieces. So, on the high end you have customized silicon, we did, everyone does, we had a Texas Memory Systems acquisition. It was the flash drawer 2U, about 375 TB, uncompressed de dup, pretty big chunky, you had to buy big chunks. So it was on the high end. >> That was the unit of granularity, right. >> But it gave you great value, but also you had great performance, latency better than you get in NVMe today, before NVMe. But you get inline compression, encryption, so it was wonderful. But it was really ultra high end. What we did was we took that great custom silicon, and we actually made it onto what it looks like a custom, or to be a standard NVMe SSD. So you take a Samsung NVMe, or a WD and you compare it to what we call our flash core module. They look the same and they go interchangeably into the NVMe standard slot. But what's in there is the same silicon, that was on this ultra high end box. So we can give the high end, exactly what we've did before. Ultra low latency, better than NVMe, but also you can get inline compression de dup and the were leveling, and the stuff that you expect in the custom silicon level. But we can take this same NVMe drive and we can put it in our lowest end model. Average sale price $15,000. Allows you to literally, no compromise on the high end, but have unbelievable surprises on the midrange and the low end, where now we can get the latency and the performance and all those benefits, to be honest on a much lower box. >> Same functionality? >> Same functionality, so you lose nothing. Now that took a lot of work, that wasn't easy. You're talking about people, there was roadmaps that had to be changed. We had to know that we were going to do that, and stick to our guns. But that'll be one. Other things is, you know you're going to get some things on the upside that you're not expecting, right. Because it's custom silicon, right, I might have a unique price performance. But also cost advantages, so I'm going to have best price performance or density across the whole product line. But also, I'm going to do things like, on the high end you used to unbelievable operational resiliency. Two site, three site, hyper-swap, you know two boxes that would act like one. Have a whole outage, or a site outage and you don't really miss a transaction, or multi-sites. But we're going to be able to do that on the low end and the midrange as well. Cyber resiliency is a big deal. So I talked about Operational Resiliency. It's very different coming back when it's cyber. But cyber incident response becomes key, so we're going to give you special capabilities there which are not available for anyone in the industry. But is cyber incident response only a high end thing, or is it a low end thing. No, it's across everywhere. So I think we're going to shock on the upside a lot of it, was the development to make sure the code stack, but also the hardware, we can at least say no compromise if you want entry-level. I'm going to meet anyone at that mote. In fact, because the features of it, I'm able to compete at an unfair level against everyone on the low end. So you say, midrange and high end, but you're not losing anything because your losing the custom silicon. >> So let's come back to the cyber piece, what exactly is that? >> All right, so, listen, this is not for data breaches. So if a data breach happens, they steal your database or they steal your customer name, you have to report to, you know you have to let people know. But it's typically than I call the storage guy and say hey, solve it. It was stolen at a different level. Now the ones that doesn't hit the media, but happens all the time actually more frequently. And it definitely, gets called down to the operations team and the storage team is for cyber or malicious code. They've locked up your system. Now they didn't steal data, so it's not something you have to report. So what happens is call comes down, and you don't know when they got you. So it's an iterative process, you have to literally find the box, bring up, maybe it's Wednesday, oh, bring it up, give it to application group, nope, it's there. Bring up Tuesday... it's an iterative process. >> It's like drilling for oil, a 100 years ago, nope, not it, drill another hole. >> So what happens is, if it's cyber without the right tools, you use your backup, one of our board advisors, literally major bank, I had four of those, I'll give you one. It took me 33 hours to bring back a box. It was a large database 30 TB, 33 hours. Now why did you backup, why didn't he use his primary storage against DR copies of everything. Well they didn't have the right tool sets, so what we were able to do is, tape is great for this air gap, but it takes time to restore and come back up and running. The modern day protection we have like Veeam or Cohesity allows the recovery being faster, because your mounting backup copies faster. But the fastest is your primary snapshot and your replicated DR snapshots. And if you can leverage those, the reason people don't leverage it, and we came upon this, almost accidentally. We were seeing our services brethren from IBM doing, IBM SO or outsourcing GTS, when they did have a hit. And what they want to do is, bring up your snapshots, but if you bring up a snapshot and you're not really careful, you start crashing production workloads, because it looks like the VM that just came up. So you need to have, and we're providing the software that allows you to visualize what your recovery points are. Allows you to orchestrate bringing up environments but more importantly, orchestrate into a fenced network environment, so it's not going to step on production workloads and address this. But allows you to do that, and provide a URL to the different business users, that they can come and say yes, it's there or it's not. So even if you don't use this software before this incident, it gives you visibility, orchestration, and then more importantly a fence, a safe fence network, a sandbox to bring these up quickly and check it out, and easily promote to production. >> So that's your safe zone? >> Safe zone, but it's just not there. You know you start bringing up snapshots, it's not like a DR case, where you're bringing things up, you have to be really smart, because you bring it up, and checking out. So without that, they don't want to trust to use the snapshots, so they just don't use primary storage. With it, it becomes the first thing you do. Because you hope you got it within a week, or week and a half of your snapshots. And it's in the environment for ninety days, now you're going to tape. Now if you do this, if you put this software in place before an incident, now you get more values, you can do orchestrated DR testing. Because where doing this orchestrated, bring up application sets it's not a VM, it's sets of VM's. Fenced network, bring it up, does it work. You can use it for Test/Dev data, you can use it for automatic DR. But even if you don't set it up, we're going to make it available so you can actually come back from these cyber incidents much faster. >> And this is the capability that I get on primary storage. Because everybody's targeting you know the backup corpus for ransomware and things of that nature. This is primary storage. >> And we do put it on our backups. So our backups allows you to do the exact same thing and do the bootable copies. And so if you have our backup product, you could already do this on primary. But, what we're saying is, regardless of who your using, we're still saying you need to do backup, you need to air a cup your backup. 'cause you know Want to Cry was in the environment for 90 days, you know your snapshots are only for a week or two. So the fact of the matter is that you need it, but in this case, if you're using the other guys, you can also, we're going to give it just for this tool set. >> How does immutability does it factor? I know like for instance AWS Reinvent they announced an immutability capability. I think IBM may have that, because of the acquisition that you made years ago, Clever Safe was fundamental to that, their architecture. Is that a way to combat ransomware? >> So immutability is obviously not just changes. So ransomware and you know malware typically is either encrypting or deleting things. Encrypting is what they do, but they have the key, so. The fact of the matter is that they're deleting things. So if it's immutable, than you can't change it. Now if you own the right controls, you can delete it, but you can't change it, they can't encrypt it on you. That becomes critical. So what you're looking for, is we do like for instance all of our flash system allows you to do these snapshots, local or remote that allow you to have, go to immutable copies either in Amazon, we support that or locally on our object storage, or in IBM's cloud. It allows you to do that. So the different platforms have this immutability that our software allows you integrate with. So I think immutability is kind of critical. >> How about consumption models? The way in which your packaging and pricing. People want to, the cloud is sort of change the way we think about this, how have you responded to that? >> So, you hit upon our Chapter II. We, IBM, actually resonates to the clients. In Chapter I, we are doing some lift and shift, and we're doing some new use cases in the cloud. And they had some challenges but it worked in general. But we're seeing the next phase II, is looking at the 80% of your key workloads, your mission critical workloads, and basically how you transfer those in. So basically, as you look at your Chapter 2, you're going to do the modernization, and you might move those into the cloud. So if you're going to move into the cloud, you might say, I'd like to modernize my storage, free my team up, because it's simple, I don't have to do a lot of things. But you need to simplify so you can now, modernize so you can transform. But, I'm going to be in the cloud in 18 months, so I don't want to modernize my storage. So what we have, is of course we have so you can buy things, you can lease things, we have a utility model, that is great for three to five years. But we have now a subscription model, which think of just cloud pricing. No long term commitment. Use what you use, up and down, and if it goes to zero, call us we'll pick it up, and there's no expense to you. So, no long term commitments and returns. So in 14 months, I've done my modernization, you've helped me free up my team. Let me go, and then we'll come and pick it up, and your bill stops that day. >> Cancel at anytime? >> Yeah, cancel at anytime. >> Do you expect people to take advantage of that? Is there a ton of demand at this point in time? >> I think everyone is on their own cloud journey. We talk a lot about meeting the client where they are, right. So how do I meet them where they're at. And everyone is on their own journey, so a lot of people are saying, hey, why would I do anything here, I need to get there. But if they can modernize and simplify what they're doing, and again these are your mission critical. We're not talking, this is how you're running your business, if we can make it better in the mean time, and then modernize it, get it in containers, get it into a new platform, that makes all the sense in the world. And because if we can give them a flexible way, say it's cheaper than using cloud storage, like in Amazon or IBM cloud. But you can use it on-prem, free you up, and then at anytime, just return it, that's a big value that people say, you know what, you're right, I'm going to go do that. You're able to give me cloud based pricing, down to zero when I'm done with it. Now I can use that to free up my team, that's the value equation. I don't think it's for everyone. But I think for a segment of the market, I think it's critical. And I think IBM's kind of perfectly positioned to do it with a balance sheet to help clients out. >> So how do you feel about this? Obviously, you've put a lot of work into it. You seem pretty excited. Do you feel as though this is going to help re-energize your business, your customer base, and how do you think competitors are going to respond? >> Good question. So, I think simplification, especially we can talk about value equation. I think I can add more value to you Mr. Customer. I can bring things you're not expected, right, and we're get to this cyber in a second, that would be one of the things they would not expected. And reduce the costs and complexity. So we've already done this a couple of times, so we did it with our Mainframe storage launch in the fall. It bar none, the best box for that workload. Lowest latency, most integration, encrypt, pervasive encryption, encryption in flight. But also, we took it from nine variants, to two. Because we could. We go, why did you need all those, we'll there's reasons for it in the past, but no longer. We also got rid of all the hard disk drives. We also add a little non-volatile cache and allowed you to get rid of all those battery backups. All these custom things that you used to have on this high end box. And now it's dramatically simpler, better. And by the way, no one asked, hey what are my other seven variants went. It was simpler, it was better, faster, but then it was the best launch we've had in the history of the product line. It think we can add better value and simplify for our clients. So that's what we'll do. You asked about how people respond. Listen, they're going to have to go through the same thing we did, right. A product line has people behind it, and it's really hard, or a founder behind it. You mentioned a couple, they're acquiring companies. I think they're going to have to go this, it's a transformational journey, that they'll have to go through. It's not as simple as doing a PowerPoint. I couldn't come to you and say, I can simplify without compromise. I can help you on the low end, the midrange, high end with same platform unless I did a lot of fundamental design work to make sure I could do that. Flash core modules being one of them, right. So I think it's going to be hard. It'll be interesting, well, they're going to have to go through the same thing I did, how about that. >> Usually when you make a major release like that, you're able to claim Top Gun, at least for a while with things like latency, and bandwidth and IOP's and performance. Are you able to make that claim? >> So, basically you saw it in the launch today. But basically you saw the latency which is one, because we're bringing a custom silicon down, our latency you'll see like I'll give you Pure bragging on their websites, their lowest latency is 70 microseconds, which by the way is pretty, you know. It's gonna be 150 microseconds, pretty good bragging rights. We're at 70 microseconds, but that's on the X90 using storage class memory. So literally we are 2x faster than on latency, how fast can you respond to something. But we can do it not only on our high end box, but we can also do it on our average sale price $15,000 box. Because I'm bringing that silicon up and down. So we can do the latency, now EMC the highest and PowerMax box. Two big chassis put together, that can do 100 microseconds. Again, still we're 70 microseconds, so we're 30% faster. And that's epitomized of the high end custom silicon software. So latency we got it. IOP's, so look at the biggest baddest two boxes of EMC, they'll do you know 15 million IOPs on their website. We'll do 18 million IOPs, but instead of two racks, it's 8U. It is 12x better IOPs per rack space, if you want to look at it that way. Throughput, which if you could do, it's all about building for our businesses. It's all about journey of the cloud and building for our businesses, everyone's trying to do this. Throughput in analytics becomes everything, and we you can do analytics in everything. Your DBA's are going to run analytics, so throughput matters. Ours is for every one of our boxes, that you can kind of add up and cluster out, it's 45 Gb/s. Pure, for instance their bragging rights, is 18, and they can't cluster anymore. So what we're able to do is on any of the, and most of those are high end, but I'll say, I can do the same thing up and down my line, because of where I'm bringing the custom silicon. So on bragging rights, and that's just kind of website, big bragging rights, I think we got a cold, and if you look price performance, and just overall price per capacity, we're inline to be the most the cost effective across everyone. >> Yeah, up and down the line, it's very interesting, it's kind of unique. >> And then you mentioned resiliency, I'll tell you that's the hottest thing, so. You mentioned the cyber incident response, that is something that we did on the Mainframe. So, we did the last Mainframe cycle, we allow you to do the same thing, and it literally drove all the demand for the product sets. It's already the number one thing people want to talk about, because it becomes a you're right, I needed that this week, I needed it last week. So, I think that's going to really drive demand? >> What worries you? >> (laughs) On this launch, not much. I think it's how fast and far we can get this message out. >> Wow, okay, so execution, obviously. You feel pretty confident about that, and yeah, getting the word out. Letting people know. Well, congratulations Ed. >> No, thank you very much, I appreciate it. I appreciate you coming in. And thank you for watching everybody. This is Dave Vellante for theCUBE. We'll see you next time. (upbeat music)

(upbeat music) >> From our studios in the heart of Silicon Valley, Palo Alto, California, this is a CUBE Conversation. >> Welcome everyone to this special CUBE Conversation Around the CUBE segment, Unpacking AI, number two, sponsored by Juniper Networks. We've got a great lineup here to go around the CUBE and unpack AI. We have Ken Jennings, all-time Jeopardy champion with us. Celebrity, great story there, we'll dig into that. John Hinson, director of AI at Evotek and Charna Parkey, who's the applied scientist at Textio. Thanks for joining us here for Around the CUBE Unpacking AI, appreciate it. First question I want to get to, Ken, you're notable for being beaten by a machine on Jeopardy. Everyone knows that story, but it really brings out the question of AI and the role AI is playing in society around obsolescence. We've been hearing gloom and doom around AI replacing people's jobs, and it's not really that way. What's your take on AI and replacing people's jobs? >> You know, I'm not an economist, so I can't speak to how easy it's going to be to retrain and re-skill tens of millions of people once these clerical and food prep and driving and whatever jobs go away, but I can definitely speak to the personal feeling of being in that situation, kind of watching the machine take your job on the assembly line and realizing that the thing you thought made you special no longer exists. If IBM throws enough money at it, your skill essentially is now obsolete. And it was kind of a disconcerting feeling. I think that what people need is to feel like they matter, and that went away for me very quickly when I realized that a black rectangle can now beat me at a game show. >> Okay John, what's your take on AI replacing jobs? What's your view on this? >> I think, look, we're all going to have to adapt. There's a lot of changes coming. There's changes coming socially, economically, politically. I think it's a disservice to us all to get to too indulgent around the idea that these things are going to change. We have to absorb these things, we have to be really smart about how we approach them. We have to be very open-minded about how these things are going to actually change us all. But ultimately, I think it's going to be positive at the end of the day. It's definitely going to be a little rough for a couple of years as we make all these adjustments, but I think what AI brings to the table is heads above kind of where we are today. >> Charna, your take around this, because the role of humans versus machines are pretty significant, they help each other. But is AI going to dominate over humans? >> Yeah, absolutely. I think there's a thing that we see over and over again in every bubble and collapse where, you know, in the automotive industry we certainly saw a bunch of jobs were lost, but a bunch of jobs were gained. And so we're just now actually getting into the phase where people are realizing that AI isn't just replacement, it has to be augmentation, right? We can't simply use images to replace recognition of people, we can't just use black box to give our FICO credit scores, it has to be inspectable. So there's a new field coming up now called explainable AI that actually is where we're moving towards and it's actually going to help society and create jobs. >> All right so let's stay on that next point for the next round, explainable AI. This points to a golden age. There's a debate around are we in a bubble or a golden age. A lot of people are negative right now on tech. You can see all the tech backlash. Amazon, the big tech companies like Apple and Facebook, there's a huge backlash around this so-called tech for society. Is this an indicator of a golden age coming? >> I think so, absolutely. We can take two examples of this. One would be where, you remember when Amazon built a hiring algorithm based upon their own resume data and they found that it was discriminating against women because they had only had men apply for it. Now with Textio we're building augmented writing across the audience and not from a single company and so companies like Johnson and Johnson are increasing the pipeline by more than nine percent which converts to 90,000 more women applying for their jobs. And so part of the difference there is one is explainable, one isn't, and one is using the right data set representing the audience that is consuming it and not a single company's hiring. So I think we're absolutely headed into more of a golden age, and I think these are some of the signs that people are starting to use it in the right way. >> John, what's your take? Obviously golden age doesn't look that to us right now. You see Facebook approving lies as ads, Twitter banning political ads. AI was supposed to solve all these problems. Is there light at the end of this dark tunnel we're on? >> Yeah, golden age for sure. I'm definitely a big believer in that. I think there's a new era amongst us on how we handle data in general. I think the most important thing we have here though is education around what this stuff is, how it works, how it's affecting our lives individually and at the corporate level. This is a new era of informing and augmenting literally everything we do. I see nothing but positives coming out of this. We have to be obviously very careful with our approaching all the biases that already exist today that are only going to be magnified with these types of algorithms at mass scale. But ultimately if we can get over that hurdle, which I believe collectively we all need to do together, I think we'd live in much better, less wasteful world just by approaching the data that's already at hand. >> Ken, what's your take on this? It's like a daily double question. Is it going to be a golden age? >> Laughs >> It's going to come sooner or later. We have to have catastrophe before, we have to have reality hit us in the face before we realize that tech is good, and shaping it? It's pretty ugly right now in some of the situations out there, especially in the political scene with the election in the US. You're seeing some negative things happening. What's your take on this? >> I'm much more skeptical than John and Charna. I feel like that kind of just blinkered, it's going to be great, is something you have to actually be in the tech industry and hearing all day to actually believe. I remember seeing kind of lay-person's exposure to Watson when Watson was on Jeopardy and hearing the questions reporters would ask and seeing the memes that would appear, and everyone's immediate reaction just to something as innocuous as a AI algorithm playing on a game show was to ask, is this Skynet from Terminator 2? Is this the computer from The Matrix? Is this HAL pushing us out of the airlock? Everybody immediately first goes to the tech is going to kill us. That's like everybody's first reaction, and it's weird. I don't know, you might say it's just because Hollywood has trained us to expect that plot development, but I almost think it's the other way around. Like that's a story we tell because we're deeply worried about our own meaning and obsolescence when we see how little these skills might be valued in 10, 20, 30 years. >> I can't tell you how much, by the way, Star Trek, Star Wars and Terminators probably affected the nomenclature of the technology. Everyone references Skynet. Oh my God, we're going to be taken over and killed by aliens and machines. This is a real fear. I thinks it's an initial reaction. You felt that Ken, so I've got to ask you, where do you think the crossover point is for people to internalize the benefits of say, AI for instance? Because people will say hey, look back at life before the iPhone, look at life before these tools were out there. Some will say society's gotten better, but yet there's this surveillance culture, things... And on and on. So what do you guys think the crossover point is for the reaction to change from oh my God, it's Skynet, gloom and doom to this actually could be good? >> It's incredibly tricky because as we've seen, the perception of AI both in and out of the industry changes as AI advances. As soon as machine learning can actually do a task, there's a tendency to say there's this no true Scotsman problem where we say well, that clearly can't be AI because I see how the trick worked. And yeah, humans lose at chess now. So when these small advances happen, the reaction is often oh, that's not really AI. And by the same token, it's not a game-changer when your email client can start to auto-complete your emails. That's a minor convenience to you. But you don't think oh, maybe Skynet is good. I really do think it's going to have to be, maybe the inflection point is when it starts to become so disruptive that actually public policy has to change. So we get serious about >> And public policy has started changing. >> whatever their reactions are. >> Charna, your thoughts. >> The public policy has started changing though. We just saw, I think it was in September, where California banned the use of AI in the body cameras, both real-time and after the fact. So I think that's part of the pivot point that we're actually seeing is that public policy is changing.` The state of Washington currently has a task force for AI who's making a set of recommendations for policy starting in December. But I think part of what we're missing is that we don't have enough digital natives in office to even attempt to, to your point Ken, predict what we're even going to be able to do with it, right? There is this fear because of misunderstanding, but we also don't have a respect of our political climate right now by a lot of our digital natives, and they need to be there to be making this policy. >> John, weigh in on this because you're director of AI, you're seeing positive, you have to deal with the uncertainty as well, the growth of machine learning. And just this week Google announced more TensorFlow for everybody. You're seeing Open Source. So there's a tech push, almost a democratization, going on with AI. So I think this crossover point might be sooner in front of us than people think. What's your thoughts? >> Yeah it's here right now. All these things can be essentially put into an environment. You can see these into products, or making business decisions or political decisions. These are all available right now. They're available today and its within 10 to 15 lines of code. It's all about the data sets, so you have to be really good stewards of the data that you're using to train your models. But I think the most important thing, back to the Skynet and all this science-fiction side, we have to collectively start telling the right stories. We need better stories than just this robots are going to take us over and destroy all of our jobs. I think more interesting stories really revolve around, what about public defenders who can have this informant augmentation algorithm that's going to help them get their job done? What about tailor-made medicine that's going to tell me exactly what the conditions are based off of a particular treatment plan instead of guessing? What about tailored education that's going to look at all of my strengths and weaknesses and present a plan for me? These are things that AI can do. Charna's exactly right, where if we don't get this into the right political atmosphere that's helping balance the capitalist side with the social side, we're going to be in trouble. So that's got to be embedded in every layer of enterprise as well as society in general. It's here, it's now, and it's real. >> Ken, before we move on to the ethics question, I want to get your thoughts on this because we have an Alexa at home. We had an Alexa at home; my wife made me get rid of it. We had an Apple device, what they're called... the Home pods, that's gone. I bought a Portal from Facebook because I always buy the earliest stuff, that's gone. We don't want listening devices in our house because in order to get that AI, you have to give up listening, and this has been an issue. What do you have to give to get? This has been a big question. What's your thoughts on all this? >> I was at an Amazon event where they were trumpeting how no technology had ever caught on faster than these personal digital assistants, and yet every time I'm in a use case, a household that's trying to use them, something goes terribly wrong. My friend had to rename his because the neighbor kids kept telling Alexa to do awful things. He renamed it computer, and now every time we use the word computer, the wall tells us something we don't want to know. >> (laughs) >> This is just anecdata, but maybe it speaks to something deeper, the fact that we don't necessarily like the feeling of being surveilled. IBM was always trying to push Watson as the star Trek computer that helpfully tells you exactly what you need to know in the right moment, but that's got downsides too. I feel like we're going to, if nothing else, we may start to value individual learning and knowledge less when we feel like a voice from the ceiling can deliver unto us the fact that we need. I think decision-making might suffer in that kind of a world. >> All right, this brings up ethics because I bring up the Amazon and the voice stuff because this is the new interface people want to have with machines. I didn't mention phones, Androids and Apple, they need to listen in order to make decisions. This brings up the ethics question around who sets the laws, what society should do about this, because we want the benefits of AI. John, you point out some of them. You got to give to get. Where are we on ethics? What's the opinion, what's the current view on this? John, we'll start with you on your ethics view on what needs to change now to move the ball faster. >> Data is gold. Data is gold at an exponential rate when you're talking about AI. There should be no situation where these companies get to collect data at no cost or no benefit to the end consumer. So ultimately we should have the option to opt out of any of these products and any of this type of surveillance wherever we can. Public safety is a little bit different situation, but on the commercial side, there is a lot of more expensive and even more difficult ways to train these models with a data set that isn't just basically grabbing everything our of your personal lives. I think that should be an option for consumers and that's one of those ethical check-marks. Again, ethics in general, the way that data's trained, the way that data's handled, the way models actually work, it has to be a primary reason for and approach of how you actually go about developing and delivering AI. That said, we cannot get over-indulgent in the fact that we can't do it because we're so fearful of the ethical outcomes. We have to find some middle ground and we have to find it quickly and collectively. >> Charna, what's your take on this? Ethics is super important to set the agenda for society to take advantage of all this. >> Yeah. I think we've got three ethical components here. We certainly have, as John mentioned, the data sets. However, it's also what behavior we're trying to change. So I believe the industry could benefit from a lot more behavioral science, so that we can understand whether or not the algorithms that we're building are changing behaviors that we actually want to change, right? And if we aren't, that's unethical. There is an entire field of ethics that needs to start getting put into our companies. We need an ethics board internally. A few companies are doing this already actually. I know a lot of the military companies do. I used to be in the defense industry, and so they've got a board of ethics before you can do things. The challenge is also though that as we're democratizing the algorithms themselves, people don't understand that you can't just get a set of data that represents the population. So this is true of image processing, where if we only used 100 images of a black woman, and we used 1,000 images of a white man because that was the distribution in our population, and then the algorithm could not detect the difference between skin tones for people of color, then we end up with situations where we end up in a police state where you put in an image of one black woman and it looks like ten of them and you can't distinguish between them. And yet, the confidence rate for the humans are actually higher, because they now have a machine backing their decision. And so they stop questioning, to your point, Ken, about what is the decision I'm making, they're like I'm so confident, this data told me so. And so there's a little bit of you need some expert in the loop and you also can't just have experts, because then you end up with Cambridge Analytica and all of the political things that happened there, not just in the US, but across 200 different elections and 30 different countries. And we are upset because it happened in the US, but this has been happening for years. So its just this ethical challenge of behavior change. It's not even AI and we do it all the time. Its why the cigarette industry is regulated (laughs). >> So Ken, what's your take on this? Obviously because society needs to have ethics. Who runs that? Companies? The law-makers? Someone's got to be responsible. >> I'm honestly a little pessimistic the general public will even demand this the way we're maybe hoping that they will. When I think about an example like Facebook, people just being able to, being willing to give away insane amounts of data through social media companies for the smallest of benefits: keeping in touch with people from high school they don't like. I mean, it really shows how little we value not being a product in this kind of situation. But I would like to see this kind of ethical decisions being made at the company-level. I feel like Google kind of surreptitiously moved away from it's little don't be evil mantra with the subtext that eh, maybe we'll be a little evil now. It just reminds me of Manhattan Project era thinking, where you could've gone to any of these nuclear scientists and said you're working on a real interesting puzzle here, it might advance the field, but like 200,000 civilians might die this summer. And I feel like they would've just looked at you and thought that's not really my bailiwick. I'm just trying to solve the fission problem. I would like to see these 10 companies actually having that kind of thinking internally. Not being so busy thinking if they can do something that they don't wonder if they should. >> That's a great point. This brings up the point of who is responsible. Almost as if who is less evil than the other person? Google, they don't do evil, but they're less evil than Amazon and Facebook and others. Who is responsible? The companies or the law-makers? Because if you look up some of the hearings in Washington, D.C., some of the law-makers we see up there, they don't know how the internet works, and it's pretty obvious that this is a problem. >> Yeah, well that's why Jack Dorsey of Twitter posted yesterday that he banned not just political ads, but also issue ads. This isn't something that they're making him do, but he understands that when you're using AI to target people, that it's not okay. At some point, while Mark is sitting on (laughs) this committee and giving his testimony, he's essentially asking to be regulated because he can't regulate himself. He's like well, everyone's doing it, so I'm going to do it too. That's not an okay excuse. We see this in the labor market though actually, where there's existing laws that prevent discrimination. It's actually the company's responsibility to make sure that the products that they purchase from any vendor isn't introducing discrimination into that process. So its not even the vendor that's held responsible, it's the company and their use of it. We saw in the NYPD actually that one of those image recognition systems came up and someone said well, he looked like, I forget the name of what the actor was, but some actor's name is what the perpetrator looked like and so they used an image of the actor to try and find the person who actually assaulted someone else. And that's, it's also the user problem that I'm super concerned about. >> So John, what's your take on this? Because these are companies are in business to make money, for profit, they're not the government. And who's the role, what should the government do? AI has to move forward. >> Yeah, we're all responsible. The companies are responsible. The companies that we work with, I have yet to interact with customers, or with our customers here, that have some insidious goal, that they're trying to outsmart their customers. They're not. Everyone's looking to do the best and deliver the most relevant products in the marketplace. The government, they absolutely... The political structure we have, it has to be really intelligent and it's got to get up-skilled in this space and it needs to do it quickly, both at the economy level, as well as for our defense. But the individuals, all of us as individuals, we are already subjected to this type of artificial intelligence in our everyday lives. Look at streaming, streaming media. Right now every single one of us goes out through a streaming source, and we're getting recommendations on what we should watch next. And we're already adapting to these things, I am. I'm like stop showing me all the stuff you know I want to watch, that's not interesting to me. I want to find something I don't know I want to watch, right? So we all have to get educated, we're all responsible for these things. And again, I see a much more positive side of this. I'm not trying to get into the fear-mongering side of all the things that could go wrong, I want to focus on the good stories, the positive stories. If I'm in a courtroom and I lose a court case because I couldn't afford the best attorney and I have the bias of a judge, I would certainly like artificial intelligence to make a determination that allows me to drive an appeal, as one example. Things like that are really creative in the world that we need to do. Tampering down this wild speculation we have on the markets. I mean, we are all victims of really bad data decisions right now, almost the worst data decisions. For me, I see this as a way to actually improve all those things. Fraud fees will be reduced. That helps everybody, right? Less speculation and these wild swings, these are all helpful things. >> Well Ken, John and Charna, thank- (audio feedback) >> Go ahead, finish. Get that word in. >> Sorry. I think that point you were making though John, is we are still a capitalist society, but we're no longer a shareholder capitalist society, we are a stakeholder capitalist society and the stakeholder is the society itself. It is us, it what we want to see. And so yes, I still want money. Obviously there are things that I want to buy, but I also care about well-being. I think it's that little shift that we're seeing that is actually you and I holding our own teams accountable for what they do. >> Yeah, culture first is a whole new shift going on in these companies that's a for-profit, mission-based. Ken, John, Charna, thanks for coming on Around the CUBE, Unpacking AI. Let's go around the CUBE Ken, John and Charna in that order, and just real quickly, unpacking AI, what's your final word? >> (laughs) I really... I'm interested in John's take that there's a democratization coming provided these tools will be available to everyone. I would certainly love to believe that. It seems like in the past, we've seen no, that access to these kind of powerful, paradigm-changing tools tend to be concentrated among a very small group of people and the benefits accrue to a very small group of people. But I hope that doesn't happen here. You know, I'm optimistic as well. I like the utopian side where we all have this amazing access to information and so many new problems can get solved with amazing amounts of data that we never could've touched before. Though you know, I think about that. I try to let that help me sleep at night, and not the fact that, you know... every public figure I see on TV is kind of out of touch about technology and only one candidate suggests the universal basic income, and it's kind of a crackpot idea. Those are the kind of things that keep me up at night. >> All right, John, final word. >> I think it's beautiful, AI's beautiful. We're on the cusp of a whole new world, it's nothing but positivity I see. We have to be careful. We're all nervous about it. None of us know how to approach these things, but as human beings, we've been here before. We're here all the time. And I believe that we can all collectively get a better lives for ourselves, for the environment, for everything that's out there. It's here, it's now, it's definitely real. I encourage everyone to hurry up on their own education. Every company, every layer of government to start really embracing these things and start paying attention. It's catching us all a little bit by surprise, but once you see it in production, you see it real, you'll be impressed. >> Okay, Charna, final word. >> I think one thing I want to leave people with is what we incentivize is what we end up optimizing for. This is the same for human behavior. You're training a new employee, you put incentives on the way that they sell, and that's, they game the system. AI's specifically find the optimum route, that is their job. So if we don't understand more complex cost functions, more complex representative ways of training, we're going to end up in a space, before we know it, that we can't get out of. And especially if we're using uninspectable AI. We really need to move towards augmentation. There are some companies that are implementing this now that you may not even know. Zillow, for example, is using AI to give you a cost for your home just by the photos and the words that you describe it, but they're also purchasing houses without a human in the loop in certain markets, based upon an inspection later by a human. And so there are these big bets that we're making within these massive corporations, but if you're going to do it as an individual, take a Coursera class on AI and take a Coursera class on ethics so that you can understand what the pitfalls are going to be, because that cost function is incredibly important. >> Okay, that's a wrap. Looks like we have a winner here. Charna, you got 18, John 16. Ken came in with 12, beaten again! (both laugh) Okay, Ken, seriously, great to have you guys on, a pleasure to meet everyone. Thanks for sharing on Around the CUBE Unpacking AI, panel number two. Thank you. >> Thanks a lot. >> Thank you. >> Thanks. I've been defeated by artificial intelligence again! (all laugh) (upbeat music)

>> Narrator: Live from New York, it's theCube! Covering AWS Global Summit 2019, brought to you by Amazon Web Services. >> Welcome back, we're here at the Javits Center in New York City for AWS Summit, I'm Stu Miniman, my cohost is Corey Quinn and happy to welcome to the program Scott Mullins, who's the head of Worldwide Financial Services Business Development with Amazon Web Services based here in The Big Apple, thanks so much for joining us. >> Thanks for having me, Stu, thanks for having me, Corey. >> All right so we had obviously financial services big location here in New York City. We just had FINRA on our program, had a great conversation about how they're using AWS for their environments, but give us a thumbnail if you will about your business, your customers and what you're seeing there. >> Sure, we're working with financial institutions all the way from the newest FinTech startups, all the way to organizations like FINRA, the largest exchanges and brokers dealers like Nasdaq, as well as insurers and the largest banks. And I've been here for five years and in that time period I actually went from being a customer speaking at the AWS Summit here in the Javits Center on stage like Steve Randich was today to watching more and more financial institutions coming forward, talking about their use in the cloud. >> Yeah before we get into technology, one of the biggest trends of moving to cloud is I'm moving from CapEx more to OpEx and oh my gosh there's uncertainty because I'm not locking in some massive contract that I'm paying up front or depreciating over five years but I've got flexibility and things are going to change. I'm curious what you're seeing as the financial pieces of how people both acquire and keep on the books what they're doing. >> Yeah it can be a little bit different, right, then what most people are used to. They're used to kind of that muscle memory and that rhythm of how you procured technology in the past and there can be a stage of adjustment, but cost isn't really the thing that people I think look to the most when it comes to cloud today, it's all about agility and FINRA is a great example. Steve has talked about over and over again over the last several years how they were able to gain such business agility and actually to do more, the fact that they're now processing 155 billion market events every night and able to run all their surveillance routines. That's really indicative of the value that people are looking for. Being able to actually get products to market faster and reducing development cycles from 18 months to three months, like Allianz, one of our customers over in Europe has been able to do. Being able to go faster I think actually trumps cost from the standpoint of what that biggest value driver that we're seeing our customers going after in financial services. >> We're starting to see such a tremendous difference as far as the people speaking at these keynotes. Once upon a time you had Netflix and folks like that on stage telling a story about how they're using cloud to achieve all these amazing things, but when you take a step back and start blinking a little bit, they fundamentally stream movies and yes, produce some awesome original content. With banks and other financial institutions if the ATM starts spitting out the wrong number, that's a different point on the spectrum of are people going to riot in the street. I'm not saying it's further along, people really like their content but it's still a different use case with a different risk profile. Getting serious companies that have world shaking impact to trust public cloud took time and we're seeing it with places like FINRA, Capital One has been very active as far as evangelizing their use of cloud. It's just been transformative. What does that look like, from being a part of that? >> Well you know it's interesting, so you know you just said it, financial services is the business of risk management. And so to get more and when you see more and more of these financial institutions coming forward and talking about their use of cloud, what that really equates to is comfort, they've got that muscle memory now, they've probably been working with us in some way, shape or form for some great period of time and so if you look at last year, you had Dean Del Vecchio from Guardian Life Insurance come out on stage at Reinvent and say to the crowd "Hey we're a 158 year old insurance company but we've now closed our data center and we're fully on AWS and we've completed the transformation of our organization". The year before you saw Goldman Sachs walk out and say "Yeah we've been working with AWS for about four years now and we're actually using them for some very interesting use cases within Goldman Sachs". And so typically what you've seen is that over the course of about a two year to sometimes a four year time period, you've got institutions that are working deeply with us, but they're not talking about it. They're gaining that muscle memory, they're putting those first use cases to begin to scale that work up and then when they're ready man, they're ready to talk about it and they're excited to talk about it. What's interesting though is today we're having this same summit that we're having here in Cape Town in Africa and we had a customer, Old Mutual, who's one of the biggest insurers there, they just started working with us in earnest back in May and they were on stage today, so you're seeing that actually beginning to happen a lot quicker, where people are building that muscle memory faster and they're much more eager to talk about it. You're going to see that trend I think continue in financial services over the next few years so I'm very excited for future summits as well as Reinvent because the stories that we're going to see are going to come faster. You're going to see more use cases that go a lot deeper in the industry and you're going to see it covering a lot more of the industry. >> It's very much not, IT is no longer what people think of in terms of Tech companies in San Francisco building products. It's banks, it's health care and these companies are transitioning to become technology companies but when your entire, as you mentioned, the entire industry becomes about risk management, it's challenging sometimes to articulate things when you're not both on the same page. I was working with a financial partner years ago at a company I worked for and okay they're a financial institution, they're ready to sign off on this but before that they'd like to tour US East one first and validate that things are as we say they are. The answer is yeah me too, sadly, you folks have never bothered to invite me to tour an active AZ, maybe next year. It's challenging to I guess meet people where they are and speak the right language, the right peace for a long time. >> And that's why you see us have a financial services team in the first place, right? Because your financial services or health care or any of the other industries, they're very unique and they have a very specific language and so we've been very focused on making sure that we speak that language that we have an understanding of what that industry entails and what's important to that industry because as you know Amazon's a very customer obsessed organization and we want to work backwards from our customers and so it's been very important for us to actually speak that language and be able to translate that to our service teams to say hey this is important to financial services and this is why, here's the context for that. I think as we've continued to see more and more financial institutions take on that technology company mindset, I'm a technology company that happens to run a bank or happens to run an exchange company or happens to run an insurance business, it's actually been easier to talk to them about the services that we offer because now they have that mindset, they're moving more towards DevOps and moving more towards agile. And so it's been really easy to actually communicate hey, here are the appropriate changes you have to make, here's how you evolve governance, here's how you address security and compliance and the different levels of resiliency that actually improve from the standpoint of using these services. >> All right so Scott, back before I did this, I worked for some large technology suppliers and there were some groups on Wall Street that have huge IT budgets and IT staffs and actually were very cutting edge in what they were building, in what they were doing and very proud of their IT knowledge, and they were like, they have some of the smartest people in the industry and they spend a ton of money because they need an edge. Talking about transactions on stock markets, if I can translate milliseconds into millions of dollars if I can act faster. So you know, those companies, how are they moving along to do the I need to build it myself and differentiate myself because of my IT versus hey I can now have access to all the services out there because you're offering them with new ones every day, but geez how do I differentiate myself if everybody can use some of these same tools. >> So that's my background as well and so you go back that and milliseconds matter, milliseconds are money, right? When it comes to trading and actually building really bespoke applications on bespoke infrastructure. So I think what we're seeing from a transitional perspective is that you still have that mindset where hey we're really good at technology, we're really good at building applications. But now it's a new toolkit, you have access to a completely new toolkit. It's almost like The Matrix, you know that scene where Neo steps into that white room and hey says "I need this" and then the shelves just show up, that's kind how it is in the cloud, you actually have the ability to leverage the latest and greatest technologies at your fingertips when you want to build and I think that's something that's been a really compelling thing for financial institutions where you don't have to wait to get infrastructure provisioned for you. Before I worked for AWS, I worked for large financial institutions as well and when we had major projects that we had to do that sometimes had a regulatory implication, we were told by our infrastructure team hey that's going to be six months before we can actually get your dev environment built so you can actually begin to develop what you need. And actually we had to respond within about thirty days and so you had a mismatch there. With the cloud you can provision infrastructure easily and you have an access to an array of services that you can use to build immediately. And that means value, that means time to market, that means time to answering questions from customers, that means really a much faster time to answering questions from regulatory agencies and so we're seeing the adoption and the embrace of those services be very large and very significant. >> It's important to make sure that the guardrails are set appropriately, especially for a risk managed firm but once you get that in place correctly, it's an incredible boost of productivity and capability, as opposed to the old crappy way of doing governance of oh it used to take six weeks to get a server in so we're going to open a ticket now whenever you want to provision an instance and it only takes four, yay we're moving faster. It feels like there's very much a right way and a wrong way to start embracing cloud technology. >> Yeah and you know human nature is to take the run book you have today and try to apply it to tomorrow and that doesn't always work because you can use that run book and you'll get down to line four and suddenly line four doesn't exist anymore because of what's happened from a technological change perspective. Yeah I think that's why things like AWS control tower and security hub, which are those guardrails, those services that we announced recently that have gone GA. We announced them a couple of weeks ago at Reinforce in Boston. Those are really interesting to financial services customers because it really begins to help automate a lot of those compliance controls and provisioning those through control tower and then monitoring those through security hub and so you've seen us focus on how do we actually make that easier for customers to do. We know that risk management, we know that governance and controls is very important in financial services. We actually offer our customers a way to look from a country specific angle, add the different countries and the rule sets and the requirements that exist in those countries and how you map those to our controls and how you map those into your own controls and all the considerations that you have, we've got them on our public website. If you went to atlas.aws right now, that's our compliance center, you could actually pick the countries you're interested in and we'll have that mapping for you. So you'll see us continue to invest in things like that to make that much easier for customers to actually deploy quickly and to evolve those governance frameworks. >> And things like with Artifact, where it's just grab whatever compliance report you need, submit it and it's done without having to go through a laborious process. It's click button, receive compliance in some cases. >> If you're not familiar with it you can go into the AWS console and you've got Artifact right there and if you need a SOC report or you need some other type of artifact, you can just download it right there through the console, yeah it's very convenient. >> Yeah so Scott you know we talked about some of the GRC pieces in place, what are you seeing trends out there kind of globally, you know GDRP was something that was on everybody's mind over the last year or so. California has new regulations that are coming in place, so anything specific in your world or just the trends that you're seeing that might impact our environments-- >> I think that the biggest trends I would point to are data analytics, data analytics, data analytics, data analytics. And on top of that obviously machine learning. You know, data is the lifeblood of financial services, it's what makes everything go. And you can look at what's happening in this space where you've got companies like Bloomberg and Refinitiv who are making their data products available on AWS so you can get B-Pipe on AWS today, you can also get the elektron platform from Refintiv and then what people are trying to do in relation to hey I want to organize my data, I want to make it much easier to actually find value in data, both either from the standpoint of regulatory reporting, as you heard Steve talk about on stage today. FINRA is building a very large data repository that they have to from the standpoint of a regulatory perspective with CAT. Broker dealers have to actually feed the CAT and so they are also worried about here in the US, how do I actually organize my data, get all the elements I have to report to CAT together and actually do that in a very efficient way. So that's a big data analytic project. Things that are helping to make that much easier are leg formations, so we came up with leg formation last year and so you've got many financial institutions that are looking at how do you make building a data leg that much easier and then how do you layer analytics on top of that, whether it's using Amazon elastic map reduce or EMR to actually run regulatory reporting jobs or how do I begin to leverage machine learning to actually make my data analytics from a standpoint of trade surveillance or fraud detection that much more enriched and actually looking for those anomalies rather than just looking for a whole bunch of false positives. So data analytics I think is what I would point to as the biggest trend and how to actually make data more useful and how to get to data insights faster. >> On the one end it seems like there's absolutely a lot of potential in this, on the other it feels in many cases with large scale data analytics, it's we have all these tools for machine learning and the rest that we can wind up passing out to you but you need to figure out what to do with them, how to make it work and it's unclear outside of a few specific use cases and I think you've alluded to a couple of those how to take in a typical business that maybe doesn't have an enormous pile of data and start applying machine learning to it in a way that makes intelligent sense. That feels right now like a storytelling failure to some extent industry wide. We're starting to see some stories emerge but it still feels a little "Gold Rush"-y to some extent. >> Yeah I would say, and my advice would be don't try to boil the ocean or don't try to boil the data leg, meaning you want to do machine learning, you've got a great amount of earnestness about that but picture use case, really hone in on what you're trying to accomplish and work backwards from that. And we offer tooling that can be really helpful in that, you know with stage maker you can train your models and you can actually make data science available to a much broader array of people than just your data scientists. And so where we see people focusing first, is where it matters to their business. So if you've got a regulatory obligation to do surveillance or fraud detection, those are great use cases to start with. How do I enhance my existing surveillance or fraud detection, so that I'm not just wading again through a sea of false positives. How do I actually reduce that workload for a human analyst using machine learning. That's a one step up and then you can go from there, you can actually continue to work deeper into the use cases and say okay how do I treat those parameters, how do I actually look for different things that I'm used to with the rules based systems. You can also look at offering more value to customers so with next best offer with Amazon Personalize, we now have encapsulated the service that we use on the amazon.com retail site as a service that we offer to customers so you don't have to build all that tooling yourself, you can actually just consume Personalize as a service to help with those personalized recommendations for customers. >> Scott, really appreciate all the updates on your customers in the financial services industry, thanks so much for joining us. >> Happy to be here guys, thanks for having me. >> All right for Corey Quinn, I'm Stu Miniman, back with more here at AWS Summit in New York City 2019, thanks as always for watching theCube.

>> Hi, I'm Peter Burroughs. And welcome to another cube conversation. This one is part of a very, very special digital community event sponsored by day trip. What we're going to be talking about today. Well, date comes here with a special product announcement that's intended to help customers do a better job of matching their technology needs with the speed and opportunities to use their data differently within their business. This is a problem that every single customer faces every single enterprise faces, and it's one that's become especially acute as those digital natives increasingly hunt down and take out some of those traditional businesses that are trying to better understand how to use their data. Now, as we have with all digital community events at the end of this one, we're gonna be running a crowd chat, so stay with us, will go through a couple of day trim and datum customer conversations, and then it'LL be your turn toe. Weigh in on what you think is important. Ask the questions of Data Room and others in the community that you think need to be addressed. Let's hear what you have to say about this increasingly special relationship between data technology and storage services. So without further ado, let's get it kicked off. Tim Page is the CEO of Datum. Tim, Welcome to the Cube. Thank you, Peter. So data give us a quick take on where you guys are. >> Yeah, Day tree ums formulated as a software to find converged infrastructure company that takes that converges to the next level. And the purpose of us is to give the user the same experience, whether you're working on Prem or across multi cloud. >> Great. So let's start by saying, that's the vision, but you've been talking a lot of customers. What's the problem that you keep hearing over and over that you're pointing towards? >> Yeah, it's funny. So it's so meet with the number CEOs over the years and specifically is related to a tree, and they'LL tell you they were on an on demand economy that expects instant outcomes, which means you have to digitally transform. And to do that, you've got to transform it, which means it's got to be easy. It's got to be consistent. You've got to get rid of a lot of the management issues, and it's got a feel and take advantage of the services that cloud has to offer. >> All right, so that's the nature of the problem. You've also done a fair amount of research looking into the specifics of what they're asking for. Give us some insight into what day terms discovering as you talk to customers about what the solutions are going to look like. >> It's interesting. So if you look at how to resolve that, you've got to conf urged to transform in some form or fashion. If you look at the first level of convergence a lot of people have done, it's been directly as relates the hardware architecture. We've taken that to a whole new level until Point were saying, How do you actually automate those mundane task that take multiple groups to solve specifically primary backup disaster recovery? All the policies involved in that is a lot of work that goes into that across multiple groups, and we set out to solve those issues, >> so there's still a need for performance. There's still the need for capacity to reduce management time and overhead etcetera. But Tim, as we move forward, how our customers responding this you're getting some sense of what percentage of them are going, Teo say Yeah, that's it >> s so interesting. So we could start a survey and got over five hundred people leaders to respond to it. It's interesting is they talk about performance management security, but they're also talking about consistency of that experience. And specifically, we asked how many of you is important to have your platform have built in backup and policy services with encryption built in et cetera. We got a seventy percent rate of those applicants of those those people interviewed saying is really important for that to be part of a plan. >> So it sounds like you're really talking about something Mohr than just a couple of products. You really talking about forcing customers or you're not forcing. Customers are starting the process of rethinking your data infrastructure, and I got that right. >> That's right. If you look at how infrastructure is grown in the last twenty years, right? Twenty years ago, san technology was related, and every time you throw open app, you had to put different policies that Apple put different one tight management to how much of my resources and go to certain things. We set out to actually automate that which is why it took us four years. To build this platform with a hundred programmers is, Well, how do we actually make you not think about how you're going to back up? How do you set a policy and no disaster recovery is going to run? And to do that, you've gotta have it one code base and we know we're on to something, even based on our survey, because the old array vendors are all buying Bolton's because they know users want an experience. But you can't have that experience with the ball time. You have to have it your fundamental platform. >> Well, let me let me step in here. So I've been around for a long time him and heard a lot of people talk about platforms. And if I have kind of one rule companies and introduce platforms that just expand, typically failed companies that bring an opinion and converge more things so it's simpler tend to be more successful. Which direction's date >> going? So we definitely That's why we took time, right? If you want to be an enterprise class company, you can't build a cheap platform in eighteen months and hit the market because were you, architect, you stay. So our purpose from the beginning was purposefully to spend four years building an enterprise clap platform that did away with a lot of the mundane task seeing management That's twenty years old. Technology right? One management. So if you're buying your multi cloud type technology experience in cages, you're just buying old stuff. We took an approach saying, We want that consistent approach that whether you're running your services on from or in any type of cloud, you could instantly take advantage of that, and it feels the same. That's a big task because you're looking to run the speed of storage with the resiliency of backup right, which is a whole different type of technology. Which is how our founders, who have built the first words in this went to the second, almost third version of that type of oven. Stan she ation of a platform. >> All right, so we know what the solution is going to look like. It's going to look like a data platform that's rethought to support the needs of data assets and introduces a set of converge services that really focus the value proposition to what the enterprise needs So what do you guys announcing? >> That's exactly right. So we've finalized what we call our auto matrix platform. So auto matrix in inherently In it we'LL have primary backup Disaster recovery D Our solution All the policies within that an encryption built in from the very beginning. Soto have those five things we believe toe actually have on the next generation experience across true multi cloud. You're not bolting on hardware technologies. You're bolting on software technologies that operate in the same manner. Those five things have to be an errand or you're a bolt on type company. >> So you're not building a platform out by acquisition. You build a platform out by architecture and development. >> That's right. And we took four years to do it with one hundred guys building this thing out. It's released, it's out and it's ready to go. So our first we're announcing is that first in Stan she ation of that as a product we're calling control shift, which is really a data mobility orchestrator. True sas based you could orchestrate from the prime from the cloud cloud to cloud, and our first generation of that is disaster recovery so truly to be able to set up your policies, check those policies and make sure you're going to have true disaster recovery with an Rto zero. It's a tough thing we've done it. >> That's upstanding. Great to hear Tim Page, CEO Data Room, talking about some of the announcement that were here more about in the second. Let's now turn our attention, Teo. A short video. Let's hear more about it. >> The bank is focused on small businesses and helping them achieve their success. We want to redesign the customer engagement in defining the bank of the future. This office is our first implementation of that concept, as you can see is a much more open floor plan design that increases the interaction between our lead bank associates and our clients with day tree and split provisioning. Oliver Data is now on the host, so we have seen eighty times lower application. Leighton. See, this gives our associates instant responses to their queries so they can answer client questions in real time. That time is always expensive in our business. In the past we had a forty eight hour recovery plan, but with the atrium we were able to far exceed that plan we've been able to recover systems in minutes now instead of backing at once per day with that backup time taking eighteen hours. Now we're doing full system snapshots our league, and we're replicating those offsite stay trim is the only vendor I know of that could provide this end to end encryption. So any cyber attacks that get into our system are neutralized with the data absolution. We don't have to have storage consultants anymore. We don't have to be stored. Experts were able to manage everything from a storage perceptive through the center, obviously spending less time and money on infrastructure. We continue to leverage new technologies to improve application performance and lower costs. We also want to animate RDR fail over. So we're looking forward to implementing daydreams. Product deloused orchestrate an automaton. RDR fell over process. >> It is always great to hear from a customer. Want to get on Peterborough's? This's a Cube conversation, part of a digital community event sponsored by Data Room. We were talking about how the relationship between the new digital business outcomes highly dependent upon data and the mismatch of technology to be able to support those new classes of outcomes is causing problems in so many different enterprises. So let's dig a little bit more deeply into someone. Tatum's announcements to try to find ways to close those gaps. We've got his already who's the CTO of data on with today, says all are welcome to the Cube, >> that being a good to see you again. >> So automate tricks give us a little bit more toe tail and how it's creating value for customers. >> So if you go to any data center today, you notice that for the amount of data they have their five different vendors and five different parts to manage the data. There is the primary storage. There is the backup on DH. There is the D R. And then there's mobility. And then there is the security or think about so this five different products, our kind of causing friction for you if you want to move, if you want to be in the undermanned economy and move fast in your business, these things are causing friction. You cannot move that fast. And so what we have done is that we took. We took a step back and built this automatics platform. It's provides this data services. We shall kind of quality that autonomous data services. The idea is that you don't have to really do much for it by converging all this functions into one simple platform that we let him with all the friction you need to manage all your data. And that's kind of what we call auto metrics that >> platform. So as a consequence, I gotta believe, Then your customers are discovering that not only is it simpler, easy to use perhaps a little bit less expertise required, but they also are more likely to be operationally successful with some of the core functions like D are that they have to work with. >> Yeah, So the other thing about these five five grandpre functions and products you need is that if you want to imagine a future, where you going, you know, leverage the cloud For a simple thing like the R, for example, the thing is that if you want to move this data to a different place with five different products, how does it move? Because, you know, all these five products must move together to some of the place. That's not how it's gonna operate for you. So by having these five different functions converge into one platform is that when the data moves between the other place, the functions move with it giving the same exist same exact, consistent view for your data. That's kind of what we were built. And on top of all the stuff is something we have this global data management applications to control the all the data you have your enterprise. >> So how are customers responding to this new architecture of autumn matrix converge services and a platform for building data applications? >> Yeah, so our customers consistently Teyla's one simple thing is that it's the most easiest platform there ever used in their entire enterprise life. So that's what we do aimed for simplicity for the customer experience. Autonomous data services give you exactly that experience. So as an example, last quarter we had about forty proof of concept sort in the field out of them, about thirty of adopted already, and we're waiting for the ten of the results to come out this quarter. So generally we found that a proof of concept don't come back because once you touch it, experience simplicity offered and how how do you get all this service is simple, then people don't tend to descend it back. They like to keep it and could have operated that way. >> So you mentioned earlier, and I kind of summarizing notion of applications, Data services, applications tell us a little bit about those and how they really toward a matrix. >> Right? So once you have data in multiple places, people have not up not a cloud. And we're going to also being all these different clouds and report that uniform experience you need this date. You need this global data management applications to extract value out off your data. And that's kind of the reason why we built some global data management applications. I SAS products, I think, install nothing to manage them. Then they sit outside and then they help you manage globally. All the data you have. >> So as a result, the I and O people, the destruction operations administrators, I can think of terms of automata whose platform the rest of the business could look at in terms of services and applications that through using and support, >> that's exactly right. So you get the single dashboard to manage all the data. You have an enterprise >> now I know you're introducing some of these applications today. Can you give us a little peek into? Yeah. >> Firstly, our automatics platform is a soft is available on prime as a software defined converge infrastructure, and you can get that we call it D V X. And then we also offer in the cloud our services. It's called Cloud Devi Ex. You could get these and we're also about kind off announcing the release ofthe control shift. It's over for one of our first date. Imagine applications, which kind of helps you manage data in a two different locations. >> So go over more specific and detail in the control shift. Specifically is which of those five data services you talk about is control shift most clearly associating with >> right. So if you go toe again back to this question about like five different services, if you have to think about B r o D. R. Is a necessity for every business, it's official protection. You need it. But the challenge is that you know that three four challenges you gently round into the most common people talk about is that one is that you have a plan. You'LL have a proper plan. It's challenging to plan something, and then you think about the fire drill. We have to run when there's a problem. And then last leaving actually pushed the button. Tofail over doesn't really work for you. Like how fast is it going to come up? So those three problems we can have one to solve really like really solidly So we call our service is a dear services fail proof tr that's actually takes a little courage to say fail proof. So control shift is our service, which actually does this. The orchestration does mobility across the two different places from could be on prime time on Prem on prompted the cloud. And because we have this into end data services ourselves, the it's easy to then to compliance checks all the time so we could do compliance checks every few minutes. So what that gives you that? Is that the confidence that that your dear plan's going to work for you when you need it? And then secondly, when you push the button because you also prime restoration back up, it's then easy to bring upon your services at once like that, and the last one is that because we are able to then work across the clouds and pride, the seamless experience. So when you move the data to the cloud, have some backups there. When you push a button to fail over, we'LL bring up your services in via MacLeod so that the idea is that it look exactly the same no matter where you are in the D. R or North India and then, you know, you watch the video, watch the demos. I think they look and see that you can tell the difference. >> Well, that's great. So give us a little bit of visibility into how day Truman intends to extend these capabilities, which give us a little visibility in the road map. Next. >> So we are already on Amazon with the cloud. The next time you're gonna be delivering his azure, that's the next step. But But if I step back a little bit and how do we think about our ourselves? Like if you look at his example Google, Google, you know, fairies, all the data and Internet data and prizes that instant search for that instant like an access to all the data you know, at your finger finger tips. So we wanted something similar for enterprise data. How do we Federated? How do we aggregate data and the property? The customer, the instant management they can get from all the data. They have already extract value from the data. So those things are set off application We're building towards organic scum. Examples are we're building, like, deep search. How do you find the things you want to find? You know, I've been a very nice into to weigh. And how do you do Compliance? GPR. And also, how do you think about you know, some dependent addicts on the data? And so we also extend our control shift not to just manage the data on all platforms. Brawls hardly manage data across different platforms. So those kind of things they're thinking about as a future >> excellent stuff is already CTO daydream. Thanks very much for talking to us about auto matrix control shift and the direction that you're taking with this very, very extreme new vision about data on business come more easily be bought together. So, you know, I'll tell you what. Let's take a look at a demo >> in today's enterprise data centers. You want a simple way to deal with your data, whether in the private or public cloud, and ensure that dealing with disaster recovery is easy to set up. Always complied and in sync with the sites they address and ready to run as the situations require built on consistent backups, allowing you to leverage any current or previous recovery point in time with near zero rto as the data does not have to be moved in order to use it. Automated orchestration lets you easily test or execute recovery plans you have constructed with greater confidence, all while monitoring actions and progress from essential resource. This, along with maintaining comprehensive run books of these actions, automatically from the orchestration framework. Managing your Systems Day Tree in autumn matrix provides this solution. Run on local host flash and get the benefits of better performance and lower. Leighton sees back up and protect your data on the same converged platform without extracting it to another system while securing the data in your enterprise with end and encryption automating salas desired for your business needs with policy driven methods. The capture the what, when and where aspects of protecting your data, keeping copies locally or at other sites efficiently Move the data from one location to another weather in your private or public cloud. This is the power of the software defined converged infrastructure with cloudy are from day tree, um, that we call Oughta Matrix. >> Hi. And welcome back to another cube conversation once again on Peter Births. And one of the biggest challenges that every user faces is How did they get Mohr out of their technology suppliers, especially during periods of significant transformation? Soto have that conversation. We've got Brian Bond, who's the director of infrastructure? The meter A seaman's business. Brian, welcome to the Cube. >> Thanks for having me. >> So tell us a little about the meteor and what you do there. >> So E Meter is a developer and supplier of smart grid infrastructure software for enterprise level clients. Utilities, water, power, energy and, ah, my team was charged with managing infrastructure for that entire business units. Everything from Deb Test Q and sales. >> Well, the you know, the intelligent infrastructure as it pertains to electronica rid. That's not a small set of applications of small city use cases. What kinds of pressure is that putting on your infrastructure >> A lot of it is the typical pressures that you would see with do more with less doom or faster. But a lot of it is wrapped around our customers and our our other end users in needing more storage, needing Mohr at performance and needing things delivered faster on a daily basis. Things change, and keeping up with the Joneses gets harder and harder to do as time moves on. >> So as you think about day trims Auto Matrix. How is it creating value for you today? Give us kind of, Ah, peek into what it's doing to alleviate some of these scaling and older and researcher pressures, >> So the first thing it does is it does allow us to do a lot more with less. We get two times the performance five times the capacity, and we spend zero time managing our storage infrastructure. And when I say zero time I mean zero time, we do not manage storage anymore. With the data in product, we can deploy thanks faster. We can recover things faster are Rto and R R P. O matrix is down two seconds instead of minutes or hours, and those types of things really allow us to provide, Ah much better level of service to our customers. >> And it's especially critical. Infrastructure like electronic grid is good to hear. That the Rto Harpo is getting is close to zero as possible. But that's the baseline today. Look out and is you and vision where the needs of these technologies are going for improving protection, consolidating converging gated services and overall, providing a better experience from a business uses data. How do you anticipate that you're goingto evolve your use of autumn matrix and related data from technologies? >> Well, we fully intend to to expand our use of the existing piece that we have. But then this new autumn matrix piece is going to help us, not witches deployments. But it's also going to help us with compliance testing, data recovery, disaster recovery, um, and also being able to deploy into any type of cloud or any type of location without having to change what we do in the back in being able to use one tool across the entire set of the infrastructure they were using. >> So what about the tool set? You're using the whole thing consistently, but what about the tool set when in easiest for you within your shop, >> installing the infrastructure pieces themselves in its entirety. We're very, very easy. So putting that into what we had already and where we were headed was very, very simple. We were able to do that on the fly in production and not have to do a whole lot of changes with the environments that were doing at the time. The the operational pieces within the D. V X, which is this the storage part of the platform were seamless as far as V Center and other tools that we're using went and allowed us to just extend what we were doing already and be able to just apply that as we went forward. And we immediately found that again, we just didn't manage storage anymore. And that wasn't something we were intending and that made our r I just go through the roof. >> So it sounds like time to value for the platform was reserved for quick and also it fit into your overall operational practices. So you have to do a whole bunch of unnatural acts to get >> right. We did not have to change a lot of policies. We didn't have to change a lot of procedures, a lot of sounds. We just shortened. We took a few steps out on a lot of cases. >> So how is it changing being able to do things like that, changing your conversation with your communities that you're serving a CZ? They asked for more stores where they ask for more capabilities. >> First off, it's making me say no, a lot less, and that makes them very, very happy. The answer usually is less. And then the answer to the question of how long will it take changes from? Oh, we can get that done in a couple of days or, oh, we can get that done in a couple hours to I did that while I was sitting here in the meeting with you, and it's it's been handled and you're off to the races. >> So it sounds like you're police in a pretty big bed and a true, uh, what's it like? Working with them is a company. >> It's been a great experience from from the start, in the initial piece of talking to them and going through the POC process. They were very helpful, very knowledgeable SCS on DH, and since then They've been very, very helpful in allowing us to tell them what our needs are, rather than them telling us what our needs are and going through and working through the new processes and the and the new procedures within our environments. They've been very instrumental and performance testing and deployment testing with things, uh, that a lot of other storage providers didn't have any interest in talking with us about so they've been very, very helpful with that and very, very knowledgeable people that air there are actually really smart, which is not surprising. But the fact that they can relay that into solutions to what my actual problems are and give me something that I can push forward on to my business and have ah, positive impact from Day one has been absolutely, without question, one of the better things. >> Well, it's always one of the big, biggest challenge when working with a company that just getting going is how do you get the smarts of that organization into the business outcomes that really succeeded? Sounds like it's working well. Absolutely. All right. Brian Bond, director Vital infrastructure demeanor, Seaman's business Thanks again for being on the Cube >> has been great >> on. Once again, this has been a cube conversation, and now what we'd like to do is don't forget this is your opportunity to participate in the crowd. Chat immediately after this video ends and let's hear your thoughts. What's important in your world is you think about new classes of data platforms, new rules of data, new approaches to taking great advantage of the data assets that air differentiating your business. Have those conversations make those comments? Asked those questions. We're here to help. Once again, Peter Bourjos, Let's go out yet.

>> From the Aria resort in Las Vegas. It's the Cube! (upbeat music) Covering AWS Marketplace. Brought to you by Amazon Web Services. Hey, welcome back everybody. Jeff Frick here with the Cube. We're at AWS Reinvent 2018. I don't know how many people are here, 60 000, 70 000, your guess is as good as mine. I'm sure we'll get an official number shortly. We're kicking things of here. Three days of coverage. Monday, Tuesday, Wednesday, Thursday, that's four days. We're at the AWS Marketplace and Service Catalog Experience here at the Aria. We're excited to be kicking stuff of with Rajeev Dutt. He is making AI that makes AI. We're going to get into it. He is the CEO president and co-founder of DimensionalMechanics. Rajeev, great to see you. >> It's great to meet ya. >> How many Reinvents have you been to? >> This would actually be my second. >> You're second? >> My second. Yeah, it's like- it's- I always feel really energized after coming here. It's like- last year was like heavy AI centered. >> Right, right. >> It was just really all these sessions in AI was really exciting. >> Let's get in to it for the folks that aren't familiar with DimensionalMechanics. What are you guys all about? >> So DimensionalMechanics is about lowering the bar for entry like to most people. So that's kind of our first focus. Our second focus is to make sure that deployment strategies allow you to deploy across any end device. So it's basically intended to be a complete end-to-end capability. >> Around AI? >> Around AI. >> The Artificial Intelligence. >> The Artificial Intelligence. >> Yeah, yeah, yeah. >> Most important part. >> Okay. >> Yeah, so, it's about reducing the bar for entry for Artificial Intelligence so that anybody without even a machine learning background can build very sophisticated models on our platform. In sometimes as little as 14 lines of code. It's just incredibly easy. We've had high school students use us, we've had university professors, who have nothing to do with AI, use us without any problems. And, really the way we do that is that we have an AI that we call the Oracle. We are all Matrix fans. (Jeff laughs) And so what this- the Oracle does is it has a vast knowledge base, has a lot of additional machine learning components and things like that. That essentially allow it adapt and learn based on the kind of problem you're trying to solve. So, every time it solves the same problem, it gets better and better at what it's doing. >> So, so, um... Is it, is it libraries, is it pre-configured, are there specific type of application that it works better on? What's kind of your go to market? >> So basically, think about AI studio as a full server application. So it, what you essentially do- we created our own language called the NeoPulse Modeling language. And the NeoPulse Modeling language, think about it as sort of the SQL for Artificial Intelligence. It does a lot of very complicated things in just a couple of lines. So essentially what you do is you compile it on the machine so when you write the NML code, the NeoPulse Modeling language code. You compile it on the machine, it looks at your data which is sitting in a bucket. It starts training the model. Once the model is ready, you can export the model as a PIM object, so Portable Inference Model object which is one of our creations. And that allows you then to deploy it on to any end target as long as it's running on runtime. And on runtime can be basically sitting in the cloud or on a device. Sometimes we're also looking at right down to FPGA kind of device levels as well. So, extremely low power devices as well as cloud computing, but gives you that flexibility, but it also, which is really important, it makes AI accessible. So anybody without like any background in it- My wife is a radiologist and she's actually looking at using it for her own internal usage... >> And how much do you have to learn? You have to learn the NeoPulse language, right? >> The NML language is really easy to learn. So we had a high school student who spent about a week learning it and so a week later she was ready to start coding and she has built her first models using that. And the way it does that is that you actually, we have a keyword auto inside NML which is context aware, and so when the compiler sees auto it goes out to the Oracle and says hey, I've seen- this person needs help building an architecture or figuring out what function to use or what hyperparameters to use and so on and so on. And the Oracle will come back and say hey, use this architecture, use these hyperparameters, use these settings or functions or these optimizations in your model and... >> So is that doin' that when I'm setting up the model in the first place to give me directions or is looking at the model once I've spun it a couple times and saying wait, this looks like one of these, maybe you should do some of this. >> So what it will look at is your data. So it will actually look in to your data, the type of data, how much data you have, the kind of problem you're trying to solve, how many, for example, if it's a classification problem, how many classes you have, and all of that basically determines the kind of model that it will use. You can also specify the level of complexity that you're interested in, like, are you interested in a very simple model, a complex model, is over fitting a risk at all It will determine all these things behind the scenes >> Right, right. >> based on the kind of problem that you're trying to solve. And the first time it solves it, it will give you a pretty good answer. It's usually very good, but then the second time you solve it or a third time you solve it, it gets better and better and better, because it's able to learn from its mistakes. So, and eventually it gets really good at its job so. >> But it's still, but it's a still a model that I built for that application. You're not drawing kind of pre-configured models down from the Oracle. >> No no, you're basically training it from scratch. >> Right. >> It's entirely intended for custom models. So companies that are- have highly customized data, like radiology or for example, looking at wind stress patterns like in polarized light and stuff like that. So things that are not normally covered by the standard image recognition and so, using things like transfer learning or fine tuning doesn't help in this particular case because if you've trained a model in dogs and cats then like, training it to recognize stress patterns, is just not- >> It's not going to work. >> It's not going to work. >> So you got to prepare for your interviews, looking through your website. You list a really dramatic example of where using your guys technology was like, I don't know, a tenth of the price >> Yes, yeah. >> And I think one month versus six. >> Yeah. >> I wonder if you could share some couple examples that, you know, people are putting this to use. >> Okay, so, we have actually a few. So one of them is with a company. They're focused on kind of a resume matching, so we built them- they were initially quoted by another company at around 450 000 and they were warned that they would not be able to exceed 40% accuracy given the data that they had. We managed to get to about 83/84% accuracy for about under 10 000. So that was like a huge huge reduction. Then the second one was just recently, another company had been spending quite a bit of time and resources on building out a technology to measure heart rate. We were able to look at that and produce, instead of spending like their 20 000 a month or so, we could bring it down to 4000 in total. So these are the kind of sort dramatic reductions in cost that our platform can offer. Stanford University, another great example. These are physicians that we're working with. None of them have any engineering background like, for them, Linux is in itself- That was the hardest thing for them to do was to get used to Linux and so once they start building on our platform it was like they actually built a model that was good enough that they were able to publish at the RSNA, which is like one of the biggest radiology conferences in the world. In this case it was for Pet CT, which is a three-dimensional model because there's a three-dimensional image if you will >> Okay. >> of the human body and so was able to determine whether somebody had a tumor or not and I think they mananged to get, with a very limited data set, about 74/75% accuracy and this was actually at Stanford, so it's a pretty, pretty big name. >> Right. So, Rajeev we're here at AWS Marketplace Experience. You're still a relatively small company. I think you said you had a good size C round, gettin' ready to go out and get a decent A round. >> Right. >> What does it mean to work with a company like Amazon? I mean, as a small company, just to get, just to get an approved vendor set up at Stanford, probably not an easy thing, right. There's all kinds of legal Ts and Cs. >> Exactly. >> As a startup their always worried about whether you're going to be around tomorrow. >> Exactly. >> So your part doin' AWS, so how's that been workin' with AWS and the Marketplace . >> Well firstly, it's definitely given us the Amazon backing in a way, so when people see you're on AWS, they see that connected to you, that automatically gives them a little bit more confidence. >> They vetted you so you must be good. >> Exactly, exactly. And the second is that it gives access to a market that we otherwise wouldn't have had like, if I'm thinking about like producing software that you have to download on our website, that's a very very limited market. You have to attract people to your website and so on and so on. Now it's like we're on the Amazon- there's a machine learning hub on AWS. We're on that, so which means that when people search for machine learning, our name does come up. >> Right, right >> It means it's very easy to launch. You don't have to worry about setting up a machine, worrying about how to configure it. Everything is done automatically, makes life really easy. >> Right. >> On top of that, the AWS team has been- the Marketplace team has been really extremely helpful connecting us with end customers. So very often they will refer people to us. In fact, one of our largest customers came through an AWS referral, so for us it's been nothing but a win-win. >> Right. What about the potential downside? Not to rain on the parade but the old joke used to be if you're a start-up makin' widgets, you know, you just got your first order with Walmart the good news. Bad news is you just got your first order with Walmart. That's opening up a huge global distribution opportunity, I mean in theory, you know, say you got a 1000 customers tomorrow, that might be a little bit of a challenge. >> Yeah, so we actually are starting to hit that. So, we- so our version two was really our go to market version and- which came out earlier this year, and so we've been trying to like wrap up the sales on that side and literally in the last three months. It's like I have not been home for six weeks now because I've been in the far East and traveling and, it' like- because of this heavy customer interaction at this point. So we have a very good story to tell the investors now, like, this has also helped with the investments rounds that we're actually looking at. So we have a very good story to tell the investors that you know, our like invoice list and so on is huge at this point so we need help now. It's actually more about raising, like building up a team now than it is about can we get orders. >> Right. It's really delivering more than sales. >> Exactly. >> I see what you're saying. And so we need to build up a delivery team, we need to- I mean, it's fairly intuitive, but at the same time it's a new technology which means, as with any platform, you're building up a team of evangelist, support individuals and so on. And there's going to be a marketing component as well, so we haven't really driven marketing that much. AWS has been great in kind of doing some of that for us, but we need to of course very actively go out and market. We haven't had that capacity yet. >> All right. We look forward to watching the story unfold and thanks for spending a few minutes with us. >> My pleasure. Thanks, thank you very much. All right, he's Rajeev Dutt, I'm Jeff. Thank you for watching the Cube. We're at the AWS Marketplace and Service Catalog Experience at the Aria, come on by. Thanks for watching. (upbeat music)

>> Announcer: From the SiliconANGLE Media office in Boston, Massachusetts, it's theCube! (bright music) Now here's your host, Dave Vellante. >> Hi everybody, welcome to this special presentation in the Marlborough offices of theCube. My name is Dave Vellante, and I'm here with a friend, a colleague, a mentor of mine, David Moschella who is an author and a Fellow at Leading Edge Forum. Dave, thanks for coming in. It's great to see you. >> Hey, great to see you again. So we're going to talk about your new book, Seeing Digital: A Visual Guide to Industries, Organizations, and Careers of the 2020s. I got it here on my laptop. Got it off of Amazon, so check it out. We're going to be unpacking what's in there today. This is your third book I believe, right? Waves of Power and... >> David: Customer-Driven IT. >> Customer-Driven IT which was under the '03 timeframe coming out of the dot-com, and to me this is your most significant work, so congratulations on that. >> Well, thank you. >> Dave: I know how much work goes into it. >> You bet. >> So what was the motivation for writing this book? >> Well it's a funny thing when books are a lot of work, and during those times you wind up asking yourself why am I (laughing) doing this because they put in so much time. But for the last seven or eight years our group, the Leading Edge Forum, we've been doing a lot of work mostly for large organizations and our clients told us that the work we've been doing in consumerization, in Cloud, in disruption, in machine intelligence was really relevant to not just them but to their wider audiences of their partners, their customers, their employees. And so people are asking can we get this to a wider audience, and really that is what the book is trying to do. >> Yeah, you guys have done some great work. I know when I can get my hands on it I consume it. For those of you who don't know, Dave originally came up with the theory of disintegration to kind of explain the shift from centralized mainframe era to the sort of open distributed competition along different lines which really defined the Wintel era. So that was kind of your work really explaining industry shifts in a way that helped people and executives really understand that. And then the nice thing about this book is you're kind of open-sourcing a decade's worth of research that yourself and your colleagues have done. So talk about the central premise of the book. We're entering a new era. We're sort of exiting the Cloud, Web 2.0 era. We're still trying to figure out what to call this. But what's the central premise of the book? >> Yeah, the central premise is that the technologies of the 2020s will indeed define a new era, and the IT era industry just evolves. We had the mainframe era, the mini era, the PC and the Internet era, the mobility era, and now we're going in this era of intelligence and automation and blockchains and speech and things that are just a entire new layer of intelligence, and that that layer to us is actually more the powerful than any of the previous layers we've seen. If you think back, the first Web was founded around technologies like search and email and surfing the Web, quite simple technologies and created tremendous companies. And then the more recently we have sort of the social era for Facebook and Salesforce. And all these companies, they sort of took advantage of the Cloud. But again, the technologies are relatively simple there. Now we're really looking at a whole wave of just fundamentally powerful technology and so trying to anticipate what that's going to mean. >> So going from sort of private networks to sort of public networks to a Cloud of remote services to now this set of interrelated digital services that are highly accessible and essentially ubiquitous is what you put forth in the book, right? >> Yeah, and we put a lot of emphasis on words. Why do words change? We had an Internet that connected computers and a Web that sort of connected pages and documents and URLs. And then we started talking about Cloud of stuff out there somewhere in cyberspace. But when we look at the world that's coming and we use those words, pervasive, embedded, aware, autonomous, these aren't words that are really associated with a Cloud. And Cloud is just a metaphor, that word, and so we're quite sure that at some point a different word will emerge because we've always had a different word for every era of change and we're going into one of those eras now. >> So a lot of people have questions about we go to these conferences and everybody talks about digital disruption and digital transformation, and it's kind of frankly lightweight a lot of times. It doesn't have a lot of substance to it. But you point out in the book that CEOs are asking the question, "How do I get digital right?" They understand that something's happening, something's changing. They don't want to get disrupted, but what are some of the questions that you get from some of your clients? >> Yeah, that first question, are we getting digital right sort of leads to almost everything. Companies look at the way that a Netflix or Amazon operates, and then they look at themselves and they see the vast difference there. And they ask themselves, "How can we be more like them? "How can we be that vast, that innovative, that efficient, "that level of simple intuitive customer service?" And one of the ways we try to define it for our clients is how do they become a digital first organization where their digital systems are their face to the marketplace? And most CEOs know that their own firm doesn't operate that way. And probably the most obvious way of seeing that is so many companies now feeling the need to appoint a Chief Digital Officer because they need to give that task to someone, and CDOs are no panacea but they speak to this need that so many companies feel now of really getting it right and having a leadership team in place that they have confidence in. And it's very hard work, and a lot of our clients, they still struggle with it. >> One of the other questions you ask in the book that is very relevant to our audience given that we have a big presence in Silicon Valley is can Silicon Valley pull off a dual disruption agenda? What do you mean by that? >> Yeah, if you look at the Valley historically you could see them essentially as arms merchants. They were selling their products and services to whoever wanted to buy them, and companies would use them as they saw fit. But today in addition to doing that they are also what we say is they're an invading army, and they are increasingly competing with the very customers they've traditionally supplied, and of course Amazon being perhaps the best example of that. So many companies dependent on AWS as a platform, but there's Amazon trying to go after them in health care or retail or grocery stores or whatever business they're in. Yeah, content, every business under the sun. And so they're wearing these two dual disruptions hats. The technologies of our time are very disruptive, machine intelligence, blockchains, virtual reality, all these things have disruptive technology. But that second disruptive agenda of how do you change insurance, how do you change health care, how do change the car industry, that's what we mean, those two different types of disruptions. And they're pursuing both at the same time. >> And because it's digital and it's data, that possibility now exists that a company, a technology company can traverse industries which historically haven't been able to be penetrated, right? >> Yeah, absolutely, in our view every industry is going to be transformed by data one way or another. Whether it is disrupted or not is a second question, but the industry'll be very different when all of these technologies come into play, and the tech companies feel like they have the expertise and the vision of it. But they also have the money, and they're going to bet heavily to pursue these areas to continue their growth agenda. >> So one of the other questions of course that IT people ask is what does it mean for my job, and maybe we can, if we have time, we can talk about that. But you answer many of these questions with a conceptual framework that you call the Matrix which is a very powerful, you said words matter, a very powerful concept. Explain the Matrix. >> Okay, yeah. If we start and go back they have this idea that every generation of technology has its own words, Internet, Web, Cloud, and now we're going to a new era, so there will be a new word. And so we use the word Matrix as our view of that, and we chose it for two reasons. Obviously there's the movie which had its machine intelligence and virtual worlds and all of that. But the real reason we chose it is this concept that a matrix as in matrix mathematics is a structure that has rows and columns. And rows and columns is sort of the fundamental dynamic of what's going on in the tech sector today, that traditionally every industry had its own sort of vertical stack of capabilities that it did and it was sort of top to bottom silo. But today those horizontal platforms, the PayPals, the AWSs, the Facebooks, they run this, Salesforce, all these horizontal services that cut across those firms. And so increasingly every industry is leveraging a common digital infrastructure, and that tension between the traditional vertical stacks and these enormously powerful horizontal technology firms is really the structural dynamic that's in play right now. >> And at the top of that Matrix you have this sort of intelligence and automation layer which is this new layer. You don't like the term artificial intelligence. You make the point in the book there's nothing really artificial about it. You use machine intelligence. But that's that top layer that you see powering the next decade. >> Absolutely, if you look at the vision that everybody tends to have, autonomous cars, personalized health care, blockchain-based accounting, digital cash, virtual education, brain implants for the media, every one of those is essentially dependent on a layer of intelligence, automation, and data that is being built right now. And so just as previous layers of technology, the Web enabled a Google or an Amazon, the Cloud enabled AWS or Salesforce, this new layer enables companies to pursue that next layer of capabilities out there to build that sort of intelligent societal infrastructure of the 2020s which will be vastly different than where we are today. >> Will the adoption of the Matrix, in your opinion, occur faster because essentially it's built on the Internet and we have the Internet, i.e. faster than say the Internet or maybe some other major innovations, or is it going to take time for a lot of reasons? >> I think the speed is actually a really interesting question because the technology of the 2020s are extremely powerful, but most of them are not going to be immediate hits. And if you look back, say, to search, when search came out it was very powerful and you could scale it massively quickly. You look at machine learning, you look at blockchains, you look at virtual realities, you look at algorithms, speech and these areas, they're tremendously powerful. But there's no scenario where those things happen overnight. And so we do not see an accelerating pace of change. In fact it might be people often overestimate the speed of change in our business and consistently do that. But what we see is a sort of fundamental transformation over time, and that's why we put a lot of emphasis on the 2020s because we do not see two years from now this stuff all being in place. >> And you have some good examples in the book going back to the early days of even telephony. So it's worth checking that out. I want to talk about, bring it back to data, Amazon, Google, Apple, Microsoft, and Facebook, top five companies, public companies in terms of market cap. Actually it's not true after the Facebook fake news thing. I mean Berkshire Hathaway is slightly past Facebook. >> It'll be back (laughs). But I agree, it'll be back, but the key point there is these companies are different, they've got data at their core. When you compare that to other companies even financial services industry companies that are really data companies but the data's very bespoken, it's in silos. Can those companies, those incumbent companies, can they close that gap? Maybe you could talk about that a little bit. >> Yeah, we do a lot of work in the area of machine intelligence, artificial, whatever you want to call it. And one of the things you see immediately is this ridiculously large gap between what these leading companies do versus most traditional firms because of the talent, the data, the business model, all the things they have. So you have this widening gap there. And so the big question is is that going to widen or is it going to continue, will it narrow? And I think that the scenario for narrowing it I think is a fairly good one. And the message we say to a lot of our clients is that you will wind up buying a lot more machine intelligence than you will build because these companies will bring it to you. Machine intelligence will be in AWS. It'll be in Azure. It'll be in Salesforce. It'll be in your devices. It'll be in your user interfaces. It'll be in the speech systems. So the supply-side innovations that are happening in the giants will be sold to the incumbents, and therefore there will be a natural improvement in today's situation where a lot of incumbents are sort of basically trying to build their own stuff internally, and they're having some successes and some not. But that's a harder challenge. But the supply side will bring intelligence to the market in a quite powerful way and fairly soon. >> Won't those incumbents, though, have to sort of reorganize in a way around those new innovations given that they've got processes and procedures that are so fossilized with their existing businesses? >> Absolutely, and the word digital transformation is thrown around everywhere. But if it means anything it is having an organization that is aligned with the way technology works. And a good example of that is when you use Netflix today there's no separate sales experience, market experience, customer service, it's just one system and you have one team that builds those systems. In a typical corporation of course you have the sales organization and the marketing organization and the IT organization and the customer service organization. And those silos is not the way to build these systems. So the message we send to our clients if you really want to transform yourself you have to have more of this team approach that is more like the way the tech players do it. And that these traditional boundaries essentially go away when you go in the digital world where the customer experience is all those things at the same time. >> So if I'm hearing you correctly it's sort of a natural progression of how they're going to be doing business and the services that they're going to be procuring, but there's probably other approaches. Maybe it's force, but you're seeing maybe M&A or you're seeing joint ventures. Do you see those things as accelerating or precipitating the transformation or do you think it's futile and it really has to be led from the top and at the core? >> It's one of the toughest issues out there. And the reason people talk about transformation is because they see the need. But the difficulty is enormous. Most companies would say this is a three- or four-year process to make significant change, and this in a marketplace that changes every few months. So incumbent firms, they see where they want to go and it's very hard, and this is why this whole thing of getting digital right is so important, that people need to commit to significant change programs, and we're seeing it. And my parent company, DXC, we do a lot of this with clients and they want to embark on this program and they need people who can help them do it. And so leading a transformation agenda in most firms is really what digital leadership is these days and who's capable of doing that which requires tremendous skills in soft skills and hard skills to do right. >> Let's talk about industries and industry disruption. When you looked at the early disrupted industries whether it was publishing, advertising, music, one maybe had the tendency to think it was a bits versus atoms thing, but you point out in the book it's really not the case because you look at taxis, you look at hotels. Those are physical businesses and they've been disrupted quite substantially. Maybe you could give us some thoughts and insight there, particularly with regard to things like health care, financial services which haven't been disrupted. >> And there's a huge part of the work that I've been doing for years. And as you say, if you look at the industries that actually have been disrupted, they're all relatively low-security, low-risk businesses, music, advertising, taxis, retail. All these businesses have had tremendous changes. But the ones that haven't are all the ones where the stakes are higher, banking, insurance, health care, aerospace, defense. They've been hardly disrupted at all. And so you have this split between the low-risk industries that have changed and the high-risk ones that haven't. But what's interesting to me about that is that these technologies of the 2020s are aimed almost directly at those high-risk industries. So machine intelligence is aimed directly at health care and autonomous systems is aimed directly at defense and blockchains are aimed directly at banking and insurance. And so the technologies of the past if you look at Internet and the Web and the Cloud eras, they were not aimed at these industries. But today's are, so you now have at least a highly plausible scenario where those industries might change too. >> When to talk to companies in those industries that haven't been disrupted do you get a sense of complacency that ah well, we haven't been disrupted, We're going to wait and see, or do you see a sense of urgency? >> No, complacency is baked in for years of people saying, "We've heard all this before. "We're doing just fine. "Maybe it's their industry but not ours." >> Dave: You don't buy it. >> Or the main one is, "I'll be (laughing) retired "before any of this stuff matters for the senior execs." And the thing about all four of those is they're probably true. They have heard all this before because there was a lot of excessive hype. Many of them are doing just fine. Well the one about the other industries is a wrong one, but and many of them will be retired before the things really bite if executive's in their late in their career. So the inertia and the complacency is an enormous issue in most traditional companies. >> So let's do a little lightning round if we can. Oh, actually I just want to make a point. In the book you lay out disruption scenarios for each industry which is really worthwhile. We don't have time to go through that here, but let's do a little lightning round here, some of the questions that you ask that I'd love to get your opinion on of which of course there are no right answers but we can maybe frame it. Let's start with retail. Do you think large retail stores are going to disappear? >> Well the first I say is that disruption is never total. There are still bookstores, there are still newspapers, there are still vinyl records. >> Dave: Mainframes, saving IBM. >> (laughing) Indeed, indeed, but real disruption means that the center of gravity is just totally moved on. And when you look at retail from that point of view, absolutely. And will large ones totally disappear? No, but Wal-Mart is teetering. If you go into a large, Best Buy, a company that strong hero locally, you go into there, there's hardly anybody in there. And so those stores are in tremendous trouble. The grocery stores, the clothing stores, they'll have probably a better future, but by and large they will shrink, and the nature of malls will change quite substantially going forward. People are going to have to find other uses for those spaces, and that's actually going on right now. >> It's funny, it is, and certainly some of the more remote malls you find that they're waning. But then some of the higher-end malls, they seem, you can't find a parking space. What's your sense of that, that that's still inevitable or it's because it's more clothing or maybe jewelry? >> And there's some parts of America that have a lot of money, and therefore they fill up malls. But I think if you look at what's going on in the malls, though, they're becoming more like indoor cities full of restaurants and health clubs and movie theaters and sometimes even college courses and health care centers, daycare centers, air conditioning. Think of them as an indoor environment where you might have the traditional anchor stores but they're less necessary over time. Quite a bit less necessary. >> You mentioned college courses. Education's something we haven't talked about which is again ripe for disruption. Machines, will they make better diagnoses than doctors? >> Yeah, you see this already in image processing, anything that has to do with an image, X-rays and mammograms, cancers, anything, tissues. The machine learning progress there has been tremendous and to the point where schools now should be seriously thinking about how many radiologists do they really want to train because those people are not going to be needed as much. However they're still part of the system. They approve things, but the work itself is increasingly done by machines. And it means increasingly that it's not just done by machine, it's done by one machine somewhere else rather than every hospital setting up its own operations to do this stuff. And health care costs are crazy high in every country in the world, especially here in America. But if you're ever going to crack those costs you have to get some sort of scale, and these machine learning-based systems are the way to do it. And so it is to me not just a question of should this happen, it's that this is so what needs to happen. It's really the only sort of economic path that might work. >> You make the point that health care in particular is really ripe for disruption of all industries. The next one's really interesting to me. You talked about blockchain being sort of aimed at banking and financial services and as an industry that has not really yet been disrupted. But do you think banks will lose control of the payment systems? >> Banks have been incredibly good at keeping control through cash and paper checks and credit cards and ATM machines. They've been really good about that and perhaps they will ride this one too. But you can see countries are clearly going to, they're getting rid of cash. They're going to digital currencies. There's the need to be able to send money around as simply as we send emails around, and the banking industry is not really supporting (laughing) those changes right now. So they are at risk, but they are very good at co-opting stuff, and I wouldn't count them out. >> And the government really wants to get rid of paper money. You've made that point, and the government and the financial services-- >> Work together, and yeah. >> They always work together, they have a lot to lose. >> Yeah, and way back when Satoshi Nakamoto, whoever he or she is or it, they, whatever it is, said that bitcoin would either be very, very big or it would vanish altogether. And I think that statement is still true, and we're still in that middle world. But if bitcoin vanishes, something doing a similar thing will emerge because the concepts and the capabilities there are really what people want. >> Yeah, the killer app for blockchain is for right now it's money. (laughing) >> Yeah, it's speculation, (laughing) I mean it's, (laughing) and no one uses it to buy anything. (Dave laughing) That was the original bitcoin vision of using it to go buy pizzas and coffees. It's become gold, it's digital gold. I mean it's all it is. >> The value store... >> It's digital gold that is very good in the dark Web. >> And if anybody does transact in bitcoin they immediately convert it to fiat currency. (laughing) >> Perhaps someday we'll learn that the Russians actually built bitcoin (Dave laughing) and it's Putin's in control. (David and Dave laughing) Stranger things have happened. >> It's possible. >> Hey, why keep it anonymous? >> They are the masters of the dark Web. (Dave laughing) >> Could be Russians, could be a woman. >> David: Right, right, nobody has any idea. >> Robotic process automation is really interesting with software robots, robots. Do you see that reversing sort of offshoring, offshore manufacturing and other services? >> Not really, I think in general people looked at robotics, they looked at 3D printing and said, "Maybe we can bring all this stuff back home." But the reality is that China uses robots and 3D printing too and they're really good at it. If anything's going to bring manufacturing back home it's much more political pressures, trade strategies, and all the stuff you see going on right now because we do have crazy imbalances in the world that probably will have to change. And as Ben Stein the economist once said, "Well if something can't go on forever, it won't." And I think there will be some reversals, but I think they'll be less about technology than they will be about political pressures and trade agreements and those sort of changes. >> Because the technology's widely accessible. So how far do you think we can take machine intelligence and how far should we take machine intelligence? >> Well I make a distinction right now that I think machine intelligence for particular purposes is tremendous if you want to recognize faces or eventually talk to something or have it read something or recognize an activity or read images and do all the things it's doing, it's very good. When they talk about a more general-wise machine intelligence it's actually really poor. But to me that's not that important. And one way we look at machine intelligence, it's almost like the app industry. There'll be an app for that, there'll be a machine learning algorithm for almost every little thing that we do that involves data. And those areas will thrive mightily. And then sort of the bottom line we try to at that as who's got the best data? Facebook is good at facial recognitions because it's got the faces, and Google's good at language translation because it has the books and language pairs better than anybody else. And so if you follow the data and where there's good data machine learning will thrive. And where there isn't it won't. >> The book is called Seeing Digital: A Visual Guide to the Industries, Organizations, and Careers of the 2020s, and part of that visual guide is every single page actually has a graphic. So really a new concept that you've... >> Yeah, and thanks for bringing that in. And the reason the book is called Seeing Digital is that the book itself is a visual book, that every page has a graphic, an image, a picture, and explains itself below. And just in our own work with our own clients people tell us it's just a more impactful way of reading. So it's a different format. It's great in the ebook format because you can use colors, you can do lots of things that the printed world doesn't do so well. And so we tried to take advantage of modern technologies to bring a different sort of book to the market. >> That's great. So Google it and you'll find it easily. Dave, again, congratulations. Thanks so much for coming on theCube. >> David: Thank you, a pleasure. >> All right, and thank you for watching, everybody. We'll see you next time. (bright music)