(upbeat music) >> Hello, and welcome to this Cube conversation. I'm Dave Nicholson, and this is part of our continuing coverage of Google Cloud Next 2021. We have a very interesting subject to discuss. I have two special guests from Google to join me in a conversation about the financial services space. I'm joined by Yolande Piazza, vice president of financial services sales for Google Cloud and Zac Mauf, managing director for global financial services solutions for Google Cloud. Yolande and Zac, welcome to the Cube. >> Thank you for having us. Looking forward to it. >> Well it's great to have you here. You know, financial services is really an interesting area when you talk about cloud because I'm sure you both remember a time, not that long ago, when we could ask a financial services organization, what their plans for cloud or what their cloud strategy was, and they would give a one word answer and that answer was, never. (laughing) So Zac, let's start out with you, what has changed? Are you and Yolande going to tell us that in fact, financial services organizations are leveraging cloud now? >> Yeah, it's a very exciting time to be in the cloud space, in financial services, because you're exactly right David. People are starting to make the transition to cloud in a real way. And a lot has gone into that, as you know, it's a highly regulated space and so there were a lot of legitimate reasons around getting kind of the regulatory frameworks in place and making sure that the risk and compliance pieces were addressed. But then there was also, as you know, technology is a major backbone for financial services. And so there's also this question of, how do we transition? And a lot of work and time has gone into moving workloads, thinking about like, what is the sort of the right migration strategy for you to get from the current situation to a more cloud native world. And to your point, we're really early, we're really early, but we're very excited and we've been investing heavily on our side to get those foundational pieces in place. But we also realized that we have to think about what are the business cases, that we want to build on top of cloud. It's not just a kind of IT modernization, which is a big part of the story, but the other part of the story is once you get all of this, technology onto the cloud platform, there are things that you can do that you couldn't do in on-prem situations. And a lot of that for us is around the data, AI and ML space. And we really see that being the way to really unlock huge amounts of value. Both of them require massive amounts of compute and breaking down all of these silos that have really developed over time within financial institutions. And really moving to the cloud is the way to unlock a lot of that. So we're really excited about a lot of those use cases that are starting to come to life now. >> Yeah. So I want to dig a little deeper on some of that Zac, but before we do, Yolande make this real for us. Give me some examples of actual real-life financial services organizations and what they're doing with Google Cloud now. >> Yeah, absolutely. And I think we're really proud to be able to announce, a number of new partnerships across the industry. You think about Wells Fargo, you think about Scotia Bank, you think about what we're doing with HSBC. They really are starting to bring to life and recognized that it's not just internally, you have to look at that transformation to cloud, it's really, how do you use this platform to help you go on the journey with your customers? I think a move to a multi-cloud common approach for our customers and our clients, is exactly what we need to be focused on. And the other- >> Hold on, hold on, Yolande. I'm sorry. Did the Google person just say multi-cloud? Because multi- cloud doesn't sound like, only Google Cloud to me. Can you- >> No, and I think Wells, absolutely, and I think Wells announced it's taking a multi-cloud approach to its digital infrastructure strategy, leveraging both Google Cloud and Microsoft Azure. And the reason being is they've openly communicated that a locked in and preparatory systems, isn't the way to go for them. They want that open flexibility. They want the ability to be able to move workloads across the different industries. And I think it's well known that this aligns completely with our principles and at Google we've always said that we support open multi and hybrid cloud strategies because we believe our customers should be able to run what they want, where they want it. And that was exactly the philosophy that that Wells took. So, and if you look at what they were trying to do is they're looking to be able to serve their customers in a different way. I think that it's true now that customers are looking for personalized services, instant gratification, the ability to interact, where they want and when they want. So we're walking with the Wells teams to really bring to life through AI, our complex AI and data solutions to really enable them to move at speed and serve their customers in a rapidly changing world. >> So Yolande, part of the move to cloud includes the fact that we're all human beings and perception can become reality. Issues like security, which are always at the forefront of someone's mind in financial services space, there is the perception, and then there is the reality. Walk us through today where perception is in the financial services space. And then Zac, I'm going to go back to you to tell us what's the reality. And is there a disconnect? Because often technology in this space has been ahead of people's comfort level for rational reasons. So Yolande, can you talk about from a perception perspective where people are. >> So I have to tell you, we are having conversations with both the incumbents and traditional organizations, as well as, the uprising, the fintechs, and the neobanks around how does technology really unlock and unleash a new business model. So we're talking about things like how does technology and help them grow that organization. How does it take out costs in that organization? How do you use all cloud platform to think about managing risks, whether that's operational, whether it's reputational, industry or regulatory type risk? And then how do we enable our partners and our customers to be able to move at speed? So all of those conversations are now on the table. And I think a big shift from when Zac and I both were sitting on the other side of the table in those financial services industries is a recognition that this couldn't and shouldn't be done alone, that it's going to require a partnership, it's going to require, really shifting to put technology at the forefront. And I think when you talk about perception, I would say a couple of years ago, I think it was more of a perception that they were really technology companies. And I think now we're really starting to see the shifts that these are technology companies serving their customers in a banking environment. >> So Zac, can you give us some- Yeah. Yeah. Zac, can you give us some examples of how that plays out from a solutions perspective? What are some of the things that you and Yolande are having conversations with these folks in? >> Yeah. - I mean, absolutely. I think there's three major trends that we're seeing, where I think we can bring the power of sort of the Google ecosystem to really change business models and change how things are done. The first is really this massive change that's been happening for like over 10 years now, but it's really this change in customers, expecting financial institutions to meet them where they are. And that started with information being delivered to them through mobile devices and online banking. And then it went to payments, and now it's going into lending and it's going into insurance. But it changes the way that financial services companies need to operate because now they need to figure out how to deliver everything digitally, embedded into the experience that their customers are having in all of these digital ecosystems. So there's lot that we're doing in that space. The second is really around modernizing the technology environment. There is still a massive amount of paper in these organizations. Most of it has been transferred to digital paper, but the workflows and the processes that are still needing to be streamlined. And there's a lot that we can do with our AI model and technology to be able to basically take unstructured data and create structured data. Thank Google Photos, you can now search for your photo library and find, pictures of you on bridges. The same thing we can now do with documents and routine interactions with chat bot. People are expecting 24/7 service. And a lot of people want to be able to interact through chat versus through voice. And the final part of this that we're seeing a lot of use cases in is in the kind of risk and regulatory space. Coming out of the financial crisis, there was this need to massively upgrade everybody's data capabilities and control and risk environments, because so much it was very manual, and a lot of the data to do a lot of the risk and control work was kind of glued together. So everybody went off and built data lakes and figured out that that was actually a really difficult challenge and they quickly became data swamps. And so really how do you unlock the value of those things? Those three use cases, and there's lots of things underneath those, are areas that we're working with customers on. And it's, like you said, it's really exciting because the perception has changed. The perception has changed that now cloud is the sort of future, and everybody is kind of now realized they have to figure out how to engage. And I think a lot of the partnership things that Yolande was talking about is absolutely true. They're looking for a strategic relationship versus a vendor relationship, and those are really exciting changes for us. >> So I just imagined a scenario where a Dave, Zac, and Yolande are at the cloud pub talking after hours over a few pints, and Dave says, "Wow, you know, 75%, 80% of IT is still on-premises." And Yolande looks at me and says, "On-premises? We're dealing with on-paper still." Such as the life of a financial services expert in this space. So Yolande, what would you consider sort of the final frontier or at least the next frontier in cloud meets financial services? What are the challenges that we have yet to overcome? I just mentioned, the large amount of stuff that's still on premises, the friction associated with legacy applications and infrastructure. That's one whole thing. But is there one thing that in a calendar year, 2022, if you guys could solve this for the financial services industry, what would it be? And if I'm putting you on the spot, so be it. >> No, no. I'm not going to hold it to just one thing. I think the shift, I think the shift to personalization and how does the power of, you know, AI and machine learning really start to change and get into way more predictive technologies. As I mentioned, customers want to be a segmentation of one. They don't want to be forced fit into the traditional banking ecosystems. There's a reason that customers have on average 14 different financial services apps on their phones. Yep. Less than three to 5% of their screen time is actually spent on them. It's because something is missing in that environment. There's a reason that you could go to any social media site and in no time at all, be able to pull up over 200 different communities of people trying to find out financial services information in layman's terms that is relevant to them. So the ability and where we're really doubling down is on this personalization. Being way more predictive, understanding where a customer is on their journey and being able to meet them at that point, whether that's the bright offers, whether that's recognizing, to Zac's point, that they've come in on one channel but they now want to switch to another channel. And how do they not have to start again every time? So these are some of the basics things, so we really doubled down on how do we start to solve in those areas. I think also the shift, I think in many cases, especially in the risk space, it's been very much what I would call, a people process technology approach, start to imagine what happens if you turn that around and think about how technology can help you be more predictive internally in your business and create better outcomes. So I think there's so many areas of opportunities, and what's really exciting is we're not restricted, we're having conversations that are titled, the art of the possible, or the future of, or help us come in and reinvent. So I think you're going to see a lot of shift probably in the next 12 to 18 months, I would say, and the capabilities and the ability to service the customer differently and meet them on their journey. >> Well, it sounds like the life of a cloud financial services person is much more pleasurable than back when it consisted of primarily running into brick walls constantly. This conversation five or 10 years ago would have been more like, please trust us, please. Just give us a shot. >> I think Zac and I both reminisce that we couldn't have joined at a more exciting time. It's the locker or whatever you want to call it, but it is a completely different world and the conversations are fun and refreshing, and you can really start to see how we have the ability to partner to change the landscape, across all of the different financial services industries. And I think that's what keeps Zac and I going every day. >> And you said earlier that you alluded to the idea that you used to be on the other side of the table, in other words, in the financial services industry on the customer side. So you pick the right time to come across. >> Without a doubt, without a doubt. Yes. >> Well, with that, I want to thank both of you for joining me today. This is really fascinating. Financial services is something that touches all of us individually in our daily lives. It's something that everyone can relate to at some level. And it also represents, that tip of the spear, the cutting edge of cloud. So very interesting. Thank you both again, pleasure to meet you both. Next time, hopefully it will be in-person and we can compare our steps that we've taken during the conference. With that I'll sign off. This has been a fantastic Cube conversation, part of our continuing coverage of Google Cloud Next 2021. I'm Dave Nicholson, Thanks again for joining us. >> Thank you. (upbeat music)

>> Narrator: From theCUBE studios in Palo Alto, in Boston, connecting with thought leaders all around the world. This is a CUBE Conversation. >> Hey, welcome back everybody Jeff Frick here with theCUBE. We're coming to you today from our Palo Alto studios with theCUBE conversation, talking about data, and we're excited to have our next guest. He's been on a number of times, many times, CUBE alum, really at the forefront of helping companies and customers be more data centric in their activities. So we'd like to welcome onto the show Satyen Sangani. He is the co founder and CEO of Alation. Satyen, great to see you. >> Great to see you, Jeff. It's good to see you again in this new world, a new format. >> It is a new world, a new format, and what's crazy is, in March and April we were talking about this light switch moment, and now we've just turned the calendar to October and it seems like we're going to be doing this thing for a little bit longer. So, it is kind of the new normal, and even I think when it's over, I don't think everything's going to go back to the way it was, so here we are, but you guys have some exciting news to announce, so let's just jump to the news and then we'll get into a little bit more of the nitty gritty. So what do you got coming out today, right? >> Yeah its so. >> What we are announcing today is basically Alation 2020, which is probably one of the biggest releases that I've been with, that we've had since I've been with the company. We with it are releasing three things. So in some sense, there's a lot of simplicity to the release. The first thing that we're releasing is a new experience around what we call the business user experience, which will bring in a whole new set of users into the catalog. The second thing that we're announcing is basically around Alation analytics and the third is around what we would describe as a cloud-native architecture. In total, it brings a fully transformative experience, basically lowering the total cost of getting to a data management experience, lower and data intelligent experience, much lower than previously had been the case. >> And you guys have a really simple mission, right? You're just trying to help your customers be more data, what's the right word? Data centric, use data more often and to help people actually make that decision. And you had an interesting quote in another interview, you talked about trying to be the Yelp for information which is such a nice kind of humanizing way to think about it because data isn't necessarily that way and I think, you mentioned before we turned on the cameras, that for a lot of people, maybe it's just easier to ignore the data. If I can just get the decision through, on a gut and intuition and get onto my next decision. >> Yeah, you know it's funny. I mean, we live in a time where people talk a lot about fake news and alternative facts and our vision is to empower a curious and rational world and I always smile when I say that a little bit, because it's such a crazy vision, right? Like how you get people to be curious and how do you get people to think rationally? But you know, to us, it's about one making the data really accessible, just allowing people to find the data they need when and as they want it. And the second is for people to be able to think scientifically, teaching people to take the facts at their disposal and interpret them correctly. And we think that if those two skills existed, just the ability to find information and interpret it correctly, people can make a lot better decisions. And so the Yelp analogy is a perfect one, because if you think about it, Yelp did that for local businesses, just like Amazon did it for really complicated products on the web and what we're trying to do at Alation is, in some sense very simple, which is to just take information and make it super usable for people who want to use it. >> Great, but I'm sure there's the critics out there, right? Who say, yeah, we've heard this before the promise of BI has been around forever and I think a lot of peoples think it just didn't work whether the data was too hard to get access to, whether it was too hard to manipulate, whether it was too hard to pull insights out, whether there's just too much scrubbing and manipulating. So, what is some of the secret sauce to take? What is a very complex world? And again and you got some very large customers with some giant data sets and to, I don't want to say humanize it, but kind of humanize it and make it easier, more accessible for that business analyst not just generally, but more specifically when I need it to make a decision. >> Yeah I mean, it's so funny because, making something, data is like a lot of software death by 1000 cuts. I mean you look at something from the outside and it looks really, really, really simple, but then you kind of dwell into any problem and that can be CRM something like Salesforce, or it can be something like service now with ITSM, but these are all really, really complicated spaces and getting into the depths and the detail of it is really hard. And data is really no different, like data is just the sort of exhaust from all of those different systems that exist inside of your company. So the detail around the data in your company is exhaustingly minute. And so, how do you make something like that simple? I think really the biggest challenge there is progressively revealing complexity, right? Giving people the right amount of information at the right amount of time. So, one of the really clever things that we do in this business user experience is we allow people to search for and receive the information that's most relevant to them. And we determined that relevance based upon the other people in the enterprise that happen to be using that data. And we know what other people are using in that company, because we look at the logs to understand which data sources are used most often, and which reports are used most often. So right after that, when you get something, you just see the name of the report and it could be around the revenues of a certain product line. But the first thing that you see is who else uses it. And that's something that people can identify with, you may not necessarily know what the algorithm was or what the formula might be, how the business glossary term relates to some data model or data artifact, but you know the person and if you know the person, then you can trust the information. And so, a lot of what we do is spend time on design to think about what is it that a person expects to see and how do they verify what's true. And that's what helps us really understand what to serve up to somebody so that they can navigate this really complicated, relevant data. >> That's awesome, cause there's really a signal to noise problem, right? And I think I've heard you speak before. >> Yeah >> And of course this is not new information, right? There's just so much data, right? The increasing proliferation of data. And it's not that there's that much more data, we're just capturing a lot more of it. So your signal to noise problem just gets worse and worse and worse. And so what you're talking about is really kind of helping filter that down to get through a lot of that, a lot of that noise, so that you can find the piece of information within the giant haystack. That is what you're looking for at this particular time in this particular moment. >> Yeah and it's a really tough problem. I mean, one of the things that, it's true that we've been talking about this problem for such a long time. And in some instance, if we're lucky, we're going to be talking about it for a lot longer because it used to be that the problem was, back when I was growing up, you were doing research on a topic and you'd go to the card catalog and you'd go to the Dewey decimal system. And in your elementary school or high school library, you might be lucky if you were to find, one, two or three books that map to the topic that you were looking for. Now, you go to Google and you find 10,000 books. Now you go inside of an enterprise and you find 4,000 relational database tables and 200 reports about an artifact that you happened to be looking for. And so really the problem is what do I trust? And what's correct and getting to that level of accuracy around information, if there's so much information out there is really the big problem of our time and I think, for me it's a real privilege to be able to work on it because I think if we can teach people to use information better and better then they can make better decisions and that can help the world in so many different. >> Right, right, my other favorite example that everybody knows is photographs, right? Back when you only got 24 and a roll and cost you six bucks to develop it. Those were pretty special and now you go buy a fancy camera. You can shoot 11, 11 frames a second. You go out and shoot the kids at the soccer game. You come home with 5,000 photos. How do you find the good photo? It's a real, >> Yeah. >> It's a real problem. If you've ever faced something like that, it's kind of a splash of water in the face. Like where do I even begin? But the other piece that you talk about a lot, which is slightly different but related is context, and in favorite concept, it's like 55, right? That's a number, but if you don't have any context for that number, is it a temperature? Is it cold inside the building? Is it a speed? Is it too slow on i5? Or is it fast because I'm on a bicycle going down a Hill and without context data is just, it's just a number. It doesn't mean anything. So you guys really by adding this metadata around the data are adding a lot more contextual information to help figure out kind of what that signal is from the noise. >> Yap, you'll get facts from anywhere, right? Like, you're going to have a Hitchcock, you've got a 55 or 42, and you can figure out like what the meaning of the universe is and apparently the answer is 42 and what does that mean? It might mean a million different things and that, to me, that context is the difference between, suspecting and knowing. And there's the difference between having confidence and basically guessing. And I think to the extent that we can provide more of that over time, that's, what's going to make us, an ever more valuable partner to the customers that we satisfy today. >> Right, well, I do know why 42 is always the answer 'cause that's Ronnie Lot and that's always the answer. So, that one I know that's an easy one. (both chuckles) But it is really interesting and then you guys just came out. I heard Aaron Kalb on, one of your co-founders the other day and we talked about this new report that you guys have sponsored the Data Culture Report and really, putting some granularity on a Data Culture Index and I thought it was pretty interesting and I'm excited that you guys are going to be doing this, longitudinally because whether you do or do not necessarily agree with the method, it does give you a number, It does give you a score, It's a relatively simple formula. And at least you can compare yourself over time to see how you're tracking. I wonder if you could share, I mean, the thing that jumps out right off the top of that report is something we were talking about before we turned the cameras on that, people's perception of where they are on this path doesn't necessarily map out when you go bottoms up and add the score versus top down when I'm just making an assessment. >> Yeah, it's funny, it's kind of the equivalent of everybody thinks they're an above average driver or everybody thinks they're above average in terms of obviously intelligence. And obviously that mathematically is not possible or true, but I think in the world of data management, we all talk about data, we all talk about how important it is to use data. And if you're a data management professional, you want people in your company to use more data. But ironically, the discipline of data management doesn't actually use a lot of data itself. It tends to be a very slow methodical process driven gut oriented process to develop things like, what data models exist and how do I use my infrastructure and where do I put my data and which data quality is best? Like all of those things tend to be, somewhat heuristic driven or gut driven and they don't have to be and a big part of our release actually is around this product called Alation Analytics. And what we do with that product is really quite interesting. We start measuring elements of how your organization uses data by team, by data source, by use case. And then we give you transparency into what's going on with the data inside of your landscape and eco-system. So you can start to actually score yourself both internally, but also as we reveal in our customer success methodology against other customers, to understand what it is that you're doing well and what it is that you're doing badly. And so you don't need necessarily to have a ton of guts instinct anymore. You can look at the data of yourselves and others to figure out where you need to improve. And so that's a pretty exciting thing and I think this notion that says, look, you think you're good, but are you really good? I mean, that's fundamental to improvement in business process and improvement in data management, improvement in data culture fundamentally for every company that we work with. >> Right, right and if you don't know, there's a problem, and if you're not measuring it, then there's no way to improve on it, right? Cause you can't, you don't know, what you're measuring is. >> Right. >> But I'm curious of the three buckets that you guys measured. So you measured data search and discovery was bucket number one, data literacy, you know what you do once you find it and then data governance in terms of managing. It feels like that the search and discovery, which is, it sounds like what you're primarily focused on is the biggest gap because you can't get to those other two buckets unless you can find and understand what you're looking for. So is that JIve or is that really not problem, is it more than manipulation of the data once you get it? >> Yeah, I mean we focus really. We focus on all three and I think that, certainly it's the case that it's a virtuous cycle. So if you think about kind of search and discovery of data, if you have very little context, then it's really hard to guide people to the right bit of information. But if I know for example that a certain data is used by a certain team and then a new member of that team comes on board. Then I can go ahead and serve them with exactly that bit of data, because I know that the human relationships are quite tight in the context graph on the back end. And so that comes from basically building more context over time. Now that context can come from a stewardship process implemented by a data governance framework. It can come from, building better data literacy through having more analytics. But however, that context is built and revealed, there tends to be a virtuous cycle, which is you get more, people searching for data. Then once they've searched for the data, you know how to necessarily build up the right context. And that's generally done through data governance and data stewardship. And then once that happens, you're building literacy in the organization. So people then know what data to search for. So that tends to be a cycle. Now, often people don't recognize that cycle. And so they focus on one thing thinking that you can do one to the exclusion of the others, but of course that's not the case. You have to do all three. >> Great and I would presume you're using some good machine, Machine Learning and Artificial Intelligence in that process to continue to improve it over time as you get more data, the metadata around the data in terms of the usage and I think, again I saw in another interview there talking about, where should people invest? What is the good data? What's the crap data? what's the stuff we shouldn't use 'cause nobody ever uses it or what's the stuff, maybe we need to look and decide whether we want to keep it or not versus, the stuff that's guiding a lot of decisions with Bob, Mary and Joe, that seems to be a good investment. So, it's a great application of applied AI Machine Learning to a very specific process to again get you in this virtuous cycle. That sounds awesome. >> Yeah, I know it is and it's really helpful to, I mean, it's really helpful to think about this, I mean the problem, one of the biggest problems with data is that it's so abstract, but it's really helpful to think about it in just terms of use cases. Like if I'm using a customer dataset and I want to join that with a transaction dataset, just knowing which other transaction datasets people joined with that customer dataset can be super helpful. If I'm an analyst coming in to try to answer a question or ask a question, and so context can come in different ways, just in the same way that Amazon, their people who bought this product also bought this product. You can have all of the same analogies exist. People who use this product also use that product. And so being able to generate all that intelligence from the back end to serve up simple seeming experience on the front end is the fun part of the problem. >> Well I'm just curious, cause there's so many pieces of this thing going on. What's kind of the, aha moment when you're in with a new customer and you finish the install and you've done all the crawling and where all the datasets are, and you've got some baseline information about who's using what I mean, what is kind of the, Oh, my goodness. When they see this thing suddenly delivering results that they've never had at their fingertips before. >> Yeah, it's so funny 'cause you can show Alation as a demo and you can show it to people with data sets that are fake. And so we have this like medical provider data set that, we've got in there and we've got a whole bunch of other data sets that are in there and people look at it and interestingly enough, a lot of time, they're like, Oh yeah, I can kind of see it work and I can kind of like understand that. And then you turn it on against their own data. The data they have been using every single day and literally their faces change. They look at the data and they say, Oh my God, like, this is a dataset that Steven uses, I didn't even know that Steven thought that this data existed and, Oh my God, like people are using this data in this particular way. They shouldn't be using that data at all, Like I thought I deprecated that dataset two years ago. And so people have all of these interesting insights and it's interesting how much more real it gets when you turn it on against the company's systems themselves. And so that's been a really fun thing that I've just seen over and over again, over the course of multiple years where people just turn on the cup, they turn on the product and all of a sudden it just changes their view of how they've been doing it all along. And that's been really fun and exciting. >> That's great yeah, cause it means something to them, right? It's not numbers on a page, It's actually, it's people, it's customers, it's relationships, It's a lot of things. That's a great story and I'm curious too, in that process, is it more often that they just didn't know that there were these other buckets of reports and other buckets of data or was it more that they just didn't have access to it? Or if they did, they didn't really know how to manipulate it or to integrate it into their own workflow. >> Yeah, It's kind of funny and it's somewhat role dependent, but it's kind of all of the above. So, if you think about it, if you're a data management professional, often you kind of know what data sources might exist in the enterprise, but you don't necessarily know how people are using the data. And so you look at data and you're like, Oh my God, I can't believe this team is using this data for this particular purpose. They shouldn't be doing that. They should be using this other data set. I deprecated that data set like two years ago. And then sometimes if you're a data scientist, you're you find, Oh my gosh, there's this new database that I otherwise didn't realize existed. And so now I can use that data and I can process that for building some new machine learning algorithms. In one case we've had a customer where they had the same data set procured five different times. So it was a pure, it was a data set that cost multiple hundreds of thousands of dollars. They were spending $2 million overall on a data set where they could have been spending literally one fifth of that amount. And then you had a sort of another case finally, where you're basically just looking at it and saying, Hey, I remember that data set. I knew I had that dataset, but I just don't remember exactly where it was. Where did I put that report? And so it's exactly the same way that you would use Google. Sometimes you use it for knowledge discovery, but sometimes you also use it for just remembering the thing you forgot. >> Right but, but the thing, like I remember when people were trying to put Google search in that companies just to find records not necessarily to support data efforts and the knock was always, you didn't have enough traffic to drive the algorithm to really have effective search say across a large enterprise that has a lot of records, but not necessarily a lot of activity. So, that's a similar type of problem that you must have. So is it really extracting that extra context of other people's usage that helps you get around kind of that you just don't have a big numbers? >> Yeah, I mean that kind of is fundamentally the special sauce. I mean, I think a lot of data management has been this sort of manual brute force effort where I get a whole bunch of consultants or a whole bunch of people in the room and we do this big documentation session. And all of a sudden we hope that we've kind of, painted the golden gate bridge is at work. But, knowing that three to six months later, you're going to have to go back and repaint the golden gate bridge overall all over again, if not immediately, depending on the size and scale of your company. The one thing that Google did to sort of crawl the web was to really understand, Oh, if a certain webpage was linked to super often, then that web page is probably a really useful webpage. And when we crawled the logs, we basically do the exact same thing. And that's really informed getting a really, really specific day one view of your data without having to have a whole bunch of manual effort. And that's been really just dramatical. I mean, it's been, it's allowed people to really see their data very quickly and new different ways and I think a big part of this is just friction reduction, right? We'd all love to have an organized data world. We'd love to organize all the information in a company, but for anybody has an email inbox, organizing your own inbox, let alone organizing every database in your company just seems like a specificity in effort. And so being able to focus people on what's the most important thing has been the most important thing. And that's kind of why we've been so successful. >> I love it and I love just kind of the human factors kind of overlay, that you've done to add the metadata with the knowledge of who is accessing these things and how are they accessing it. And the other thing I think is so important Satyen is, we talk about innovation all the time. Everybody wants more innovation and they've got DevOps so they can get software out faster, et cetera, et cetera. But, I fundamentally believe in my heart of hearts that it's much more foundational than that, right? That if you just get more people, access to more information and then the ability to manipulate and clean knowledge out of that information and then actually take action and have the power and the authority to take action. And you have that across, everyone in the company or an increasing number of people in the company. Now suddenly you're leveraging all those brains, right? You're leveraging all that insight. You're leveraging all that kind of First Line experience to drive kind of a DevOps type of innovation with each individual person, as opposed to, kind of classic waterfall with the Chief Innovation Officer, Doing PowerPoints in his office, on his own time. And then coming down from the mountain and handing it out to everybody to go build. So it's a really a kind of paradox that by adding more human factors to the data, you're actually making it so much more usable and so much more accessible and ultimately more valuable. >> Yeah, it's funny we, there's this new term of art called data intelligence. And it's interesting because there's lots of people who are trying to define it and there's this idea and I think IDC, IDC has got a definition and you can go look it up, but if you think about the core word of intelligence, it basically DevOps down to the ability to acquire information or skills, right? And so if you then apply that to companies and data, data intelligence then stands to reason. It's sort of the ability for an organization to acquire, information or skills leveraging their data. And that's not just for the company, but it's for every individual inside of that company. And we talk a lot about how much change is going on in the world with COVID and with wildfires here in California. And then obviously with the elections and then with new regulations and with preferences, cause now that COVID happened everybody's at home. So what products and what services do you have to deliver to them? And all of this change is, basically what every company has to keep up with to survive, right? If capitalism is creative destruction, the world's getting destroyed, like, unfortunately more often than we'd like it to be,. >> Right. >> And so then you're say there going, Oh my God, how do I deal with all of this? And it used to be the case that you could just build a company off of being really good at one thing. Like you could just be the best like logistics delivery company, but that was great yesterday when you were delivering to restaurants. But since there are no restaurants in business, you would just have to change your entire business model and be really good at delivering to homes. And how do you go do that? Well, the only way to really go do that, is to be really, really intelligent throughout your entire company. And that's a function of data. That's a function of your ability to adapt to a world around you. And that's not just some CEO cause literally by the time it gets to the CEO, it's probably too late. Innovations got to be occurring on the ground floor. And people have got to repackage things really quickly. >> I love it, I love it. And I love the other human factor that we talked about earlier. It's just, people are curious, right? So if you can make it easy for them to fulfill their curiosity, they're going to naturally seek out the information and use it versus if you make it painful, like a no fun lesson, then people's eyes roll in and they don't pay attention. So I think that it's such an insightful way to address the problem and really the opportunity and the other piece I think that's so different when you're going down the card catalog analogy earlier, right? Is there was a day when all the information was in that library. And if you went to the UCLA psych library, every single reference that you could ever find is in that library, I know I've been there, It was awesome, but that's not the way anymore, right? You can't have all the information and it's pulling your own information along with public information and as much information as you can. where you start to build that competitive advantage. So I think it's a really great way to kind of frame this thing where information in and of itself is really not that valuable. It's about the context, the usability, the speed of these ability and that democratization is where you really start to get these force multipliers and using data as opposed to just talking about data. >> Yeah and I think that that's the big insight, right? Like if you're a CEO and you're kind of looking at your Chief Data Officer or Chief Data and Analytics Officer. The real question that you're trying to ask yourself is, how often do my people use data? How measurable is it? Like how much do people, what is the level at which people are making decisions leveraging data and that's something that, you can talk about in a board room and you can talk about in a management meeting, but that's not where the question gets answered. The question gets really answered in the actual behaviors of individuals. And the only way to answer that question, if you're a Chief Analytics Officer or somebody who's responsible for data usage within the company is by measuring it and managing it and training it and making sure it's a part of every process and every decision by building habit and building those habits are just super hard. And that's, I think the thing that we've chosen to be sort of the best in the world at, and it's really hard. I mean, we're still learning about how to do it, but, from our customers and then taking that knowledge and kind of learning about it over time. >> Right, well, that's fantastic. And if it wasn't hard, it wouldn't be valuable. So those are always the best problems to solve. So Satyen, really enjoyed the conversation. Congratulations to you and the team on the new release. I'm sure there's lots of sweat, blood and tears that went into that effort. So congrats on getting that out and really great to catch up. Look forward to our next catch up. >> You too Jeff, It's been great to talk. Thank you so much. >> All right, take care. All righty Satyen and I'm Jeff, you're watching theCUBE. We'll see you next time. Thanks for watching. (ethereal music)

( bright upbeat music) >> Announcer: From theCUBE studios in Palo Alto in Boston, connecting with thought leaders all around the world, this is a Cube conversation. >> Hey, welcome back. You're ready, Jeff Frick here with theCUBE, we are still getting through COVID. It's a hot August day here in San Francisco Bay Area. It is 99, somebody said in the city that's hot, but we're still getting through it. We're still reaching out to the community, we're still talking to leaders in all the areas that we cover. And one of the really interesting areas is natural language processing. And it's a small kind of subset. We'll get into it a little bit more detail, we are very specific place within the applied AI world. And one of my very good friends and Cube alumni, who's really an expert in the space, he's coming back for his second startup in the space. And we're joined by Ben, he's Ben Chung, the Co-founder of Ogmagod. Did I get that right Ben, Ogmagod. >> That's correct. That's right. >> Great to see you again. >> Thank you for inviting to the show. >> Well, I love it. One of the topics that we've been covering a lot Ben is applied AI. 'Cause there's just so much kind of conversation about artificial intelligence, the machine learning is kind of this global big thing. And it kind of reminds me of kind of big data or cloud, in the generic it's interesting but it's really not that interesting, 'cause that's really not where it gets applied. Where I think what's much more interesting and why I wanted to have you back on is, where is it actually being applied in applications? And where are we seeing it in solutions? And where is it actually changing people's lives, changing people's days, changing people's behavior, and you seem to have a propensity for this stuff. It was five years ago, I looked July five years ago, we had you on and you had found Genie, which was a natural anti processing company focused on scheduling. Successful exit, sold that to Microsoft and they baked it into who knows, there probably baked in all over the place. Left there now you've done it again. So before we get into it. What so intriguing to you about natural language processing for all the different kind of opportunities that you might go after from an AI perspective? What's special about this realm that keeps drawing you back? >> Yeah, sure, I mean it, to be honest it was not anything premeditated, I kind of stumbled on it. I before this, I was more like an infrastructure guy spent a number of years at VMware and had a blast there and learned a lot. Then I kind of just stumble on it. Because when we started doing the startup, we didn't intend it to be a AI startup or anything like that. We just had a problem that my co-founder Charles Lee and I really wanted to solve, which is to help people, solve people's scheduling problem. But very shortly after getting into and start looking at some use cases, we thought that the easiest way is to communicate with people like humans do to help them do the scheduling. And that's kind of how I stumbled on it. And it wasn't until that I stumbled on it that I realized that it has a lot of attraction to me, because I throughout my whole life, I'm always very interested in the human emotions of it, how humans relate to each other. And that's always been the hidden side project thing, I do traveling to figure out stuff and get a little bit of that. But once I start getting into this field, I realized that there's a lot about it, about humanity and how humans communicate that it was kind of like a hidden interest for me. That now suddenly coming out and it kind of just got me hooked. >> Right, that's awesome. So one of the things and we'll just get into it is people are a little bit familiar with natural language processing, probably from Siri and from Google and from Alexa and increasingly some of these tools but I think, you kind of rapidly find out beyond what's the weather and play a song and tell me a joke that the functionality is relatively limited. So when people think about natural language and they have that as a reference point, how do you help them see that it's a lot more than, asking Siri for the weather. >> Yeah, there are a lot of capability but also hopefully not offensive to some of the tech visionaries. Just as a guy who is dealing with it every day, there are also lots of limitation is not nearly to the degree of refinements. Like what might being preach out there saying that the machines are going to take over everything in one day, we have a lot of struggles that are very basic stuff with machines. However, there has been definitely a lot of breakthroughs in the last few years and that's why I'm dedicating my life and my time into this area because I think that it just, there's going to be huge amount of innovation continuously going in this area. So that's at the high level, but if you talk about, in terms of artificial intelligence and in general, I think, I have my own understanding, I'm more like an apply guy, lot of academics so what I'm going to say might make some academics cringe because I'm more like a everyday practical guy and try to re conciliate these concepts myself. The way that I view is that artificial intelligence has really tried to help mimic some human capabilities that originally thought that is the domain of human, only humans are able to do it, but machines now try to demonstrate that machine can do it, like as though the humans could. So and then usually people get that mixed up with machine learning, to me is actually quite different thing. Artificial intelligence just like what I mentioned, machine learning is just a technique or a science or way of applying like to leverage this capability, machine learning capability in solving these artificial intelligent problems, to make it more achievable to raise the bar on it. So I don't think we should use them interchangeably, artificial intelligence and machine learning. Because today machine learning is the big deal that are making the progress wise, tomorrow might be something else to help improve artificial intelligence. And in the past, it was something else before machine learning. So it's a progression, the machine learning is the very powerful and popular technique right now to being used. Now within artificial intelligence, I think you mentioned that there are various different domains and topics, there is like object recognition deals with image processing, there's speech detection, there's a video and what I would call action or situation detection. And then there's natural language processing, which is the domain that I'm in that is really in that stage of where we seeing quite a bit of break through, but it's not quite there yet. Whereas versus speech detection and image processing actually has done a tremendous progress in the past. So and in you can say that like the innovation there is not as obvious or as leap frogging as the natural language processing. >> Right, so some of the other examples that we know about that are shared often for machine learning or say, the visual thing, can you identify a chihuahua from the blueberry muffin, which sounds kind of funny until you see the pictures, they actually look very, very similar. And the noise stated that Google and their Google Photos, right, has so many pictures such a huge and diverse data set in which to train the machines to identify a chihuahua versus a blueberry muffin. Or you take the case in Tesla, if you've watched any of their autonomous vehicle stuff and their computer vision process and they have the fleet, hundreds of thousands of cars that are recording across many, many cameras reporting back every night. With natural language processing you don't have that kind of a data set. So when you think about training the machine to the way that I speak, which is different than the way you speak and the little nuances, even if we're trying to say the same thing, I would imagine that the variety in the data set is so much higher and the quantity of the data set is so much lower that's got to be a kind of special machine learning challenge. >> Yes, it is. I think the people say that there is, we are at the cusp of, being able to understand language in general, I don't believe that we are very far away from that. And even if when you narrow scope to say, like focus on one single language like English, even within that, we still very far from it. So I think the reality, at least for me, speaking from the ground level, kind of person tried to make use of these capabilities is that you really have to narrow it to a very narrow domain to focus on and bound it. And my previous startup is really that our assistant to help you schedule meetings, that assistant doesn't understand anything else other than scheduling, we were only able to train it to really focus on doing scheduling, if you try to ask it about joke or ask anything else, it wouldn't be able to understand that. So, I think the reality on the ground at least from what I see of a practical application and being successful at it, you really need to like have a very narrow domain in which you apply these capabilities. And then in terms of technology being used broadly in natural language processing in my view there are two parts of it, one is the input, which is sometimes call natural language understanding. And then that part is actually very good progress. And then the other part is the natural language generation, meaning that the machine knows how to compose sentences and generate back to you, that is still very, very early days. So there is that break up and then if you go further, I don't want to bore you Jeff here with all these different nuances, but when you look at natural language understanding, there are a lot of areas like what we call topic extraction or entity extraction, event extraction. So that's to extract the right things and understand those things from the sentences, there is sentimental analysis knowing that where some a sentence expresses somebody angry or some different kinds of emotions, there is summarization, meaning that I can take sets of texts or paragraphs of text and summarize with fewer words for you. So and then there is like dialogue management, which manages the dialogue with the person. So they're like these various different fields within it. So the deeper you look, there's like the more stuff within it and there's more challenges. So it's not like a blanket statement, say like, "Hey, we could conquer on this." And if you digging deep there's some good progress in certain this area. But some areas like it's really just getting started. >> Right, well we talked about in getting ready for this call and kind of reviewing some of the high level concepts of and you brought up, what is the vocab? So first you have to just learn what is the vocabulary, which a lot of people probably think it stops there. But really then what is the meaning of the vocabulary, but even more important is the intent, right, which is all driven by context. And so the complexity, beyond vocabulary is super high and extremely nuanced. So how do you start to approach algorithmically, to start to call out these things like intent or I mean, people talk about sentiment all the time, that's kind of an old marketing thing, but when you're talking about specific details, to drive a conversation, and you're also oh, by the way, converting back and forth between voice and text to run the algorithms in a text based system, I assume inside the computer, not a voice system. How do you start to identify and programmatically define intent and context? >> Yeah, just to share a little anecdote, like one of the most interesting part of, since I started this journey six years ago and also interesting was a very frustrating part is that, especially when I was doing the scheduling system, is that how sloppy people are with their communication and how little that they say they communicate to you and expect you to understand. And when we were doing the scheduling assistant, we're constantly challenged by somebody telling us certain things and we look at it's like, well, what do they mean exactly? For example, like one of the simple thing that we used to talk a lot with new people coming on the team about is that when people say they want to schedule next week, they don't necessarily mean next week, what they mean is not this week. So it doesn't, if you like take it literally and you say, "Oh, sorry, Jeff, there is no time available next week." And actually Jeff probably not even remember that he told you to schedule next week, to what he remember, what he told you not to schedule it this week. So when you come back to them and say, "Jeff, you have nothing available this week or next week." And Jeff might say like, while your assistant is kind of dumb, like, why are you asking me this question? If there was nothing available next week, just scheduled the week after next week. But the problem is that you literally said next week, so if we took you literally, we would cause unhappiness for you. But we kind of have to guess like what exactly you mean. So don't like this a good example where they're like lot of sloppiness and lot of contextual things that we have to take into account when we communicate what humans, or when we try to understand what they say. So yeah, is exactly your point is not like mathematics is not simple logic. There are a lot of things to it. So the way that I look at it, there are really two parts of it. There's the science part and then there's art part to it. The science part is like what people normally talk about and I mentioned earlier, you have to narrow your domain to a very narrow domain. Because you cannot, you don't have the luxury of collecting infinite data set like Google does. You as a startup, or any team within a corporation, you cannot expect to have that kind of data set that Google or Microsoft or Facebook has. So without the data set, huge data set, so you want to deliver something with a smaller data set. So you have to narrow your domain. So that's one of the science part. The other part is I think people talk about all the time to be very disciplined about data collection and creating training data sets so that you have a very clean and good training data set. So these two are very important on the science part and that's expected. But I think a lot of people don't realize this, what I would call the art part of it, is really there are two parts to that. One is exactly like what you said Jeff is to narrow your domain or make some assumption within the domain, so that you can make some guesses about the context because the user is not giving it to you verbally or giving you to you into text. A lot of us we find out visually by looking at the person as we communicate with them. Or even harder we have some kind of empathetic understanding or situational understanding, meaning that there is some knowledge that we know that Jeff is in this situation, therefore, I understand what he's saying right now means this or that Jeff is a tech guy like me, therefore, he's saying certain thing, I have the empathetic understanding that he meant this as a tech guy. So that's a really hard kind of part of it to capture or make some good guesses about the context. So that's one part. The other part is that you can only guess so much. So you have to really design the user experience, you have to be very careful how you design the user experience to try what you don't know. So that it's not frustrating to the user or to put guardrails in place such that the user doesn't go out of balm and start going to the place where you are not trained for that you don't have to understand it. >> Right, because it's so interesting, 'cause we talked about that before that so much of communication, it's not hard to know that communication is really hard, emails are horrible. We have a hard time as humans, unless we're looking at each other and pick up all these nonverbal cues that add additional context and am I being heard, am I being understood? Does this person seem to understand what I'm trying to say? Is it not getting in? I mean, there's so many these kind of nonverbal cues as you've expressed, that really support the communication of ideas beyond simply the words in which we speak. So and then the other thing you got to worry about too, as you said, ultimately, it's user experience if the user experience sucks, for instance, if you're just super slow, 'cause you're not ready to make some guesses on context and it just takes for a long time, people are not going to to use the thing. So I'm curious on the presentation of the results, right? Lots of different ways that that can happen. Lots of different ways to screw it up. But how do you do it in such a way that it's actually adding value to some specific task or job and maybe this is a good segue to talk about what you're doing now at Ogmagod, I'm sorry I have to look again. I haven't memorized that yet. 'Cause what you're also doing if I recall is you're taking out an additional group of data and additional datasets in beyond simply this conversational flow. But ultimately, you've got to suck it in, as you said, you've got to do the analysis on it. But at the end of the day, it's really about effective presentation of that data in a way that people can do something with it. So tell us a little bit about what you're doing now beyond scheduling in the old days. >> Sure, yeah, I left Microsoft late last year and started a new startup. It's called Ogmagod. And what we do is to help salespeople to be more effective, understand the customer better so that they have higher probability of winning the deal or to be able to shorten the sales cycle. And oftentimes, a lot of the sales cycle got LinkedIn is because of the lack of understanding and there's also, I say, we focus on B2B sales. So for B2B salespeople, the world's really changed a lot since the internet came about. In the old days is really about, tell it to explain what your product is and so that your customer understand your product, but the new days is really about not explaining your product because the customer can find out everything about your product by looking at your website or maybe your marketing people did do such a good job, they already communicated to the customer exactly what your product does. But really to win out against other people you really like almost like a consultant to go to your customer and say, like, I have done your job, almost like I've done your job before I know about your company. And let me try to help you to fix this problem. And our product fit in as part of that, but our focus is let's fix this problem. So how would you be able to talk like that, like you've done this job before? Like you worked at this company before? How do you get at the level of information that you can present yourself that way to the customer and differentiate yourself against all the other people who try to get their attention, all the people sending them email every day automatically, how do you differentiate that? So we felt that the way that you do it is really have the depth of understanding where your customer that is unrivaled by anybody else. Now sure, you can do that, you can Google your customer all day, reorder news report, know all the leadership, could follow them on social media-- >> Right, they're supposed to be doing all this stuff, right Ben, they're supposed to be doing all this stuff and with Google and the internet there's no excuse anymore. It's like, how did you not do your homework? You just have to get the Yellow Pages. >> Yeah, why didn't you do your work? Yes, people get beat up by their management saying like, "Oh, how come you miss this? "It's right there go on Google." But the truth is that you have to be empathetic to a salesperson. A lot of people don't realize that for a salesperson, every salesperson, you might own 300 accounts in your territory. And a lot of times in terms of companies, there might be thousands of companies in your territory. Are you going to spend seven hours, follow all these 300 companies and read all tweet. Check out the thousands of employees in each of these company, their LinkedIn profiles, look at their job listings, look at all the news articles. It's impossible to do as a human, as a person. If you do that you'll be sitting in your computer all day and you never even get to the door to have a conversation with the customer. So that is the challenge so I felt like salespeople really put up impossible tasks, because all this information out there, you're expected it to know. And if you screwed up because you didn't check, then it's your fault. But then on the same time, how can they check all 300 accounts and be on top of everything? So, what we thought is that like, "Hey, we made a lot of progress "on natural language processing "and natural language understanding." And salespeople what they look for is a quite narrow domain. They are looking for some very specific thing related to what they selling, and very specific projects, pinpoints budget related to what they're selling. So it's a very narrow domain, we felt like it's not super narrow. It's a little bit broader than I would say scheduling. But it's still very narrow the kind of things that they're looking for. They're looking for those buying triggers. They're looking for problem statements within the customers that relate to what they selling. So we think that we can use, develop a bunch of machine learning models and use what's available in terms of the web. What's out there on the web, the type of information out there. And to be able to say, like, salesperson, you don't need to go and keep up and scan, all the tweets and all the news and everything else for these 300 companies that you cover, we'll scan all of them, we will put them into our machine learning pipeline and filter out all the junk, because there are lots of junk out there, like Nike, that's like, I don't know, hundreds of news release probably per week. And most of them are not relevant to you. It doesn't make sense for you to read all of those. So but how about we read all of them and we extract out, we it's difficult topic extraction, we extract out the topic that you're looking for and then we organize it and present to you. Not just we extracting out the topic. Once we get the topic how about we look up all the people that are related to that topic in the company for you so that you can call on them. So you know what you want to talk to them about, which is this topic or this pin point. And you know who to talk to, these are the people. So that's what we do. That's that's really interesting. It's been a tagline around here for a long time, right separating the signal from the noise. And I think what you have identified, right is, as you said, now we live in the age where all the information is out there. In fact, there's too much information. So you should be able to find what you're looking for. But to your point, there's too much. So how do you find the filter? How do you find the trusted kind of conduit for information so that you're not just simply overwhelmed that what you're talking about, if I hear you right, is you're actually querying publicly available data for particular types of I imagine phrases, keywords, sentences, digital transformation initiative, blah, blah, blah. And then basically then coalescing the ecosystem around that particular data point. And then how do you then present that back to the salesperson who's trying to figure out what he's going to work on today. >> B2B salespeople, they start with an opportunity. So opportunity is actually a very concrete word at least in the tech B2B sales-- >> We know, we see the 60 stories in downtown San Francisco will validate statement. (laughs) >> Yes, so yeah, so it starts with the word opportunity. So the output is a set of potential opportunity. So it speaks to the salespersons language and say, when you use us, we don't just say "Hey, Jeff, there's this news article about Twilio and you cover Twilio, that's interesting to you." "Oh, there's a guy at Twilio that matches the kind of persona that you sell into." We don't start with that, we start with, "Jeff, there are six Opportunities for you at Twilio. "Let's explain what those things are." And then explain the people behind these opportunities so that you can start qualify them. So get you started, right way in your vocabulary in a package that you understand. So that I think that's what differentiates us. >> Right, and at some point in time, would you potentially just thinking logically down the road, you have some type of Salesforce API. So it just pumps into whatever their existing system is. That they're working every day. And then it describes based on the algorithm, why the system identified this opportunity, what the attributes are that flagged it, who are the right people, et cetera. Awesome, so what kind of data are you requiring-- >> Yes, you are designing our product wise. >> (laughs) Since Dave and John, watch this. They're going to want to talk to you, I'm sure. But what type of data sets are you querying? >> There are lots of them. We learned most of it by through the process working for salespeople, meaning that we work for salespeople, we may be quote, unquote, stand behind their back and see what they're searching. They're searching LinkedIn. They're searching jobs. They're searching endless court transcripts, they're looking at 10K 10Q's, they dig up various, some people are very, very creative, digging out various parts of the web and find really good information. The challenge is that they can't do this to scale. They can't do it for 300 accounts, 'cause we're doing for one accounts very is laborious. So there are various different places that we can find information. And in terms of the pattern that we're looking for. It's not just keyword, it's really concepts. We call it a topic. We really looking for very specific topics that the salesperson looks for. And that's not just a word, because sometimes words is very misleading. For example, I tell you one of the common words in tech is called Jenkins. Jenkins is a very popular technologies, continuous delivery technologies step but Jenkins is also happens to be a very common last name for people. >> (laughs) Well, I'm always reminded of our Intel days with all the acronyms, but my favorite is ASP 'Cause you could use ASP twice in the same sentence and mean two different things, right? Average selling price or application service provider back in the days before we call them clouds, but yeah, so the nuances is so tricky. So within kind of what you're doing then and as you described working within defined data sets and keeping the UX and user experience pretty dialed in and within the rails, are there particular types of opportunities in terms of B2B types of opportunities that fit better that have kind of a richer data set, a higher efficacy in the returns what do you kind of seeing in terms of great opportunities for you guys. >> We're still early, so I can't tell you that like from a global view because we are like less than one year old experience, quite honestly. But so far we are being led by the customer. So meaning that there is an interesting customer, they ask us to look for certain topics or certain things. And we always find it to my surprise, because and that really is, like, I'm constantly surprised by how much is there out in the web, like what you were saying, like customer ask us to look for something. And I thought for sure, this thing we couldn't do it, we can find it. And we gave it a try and low and behold, there it is. It's out there. So, to be honest, I can't tell you at this point, because I have not run into any limits. But that is because we are still a very young startup. And we are not like Google. We're not trying to be all encompassing looking for everything and looking over everything. We're just looking over everything that a salesperson wants, that's it. >> So I'm going to make you jump up a couple levels. Since you've been thinking about this and working on this for a long time, there's a lot of conversation about machines taking everybody's jobs, then there's the whole kind of sidetrack launch to that, which is no, it's all about helping people do better jobs and helping people do more higher value work and less drudgery. I mean, that sounds so consistent with what you're talking about, I wonder as somebody down in the weeds of artificial intelligence, if you can kind of tell us your vision of how this is going to unfold over the next several years, is it just going to be many, many, many little applications that slowly before we know it are going to have moved, along many fronts very far, or do you still see it's such a fundamental human thing in terms of the communication that the these machines will get better at learning, but ultimately, they can kind of fulfill this promise of taking care of the drudgery and freeing up the people to make what are actually much harder decisions from a computer's point of view than maybe the things that we think about that a three year old could ascertain with very little extra effort. >> Yeah, if you take a look at what we do and hopefully it didn't sound like we're underselling our startup but a lot of it really is we taking away to time consumer and also grunt work process of the data collection and cleaning up the data. The humans, the real human intelligence should be focused on data analysis to be able to derive lots of insights of the data. So and to be able to formulate a strategy, how to win the account, how to win the deal. That's what's the human intelligence should be focused on. The other part by struggling with doing the Google search and in return 300 entries, in 30 different pages and you have to click through each one and then give up the first week, that kind of data collection data hunting work, we are really, it should not, I don't think it's worthy, quite honestly, for a very educated person to deal with. And we can invest it back in helping the human to do what the humans are really good at is that, how do I talk to Jeff? And I'm going to get a deal out to Jeff, how can I help and through helping him solving his problem, how can I take the burden of solving the problem from Jeff's head and solve the problem for him? That's what human intelligence for me as a salesperson, I would prefer to do that instead of sitting in front of my desk and doing googling, so net net what I'm trying to say using ourselves as an example is that we're not taking over the job of a salesperson, there was no way that we can close a deal for you. But what we're doing is that we're empowering you so that you look like you're on top of 300 accounts and you talk to any of those accounts, you'll be able to talk to the people, your customer, their particular customer, like you know them inside out. And without you being the superhuman to be able to do all this stuff, but as far as that customer is concerned, sounds like you were on top of all this stuff all day and that's all you do, you have no other customers, they're the only customer. In fact, you on top of 300 customers. So that's kind of the value that we see, to provide to the human is to allow you to scale by removing these grunt work that are preventing you from scaling or living up to your potential how you wanted to present yourself, how you want to deliver yourself. There's no way that we can be smarter than human, no way. I just don't see it not in my lifetime. >> I just love, we've had a lot of conversations over the years and you talking about the difficulty in training the computers on some really nuanced kind of human things versus the things that they're very very good at and keeping the AI in the right guard wheel is probably just as important as keeping the user interface in the right lane as well to make sure that it's a mutually beneficial exchange and one doesn't go off and completely miss the benefit to the other. Well, Ben, it's a great story. Really exciting place to dedicate yourself and we are just digging watching the story and we're going to enjoy watching this one unfold. So thanks for taking a few minutes in sharing your insight on natural language processing and this applied machine learning techniques. >> Thank you, Jeff. It's always a pleasure. >> Yep, all right. He's Ben, am Jeff, you're watching theCUBE. Thanks for watching. We'll see you next time. (bright upbeat music)

>> Announcer: From San Francisco, it's theCUBE, covering Accenture Tech Vision 2020, brought to you by Accenture. >> Hey, welcome back, everybody. Jeff Rick here with theCUBE. We're high atop San Francisco on a beautiful day at the Accenture San Francisco Innovation Hub, 33rd floor of the Salesforce Tower, for the Accenture Tech Vision 2020 reveal. It's where they come up with four or five themes to really look forward to, a little bit innovative, a little bit different than cloud will be big or mobile will be big. And we're excited to have, really, one of the biggest brains here on the 33rd floor. She's Teresa Tung, the managing director of Accenture Labs. Teresa, great to see you. >> Nice to see you again. >> So I have to tease you because the last time we were here, everyone was bragging on all the patents that you've filed over the years, so congratulations on that. It's almost kind of like a who's who roadmap of what's happening in tech. I looked at a couple of them. You've got a ton of stuff around cloud, a ton of stuff around Edge, but now, you're getting excited about robots and AI. >> That's right. >> That's the new passion. >> That's the new passion. >> All right, so robots, one of the five trends was robots in the wild, so what does that mean, robots in the wild, and why is this something that people should be paying attention to? >> Well, robots have been around for decades, right? So if you think about manufacturing, you think about robots. But as your kid probably knows, robots are now programmable, kids can do it, so why not enterprise? And so, now that robots are programmable, you can buy them and apply them. We're going to unlock a whole bunch of new use cases beyond just those really hardcore manufacturing ones that are very strictly designed in a very structured environment, to things in an unstructured and semi-structured environment. >> So does the definition of robot begin to change? We were just talking before we turned on the cameras about, say, Tesla. Is a Tesla a robot in your definition or does that not quite make the grade? >> I think it is, but we're thinking about robots as physical robots. So sometimes people think about robotics process automation, AI, those are robots, but here, I'm really excited about the physical robots; the mobile units, the gantry units, the arms. This is going to allow us to close that sense-analyze-actuate loop. Now the robot can actually do something based off of the analytics. >> Right, so where will we see robots kind of operating in the wild versus, as we said, the classic manufacturing instance, where they're bolted down, they do a step along the process? Where do you see some of the early adoption is going to, I guess, see them on the streets, right, or wherever we will see them? >> Well, you probably do see them on the streets already. You see them for security use cases, maybe mopping up a store after, where the employees can actually focus on the customers, and the robot's maybe restocking. We see them in the airports, so if you pay attention to modern airports, you see robots bringing out the baggage and doing some of the baggage handling. So really, the opportunities for robots are jobs that are dull, dirty, or dangerous. These are things that humans don't want to or shouldn't be doing. >> Right, so what's the breakthrough tech that's enabling the robots to take this next step? >> Well, a lot of it is AI, right? So the fact that you don't have to be a data scientist and you can apply these algorithms that do facial recognition, that can actually help you to find your way around, it's actually the automation that's programmable. As I was saying, kids can program these robots, so they're not hard to do. So if a kid can do it, maybe somebody who knows oil and gas, insurance, security, can actually do the same thing. >> Right, so a lot of the AI stuff that people are familiar with is things like photo recognition and Google Photos, so I can search for my kids, I can search for a beach, I can search for things like that, and it'll come back. What are some of the types of AI and algorithms that you're applying with kind of this robot revolution? >> It's definitely things like the image analytics. It's for the routing. So let me give you an example of how easy it is to apply. So anybody who can play a video game, you have a video game type controller, so when your kid's, again, playing games, they're actually training for on the skilled jobs. Right, so you map a scene by using that controller to drive the robot around a factory, around the airport, and then, the AI algorithm is smart enough to create the map. And then, from that, we can actually use the robot just out of the box to be able to navigate and you have a place to, say, going from Teresa, here, and then, I might be able to go into the go get us a beer, right? >> Right, right. >> Maybe we should have that happen. (laughs) >> They're setting up right over there. >> They are setting up right there. >> That's right. So it's kind of like when you think of kind of the revolution of drones, which some people might be more familiar with 'cause they're very visible. >> Yes. >> Where when you operate a DJI drone now, you don't actually fly the drone. You're not controlling pitch and yaw and those things. You're just kind of telling it where you want it to go and it's the actual AI under the covers that's making those adjustments to thrust and power and angle. Is that a good analogy? >> That is a great analogy. >> And so, the work that we would do now is much more about how you string it together for the use case. If a robot were to come up to us now, what should it do, right? So if we're here, do we want the robot to even interact with us to get us that beer? So robots don't usually speak. Should speaking be an option for it? Should maybe it's just gesturing and it has a menu? We would know how to interact with it. So a lot of that human-robot interface is some of the work that we're doing. So that was kind of a silly example, but now, imagine that we were surveying an oil pipeline or we were actually as part of a manufacturing line, so in this case it's not getting us a beer, but it might need to do the same sort of thing. What sort of tool does Theresa need to actually finish her job? >> Yeah, and then, the other one is AI and me. And you just said that AI is getting less complicated to program, these machines are getting less complicated to program, but I think most people still are kind of stuck in the realm of we need a data scientist and there are not a lot of data scientists and they got to be super, super smart. You've got to have tons and tons of data and these types of factors, so how is it becoming AI and me, Jeff who's not necessarily a data scientist. I don't have a PhD in molecular physics, how's that going to happen? >> I think we need more of that democratization for the people who are not data scientists. So data scientists, they need the data, and so, a lot of the hard part is getting the data as to how it should interact, right? So in that example, we were saying how does Teresa and Jeff interact with the robot? The data scientist needs tons, right, thousands, tens of thousands of instances of those data types to actually make an insight. So what if, instead, when we think about AI and me, what about we think about, again, the human, not the, well, data scientists are people too. >> Right, right. >> But let's think about democratizing the rest of the humans to saying, how should I interact with the robot? So a lot of the research that we do is around how do you capture this expert knowledge. So we don't actually need to have tens of thousands of that. We can actually pretty much prescribe we don't want the robot to talk to us. We want him to give us the beer. So why don't we just use things like that? We don't have to start with all the data. >> Right, right, so I'm curious because there's a lot of conversation about machines plus people is better than one or the other, but it seems like it's much more complicated to program a robot to do something with a person as opposed to just giving it a simple task, which is probably historically what we've done more. Here, you go do that task. Now, people are not involved in that task. They don't have to worry about the nuance. They don't have to worry about reacting, reading what I'm trying to communicate. So is it a lot harder to get these things to work with people as opposed to kind of independently and carve off a special job? >> It may be harder, but that's where the value is. So if we think about the AI of, let's say, yesterday, there's a lot of dashboards. So it's with the pure data-driven, the pure AI operating on its own, it's going to look at the data. It's going to give us the insight. At the end of the day, the human's going to need to read, let's say, a static report and make a decision. Sometimes, I look at these reports and I have a hard time even understanding what I'm seeing, right? When they show me all these graphs, I'm supposed to be impressed. >> Right, right. >> I don't know what to do versus if you do. I use TurboTax as an example. When you're filing TurboTax, there's a lot of AI behind the scenes, but it's already looked at my data. As I'm filling in my return, it's telling me maybe you should claim this deduction. It's asking me yes or no questions. That's how I imagine AI at scale being in the future, right? It's not just for TurboTax, but everything we do. So in the robot, in the moment that we were describing, maybe it would see that you and I were talking, and it's not going to interrupt our conversation. But in a different context, if Teresa's by herself, maybe it would come up and say, hey, would you like a beer? >> Right, right. >> I think that's the sort of context that, like a TurboTax, but more sexy of course. >> Right, right, so I'm just curious from your perspective as a technologist, again, looking at your patent history, a lot of stuff on cloud, a lot of stuff on edge, but we've always kind of operated in this kind of new world, which is, if you had infinite compute, infinite storage, and infinite bandwidth, which was taking another. >> Yes. >> Big giant step with 5G, kind of what would you build and how could you build it? You got to just be thrilled as all three of those vectors are just accelerating and giving you, basically, infinite power in terms of tooling to work with. >> It is, I mean, it feels like magic. If you think about, I watch things like "Harry Potter", and you think about they know these spells and they can get things to happen. I think that's exactly where we are now. I get to do all these things that are magic. >> And are people ready for it? What's the biggest challenge on the people side in terms of getting them to think about what they could do, as opposed to what they know today? 'Cause the future could be so different. >> That is the challenge, right, because I think people, even with processes, they think about the process that existed today, where you're going to take AI and even robotics, and just make that process step faster. >> Right. >> But with AI and automation, what if we jumped that whole step, right? If as humans, if I can see everything 'cause I had all the data and then, I had AI telling me these are the important pieces, wouldn't you jump towards the answer? A lot of the processes that we have today are meant so that we actually explore all the conditions that need to be explored, that we do look at all the data that needs to be looked at. So you're still going to look at those things, right? Regulations, rules, that still happens, but what if AI and automation check those for you and all you're doing is actually checking the exceptions? So it's going to really change the way we do work. >> Very cool, well, Teresa, great to catch up and you're sitting right in the catbird seat, so exciting to see what your next patents will be, probably all about robotics as you continue to move this train forward. So thanks for the time. >> Thank you. >> All right, she's Teresa, I'm Jeff. You're watching theCUBE. We're at the Accenture Tech Vision 2020 Release Party on the 33rd floor of the Salesforce Tower. Thanks for watching. We'll see you next time. (upbeat music)

>> Announcer: From San Francisco, it's theCUBE, covering Accenture Tech Vision 2020. Brought to you by Accenture. >> Hey, welcome back, everybody. Jeff Frick here from theCUBE. We are high atop San Francisco at the Accenture Innovation Hub, 33rd floor of the Salesforce Tower. It's a beautiful night, but we're here for a very special occasion. It's the Tech Vision 2020 reveal, and we are happy to have the guy that runs the whole thing, he's going to reveal on stage a little bit later, but we got him in advance. He's Paul Daugherty, the chief technology and innovation officer for Accenture. Paul, great to see you as always. >> Great to see you, Jeff, too. It is a beautiful evening here, looking out over the Bay. >> If only we could turn the cameras around, but, sorry, we can't do that. >> Yeah. >> All right, so you've been at this now, the Tech Vision's been going on for 20 years, we heard earlier today. >> Yeah. >> You've been involved for almost a decade. How has this thing evolved over that time? >> Yeah, you know, we've been doing the Vision for 20 years, and what we've been trying to do is forecast what's happening with business and technology in a way that's actionable for executives. There's lots of trend forecasts and lists and things, but if you just see a list of cloud, or-- >> Jeff: Mobile's going to be really big. (laughs) >> AI, mobile, it doesn't really help you. We're trying to talk a little bit about the impact on business, impact to the world, and the decisions that you need to make. What's changed over that period of time is just the breadth of the impact that technology's having on people, so we focus a lot of our Visions on the impact on humans, on individuals, what's happening with technology, what the impact on business, we can talk about that a little bit more, but business is certainly not the back office of companies anymore. It's not just the back office and front office, either. In business, it's instrumental in the fabric of how every part of the company operates, their strategy, their operations, their products and services, et cetera, and that's really the trajectory we've seen. As technology advances, we have this accelerating exponential increase in technology, the implications for executives and the stakes just get higher and higher. >> It's weird, there are so many layers to this. One of the things we've talked about a lot is trust, and you guys talk about trust a lot. But what strikes me as kind of this dichotomy is on one hand, do I trust the companies, right? Do I trust Mark Zuckerberg with my data, to pick on him, he gets picked on all the time. That might be a question, but do I trust that Facebook is going to work? Absolutely. And so, our reliance on the technology, our confidence in the technology, our just baseline assumption that this stuff is going to work, is crazy high, up to and including people taking naps in their Teslas, (laughs) which are not autonomous vehicles! >> Not an advisable practice. >> Not autonomous vehicles! So it's this weird kind of split where it's definitely part of our lives, but it seems like kind of the consciousness is coming up as kind of the second order. What does this really mean to me? What does this mean to my data? What are people actually doing with this stuff? And am I making a good value exchange? >> Well, that's the, we talk in the Vision this year about value versus values, and the question you're asking is getting right at that, the crux between value and values. You know, businesses have been using technology to drive value for a long time. That's how applying different types of technology to enterprise, whether it be back to the mainframe days or ERP packages, cloud computing, et cetera, artificial intelligence. So value is what they were talking about in the Vision. How do you drive value using the technology? And one thing we found is there's a big gap. Only 10% of organizations are really getting full value in the way they're applying technology, and those that are are getting twice the revenue growth as companies that aren't, so that's one big gap in value. And this values point is really getting to be important, which is, as technology can be deployed in ways that are more pervasive and impact our experience, they're tracking our health details-- >> Right, right. >> They know where we are, they know what we're doing, they're anticipating what we might do next. How does that impact the values? And how are the values of companies important in other ways? The values you have around sustainability and other things are increasingly important to new generations of consumers and consumers who are thinking in new ways. This value versus values is teeing up what we call a tech-clash, which isn't a tech-lash, just, again, seeing people reacting against tech companies, as you said earlier, it's a tech-clash, which is the values that consumer citizens and people want sometimes clashing with the value of the models that companies have been using to deliver their products and services. >> Right. Well, it seems like it's kind of the "What are you optimizing for?" game, and it seems like it was such an extreme optimization towards profitability and shareholder value, and less, necessarily, employees, less, necessarily, customers, and certainly less in terms of the social impact. So that definitely seems to be changing, but is it changing fast enough? Are people really grasping it? >> Well, I think the data's mixed on that. I think there's a lot of mixed data on "What do people really want?" So people say they want more privacy, they say they want access and control of their data, but they still use a lot of the services that it may be inconsistent with the values that they talk about, and the values that come out in surveys. So, but that's changing. So consumers are getting more educated about how they want their data to be used. But the other thing that's happening is that companies are realizing that it's really a battle for experience. Experience is what, creating broader experiences, better experiences for consumers is what the battleground is. A great experience, whether you're a travel company or a bank or a manufacturing company, or whatever you might be, creating the experience requires data, and to get the data from an individual or another company, it takes trust. So this virtuous circle of experience, data, and trust is something that companies are realizing is essential to their competitive advantage going forward. We say trust is the currency of the digital and post-digital world that we're moving into. >> Right, it's just how explicit is that trust, or how explicit does it need to be? And as you said, that's unclear. People can complain on one hand, but continue to use the services, so it seems to be a little bit kind of squishy. >> It's a sliding scale. It's really a value exchange, and you have to think about it. What's the value exchange and the value that an individual consumer places on their privacy versus free access to a service? That's what's being worked out right now. >> Right, so I'm going to get your take on another thing, which is exponential curves, and you've mentioned time and time again, the pace of change is only accelerating. Well, you've been saying that, probably, for (laughs) 20 years. (Paul laughs) So the curve's just getting steeper. How do you see that kind of playing out over time? Will we eventually catch up? Is it just presumed that this is kind of the new normal? Or how is this going to shake out? 'Cause people aren't great at exponential curves. It's just not really in our DNA. >> Yeah, but I think that's the world we're operating in now, and I think the exponential potential is going to continue. We don't see a slowdown in the exponential growth rates of technology. So artificial intelligence, we're at the early days. Cloud computing, only about 20% enterprise adoption, a lot more to go. New adoptions are on the horizon, things like central bank digital currencies that we've done some research and done some work on recently. Quantum computing and quantum cryptography for networking, et cetera. So the pace of innovation is going to accelerate, and the challenge for organizations is rationalizing that and deciding how to incorporate that into their business, change their business, and change the way that they're leveraging their workforce and change the way that they're interacting with customers. And that's why what we're trying to address in the Vision is provide a little bit of that road map into how you digest it down. Now, there's also technology foundations of this. We talk about something at Accenture called living systems. Living systems is a new way of looking at the architecture of how you build your technology, because you don't have static systems anymore. Your systems have to be living and biological, adapting to the new technology, adapting to the business, adapting to new data over time. So this concept of living systems is going to be really important to organizations' success going forward. >> But the interesting thing is, one of the topics is "AI and Me," and traditional AI was very kind of purpose-built. For instance, Google Photos, can you find the cat? Can I find the kids at the beach? But you're talking about models where the AI can evolve and not necessarily be quite so data-centric around a specific application, but much more evolutionary and adaptable, based on how things change. >> Yeah, I think that's the future of AI that we see. There's been a lot of success in applying AI today, and a lot of it's been based on supervised learning, deep learning techniques that require massive amounts of data. Solving problems like machine vision requires massive amounts of data to do it right. And that'll continue. There'll continue to be problem sets that need large data. But what we're also seeing is a lot of innovation and AI techniques around small data. And we actually did some research recently, and we talk about this a little bit in our Vision, around the future being maybe smaller data sets and more structured data and intelligence around structured data, common-sense AI, and things that allow us to make breakthroughs in different ways. And that's, we used to look at "AI and Me," which is the trend around the workforce and how the workforce changes. It's those kinds of adaptations that we think are going to be really important. >> So another one is robotics, "Robots in the Wild." And you made an interesting comment-- >> Paul: Not "Robots Gone Wild," "Robots in the Wild," "Robots in the Wild." >> Well, maybe they'll go wild once they're in the wild. You never know. Once they get autonomy. Not a lot of autonomy, that's probably why. But it's kind of interesting, 'cause you talk about robots being designed to help people do a better job, as opposed to carving out a specific function for the robot to do without a person, and it seems like that's a much easier route to go, to set up a discrete thing that we can carve out and program the robot to do. Probably early days of manufacturing and doing spot welding in cars, et cetera. >> Right. >> So is it a lot harder to have the robot operate with its human partner, if you will, but are the benefits worth it? How do you kind of see that shaking out, versus, "Ah, I can carve out one more function"? >> Yeah, I think it's going to be a mix. I think there'll be, we see a lot of application of the robots paired with people in different ways, cobots in manufacturing being a great example, and something that's really taking off in manufacturing environments, but also, you have robots of different forms that serve human needs. There's a lot of interesting things going on in healthcare right now. How can you support autistic children or adults better using human-like robots and agents that can interact in different ways? A lot of interesting things around Alzheimer's and dealing with cognitive impairment and such using robots and robotics. So I think the future isn't, there's a lot of robots in the wild in the form of C-3POs and R2-D2s and those types of robots, and we'll see some of those. And those are being used widely in business today, even, in different contexts, but I think the interesting advance will be looking at robots that complement and augment and serve human needs more effectively. >> Right, right, and do people do a good enough job of getting some of the case studies? Like, you just walked through kind of the better use cases, the more humane use cases, the kind of cool medical breakthroughs, versus just continued optimization of getting me my Starbucks coupon when I walk by out front? (Paul laughs) >> Yeah, I'm not sure. >> Doesn't seem like I get the pub, like they just don't get the pub, I don't think. >> Yeah, yeah, yeah, maybe not. A little mixology is another (Jeff laughs) inflection that robots are getting good at. But I think that's what we're trying to do, is through the effort we do with the Vision, as well as our Tech for Good work and other things, is look at how we amplify and highlight some of the great work that is happening in those areas. >> So, you've been doing it for a decade. What struck you this year as being a little bit different, a little bit unexpected, not necessarily something you may have anticipated? >> I think the thing that is maybe a tipping point that I see in this Vision that I didn't anticipate is this idea that every company's really becoming a technology company. We said eight years ago, "Every business "will be a digital business," and that was, while ridiculed by some at the time, that really came true, and every business and every industry really is becoming digital or has already become digital. But I think we might've gotten it slightly wrong. Digital was kind of a step, but every company is deploying technology in the way they serve their customers, in the way they build their products and services. Every product and service is becoming technology-enabled. The ecosystem of technology providers is critical to companies in every industry. So every company's really becoming a technology company. Maybe every company needs to be as good as a digital native company at developing products and services, operating them. So I think that this idea of every company becoming a technology company, every CEO becoming a technology CEO, technology leader, is something that I think will differentiate companies going forward as well. >> Well, really, good work, you, Michael, and the team. It's fun to come here ever year, because you guys do a little twist. Like you said, it's not "Cloud's going to be really big, "mobile's going to be really big," but a little bit more thoughtful, a little bit more deep, a little bit longer kind of thought cycles on these trends. >> Yeah, and I think the, if you read through the Vision, we're trying to present a complete story, too, so it's, as you know, "We, the post-digital people." But if you look at innovation, "The I in Experience" is about serving your customers differently. "The Dilemma of Smart Machines" and "Robots in the Wild" is about your new products and services and the post-digital environment powered by technology. "AI and Me" is about the new workforce, and "Innovation DNA" is about driving continuous innovation in your organization, your culture, as you develop your business into the future. So it really is providing a complete narrative on what we think the future looks like for executives. >> Right, good, still more utopian than dystopian, I like it. >> More utopia than dystopia, but you got to steer around the roadblocks. (Jeff chuckles) >> All right, Paul, well, thanks again, and good luck tonight with the big presentation. >> Thanks, Jeff. >> All right, he's Paul, I'm Jeff. You're watching theCUBE. We're at the Accenture innovation reveal 2020, when we're going to know everything with the benefit of hindsight. Thanks for watching, (laughs) we'll see you next time. (upbeat pop music)

(upbeat music) >> From our studios in the heart of Silicon Valley, Palo Alto, California, this is a CUBE conversation. >> Hello, and welcome to theCUBE Studios here in Palo Alto, California. We have a special Around theCUBE segment, Unpacking AI. This is a Get Smart Series. We have three great guests. Rajen Sheth, VP of AI and Product Management at Google. He knows well the AI development for Google Cloud. Dr. Kate Darling, research specialist at MIT media lab. And Professor Barry O'Sullivan, Director SFI Centre for Training AI, University of College Cork in Ireland. Thanks for coming on, everyone. Let's get right to it. Ethics in AI as AI becomes mainstream, moves out to the labs and computer science world to mainstream impact. The conversations are about ethics. And this is a huge conversation, but first thing people want to know is, what is AI? What is the definition of AI? How should people look at AI? What is the definition? We'll start there, Rajen. >> So I think the way I would define AI is any way that you can make a computer intelligent, to be able to do tasks that typically people used to do. And what's interesting is that AI is something, of course, that's been around for a very long time in many different forms. Everything from expert systems in the '90s, all the way through to neural networks now. And things like machine learning, for example. People often get confused between AI and machine learning. I would think of it almost the way you would think of physics and calculus. Machine learning is the current best way to use AI in the industry. >> Kate, your definition of AI, do you have one? >> Well, I find it interesting that there's no really good universal definition. And also, I would agree with Rajen that right now, we're using kind of a narrow definition when we talk about AI, but the proper definition is probably much more broad than that. So probably something like a computer system that can make decisions independent of human input. >> Professor Barry, your take on the definition of AI, is there one? What's a good definition? >> Well, you know, so I think AI has been around for 70 years, and we still haven't agreed the definition for it, as Kate said. I think that's one of those very interesting things. I suppose it's really about making machines act and behave rationally in the world, ideally autonomously, so without human intervention. But I suppose these days, AI is really focused on achieving human level performance in very narrowly defined tasks, you know, so game playing, recommender systems, planning. So we do those in isolation. We don't tend to put them together to create the fabled artificial general intelligence. I think that's something that we don't tend to focus on at all, actually if that made sense. >> Okay the question is that AI is kind of elusive, it's changing, it's evolving. It's been around for awhile, as you guys pointed out, but now that it's on everyone's mind, we see it in the news every day from Facebook being a technology program with billions of people. AI was supposed to solve the problem there. We see new workloads being developed with cloud computing where AI is a critical software component of all this. But that's a geeky world. But the real world, as an ethical conversation, is not a lot of computer scientists have taken ethics classes. So who decides what's ethical with AI? Professor Barry, let's start with you. Where do we start with ethics? >> Yeah, sure, so one of the things I do is I'm the Vice-Chair of the European Commission's High-Level Expert Group on Artificial Intelligence, and this year we published the Ethics Guidelines for Trustworthy AI in Europe, which is all about, you know, setting an ethical standard for what AI is. You're right, computer scientists don't take ethical standards, but I suppose what we are faced with here is a technology that's so pervasive in our lives that we really do need to think carefully about the impact of that technology on, you know, human agency, and human well-being, on societal well-being. So I think it's right and proper that we're talking about ethics at this moment in time. But, of course, we do need to realize that ethics is not a panacea, right? So it's certainly something we need to talk about, but it's not going to solve, it's not going to rid us of all of the detrimental applications or usages of AI that we might see today. >> Kate, your take on ethics. Start all over, throw out everything, build on it, what do we do? >> Well, what we do is we get more interdisciplinary, right? I mean, because you asked, "Who decides?". Until now it has been the people building the technology who have had to make some calls on ethics. And it's not, you know, it's not necessarily the way of thinking that they are trained in, and so it makes a lot of sense to have projects like the one that Barry is involved in, where you bring together people from different areas of expert... >> I think we lost Kate there. Rajen, why don't you jump in, talk about-- >> (muffled speaking) you decide issues of responsibility for harm. We have to look at algorithmic bias. We have to look at supplementing versus replacing human labor, we have to look at privacy and data security. We have look at the things that I'm interested in like the ways that people anthropomorphize the technology and use it in a way that's perhaps different than intended. So, depending on what issue we're looking at, we need to draw from a variety of disciplines. And fortunately we're seeing more support for this within companies and within universities as well. >> Rajen, your take on this. >> So, I think one thing that's interesting is to step back and understand why this moment is so compelling and why it's so important for us to understand this right now. And the reason for that is that this is the moment where AI is starting to have an impact on the everyday person. Anytime I present, I put up a slide of the Mosaic browser from 1994 and my point is that, that's where AI is today. It's at the very beginning stages of how we can impact people, even though it's been around for 70 years. And what's interesting about ethics, is we have an opportunity to do that right from the beginning right now. I think that there's a lot that you can bring in from the way that we think about ethics overall. For example, in our company, can you all hear me? >> Yep. >> Mm-hmm. >> Okay, we've hired an ethicist within our company, from a university, to actually bring in the general principles of ethics and bring that into AI. But I also do think that things are different so for example, bias is an ethical problem. However, bias can be encoded and actually given more legitimacy when it could be encoded in an algorithm. So, those are things that we really need to watch out for where I think it is a little bit different and a little bit more interesting. >> This is a great point-- >> Let me just-- >> Oh, go ahead. >> Yeah, just one interesting thing to bear in mind, and I think Kate said this, and I just want to echo it, is that AI is becoming extremely multidisciplinary. And I think it's no longer a technical issue. Obviously there are massive technical challenges, but it's now become as much an opportunity for people in the social sciences, the humanities, ethics people. Legal people, I think need to understand AI. And in fact, I gave a talk recently at a legal symposium, and the idea of this on a parallel track of people who have legal expertise and AI expertise, I think that's a really fantastic opportunity that we need to bear in mind. So, unfortunately us nerds, we don't own AI anymore. You know, it's something we need to interact with the real world on a significant basis. >> You know, I want to ask a question, because you know, the algorithms, everyone talks about the algorithms and the bias and all that stuff. It's totally relevant, great points on interdisciplinary, but there's a human component here. As AI starts to infiltrate the culture and hit everyday life, the reaction to AI sometimes can be, "Whoa, my job's going to get automated away." So, I got to ask you guys, as we deal with AI, is that a reflection on how we deal with it to our own humanity? Because how we deal with AI from an ethics standpoint ultimately is a reflection on our own humanity. Your thoughts on this. Rajen, we'll start with you. >> I mean it is, oh, sorry, Rajen? >> So, I think it is. And I think that there are three big issues that I see that I think are reflective of ethics in general, but then also really are particular to AI. So, there's bias. And bias is an overall ethical issue that I think this is particular here. There's what you mentioned, future of work, you know, what does the workforce look like 10 years from now. And that changes quite a bit over time. If you look at the workforce now versus 30 years ago, it's quite a bit different. And AI will change that radically over the next 10 years. The other thing is what is good use of AI, and what's bad use of AI? And I think one thing we've discovered is that there's probably 10% of things that are clearly bad, and 10% of things that are clearly good, and 80% of things that are in that gray area in between where it's up to kind of your personal view. And that's the really really tough part about all this. >> Kate, you were going to weigh in. >> Well, I think that, I'm actually going to push back a little, not on Rajen, but on the question. Because I think that one of the fallacies that we are constantly engaging in is we are comparing artificial intelligence to human intelligence, and robots to people, and we're failing to acknowledge sufficiently that AI has a very different skillset than a person. So, I think it makes more sense to look at different analogies. For example, how have we used and integrated animals in the past to help us with work? And that lets us see that the answer to questions like, "Will AI disrupt the labor market?" "Will it change infrastructures and efficiencies?" The answer to that is yes. But will it be a one-to-one replacement of people? No. That said, I do think that AI is a really interesting mirror that we're holding up to ourselves to answer certain questions like, "What is our definition of fairness?" for example. We want algorithms to be fair. We want to program ethics into machines. And what it's really showing us is that we don't have good definitions of what these things are even though we thought we did. >> All right, Professor Barry, your thoughts? >> Yeah, I think there's many points one could make here. I suppose the first thing is that we should be seeing AI, not as a replacement technology, but as an assistive technology. It's here to help us in all sorts of ways to make us more productive, and to make us more accurate in how we carry out certain tasks. I think, absolutely the labor force will be transformed in the future, but there isn't going to be massive job loss. You know, the technology has always changed how we work and play and interact with each other. You know, look at the smart phone. The smart phone is 12 years old. We never imagined in 2007 that our world would be the way it is today. So technology transforms very subtly over long periods of time, and that's how it should be. I think we shouldn't fear AI. I think the thing we should fear most, in fact, is not Artificial Intelligence, but is actual stupidity. So I think we need to, I would encourage people not to think, it's very easy to talk negatively and think negatively about AI because it is such a impactful and promising technology, but I think we need to keep it real a little bit, right? So there's a lot of hype around AI that we need to sort of see through and understand what's real and what's not. And that's really some of the challenges we have to face. And also, one of the big challenges we have, is how do we educate the ordinary person on the street to understand what AI is, what it's capable of, when it can be trusted, and when it cannot be trusted. And ethics gets of some of the way there, but it doesn't have to get us all of the way there. We need good old-fashioned good engineering to make people trust in the system. >> That was a great point. Ethics is kind of a reflection of that mirror, I love that. Kate, I want to get to that animal comment about domesticating technology, but I want to stay in this culture question for a minute. If you look at some of the major tech companies like Microsoft and others, the employees are revolting around their use of AI in certain use cases. It's a knee-jerk reaction around, "Oh my God, "We're using AI, we're harming the world." So, we live in a culture now where it's becoming more mission driven. There's a cultural impact, and to your point about not fearing AI, are people having a certain knee-jerk reaction to AI because you're seeing cultures inside tech companies and society taking an opinion on AI. "Oh my God, it's definitely bad, our company's doing it. "We should not service those contracts. "Or, maybe I shouldn't buy that product "because it's listening to me." So, there's a general fear. Does this impact the ethical conversation? How do you guys see this? Because this is an interplay that we see that's a personal, it's a human reaction. >> Yeah, so if I may start, I suppose, absolutely there are, you know, the ethics debates is a critical one, and people are certainly fearful. There is this polarization in debate about good AI and bad AI, but you know, AI is good technology. It's one of these dual-use technologies. It can be applied to bad situation in ways that we would prefer it wasn't. And it can also, it's a force for tremendous good. So, we need to think about the regulation of AI, what we want it to do from a legal point of view, who is responsible, where does liability lie? We also think about what our ethical framework is, and of course, there is no international agreement on what is, there is no universal code of ethics, you know? So this is something that's very very heavily contextualized. But I think we certainly, I think we generally agree that we want to promote human well-being. We want to compute, we want to have a prosperous society. We want to protect the well-being of society. We don't want technology to impact society in any negative way. It's actually very funny. If you look back about 25-30 years ago, there was a technology where people were concerned that privacy was going to be a thing of the past. That computer systems were going to be tremendously biased because data was going to be incomplete and not representative. And there was a huge concern that good old-fashioned databases were going to be the technology that will destroy the fabric of society. That didn't happen. And I don't think we're going to have AI do that either. >> Kate? >> Yeah, we've seen a lot of technology panic, that may or may not be warranted, in the past. I think that AI and robotics suffers from a specific problem that people are influenced by science fiction and pop culture when they're thinking about the technology. And I feel like that can cause people to be worried about some things that maybe perhaps aren't the thing we should be worrying about currently. Like robots and jobs, or artificial super-intelligence taking over and killing us all, aren't maybe the main concerns we should have right now. But, algorithmic bias, for example, is a real thing, right? We see systems using data sets that disadvantage women, or people of color, and yet the use of AI is seen as neutral even though it's impinging existing biases. Or privacy and data security, right? You have technologies that are collecting massive amounts of data because the way learning works is you use lots of data. And so there's a lot of incentive to collect data. As a consumer, there's not a lot of incentive for me to want to curb that, because I want the device to listen to me and to be able to perform better. And so the question is, who is thinking about consumer protection in this space if all the incentives are toward collecting and using as much data as possible. And so I do think there is a certain amount of concern that is warranted, and where there are problems, I endorse people revolting, right? But I do think that we are sometimes a little bit skewed in our, you know, understanding where we currently are at with the technology, and what the actual problems are right now. >> Rajen, I want your thoughts on this. Education is key. As you guys were talking about, there's some things to pay attention to. How do you educate people about how to shape AI for good, and at the same time calm the fears of people at the same time, from revolting around misinformation or bad data around what could be? >> Well I think that the key thing here is to organize kind of how you evaluate this. And back to that one thing I was saying a little bit earlier, it's very tough to judge kind of what is good and what is bad. It's really up to personal perception. But then the more that you organize how to evaluate this, and then figure out ways to govern this, the easier it gets to evaluate what's in or out . So one thing that we did, was that we created a set of AI principles, and we kind of codified what we think AI should do, and then we codified areas that we would not go into as a company. The thing is, it's very high level. It's kind of like the constitution, and when you have something like the constitution, you have to get down to actual laws of what you would and wouldn't do. It's very hard to bucket and say, these are good use cases, these are bad use cases. But what we now have is a process around how do we actually take things that are coming in and figure out how do we evaluate them? A last thing that I'll mention, is that I think it's very important to have many many different viewpoints on it. Have viewpoints of people that are taking it from a business perspective, have people that are taking it from kind of a research and an ethics perspective, and all evaluate that together. And that's really what we've tried to create to be able to evaluate things as they come up. >> Well, I love that constitution angle. We'll have that as our last final question in a minute, that do we do a reset or not, but I want to get to that point that Kate mentioned. Kate, you're doing research around robotics. And I think robotics is, you've seen robotics surge in popularity from high schools have varsity teams now. You're seeing robotics with software advances and technology advances really become kind of a playful illustration of computer technology and software where AI is playing a role, and you're doing a lot of work there. But as intelligence comes into, say robotics, or software, or AI, there's a human reaction to all of this. So there's a psychology interaction to either AI and robotics. Can you guys share your thoughts on the humanization interaction between technology, as people stare at their phones today, that could be relationships in the future. And I think robotics might be a signal. You mentioned domesticating animals as an example back in the early days of when we were (laughing) as a society, that happened. Now we all have pets. Are we going to have robots as pets? Are we going to have AI pets? >> Yes, we are. (laughing) >> Is this kind of the human relationship? Okay, go ahead, share your thoughts. >> So, okay, the thing that I love about robots, and you know, in some applications to AI as well, is that people will treat these technologies like they're alive. Even though they know that they're just machine. And part of that is, again, the influence of science fiction and pop culture, that kind of primes us to do this. Part of it is the novelty of the technology moving into shared spaces, but then there's this actual biological element to it, where we have this inherent tendency to anthropomorphize, project human-like traits, behaviors, qualities, onto non-humans. And robots lend themselves really well to that because our brains are constantly scanning our environments and trying to separate things into objects and agents. And robots move like agents. We are evolutionarily hardwired to project intent onto the autonomous movement in our physical space. And this is why I love robots in particular as an AI use case, because people end up treating robots totally differently. Like people will name their Roomba vacuum cleaner and feel bad for it when it gets stuck, which they would never do with their normal vacuum cleaner, right? So, this anthropomorphization, I think, makes a huge difference when you're trying to integrate the technology, because it can have negative effects. It can lead to inefficiencies or even dangerous situations. For example, if you're using robots in the military as tools, and they're treating them like pets instead of devices. But then there are also some really fantastic use cases in health and education that rely specifically on this socialization of the robot. You can use a robot as a replacement for animal therapy where you can't use real animals. We're seeing great results in therapy with autistic children, engaging them in ways that we haven't seen previously. So there are a lot of really cool ways that we can make this work for us as well. >> Barry, your thoughts, have you ever thought that we'd be adopting AI as pets some day? >> Oh yeah, absolutely. Like Kate, I'm very excited about all of this too, and I think there's a few, I agree with everything Kate has said. Of course, you know, coming back to the remark you made at the beginning about people putting their faces in their smartphones all the time, you know, we can't crowdsource our sense of dignity, or that we can't have social media as the currency for how we value our lives or how we compare ourselves with others. So, you know, we do have to be careful here. Like, one of the really nice things about, one of the really nice examples of an AI system that was given some significant personality was, quite recently, Tuomas Sandholm and others at Carnegie Mellon produced this Liberatus poker playing bot, and this AI system was playing against these top-class Texas hold' em players. And all of these Texas hold 'em players were attributing a level of cunning and sophistication and mischief on this AI system that clearly it didn't have because it was essentially trying to just behave rationally. But we do like to project human characteristics onto AI systems. And I think what would be very very nice, and something we need to be very very careful of, is that when AI systems are around us, and particularly robots, you know, we do need to treat them with respect. And what I mean is, we do make sure that we treat those things that are serving society in as positive and nice a way as possible. You know, I do judge people on how they interact with, you know, sort of the least advantaged people in society. And you know, by golly, I will judge you on how you interact with a robot. >> Rajen-- >> We've actually done some research on that, where-- >> Oh, really-- >> We've shown that if you're low empathy, you're more willing to hit a robot, especially if it has a name. (panel laughing) >> I love all my equipment here, >> Oh, yeah? >> I got to tell ya, it's all beautiful. Rajen, computer science, and now AIs having this kind of humanization impact, this is an interesting shift. I mean, this is not what we studied in computer science. We were writin' code. We were going to automate things. Now there's notions of math, and not just math cognition, human relations, your thoughts on this? >> Yeah, you know what's interesting is that I think ultimately it boils down to the user experience. And I think there is this part of this which is around humanization, but then ultimately it boils down to what are you trying to do? And how well are you doing it with this technology? And I think that example around the Roomba is very interesting, where I think people kind of feel like this is more, almost like a person. But, also I think we should focus as well on what the technology is doing, and what impact it's having. My best example of this is Google Photos. And so, my whole family uses Google Photos, and they don't know that underlying it is some of the most powerful AI in the world. All they know is that they can find pictures of our kids and their grandkids on the beach anytime that they want. And so ultimately, I think it boils down to what is the AI doing for the people? And how is it? >> Yeah, expectations become the new user experience. I love that. Okay, guys, final question, and also humanization, we talked about the robotics, but also the ethics here. Ethics reminds me of the old security debate, and security in the old days. Do you increase the security, or do you throw it all away and start over? So with this idea of how do you figure out ethics in today's modern society with it being a mirror? Do we throw it all away and do a do-over, can we recast this? Can we start over? Do we augment? What's the approach that you guys see that we might need to go through right now to really, not hold back AI, but let it continue to grow and accelerate, educate people, bring value and user experience to the table? What is the path? We'll start with Barry, and then Kate, and then Rajen. >> Yeah, I can kick off. I think ethics gets us some of the way there, right? So, obviously we need to have a set of principles that we sign up to and agree upon. And there are literally hundreds of documents on AI ethics. I think in Europe, for example, there are 128 different documents around AI ethics, I mean policy documents. But, you know, we have to think about how are we actually going to make this happen in the real world? And I think, you know, if you take the aviation industry, that we trust in airplanes, because we understand that they're built to the highest standards, that they're tested rigorously, and that the organizations that are building these things are held account when things go wrong. And I think we need to do something similar in AI. We need good strong engineering, and you know, ethics is fantastic, and I'm a strong believer in ethical codes, but we do need to make it practical. And we do need to figure out a way of having the public trust in AI systems, and that comes back to education. So, I think we need the general public, and indeed ourselves, to be a little more cynical and questioning when we hear stories in the media about AI, because a lot of it is hyped. You know, and that's because researchers want to describe their research in an exciting way, but also, newspaper people and media people want to have a sticky subject. But I think we do need to have a society that can look at these technologies and really critique them and understand what's been said. And I think a healthy dose of cynicism is not going to do us any harm. >> So, modernization, do you change the ethical definition? Kate, what's your thoughts on all this? >> Well, I love that Barry brought up the aviation industry because I think that right now we're kind of an industry in its infancy, but if we look at how other industries have evolved to deal with some thorny ethical issues, like for example, medicine. You know, medicine had to develop a whole code of ethics, and develop a bunch of standards. If you look at aviation or other transportation industries, they've had to deal with a lot of things like public perception of what the technology can and can't do, and so you look at the growing pains that those industries have gone through, and then you add in some modern insight about interdisciplinary, about diversity, and tech development generally. Getting people together who have different experiences, different life experiences, when you're developing the technology, and I think we don't have to rebuild the wheel here. >> Yep. >> Rajen, your thoughts on the path forward, throw it all away, rebuild, what do we do? >> Yeah, so I think this is a really interesting one because of all the technologies I've worked in within my career, everything from the internet, to mobile, to virtualization, this is probably the most powerful potential for human good out there. And AI, the potential of what it can do is greater than almost anything else that's out there. However, I do think that people's perception of what it's going to do is a little bit skewed. So when people think of AI, they think of self-driving cars and robots and things like that. And that's not the reality of what AI is today. And so I think two things are important. One is to actually look at the reality of what AI is doing today and where it impacts people lives. Like, how does it personalize customer interactions? How does it make things more efficient? How do we spot things that we never were able to spot before? And start there, and then apply the ethics that we've already known for years and years and years, but adapt it to a way that actually makes sense for this. >> Okay, like that's it for the Around theCUBE. Looks like we've tallied up. Looks like Professor Barry 11, third place, Kate in second place with 13. Rajen with 16 points, you're the winner, so you get the last word on the segment here. Share your final thoughts on this panel. >> Well, I think it's really important that we're having this conversation right now. I think about back to 1994 when the internet first started. People did not have that conversation nearly as much at that point, and the internet has done some amazing things, and there have been some bad side effects. I think with this, if we have this conversation now, we have this opportunity to shape this technology in a very very positive way as we go forward. >> Thank you so much, and thanks everyone for taking the time to come in. All the way form Cork, Ireland, Professor Barry O'Sullivan. Dr. Kate Darling doing some amazing research at MIT on robotics and human psychology and like a new book coming out. Kate, thanks for coming out. And Rajen, thanks for winning and sharing your thoughts. Thanks everyone for coming. This is Around theCUBE here, Unpacking AI segment around ethics and human interaction and societal impact. I'm John Furrier with theCUBE. Thanks for watching. (upbeat music)

>> Announcer: Live from New York, it's theCube, covering AWS Global Summit 2019. Brought to you by Amazon Web Services. >> Welcome back. We're reaching towards the end of theCube's coverage of AWS Summit in New York City. I'm Stu Miniman, my co-host is Corey Quinn. Behind us, they're starting to roll out the beer trucks, but before we get there, we're really excited to have on the program first-time guest, Dmitry Traytel, who's the CTO of Timehop. Dmitry, thanks so much for joining us. >> Thanks for having me. >> All right, so Timehop, for our audience that's not familiar with it, I'm familiar with it on social media, is the "oh hey, here's your memory from a year ago, three years ago, five years ago." It's interesting always to know. I know I go to a lot of events, so it's like "Groundhog Day" to me. It's like, "oh hey, AWS New York City, I remember two years ago where I saw this person, this person, this person." We capture lots of videos and photos. We should probably figure out some partnership to bring some of those memories back when we do it, but >> Dmitry: Exactly. give us a little bit for those of us that might not know Timehop. Seems like there's more than just kind of the one thing. What's the company do? >> So, Timehop, the consumer product, the mobile app, is essentially a place for you to celebrate your digital memories, right? We are the nostalgia company, where you can look back on what you did on this day, and the kind of things that you posted on social media, Facebook, Twitter, Instagram, et cetera. And relive those things, share them with your friends, and also look at what's on your phone, in your local device. Stuff you haven't shared. So, the thousand photos you took of your kid at year one, you'll see a year later, and the year after that, and you get to relive those moments. >> Okay, very cool. So, boy there must be some good metadata underneath there. You talk about the content creation that goes on with most people. It's nice that in 2019, I don't really think too much about the thousands of photos that I have in my library. Boy, I know people that are pretty noisy on social media, and boy, you'd think their feed would be overwhelmed looking back on certain days, especially the guy sitting next to me. If it's a keynote day at a conference, Corey would be like, "oh boy, did I say those things?" Is it just, I get all of it, or is there some intelligence behind that? Give us a little bit of insight. What happens? >> Sure, there's definitely some intelligence behind it, a random link you might've shared out probably won't make it, but photos and videos certainly do. And any sort of text posts, tweet threads, Facebook statuses that you might've added, particularly those from 10 plus years ago, those are the most interesting ones, because people used Facebook in a very different way back then, then they do these days. Some people used it more, some less, and we try to feature especially those that have the most engagement, we try to surface those ahead of everything else. >> Yeah, I remember back in the old days of Facebook, where it was like, "Stu is," and then it was my thing there, it's like wow. The engagement that you'd have, and photos were all very different on all of these platforms before Facebook realized, "oh hey, photos are a pretty important thing there." So, you're the CTO. Bring us a little bit inside. I'm sure architecture is something you're talking about at a show like this. I have to believe AWS is a piece, if not a major piece, of what goes behind the scenes. So, bring us inside the technology a little bit. >> Absolutely. AWS is the bedrock upon which everything is built. We run over 200 instances on EC2. We're probably running about 20 different back-end services across around 15 to 20 different AWS services, and we're doing all of this with four back-end engineers. We're a very small company. One of those engineers, Mark, he's here, he spoke earlier today about how we were able to leverage AWS to essentially spin-off a whole new line of business that's not a consumer product, but a B2B offering for the ad industry. And that's kind of what we're announcing and talking about this week. We launched a new website about it, we have some early partners that we're working with, and this is the sort of thing that saved us as a company, and allowed us to become financially independent. Amazon was the bedrock of our ability to do that without increasing staff at all. >> So, what is the capability story that AWS unlocked as a part of that, or Cloud to the larger point. We don't necessarily need to be vendor-specific, despite the room we're sitting in. What was it that empowered for you that unlocked, I guess, the opportunity? >> There were a few things. Skill ability for one thing. We were able to go from 115, 120 instances, up to 200 very quickly when our clients needed us to, because a lot of them are larger than Timehop is, in terms of user base and access. The second one would've been global reach. We expanded from one availability zone, or rather one region, out to seven, because some of them are international, or have an international user base that requires us to be global. And then beyond that, just the breadth of services, like Elasticsearch, Kinesis Firehose. All of those things that let us connect the data from what we import from social media services, over to the user themselves, when they send push notifications or show the memories. The breadth of services that Amazon as a Cloud provider offers, means we don't have to write this stuff ourselves. We can just leverage what's already there, and we can connect all those dots, and deploy quickly. >> Yeah, the undifferentiated heavy lifting is the phrase that they're in love with to describe that. I always used to frame it slightly differently, as far as you're spending time locally, solving a global problem, where the things that the infrastructure provider can do at massive scale, it just makes sense. There's no competitive value for anyone anymore, and being able to go down to the data center, and replace a failing hard drive. So, why not make that someone who can get economies and scale out of it? And focus on >> Exactly. the way they're doing things that drive business value. But, that said, you said this awhile as well, and then the slide deck yet again today for the keynote, in the future, the only code you write is business value. And then, in a very tiny font that no one except me could read was probably in JavaScript, but that's neither here nor there. How close are we to that future, based upon what you're seeing? >> Close. I know we demonstrated the CDK, and the demonstration was in TypeScript, so we're one step away from the JavaScript world. Everything that we do, we do in Go, obviously other than some of the descriptor files that allow us to spin-off that infrastructure. But, we're incredibly close to being there, and Go is so close to the hardware itself, that I'm assuming Amazon will eventually support Go for that kind of CDK as well. I know they already do for Lambda, and that's relatively recent. I think it'll take a lot of companies a long time to get there, because there's a lot of processes than some of the larger enterprise words. We're fairly small, and we can pivot very quickly, as we've proven with the ad server called Nimbus. But, we're not that far away, at least at Timehop. >> So Dmitry, we live in the enterprise world a lot, and I have to imagine that there's some companies that would be like, "why am I going to work with this consumer social media company?" So, is being on a public Cloud, and specifically AWS, does that help give credibility behind the new services that you're offering? >> I think so. I think from a reliability and dependability standpoint, when we tell a mobile app publisher that they can trust us to run their ads for them, they know because we're on AWS that that's always going to be there. And, because we monetize for them, we end up having to depend on that reliability in order to promise them four nines above time. And, the fact that they can keep a revenue stream going at all times to keep the lights on and the doors open. >> And it's funny we're having this conversation today, when Twitter was hard down globally for an hour. So, nothing is going to be impenetrable. Nothing's going to stay up forever. I don't believe in making fun of companies for their down time, but at some point, past a certain point, it's okay. If there is a region-wide outage in AWS, for example, on that day, the internet's not going to be working super great for an awful lot of people. Depending on what your business model is, and what your use case is, maybe that's acceptable. Maybe in the case of my nonsense, the world is better off if it's not on the internet for that hour or two. But, it is a difference, I think, in the business modeling, between life-critical things, versus things that people use as entertainment. It feels like the B2B story that you're telling is somewhere in between those two ends of the spectrum. >> It certainly can be. One of the reasons we did go global is to prevent that sort of thing from happening. So, everything has a backup somewhere in a different hemisphere, which is awesome. But, depending on the kind of partner that we're working with, some of them are for looking through memories like us. Some of them are for reading short stories on the internet, which you can pause on that for an hour if Amazon goes down. For some others, they might be more mission-critical, like posting portfolios or resumes, and the free version might show ads. And in that case, you might be at a job interview, and you don't need that to go down. Now, the ad side can take a minute, and I'm sure whoever's depending on it has other fires to fight at the time. But for us, we have an obligation to all of our partners to make sure that we deliver on what we promised to them, and the same way that Amazon has to us. >> So Dmitry, what learnings can you share spinning off this new line of business, moving forward, working with Amazon there. What would you be talking to your peers about as to, is there anything you would've done a little bit differently, or now that you've gone through this, that you might recommend to them? >> I would say, build in-house what you can, if nobody else is doing it better than you can. I kind of wish that we had built Nimbus a lot earlier in our life cycle, because as soon as we built it, we prototyped it over a weekend, and we learned immediately that it was going to work better than any third-party ad-tech that we could've tried. At the same time, always evaluate what you're doing against your competition. Run those A/B tests, run them properly, measure, instrument everything, and in the end, understand where your dependencies are on third-parties. And eliminate them as much as possible. Again, we're so small that we do leverage as much third-party code. The best kind of code is the code you didn't have to write in the first place. But, in certain cases, you end up bringing a lot of value to the table by writing something proprietary, and kind of the way Amazon did with AWS when they built Rotor for themselves, and started offering it to everybody else. We're doing the same with Nimbus, where we wrote this Cloud-based ad platform, and we realized that it could help us. We're now realizing that it could help everybody else in our position. >> Okay. >> So Dmitry, we want to give you the final word here. Coming to an Amazon event like this, what's it mean to Timehop? What do you personally, you and the team, get out of it? >> It means a lot. It meant a lot to my colleague Mark to be able to speak today, to share with people some of our journey. Amazon is one of the partners that we work with, even on the ad side, 'cause that is a line of business Amazon has. And, we get to announce Nimbus as a service on adsbynimbus.com, with a website we just launched this week, to share with the world that Timehop is not just the consumer Timehop product. But, we are also this ad-tech company at this point that is growing very quickly, that is hiring. And, we want to continue to work with Amazon, and all of our other partners in order to scale that business. >> All right, well Dmitry, congratulations on the launch of the new product. We know a year from now what you'll be looking back at from this event. Apologies for that, but thank you so much for joining us. >> Thank you too. >> All right. For Corey Quinn, I'm Stu Miniman. We're at the end now of our day of coverage here from AWS New York City Summit for 2019. As always, go to thecube.net for all of the content here. We're at lots of AWS shows, many of the other Cloud infrastructure, big-data, AI, IOT, you name it. If there's a show out there with great information, great content, please contact us. Thank you as always for watching theCube.

>> Announcer: Live from Nashville, Tennessee, it's theCUBE, covering Commvault GO 2018. Brought to you by Commvault. >> Welcome to the Music City. You're watching theCUBE, the worldwide leader in live tech coverage. This is Commvault GO. 20-year-old company, Commvault, the third year of their show, and the first time we have theCUBE here, and the first time we've been in Nashville, Tennessee. I'm Stu Miniman, your host for one day of coverage and joining me to help unlock the Commvault is the CTO advisor, Keith Townsend. >> Good to be back on theCUBE. >> Yeah, Keith, so you've actually been to this show before. It's my first time. I've known Commvault for a long time, but, you know, we talk about companies, they're all going through some kind of digital transformation and Commvault is no exception. I love the energy that I'm seeing at this show. They've got great puns around data. Data is at the center of everything, and really comes to what we see. You know, we know that data is so important. All the tropes out there. It's the new oil, it's the new currency, it is one of the most important things, not only in IT, but in business. So what's your experience been, so far? >> So far great. You know, they did a great job, second go for me. Last year, they had Captain Sully, great inspirational talk. This year they had a comedian, Connell on it, did a fabulous job of fast-paced multimedia sessions, talking about the connection of data, our everyday lives, lives as a technologist. Really high-powered show, a lot of great conversation around data and its applicability. >> Yeah, I did love that. Steve Connell, he is a poet, and some humor, and a lot of geeky things in there, talking about, right, how data fits into all of our lives, and what we do. And then that's one of the reason's why we're here, why the customers are here, and that's what it's about. You look at a company like Commvault. They've got 10s of thousands of customers, and as the big wave's coming in, what is Cloud Mead? I like some of the messages. I know we're going to dig in, both in our analysis, as well as with the guests, how cloud is impacting this, as well as things like the wave of AI. How is that changing the product? How can I access the information? I hear things like ransomware and GDPR, and hacking. It's a dangerous time in technology, whether you're talking social media, or talking in business. So give us a little bit of background, what you're hearing. Keith, you're talking to customers in your day job all the time. How important is data? And things like backup and data recovery, where do they fit in their world? >> Well, you know what? Customers are still learning this journey. I've talked to plenty of customers that have used Commvault, competing products, and a lot of, at the low level, a lot of these guys are still thinking about it as backup, but great, great testimony from one of the larger customers, out there, Merck, who talked about using backup or data protection, as part of their data management strategy, moving workloads from worker mobility, moving workloads from cloud to cloud, location to location. Every customer is dealing with multi-cloud challenges. Stu, we've talked about multi-cloud and the keys to multi-cloud data is absolutely the most important part of getting your multi-cloud strategy, or even cloud strategy, straight. So, I'm looking forward to continuing the conversation I've had out in the field, which is customers challenged with how do I simply identify a data management strategy? To hearing Commvault's message today and throughout the guests that we'll have on, customers, partners, the entire ecosystem, about how Commvault enables multi-cloud through data management. >> Yeah, I was curious what I would see coming in. Would this be, kind of, a hard core, let's get in to the product and understand things like backup and recovery. As you know, backup's important, but recovery is everything. We heard some of the customer stories about how fast they can recover. Those are great stories. How does cloud fit into it? You had the CEO and the COO on stage talking about do you go, when you go to the cloud, do you go simple or do you go smart? And there's some nuance there that you'll want to unpack as to understanding. You know, as we look at cloud, it's not just take the way we were doing things and throw them up there. I mean Keith, they talked about tape and virtual tape. You know, I remember back when, like, the VTLs were first being a thing, I was working at a storage company back then. You know, it was a huge move. Backup, those processes, are really hardened into an environment. What do the admins have to do? What do they have to change in the way they're doing things? Let's look at the news a little bit. So, you know, there was the, Commvault did a good job, I think, of checking all the check boxes. While there was nothing that jumped out at me as, like, wow this is the first time I've heard it, it's what I'm hearing from customers. So, moving to, and as a service portfolio, they've got a full line of appliances, but it's not only hardware. If you'd like to buy the software from them, of course you could do that. Got a number of big partners. We're going to HPE on the program. We're going to have Cisco on the program. NetUP is another big, big partner here. As well as, I think that the product that they're most excited to talk about is Commvault Activate, which is really looking a lot of the governance, which, when you talk in a cloud world, is one of the biggest challenges. By the way, if people in the background hear these cheering, the Commvault employees are really excited, everybody's starting to walk on the show floor. We're in the center of it all, Keith. So, we got a preview yesterday, they actually announced it to the tech field day crew, which you and I sat in with. So, give me your thoughts as to what you saw in the product line. How does that line up with what you're hearing from customers in a competitive nature? >> So, I think I tweeted out yesterday, doing the tech field day session, Commvault does not sleep at the wheel. As you said, Stu, there's nothing amazingly new about what they announced, but a 20-year-old technology company is definitely keeping pace with the innovation that we've seen in the field. Customers want options when it comes to consuming backup and recovery. From a storage layer, they want the storage bricks, they want a hardware solution, they want to consume it via subscription, or perpetual license. They want this cloud-type capability. More importantly, they want, and they talked about it on stage today, this analytics capability. The ability to extract intelligence out of your data. Commvault calls is 4-D indexing. Other vendors just call it, simply, meta-data. But taking advantage of 15, 20 year-old data, to drive innovation in today's society, while keeping compliant with GDPR and other regulations that are coming up, sprouting up as it seems, every other week. >> I did like that terminology that you used. The 4-D innovation, because of course the fourth dimension is time and we're using intelligence. The challenge we have, as we know, is we have so much data and what do we the analytics for? They said we can use the analytics, first of all, compliance. I need to understand that I take care of that. Secondly, what if I want to cull data? What data don't I need anymore? What can I get rid of? There's huge cost savings that I can have there. And lastly, what can I get from analytics? How can I get value out of that information? And more. So, the use of analytics is something I was looking for, obviously want to talk to some of the product people, some of the customers, about what I've heard so far and talking to people. People were excited. I was actually talking to one of the partners of Commvault, they said one of the reasons they partnered deeper and are looking to work with Commvault, is they've got good tech. There's a reason they've been around for 20 years. They're a publicly traded stock. They've been doing well. They have been growing. Revenue wise, I looked, the last three years, I think they're at 700 million, they've been growing in the kind of eight to 9% year over year for the last couple years. Which, as a software company, it's not taking the world by storm, but for, in the infrastructure space, that is good growth. I do have to mention, there was some activist investor activity that came on. We actually we're going to have the CMO, we're going to have the COO on the program. We won't have the CEO, they are in the midst of going through a change there. And, you know, look, say what you will about activist investors. The reason they're getting involved is because they believe that there is more value that can be unlocked in Commvault with some changes and with product line and the things happening that's what we're starting to see here. That's why were excited to dig in and kind of understand. >> Yeah, we can see that even in some of the tech customer's testimonials. The state of Colorado net new customer. This is amazing in an area that we've seen 90 million, 250 million, easily a half a million dollars of investment in the data protection space. Commvault, 20-year-old company, still gaining traction with net new use cases and if I was an activist investor, I'd look at that. I'd look at the overall industry and thinking what can we do to unlock some of the potential of a fairly large customer base? Pretty stable company, but a very, very exciting part of the industry. >> Yeah, and Keith, you brought up meta-data. Meta-data's something that, you know, in the industry we've been talking about for a long time. It's really that intelligence that's going to allow the systems to gather everything. I know, when I get my brand new phone now, I can search my 4,000 photos by location, by date, everything like that. It's auto-recognizing information. The same thing we're getting on the business side. It used be oh okay, let's make sure when you put your photo, your file, in there that you tag it. Come on. Nobody can do this. Nobody's thinking when I'm doing my job, well I really need to think about the meta data 'cause five years from now, I might want to do it. Oh, I can search by person or project or things like that. But it's the intelligence in the system to be able to learn and grow and the more data we have, actually the more that the intelligence can get there. >> And that's critically important for even compliance. Again, culling data. You know, Bill Nye got up on stage and talked about being able to use data, or I'm sorry, AstraZeneca got up on stage and talked about using data that was 15-years-old to rerun through today's algorithms and trials. If you were to cull the wrong data, then they could not have the innovation that they've created by having 15-year-old data. So, the meta data, the ability to go back again, search your repository for key words, content, surface up that data and leverage that data. This is why we say data is the new currency, it's the new oil, it's the most critical. I even heard on stage today, data's the new water. I don't know if I'd go quite that far, you know I like my old-fashioned glass of water, but this is why we hear these terms because companies are reinventing themselves with the data. >> Alright, so Keith, what Dave Allante would point out is water is a limited resource. Data, we can reuse it. We can take a drink of data, we can share it. Data helps complete us. It's the shirts that they have at the show. We've got AstraZeneca, we've got the state of Colorado, we've got other users. The key partners, key executives. We're going to bring you the key data to help you extract the signal from the noise here at Commvault GO. For Keith Townsend, I'm Stu Miniman. Thanks for joining theCUBE. (upbeat music)

>> Narrator: Live from Las Vegas, it's The Cube. Covering IBM Think 2018. Brought to you by IBM. >> Hello everyone and welcome to The Cube here at IBM Think in Las Vegas, the Mandalay Bay. I'm John Furrier, the host of The Cube. We're here in this Cube studio as a set for IBM Think. My next guest is Jennifer Shiin who's the founder of 8 Path Solutions. Twitter handle Jenn, J-S-H-I-N. Great to see you. Thanks for joining me. >> Yeah, happy to be here. >> I'm glad you stopped by. I wanted to get your thoughts. You're thought leader in the industry. You've been on multiple Cube panels. Thank you very much. And also Cube alumni. You know, IBM with the data center of the value proposition. The CEO's up on the stage today saying you got data, you got blockchain and you got AI, which is such the infrastructure of the future. And AI is the software of the future, data's at the middle. Dave and I were talking about that as the innovation sandwich. The data is being sandwiched between blockchain and AI, two super important things. And she also mentioned Moore's law. Faster, smaller, cheaper. Every 6 months doubling in speed and performance. And then Metcalfe's law, which is more of a network effect. Kind of teasing out token economics. You see kind of where the world's going. This is an interesting position from IBM. I like it. Is it real? >> Well it sounds very data sciency, right? You have the economics part, you have the networking. You have all these things in your plane. So I think it's very much in line with what you would expect if data science actually sustains (mumbles), which thankfully it has. >> Yeah. >> And I think the reality is you know, we like to boil things down into nice, simple concepts but in the real world when you're actually figuring it all out its going to be multiple effects. It's going to be, you know a lot of different things that interact. >> And they kind of really tease out their cloud strategy in a very elegant way. I mean they essentially said, 'Look we're into the cloud and we're not going to try to.' They didn't say it directly, but they basically said it. We're not going to compete with Amazon head-to-head. We're going to let our offerings to do the talking. We're going to use data and give customers choice with multi cloud. How does that jive for you? How does that work because at the end of the day I got to have business logics. I need applications. >> Yes. >> You know whether its blockchains, cryptocurrency or apps. The killer app's now money. >> Yep. >> If no one's making any money. >> Sure. >> No commerce is being done. >> Right. I mean I think it makes sense. You know, Amazon has such a strong hold in the infrastructure part, right? Being able to store your data elsewhere and have it be cloud. I don't think that was really IBM's core business. You know, a lot of I think their business model was built around business and business relationships and these days, one of the great things about all these data technologies is that one company doesn't have to do all of it, right? You have partnerships and actually partners so that you know, one company does AI. You partner with another company that has data. And that way you can actually both make money, right? There's more than enough work to go around and that much you can say having worked in data science teams right? If I can offload some of my work to different divisions, fantastic. That'd be great. Saves us time. You get to market faster. You can build things quicker. So I think that's one of the great things about what's happening with data these days, right? There's enough work to get around. >> And it's beautiful too because if you think about the concept that made cloud great is DevOps. Blockchain is an opportunity to use desensualization to take away a lot of inefficiencies. AI is also an automation opportunity to create value. So you got inefficiencies on block chains side and AI to create value, your thoughts and reaction to where that's going to go. You know, in light of the first death on a Uber self-driving car. Again, historic yesterday right? And so you know, the reality is right there. We're not perfect. >> Yeah. >> But there's a path. >> Well so most of its inefficiency out there. It's not the technology. It's all the people using technology, right? You broke the logic by putting in something you shouldn't have put in that data set, you know? The data's now dirty because you put in things that you know, the developer didn't think you'd put in there. So the reality is we're going to keep making mistakes and there will be more and more opportunities for new technologies to help you know, cheer that up. >> So I was talking to Rob Thomas, GM of the analytics team. You know Rob, great guy. He's smart. He's also an executive but he knows the tech. He and I were talking about this notion of data containers. So with Kubernetes now front and center as an orchestration layer for cloud and application workloads, IBM has an interesting announcement with this cloud private approach. Where data is the central thing in this. Because you've got things like GDPR out there and the regulatory environment not going to get any easier. You got blockchain crypto. That's a regulatory nightmare. We know a GDBR. That's a total nightmare. So this is happening, right? So what should customers be doing, in your experience? Customers are scratching their head. They don't want to make a wrong bet, but they need good data, good strategy. They need to do things differently. How do they get the best out of their data architecture knowing that there's hurdles and potential blockers in front of them? >> Well so I think you want to be careful of what you select. and how much are you going to be indebted to that one service that you selected, right? So if you're not sure yet maybe you don't want to invest all of your budget into this one thing you're not sure is going to be what you really want to be paying for a year or two, right? So I think being really open to how you're going to plan for things long term and thinking about where you can have some flexibility, whereas certain things you can't. For instance, if you're going to be in an industry that is going to be you know, strict on regulatory requirements right? Then you have less wiggle room than let's say an industry where that's not going to be an absolute necessary part of your technology. >> Let me ask you a question and being kind of a historian you know, what say one year is seven dog years or whatever the expression is in the data space. It just seems like yesterday that Hadoop was going to save the world. So that as kind of context, what is some technologies that just didn't pan out? Is the data link working? You know, what didn't work and what replaced it if you can make an observation? >> Well, so I think that's hard because I think the way I understood technology is probably not the way everyone else did right? I mean, you know at the end of the day it just is being a way to store data right? And just being able to use you know, more information store faster, but I'll tell you what I think is hilarious. I've seen people using Hadoop and then writing sequel queries the same way we did like ten plus years ago, same inefficiencies and they're not leveling the fact that it's Hadoop. Right? They're treating it like I want to create eight million tables and then use joins. So they're not really using the technology. I think that's probably the biggest disappointment is that without that knowledge sharing, without education you have people making the same mistakes you made when technology wasn't as efficient. >> I mean if you're a hammer, everything else is like a nail I guess if that's the expression. >> Right. >> On the exciting side, what are you excited about in technology right now? What are you looking at that's a you know, next 20 mile stare of potential goodness that could be coming out of the industry? >> So I think anytime you have better science, better measurements. So measurement's huge, right? If you think about media industry, right? Everyone's trying to measure. I think there was an article that came out about some of YouTube's failure about measurement, right? And I think in general like Facebook is you know, very well known for measurement. That's going to be really interesting to see, right? What methodologies come out in terms of how well can we measure? I think another one will be say, target advertising right? That's another huge market that you know, a lot of companies are going after. I think what's really going to be cool in the next few years is to see what people come up with, right? It's really the human ingenuity of it, right? We have the technology now. We have data engineers. What can we actually build? And how are we going to be able to partner to be able to do that? >> And there's new stacks that are developing. You think about the ecommerce stack. It's a 30 year old stack. AdTech and DNS and cookiing, now you've got social and network effects going on. You mentioned you know, the Metcalfe's law. So with all that, I want to get just your personal thoughts on blockchain. Beyond blockchain, token economics because there are a lot people who are doing stuff with crypto. But what's really kind of pointing as a mega trands standpoint is a new class of desensualized application developers are coming in. >> Right. >> Okay. They're dealing with data now on a desensualized basis. At the heart of that is the token economics, which is changing some of the business model dynamics. Have you seen anything? Your thoughts on token economics? >> So I haven't seen it from the economics standpoint. I've seen it from more of the algorithms and that standpoint. I actually have a good friend of mine, she's at Yale. And she actually runs the, she's executive director of their corporate law center. So I hear some from her on the legal side. I think what's really interesting is there's all these different arenas. Legal being a very important component in blockchain. As well as, from the mathematical standpoint. You know when I was in school way back when, we studied things like hash keys and you know, RSA keys and so from a math standpoint that's also a really cool aspect of it. So I think it's probably too early to say for sure what the economics part is going to actually look like. I think that's going to be a little more longterm. But what is exciting about this, is you actually see different parts of businesses, right? Not just the financial sector but also the legal sector and then you know say, the math and algorithms and you know. Having that integration of being able to build cooler things for that reason. >> Yeah the math's certainly exciting. Machine learning, obviously that's well documented. The growth and success of what, and certainly the interests are there. You seeing Amazon celebrating all the time. I just saw Werner Vogels, the CTO. Talking about another SageMaker, a success. They're looking at machine learning that way. You got Google with TensorFlow. You've got this goodness in these libraries now that are in the community. It's kind of a perfect storm of innovation. What's new in the ML world that developers are getting excited about that companies are harnessing for value? You seeing anything there? Can you share some commentary on the current machine learning trends? >> So I think a lot of companies have gotten a little more adjusted to the idea of ML. At the beginning everyone was like, 'Oh this is all new.' They loved the idea of it but they didn't really know what they were doing, right? Right now they know a little bit more. I think in general everyone thinks deep learning is really cool, neural networks. I think what's interesting though is everyone's trying to figure out where's the line. What's the different between AI versus machine learning versus deep learning versus neural networks. I think it's a little bit fun for me just to see everyone kind of struggle a little bit and actually even know the terminology so we can have a conversation. So I think all of that, right? Just anything related to that you know, when do you TensorFlow? What do you use it for? And then also say, from Google right? Which parts do you actually send through an API? I mean that's some of the conversations I've been having with people in the business industry, like which parts do you send through an API. Which parts do you actually have in house versus you know, having to outsource out? >> And that's really kind of your thinking there is what, around core competencies where people need to kind of own it and really build a core competency and then outsource where its more a femoral invalue. Is there a formula, I guess to know when to bring it in house and build around? >> Right. >> What's your thoughts there? >> Well part of it, I think is scalability. If you don't have the resources or the time, right? Sometimes time. If you don't have the time to build it in house, it does make sense actually to outsource it out. Also if you don't think that's part of your core business, developing that within house do you're spending all that money and resources to hire the best data scientists, may not be worth it because in fact the majority of your actual sales is with the sale department. I mean they're the ones that actually bring in that revenue. So I think it's finding a balance of what investment's actually worth it. >> And sometimes personnel could leave and you could be a big problem, you know. Someone walks about the door, gets another job because its a hot commodity to be. >> That's actually one of the big complaints I've heard is that we spend all this time investing in certain young people and then they leave. I think part of this is actually that human factor. How do you encourage them to stay? >> Let's talk about you. How did you get here? School? Interests? Did you go off the path? Did you come in from another vector? How did you get into what you're doing now and share a little bit about who you are? >> Yeah so I studied economics, mathematics, creative writing as an undergrad and statistics as a grad student. So you know, kind of perfect storm. >> Natural math, bring it all together. >> Yeah but you know its funny because I actually wrote about and talked about how data is going to be this big thing. This is like 2009, 2010 and people didn't think it was that important, you know? I was like next three to five years mathematicians are going to be a hot hire. No one believed me. So I ended up going, 'Okay well, the economy crashed.' I was in management consulting in finance, private equity hedge funds. Everyone swore like, if you do this you're going to be set for life, right? You're on the path. You'll make money and then the economy crashed. All the jobs went away. And I went, 'Maybe not the best career choice for me.' So I did what I did at companies. I looked at the market and I went, 'Where's their growth?' I saw tech had growth and decided I'm going to pick up some skills I've never had before, learn to develop more. I mean in the beginning I had no idea what an application development process was, right? I'm like, 'What does that mean to actually develop an application?' So the last few years I've really just been spending, just learning these things. What's really cool though is last year when my patents went through and I was able to actually able to launch something with Box at their keynote. That was really awesome. >> Awesome. >> So I became a long way from I think, have the academic knowledge to being able to apply it and then learn the technologies and then developing the technologies, which is a cool thing. >> Yeah and that's a good path because you came in with a clean sheet of paper. You didn't have any dogma of waterfall and all the technologies. So you kind of jumped in. Did you use like a cloud to build on? Was it Amazon? Was it? >> Oh that's funny too. Actually I do know Legacy's technology quite well because I was in corporate America before. Yeah, so like Sequel. For instance like when I started working data science, funny enough we didn't call it data science. We just called it like whatever you call it, you know. There was no data science term at that point. You know we didn't have that idea of whether to use R or Python. I mean I've used R over ten years, but it was for statistics. It was never for like actual data science work. And then we used Sequel in corporate America. When I was taking data it was like in 2012. Around then, everyone swore that no, no. They're going to programmers. Got to know programming. To which, I'm like really? In corporate America, we're going to have programmers? I mean think about how long it's going to take to get someone to learn any language and of course, now everyone's learning. It's on Sequel again right? So. >> Isn't it fun to like, when you see someone on Facebook or Linkdin, 'Oh man data's a new oil.' And then you say, 'Yeah here's a blog post I wrote in 2009.' >> Right. Yeah, exactly. Well so funny enough Ginni Rometty today was saying about exponential versus linear and that's one of the things I've been saying over the last year about because you know, you want exponential growth. Because linear anyone can do. That's a tweet. That's not really growth. >> Well we value your opinion. You've been great on The Cube. Great to help us out on those panels, got a great view. What's going on with your company? What are you working on now? What's exciting you these days? >> Yeah so one of the cool things we worked on, it's very much in line with what the IBM announcement was, so being smarter, right? So I developed some technology in the photo industry, digital assent management as well as being able to automate the renaming of files, right? So you think you probably a picture on your digital camera you never moved over because you, I remember the process. You open it, you rename it, you saved it. You open the next one. Takes forever. >> Sometimes its the same number. I got same version files. It's a nightmare. >> Exactly. So I basically automated that process of having all of that automatically renamed. So the demo that I did I had 120 photos renamed in less than two minutes, right? Just making it faster and smarter. So really developing technologies that you can actually use every day and leverage for things like photography and some cooler stuff with OCR, which is the long term goal. To be able to allow photographers to never touch the computer and have all of their clients photos automatically uploaded, renamed and sent to the right locations instantly. >> How did you get to start that app? Are you into photography or? >> No >> More of, I got a picture problem and I got to fix it? >> Well actually its funny. I had a photographer taking my picture and she showed me what she does, the process. And I went, 'This is not okay. You can do better than this.' So I can code so I basically went to Python and went, 'Alright I think this could work,' built a proof of concept and then decided to patent it. >> Awesome. Well congratulations on the patent. Final thoughts here about IBM Think? Overall sentiment of the show? Ginni's keynote. Did you get a chance to check anything out? What's the hallway conversations like? What are some of the things that you're hearing? >> So I think there's a general excitement about what might be coming, right? So a lot of the people who are here are actually here to, I think share notes. They want to know what everyone else is doing, so that's actually great. You get to see more people here who are actually interested in this technology. I think there's probably some questions about alignment, about where does everything fit. That seems to be a lot of the conversation here. It's much bigger this year as I'm sure you've noticed, right? It's a lot bigger so that's probably the biggest thing I've heard like there's so many more people than we expected there to be so. >> I like the big tent events. I'm a big fan of it. I think if I was going to be critical I would say, they should do a business event and do a technical one under the same kind of theme and bring more alpha geeks to the technical one and make this much more of a business conversation because the business transformation seems to be the hottest thing here but I want to get down in the weeds, you know? Get down and dirty so I would like to see two. That's my take. >> I think its really hard to cater to both. Like whenever I give a talk, I don't give a really nerdy talk to say a business crowd. I don't give a really business talk to a nerdy crowd, you know? >> It's hard. >> You just have to know, right? I think they both have a very different sensibility, so really if you want to have a successful talk. Generally you want both. >> Jennifer thanks so much for coming by and spending some time with The Cube. Great to see you. Thanks for sharing your insights. Jennifer Shin here inside The Cube at IBM Think 2018. I'm John Furrier, host of The Cube. We'll be back with more coverage after this short break.

>> Announcer: Live from San Jose, it's theCUBE. Presenting Big Data, Silicon Valley, brought to you by Silicon Angle Media and its ecosystem partners. >> Welcome back to theCUBE's continuing coverage of our event Big Data SV. I'm Lisa Martin, joined by Dave Vellante and we've been here all day having some great conversations really looking at big data, cloud, AI machine-learning from many different levels. We're happy to welcome back to theCUBE one of our distinguished alumni, Ron Bodkin, who's now the Technical Director of Applied AI at Google. Hey Ron, welcome back. >> It's nice to be back Lisa, thank you. >> Yeah, thanks for coming by. >> Thanks Dave. >> So you have been a friend of theCUBE for a long time, you've been in this industry and this space for a long time. Let's take a little bit of a walk down memory lane, your perspectives on Big Data Hadoop and the evolution that you've seen. >> Sure, you know so I first got involved in big data back in 2007. I was VP in generating a startup called QuantCast in the online advertising space. You know, we were using early versions of Hadoop to crunch through petabytes of data and build data science models and I saw a huge opportunity to bring those kind of capabilities to the enterprise. You know, we were working with early Hadoop vendors. Actually, at the time, there was really only one commercial vendor of Hadoop, it was Cloudera and we were working with them and then you know, others as they came online, right? So back then we had to spend a lot of time explaining to enterprises what was this concept of big data, why it was Hadoop as an open source could get interesting, what did it mean to build a data lake? And you know, we always said look, there's going to be a ton of value around data science, right? Putting your big data together and collecting complete information and then being able to build data science models to act in your business. So you know, the exciting thing for me is you know, now we're at a stage where many companies have put those assets together. You've got access to amazing cloud scale resources like we have at Google to not only work with great information, but to start to really act on it because you know, kind of in parallel with that evolution of big data was the evolution of the algorithms as well as the access to large amounts of digital data that's propelled, you know, a lot of innovation in AI through this new trend of deep learning that we're invested heavily in. >> I mean the epiphany of Hadoop when I first heard about it was bringing, you know, five megabytes of code to a petabyte of data as sort of the bromide. But you know, the narrative in the press has really been well, they haven't really lived up to expectations, the ROI has been largely a reduction on investment and so is that fair? I mean you've worked with practitioners, you know, all your big data career and you've seen a lot of companies transform. Obviously Google as a big data company is probably the best example of one. Do you think that's a fair narrative or did the big data hype fail to live up to expectations? >> I think there's a couple of things going on here. One is, you know, that the capabilities in big data have varied widely, right? So if you look at the way, for example, at Google we operate with big data tools that we have, they're extremely productive, work at massive scale, you know, with large numbers of users being able to slice and dice and get deep analysis of data. It's a great setup for doing machine learning, right? That's why we have things like BigQuery available in the cloud. You know, I'd say that what happened in the open source Hadoop world was it ended up settling in on more of the subset of use cases around how do we make it easy to store large amounts of data inexpensively, how do we offload ETL, how do we make it possible for data scientists to get access to raw data? I don't think that's as functional as what people really had imagined coming out of big data. But it's still served a useful function complementing what companies were already doing at their warehouse, right? So I'd say those efforts to collect big data and to make them available have really been a, they've set the stage for analytic value both through better building of analytic databases but especially through machine learning. >> And there's been some clear successes. I mean, one of them obviously is advertising, Google's had a huge success there. But much more, I mean fraud detection, you're starting to see health care really glom on. Financial services have been big on this, you know, maybe largely for marketing reasons but also risk, You know for sure, so there's been some clear successes. I've likened it to, you know, before you got to paint, you got to scrape and you got to, you put in caulking and so forth. And now we're in a position where you've got a corpus of data in your organization and you can really start to apply things like machine learning and artificial intelligence. Your thoughts on that premise? >> Yeah, I definitely think there's a lot of truth to that. I think some of it was, there was a hope, a lot of people thought that big data would be magic, that you could just dump a bunch of raw data without any effort and out would come all the answers. And that was never a realistic hope. There's always a level of you have to at least have some level of structure in the data, you have to put some effort in curating the data so you have valid results, right? So it's created a set of tools to allow scaling. You know, we now take for granted the ability to have elastic data, to have it scale and have it in the cloud in a way that just wasn't the norm even 10 years ago. It's like people were thinking about very brittle, limited amounts of data in silos was the norm, so the conversation's changed so much, we almost forget how much things have evolved. >> Speaking of evolution, tell us a little bit more about your role with applied AI at Google. What was the genesis of it and how are you working with customers for them to kind of leverage this next phase of big data and applying machine learning so that they really can identify, well monetize content and data and actually identify new revenue streams? >> Absolutely, so you know at Google, we really started the journey to become an AI-first company early this decade, a little over five years ago. We invested in the Google X team, you know, Jeff Dean was one of the leaders there, sort of to invest in, hey, these deep learning algorithms are having a big impact, right? Fei-Fei Li, who's now the Chief Scientist at Google Cloud was at Stanford doing research around how can we teach a computer to see and catalog a lot of digital data for visual purposes? So combining that with advances in computing with first GPUs and then ultimately we invested in specialized hardware that made it work well for us. The massive-scale TPU's, right? That combination really started to unlock all kinds of problems that we could solve with machine learning in a way that we couldn't before. So it's now become central to all kinds of products at Google, whether it be the biggest improvements we've had in search and advertising coming from these deep learning models but also breakthroughs, products like Google Photos where you can now search and find photos based on keywords from intelligence in a machine that looks at what's in the photo, right? So we've invested and made that a central part of the business and so what we're seeing is as we build up the cloud business, there's a tremendous interest in how can we take Google's capabilities, right, our investments in open source deep learning frameworks, TensorFlow, our investments in hardware, TPU, our scalable infrastructure for doing machine learning, right? We're able to serve a billion inferences a second, right? So we've got this massive capability we've built for our own products that we're now making available for customers and the customers are saying, "How do I tap into that? "How can I work with Google, how can I work with "the products, how can I work with the capabilities?" So the applied AI team is really about how do we help customers drive these 10x opportunities with machine learning, partnering with Google? And the reason it's a 10x opportunity is you've had a big set of improvements where models that weren't useful commercially until recently are now useful and can be applied. So you can do things like translating languages automatically, like recognizing speech, like having automated dialog for chat bots or you know, all kinds of visual APIs like our AutoML API where engineers can feed up images and it will train a model specialized to their need to recognize what you're looking for, right? So those types of advances mean that all kinds of business process can be reconceived of, and dramatically improved with automation, taking a lot of human drudgery out. So customers are like "That's really "exciting and at Google you're doing that. "How do we get that, right? "We don't know how to go there." >> Well natural language processing has been amazing in the last couple of years. Not surprising that Google is so successful there. I was kind of blown away that Amazon with Alexa sort of blew past Siri, right? And so thinking about new ways in which we're going to interact with our devices, it's clearly coming, so it leads me into my question on innovation. What's driven in your view, the innovation in the last decade and what's going to drive innovation the next 10 years? >> I think innovation is very much a function of having the right kind of culture and mindset, right? So I mean for us at Google, a big part of it is what we call 10x thinking, which is really focusing on how do you think about the big problem and work on something that could have a big impact? I also think that you can't really predict what's going to work, but there's a lot of interesting ideas and many of them won't pan out, right? But the more you have a culture of failing fast and trying things and at least being open to the data and give it a shot, right, and say "Is this crazy thing going to work?" That's why we have things like Google X where we invest in moonshots but that's where, you know, throughout the business, we say hey, you can have a 20% project, you can go work on something and many of them don't work or have a small impact but then you get things like Gmail getting created out of a 20% project. It's a cultural thing that you foster and encourage people to try things and be open to the possibility that something big is on your hands, right? >> On the cultural front, it sounds like in some cases depending on the enterprise, it's a shift, in some cases it's a cultural journey. The Google on Google story sounds like it could be a blueprint, of course, how do we do this? You've done this but how much is it a blueprint on the technology capitalizing on deep learning capabilities as well as a blueprint for helping organizations on this cultural journey to be actually being able to benefit and profit from this? >> Yeah, I mean that's absolutely right Lisa that these are both really important aspects, that there's a big part of the cultural journey. In order to be an AI-first company, to really reconceive your business around what can happen with machine learning, it's important to be a digital company, right? To have a mindset of making quick decisions and thinking about how data impacts your business and activating in real time. So there's a cultural journey that companies are going through. How do we enable our knowledge workers to do this kind of work, how do we think about our products in a new way, how do we reconceive, think about automation? There's a lot of these aspects that are cultural as well, but I think a big part of it is, you know, it's easy to get overwhelmed for companies but it's like you have pick somewhere, right? What's something you can do, what's a true north, what's an area where you can start to invest and get impact and start the journey, right? Start to do pilots, start to get something going. What we found, something I've found in my career has been when companies get started with the right first project and get some success, they can build on that success and invest more, right? Whereas you know, if you're not experimenting and trying things and moving, you're never going to get there. >> Momentum is key, well Ron, thank you so much for taking some time to stop by theCUBE. I wish we had more time to chat but we appreciate your time. >> No, it's great to be here again. >> See ya. >> We want to thank you for watching theCUBE live from our event, Big Data SV in San Jose. I'm Lisa Martin with Dave Vellante, stick around we'll be back with our wrap shortly. (relaxed electronic jingle)

>> Announcer: Live from Las Vegas, it's theCUBE. Covering AWS re:Invent 2017. Presented by AWS, Intel and our ecosystem of partners. >> Hey, welcome back everyone. Live here this is theCUBE in Las Vegas for AWS Amazon Web Services re:Invent 2017. Our 5th year covering the event. Wall to wall coverage. Three days, this is our day two. 45,000 people here. Developers and business connecting together this year. Big show. Amazon continues its growth. I'm John Furrier, my co-host Justin Warren. Our next guest is from Washington County Sheriff's Office using Amazon, Amazon Recognition, Chris Adzima, who is the Senior Information Systems Analyst at the Washington County Sheriff. Welcome to theCUBE. >> Nice to have you. >> So Chris. >> be here. >> So, so tons of cool stuff we saw on stage today. You know they've had polylex out for awhile. But you're gonna start to see some of these multi-media services around. Human identification, transcription, Recognition's been out for awhile. With the power of the cloud, you can start rollin' out some pretty cool services. You have one of 'em, talk about your solution and what you guys are doing with it. >> Sure, about last year when Recognition was announced, I wanted to provide our deputies at the Sheriff's office with the way to identify people based on videos that we get from either surveillance or eyewitnesses. So, I looked into Recognition and decided that we should give it a try by giving all of our booking photos or mugshots up to the cloud for it to be indexed. So, that's what I did. I indexed all, about 300,000 booking photos, we have in the last 10 years, and put that into a Recognition Collection. And now I can use the simple tools that AWS gives me to search against that index for any new image that we get in, either from surveillance or an eyewitness, allowing us to get identification within seconds as opposed to having to go through all 700 employees at the Sheriff's Office for the chance that they might have known the person. >> So the old way was essentially grab the footage, and then do the old mugshot kinda scan manually, right? >> Yeah, manually. It wasn't in a book, it was on a website, but essentially, yeah, you had to-- >> I made my point, it sucks. It's hard as hell. >> It's very difficult, very difficult. >> You see on TV all the magic pictures goin' on and the facial recognition, you see on the movies and stuff. How close are we to that right now in terms of that capability? >> Well as far as facial recognition goes it all depends on the data that you have at your fingertips. Right now I have booking photos, so I can identify people with a very high level of certainty if they've been in our jail. If they haven't been in our jail, I obviously don't have much of a chance of identifying them. So, what you see on the TV where it's like, we looked through all the DMV records. We looked through all of the people on the street and all this stuff, We're pretty far off from that because nobody has a catalog of all those images. >> You need to incorporate of all the pictures, all the data. >> Yeah, but when you have the data, it's very simple. >> Right, and it's a lot like scanning for fingerprints. It's like, people would have seen that. You know, you have a fingerprint that you've collected from the crime scene-- >> Chris: Exactly. >> We see it on NCIS or something where you scan through all of that. So, it's pretty similar to that. >> Yeah, it's similar to that, or DNA, or anything like that. If you have the data set, it's very easy to search for those people. >> Yeah. >> So, faces are no different. >> So, how long did it take you to get up and running? Did you have to ingest the photos? How did you do that or? >> So... >> John: They're on a website so you had 'em on digital already. >> From never knowing anything about Amazon Web Services, to a fully-functional prototype of this product took me 30 days. >> John: Wow. >> I had the photos uploaded and the ability to actually run the searches via the API in three. So, extremely easy. Extremely easy. >> So, given the success that you've had with that particular producr, are there other services at AWS that you're looking into? That say, hey, that would actually be really useful for us? >> Yes, a couple that were announced today. First off, the recognition for video. Something that we have a problem with, and I'm hoping recognition for video's going to help with is when you have a surveillance camera, people are moving all the time. Therefore, trying to get a screenshot is going to get a blurry image. We're not getting good results with low-light or low frame rate. But recognition for video is gonna be able to take that movement and still look at the face. Hopefully we're gonna be able to get a better facial identification that way. >> Justin: Okay. >> Another thing that I want to look into is this DeepLens they just announced today. >> John: Awesome. >> That looks extremely promising in the way of me being able to teach it things that we need. A great example of what I would use this for is when a inmate comes in, we take pictures of scars, marks and tattoos. That way, we have a database of all the scars, marks and tattoos on somebody. In case, if they recommit a crime and our eye-witness says, "They had a skull tattoo on their chest" we can then look through all of the people that have a skull tattoo and say, "These are our list of possible suspects." The problem with that is, is that you may enter somebody in as a skull, and you may enter it in as crossbones. Somebody else might put an accidental I in there. So it's very hard to do a text search against that. But if recognition were to come through, or it wouldn't be recognition in this case. If whatever model I built with the DeepLens came through, and said this is a skull and this is the word we use, then I'd be able to index all of those images, quickly pull them up, so we wouldn't even need a picture. We would just need to know, from an eye-witness, that there was a skull on that person's chest. >> John: We had a guest on yesterday from Thorn, which Intel is doing AI for good, and they use essentially, and they didn't say Craigslist, but trying to look for women who were being sold for prostitution, and exploited children and whatnot. And it's all machine learning, and some natural language processing. When you look at the Sage announcement, that looks promising, 'cause they're gonna make, as I was try to democratize the heavy-lifting around all of this, you know, voodoo machine learning. Which, I mean, if you're totally a computer science geek and that's all you do, yeah, you could probably master machine learning. But if you're a practitioner, you're just whipping up. >> Well, yeah, and that's a good example. Because I am not a data scientist. I have no idea how this stuff works in the back end. But being able to utilize, stand on the shoulders of these giants, so to speak, is allowing people like me who A, I only have seven people on my team to devote to this kind of thing. We don't have a lot of resources. We wouldn't be able to get a data scientist. But opening this stuff up to us allows us to build these things, like this facial recognition and other things based on machine learning. And ultimately keep our citizens safe through the work that AWS does in getting this to us. >> Justin: Yeah, and we've been saying at a couple of different interviews so far, that humans don't scale. So these tools that provide the humans that you do have a lot more leverage to get things done. So, we were talking just before, before we started recording that these are tools that assist the humans. You're not replacing the humans with machines that just go oh we're gonna cede all decision-making to you. This is just another tool like being able to fingerprint people and search that. It's one more way of doing the standard policing that you are already doing. >> Exactly, and the tool that I've already created, and any tool I create after that, doesn't ever look to replace our deputies or our detectives. We give them things so that they don't have to do the things like flipping through that book for hours upon hours. They can be out in the field, following the leads, keeping the community safe and apprehending these criminals. >> Do they have on body cameras too? >> Not yet. We are currently looking into body cameras. >> John: That's a trend. They're gonna be instrumented basically like warriors: fully loaded, you know, cameras. >> I tend not to think of it like that. Only because, again, that's a tool that we use. Not to, you know, be that land-warrior so to speak. But more of a-- >> Documentation, I mean, you see 'em on cars when people get pulled over. >> Exactly. >> You've got the evidence. >> It's documentation, just like anything else. It's just that one more tool that helps that deputy, that detective, that police officer get a better idea of the entire situation. >> Maybe I shouldn't have said war. Maybe I'm just into the Twitch culture where they're all geared up with all the gear. Okay, so next question for you is what's your vibe on the show? Obviously you have great experience working at Amazon. You're a success study because you're trying to get a job done, you got some tools and, >> Right. >> making it happen. What's your take this year? What's your vibe of the show? >> I'm really excited about a lot of stuff I'm seeing at the show. A lot of the announcements seemed like they were almost geared towards me. And I know they weren't obviously, but it really felt like announcement after announcement were these things that I'm wanting to go home and immediately start to play with. Anywhere from the stuff that was in the machine learning to the new elastic containers that they are announcing, to the new LAM defunctions that they're talking about. I mean, just all over the board. I'm very excited for all these new things that I get to go home and play with. >> What do you think, Justin? What's your take on the vibe show? >> I find that it's an interesting show. I'm finding it a little different than what I was expecting. This is my first time here at AWS re:Invent. I go to a lot of other trade shows and I was expecting more of like a developer show. Like I'm going to CubeCon next week and that's full of people with spiky hair, and pink shoes, and craziness. >> John: That's the area, by the way. >> Oh that's the area, right. It's a bit more casual than some of the other more businessy sort of conferences. I mean, here I am, wearing a jacket. So I don't feel completely out of place here, but it does feel like it's that blending of business and use cases and the things that you actually get done with it as well as there being people who have the tools that they want to go and build amazing new things with. >> Chris: Right, right, yeah. >> So it's a nice blend, I think. >> Yeah, I've found that it definitely doesn't feel like any other developer conference I've been to. But being in the public sector, I tend to go to the more business-suit conferences. >> John: This is like total developer for you, from a public sector perspective. >> From where I'm coming from, this is very laid back. And extremely... >> Oh yeah. >> But at the same time, it's very like a mixture. Like you said, you see executives mingling with the developers talking about things-- >> John: You're a good example I think of Amazon. First of all, there's the builder thing in the area is supposed to be pretty cool. I was told to go there last night. People came back, it was very much builder, kind of maker culture. They're doing prototypes, it was very developer-oriented. But the public sector, I'm astonished by Amazon's success there because the stuff is easy and low-cost to get in. And public sector is not known for its agility. >> Chris: No. >> I mean, it's music to your ears, right? I mean, if you're in the public sector, you're like, "What? Now I can get it done?" >> Very much so. And one thing I love to share about our solution is the price, right? Because I spent $6 a month for my AWS bill. Right? >> John: Wow. >> That's extremely easy to sell to tax payers, right? It's extremely easy to sell to the higher-ups in government to say, I'm gonna tinker around with this, but even if we solve one crime, we've already seen a return on our investment above and beyond what we expected. >> Yeah. >> No brainer, no brainer. Chris, thanks so much for sharing your story. We really appreciate it. Congratulations on your success and keep in touch with theCube. Welcome to theCube Alumni Club. >> Alright. >> John: For coming out, it's theCube here. Amazon re:Invent, bringing all the action down, all of the success stories, all of the analysis. I'm John Furrier with theCube. More live coverage after this short break. (upbeat music)

(upbeat techno music) >> Announcer: Good morning ladies and gentlemen. This is Lenovo Transform. Please welcome to the stage Lenovo's Rod Lappin. (upbeat instrumental) >> Alright, ladies and gentlemen. Here we go. I was out the back having a chat. A bit faster than I expected. How are you all doing this morning? (crowd cheers) >> Good? How fantastic is it to be in New York City? (crowd applauds) Excellent. So my name's Rod Lappin. I'm with the Data Center Group, obviously. I do basically anything that touches customers from our sales people, our pre-sales engineers, our architects, et cetera, all the way through to our channel partner sales engagement globally. So that's my job, but enough of that, okay? So the weather this morning, absolutely fantastic. Not a cloud in the sky, perfect. A little bit different to how it was yesterday, right? I want to thank all of you because I know a lot of you had a lot of commuting issues getting into New York yesterday with all the storms. We have a lot of people from international and domestic travel caught up in obviously the network, which blows my mind, actually, but we have a lot of people here from Europe, obviously, a lot of analysts and media people here as well as customers who were caught up in circling around the airport apparently for hours. So a big round of applause for our team from Europe. (audience applauds) Thank you for coming. We have some people who commuted a very short distance. For example, our own server general manager, Cameron (mumbles), he's out the back there. Cameron, how long did it take you to get from Raleigh to New York? An hour-and-a-half flight? >> Cameron: 17 hours. >> 17 hours, ladies and gentleman. That's a fantastic distance. I think that's amazing. But I know a lot of us, obviously, in the United States have come a long way with the storms, obviously very tough, but I'm going to call out one individual. Shaneil from Spotless. Where are you Shaneil, you're here somewhere? There he is from Australia. Shaneil how long did it take you to come in from Australia? 25 hour, ladies and gentleman. A big round of applause. That's a pretty big effort. Shaneil actually I want you to stand up, if you don't mind. I've got a seat here right next to my CEO. You've gone the longest distance. How about a big round of applause for Shaneil. We'll put him in my seat, next to YY. Honestly, Shaneil, you're doing me a favor. Okay ladies and gentlemen, we've got a big day today. Obviously, my seat now taken there, fantastic. Obviously New York City, the absolute pinnacle of globalization. I first came to New York in 1996, which was before a lot of people in the room were born, unfortunately for me these days. Was completely in awe. I obviously went to a Yankees game, had no clue what was going on, didn't understand anything to do with baseball. Then I went and saw Patrick Ewing. Some of you would remember Patrick Ewing. Saw the Knicks play basketball. Had no idea what was going on. Obviously, from Australia, and somewhat slightly height challenged, basketball was not my thing but loved it. I really left that game... That was the first game of basketball I'd ever seen. Left that game realizing that effectively the guy throws the ball up at the beginning, someone taps it, that team gets it, they run it, they put it in the basket, then the other team gets it, they put it in the basket, the other team gets it, and that's basically the entire game. So I haven't really progressed from that sort of learning or understanding of basketball since then, but for me, personally, being here in New York, and obviously presenting with all of you guys today, it's really humbling from obviously some of you would have picked my accent, I'm also from Australia. From the north shore of Sydney. To be here is just a fantastic, fantastic event. So welcome ladies and gentlemen to Transform, part of our tech world series globally in our event series and our event season here at Lenovo. So once again, big round of applause. Thank you for coming (audience applauds). Today, basically, is the culmination of what I would classify as a very large journey. Many of you have been with us on that. Customers, partners, media, analysts obviously. We've got quite a lot of our industry analysts in the room. I know Matt Eastwood yesterday was on a train because he sent a Tweet out saying there's 170 people on the WIFI network. He was obviously a bit concerned he was going to get-- Pat Moorhead, he got in at 3:30 this morning, obviously from traveling here as well with some of the challenges with the transportation, so we've got a lot of people in the room that have been giving us advice over the last two years. I think all of our employees are joining us live. All of our partners and customers through the stream. As well as everybody in this packed-out room. We're very very excited about what we're going to be talking to you all today. I want to have a special thanks obviously to our R&D team in Raleigh and around the world. They've also been very very focused on what they've delivered for us today, and it's really important for them to also see the culmination of this great event. And like I mentioned, this is really the feedback. It's not just a Lenovo launch. This is a launch based on the feedback from our partners, our customers, our employees, the analysts. We've been talking to all of you about what we want to be when we grow up from a Data Center Group, and I think you're going to hear some really exciting stuff from some of the speakers today and in the demo and breakout sessions that we have after the event. These last two years, we've really transformed the organization, and that's one of the reasons why that theme is part of our Tech World Series today. We're very very confident in our future, obviously, and where the company's going. It's really important for all of you to understand today and take every single snippet that YY, Kirk, and Christian talk about today in the main session, and then our presenters in the demo sections on what Lenovo's actually doing for its future and how we're positioning the company, obviously, for that future and how the transformation, the digital transformation, is going ahead globally. So, all right, we are now going to step into our Transform event. And I've got a quick agenda statement for you. The very first thing is we're going to hear from YY, our chairman and CEO. He's going to discuss artificial intelligence, the evolution of our society and how Lenovo is clearly positioning itself in the industry. Then, obviously, you're going to hear from Kirk Skaugen, our president of the Data Center Group, our new boss. He's going to talk about how long he's been with the company and the transformation, once again, we're making, very specifically to the Data Center Group and how much of a difference we're making to society and some of our investments. Christian Teismann, our SVP and general manager of our client business is going to talk about the 25 years of ThinkPad. This year is the 25-year anniversary of our ThinkPad product. Easily the most successful brand in our client branch or client branch globally of any vendor. Most successful brand we've had launched, and this afternoon breakout sessions, obviously, with our keynotes, fantastic sessions. Make sure you actually attend all of those after this main arena here. Now, once again, listen, ask questions, and make sure you're giving us feedback. One of the things about Lenovo that we say all the time... There is no room for arrogance in our company. Every single person in this room is a customer, partner, analyst, or an employee. We love your feedback. It's only through your feedback that we continue to improve. And it's really important that through all of the sessions where the Q&As happen, breakouts afterwards, you're giving us feedback on what you want to see from us as an organization as we go forward. All right, so what were you doing 25 years ago? I spoke about ThinkPad being 25 years old, but let me ask you this. I bet you any money that no one here knew that our x86 business is also 25 years old. So, this year, we have both our ThinkPad and our x86 anniversaries for 25 years. Let me tell you. What were you guys doing 25 years ago? There's me, 25 years ago. It's a bit scary, isn't it? It's very svelte and athletic and a lot lighter than I am today. It makes me feel a little bit conscious. And you can see the black and white shot. It shows you that even if you're really really short and you come from the wrong side of the tracks to make some extra cash, you can still do some modeling as long as no one else is in the photo to give anyone any perspective, so very important. I think I might have got one photo shoot out of that, I don't know. I had to do it, I needed the money. Let me show you another couple of photos. Very interesting, how's this guy? How cool does he look? Very svelte and athletic. I think there's no doubt. He looks much much cooler than I do. Okay, so ladies and gentlemen, without further ado, it gives me great honor to obviously introduce our very very first guest to the stage. Ladies and gentlemen, our chairman and CEO, Yuanqing Yang. or as we like to call him, YY. A big round of applause, thank you. (upbeat techno instrumental) >> Good morning everyone. Thank you, Rod, for your introduction. Actually, I didn't think I was younger than you (mumbles). I can't think of another city more fitting to host the Transform event than New York. A city that has transformed from a humble trading post 400 years ago to one of the most vibrant cities in the world today. It is a perfect symbol of transformation of our world. The rapid and the deep transformations that have propelled us from the steam engine to the Internet era in just 200 years. Looking back at 200 years ago, there was only a few companies that operated on a global scale. The total value of the world's economy was around $188 billion U.S. dollars. Today, it is only $180 for each person on earth. Today, there are thousands of independent global companies that compete to sell everything, from corn and crude oil to servers and software. They drive a robust global economy was over $75 trillion or $1,000 per person. Think about it. The global economy has multiplied almost 450 times in just two centuries. What is even more remarkable is that the economy has almost doubled every 15 years since 1950. These are significant transformation for businesses and for the world and our tiny slice of pie. This transformation is the result of the greatest advancement in technology in human history. Not one but three industrial revolutions have happened over the last 200 years. Even though those revolutions created remarkable change, they were just the beginning. Today, we are standing at the beginning of the fourth revolution. This revolution will transform how we work (mumbles) in ways that no one could imagine in the 18th century or even just 18 months ago. You are the people who will lead this revolution. Along with Lenovo, we will redefine IT. IT is no longer just information technology. It's intelligent technology, intelligent transformation. A transformation that is driven by big data called computing and artificial intelligence. Even the transition from PC Internet to mobile Internet is a big leap. Today, we are facing yet another big leap from the mobile Internet to the Smart Internet or intelligent Internet. In this Smart Internet era, Cloud enables devices, such as PCs, Smart phones, Smart speakers, Smart TVs. (mumbles) to provide the content and the services. But the evolution does not stop them. Ultimately, almost everything around us will become Smart, with building computing, storage, and networking capabilities. That's what we call the device plus Cloud transformation. These Smart devices, incorporated with various sensors, will continuously sense our environment and send data about our world to the Cloud. (mumbles) the process of this ever-increasing big data and to support the delivery of Cloud content and services, the data center infrastructure is also transforming to be more agile, flexible, and intelligent. That's what we call the infrastructure plus Cloud transformation. But most importantly, it is the human wisdom, the people learning algorithm vigorously improved by engineers that enables artificial intelligence to learn from big data and make everything around us smarter. With big data collected from Smart devices, computing power of the new infrastructure under the trend artificial intelligence, we can understand the world around us more accurately and make smarter decisions. We can make life better, work easier, and society safer and healthy. Think about what is already possible as we start this transformation. Smart Assistants can help you place orders online with a voice command. Driverless cars can run on the same road as traditional cars. (mumbles) can help troubleshoot customers problems, and the virtual doctors already diagnose basic symptoms. This list goes on and on. Like every revolution before it, intelligent transformation, will fundamentally change the nature of business. Understanding and preparing for that will be the key for the growth and the success of your business. The first industrial revolution made it possible to maximize production. Water and steam power let us go from making things by hand to making them by machine. This transformed how fast things could be produced. It drove the quantity of merchandise made and led to massive increase in trade. With this revolution, business scale expanded, and the number of customers exploded. Fifty years later, the second industrial revolution made it necessary to organize a business like the modern enterprise, electric power, and the telegraph communication made business faster and more complex, challenging businesses to become more efficient and meeting entirely new customer demands. In our own lifetimes, we have witnessed the third industrial revolution, which made it possible to digitize the enterprise. The development of computers and the Internet accelerated business beyond human speed. Now, global businesses have to deal with customers at the end of a cable, not always a handshake. While we are still dealing with the effects of a digitizing business, the fourth revolution is already here. In just the past two or three years, the growth of data and advancement in visual intelligence has been astonishing. The computing power can now process the massive amount of data about your customers, suppliers, partners, competitors, and give you insights you simply could not imagine before. Artificial intelligence can not only tell you what your customers want today but also anticipate what they will need tomorrow. This is not just about making better business decisions or creating better customer relationships. It's about making the world a better place. Ultimately, can we build a new world without diseases, war, and poverty? The power of big data and artificial intelligence may be the revolutionary technology to make that possible. Revolutions don't happen on their own. Every industrial revolution has its leaders, its visionaries, and its heroes. The master transformers of their age. The first industrial revolution was led by mechanics who designed and built power systems, machines, and factories. The heroes of the second industrial revolution were the business managers who designed and built modern organizations. The heroes of the third revolution were the engineers who designed and built the circuits and the source code that digitized our world. The master transformers of the next revolution are actually you. You are the designers and the builders of the networks and the systems. You will bring the benefits of intelligence to every corner of your enterprise and make intelligence the central asset of your business. At Lenovo, data intelligence is embedded into everything we do. How we understand our customer's true needs and develop more desirable products. How we profile our customers and market to them precisely. How we use internal and external data to balance our supply and the demand. And how we train virtual agents to provide more effective sales services. So the decisions you make today about your IT investment will determine the quality of the decisions your enterprise will make tomorrow. So I challenge each of you to seize this opportunity to become a master transformer, to join Lenovo as we work together at the forefront of the fourth industrial revolution, as leaders of the intelligent transformation. (triumphant instrumental) Today, we are launching the largest portfolio in our data center history at Lenovo. We are fully committed to the (mumbles) transformation. Thank you. (audience applauds) >> Thanks YY. All right, ladies and gentlemen. Fantastic, so how about a big round of applause for YY. (audience applauds) Obviously a great speech on the transformation that we at Lenovo are taking as well as obviously wanting to journey with our partners and customers obviously on that same journey. What I heard from him was obviously artificial intelligence, how we're leveraging that integrally as well as externally and for our customers, and the investments we're making in the transformation around IoT machine learning, obviously big data, et cetera, and obviously the Data Center Group, which is one of the key things we've got to be talking about today. So we're on the cusp of that fourth revolution, as YY just mentioned, and Lenovo is definitely leading the way and investing in those parts of the industry and our portfolio to ensure we're complimenting all of our customers and partners on what they want to be, obviously, as part of this new transformation we're seeing globally. Obviously now, ladies and gentlemen, without further ado once again, to tell us more about what's going on today, our announcements, obviously, that all of you will be reading about and seeing in the breakout and the demo sessions with our segment general managers this afternoon is our president of the data center, Mr. Kirk Skaugen. (upbeat instrumental) >> Good morning, and let me add my welcome to Transform. I just crossed my six months here at Lenovo after over 24 years at Intel Corporation, and I can tell you, we've been really busy over the last six months, and I'm more excited and enthusiastic than ever and hope to share some of that with you today. Today's event is called "Transform", and today we're announcing major new transformations in Lenovo, in the data center, but more importantly, we're celebrating the business results that these platforms are going to have on society and with international supercomputing going on in parallel in Frankfurt, some of the amazing scientific discoveries that are going to happen on some of these platforms. Lenovo has gone through some significant transformations in the last two years, since we acquired the IBM x86 business, and that's really positioning us for this next phase of growth, and we'll talk more about that later. Today, we're announcing the largest end-to-end data center portfolio in Lenovo's history, as you heard from YY, and we're really taking the best of the x86 heritage from our IBM acquisition of the x86 server business and combining that with the cost economics that we've delivered from kind of our China heritage. As we've talked to some of the analysts in the room, it's really that best of the east and best of the west is combining together in this announcement today. We're going to be announcing two new brands, building on our position as the number one x86 server vendor in both customer satisfaction and in reliability, and we're also celebrating, next month in July, a very significant milestone, which will we'll be shipping our 20 millionth x86 server into the industry. For us, it's an amazing time, and it's an inflection point to kind of look back, pause, but also share the next phase of Lenovo and the exciting vision for the future. We're also making some declarations on our vision for the future today. Again, international supercomputing's going on, and, as it turns out, we're the fastest growing supercomputer company on earth. We'll talk about that. Our goal today that we're announcing is that we plan in the next several years to become number one in supercomputing, and we're going to put the investments behind that. We're also committing to our customers that we're going to disrupt the status quo and accelerate the pace of innovation, not just in our legacy server solutions, but also in Software-Defined because what we've heard from you is that that lack of legacy, we don't have a huge router business or a huge sand business to protect. It's that lack of legacy that's enabling us to invest and get ahead of the curb on this next transition to Software-Defined. So you're going to see us doing that through building our internal IP, through some significant joint ventures, and also through some merges and acquisitions over the next several quarters. Altogether, we're driving to be the most trusted data center provider in the industry between us and our customers and our suppliers. So a quick summary of what we're going to dive into today, both in my keynote as well as in the breakout sessions. We're in this transformation to the next phase of Lenovo's data center growth. We're closing out our previous transformation. We actually, believe it or not, in the last six months or so, have renegotiated 18,000 contracts in 160 countries. We built out an entire end-to-end organization from development and architecture all the way through sales and support. This next transformation, I think, is really going to excite Lenovo shareholders. We're building the largest data center portfolio in our history. I think when IBM would be up here a couple years ago, we might have two or three servers to announce in time to market with the next Intel platform. Today, we're announcing 14 new servers, seven new storage systems, an expanded set of networking portfolios based on our legacy with Blade Network Technologies and other companies we've acquired. Two new brands that we'll talk about for both data center infrastructure and Software-Defined, a new set of premium premiere services as well as a set of engineered solutions that are going to help our customers get to market faster. We're going to be celebrating our 20 millionth x86 server, and as Rod said, 25 years in x86 server compute, and Christian will be up here talking about 25 years of ThinkPad as well. And then a new end-to-end segmentation model because all of these strategies without execution are kind of meaningless. I hope to give you some confidence in the transformation that Lenovo has gone through as well. So, having observed Lenovo from one of its largest partners, Intel, for more than a couple decades, I thought I'd just start with why we have confidence on the foundation that we're building off of as we move from a PC company into a data center provider in a much more significant way. So Lenovo today is a company of $43 billion in sales. Absolutely astonishing, it puts us at about Fortune 202 as a company, with 52,000 employees around the world. We're supporting and have service personnel, almost a little over 10,000 service personnel that service our servers and data center technologies in over 160 countries that provide onsite service and support. We have seven data center research centers. One of the reasons I came from Intel to Lenovo was that I saw that Lenovo became number one in PCs, not through cost cutting but through innovation. It was Lenovo that was partnering on the next-generation Ultrabooks and two-in-ones and tablets in the modem mods that you saw, but fundamentally, our path to number one in data center is going to be built on innovation. Lastly, we're one of the last companies that's actually building not only our own motherboards at our own motherboard factories, but also with five global data center manufacturing facilities. Today, we build about four devices a second, but we also build over 100 servers per hour, and the cost economics we get, and I just visited our Shenzhen factory, of having everything from screws to microprocessors come up through the elevator on the first floor, go left to build PCs and ThinkPads and go right to build server technology, means we have some of the world's most cost effective solutions so we can compete in things like hyperscale computing. So it's with that that I think we're excited about the foundation that we can build off of on the Data Center Group. Today, as we stated, this event is about transformation, and today, I want to talk about three things we're going to transform. Number one is the customer experience. Number two is the data center and our customer base with Software-Defined infrastructure, and then the third is talk about how we plan to execute flawlessly with a new transformation that we've had internally at Lenovo. So let's dive into it. On customer experience, really, what does it mean to transform customer experience? Industry pundits say that if you're not constantly innovating, you can fall behind. Certainly the technology industry that we're in is transforming at record speed. 42% of business leaders or CIOs say that digital first is their top priority, but less than 50% actually admit that they have a strategy to get there. So people are looking for a partner to keep pace with that innovation and change, and that's really what we're driving to at Lenovo. So today we're announcing a set of plans to take another step function in customer experience, and building off of our number one position. Just recently, Gartner shows Lenovo as the number 24 supply chains of companies over $12 billion. We're up there with Amazon, Coca-Cola, and we've now completely re-architected our supply chain in the Data Center Group from end to end. Today, we can deliver 90% of our SKUs, order to ship in less than seven days. The artificial intelligence that YY mentioned is optimizing our performance even further. In services, as we talked about, we're now in 160 countries, supporting on-site support, 50 different call centers around the world for local language support, and we're today announcing a whole set of new premiere support services that I'll get into in a second. But we're building on what's already better than 90% customer satisfaction in this space. And then in development, for all the engineers out there, we started foundationally for this new set of products, talking about being number one in reliability and the lowest downtime of any x86 server vendor on the planet, and these systems today are architected to basically extend that leadership position. So let me tell you the realities of reliability. This is ITIC, it's a reliability report. 750 CIOs and IT managers from more than 20 countries, so North America, Europe, Asia, Australia, South America, Africa. This isn't anything that's paid for with sponsorship dollars. Lenovo has been number one for four years running on x86 reliability. This is the amount of downtime, four hours or more, in mission-critical environments from the leading x86 providers. You can see relative to our top two competitors that are ahead of us, HP and Dell, you can see from ITIC why we are building foundationally off of this, and why it's foundational to how we're developing these new platforms. In customer satisfaction, we are also rated number one in x86 server customer satisfaction. This year, we're now incentivizing every single Lenovo employee on customer satisfaction and customer experience. It's been a huge mandate from myself and most importantly YY as our CEO. So you may say well what is the basis of this number one in customer satisfaction, and it's not just being number one in one category, it's actually being number one in 21 of the 22 categories that TBR talks about. So whether it's performance, support systems, online product information, parts and availability replacement, Lenovo is number one in 21 of the 22 categories and number one for six consecutive studies going back to Q1 of 2015. So this, again, as we talk about the new product introductions, it's something that we absolutely want to build on, and we're humbled by it, and we want to continue to do better. So let's start now on the new products and talk about how we're going to transform the data center. So today, we are announcing two new product offerings. Think Agile and ThinkSystem. If you think about the 25 years of ThinkPad that Christian's going to talk about, Lenovo has a continuous learning culture. We're fearless innovators, we're risk takers, we continuously learn, but, most importantly, I think we're humble and we have some humility. That when we fail, we can fail fast, we learn, and we improve. That's really what drove ThinkPad to number one. It took about eight years from the acquisition of IBM's x86 PC business before Lenovo became number one, but it was that innovation, that listening and learning, and then improving. As you look at the 25 years of ThinkPad, there were some amazing successes, but there were also some amazing failures along the way, but each and every time we learned and made things better. So this year, as Rod said, we're not just celebrating 25 years of ThinkPad, but we're celebrating 25 years of x86 server development since the original IBM PC servers in 1992. It's a significant day for Lenovo. Today, we're excited to announce two new brands. ThinkSystem and ThinkAgile. It's an important new announcement that we started almost three years ago when we acquired the x86 server business. Why don't we run a video, and we'll show you a little bit about ThinkSystem and ThinkAgile. >> Narrator: The status quo is comfortable. It gets you by, but if you think that's good enough for your data center, think again. If adoption is becoming more complicated when it should be simpler, think again. If others are selling you technology that's best for them, not for you, think again. It's time for answers that win today and tomorrow. Agile, innovative, different. Because different is better. Different embraces change and makes adoption simple. Different designs itself around you. Using 25 years of innovation and design and R&D. Different transforms, it gives you ThinkSystem. World-record performance, most reliable, easy to integrate, scales faster. Different empowers you with ThinkAgile. It redefines the experience, giving you the speed of Cloud and the control of on-premise IT. Responding faster to what your business really needs. Different defines the future. Introducing Lenovo ThinkSystem and ThinkAgile. (exciting and slightly aggressive digital instrumental) >> All right, good stuff, huh? (audience applauds) So it's built off of this 25-year history of us being in the x86 server business, the commitment we established three years ago after acquiring the x86 server business to be and have the most reliable, the most agile, and the most highest-performing data center solutions on the planet. So today we're announcing two brands. ThinkSystem is for the traditional data center infrastructure, and ThinkAgile is our brand for Software-Defined infrastructure. Again, the teams challenge themselves from the start, how do we build off this rich heritage, expanding our position as number one in customer satisfaction, reliability, and one of the world's best supply chains. So let's start and look at the next set of solutions. We have always prided ourself that little things don't mean a lot. Little things mean everything. So today, as we said on the legacy solutions, we have over 30 world-record performance benchmarks on Intel architecture, and more than actually 150 since we started tracking this back in 2001. So it's the little pieces of innovation. It's the fine tuning that we do with our partners like an Intel or a Microsoft, an SAP, VMware, and Nutanix that's enabling us to get these world-record performance benchmarks, and with this next generation of solutions we think we'll continue to certainly do that. So today we're announcing the most comprehensive portfolio ever in our data center history. There's 14 servers, seven storage devices, and five network switches. We're also announcing, which is super important to our customer base, a set of new premiere service options. That's giving you fast access directly to a level two support person. No automated response system involved. You get to pick up the phone and directly talk to a level two support person that's going to have end-to-end ownership of the customer experience for ThinkSystem. With ThinkAgile, that's going to be completely bundled with every ThinkAgile you purchase. In addition, we're having white glove service on site that will actually unbox the product for you and get it up and running. It's an entirely new set of solutions for hybrid Cloud, for big data analytics and database applications around these engineered solutions. These are like 40- to 50-page guides where we fine-tuned the most important applications around virtual desktop infrastructure and those kinds of applications, working side by side with all of our ISP partners. So significantly expanding, not just the hardware but the software solutions that, obviously, you, as our customers, are running. So if you look at ThinkSystem innovation, again, it was designed for the ultimate in flexibility, performance, and reliability. It's a single now-unified brand that combines what used to be the Lenovo Think server and the IBM System x products now into a single brand that spans server, storage, and networking. We're basically future-proofing it for the next-generation data center. It's a significantly simplified portfolio. One of the big pieces that we've heard is that the complexity of our competitors has really been overwhelming to customers. We're building a more flexible, more agile solution set that requires less work, less qualification, and more future proofing. There's a bunch of things in this that you'll see in the demos. Faster time-to-service in terms of the modularity of the systems. 12% faster service equating to almost $50 thousand per hour of reduced downtime. Some new high-density options where we have four nodes and a 2U, twice the density to improve and reduce outbacks and mission-critical workloads. And then in high-performance computing and supercomputing, we're going to spend some time on that here shortly. We're announcing new water-cooled solutions. We have some of the most premiere water-cooled solutions in the world, with more than 25 patents pending now, just in the water-cooled solutions for supercomputing. The performance that we think we're going to see out of these systems is significant. We're building off of that legacy that we have today on the existing Intel solutions. Today, we believe we have more than 50% of SAP HANA installations in the world. In fact, SAP just went public that they're running their internal SAP HANA on Lenovo hardware now. We're seeing a 59% increase in performance on SAP HANA generation on generation. We're seeing 31% lower total cost to ownership. We believe this will continue our position of having the highest level of five-nines in the x86 server industry. And all of these servers will start being available later this summer when the Intel announcements come out. We're also announcing the largest storage portfolio in our history, significantly larger than anything we've done in the past. These are all available today, including some new value class storage offerings. Our network portfolio is expanding now significantly. It was a big surprise when I came to Lenovo, seeing the hundreds of engineers we had from the acquisition of Blade Network Technologies and others with our teams in Romania, Santa Clara, really building out both the embedded portfolio but also the top racks, which is around 10 gig, 25 gig, and 100 gig. Significantly better economics, but all the performance you'd expect from the largest networking companies in the world. Those are also available today. ThinkAgile and Software-Defined, I think the one thing that has kind of overwhelmed me since coming in to Lenovo is we are being embraced by our customers because of our lack of legacy. We're not trying to sell you one more legacy SAN at 65% margins. ThinkAgile really was founded, kind of born free from the shackles of legacy thinking and legacy infrastructure. This is just the beginning of what's going to be an amazing new brand in the transformation to Software-Defined. So, for Lenovo, we're going to invest in our own internal organic IP. I'll foreshadow: There's some significant joint ventures and some mergers and acquisitions that are going to be coming in this space. And so this will be the foundation for our Software-Defined networking and storage, for IoT, and ultimately for the 5G build-out as well. This is all built for data centers of tomorrow that require fluid resources, tightly integrated software and hardware in kind of an appliance, selling at the rack level, and so we'll show you how that is going to take place here in a second. ThinkAgile, we have a few different offerings. One is around hyperconverged storage, Hybrid Cloud, and also Software-Defined storage. So we're really trying to redefine the customer experience. There's two different solutions we're having today. It's a Microsoft Azure solution and a Nutanix solution. These are going to be available both in the appliance space as well as in a full rack solution. We're really simplifying and trying to transform the entire customer experience from how you order it. We've got new capacity planning tools that used to take literally days for us to get the capacity planning done. It's now going down to literally minutes. We've got new order, delivery, deployment, administration service, something we're calling ThinkAgile Advantage, which is the white glove unboxing of the actual solutions on prem. So the whole thing when you hear about it in the breakout sessions about transforming the entire customer experience with both an HX solution and an SX solution. So again, available at the rack level for both Nutanix and for Microsoft Solutions available in just a few months. Many of you in the audience since the Microsoft Airlift event in Seattle have started using these things, and the feedback to date has been fantastic. We appreciate the early customer adoption that we've seen from people in the audience here. So next I want to bring up one of our most important partners, and certainly if you look at all of these solutions, they're based on the next-generation Intel Xeon scalable processor that's going to be announcing very very soon. I want to bring on stage Rupal Shah, who's the corporate vice president and general manager of Global Data Center Sales with Intel, so Rupal, please join me. (upbeat instrumental) So certainly I have long roots at Intel, but why don't you talk about, from Intel's perspective, why Lenovo is an important partner for Lenovo. >> Great, well first of all, thank you very much. I've had the distinct pleasure of not only working with Kirk for many many years, but also working with Lenovo for many years, so it's great to be here. Lenovo is not only a fantastic supplier and leader in the industry for Intel-based servers but also a very active partner in the Intel ecosystem. In the Intel ecosystem, specifically, in our partner programs and in our builder programs around Cloud, around the network, and around storage, I personally have had a long history in working with Lenovo, and I've seen personally that PC transformation that you talked about, Kirk, and I believe, and I know that Intel believes in Lenovo's ability to not only succeed in the data center but to actually lead in the data center. And so today, the ThinkSystem and ThinkAgile announcement is just so incredibly important. It's such a great testament to our two companies working together, and the innovation that we're able to bring to the market, and all of it based on the Intel Xeon scalable processor. >> Excellent, so tell me a little bit about why we've been collaborating, tell me a little bit about why you're excited about ThinkSystem and ThinkAgile, specifically. >> Well, there are a lot of reasons that I'm excited about the innovation, but let me talk about a few. First, both of our companies really stand behind the fact that it's increasingly a hybrid world. Our two companies offer a range of solutions now to customers to be able to address their different workload needs. ThinkSystem really brings the best, right? It brings incredible performance, flexibility in data center deployment, and industry-leading reliability that you've talked about. And, as always, Xeon has a history of being built for the data center specifically. The Intel Xeon scalable processor is really re-architected from the ground up in order to enhance compute, network, and storage data flows so that we can deliver workload optimized performance for both a wide range of traditional workloads and traditional needs but also some emerging new needs in areas like artificial intelligence. Second is when it comes to the next generation of Cloud infrastructure, the new Lenovo ThinkAgile line offers a truly integrated offering to address data center pain points, and so not only are you able to get these pretested solutions, but these pretested solutions are going to get deployed in your infrastructure faster, and they're going to be deployed in a way that's going to meet your specific needs. This is something that is new for both of us, and it's an incredible innovation in the marketplace. I think that it's a great addition to what is already a fantastic portfolio for Lenovo. >> Excellent. >> Finally, there's high-performance computing. In high-performance computing. First of all, congratulations. It's a big week, I think, for both of us. Fantastic work that we've been doing together in high-performance computing and actually bringing the best of the best to our customers, and you're going to hear a whole lot more about that. We obviously have a number of joint innovation centers together between Intel and Lenovo. Tell us about some of the key innovations that you guys are excited about. >> Well, Intel and Lenovo, we do have joint innovation labs around the world, and we have a long and strong history of very tight collaboration. This has brought a big wave of innovation to the marketplace in areas like software-defined infrastructure. Yet another area is working closely on a joint vision that I think our two companies have in artificial intelligence. Intel is very committed to the world of AI, and we're committed in making the investments required in technology development, in training, and also in R&D to be able to deliver end-to-end solutions. So with Intel's comprehensive technology portfolio and Lenovo's development and innovation expertise, it's a great combination in this space. I've already talked a little bit about HPC and so has Kirk, and we're going to hear a little bit more to come, but we're really building the fastest compute solutions for customers that are solving big problems. Finally, we often talk about processors from Intel, but it's not just about the processors. It's way beyond that. It's about engaging at the solution level for our customers, and I'm so excited about the work that we've done together with Lenovo to bring to market products like Intel Omni-Path Architecture, which is really the fabric for high-performance data centers. We've got a great showing this week with Intel Omni-Path Architecture, and I'm so grateful for all the work that we've done to be able to bring true solutions to the marketplace. I am really looking forward to our future collaboration with Lenovo as we have in the past. I want to thank you again for inviting me here today, and congratulations on a fantastic launch. >> Thank you, Rupal, very much, for the long partnership. >> Thank you. (audience applauds) >> Okay, well now let's transition and talk a little bit about how Lenovo is transforming. The first thing we've done when I came on board about six months ago is we've transformed to a truly end-to-end organization. We're looking at the market segments I think as our customers define them, and we've organized into having vice presidents and senior vice presidents in charge of each of these major groups, thinking really end to end, from architecture all the way to end of life and customer support. So the first is hyperscale infrastructure. It's about 20% on the market by 2020. We've hired a new vice president there to run that business. Given we can make money in high-volume desktop PCs, it's really the manufacturing prowess, deep engineering collaboration that's enabling us to sell into Baidu, and to Alibaba, Tencent, as well as the largest Cloud vendors on the West Coast here in the United States. We believe we can make money here by having basically a deep deep engineering engagement with our key customers and building on the PC volume economics that we have within Lenovo. On software-defined infrastructure, again, it's that lack of legacy that I think is propelling us into this space. We're not encumbered by trying to sell one more legacy SAN or router, and that's really what's exciting us here, as we transform from a hardware to a software-based company. On HPC and AI, as we said, we'll talk about this in a second. We're the fastest-growing supercomputing company on earth. We have aspirations to be the largest supercomputing company on earth, with China and the U.S. vying for number one in that position, it puts us in a good position there. We're going to bridge that into artificial intelligence in our upcoming Shanghai Tech World. The entire day is around AI. In fact, YY has committed $1.2 billion to artificial intelligence over the next few years of R&D to help us bridge that. And then on data center infrastructure, is really about moving to a solutions based infrastructure like our position with SAP HANA, where we've gone deep with engineers on site at SAP, SAP running their own infrastructure on Lenovo and building that out beyond just SAP to other solutions in the marketplace. Overall, significantly expanding our services portfolio to maintain our number one customer satisfaction rating. So given ISC, or International Supercomputing, this week in Frankfurt, and a lot of my team are actually over there, I wanted to just show you the transformation we've had at Lenovo for delivering some of the technology to solve some of the most challenging humanitarian problems on earth. Today, we are the fastest-growing supercomputer company on the planet in terms of number of systems on the Top 500 list. We've gone from zero to 92 positions in just a few short years, but IDC also positions Lenovo as the fast-growing supercomputer and HPC company overall at about 17% year on year growth overall, including all of the broad channel, the regional universities and this kind of thing, so this is an exciting place for us. I'm excited today that Sergi has come all the way from Spain to be with us today. It's an exciting time because this week we announce the fastest next-generation Intel supercomputer on the planet at Barcelona Supercomputer. Before I bring Sergi on stage, let's run a video and I'll show you why we're excited about the capabilities of these next-generation supercomputers. Run the video please. >> Narrator: Different creates one of the most powerful supercomputers for the Barcelona Supercomputer Center. A high-performance, high-capacity design to help shape tomorrow's world. Different designs what's best for you, with 25 years of end-to-end expertise delivering large-scale solutions. It integrates easily with technology from industry partners, through deep collaboration with the client to manufacture, test, configure, and install at global scale. Different achieves the impossible. The first of a new series. A more energy-efficient supercomputer yet 10 times more powerful than its predecessor. With over 3,400 Lenovo ThinkSystem servers, each performing over two trillion calculations per second, giving us 11.1 petaflop capacity. Different powers MareNostrum, a supercomputer that will help us better understand cancer, help discover disease-fighting therapies, predict the impact of climate change. MareNostrom 4.0 promises to uncover answers that will help solve humanities greatest challenges. (audience applauds) >> So please help me in welcoming operations director of the Barcelona Supercomputer Center, Sergi Girona. So welcome, and again, congratulations. It's been a big week for both of us. But I think for a long time, if you haven't been to Barcelona, this has been called the world's most beautiful computer because it's in one of the most gorgeous chapels in the world as you can see here. Congratulations, we now are number 13 on the Top500 list and the fastest next-generation Intel computer. >> Thank you very much, and congratulations to you as well. >> So maybe we can just talk a little bit about what you've done over the last few months with us. >> Sure, thank you very much. It is a pleasure for me being invited here to present to you what we've been doing with Lenovo so far and what we are planning to do in the next future. I'm representing here Barcelona Supercomputing Center. I am presenting high-performance computing services to science and industry. How we see these science services has changed the paradigm of science. We are coming from observation. We are coming from observation on the telescopes and the microscopes and the building of infrastructures, but this is not affordable anymore. This is very expensive, so it's not possible, so we need to move to simulations. So we need to understand what's happening in our environment. We need to predict behaviors only going through simulation. So, at BSC, we are devoted to provide services to industry, to science, but also we are doing our own research because we want to understand. At the same time, we are helping and developing the new engineers of the future on the IT, on HPC. So we are having four departments based on different topics. The main and big one is wiling to understand how we are doing the next supercomputers from the programming level to the performance to the EIA, so all these things, but we are having also interest on what about the climate change, what's the air quality that we are having in our cities. What is the precision medicine we need to have. How we can see that the different drugs are better for different individuals, for different humans, and of course we have an energy department, taking care of understanding what's the better optimization for a cold, how we can save energy running simulations on different topics. But, of course, the topic of today is not my research, but it's the systems we are building in Barcelona. So this is what we have been building in Barcelona so far. From left to right, you have the preparation of the facility because this is 160 square meters with 1.4 megabytes, so that means we need new piping, we need new electricity, at the same time in the center we have to install the core services of the system, so the management practices, and then on the right-hand side you have installation of the networking, the Omni-Path by Intel. Because all of the new racks have to be fully integrated and they need to come into operation rapidly. So we start deployment of the system May 15, and we've now been ending and coming in production July first. All the systems, all the (mumbles) systems from Lenovo are coming before being open and available. What we've been installing here in Barcelona is general purpose systems for our general workload of the system with 3,456 nodes. Everyone of those having 48 cores, 96 gigabytes main memory for a total capacity of about 400 terabytes memory. The objective of this is that we want to, all the system, all the processors, to work together for a single execution for running altogether, so this is an example of the platinum processors from Intel having 24 cores each. Of course, for doing this together with all the cores in the same application, we need a high-speed network, so this is Omni-Path, and of course all these cables are connecting all the nodes. Noncontention, working together, cooperating. Of course, this is a bunch of cables. They need to be properly aligned in switches. So here you have the complete presentation. Of course, this is general purpose, but we wanted to invest with our partners. We want to understand what the supercomputers we wanted to install in 2020, (mumbles) Exascale. We want to find out, we are installing as well systems with different capacities with KNH, with power, with ARM processors. We want to leverage our obligations for the future. We want to make sure that in 2020 we are ready to move our users rapidly to the new technologies. Of course, this is in total, giving us a total capacity of 13.7 petaflops that it's 12 times the capacity of the former MareNostrum four years ago. We need to provide the services to our scientists because they are helping to solve problems for humanity. That's the place we are going to go. Last is inviting you to come to Barcelona to see our place and our chapel. Thank you very much (audience applauds). >> Thank you. So now you can all go home to your spouses and significant others and say you have a formal invitation to Barcelona, Spain. So last, I want to talk about what we've done to transform Lenovo. I think we all know the history is nice but without execution, none of this is going to be possible going forward, so we have been very very busy over the last six months to a year of transforming Lenovo's data center organization. First, we moved to a dedicated end-to-end sales and marketing organization. In the past, we had people that were shared between PC and data center, now thousands of sales people around the world are 100% dedicated end to end to our data center clients. We've moved to a fully integrated and dedicated supply chain and procurement organization. A fully dedicated quality organization, 100% dedicated to expanding our data center success. We've moved to a customer-centric segment, again, bringing in significant new leaders from outside the company to look end to end at each of these segments, supercomputing being very very different than small business, being very very different than taking care of, for example, a large retailer or bank. So around hyperscale, software-defined infrastructure, HPC, AI, and supercomputing and data center solutions-led infrastructure. We've built out a whole new set of global channel programs. Last year, or a year passed, we have five different channel programs around the world. We've now got one simplified channel program for dealer registration. I think our channel is very very energized to go out to market with Lenovo technology across the board, and a whole new set of system integrator relationships. You're going to hear from one of them in Christian's discussion, but a whole new set of partnerships to build solutions together with our system integrative partners. And, again, as I mentioned, a brand new leadership team. So look forward to talking about the details of this. There's been a significant amount of transformation internal to Lenovo that's led to the success of this new product introduction today. So in conclusion, I want to talk about the news of the day. We are transforming Lenovo to the next phase of our data center growth. Again, in over 160 countries, closing on that first phase of transformation and moving forward with some unique declarations. We're launching the largest portfolio in our history, not just in servers but in storage and networking, as everything becomes kind of a software personality on top of x86 Compute. We think we're very well positioned with our scale on PCs as well as data center. Two new brands for both data center infrastructure and Software-Defined, without the legacy shackles of our competitors, enabling us to move very very quickly into Software-Defined, and, again, foreshadowing some joint ventures in M&A that are going to be coming up that will further accelerate ourselves there. New premiere support offerings, enabling you to get direct access to level two engineers and white glove unboxing services, which are going to be bundled along with ThinkAgile. And then celebrating the milestone of 25 years in x86 server compute, not just ThinkPads that you'll hear about shortly, but also our 20 million server shipping next month. So we're celebrating that legacy and looking forward to the next phase. And then making sure we have the execution engine to maintain our position and grow it, being number one in customer satisfaction and number one in quality. So, with that, thank you very much. I look forward to seeing you in the breakouts today and talking with many of you, and I'll bring Rod back up to transition us to the next section. Thank you. (audience applauds) >> All right, Kirk, thank you, sir. All right, ladies and gentlemen, what did you think of that? How about a big round of applause for ThinkAgile, ThinkSystems new brands? (audience applauds) And, obviously, with that comes a big round of applause, for Kirk Skaugen, my boss, so we've got to give him a big round of applause, please. I need to stay employed, it's very important. All right, now you just heard from Kirk about some of the new systems, the brands. How about we have a quick look at the video, which shows us the brand new DCG images. >> Narrator: Legacy thinking is dead, stuck in the past, selling the same old stuff, over and over. So then why does it seem like a data center, you know, that thing powering all our little devices and more or less everything interaction today is still stuck in legacy thinking because it's rigid, inflexible, slow, but that's not us. We don't do legacy. We do different. Because different is fearless. Different reduces Cloud deployment from days to hours. Different creates agile technology that others follow. Different is fluid. It uses water-cooling technology to save energy. It co-innovates with some of the best minds in the industry today. Different is better, smarter. Maybe that's why different already holds so many world-record benchmarks in everything. From virtualization to database and application performance or why it's number one in reliability and customer satisfaction. Legacy sells you what they want. Different builds the data center you need without locking you in. Introducing the Data Center Group at Lenovo. Different... Is better. >> All right, ladies and gentlemen, a big round of applause, once again (mumbles) DCG, fantastic. And I'm sure all of you would agree, and Kirk mentioned it a couple of times there. No legacy means a real consultative approach to our customers, and that's something that we really feel is differentiated for ourselves. We are effectively now one of the largest startups in the DCG space, and we are very much ready to disrupt. Now, here in New York City, obviously, the heart of the fashion industry, and much like fashion, as I mentioned earlier, we're different, we're disruptive, we're agile, smarter, and faster. I'd like to say that about myself, but, unfortunately, I can't. But those of you who have observed, you may have noticed that I, too, have transformed. I don't know if anyone saw that. I've transformed from the pinstripe blue, white shirt, red tie look of the, shall we say, our predecessors who owned the x86 business to now a very Lenovo look. No tie and consequently a little bit more chic New York sort of fashion look, shall I say. Nothing more than that. So anyway, a bit of a transformation. It takes a lot to get to this look, by the way. It's a lot of effort. Our next speaker, Christian Teismann, is going to talk a lot about the core business of Lenovo, which really has been, as we've mentioned today, our ThinkPad, 25-year anniversary this year. It's going to be a great celebration inside Lenovo, and as we get through the year and we get closer and closer to the day, you'll see a lot more social and digital work that engages our customers, partners, analysts, et cetera, when we get close to that birthday. Customers just generally are a lot tougher on computers. We know they are. Whether you hang onto it between meetings from the corner of the Notebook, and that's why we have magnesium chassis inside the box or whether you're just dropping it or hypothetically doing anything else like that. We do a lot of robust testing on these products, and that's why it's the number one branded Notebook in the world. So Christian talks a lot about this, but I thought instead of having him talk, I might just do a little impromptu jump back stage and I'll show you exactly what I'm talking about. So follow me for a second. I'm going to jaunt this way. I know a lot of you would have seen, obviously, the front of house here, what we call the front of house. Lots of videos, et cetera, but I don't think many of you would have seen the back of house here, so I'm going to jump through the back here. Hang on one second. You'll see us when we get here. Okay, let's see what's going on back stage right now. You can see one of the team here in the back stage is obviously working on their keyboard. Fantastic, let me tell you, this is one of the key value props of this product, obviously still working, lots of coffee all over it, spill-proof keyboard, one of the key value propositions and why this is the number one laptop brand in the world. Congratulations there, well done for that. Obviously, we test these things. Height, distances, Mil-SPEC approved, once again, fantastic product, pick that up, lovely. Absolutely resistant to any height or drops, once again, in line with our Mil-SPEC. This is Charles, our producer and director back stage for the absolute event. You can see, once again, sand, coincidentally, in Manhattan, who would have thought a snow storm was occurring here, but you can throw sand. We test these things for all of the elements. I've obviously been pretty keen on our development solutions, having lived in Japan for 12 years. We had this originally designed in 1992 by (mumbles), he's still our chief development officer still today, fantastic, congratulations, a sand-enhanced notebook, he'd love that. All right, let's get back out front and on with the show. Watch the coffee. All right, how was that? Not too bad (laughs). It wasn't very impromptu at all, was it? Not at all a set up (giggles). How many people have events and have a bag of sand sitting on the floor right next to a Notebook? I don't know. All right, now it's time, obviously, to introduce our next speaker, ladies and gentlemen, and I hope I didn't steal his thunder, obviously, in my conversations just now that you saw back stage. He's one of my best friends in Lenovo and easily is a great representative of our legendary PC products and solutions that we're putting together for all of our customers right now, and having been an ex-Pat with Lenovo in New York really calls this his second home and is continually fighting with me over the fact that he believes New York has better sushi than Tokyo, let's welcome please, Christian Teismann, our SVP, Commercial Business Segment, and PC Smart Office. Christian Teismann, come on up mate. (audience applauds) >> So Rod thank you very much for this wonderful introduction. I'm not sure how much there is to add to what you have seen already back stage, but I think there is a 25-year of history I will touch a little bit on, but also a very big transformation. But first of all, welcome to New York. As Rod said, it's my second home, but it's also a very important place for the ThinkPad, and I will come back to this later. The ThinkPad is thee industry standard of business computing. It's an industry icon. We are celebrating 25 years this year like no other PC brand has done before. But this story today is not looking back only. It's a story looking forward about the future of PC, and we see a transformation from PCs to personalized computing. I am privileged to lead the commercial PC and Smart device business for Lenovo, but much more important beyond product, I also am responsible for customer experience. And this is what really matters on an ongoing basis. But allow me to stay a little bit longer with our iconic ThinkPad and history of the last 25 years. ThinkPad has always stand for two things, and it always will be. Highest quality in the industry and technology innovation leadership that matters. That matters for you and that matters for your end users. So, now let me step back a little bit in time. As Rod was showing you, as only Rod can do, reliability is a very important part of ThinkPad story. ThinkPads have been used everywhere and done everything. They have survived fires and extreme weather, and they keep surviving your end users. For 25 years, they have been built for real business. ThinkPad also has a legacy of first innovation. There are so many firsts over the last 25 years, we could spend an hour talking about them. But I just want to cover a couple of the most important milestones. First of all, the ThinkPad 1992 has been developed and invented in Japan on the base design of a Bento box. It was designed by the famous industrial designer, Richard Sapper. Did you also know that the ThinkPad was the first commercial Notebook flying into space? In '93, we traveled with the space shuttle the first time. For two decades, ThinkPads were on every single mission. Did you know that the ThinkPad Butterfly, the iconic ThinkPad that opens the keyboard to its size, is the first and only computer showcased in the permanent collection of the Museum of Modern Art, right here in New York City? Ten years later, in 2005, IBM passed the torch to Lenovo, and the story got even better. Over the last 12 years, we sold over 100 million ThinkPads, four times the amount IBM sold in the same time. Many customers were concerned at that time, but since then, the ThinkPad has remained the best business Notebook in the industry, with even better quality, but most important, we kept innovating. In 2012, we unveiled the X1 Carbon. It was the thinnest, lightest, and still most robust business PC in the world. Using advanced composited materials like a Formula One car, for super strengths, X1 Carbon has become our ThinkPad flagship since then. We've added an X1 Carbon Yoga, a 360-degree convertible. An X1 Carbon tablet, a detachable, and many new products to come in the future. Over the last few years, many new firsts have been focused on providing the best end-user experience. The first dual-screen mobile workstation. The first Windows business tablet, and the first business PC with OLED screen technology. History is important, but a massive transformation is on the way. Future success requires us to think beyond the box. Think beyond hardware, think beyond notebooks and desktops, and to think about the future of personalized computing. Now, why is this happening? Well, because the business world is rapidly changing. Looking back on history that YY gave, and the acceleration of innovation and how it changes our everyday life in business and in personal is driving a massive change also to our industry. Most important because you are changing faster than ever before. Human capital is your most important asset. In today's generation, they want to have freedom of choice. They want to have a product that is tailored to their specific needs, every single day, every single minute, when they use it. But also IT is changing. The Cloud, constant connectivity, 5G will change everything. Artificial intelligence is adding things to the capability of an infrastructure that we just are starting to imagine. Let me talk about the workforce first because it's the most important part of what drives this. The millennials will comprise more than half of the world's workforce in 2020, three years from now. Already, one out of three millennials is prioritizing mobile work environment over salary, and for nearly 60% of all new hires in the United States, technology is a very important factor for their job search in terms of the way they work and the way they are empowered. This new generation of new employees has grown up with PCs, with Smart phones, with tablets, with touch, for their personal use and for their occupation use. They want freedom. Second, the workplace is transforming. The video you see here in the background. This is our North America headquarters in Raleigh, where we have a brand new Smart workspace. We have transformed this to attract the new generation of workers. It has fewer traditional workspaces, much more meaning and collaborative spaces, and Lenovo, like many companies, is seeing workspaces getting smaller. An average workspace per employee has decreased by 30% over the last five years. Employees are increasingly mobile, but, if they come to the office, they want to collaborate with their colleagues. The way we collaborate and communicate is changing. Investment in new collaboration technology is exploding. The market of collaboration technology is exceeding the market of personal computing today. It will grow in the future. Conference rooms are being re-imagined from a ratio of 50 employees to one large conference room. Today, we are moving into scenarios of four employees to one conference room, and these are huddle rooms, pioneer spaces. Technology is everywhere. Video, mega-screens, audio, electronic whiteboards. Adaptive technologies are popping up and change the way we work. As YY said earlier, the pace of the revolution is astonishing. So personalized computing will transform the PC we all know. There's a couple of key factors that we are integrating in our next generations of PC as we go forward. The most important trends that we see. First of all, choose your own device. We talked about this new generation of workforce. Employees who are used to choosing their own device. We have to respond and offer devices that are tailored to each end user's needs without adding complexity to how we operate them. PC is a service. Corporations increasingly are looking for on-demand computing in data center as well as in personal computing. Customers want flexibility. A tailored management solution and a services portfolio that completes the lifecycle of the device. Agile IT, even more important, corporations want to run an infrastructure that is agile, instant respond to their end-customer needs, that is self provisioning, self diagnostic, and remote software repair. Artificial intelligence. Think about artificial intelligence for you personally as your personal assistant. A personal assistant which does understand you, your schedule, your travel, your next task, an extension of yourself. We believe the PC will be the center of this mobile device universe. Mobile device synergy. Each of you have two devices or more with you. They need to work together across different operating systems, across different platforms. We believe Lenovo is uniquely positioned as the only company who has a Smart phone business, a PC business, and an infrastructure business to really seamlessly integrate all of these devices for simplicity and for efficiency. Augmented reality. We believe augmented reality will drive significantly productivity improvements in commercial business. The core will be to understand industry-specific solutions. New processes, new business challenges, to improve things like customer service and sales. Security will remain the foundation for personalized computing. Without security, without trust in the device integrity, this will not happen. One of the most important trends, I believe, is that the PC will transform, is always connected, and always on, like a Smart phone. Regardless if it's open, if it's closed, if you carry it, or if you work with it, it always is capable to respond to you and to work with you. 5G is becoming a reality, and the data capacity that will be out there is by far exceeding today's traffic imagination. Finally, Smart Office, delivering flexible and collaborative work environments regardless on where the worker sits, fully integrated and leverages all the technologies we just talked before. These are the main challenges you and all of your CIO and CTO colleagues have to face today. A changing workforce and a new set of technologies that are transforming PC into personalized computing. Let me give you a real example of a challenge. DXC was just formed by merging CSE company and HP's Enterprise services for the largest independent services company in the world. DXC is now a 25 billion IT services leader with more than 170,000 employees. The most important capital. 6,000 clients and eight million managed devices. I'd like to welcome their CIO, who has one of the most challenging workforce transformation in front of him. Erich Windmuller, please give him a round of applause. (audience applauds). >> Thank you Christian. >> Thank you. >> It's my pleasure to be here, thank you. >> So first of all, let me congratulation you to this very special time. By forming a new multi-billion-dollar enterprise, this new venture. I think it has been so far fantastically received by analysts, by the press, by customers, and we are delighted to be one of your strategic partners, and clearly we are collaborating around workforce transformation between our two companies. But let me ask you a couple of more personal questions. So by bringing these two companies together with nearly 200,00 employees, what are the first actions you are taking to make this a success, and what are your biggest challenges? >> Well, first, again, let me thank you for inviting me and for DXC Technology to be a part of this very very special event with Lenovo, so thank you. As many of you might expect, it's been a bit of a challenge over the past several months. My goal was really very simple. It was to make sure that we brought two companies together, and they could operate as one. We need to make sure that could continue to support our clients. We certainly need to make sure we could continue to sell, our sellers could sell. That we could pay our employees, that we could hire people, we could do all the basic foundational things that you might expect a company would want to do, but we really focused on three simple areas. I called it the three Cs. Connectivity, communicate, and collaborate. So we wanted to make sure that we connected our legacy data centers so we could transfer information and communicate back and forth. We certainly wanted to be sure that our employees could communicate via WIFI, whatever locations they may or may not go to. We certainly wanted to, when we talk about communicate, we need to be sure that everyone of our employees could send and receive email as a DXC employee. And that we had a single-enterprise directory and people could communicate, gain access to calendars across each of the two legacy companies, and then collaborate was also key. And so we wanted to be sure, again, that people could communicate across each other, that our legacy employees on either side could get access to many of their legacy systems, and, again, we could collaborate together as a single corporation, so it was challenging, but very very, great opportunity for all of us. And, certainly, you might expect cyber and security was a very very important topic. My chairman challenged me that we had to be at least as good as we were before from a cyber perspective, and when you bring two large companies together like that there's clearly an opportunity in this disruptive world so we wanted to be sure that we had a very very strong cyber security posture, of which Lenovo has been very very helpful in our achieving that. >> Thank you, Erich. So what does DXC consider as their critical solutions and technology for workplace transformation, both internally as well as out on the market? >> So workplace transformation, and, again, I've heard a lot of the same kinds of words that I would espouse... It's all about making our employees productive. It's giving the right tools to do their jobs. I, personally, have been focused, and you know this because Lenovo has been a very very big part of this, in working with our, we call it our My Style Workplace, it's an offering team in developing a solution and driving as much functionality as possible down to the workstation. We want to be able, for me, to avoid and eliminate other ancillary costs, audio video costs, telecommunication cost. The platform that we have, the digitized workstation that Lenovo has provided us, has just got a tremendous amount of capability. We want to streamline those solutions, as well, on top of the modern server. The modern platform, as we call it, internally. I'd like to congratulate Kirk and your team that you guys have successfully... Your hardware has been certified on our modern platform, which is a significant accomplishment between our two companies and our partnership. It was really really foundational. Lenovo is a big part of our digital workstation transformation, and you'll continue to be, so it's very very important, and I want you to know that your tools and your products have done a significant job in helping us bring two large corporations together as one. >> Thank you, Erich. Last question, what is your view on device as a service and hardware utility model? >> This is the easy question, right? So who in the room doesn't like PC or device as a service? This is a tremendous opportunity, I think, for all of us. Our corporation, like many of you in the room, we're all driven by the concept of buying devices in an Opex versus a Capex type of a world and be able to pay as you go. I think this is something that all of us would like to procure, product services and products, if you will, personal products, in this type of a mode, so I am very very eager to work with Lenovo to be sure that we bring forth a very dynamic and constructive device as a service approach. So very eager to do that with Lenovo and bring that forward for DXC Technology. >> Erich, thank you very much. It's a great pleasure to work with you, today and going forward on all sides. I think with your new company and our lineup, I think we have great things to come. Thank you very much. >> My pleasure, great pleasure, thank you very much. >> So, what's next for Lenovo PC? We already have the most comprehensive commercial portfolio in the industry. We have put the end user in the core of our portfolio to finish and going forward. Ultra mobile users, like consultants, analysts, sales and service. Heavy compute users like engineers and designers. Industry users, increasingly more understanding. Industry-specific use cases like education, healthcare, or banking. So, there are a few exciting things we have to announce today. Obviously, we don't have that broad of an announcement like our colleagues from the data center side, but there is one thing that I have that actually... Thank you Rod... Looks like a Bento box, but it's not a ThinkPad. It's a first of it's kind. It's the world's smallest professional workstation. It has the power of a tower in the Bento box. It has the newest Intel core architecture, and it's designed for a wide range of heavy duty workload. Innovation continues, not only in the ThinkPad but also in the desktops and workstations. Second, you hear much about Smart Office and workspace transformation today. I'm excited to announce that we have made a strategic decision to expand our Think portfolio into Smart Office, and we will soon have solutions on the table in conference rooms, working with strategic partners like Intel and like Microsoft. We are focused on a set of devices and a software architecture that, as an IoT architecture, unifies the management of Smart Office. We want to move fast, so our target is that we will have our first product already later this year. More to come. And finally, what gets me most excited is the upcoming 25 anniversary in October. Actually, if you go to Japan, there are many ThinkPad lovers. Actually beyond lovers, enthusiasts, who are collectors. We've been consistently asked in blogs and forums about a special anniversary edition, so let me offer you a first glimpse what we will announce in October, of something we are bring to market later this year. For the anniversary, we will introduce a limited edition product. This will include throwback features from ThinkPad's history as well as the best and most powerful features of the ThinkPad today. But we are not just making incremental adjustments to the Think product line. We are rethinking ThinkPad of the future. Well, here is what I would call a concept card. Maybe a ThinkPad without a hinge. Maybe one you can fold. What do you think? (audience applauds) but this is more than just design or look and feel. It's a new set of advanced materials and new screen technologies. It's how you can speak to it or write on it or how it speaks to you. Always connected, always on, and can communicate on multiple inputs and outputs. It will anticipate your next meeting, your next travel, your next task. And when you put it all together, it's just another part of the story, which we call personalized computing. Thank you very much. (audience applauds) Thank you, sir. >> Good on ya, mate. All right, ladies and gentlemen. We are now at the conclusion of the day, for this session anyway. I'm going to talk a little bit more about our breakouts and our demo rooms next door. But how about the power with no tower, from Christian, huh? Big round of applause. (audience applauds) And what about the concept card, the ThinkPad? Pretty good, huh? I love that as well. I tell you, it was almost like Leonardo DiCaprio was up on stage at one stage. He put that big ThinkPad concept up, and everyone's phones went straight up and took a photo, the whole audience, so let's be very selective on how we distribute that. I'm sure it's already on Twitter. I'll check it out in a second. So once again, ThinkPad brand is a core part of the organization, and together both DCG and PCSD, what we call PCSD, which is our client side of the business and Smart device side of the business, are obviously very very linked in transforming Lenovo for the future. We want to also transform the industry, obviously, and transform the way that all of us do business. Lenovo, if you look at basically a summary of the day, we are highly committed to being a top three data center provider. That is really important for us. We are the largest and fastest growing supercomputing company in the world, and Kirk actually mentioned earlier on, committed to being number one by 2020. So Madhu who is in Frankfurt at the International Supercomputing Convention, if you're watching, congratulations, your targets have gone up. There's no doubt he's going to have a lot of work to do. We're obviously very very committed to disrupting the data center. That's obviously really important for us. As we mentioned, with both the brands, the ThinkSystem, and our ThinkAgile brands now, highly focused on disrupting and ensuring that we do things differently because different is better. Thank you to our customers, our partners, media, analysts, and of course, once again, all of our employees who have been on this journey with us over the last two years that's really culminating today in the launch of all of our new products and our profile and our portfolio. It's really thanks to all of you that once again on your feedback we've been able to get to this day. And now really our journey truly begins in ensuring we are disrupting and enduring that we are bringing more value to our customers without that legacy that Kirk mentioned earlier on is really an advantage for us as we really are that large startup from a company perspective. It's an exciting time to be part of Lenovo. It's an exciting time to be associated with Lenovo, and I hope very much all of you feel that way. So a big round of applause for today, thank you very much. (audience applauds) I need to remind all of you. I don't think I'm going to have too much trouble getting you out there, because I was just looking at Christian on the streaming solutions out in the room out the back there, and there's quite a nice bit of lunch out there as well for those of you who are hungry, so at least there's some good food out there, but I think in reality all of you should be getting up into the demo sessions with our segment general managers because that's really where the rubber hits the road. You've heard from YY, you've heard from Kirk, and you've heard from Christian. All of our general managers and our specialists in our product sets are going to be out there to obviously demonstrate our technology. As we said at the very beginning of this session, this is Transform, obviously the fashion change, hopefully you remember that. Transform, we've all gone through the transformation. It's part of our season of events globally, and our next event obviously is going to be in Tech World in Shanghai on the 20th of July. I hope very much for those of you who are going to attend have a great safe travel over there. We look forward to seeing you. Hope you've had a good morning, and get into the sessions next door so you get to understand the technology. Thank you very much, ladies and gentlemen. (upbeat innovative instrumental)

>> Announcer: Live from San Francisco, it's The Cube. Covering Spark Summit 2017. Brought to you by Databricks. >> The Cube is live once again from Spark Summit 2017, I'm David Goad, your host, here with George Gilbert, and we are interviewing many of the speakers that we saw on stage this morning at the keynote. Happy to introduce our next guest on the show, his name is Matt Fryer, Matt, how're you doing? >> Matt: Very well. >> You're the chief, Chief Data Science Officer, I don't see many CDSOs out there, is that a common-- >> I think to say, it's a newer title, and it's coming, I think, where companies that feel the use of data, data science and algorithms, are fundamental to their, their futures. They're creating both the mix of commercial, technical, and algorithmic skill sets, this one team, and to execute together, and that's where the title came from. There's more coming, there's a number of-- Facebook have a few, that's one for example, but it's a newer title, I think it's going to become larger and larger, as time goes on. >> David: So, the CDSO for Hotels.com, something else we learned about you that you may not want me to reveal, but I heard you were the inspiration for Captain Obvious, is that true? >> Uh, that's not true. (laughter) I think Captain Obvious is only an expression of my brand, so there's an awesome brand team, at our office in Dallas. (crosstalk) We all love the captain, he has some good humorous moments, and he keeps us all kind of happy. >> Oh, yeah, he states the obvious, we're going to talk about some of the obvious, and maybe some of the not-so obvious here in this interview. So let's talk a little bit about company culture, because you talked a lot on the stage this morning about customer-first kind of approach, rather than a, "Ooh, look what I can do with the technology." Talk a little bit more about the culture at Hotels.com. >> And that's important, and I think, we're a very data-driven culture, I think most tech companies, and travel, technology companies have that kind of ethos. But fundamentally, the focus and the reason we exist is for the customer. So we want to bring, and actually-- in even better ways than that, I think it's the people. So whether it's the focus on the customer, if we did the right thing by the customer, we fundamentally want you to use our platform time and time again. Whatever need you have, booking, lodging and travel, please use our platform. That's the crucial win. So, to do that, we have to always delight you in every experience you have with us. And equally about people, it's about the team, so we have an internal concept called being supportive. So the whole part of our team culture, is that everybody helps everybody else out, we don't single things out, we're all part of the same team, and we all win if all of us pull together. That makes it a great place, a fun place to work, we're going to play with some new technologies, tech is important to us, but actually the people are even more important to us. >> In part why you love the Spark Summit then, huh? Same kind of spirit here, right? >> It's great, I think it's my third Spark Summit, my second time over in San Francisco, and the size of it is very impressive now. I just love meeting other people learning about some of the things they're up to, how we can apply those back to our business, and hopefully sharing a little bit of what we're up to. >> David: Let's dive into how you're applying it to your business, you talked about this evolution toward becoming an algorithm business, what does that mean and what part does Spark play in that? >> Matt: I think what it is, is about how do you, if you think about a bit of the journey, historically, a lot of the opportunity came in building new features, constantly building it, it's almost like a semi arms race, about how to build more and more features. The crucial thing I think going forward, and particularly with mobile devices now, we have over half our traffic, comes from people using smartphones, on both the app and mobile web. That bringing together means that, be more targeted, in understanding your journey, and people are, last on to time, speed is much more important, people expect things to be right there when they need it, relevance is much more important to people, so we need to bring all those things together to offer a much more targeted experience, and a much more real-time experience. People expect you to have understood what they did milliseconds ago, and respond to that. The only way you can do that is using data science and algorithms. You balance out on a business operation side, just how do you scale? The analogy I use with, say, anomaly detection, which is a crucial feature for enterprises. Used to have a large business intelligence, lots of reports, pages of paper, now people have things like Tablo, Power BI, those are great and you need those to start with, but really as a business leader, you want to know, "Tell me what's broken, tell me what's changed, "because if it's changed something caused the change, "tell me why it's slowly moving, and most importantly, "tell me where the opportunity is." And that transforms the conversation where algorithms can really surface that to users, and it's about organic intelligence, it's not about artificial intelligence, it's about how would you bring together the people, and the advance in technology to really do a great job for customers. >> David: Well, you mentioned AI, you made a big bold claim about AI, I'm going to ask George to weigh in on this in just a moment, you said AI was going to be the next big thing in the travel industry, can you explain? >> One of the next big things, I think. Yeah, I think it's already happening, in fact, our chairman, Mr. Diller made that statement very recently, also backed up by both the CEO and the brand president, where it's... If you think about 20 years ago, one of the things both Expedia and Hotels.com, and travel online space did, were democratize price information, and made it transparent to users. So previously, the power was with the travel agents, that power moved to the user, they had the information. And that's evolved over time, and what we feel with artificial intelligence, particularly organic intelligence, enablers like mobile, messaging and having conversations, have a machine learning how to make this happen, that you can turn the screen around and actually empower users always with the second revolution. They actually have the advice, and the benefits you had a number of years ago from travel agents: A, they had the price transparency, they have the other part now, which is the content, advice, and what's the most relevant to help them. And you can listen to what they're saying to you, as a customer, and actually we can now replay the perfect information back to them, or increasingly perfect as time goes on. (crosstalk) >> That is fascinating, 'cause in the way you broke that out, with--it wasn't actually only travel, but over the last couple decades, price transparency became an issue for many industries, but what you're saying now is, by giving the content to surprise and delight the customer, as long as you're collecting the data breadcrumbs to help you do that, you're not giving up control, you're actually creating stickiness. >> Matt: We're empowering, is the language I use. And if you empower the user, the more likely to come back to use your service in the future, and that's really what we want, we want happy customers. >> George: Tell us a little bit, at the risk of dropping a little in the wait, tell us a little bit about how you empower, in other words, how do you know what type of content to serve up, and how do you measure how they engage with it? >> It's a great question, and I think it's quite embryonic, part of the world right now. I don't think anybody's-- have we made some great developments? I said it was a long journey we have, but it's a lot about how do you, and this is true across data science machine learning, great data science is fundamental to having great feedback loops. So, there's lots of different techniques and tactics around how you might discover those feedback loops, and customers demand that you use their data to help them. So, we need to get faster, and streaming is one way, that's becoming feasible, and the advances in streaming and it's great Databricks are working on that, but the advances in streaming allows it to feed that loop, to take that much--those real-time signals, as well as previous signals, to really help figure out what you're trying to do today, what content-- interesting thing is, Netflix and Amazon were some pioneers in this space, where if you use Netflix service, often you go, "How the hell did they know "this video was going to be right for me?" And, some of the comments, and you can say, well, what they're actually doing is they're looking at microsegments, so previously everyone talked about custom segments as these very large groups, and they have their place, but increasing machine learning allows you to build microsegments. What I can start to do is actually discover from the behavior of others, things you likely-- very relevant things that you're going to be very interested in, and actually help inspire you and discover things you didn't even know existed. And by filling that gap and using those microsegments as well as put truly personal, personalization, I can bring that together to offer you a much more enhanced service. >> George: And so, help make that concrete in terms of, what would I as a potential--I want to plan a vacation for the summer, I have my five and a half inch or, five-seven iPhone, and that's my primary device. And in banking, it's moved from tying everything to the checking account, to tying every interaction to your mobile device. So what would you show me on my mobile device, that would get me really engaged about going to some location? >> So I think a lot of it is about where you are in that journey. So, you think, there's so many different routes customers can take, through that buying decision. And depends on the trip type, whether it's a leisure trip, seeing your family and friends, how much knowledge you may have about them, have you been there before? We look for all those signals, to try and help inspire. So a great example might be, if you stayed in a hotel on our site before, and you liked that hotel, and you come back and do a search again, we try and make it easy to continue by putting that hotel at the top. Trying to make it easy to task-complete. We have a trip planner capability you'll see on the home screen, which allows you to record and play back some of your previous searches, so you can quickly see and compare where you've been, and what's interesting for you. But on top of that, we can then use the signals, and increasingly, we have a very advanced filter list, and that's a key, and we're looking in stuff, how we do conversations in the chatbox, is this sort of future, how to have a conversation to say, "Hey, here's a list of hotels, which we used a mix of your, "the types of preferences understood about you, "and the wider thing, where you are in the world, "what's going on, what time of day." We take hundreds of different signals to try and figure out what the right list is for you, and from that list, the great thing is most people interact with that list and give us more signals, exactly what you wanted. We can hone and hone and hone, and repeat, 'cause I said at the start, for example, those majority of customers will do multiple searches. They want to understand what the market is, they may not be interested in one particular place, they may have a sweeter place there instead. Even now, where we've moved further up the funnel, investing behind, how can you figure out what destination you're interested in? So you may not even know what destination you're interested in, or there might be other destinations that you didn't know--with a very relevant for your use case, particularly if you're going on vacation, we can help inspire you to find that hidden gem, that hidden great prize, you may not even know it existed. Being the much better job, but to show you how busy the market is, to how fast you should be looking to book there, if it's a very compressed, busy market, you need to get in there quick to lock your price in, and we're now providing that information to help you make a better decision. And we can mine all that data, to empower you to make smart decisions with smart data. >> I want to clarify something I saw in your demonstration this morning, you were talking about detecting the differences between photos and user-generated content, so do you have users actually posting their own photos of the hotel, right next to the photoshopped pictures of the hotel? >> Matt: We do, yeah. >> David: What are the ramifications of that? >> So it's an interesting advancement we've made, so we've... In the last of the year, we now offer and asking users to submit their photos, to help other users. I think one of the crucial things is about how to be authentic. Over the years, we've had tens of millions of testimonial reviews, text reviews, and we can see they're really, crucially important to users, and their buying decisions. >> David: It scares the hotel owners to death though, doesn't it? >> Matt: Well, I think it does, but I think the testimony of the customer, could be one of the key things we call them, as we have verified reviews, so to leave a review on our site, you've had to stay in that hotel. We think that's a crucial step in really helping to say, "These are your customers." In recent times, we've taken that product further, to now when you actually arrive at the hotel within a few hours, We'll ask you what your first impressions were. We would ask if you want to share that with the hotel owner. To get the hotel owner a chance to actually rectify any early challenges, so you can have a great stay. And one of the crucial things we have is that, what's really, really important, is that users and customers have a great stay, that reflects on our Net Promoter score, and their view of us, and we need to fill that cycle and make sure we have happy users. So that real-time review is super crucial, in basing how can hotels--if they want happy users and customers as well, it helps them to cut a course correct, if there's an issue, and we can step in as well to help the user if it's a really deep issue. And then with the photos, the key to think is how to navigate and understand what the photo is, so the user helps us by tagging that, which is great, but how we-- >> David: Possibly mistagging it. >> Possibly mistagging it on occasion, that's something we've, we've built in some skill as you've heard, on how to tackle that, but the crucial thing is how to bring these together, if you're on a mobile device, you've got to scan through each photo, and in places around the world have limited bandwidth, a limited time to go through them, so what we're now working on is how to assess the quality of those photos, to try and make sure we authentically--what we want to do, is get the customer the most lively experience they will have. As I said before, we're on the customer's kind of focus, we want to make sure they get the best photos, the most realistic of what's going to happen, and doing the most diverse. You want to see three photos, exactly the same, and we're working on the moment, you can swipe left and swipe right, we're working on how that display evolves over time, but it's exciting. >> David: Very exciting, fascinating stuff. Sorry that we're up against a hard break, coming here in just a moment, but I wanted to give you just 30 seconds to kind of sum up, maybe the next big technical challenge you're looking at that involves Spark, and we'll close with that. >> Cool, it's a great question. I think I talked a little about that in the keynote, totally caught the kind of out challenge. How to scale a mountain, which has been-- there's been great advance on how to stream data into platforms, Spark is a core part of that, and the platforms that we've been building, both internally, and partnering with Databricks and using their platform, has really given us a large boost going forwards, but how you turn those algorithms and that competitive algorithmic advantage, into a live production environment, whether it's marketplaces, Adtech marketplaces or websites, or in call centers, or in social media, wherever the platform needs to go, that's a hard problem right now. Or, I think it's too hard a problem right now. And I'd love to see--and we're going to invest behind that, a transformation, that hopefully this time next year, that is no longer a problem, and is actually an asset. >> David: Well I hope I'm not Captain Obvious to say, I know you're up to the challenge. Thank you so much, Matt Fryer, we appreciate you being on the show, thank you for sharing what's going on at Hotels.com. And thank you all for watching The Cube, we'll be back in a few moments with our next guest, here at Spark Summit 2017. (electronic music) (wind blowing)

>> Announcer: Live from San Francisco it's the CUBE covering Spark Summit 2017 brought to you by Data Bricks. >> Welcome back to the CUBE, we're continuing to take about two people who are not just talking about things but doing things. We're happy to have, from Novetta, the Director of Predictive Analytics, Mr. Rob Lantz. Rob, welcome to the show. >> Thank you. >> And off to my right, George, how are you? >> Good. >> We've introduced you before. >> Yes. >> Well let's talk to the guest. Let's get right to it. I want to talk to you a little bit about what does Novetta do and then maybe what apps you're building using Spark. >> Sure, so Novetta is an advanced analytics company, we're medium sized and we develop custom hardware and software solutions for our customers who are looking to get insights out of their big data. Our primary offering is a hard entity resolution engine. We scale up to billions of records and we've done that for about 15 years. >> So you're in the business end of analytics, right? >> Yeah, I think so. >> Alright, so talk to us a little bit more about entity resolution, and that's all Spark right? This is your main priority? >> Yes, yes, indeed. Entity resolution is the science of taking multiple disparate data sets, traditional big data, and taking records from those and determining which of those are actually the same individual or company or address or location and which of those should be kept separate. We can aggregate those things together and build profiles and that enables a more robust picture of what's going on for an organization. >> Okay, and George? >> So what did you do... What was the solution looking like before Spark and how did it change once you adopted Spark? >> Sure, so with Spark, it enabled us to get a lot faster. Obviously those computations scaled a lot better. Before, we were having to write a lot of custom code to get those computations out across a grid. When we moved to Hadoop and then Spark, that made us, let's say able to scale those things and get it done overnight or in hours and not weeks. >> So when you say you had to do a lot of custom code to distribute across the cluster, does that include when you were working with MapReduce, or was this even before the Hadoop era? >> Oh it was before the Hadoop era and that predates my time so I won't be able to speak expertly about it, but to my understanding, it was a challenge for sure. >> Okay so this sounds like a service that your customers would then themselves build on. Maybe an ETL customer would figure out master data from a repository that is not as carefully curated as the data warehouse or similar applications. So who is your end customer and how do they build on your solution? >> Sure, so the end customer typically is an enterprise that has large volumes of data that deal in particular things. They collect, it could be customers, it could be passengers, it could be lots of different things. They want to be able to build profiles about those people or companies, like I said, or locations, any number of things can be considered an entity. The way they build upon it then is how they go about quantifying those profiles. We can help them do that, in fact, some of the work that I manage does that, but often times they do it themselves. They take the resolve data and that gets resolved nightly or even hourly. They build those profiles themselves for their own purpose. >> Then, to help us think about the application or the use case holistically, once they've built those profiles and essentially harmonized the data, what does that typically feed into? >> Oh gosh, any number of things really. Oh, shoot. We've got deployments in AWS in the cloud, we've got deployments, lots of deployments on premises obviously. That can go anywhere from relational databases to graph query language databases. Lots of different places from there for sure. >> Okay so, this actually sounds like everyone talks now about machine learning and forming every category of software. This sounds like you take the old style ETL, where master data was a value add layer on top, and that was, it took a fair amount of human judgment to do. Now, you're putting that service on top of ETL and you're largely automating it, probably with, I assume, some supervised guidance, supervised training. >> Yes, so we're getting into the machine learning space as far as entity extraction and resolution and recognition because more and more data is unstructured. But machine learning isn't necessarily a baked in part of that. Actually entity resolution is a prerequisite, I think, for quality machine learning. So if Rob Lantz is a customer, I want to be able to know what has Rob Lantz bought in the past from me. And maybe what is Rob Lantz talking about in social media? Well I need to know how to figure out who those people are and who's Rob Lantz and who's Robert Lantz is a completely different person, I don't want to collapse those two things together. Then I would build machine learning on top of that to say, right, now what's his behavior going to be in the future. But once I have that robust profile built up, I can derive a lot more interesting features with which to apply the machine learning. >> Okay, so you are a Data Bricks customer and there's also a burgeoning partnership. >> Rob: Yeah, I think that's true. >> So talk to us a little bit about what are some of the frustrations you had before adopting Data Bricks and maybe why you choose it. >> Yeah, sure. So the frustrations primarily with a traditional Hadoop environment involved having to go from one customer site to another customer site with an incredibly complex technology stack and then do a lot of the cluster management for those customers even after they'd already set it up because of all the inner workings of Hadoop and that ecosystem. Getting our Spark application installed there, we had to penetrate layers and layers of configuration in order to tune it appropriately to get the performance we needed. >> David: Okay, and were you at the keynote this morning? >> I was not, actually. >> Okay, I'm not going to ask you about that then. >> Ah. >> But I am going to ask you a little bit about your wishlist. You've been talking to people maybe in the hallway here, you just got here today but, what do you wish the community would do or develop, what would you like to learn while you're here? >> Learning while I'm here, I've already picked up a lot. So much going on and it's such a fast paced environment, it's really exciting. I think if I had a wishlist, I would want a more robust ML Lib, machine learning library. All the things that you can get on traditional, in scientific computing stacks moved onto a Spark ML Lib for easier access. On a cluster would be great. >> I thought several years ago ML Lib took over from Mahoot as the most active open source community for adding, really, I thought, scale out machine learning algorithms. If it doesn't have it all now, or maybe all is something you never reach, kind of like Red Queen effect, you know? >> Rob: For sure, for sure. >> What else is attracting these scale out implementations of the machine learning algorithms? >> Um? >> In other words, what are the platforms? If it's not Spark then... >> I don't think it exists frankly, unless you write your own. I think that would be the way to go. That's the way to go about it now. I think what organizations are having to do with machine learning in a distributed environment is just go with good enough, right. Whereas maybe some of the ensemble methods that are, actually aren't even really cutting edge necessarily, but you can really do a lot of tuning on those things, doing that tuning distributed at scale would be really powerful. I read somewhere, and I'm not going to be able to quote exactly where it was but, actually throwing more data at a problem is more valuable than tuning a perfect algorithm frankly. If we could combine the two, I think that would be really powerful. That is, finding the right algorithm and throwing all the data at it would get you a really solid model that would pick up on that signal that underlies any of these phenomena. >> David: Okay well, go ahead George. >> I was going to ask, I think that goes back to, I don't know if it was Google Paper, or one of the Google search quality guys who's a luminary in the machine learning space says, "data always trumps algorithms." >> I believe that's true and that's true in my experience certainly. >> Once you had this machine learning and once you've perhaps simplified the multi-vendor stack, then what is your solution start looking like in terms of broadening its appeal, because of the lower TCO. And then, perhaps embracing more use cases. >> I don't know that it necessarily embraces more use cases because entity resolution applies so broadly already, but what I would say is will give us more time to focus on improving the ER itself. That's I think going to be a really, really powerful improvement we can make to Novetta entity analytics as it stands right now. That's going to go into, we alluded to before, the machine learning as part of the entity resolution. Entity extraction, automated entity extraction from unstructured information and not just unstructured text but unstructured images and video. Could be a really powerful thing. Taking in stuff that isn't tagged and pulling the entities out of that automatically without actually having to have a human in the loop. Pulling every name out, every phone number out, every address out. Go ahead, sorry. >> This goes back to a couple conversations we've had today where people say data trumps algorithms, even if they don't say it explicitly, so the cloud vendors who are sitting on billions of photos, many of which might have house street addresses and things like that, or faces, how do you make better... How do you extract better tuning for your algorithms from data sets that I assume are smaller than the cloud vendors? >> They're pretty big. We employ data engineers that are very experienced at tagging that stuff manually. What I would envision would happen is we would apply somebody for a week or two weeks, to go in and tag the data as appropriate. In fact, we have products that go in and do concept tagging already across multiple languages. That's going to be the subject of my talk tomorrow as a matter of fact. But we can tag things manually or with machine assistance and then use that as a training set to go apply to the much larger data set. I'm not so worried about the scale of the data, we already have a lot, a lot of data. I think it's going to be getting that proof set that's already tagged. >> So what you're saying is, it actually sounds kind of important. That actually almost ties into what we hear about Facebook training their messenger bot where we can't do it purely just on training data so we're going to take some data that needs semi-supervision, and that becomes our new labeled set, our new training data. Then we can run it against this broad, unwashed mass of training data. Is that the strategy? >> Certainly we would get there. We would want to get there and that's the beauty of what Data Bricks promises, is that ability to save a lot of the time that we would spend doing the nug work on cluster management to innovate in that way and we're really excited about that. >> Alright, we've got just a minute to go here before the break, so I wanted to ask you maybe, the wish list question, I've been asking everybody today, what do you wish you had? Whether it's in entity resolution or some other area in the next couple of years for Novetta, what's on your list? >> Well I think that would be the more robust machine learning library, all in Spark, kind of native, so we wouldn't have to deploy that ourselves. Then, I think everything else is there, frankly. We are very excited about the platform and the stack that comes with it. >> Well that's a great ending right there, George do you have any other questions you want to ask? Alright, we're just wrapping up here. Thank you so much, we appreciate you being on the show Rob, and we'll see you out there in the Expo. >> I appreciate it, thank you. >> Alright, thanks so much. >> George: It's good to meet you. >> Thanks. >> Alright, you are watching the CUBE here at Spark Summit 2017, stay tuned, we'll be back with our next guest.