so at the starting at the Google level we have data centers in four continents so we're in North America South America Asia and Europe of course we have a probably one of the world's largest global private networks with you know 13 undersea cables that are our own and hundreds of thousands of miles of dark fiber and and lit fiber that we you know we operate like I said probably one of the world's largest networks we have in in Europe were in five countries in Europe we're in two countries in Asia were in one country in South America and that's at the Google and in North America of course we have many many many sites across all of North America that's at the Google level now cloud has 19 regions that they operate in and 58 zones so each region of course has multiple zones in it you know we cover Google has presence in over 200 countries worldwide so really it is truly global operations so AccuWeather has been running an API service for the past ten years and we have lots of enterprise clients but we started to realize we are missing a whole business opportunity so we partnered with a eg and we created a new self-serve API developer portal that allows developers to go in there sign up on their own and get started and it's been great for us as far as like basically unlocking new revenue opportunities with api's because as you said everything is api's we also say everything is impacted by the weather so why not have everyone use a cue other api's to fulfill their weather needs I think if you look at what's going on and I talk to a lot of customers and developers and IT teams and clearly I think they are overwhelmed with the different things which are going on in this space so how do you make it simple how do you make it open how do you make it hybrid so you have flexibility of choices becoming top of the mind for many of the users now the lock in which many vendors currently provide it becomes very difficult for many of this users people moving around and meet the business requirements so I think having a solution and technology stack which is really understanding that complexity around that and making it simple in after dock I think is important so the focus well there's a theme in a couple different levels the broad theme is a cloud like no other because we've introduced a lot of new different features and products and programs we introduced anthos this morning which was really revolutionary way of using containers broadly Multi cloud hybrid cloud so it's from a product standpoint but it's also a cloud like no other because it's about the community that's here and it's truly a partnership with our customers and our partners about building this cloud together and we see the community as a really key part of that it's really core to Google's values around openness open-source technology and really embracing the broader community to build the cloud together well you know I think it continues to be continues to cooperate in the technical community very well and a couple of data points right one is around kubernetes that started what four or five years ago and that's going really strong but more importantly you know as the industry matures there are what I would call special interest groups that are starting to emerge in the kubernetes community one thing that we are playing very close attention to is the storage sake which is the ability to federated storage across multiple clouds and how do you do it seamlessly within the framework of googan IDs as opposed to trying to create a hack or a one-off that some vendors attempted to do so we try to take a very holistic view of it and make sure I mean the industry we are in it's time to drive volumes and volumes drive standards so I think we play very very close I think one of the biggest things that I'm seeing in this entire conference to date has been almost a mind shift change I mean this is conferences called Google Next and for a long time that's been one of their biggest problems they're focusing on what's next rather than what is today and they're inventing the future - almost at the expense of the present I think the big messaging today was both about reassuring enterprises that they're serious about this and also building a narrative where they're now talking about coming at this from a position of being able to embrace customers where they are and speak their language I think that that's transformative for Google and it's something I don't think that we've seen them do seriously at least not for very long I think that there's no question that this is a data game and we said early on John and the cube that big data war was going to be one in the cloud the data was going to reside in the cloud and having now machine intelligence applied to that data is what's giving companies competitive advantage and scale and economics I was struck by the stats that Google gave at the beginning of the keynote today Google in the last three years has spent 47 billion dollars on capital expenditures this year to date alone they've spent 13 billion dollars in capex and data centers 13 billion it would take IBM three and a half years to spend that much in capex it would take Oracle six years so from an economic standpoint in a scale standpoint Google Microsoft and Amazon are gonna win that game there's no question in my mind I am a student of AI I did my masters and PhD in that and I went through that change in my career because we had to collect the data match it and now analyze it and actually make a decision about it and we had a lot of false positives in some cases know something of which you don't want that either and what happened is our modeling capabilities became much better and we with this rich data and you actually tap into that data like you can go in there the data is there and disparate data we can pull in data from different sources and actually remove the outliers and make our decision real time right there we didn't have the processing capability we didn't have a place like pops up where global can scan and bring in data at hundreds of gigabytes of data that's messaging that you want to deal with at scale no matter where it is and process that that wasn't available for us now it's a real it's like a candy shop for technologists all the technologies in our hands and we want all these things so if you look at that category of that repetitive work AI can play a really amazing role in helping alleviate that mundane repetitive work and so you know great example of that as smart composed which hopefully you've used yep and so what we look at is things like say a salutation in an email where you have to think about who are you addressing how do you want to address them how do you spell their name we can alleviate that and make your composition much faster so the exciting announcement that we had today was that we are leveraging the Google assistant so the assistant that you're used to using at home via your home devices or on your phone and we're connecting that to your Google Calendar and so you'll be able to ask your assistant what you have on your schedule you know know what's ahead of you during your day and be able to do that on the go so you know I think in general one of the unique opportunities that we have with G suite is not only AI for taking these products that consumers know in love and bringing them into the enterprise and so we see that that helps people adopt and understand the products if it also just brings that like consumer grade simplicity and elegance in the design into the enterprise which brings joy to the workplace yeah so we've been working we've been hard at work over the last eight months since our last next can you believe that it's only been eight months and we last last year we were here announcing gk on prem this year we've rebranded CSP to anthos and enlarged it and we've moved it to GA so that's the big announcements in our spotlight we actually walk through all the pieces and gave three live demos as well as had two customers on stage and really the big difference in the eight months is while we're moving to GA now we've been working throughout this time with a set of customers we saw unprecedented demand for what we announced last year and we've had that privilege of working with customers to build a product which is what's unique really yeah and so we had two of those folks up on stage talking about the transformation that anthos is creating in their companies yeah absolutely I think particularly most of the larger enterprise accounts tend to have a multi vendor strategy for almost every category right including cloud which typically is one of the largest pens and you know it's it's typically what we see is people looking at certain classes or workloads running on particular clouds so it may be transactional systems running on AWS you know a lot of their more traditional enterprise workloads that were running on Windows servers potentially running on this year we see a lot of interest in data intensive sorts of analytics workloads potentially running on GCP and so I think larger companies tend to kind of look at it in terms of what's the best platform for the use case that they have in mind but in general you know I they are looking at multiple cloud vendors [Music] you

>> Announcer: From Seattle, Washington it's The Cube covering AWS Imagine. Brought to you by Amazon Web Services. >> Welcome back everybody, Jeff Frick here with The Cube. We're here in downtown Seattle at the AWS Imagine Education Conference. It's the second year of the conference. It's really successful so much now they have another education conference, excuse me, Imagine Conference coming up for nonprofits, but this is the education one. About 800 people and we're excited to have, I think they had representatives from like 40 countries here. It's amazing, such a small conference with such great global representation. We've got our first guest, all the way from Valencia, Spain. He is Inaki Bilbao Estrada and the Vice Chancellor for Internationalization and Innovation at the CEU Universidad Cardenal Herrera. It's a mouthful, welcome. >> Thank you very much. >> So first off, impressions from the show, from the keynotes this morning. >> It was very impressive, the keynote by Andrew Co Intersession by Amazon. We were impressed, we were included in the keynote and we are very proud of having been included in the keynote for our Alexa skill. >> Great, so before we get into kind of what they talked about, let's back up a few steps in terms of what you are trying to accomplish as an institution. So give us a little bit of background on the college, how big it was, and kind of what was going on and what you wanted to really do differently. >> We are a Spanish University. We belong with CEU San Pablo Foundation which owns three universities in Spain, Barcelona, Madrid, and Valencia. We are a not for profit universities and in Valencia, in our case, we are very proud that we used to be a local university with only 300 international students eight years ago and right now we have reached 2500 international students which represents around 30-33% of the population of the university. We are right now 8000 undergraduate students and 3000 graduate students. >> So that's pretty amazing. So as you said, you were really kind of a regional university and you decided you wanted more international students. Why did you want more international students and then once you made that goal, what were some of the major objectives at the beginning of this process or problems that you had to overcome? >> It was a trend in higher education institutions but for us it was very important for two reasons, one the sustainability of the university, but also and I think the main reason is that we wanted to have our students to have a global experience. We wanted to become a global university based in Valencia, but we have right now more than 80 countries represented on campus. >> Wow, so what were some of the big hurdles that you saw that were going to get in the way of attracting more of these international students? >> So it was very important for us to adapt all our processes to our students. For this we have a very helpful firm partnered on campus. It was the IT department with Jose Roch in charge of this department and through technology we have been able to escalate and automate, get the automation of all of this process in order to reach bigger number of international students. So we have adapted all the processes to the needed of our international students, our new population of international students. >> Right, so you were highlighted today for a very specific thing, for a very specific device, which is Alexa, and voice as an interface and we saw some of the Alexa stuff last year, in terms of the kids asking it, you know, when is my test, is my homework due, these types of things, but you guys are actually taking it to the next level. Explain to the folks what you guys have done with Alexa. >> So we have used Alexa to introduce a virtual assistant for all our students national and international students and one other things that have been highlighted in the keynote is that is not only in English, but also in Spanish. Like this we are covering the two most speaked language on campus, English and Spanish. >> So it's bi, so you've got a bilingual Alexa in the room. >> Yeah, yeah, yeah. So for us it was very important as explained before that technologies had been asked to cover all the population of students, not only part of them. >> Right and using English is kind of universal language, regardless of what their native tongue is. >> Yeah, yeah. >> So did you have to build all this from scratch? How much was Amazon helping you to do the English to Spanish translation, was it written in Spanish, how did some of those logistics work out? >> So we began six months ago with the project with the help of Amazon, they were very, very, very helpful for us. With Ana Cabez and Juan Manuel Gomez from the UK team of Amazon and they guided us how to develop the Alexa skills for the goals that we set with them, what we wanted to achieve with the virtual assistant for our students. >> And yeah, so the skills are the things that you actually write, so how many different skills did you write especially for your students? >> So we, what we are doing is to build only one, but we are integrating all the services in one only skill. So we are integrating services related with what my next assignment on Blackboard, which are my grades, how can I book a room in the library or another space of the university, locations of the different services or professors of the university. We are integrating a lot of services, but in one skill because we don't want the students to have to switch between skills. >> Jeff: Right, right. >> So we're aiming to have one virtual assistant for the students in only one skill. >> So that's interesting, I didn't even think about all the integration points that you have. But you've got integration points in all these other systems. The room booking services, the library services, Blackboard and the other educational services. >> The learning management system. >> So how many points of integration are there? >> A lot we are working right now, we are focusing around five, seven integration points, because also we are integrating it with our CRM in order to have personalized message to different segments of our students, depending of if they are due to get some documentation to the registrar office. We think that integration with CRM allows us to give personalized message and notification to our students depending on the situations. >> Jeff: Right. >> So it's not a general notification for all of the students on campus. >> Right, that's awesome. Again, highlighted in the keynote really I think is the first kind of bilingual implementation of Alexa. So that's terrific. I want to shift gears a little bit about innovation and transformation. We go to a lot of tech shows, talk to a lot of big companies, everybody wants to digitally transform and innovate. Traditionally education hasn't been known as the most progressive industry in terms of transforming. You said right off the bat, that's your job is about transformation and innovation. Where's that coming from? Is that from the competitive world in which you live? Is that a top-down leadership directive? What's kind of pushing basically the investment in this innovation around your guys' school? >> So I believe that education can be disrupt in the next five, ten years. So what we think at the university is that we have to be closer to this disruption and in this sense we are working a lot to improve the students' experience of our students on campus because if not we think that it makes no sense to study on campus when you can go online. >> Jeff: Right. >> So that's why we're using technology to improve the students' experience on campus. So we are trying to avoid those things that have no value added for the students through technology and through the digital transformation. In order that we have more time for these value added interaction between the staff, academic and nonacademic staff, with the students. >> Right, and then how has the reception been with the staff, both the academic staff and the nonacademic staff because clearly the students are your customers, your primary customer, but they're a customer as well. So how have they embraced this and got behind it? >> So I seen all the institutions and you have a part of the institution that is not so in favor of these innovations, but the big number of professors and staff have seen the benefits of not to have to answer email Saturday night because the virtual assistant is 24 hours seven days a week. So they've seen the benefits of how technology can give them more time for these value added interaction with their students. For this in order to avoid only top-down decisions we have created digital ambassador programs which this program what we do is to share with our professors and with our nonacademic staff what we are planning and how they see the project. >> Jeff: Right, right. >> And we are integrating their opinions and their suggestions in the program. >> So you're six months into it you said since you launched it. >> Yeah. >> Okay, I'm just curious if you could share any stories, biggest surprises, things that you just didn't expect. I always like long and unintended consequences, you know, as you go through this process. >> So one of things is in Spain, Alexa was launched in November, last November so it's very new. >> Jeff: Very new. >> Very new in Spain. There's no voice assistant in the last nine months, it have exploded, but we didn't have before. So the students have been very impressed that the university were working at this level with the technology so new because it was even new for them, even if they are younger and they knew a lot about this technology. They were impressed that the university so quickly reacted to the introduction of the technology. The other point is through innovation, we are also using Alexa for the digital transformation of learning and teaching. We have launched an innovation program for quizzes for the students. And we have the huge amount of volunteers that they want to see how it works. >> Right, right, just curious too, to get your take on voice as an interface. You made an interesting comment before we turned the cameras on that email just doesn't work very well for today's kids. They don't use it. They're not used to using it. But voice still seems to really be lagging. I get an email from Google every couple of days saying, here ask your Google Assistant this or ask your Alexa this, you know, we still haven't learned it. From where you're sitting and seeing kind of this new way to interact and as you said get away from these emails in the middle of the night that ask, when's my paper due and I could ask the assistant. How do you see that evolving? Are you excited about it? Do you see voice as really the centerpieces of a lot of these new innovations or is it just one of many things that you're working on? >> So I think the difference is that usually higher education institutions would have use of email for communication with students with so massive amount of emails. I think what they feel with the voice assistants is that they have the freedom to choose what they want to know or not to know. So if they can ask voice, virtual assistant, as in one case, they have the freedom when they want the information. >> Jeff: Right. >> So I think its a big difference between emails, in an email you decide when you send the information to the students, with voice technologies, the student, it's the student who is asking when they want the information. >> Jeff: Right. >> So I think it's important for them. >> It's huge because they never ask for the email. >> No, they, and after they tell us that it wasn't important information that they didn't check the email. >> Right. >> They complain that they don't have the right information. >> Right, well Inaki, thank you for sharing your story and congratulations on this project. Sounds like you're just getting started, you've got a long ways to go. >> Thank you so much. >> All right, thank you. He's Inaki, I'm Jeff. You're watching the Cube, we're in downtown Seattle at AWS Imagine Education Conference. Thanks for watching. See you next time. 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>> fly from San Francisco. It's the queue covering Google Cloud next nineteen, Tio by Google Cloud and its ecosystem Partners. >> Okay, welcome back, everyone. We hear it live coverage here in San Francisco, in Moscow, near on the show floor at Google Cloud. Next. Hashtag Google next nineteen on John Barrier with Dave. A long thing with the Cube, where he with Amy Loki G Sweet vice president of U X for Google. Great to see you. Thanks for coming on. >> Thank you so much for having me. >> So we've been here. It's day two of three days of coverage. A lot of action here. Great profile of of attendees. You got developers. You've got a lot of corporate enterprise focus kind of cloud coming. Maid. She has been the part of the theme, But I loved your key. No, you're showing all the cool features of G. Sweep of the new innovations was kind of going away. What's coming around the corner? What was the mean exercise of Aquino was the main theme. What was the key message? >> Yeah, well, I think in general we are really excited about how g speed is adapting to the changing landscape of work. And so what you heard me talk about was really how we're seeing how ghee sweets, playing a key role and connecting mobile remote workforces. So those front line workers with the back office. And that's a scenario that we're seeing happening today with our customers and many different industries, some unexpected, some expected. So, you know, we heard about AirAsia aviation industry on DH. Then we also talked about a scenario in the retail industry. And so what we're seeing is that these frontline workers are using products like hangouts, chat to communicate very quickly and send data and information back to the back office. S O G. Sweets. Really helping make this immediate sharing of information available so that, you know, strategic decisions can be made based on the data and the information that this remote workforce has available to them. And so, you know, helping connect those groups is a key piece of, I think, where we see work going in the future. What if some >> of the innovations, because one thing is that we're power uses of G sweet disclosure, we use G sweet, happy customers. The productivity has always been a big one stand up very easily. Don't need it. Get search all this great. All these great features. But as people keep using it, you guys are innovating more. What of the key design and user experience? Innovations to help people remember more productive because no males not going away. You've got good filtering. What if some of the new things >> right, Right. Well, you know, I think I certainly a hot word, right? But that is something where we see, you know, plays a key role in the enterprise. Because what we found through a lot of the user research that my team has done and also just largely in the industry, is that people categorized their work into two things. One is kind of repetitive, mundane work that the things that they have to do but they don't really enjoy and the other would be their core work. That, they see, is their intellectual contribution that builds their profile, builds their reputation, makes the marketable, unemployable and so on. And so if you look at that category of that repetitive work hey, I can play a really amazing role in helping alleviate that mundane, repetitive work. And so, you know, great example of that. A smart compose which hopefully you views on. So what we look at is things like, say, a salutation in an email where you have to think about who are you addressing? How do you want to address some? How do you spell their name? We can alleviate that and make your composition much faster. S o The exciting announcement that we had today was that we are leveraging the Google assistant. So the assistant that you're used to using at home via your home devices are on your phone and we're connecting that to your Google calendar. And so you'LL be able to ask your assistant what you have on your schedule. You know what's ahead of you during your day. Be able to do that on the go. So, you know, I think in general one of the unique opportunities that we have with G suite is not only I, but taking these products that consumers know in love and bringing them into the enterprise. And so we see that that helps people adopted understand the products, but also just brings that like consumer grade simplicity and elegance in the design into the enterprise, which brings joy to the workplace. >> You talk about this kind of new vision of of how you're gonna work. And I I first started. It was introduced with the sweet because of collaboration features. I mean, to this day, if somebody wants to be to edit a document, if it's not in Google docks, I'm going to look at it. >> Not gonna tell >> you I'm not going to do when I got it. You get it? It's just a waste of time. So I want to work faster. Smarter? I want more productive. I wanted to be secure. And the great thing is, these features just show up. Yes. Yeah. You call that smart? Composed. I call it, finish my thought. So. So paint a vision of what that future of work looks like. >> Yeah, well, I mean, certainly we see that work is getting more distributed. Work is getting more mobile. You know, we see more and more that work forces are in many different locations, not just all together in one office. So what excites me about these tools is I really see them in ways that we kind of build relationships amongst colleagues that may not get to spend face to face time together. So whether that's through video conferencing, whether that's through chat, all of these tools play a critical role in really building connective ity and culture of a team so that they can do their best work together. And so I really think of them not just a CZ like productivity tools, but as relationship building tools on DH. So I think the more that the tools can almost just help facilitate humans connecting and communicating. That's when we're really going to elevate the way that people can work together. >> I think cloud is so disrupted. We've been talking all today and yesterday around how the disruptive business miles changed with SAS and Cloud and databases from databases to the front end and one of the things that we've seen over the years. The trends is O Cloud. First Mobile first, first Mobile first and cloud First data first. But one of the things we're seeing is that no one's really cracked the code yet on virtual First, where companies now could be virtual. You don't really need maybe even need an office for me when you say virtual first. That means having an HR app that's designed for remote and distributed work teams. This's becoming a trend. Now we're starting to see some visibility around this new virtual first. >> Yeah, you guys look >> at it that way You guys have any conversation about? Can you share any reaction to that concept of virtual first companies where the processes were tailored for those remote work forces that might gather for meetings physical face to face, but then have to go back and be digital? Yeah, it's on that. >> Uh, Well, yeah. I mean, I think it goes back. Tio, this distributed idea, right? People are working in different places, but I think also different time place an element as well to solve, you know, speak for Google. In particular, we have a global team, right? Which means my team is working on different time zones. It's different, you know, different places as well. So you have to find kind of like you said that virtual way to connect. It's definitely something that we're seeing. I don't know that I have anything specific to comment on it this time, and it's definitely a trend that we're aware of. How >> about you? I designed and user experience what some of the cutting edge techniques that are emerging that you're seeing that's working that you're doubling down on. Can you share some insight into what u ex think customers and users like? >> Sure, Well, I mean, I think one of the big thing is voice input, right? And so you hear a lot about conversational You y is certainly very much an emerging discipline within the field. So, you know, when I started this career path, it was all about pixels on a screen and how you might move and manipulate those pixels and interact with them. But now, with all the voice to text capability, it's really about how can you communicate in an interactive way with digital experience? But you don't necessarily have to use your hands right. You don't necessarily have to have an input device like a mouse or a keyboard, which is a really exciting space, right, because it also opens up a world of, you know, ways that we can bring in more diverse workforce together through assistive technology and accessibility features. Right? So one of the things that I was excited to demonstrate today eyes the transcription capability within a meeting. So using hangouts meet you'LL be able to transcribe the meeting and have that show up on text on the screen, which helps people with varying ways that they might want to engage, be able to engage with the conversation right >> there. Just taking notes >> first is taking the right person. You >> are listening to the whole, you know, recorded video aft. The fact, Yeah, yeah, time consuming. >> Absolutely. You could look at a transcription. So I do think that, like interaction, is going to be less necessarily about using a device that helps you interact and more about using a natural interface like a conversation. >> We had a highlight reel for the meetings. That >> way you get the hard life. That's machine learning could come in. I was asking about the inbox before. What did you learn from that initiative? What do you carrying over what could use his expect? >> Yeah, well, I mean, inbox certainly was a great way for us to experiment and try out different features. There was a lot that we learn from that product. Onda lot of it. We have brought over ways that we kind of come prioritized your messages. Help kind of remind you what to get back Teo and categorize them. And those are all things that we've learned from inbox and we'LL continue to carry for it and it to Gino >> One of things we hear all the time that we've been covering Google clouds. Really, since the beginning, security has always been a big part of it. One things that you guys do that I like is identifying malicious e mails. Right? So talk about how you guys interface because also, you've got a little warning. Gotta warn users. Well, maybe a visual thing as well. But also this tech involved, right? Security's a huge concern for fishing. Spear fishing, Right, So we're talking about that. >> What's fantastic about what we could do a female is like I mentioned this morning. This is a product that, you know, I think over one point five billion people use right, which means that our machine learning on that data is incredibly powerful. And that's how we're able to detect malicious e mails and protect you from them and also warn you. And it's where design plays a role, too, because, like you may have seen it, I know it for myself. I rarely see them, but when I d'Oh, there's a big red banner at the top of the email that warns you that this is an email you should probably be cautious around, right? Eso ITT's were designed plays a role in security. But also our technology really is, you know, kind of far above on. You know what >> you do notice? It's like, Are you sure you want to hit? Send this makes your right. Thank you. Thank >> you. The productivity is is also a double edged sword. You guys have been so good with filtering. I can't use the excuse almost being my spam folder. You guys do a great job of filtering out spam, and it's kind of killing the newsletter business. But there's a lot of stuff that you guys categorize this this kind of again back to the collective intelligence across the billions of signals or users. How do you guys look at that? What's the Can you share some insight on how that works is their secret sauce is there, You know, because you've got spam, you got, you know, not urgent. You got a ways to kind of bring all that out >> Yeah. You know, I'm probably not the best to comment on how that all works, you know, coming from or is it a secret arrest after >> some machine learning? >> So that's an element. But, you know, essentially, what we want to do is make sure that your most important messages are in the foreground. And then you Khun, respond to the other messages when you have the right time and you want to address this thing. So you know, I find for me it's actually useful to go through, and I'm in that mindset like maybe it's a Sunday morning while I'm having my lot go through the newsletters and see the things that I want to catch in Terms of promotions are offers things like that, and I like being able to compartmentalize my time that way. One of >> the nice things that I noticed that you guys a collective intelligence, always a good thing that's where data comes in is that you have these now reminded. Sometimes I see some stuff on my email or says, Hey, you might want to pay attention this evening. >> A little >> kind of pops up the nudge. Is that new? When does that come out. Is that something that's been around >> something that's been out for a bit? I don't remember specifically when we launched it, but it was probably in the last few months, kind of time frame. But yeah, that's another way that we want to make sure that you're not missing important messages. I find it incredibly useful at work because there are those messages that I read, and I think I'm going to respond right away, but something to divert me to something else. And then I pushes down the list, so I find that the accuracy on this is amazing as well. >> About search of discovery I was just one of the benefits of of G Suite is across the board surgeon. Cross correlation. Any innovations there? Any new kind of techniques that you're seeing around search and layout holders is going because anything new there were thinking around that. >> I spoke a bit this morning about clouds search, which is, you know, a product that we launched about two years ago and that really, that enables businesses bring the power of Google search into their business, and it's also a standalone products. So if businesses aren't totally ready to make the move to G suite. They can kind of dip a toe in the water by trying search within their business on DH. Then what was exciting that we announced today is we now allow third party connectivity, so clouds search will not just searched. Your corpus of G sweet data are Google data. It will search all types of data at your company. So you know, including things like cells for us or SAPI data on. So that means that now, for the end user benefit, they can search all of the digital assets at their company and all the people and get those results in one place >> because, I mean, I know I personally creating data faster than I could manage it. So having a powerful search like that, So that sounds like was gonna ask you that sounds like you help how you'LL help use your solve that problem. Yeah, absolutely. So that's a product that I can purchase a standalone you completely standalone. Whatever data I want >> all the data within your business. Yeah, and, you know, based on our research, we find that people spend an inordinate, inordinate amount of time at work, searching for information, right? So we can help cut down that time and help them find the thing that they need That saves people that kind of time at work. >> How do you price it is for users that there's a terabyte or >> I have to get back? >> Don't know. Don't >> know off the top >> of citrus and I'm ready to buy a castle only objective. Come on. Any >> question for you on a CZ you look at the Enterprise is a big enterprise. Focus. What have you learned in dealing with the enterprise? Because great born in the clouds standing up Jeez, we, like we've done ten years ago on then certainly won't get the corporate account been great for our business. But as enterprising had the legacy stuff, whether Microsoft outlook or whatever they have existing stuff that they're used to. What have you learned dealing with the enterprise either? Integration. Sarah experienced What? Can you share any insights to some of those learnings? >> Yeah, absolutely. I mean so one of the things that's tantamount the enterprises interoperability. And so we've been really focused on ensuring that the sweet works well with other products in the enterprise, and I think that is a continuing trend way. See more and more when we speak with our customers. They're not looking for a one size fits all solution for all of their software needs. They understand now that really employees have a lot more control and influence on the tools that they want to use on DH. That's where you really looking at. You know, an employee will try to seek out the tool that they think is the best user experience, and that's what they want to use in the work place. And so that means the employer, the enterprise has to be much more nimble about how they might put a complimentary group of tools together. Eh? So we've been very, very focused on ensuring that our products work well with other products, including Microsoft, but including, you know, other video conferencing solutions, hardware solutions and so on. >> Security. Something neat. Thanks so much for sharing the inside. The update on G Suite. Final question for him. Curious because you're going unique position. Vice president of U Ex share what your job is. What do you do on a day to day basis? There's through the day in the life for a year in the life. What do you work on? What's in the projects? What do your objective? What do you do for your job? Specifically? Were the key things? >> Yeah. I mean, the best part of my job is I get to be, you know, really close with our customers and users. And I see my job is kind of like cheap chief. Empathize, er right. And so really understanding the human need behind you know, users and what they need to accomplish. And I spoke today about one of the most rewarding aspects is helping people accomplish their most important goals. And that could be in their personal life. It could be for education on it could be in the workplace is well, too. And so for us, like my team does a lot of user research and design to understand. What are those big bulls that people have? What is the friction that they have in accomplishing those goals? And then how can our tools solve those problems for them and make a frictionless experience that brings delight and helps him accomplish great things? >> You're like a life coaching a psychologist, same time. Hear my problems? Amy, Thank you so much for sharing the inside. Great. Inside here in the Cube on the U ex behind G suite. Really successful platform. I've seen innovation on Web mail taking to a home of the level now into the enterprise. Excuse coverage here on the the show floor of Google Cloud. Next. I'm John for a day. Volonte, stay with us for more coverage after this short break.

live from San Francisco it's the cube covering micron insight 2018 brought to you by micron welcome back to the San Francisco Bay everybody we saw the Sun rise in the bay this morning of an hour so we're gonna see the Sun set this gorgeous setting here at Pier 27 Nob Hills up there the Golden Gate Bridge over there and of course we have this gorgeous view of the bay you're watching the cube the leader in live tech coverage we're covering micron insight 2018 ai accelerating intelligence a lot of talk on on on memory and storage but a lot more talk around the future of AI so we got a great discussion here on the auto business and how AI is powering that business Jeff Bader is here is the corporate vice president and general manager of the embedded business unit at micron good to see you again Jeff thanks for coming on and Simon and rigor is the vice president BMW and he's also joined by Vijay Nadkarni who was the global head of AI and augmented reality at Visteon which is a supplier to Automobile Manufacturers gentlemen welcome to the cube thanks so much for coming on thank you so you guys had a panel earlier today which was pretty extensive and just a lot of talk about AI how AI will be a platform for interacting with the vehicle the consumer the driver interacting with the vehicle also talked a lot about autonomous vehicles but Simon watch you kick it off your role at BMW let's let's just start there it will do the same for Vijay and then get into it research portion that we do globally in which is represented here in North America and so obviously we're working on autonomous vehicles as well as integrating assistance into the car and basically what we're trying to do is to get use AI as much as possible in all of the behavioral parts of the vehicle that uses have an expectations towards being more personalized and having a personalized experience whereas we have a solid portion of the vehicle is going to be as a deterministic anesthetic as we have it before like all of the safety aspects for example and that is what we're working on here right now Vijay Visteon is a supplier to BMW and other auto manufacturers yes we are a tier 1 supplier so we basically don't make cars but we supply auto manufacturers of which BMW is one and my role is essentially AI technology adversity on and also augmented reality so in AI there are basically two segments that we cater to and one of them is that almost driving which is fully our biggest segment and the second one is infotainment and in that the whole idea is to give the driver a better experience in the car by way of recommendations or productivity improvements and such so that is so my team basically develops the technology and then we centrally integrate that into our products so so not necessarily self-driving it's really more about the experience inside the vehicle that is the and then on the autonomous driving side we of course very much are involved with the autonomous driving technology which is tested with detecting objects are also making the proper maneuvers for the Waker and we're definitely going to talk about that now Jeff you sell to the embedded industry of fooding automobile manufacturers we hear that cars have I forget the number of microprocessors but there's also a lot of memory and storage associate yeah I mean if you follow the chain you have our simon representing the OEMs Vijay represented the Tier one suppliers were supplier to those Tier one suppliers in essence right so so we're providing memory and storage that then goes in to the car in as you said across all of the different sort of control and engine drone and computing units within the car in particular into that infotainment application and increasingly into the a TAS or advanced driver assistance systems that are leading toward autonomous driving so there's a lot of AI or some AI anyway in vehicles today right presumably yeah affected David who did a wonderful job on the panel he was outstanding but he kind of got caught up in having multiple systems like a like an apple carplay your own system I actually have a bit about kind of a BMW have a mini because I'm afraid it's gonna be self-driving cars and I just want to drive a drive on car for this take it away from me though but but you push a button if you want to talk to a Syrian yeah push another button if you want to talk to the mini I mean it's it's gonna use it for different use cases right exactly may I is also about adaption and is also about integrating so AI is is is coming with you with the devices that you have with you anyway right so your might be an Alexa user rather than a Google assistant user and you would have that expectation to be able to ask to chat with your Alexa in your car as well that's why we have them in the vehicle also we have an own voice assistant that we recently launched in Paris Motorshow which augments the experience that you have with your own assistants because it factors in all of the things you can do with the car so you can say there is a solid portion of AI already in the vehicle it's mainly visible in the infotainment section right and of course I remember the first time I'm sure you guys experienced to that the the car braked on my behalf and then kind of freaked me out but then I kind of liked it too and that's another form of machine intelligence well that out well that counts for you that had not that has not necessarily been done by AI because in in in let's say self-driving there is a portion of pretty deterministic rule based behavior and exactly that one like hitting an object at parking you don't need AI to determine to hit the right there is no portion or of AI necessary in order to improve that behavior whereas predicting the best driving strategy for your 20-mile ride on the highway this is where AI is really beneficial in fact I was at a conference last week in Orlando it's the Splunk show and it was a speaker from BMW talking about what you're doing in that regard yeah it's all about the data right learning about it and and in turning data into insights into better behavior yes into better expected behavior from whatever the customer wants so Vijay you were saying before that you actually provide technology for autonomous vehicles all right I got a question for you could it autonomous - could today's state of autonomous vehicles pass a driver's test no no would you let it take one no it depends I mean there are certain companies like way mo for example that do a lot but I still don't think way mo can take a proper driver's test as of today but it is of course trying to get there but what we are essentially doing is taking baby steps first and I think you may be aware of the SAE levels so level 1 level 2 level 3 level 4 SF and a 5 so we and most of the companies in the industry right now are really focusing more on the level 2 through level 4 and a few companies like Google or WAV or other and uber and such are focusing on the level 5 we actually believe that the level 2 through 4 is the market would be ready for that essentially in the shorter term whereas the level 5 will take a little while to get that so everybody Christmas and everyone we're gonna have autonomous because I'm not gonna ask you that question because there's such a spectrum of self-driving but I want to ask you the question differently and I ask each of you when do you think that driving your own car will become the exception rather than than the rule well I'd rather prefer actually to rephrase the question maybe to where not when because we're on a highway setting this question can be answered precisely in roughly two to three years the the functionality will kick in and then it's going to be the renewal of the vehicles so if you answer if you if you ask where then there is an answer within the next five years definitely if we talk about an urban downtown scenario the question when is hard to answer yeah well so my question is more of a social question it is a technology question because I'm not giving up my stick shift high example getting my 17 year old to get his permit was like kicking a bird out of the nest I did drive his permanent driver on staff basically with me right so why but I mean when I was a kid that was freedom 16 years old you racing out and there is a large generational group growing up right now that doesn't necessarily see it as a necessity right so not driving your own car I think car share services right share who bore the so and so forth are absolutely going to solve a large portion of the technology of the transportation challenge for a large portion of the population I think but I agree with the the earlier answers of it's gonna be where you're not driving as opposed to necessarily win and I think we heard today of course the you know talking about I think the number is 40,000 fatalities on the roadways in the u.s. in the u.s. yeah everybody talks about how autonomous vehicles are going to help attack that problem um but it strikes me talk about autonomous cars it why don't we have autonomous carts like in a hospital or even autonomous robots that aren't relying on lines or stripes or beacons you one would think that that would come before in our autonomous vehicle am I missing something are there are there there there systems out there that that I just haven't seen well I don't know if you've ever seen videos of Amazon distribution centers yeah but they're there they're going to school on lines and beacons and they are they're not really autonomous yeah that's fair that's fair yeah so will we see autonomous carts before we see autonomous cars I think it's a question what problem that solves necessarily yeah it's just as easy for them to know where something is yeah you think about microns fabs every one of our fabs is is completely automated as a material handling system that runs up and down around the ceilings handling all the wafers and all the cartridges the wafers moving it from one tool to the next tool to the next tool there's not people anymore carrying that around or even robots on the floor right but it's a guided track system that only can go to certain you know certain places well the last speaker today ii was talking about it I remember when robots couldn't climb stairs and now they can do backflips and you know you think about the list of things that humans can do that computers can't do it let's get smaller and smaller every year so it's kind of scary to think about one hand is that does the does the concept of Byzantine fault-tolerance you guys familiar with that does that does that come into play here you guys know what that's about I don't know what it is exactly so that's a problem and I first read about it with it's the Byzantine general problem if you have nine generals for one Oh attack for one retreat and the ninth sends a message to half to retreat or not and then you don't have the full force of the attack so the concept is if you're in a self-driving boat within the vehicle and within the ecosystem around the city then you're collectively solving the problem so there these are challenging math that need to be worked out and and I'm not saying I'm a skeptic but I just wanted more I read about it the more hurdles we have there's some isolated examples of where AI I think fits really well and is gonna solve problems today but this singularity of vehicle seems to be we have a highly regulated environment obviously public transportation or public roads right are a highly regulated environment so it's like it's different than curating playlists or whatever right this is not so much regulated traffic and legislation isn't there yet so especially and it's it's designed for humans right traffic cars roads are designed for human to use them and so the adoption to they the design of any legislation any public infrastructure would be completely different if we didn't drive as humans but we have it we have machines drive them so why are robots and carts not coming because the infrastructure really is designed for humans and so I think that's what's going to be the ultimate slow down is how fast we as a society that comes up with legislation with acceptance of behavioral aspects that are driven by AI on how fast we adopt it technically I think it can happen faster than yeah yeah it's not a technology problem as much as it is the public policy insurance companies think about one of the eventually you can think of from from let's say even level four capable car on a highway is platooning yeah right instead of having X number of car lengths to the turn fryer you just stack them up and they're all going on in a row that sounds great until Joe Blow with their 20 year old Honda you know starts to pull into that Lane right so you either say this Lane is not allowed for that or you create special infrastructure essentially that isn't designed for humans there is more designed specifically for the for the machine driven car right how big is this market it's it feels like it's enormous I don't know how do you look at the tan we can talk to the memory I can talk the memory storage part of it right but today memory and storage all of memory storage for automotive is about a two and a half billion dollar market that is gonna triple in the next three years and probably beyond that my visibility is not so good maybe yours is better for sure but it then really driven by adoption rate and how fast that starts to penetrate through the car of OAM lines and across the different car in vijay your firm is when were you formed how long you've been around or vistas be around basically since around 2001 okay we were part of relatively old spun out whiskey on that at work right okay so so alright so that's been around forever yeah for this Greenfield for you for your your group right where's the aw this is transitional right so is it is it is it you try not to get disrupted or you trying to be the disrupter or is it just all sort of incremental as a 101 year old company obviously people think about you as being ripe for disruption and I think we do quite well in terms of renewing ourselves coming from aeroplane business to a motorcycle business to garbage and so I think the answer is are we fast enough I'll be fast enough in adoption and on the other hand it's fair to say that BMW with all of its brands is part of a premium thing and so it's not into the mass transportation so everything that's going to be eaten up by something like multi occupancy vehicle mass transportation in a smaller effort right this is probably not going to hurt the premium brand so much as a typical econo type of boxy car exciting time so thanks so much for coming on the cube you got a run appreciate thank you so much okay thanks for watching everybody we are out from San Francisco you've watched the cube micron inside 2018 check out Silicon angle comm for all the published research the cube dotnet as well you'll find these videos will keep on calm for all the research thanks for watching everybody we'll see you next time you

>> Live from San Francisco, it's The Cube, Covering, Google Cloud Next 2018, brought to you by, Google Cloud and it's ecosystem partners. >> Everyone, welcome back, this is The Cube, live in San Francisco for Google Cloud, big event here, called Google Next 2018, #GoogleNext18, I'm John Furrier, Dave Vellante, bringing down all the top stories, all the top technology news, all the stuff that they're announcing on stage, some of the executives, the product managers, customers, analysts, you name it we want to get that signal and extract it and share that with you. Our next guest is Dan here and he's the product manager for Cloud AI at Google, and dialogue flow with a hot product here under his preview. Thanks for joining us! Good to see you! >> Ah, yeah, excited to be here! >> We were bantering off camera because we love video, we love speech to text, we love all kinds of automation that can add value to someone's products rather than having to do a lot of grunt work, or not having any capabilities, so super excited about what your working on, the variety of things, this one's the biggest, dialogue flow, talk about the product. >> Sure, yeah, yeah. >> What is it? Yeah, so Dialogue Flow it's a platform for building conversational applications, conversation interfaces, so could be chatbox, it could be voicebox, and it started from the acquisition of APIAI, that we did a year and a half ago, and its been gaining a lot of momentum since then so last year at Google Cloud Next, we announced that we just crossed 150,000 developers in the Dialog Flow community, yesterday we just announced that we now crossed 600,000 and yeah its uh-- >> Hold on, back up, slow down. I think I just missed that. You had what and then turned in to what? Say it again. >> So it was a 150,000 last year or over a 150,000 and now its now its over 600,000. >> Congratulations, that's massive. >> So yeah, I-- >> That's traction! >> It's very, very exciting. >> Four X. (laughs) >> And yeah, we you know, were still seeing like a lot of strong growth and you know with the new announcements we made yesterday, we think it's going to take a much larger role, especially in larger enterprises and especially in sort of powering enterprise contact centers. >> You know, natural language processing, also know as NLP for the folks that you know, know the jargon, or don't know the jargon, its been around for a long time, there's been a series of open sores, academias done it, just, it just, ontologys been around, its like, it just never cracked the code. Nothing has actually blown me away over the years, until cloud came. So with cloud, you're seeing a rebirth of NLP because now you have scale, you've got compute power, more access to data, this is a real big deal, can you just talk about the importance of why Cloud and NLP and other things that were, I won't say stunted or hit a glass ceiling and the capability, why is cloud so important because you're seeing a surge in new services. >> Yeah, sure, so there's two big things, one is cloud, the other is machine learning and the AI, and they kind of advanced speech recognition, natural language understanding, speech symphysis, all of the big technologies that we're working on, so with Cloud, there's now sort of a lot more processing that's done centrally and there's more availability of data, that he could use to trains models and that feeds well into machine learning and so you know with machine learning we can do stuff that was much harder to do before machine learning existed. And with some of these new tools, like what makes Dialog Flow special is you could use it to build stuff very, very easily, so I showed last year at Google Cloud Next how you build a bot for an imaginary Google Hardware store, we built the whole thing in 15 minutes, and deployed it on a messaging platform and it was done and its so quick and easy anyone can do it now. >> So Dave we could an ask the cube bot, take our transcripts and just have canned answers maybe down the road you automate it away. >> Yeah, yeah, yeah! >> You'd kill our job! (laughs) >> No its pretty awesome. What's interesting is its shifting the focus from kind of developers and IT more to the business users, so what we're seeing is a lot of our customers, one of the people that went on stage yesterday in the Dialog Flow section, they were saying that now 90% of the work is actually done by the business users that are programming the tool. >> Really? Because a low code type of environment? >> Yeah, you can build simple things without coding, now you know, if you were a large enterprise you're probably going to need to have a fulfillment layer, that has code, but it's somewhat abstracted from the analoopies, and so you can do a lot of things directly on the UY without any code. >> So I get started as a business user, develop some function, get used to it and then learn over time and add more value and then bring in my real hardcore devs when I really want some new functions. >> Right. So what it handles is understanding what the user wants. So if you're building a cube bot, and what Dialog Flow will do is help you understand what the user is saying to the cube bot and then what you need to bring in a developer for is to then fulfill it so if you want that, for example, every time they ask for cube merchandise, you want to send them a shirt or a toy or something, you want your developer to connect it to your warehouse or wherever. >> Give us the best bot chain content you have? >> Right. >> There it is. >> So how would we go about that? We have all this corpus of data that we ingest and and we would just, what would we do with that? Take us through an example. >> So you would want to identify what are the really important use cases, that you want to fulfill, you don't want to do everything, you're going to spread yourself thin and it won't be high quality, you want to pick what are the 20% of things that drive 80% of of the traffic, and then fulfill those, and then for the rest, you probably want to just transition to a human and have it handled by a human. >> So, lets say for us we want it to be topical, right, so would we somehow go through and auto categorize the data and pick the top topics and say okay now we want to chat bot to be able to ask questions about the most relevant content in these five areas, ten areas, or whatever, would that be a reasonable use case that you could actually tackle? >> Yeah, definitely. You know there's a lot of tools, some Google offer, some that other offer that can do that kind of of categorization but you would want to kind of figure out what the important use cases that you want to fulfill and then sort of build paths around them. >> Okay and then you've got ML behind this and this is a function I can, this fits into your servalist strategy, your announced GA today, >> We announced GA a few months ago, but what we announced yesterday was five new features that help transform Dialog Flow into sort or from a tool-- >> What are those features take a minute to explain. >> Sure, yeah, yeah, so first is our Dialog Flow phone gateway, what is does is it can turn any bot into a an IVR that can respond within, it take 30 seconds to set up. You basically just choose a phone number and it attaches a phone number and it cost zero dollars per month, zero, nothing, you juts pay for usage if it actually goes above a certain limit, and then it does all of the speech recognition, speech symphysis, natural language understanding orchestration, it does it all for you. So setting up and IVR, a few years ago used to be something that you needed millions of dollars to set up. >> A science project! Yeah absolutely! >> Now you can do it in a few minutes. >> Wow! >> Second is our knowledge connectors. What it does it lets you incorporate enterprise knowledge into your chat bot, it could either be FAQs or articles, and so now if you have some sort of FAQ, again in like less than a minute, you can build it into Dialog Flow without having to intense for it. Then there are a few other smaller ones that we introduced also are speech symphysis, automatic spell correction, which is really important for a chat box because people always have typos, I'm guilty just as much as everyone. Last but not least sentiment analysis, so when it helps you understand when you want to transition to a human, for example, if you have someone sort of that's not super happy-- >> Agent! >> Yeah exactly! >> And some of these capabilities were available separately so for example you could have built a phone gateway and connected it to Dialog Flow before, but it used to be a big project that took a lot of work so, we had a guest speaker yesterday, in the session for Dialog Flow and they've been running POC with a few vendors right now, its been going on for a few months, and they told us that with Dialog Flow, phone gateway and knowledge connectors, they were able to build something in a few hours that took a few months to do with other vendors because they have to stitch together multiple services, configure them, set them up, do all of that. >> So the use case for this, just to kind of, first of all to, chat box have been hot for a while, super great, but now you have an integrated complex system behind it powering an elegant front end, I could see this as a great bolt on to products, whether it's websites or apps, how-tos, instrumentation, education, lot of different apps, that seems to be the use case. How does someone learn more about how they get involved? Do they go to the website, download some code? Just take us through. I want to jump in tomorrow or now, what do I do? >> There's a free edition I can have right? >> Exactly, yeah, so the good news is you could go to either cloud@google.com/dialogflow or dialogflow.com, there's, if you go to dialogflow.com you can sign up for the standard edition which is 100% free, its for text interactions, its unlimited up to small amount of traffic, and you can even play around with the phone gateway and knowledge connectors with a limited amount, without even giving a credit card. If you want cloud terms of service and enterprise grade reliability, we also offer Dialog Flow enterprise edition, which is available on cloud or google.com, and you can sign up there. >> That comes with an SLA that-- >> Exactly, an SLA and like cloud data terms of service, and everything that's kind of attached with that. I'd also encourage people to check out the YouTube clip for the session that was yesterday that was where we demoed all of these new features. >> What was the name of the session? >> Automating you contact center with a virtual agents. >> Okay check that out on YouTube, good session. Okay so take us through the road map, your on the products, so you're product manager so this is, you got to decide priorities, maybe cut some things, make things work better, what's on the roadmap, what's the guiding principles, what's the north star for this product? >> Yeah, so, for us it's all about the quality of the end user experience, so the reality is there's many thousands of bots out there in the world, and most of them are not great. >> I'll say, most of them really suck. (laughs) >> If you Google for why chat bots, why chat bots fail is the first result, and so that's kind of our north star, we want to solve that, we want to help different developers, whether they're start ups, experience they're enterprises, we want to help them build a high quality bots, and so a lot of the features we announced yesterday, are kind of part of that journey, for example, send integrated sentiment experience that as you transition to humans, cause we know we can't solve everything so helps you understand, or knowledge connectors-- >> Automation helps to a certain point but humans are really important, that crossover point. Trying to understand that's important. >> Exactly, and we'd rather help people build bots that are focused on specific use cases, but do them really, really well, versus do a lot, but leave users with a feeling that they were talking to a bot that doesn't understand them and have a bad experience. >> We could take all the questions we've done on the cube, Dave, and turn them into a chat bot. What's the future of bots? >> Yeah. >> Go ahead, answer the question. (laughs) >> So I think, so we're kind of in the last year or two, we've been at an inflection point, where speech recognition has advanced dramatically, and it's now good enough it can understand really complex questions, so you can see with, sort of Google Assistant and Google Home and bunch of other things that people can now converse with bots and get sort of reasonably good answers back. >> And that just feed ML in a big way. >> Right, exactly, so now, you know, Dialog Flow introduced speech recognition in recognition, which just introduced speech recognition yesterday, and so we're now looking to empower all of our developers to build these amazing voice voice based experiences with Dialog-- >> Give an anecdote or an experience that the customers had where you guys are like wow, that blow me away! That is so cool, or that is just so technically amazing, or that was unique and we've never seen that coming, give us, share some color commentary around some of the implementations of the bot, bot world and the Dialog Flow's impact to someones business or life. >> Sure, so I think yesterday the ticketmaster team was showing how they look at their current idea of that's based in the old world, where you have to give very short response like yes or no or like San Francisco California, and because it's built on these short responses, it kind of a guided IVR, it takes 11 steps-- >> What's an IVR again? >> Integrated Voice Response or Interactive Voice Response, it's a system that answers the phone. >> Just want to get the jargon right. >> So now that with something like Dialog Flow they can go and build something like that instead of 11 steps, takes 3 steps. So because someone can just say, I'd like to buy tickets for so and so and complete the sentence. And the cool thing is sort of the example that they gave a recording that I made with them about a year, plus ago, and the example was, I'd like to book tickets for Chainsmokers and then they were showing it yesterday in the conference, they were like oh we know why you chose it, its because the Chainsmokers are preforming at Google Cloud Next! Its probably just a funny coincidence but... >> So they've deployed this now or they're in the processes of deploying it? >> They're in the process of deploying it, first for customer service, and at a later stage its going to be for sales as well. >> Yeah, because of the IVR for Ticketmaster today, I know it well, I'm a customer, I love Ticketmaster, but you're right, it tells you what you just asked them pretty well, but it really doesn't quite solve your problem well so. >> I mean the recognize the sales one was built a long time ago, but they're kind of overhauling all of that. >> I'm excited to see it because its a good point of comparison, you know good reference point that you understand, it's , the takeaway that I'm getting, Dan, is the advice you're giving is, nail the use case, narrow it down, and then start there, don't try to do too wide of a scope. >> Exactly, exactly. Handle the most important thing is delivering great end user experiences because you want people to really enjoy talking to the bot, so in surveys people say, 60% of consumers say that the thing they want to improve most in customer service is getting more self serve tools. They're not looking to talk to humans, but they're forced to because the self services, yeah they're terrible. >> If can get it quickly self served, I'd love that every time, I'd serve myself gas and a variety of other things, airport kiosks have gotten so much better, I don't mind those anymore. Okay one quick follow up on Dave's point about making a focus, I totally agree, that's a great point. Is there a recommendation on how the data should be structured on the ingest side? What's the training data, si there a certain best practice you recommend on having certain kinds of data, is it Q and A, is it just text, speaks this way, is it just a blob of data that gets parsed by the engine? Take us through on the data piece. >> So that really changes a lot, depending on the specific use case, the specific companies, the specific customers, so someone asked in the adience yesterday, asked the guest speaker has many intense they felt in Dialog Flow and each one of them had very different answer, so it depends a lot. But I would say the goal is to kind of focus on the top use cases that really matter, built high quality conversations, and then built a lot of intents and text examples in those, and when I say a lot, it doesn't, we don't need a lot because Dialog Flow is built on machine learning, sometimes a few dozen is enough, or maybe a couple hundred if you need to, but like we see people trying tens of thousands, we don't need that much data. And then for the other stuff that's not in your core use cases, that's where you can use things like knowledge connectors, or other ways to respond to people rather than to manually build them in, or just divert them to human associates that can fill those. >> Great job Dan! So you're the lead product manager? >> I'm the lead product manager on Dialog Flow Enterprise Edition, and there's a large team kind of working with me. >> How big is the team? Roughly. >> We don't talk about that actually. >> What other products do you own? >> I'm also product manager for cloud speech to text and cloud text to speech. >> Well awesome. Glad to have you on, thanks for sharing. Super exciting, love the focus. I think its a great strategy of having something that's not a one trick pony bot kind model, having something that is more comprehensive, see that's why bots fail. But I think there's a real need for great self service, its the Google way, search yourself, get out quick. Get your results, I mean its the Google ethos. (laughs) Get in, get your answer. >> Yeah, we're all about democratizing AI so now with cloud speech to text and cloud text to speech, put the power of Google speech recognition, speech synthesis into the hands of any developer, now with Dialog Flow we are taking that a step further, anyone can build their voice bots with ease, what used to cost like millions of dollars, you don't need special expertise. >> Alright, Dan Harron is the product manager for the Dialog Flow Enterprise Edition and doing Cloud AI for Google to bring you all the best dialog here in the cube, doing our part, soon we'll have a cube bot, you can ask us any question, we'll have a canned answer from one of the cube interviews. Dave Vellante is here with me, I'm John Furrier, thanks for watching! Stay with us we'll be right back! (music)

>> Announcer: Live from San Francisco it's TheCUBE covering Samsung Developer Conference 2017. Brought to you by Samsung. (digital music) >> Hey welcome back and we're live here in San Francisco this is TheCUBE's exclusive coverage Samsung Developer Conference #SDC2017, I'm John Furrier co-founder of SiliconANGLE Media Coast My next guest is Ken Yeoung tech reporter here inside TheCUBE. I've known Ken for almost 10 years now plus been in the Silicon Valley beat scene covering technology, communities, and all the cutting edge tech but also some of the old established companies. Great to see you. >> Likewise, thanks for having me. >> So tech reporter, let's have a little reporter session here because reporting here at Samsung, to me, is my first developer conference with Samsung. I stopped going to the Apple World Developer Conference when it became too much of a circus around, you know, close to a couple of years before Steve Jobs died. >> Right. >> Now this whole scene well we will have to talk to Steve Gall when we get down there but here, my first one, my reports an awakening I get the TV thing but I'm like IoT that's my world. >> Ken: Oh really? >> I want to see more IoT >> Ken: Yeah. >> So it's good to see Samsung coming into the cloud and owning that. So, that's exciting for me. What do you see as a report that you could file? >> You know, so it's funny because I actually did write a post this morning after watching the keynote yesterday. While I was at VentureBeat a few months ago I reported on Bixby's launch when it came out with the Galaxy S8 and when I heard about what that was it was kind of interesting. That was one of the biggest selling points for me to switch over from my iPhone. And when I tried it out it was interesting. I was kind of wondering how it would stand up against Google Assistant because both of them are installed on the same device. But now as you see with Bixby 2.0 and now with the SmartThings you start to see Samsung's vision. Right now it's on a mobile, it's just very piecemeal. But now when you tackle it on with the TVs, with the fridges, monitors, ovens and everything like that it becomes your entire home. It becomes your Jarvis. You don't actually have to spend 150 bucks or 200 bucks on an Alexa-enabled device or Google Home that most people may not be totally familiar with. But if you have a TV you're familiar with it. >> Obviously you mentioned Jarvis. That's reference to the old sitcom and when Mark Zuckerberg tried his Jarvis project which was, you know, wire his home from scratch. Although a science project, you talk about real utility. I mean so we're getting down to the consumerization so let's take that to the next level. >> Ken: Right. >> If you look at the trends in Silicon Valley it's certainly in the tech industry block, chain and ICOs are really hot. Mission point offerings. That's based on utility right? So, utility-based ICOs, so communities using gamification. Game apps, utility. Samsung, SmartThings. Using their intelligence to not just be the next Amazon. >> Right >> The commerce cloud company, they're just trying to be a better Samsung. >> Ken: Exactly. >> Which they've had some problems in the past and we've heard from analysts here Patrick Morgan was on, pointed out... Illustrated the point. They're a stovepipe company. And with Bixby 2.0 they're like breaking down the silos. We had the execs on here saying that's their goal. >> Ken: Exactly. Yeah if you look on here everything has been siloed. You look at a lot of tech companies now and you don't get to see their grand vision. Everyone has this proto-program when they start these companies and when they expand then you start to see everything come together. Like for example, whether it's Square, whether it's Apple, whether it's Google or Facebook, right? And Samsung, a storied history, right, they've been around for ages with a lot of great technology and they've got their hands in different parts. But from a consumer standpoint you're like likelihood of you having a Samsung device in your home is probably pretty good and so why not just expand that leverage that technology. Right now tech is all about AI. You start to see a lot of the AI stars get acquired or heavily funded and heavily invested. >> Really The Cube is AI, we're AI machine right here. Right here is the bot, analyst report. People are AI watching. But I mean what the hell is AI? AI is machine learning, using software, >> Data collection. >> Nailed it. >> And personalization. And you look at I interviewed a Samsung executive at CAS last year this January, and he was telling me about the three parts. It has to be personal, it has to be contextual and it has to be conversational in terms of AI. What you saw yesterday during the keynote and what executives and the companies have been repeatedly saying is that's what Bixby is. And you could kind of say that's similar to what Google has with Google Assistant you can see that with Alexa but it's still very... Those technologies are very silent. >> What were those three things again? Personal, >> Personable, contextual, and conversational. >> That is awesome, in fact, that connects with what Amy Joe Kim, CEO of ShuffleBrain. She took it from a different angle; she's building these game apps but she's becoming more of a product development. Because it's not just build a game like a Zynga game or you know, something on a mobile phone. She's bringing gaming systems. Her thesis was people are now part of the game. Now those are my words but, she's essentially saying the game system includes data from your friends. >> Right. >> The game might suck but my friends are still there. So there's still some social equity in there. You're bringing it over to the contextual personal, this is the new magic for app developers. Is this leading to AR? >> Oh absolutely. >> I mean we're talking about ... This is the convergence of the new formulas for successful app development. >> Right, I mean we were talking about earlier what is AI and I mentioned all about data and it's absolutely true. Your home is collecting so much data about you that it's going to offer that personal response. So you're talking about is this going to lead to AR? Absolutely, so whatever data it has about your home you might bring your phone out as you go shopping or whatnot. You might be out sight-seeing and have your camera out. And it might bring back some memories, right or might display a photo from your photo album or something. So there's a lot of interesting ties that could come into it and obviously Samsung's camera on their phones are one of the top ones on the market. So there's potential for it, yeah. >> Sorry Ken, I've got to ask you. So looking at the bigger picture now let's look outside of Samsung. We can look at some tell signs here Google on stage clearly not grand-standing but doing their thing. Android, you know, AR core, starting to see that Google DNA. Now they've got tensor flow and a lot of goodness happening in the cloud with Sam Ramji over there kicking ass at Google doing a great job. Okay, they're the big three, some people call it the big seven I call it the big three. It's Amazon, Microsoft, Google. Everyone else is fighting for four, five, six. Depending on who you want to talk to. But those are the three, what I call, native clouds. Ones that are going to be whole-saleing resource. Amazon is not Google, Amazon has no Android. They dropped their phones. Microsoft, Joe Belfiore said hey I'm done with phones they tapped out. So essentially Microsoft taps out of device. They've still got the Xbox. Amazon tapping out of phones. They've got commerce. They've got web service. They've got entertainment. This is going to be interesting. What's your take? >> Well interesting is an under-statement there. I mean, you look at what the ... Amazon, right now, is basically running the show when it comes to virtual assistant or voice-powered assistance. Alexa, Amazon launched a bunch of Alexa products recently and then soon after, I believe it was the last month, Google launches a whole bunch of Google home devices as well. But what's interesting is that both of those companies are targeting... Have a different approach to what Samsung is, right? Remember Samsung's with Bixby 2.0 is all about consolidating the home, right? In my post I coined that it was basically their fight to unite the internet of things kind of thing. But, you know, when it comes to Alexa with Amazon and Google they're targeting not only the smaller integrations with maybe like August or SmartLocks or thermostats and whatnot but they're also going after retailers and businesses. So how many skills can you have on Alexa? How many, what are they called, actions can you have on Google Home? They're going after businesses. >> Well this is the edge of the network so the reason why, again coming back full-circle, I was very critical on day one yesterday. I was kind of like, data IoT that's our wheelhouse in TheCUBE. Not a lot of messaging around that because I don't think Samsung is ready yet and nor should they be given their evolution. But in Amazon's world >> I think they're ... The way they played it yesterday was pretty good a little humble, like they didn't set that expectation like oh my god this is going to >> They didn't dismiss it but they were basically not highlighting it right. >> Well they did enough. They did enough to entice you to tease it but like, look, they have a long way to go to kind of unite it. SmartThings has been around for a while so they've been kind of building it behind the scenes. Now this is like hey now we're going to slap on AI. It's similar to ... >> What do you hear from developers? I've been hearing some chirping here about AI it's got to be standardized and not sure. >> Oh, absolutely. I think a lot of developers will probably want to see hey if I'm going to build... If I want to leverage AI and kind of consolidate I want to be able to have it to maximize my input maximize my reach. Like I don't want to have to build one action here one service skill here. Whatever Samsung's going to call for Bixby. You know I want to make it that one thing. But Samsung's whole modernization that's going to be interesting in terms of your marketplace. How does that play out? You know, Amazon has recently started to monetize or start to incentivize, as it were, developers. And Google if they're not already doing that will probably has plenty of experience in doing that. With Android and now they can do that with Google. >> So I've got to ask you about Facebook. Facebook has been rumored to have a phone coming but I mean Facebook's >> Ken: They tried that once. >> They're Licking their wounds right now. I mean the love on Facebook is not high. Fake news, platform inconsistencies. >> Ken: Ad issues. >> Moves fast, breaks stuff. Zuck is hurting. It's hurting Zuck. Certainly the Russian stuff. I think, first of all, it's really not Facebook's fault. They never claimed to be some original content machine. They just got taken advantage of through bad arbitrage. >> It's gets it to some scale. >> People are not happy with Facebook right now so it's hard for them to choose a phone. >> Well, you're right. There are rumors that they were going to introduce the phone again after... We all remember Facebook Home which was, you know, we won't talk about that anymore. But I think there was talk about them doing a speaker some sort of video thing. I think they were calling it... I believe it's called Project Aloha. I believe Business ETC. and TechCrunch have reported on that extensively. That is going to compete with what Amazon's going. So everyone is going after Amazon, right. So I think don't discount Samsung on this part I think they are going to be I don't want to call them the dark horse but you know, people are kind of ignoring them right now. >> Well if Samsung actually aligned with Amazon that would be very because they'd have their foot in both camps. Google and Amazon. Just play Switzerland and win on both sides. >> Samsung, I think Samsung >> That might be a vital strategy. Kinesis if the customers wanted to do that. Google can provide some cloud for them, don't know how they feel about that. >> Yeah I mean Samsung will definitely be... I think has the appeal with their history they can go after the bigger retailers. The bigger manufacturers to leverage them because there's some stability as opposed to well I'm not going to give access to my data to Amazon you look at Amazon now as Amazon's one of the probably the de facto leader in that space. You see people teaming up with Google to compete against them. You know, there's a anti-Amazony type of alliance out there. >> Well I would say there's a jealousy factor. >> Ken: True, true. >> But a lot of the fud going out there... I saw Matt Asay's article in InfoWorld... And it was over the top basically saying that Amazon's not giving back an open source. I challenged Andy Jesse two years ago on that and Matt's behind the times. Matt you've got to get with the program you're a little bit hardcore pushed there. But I think he's echoing the fear of the community. Amazon's definitely doing open source first of all but the same thing goes for Ali Baba. I asked the founder of Ali Baba cloud last week when I was in China. You guys are taking open source what are you giving back and it was off the record comment and he was like, you know, they want to give back. So, just all kinds of political and or incumbent positions on open source, that to me is going to be the game-changer. Linux foundation, Hipatchi is growing, exponential growth in open source over the next five to ten years. Just in terms of lines of code shipped. >> Right. >> Linux foundation's shown those numbers and 10% of that code is going to be new. 90% of the code's going to be re-used and so forth. >> Ken: Oh absolutely. I mean you're going to need to have a lot of open source in order for this eco-system to really flourish. To build it on your own and build it proprietary it basically locks it down. Didn't Sony deal with that when they were doing, like, they're own memory cards for cameras and stuff and now their cameras are using SD cards now. So you're starting to see, I think, a lot of companies will need to be supportive of open source. In tech you start to see people boasting that, you know, we are doing this in open source. Or you know, Facebook constantly announces hey we are releasing this into open source. LinkedIn will do that. Any company that you talk to will... >> Except Apple. Apple does some open source. >> Apple does some open source, yeah. >> But they're a closed system and they are cool about it. They're up front it. Okay final question, bottom line, Samsung Developer Conference 2017 what should people know that didn't make it or are watching this, what should they know about what they missed and what Samsung's doing, what they need to do better. >> You know I think what really took the two-day conference is basically Bixby. You look at all the sessions; all about Bixby. SmartThings, sure they consolidated everything into the SmartThings cloud, great. But you know SmartThings has been around for a while and I'm interested to see how well they've been doing. I wish they released a little bit more numbers on those. But Bixby it was kind of an interesting 10 million users on them after three months launching in the US which is very is a pretty good number but they still have a bit of a ways to go and they're constantly making improvements which is a very good, good, good thing as well. >> Ken Yeoung, a friend of TheCUBE, tech reporter formerly with VentureBeat now onto his next thing what are you going to do? Take some time off? >> Take some time off, continue writing about what I see and who knows where that takes me. >> Yeah and it's good to get decompressed, you know, log off for a week or so. I went to China I was kind of off Facebook for a week. It felt great. >> Yeah. (laughs) >> No more political posts. One more Colin Kaepernick kneeling down during the national anthem or one more anti-Trump post I'm going to... It was just disaster and then the whole #MeToo thing hit and oh my god it was just so much hate. A lot of good things happening though in the world and it's good to see you writing out there. It's TheCUBE, I'm John Furrier, live in San Francisco, Samsung Developer Conference exclusive Cube coverage live here we'll be right back with more day two coverage of two days. We'll be right back.

(upbeat electronic music) >> Hello, everyone. Welcome to the Cube Studios in Palo Alto, California. I'm John Furrier, the co-host of the Cube and co-founder of SiliconANGLE Media Inc. I'm here with George Gilbert for a Wikibon conversation on the state of the big data. George Gilbert is the analyst at Wikibon covering big data. George, great to see you. Looking good. (laughing) >> Good to see you, John. >> So George, you're obviously covering big data. Everyone knows you. You always ask the tough questions, you're always drilling down, going under the hood, and really inspecting all the trends, and also looking at the technology. What are you working on these days as the big data analyst? What's the hot thing that you're covering? >> OK, so, what's really interesting is we've got this emerging class of applications. The name that we've used so far is modern operational analytic applications. Operational in the sense that they help drive business operations, but analytical in the sense that the analytics either inform or drive transactions, or anticipate and inform interactions with people. That's the core of this class of apps. And then there are some sort of big challenges that customers are having in trying to build, and deploy, and operate these things. That's what I want to go through. >> George, you know, this is a great piece. I can't wait to (mumbling) some of these questions and ask you some pointed questions. But I would agree with you that to me, the number one thing I see customers either fumbling with or accelerating value with is how to operationalize some of the data in a way that they've never done it before. So you start to see disciplines come together. You're starting to see people with a notion of digital business being something that's not a department, it's not a marketing department. Data is everywhere, it's horizontally scalable, and the smart executives are really looking at new operational tactics to do that. With that, let me kick off the first question to you. People are trying to balance the cloud, On Premise, and The Edge, OK. And that's classic, you're seeing that now. I've got a data center, I have to go to the cloud, a hybrid cloud. And now the edge of the network. We were just taking about Block Chain today, there's this huge problem. They've got the balance that, but they've got to balance it versus leveraging specialized services. How do you respond to that? What is your reaction? What is your presentation? >> OK, so let's turn it into something really concrete that everyone can relate to, and then I'll generalize it. The concrete version is for a number of years, everyone associated Hadoop with big data. And Hadoop, you tried to stand up on a cluster on your own premises, for the most part. It was on had EMR, but sort of the big company activity outside, even including the big tech companies was stand up a Hadoop cluster as a pilot and start building a data lake. Then see what you could do with sort of huge amounts of data that you couldn't normally sort of collect and analyze. The operational challenges of standing up that sort of cluster was rather overwhelming, and I'll explain that later, so sort of park that thought. Because of that complexity, more and more customers, all but the most sophisticated, are saying we need a cloud strategy for that. But once you start taking Hadoop into the cloud, the components of this big data analytic system, you have tons more alternatives. So whereas in Cloudera's version of Hadoop you had Impala as your MPP sequel database. On Amazon, you've got Amazon Redshift, you've got Snowflake, you've got dozens up MPP sequel databases. And so the whole playing field shifts. And not only that, Amazon has instrumented their, in that particular case, their application, to be more of a more managed service, so there's a whole lot less for admins to do. And you take that on sort of, if you look at the slides, you take every step in that pipeline. And when you put it on a different cloud, it's got different competitors. And even if you take the same step in a pipeline, let's say Spark on HDFS to do your ETL, and your analysis, and your shaping of data, and even some of the machine learning, you put that on Azure and on Amazon, it's actually on different storage foundation. So even if you're using the same component, it's different. There's a lot of complexity and a lot of trade off that you got to make. >> Is that a problem for customers? >> Yes, because all of a sudden, they have to evaluate what those trade offs are. They have to evaluate the trade off between specialization. Do I use the best to breed thing on one platform. And if I do, it's not compatible with what I might be running on prem. >> That'll slow a lot of things down. I can tell you right now, people want to have the same code base on all environments, and then just have the same seamless operational role. OK, that's a great point, George. Thanks for sharing that. The second point here is harmonizing and simplifying management across hybrid clouds. Again, back to your point. You set that up beautifully. Great example, open source innovation hits a roadblock. And the roadblock is incompatible components in multiple clouds. That's a problem. It's a management nightmare. How do harmonization about hybrid cloud work? >> You couldn't have asked it better. Let me put it up in terms of an X Y chart where on the x-axis, you have the components of an analytic pipeline. Ingest, process, analyze, predict, serve. But then on the y-axis, this is for an admin, not a developer. These are just some of the tasks they have to worry about. Data governance, performance monitoring, scheduling and orchestration, availability and recovery, that whole list. Now, if you have a different product for each step in that pipeline, and each product has a different way of handling all those admin tasks, you're basically taking all the unique activities on the y-axis, multiplying it by all the unique products on the x-axis, and you have overwhelming complexity, even if these are managed services on the cloud. Here now you've got several trade offs. Do I use the specialized products that you would call best to breed? Do I try and do end to end integration so I get simplification across the pipeline? Or do I use products that I had on-prem, like you were saying, so that I have seamless compatibility? Or do I use the cloud vendors? That's a tough trade off. There's another similar one for developers. Again, on the y-axis, for all the things that a developer would have to deal with, not all of them, just a sample. The data model and the data itself, how to address it, the programing model, the persistence. So on that y-axis, you multiply all those different things you have to master for each product. And then on the x-axis, all the different products and the pipeline. And you have that same trade off, again. >> Complexity is off the charts. >> Right. And you can trade end to end integration to simplify the complexity, but we don't really have products that are fully fleshed out and mature that stretch from one end of the pipeline to the other, so that's a challenge. Alright. Let's talk about another way of looking at management. This was looking at the administrators and the developers. Now, we're getting better and better software for monitoring performance and operations, and trying to diagnose root cause when something goes wrong and then remediate it. There's two real approaches. One is you go really deep, but on a narrow part of your application and infrastructure landscape. And that narrow part might be, you know, your analytic pipeline, your big data. The broad approach is to get end to end visibility across Edge with your IOT devices, across on-prem, perhaps even across multiple clouds. That's the breadth approach, end to end visibility. Now, there's a trade off here too as in all technology choices. When you go deep, you have bounded visibility, but that bounded visibility allows you to understand exactly what is in that set of services, how they fit together, how they work. Because the vendor, knowing that they're only giving you management of your big data pipeline, they can train their models, their machine learning models, so that whenever something goes wrong, they know exactly what caused it and they can filter out all the false positives, the scattered errors that can confuse administrators. Whereas if you want breadth, you want to see end to end your entire landscape so that you can do capacity planning and see if there was an error way upstream, something might be triggered way downstream or a bunch of things downstream. So the best way to understand this is how much knowledge do you have of all the pieces work together, and how much knowledge you have of all the pieces, the software pieces fit together. >> This is actually an interesting point. So if I kind of connect the dots for you here is the bounded root cause analysis that we see a lot of machine learning, that's where the automation is. >> George: Yeah. >> The unbounded, the breadth, that's where the data volume is. But they can work together, that's what you're saying. >> Yes. And actually, I hadn't even got to that, so thanks for taking it out. >> John: Did I jump ahead on that one? (laughing) >> No, no, you teed it out. (laughing) Because ultimately-- >> Well a lot of people want to know where it's going to be automated away. All the undifferentiated labored and scale can be automated. >> Well, when you talk about them working together. So for the deep depth first, there's a small company called Unravel Data that sort of modeled eight million jobs or workloads of big data workloads from high tech companies, so they know how all that fits together and they can tell you when something goes wrong exactly what goes wrong and how to remediate it. So take something like Rocana or Splunk, they look end to end. The interesting thing that you brought up is at some point, that end to end product is going to be like a data warehouse and the depth products are going to sit on top of it. So you'll have all the contextual data of your end to end landscape, but you'll have the deep knowledge of how things work and what goes wrong sitting on it. >> So just before we jump to the machine learning question which I want to ask you, what you're saying is the industry is evolving to almost looking like a data warehouse model, but in a completely different way. >> Yeah. Think of it as, another cue. (laughing) >> John: That's what I do, George. I help you out with the cues. (laughing) No, but I mean the data warehouse, everyone knows what that was. A huge industry, created a lot of value, but then the world got rocked by unstructured data. And then their bounded, if you will, view has got democratized. So creative destruction happened which is another word for new entrants came in and incumbents got rattled. But now it's kind of going back to what looks like a data warheouse, but it's completely distributed around. >> Yes. And I was going to do one of my movie references, but-- >> No, don't do it. Save us the judge. >> If you look at this starting in the upper right, that's the data lake where you're collecting all the data and it's for search, it's exploratory. As you get more structure, you get to the descriptive place where you can build dashboards to monitor what's going on. And you get really deep, that's when you have the machine learning. >> Well, the machine learning is hitting the low hanging fruit, and that's where I want to get to next to move it along. Sourcing machine learning capability, let's discuss that. >> OK, alright. Just to set contacts before we get there, notice that when you do end to end visibility, you're really seeing across a broad landscape. And when I'm showing my public cloud big data, that would be depth first just for that component. But you would do breadth first, you could do like a Rocana or a Splunk that then sees across everything. The point I wanted to make was when you said we're reverting back to data warehouses and revisiting that dream again, the management applications started out as saying we know how to look inside machine data and tell you what's going on with your landscape. It turns out that machine data and business operations data, your application data, are really becoming one and the same. So what used to be a transaction, there was one transaction. And that, when you summarized them, that went into the data warehouse. Then we had with systems of engagement, you had about 100 interaction events that you tracked or sort of stored for everything business transaction. And then when we went out to the big data world, it's so resource intensive that we actually had 1,000 to 10,000 infrastructure events for every business transaction. So that's why the data volumes have grown so much and why we had to go back first to data lake, and then curate it to the warehouse. >> Classic innovation story, great. Machine learning. Sourcing machine learning capabilities 'cause that's where the rubber starts hitting the road. You're starting to see clear skies when it comes to where machine learning is starting fit in. Sourcing machine learning capabilities. >> You know, even though we sort of didn't really rehearse this, you're helping cue me on perfectly. Let me make the assertion that with machine learning, we have the same shortage of really trained data scientists that we had when we were trying to stand up Hadoop clusters and do big data analytics. We did not have enough administrators because these were open source components built from essentially different projects, and putting them all together required a huge amount of skills. Data science requires, really, knowledge of algorithms that even really sophisticated programmers will tell you, "Jeez, now I need a PhD "to really understand how this stuff works." So the shortage, that means we're not going to get a lot of hand-built machine learning applications for a while. >> John: In a lot of libraries out there right now, you see TensorFlow from Google. Big traction with that application. >> George: But for PhDs, for PhDs. My contention is-- >> John: Well developers too, you could argue developers, but I'm just putting it out there. >> George: I will get to that, actually. A slide just on that. Let me do this one first because my contention is the first big application, widespread application of machine learning, is going to be the depth first management because it comes with a model built in of how all the big data workloads, services, and infrastructure fit together and work together. And if you look at how the machine learning model operates, when it knows something goes wrong, let's say an analytic job takes 17 hours and then just falls over and crashes, the model can actually look at the data layout and say we have way too much on one node, and it can change the settings and change the layout or the data because it knows how all the stuff works. The point about this is the vendor. In this particular example, Unravel Data, they built into their model an understanding of how to keep a big data workload running as opposed to telling the customer, "You have to program it." So that fits into the question you were just asking which is where do you get this talent. When you were talking about like TensorFlow, and Cafe, and Torch, and MXnet, those are all like assembly language. Yes, those are the most powerful places you could go to program machine learning. But the number of people is inversely proportional to the power of those. >> John: Yeah, those are like really unique specialty people. High, you know, the top guys. >> George: Lab coats, rocket scientists. >> John: Well yeah, just high end tier one coders, tier one brains coding away, AI gurus. This is not your working developer. >> George: But if you go up two levels. So go up one level is Amazon machine learning, Spark machine learning. Go up another level, and I'm using Amazon as an example here. Amazon has a vision service called Recognition. They have a speech generation service, Natural Language. Those are developer ready. And when I say developer ready, I mean developer just uses an API, you know, passes in the data that comes out. He doesn't have to know how the model works. >> John: It's kind of like what DevOps was for cloud at the end of the day. This slide is completely accurate in my opinion. And we're at the early days and you're starting to see the platforms develop. It's the classic abstraction layer. Whoever can extract away the complexity as AI and machine learning grows is going to be the winning platform, no doubt about it. Amazon is showing some good moves there. >> George: And you know how they abstracted away. In traditional programming, it was just building higher and higher APIs, more accessible. In machine learning, you can't do that. You have to actually train the models which means you need data. So if you look at the big cloud vendors right now. So Google, Microsoft, Amazon, and IBM. Most of them, the first three, they have a lot of data from their B to C businesses. So you know, people talking to Echo, people talking to Google Assistant or Siri. That's where they get enough of their speech. >> John: So data equals power? >> George: Yes. >> By having data, you have the ingredients. And the more data that you have, the more data that you know about, the more data that has information around it, the more effective it can be to train machine learning algorithms. >> Yes. >> And the benefit comes back to the people who have the data. >> Yes. And so even though your capabilities get narrower, 'cause you could do anything on TensorFlow. >> John: Well, that's why Facebook is getting killed right now just to kind of change tangents. They have all this data and people are very unhappy, they just released that the Russians were targeting anti-semitic advertising, they enabled that. So it's hard to be a data platform and still provide user utility. This is what's going on. Whoever has the data has the power. It was a Frankenstein moment for Facebook. So there's that out there for everyone. How do companies do the right thing? >> And there's also the issue of customer intellectual property protection. As consumers, we're like you can take our voice, you can take all our speech to Siri or to Echo or whatever and get better at recognizing speech because we've given up control of that 'cause we want those services for free. >> Whoever can shift the data value to the users. >> George: To the developers. >> Or to the developers, or communities, better said, will win. >> OK. >> In my opinion, that's my opinion. >> For the most part, Amazon, Microsoft, and Google have similar data assets. For the most part, so far. IBM has something different which is they work closely with their industry customers and they build progressively. They're working with Mercedes, they're working with BMW. They'll work on the connected car, you know, the autonomous car, and they build out those models slowly. >> So George, this slide is really really interesting and I think this should be a roadmap for all customers to look at to try to peg where they are in the machine learning journey. But then the question comes in. They do the blocking and tackling, they have the foundational low level stuff done, they're building the models, they're understanding the mission, they have the right organizational mindset and personnel. Now, they want to orchestrate it and implement it into action. That's the final question. How do you orchestrate the distributed machine learning feedback and the data coherency? How do you get this thing scaling? How do these machines and the training happen so you have the breadth, and then you could bring the machine learning up the curve into the dashboard? >> OK. We've saved the best for last. It's not easy. When I show the chevrons, that's the analytic data pipeline. And imagine in the serve and predict at the very end, let's take an IOT app, a very sophisticated one. which would be an autonomous car. And it doesn't actually have to be an autonomous one, you could just be collected a lot of information off the car to do a better job insuring it, the insurance company. But the key then is you're collecting data on a fleet of cars, right? You're collecting data off each one, but you're also collecting then the fleet. And that, in the cloud, is where you keep improving your model of how the car works. You run simulations to figure out not just how to design better ones in the future, but how to tune and optimize the ones that are on the road now. That's number three. And then in four, you push that feedback back out to the cars on the road. And you have to manage, and this is tricky, you have to make sure that the models that you trained in step three are coherent, or the same, when you take out the fleet data and then you put the model for a particular instance of a car back out on the highway. >> George, this is a great example, and I think this slide really represents the modern analytical operational role in digital business. You can't look further than Tesla, this is essentially Tesla, and now all cars as a great example 'cause it's complex, it's an internet (mumbling) device, it's on the edge of the network, it's mobility, it's using 5G. It encapsulates everything that you are presenting, so I think this is example, is a great one, of the modern operational analytic applications that supports digital business. Thanks for joining this Wikibon conversaion. >> Thank you, John. >> George Gilbert, the analyst at Wikibon covering big data and the modern operational analytical system supporting digital business. It's data driven. The people with the data can train the machines that have the power. That's the mandate, that's the action item. I'm John Furrier with George Gilbert. Thanks for watching. (upbeat electronic music)