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

>> Announcer: Live from Orlando, Florida, it's theCube, covering Grace Hopper Celebration of Women in Computing. Brought to you by SiliconANGLE media. >> Welcome back to theCube's coverage of Grace Hopper Conference here in Orlando, Florida. I'm your host, Rebecca Knight. We're joined by Vicki Mealer-Burke, she is the Vice-President and Chief Diversity Officer at Qualcomm. Thanks so much for joining us, Vicki. >> Thank you, Rebecca, it's great to be here. >> So, before we the camera's were rolling, you were describing how you've been at Qualcomm for 20 years, but you've been in this job for one year. And you're the first person to ever hold the position. >> That's right. >> So, tell our viewers how it came about. >> Yeah, I have been at Qualcomm almost 21 years now, and mostly in product development, product management, and then, my last role was as a general manager of one of our wholly-owned subsidiaries and I really thought that my run at Qualcomm was done, because we're consolidating a lot of our businesses. I started working on some women's programs while I was shutting down our last business, and it just so happened, it was the same time the company decided to create a chief diversity officer. My initial reaction was, "That's so great, we're going to "get one of those people, and we really need them." I wanted to be a champion for that person, and then I started getting myself interested and thinking that I could really be a change agent and a leader for the company. And kind of leave a legacy back to the company, a company that's actually been really, really good to me. >> So, when you were thinking about this job, you described it as a business problem that needed to be solved. And as someone who'd been at Qualcomm for two decades, how did you define the business problem? >> The way that my brain works is, I'm a problem solver and that's why I got into product management. And so, I really thought that if the company saw this as compliance or some sort of regulatory issue, I would really have no real interest, but I really knew that we could solve the probably by likely re-engineering some of the processes that had been in place. And, Qualcomm has had a tremendous growth over the years, and we've ramped from, I was employee 5,000 to now well over 30,000, so many of our processes really just had to be re-engineered. And I knew that I could speak that language to our leaders, we understand re-engineering problems. So, I really tried to get down to root cause and focus on a couple of the areas that would really make a big difference, and discuss the business value of why we were doing this. >> So, what are the areas that you are focusing on? Just give our viewers of a sense of the the top two or three areas where you think you can have the most impact? >> There's really two levers that I'm focused on. One is talent acquisition, so continuing to bring the best and brightest minds, the most innovative people in the world now to help us move our wireless technology into the 5G world. The possibilities are endless so we need all kinds of bright minds looking at this from all different kinds of directions. That's the diversity piece of it. The second big lever is, once we get them in, we have to keep 'em. I mean, this show shows how talented women engineers are really at premium, and so the more we're hiring, the more we're losing people on the other side. People call that the leaky pipeline or the leaky bucket. So, I'm working on retention programs to make sure that once we get our diverse talent in the door that we can keep them by really supporting, promoting, progressing them, making sure that they have wide variety of opportunities and that they see a bright future for themselves at Qualcomm. >> So, are you starting new programs? Is this about mentorship, is this about making sure there is flexible work? I mean, what are some of the nitty-gritty things that Qualcomm is doing? >> Yeah, we have started a series of sessions with our senior-most leaders, what we call, like, our directors and above. We have terrific support at the C-level at Qualcomm, terrific support. But at a 30,000 person company, you really need to get into that next couple-down layers. And so, we're doing training about, basically, how to run an inclusive team, how to empower. One of the big things that we're training on is the process of, how do you pick people for that next big project? And, like many managers, they go back to the people that have been successful year after year. What we're trying to do is disrupt that and either create, like, a apprenticeship, product leader positions where someone can tag along and lead and understand how those projects were run so well. But that's what we need to do is really try to expand the project opportunities, that's when people get a lot of visibility, a lot of experience, and that's where their own talents will just then accelerate them through our levels. >> You were talking about the need to make sure that a couple rungs down from the senior brass, really understand that there is a real business case for diverse teams that are collaborative. How receptive are these managers in your experience, and what do you say that really tips them over? >> So, Qualcomm is full of extremely bright people. There's an awareness and the benefit of the doubt that we're giving all of our employees is, "Let's give you the "facts, let's make you aware, let's let you drive the "solution, so that we're all working together." We don't have any kind of quotas, we just want to make managers, give them all the data and have them make good decisions, and empower them to be part of the solution. That empowerment need is where we're building trust with those managers. We're not saying, "Oh, you've been doing it wrong for "a million years." We're saying, "Here's what you can do to get better. "Here's what you can do to have a more engaged team. "Here's what you can do to have a more empowered team." That leads to productivity, productivity goes straight to the bottom line, and it makes sense. So, we're trying to do it more in a partnership, giving them the respect that they've earned with the positions that they're in, and empowering them to be the change. >> So, earlier in your career, you worked on some really exciting projects in terms of wearables, in terms of smart-cities, in terms of home-base technology. Do you miss the tech, I mean, do you see yourself going back and working in that? >> Yeah, it's a great question. When you're in the business, there are daily, weekly, incremental successes. We fixed that bug, we got that contract, this is really more, I call it kind of like forming jello, it's hard to get those feelings everyday like you're making progress on something. I do miss the technology, this is the biggest problem I think I've ever been tasked to solve, so that is extremely inspiring, and luckily, I get to work side-by-side with a lot of our best technology leaders. But, I do miss the technology, for sure. >> And working in the business? >> Sure. >> So, you talked about the, sort of, difficulty with measuring incremental progress, and then really we're at a point in time where the Google manifesto and Travis Kalanick's antics are front-page news. Is this discouraging, or is does it make you more excited by the cause and what you're doing? >> There are aspects to it that are discouraging, but I am really a glass half-full type of person, I think shining the light, really shining this big, bright light on the issue makes 99% of the people in our business really say, "Wow, I can't believe that's really going on." So, I actually think it's good, it's allowing us to have these conversations which are uncomfortable and a lot of leaders want to have the conversations but they don't know what to say. So, all of these things coming out in the press just give us that entry to be able to say, "Let's talk about it." And we've been doing that at Qualcomm, we do it with our employees, I want people to feel free to ask questions and not think that they should know it all. This is actually a fairly new area, so we've got to allow all of our leaders to have a level of comfort, but also know they don't have to be perfect in every single thing they say, just be inquisitive and really start the discussions. >> When you are pitching Qualcomm as a potential employer to young women, what is your value proposition? We heard Fei-Fei Li during the keynote talk about there is a real crisis if women are not actively involved in creating the next generation of artificial intelligence, and we're half of the end users, that there is going to be this real disconnect between the technology and how it's used. >> And as a product leader, I have always been fascinated by these public stories of product failures that no one was trying to make them fail but it was very clear that they didn't have a diverse team, because they just had some really big misses. So, one of the things we talk about at Qualcomm, you know, we're a wireless technology company, we started with 3G and now 4GLTE, that whole wireless technology, that backbone of it, is all Qualcomm tech, and it allows us to go into 5G. 5G is where the thing gets exponentially more interesting, more exciting, a much lighter set of problems to solve can be solved through 5G. So, if we don't have a diverse set of people thinking about all the different use-cases, variables, that we can use 5G technology, we'll miss something big. And I know that our CEO believes that, we've talked about it, we are inventors, we are innovators, and we have to have a wider variety of people that are being inventors of the future. >> So, I just want to wrap up here but finally ask you about this conference, this is not your first Grace Hopper, and it's a very young conference and you're really looked at as a veteran, I mean, me, too. We're the old bags about this place. (laughing) Can you just describe a little bit, I know you said that you were introducing one of the keynote speakers and you got to meet a personal hero of yours, just what it's like to be here? >> It's really amazing, last year was my first year. I was not the Chief Diversity Officer a year ago, yet, and I came here and people like, Telle Whitney, who you read about, I've gotten to meet here, I can hug her. >> Rebecca: You'll never was your hand again. >> I know, it's amazing. The women that have been leading this for years and years and years, and now what this has turned out to be, I was talking to one of my colleagues, and I go to a lot of technical conferences and business conferences like CES, CES is almost where we should be here meeting in the middle, a lot more men here, in years to come, and a lot more women at CES. And I think that's when we'll know that we're actually making progress. >> Well, Vicki, thank you so much for joining us. >> Yes, thank you, thanks for having me. >> I'm Rebecca Knight, we'll have more from theCube's coverage of the Grace Hopper just after this. (upbeat music)

>> Live from Orlando, Florida, it's theCUBE covering Grace Hopper's Celebration of women in computing. Brought to you by SiliconANGLE Media. >> Hey welcome back everybody. Jeff Frick here at theCUBE. We're at the Grace Hopper Celebration of Women in Computing, the best name in tech conferences. 18,000 women here in Orlando, filling up the Orange County Conference Center. We're excited to be here for our fourth year, and part of the whole program is getting some of the leadership from AnitaB.org on to give us an update and we're really excited to have Elizabeth Ames. She's the SVP of Marketing and Alliances and Programs but we just think of her as Elizabeth at AnitaB.org. So, Elizabeth, great to see you. >> Great to be here. >> Absolutely >> We're thrilled to have you here at the Celebration. >> I can't believe it's been four years. I've been telling so many people. There are still so many people that have never been here. I was amazed at the keynote, the first day, there was the call, the houselights went up, how many people it's their first time, and as big as this conference is, as much the people that know it love it, there's still a lot of people that have not been exposed to this show. >> It's absolutely the case. We have every year it seems like more and more sort of first timers. Which is great because we love to have them come but we'd love to have them come back. I think it's really an expression of how this issue has become a big issue and that the women are really engaged and excited and they want to be a part of it, so it's great. >> The other thing I don't think a lot of people know is there's obviously a lot of recruiting going on, there's a lot of young people here which is really what I think gives it its flavor, but we had Workday on. They said they had 140 people here from Workday. I talked to a guy last night at dinner from Google, I think they had 180 people and I said to her, "Do you have any show "that you bring that many people to "that's not your own show, so the amount of investment" And then I said, it's all young, fresh out of school No, it's all ranges, all ages. So again, I think there's a lot going on here that people are just not that exposed to. >> Yeah, that's absolutely true. So, if you look at our attendance overall, about 70% are industry and a lot of those are companies that are bringing their women and some of them are their younger women who have maybe been in the firm, in the company for a year or two or three or something like that, but the place where a lot of women drop out of the industry is more mid-career and so I think more and more companies are seeing this as a way to help their mid-career women recommit to the field and make those connections with the community at large and get a little bit more reinvigorated so we definitely see companies bringing all kinds of women out of their organization, and they like to bring a mix, so that they have some of their senior women that are sort of mentoring women who are mid-career or women who are more junior and it just gives them a really good mix. And then about 30% of our attendees are academic, we call it academic, but it's primarily students, so undergraduate, graduate, post doc, and research type people, and then some amount of professors and teaching assistants, those types of people. >> Yeah, and I really think it's the youth that give this show its special vibe. I mean there's a lot of great keynotes and some fantastic stories and really great global representation, a ton of African representation. But I do think it's the youth, it's the youngsters that bring a really unique and positive energy that you don't really see at many other conferences. >> Yeah, and I think part of that is that the community at large, you know women that are in the field they care about the women coming up and they want them to succeed and they want them to have every single opportunity so everybody's kind of invested in them and interested in nurturing and helping them along. So it does create this really, I don't know, positive environment, right. We always jokingly say there's a reason we call it a celebration. We don't call it a conference, we call it a celebration. >> Everyone's a delegate too. I like that too. It's not attendees. And that's come up on a number of interviews too where when people have reflected back on people that have helped them along the way the payback, it's almost like it's been scripted is, OK, now you need to do this to the next person to really pay it forward and that again is a consistent theme that we have also heard from the keynotes earlier today, that it is about paying it forward, which is funny because sometimes you'll hear kind of a catty women reputation that they're trying to keep each other down, you know that that was kind of a classic, another hurdle that women had to face in the professional world that they weren't necessarily supporting each other, and that is not the case here, at all. It's very much a supportive environment. >> We may have a self selection bias going on here >> Well that's okay >> But I think there's nothing but support for one another in the community and everybody recognizes that we all have to pull together. >> Right. So interesting times at AnitaB.org, the organization that puts on Grace Hopper, change of leadership, we had Brenda on, so kind of a fresh face, fresh energy. Telle. I'm going to see if I can get her a horse tomorrow to ride off into the sunset if the sun breaks out here in Orlando, so it's exciting times. It's a time of transition, always a little kind of mixed feelings, but also tremendous excitement and kind of new chapter, if you will. So tell us a little bit about what's going on at AnitaB.org >> It's an incredibly exciting time. First of all, a nod to Telle. She's been at the helm for 15 years. She's seen an incredible amount of growth. She took this on really as a favor to her dear dear friend and then took on the mantle upon Anita's death. She's done an amazing job. She's certainly an icon within the community overall I'm sure you'll hear more from her in the future. It's been great. Brenda is new fresh face. She has accomplished some pretty amazing things with the Chicago Public Schools. She's really invigorated to step into this space and it's great having her. I think the thing that you really, hopefully you got from her when she was here is that she is just this incredibly genuine person. She's lived the experience. She can relate to what all of these women have gone through. She has this profound commitment to make things different. And just the biggest heart that you could possibly imagine. >> Right, and a little chip on her shoulder. Which she talked about and it's come up time and time again where when people are told they can't do things for a lot of people, there's no greater motivator than being told you can't do this, you shouldn't do this, you're not qualified. She said "I've been in positions "where I've been told I can't be there." So to have that little chip on her shoulder I think is a real driver for many folks. >> It is. We recently did a little written piece it hasn't actually gotten published yet where we kind of went back and looked at a lot of the language that we're hearing today about women are not biologically suited to be programmers or women aren't this or women aren't that. And we did this little let's look back historically, and when did women get certain rights, and one of the things that really stood out for us in looking at that was women weren't admitted to all of the premier colleges, Harvard, Yale, whatever, until the 1960s. Which is kind of shocking when you think about it. >> Yeah, it's like yesterday practically. >> The language that was used at the time was almost identical to the language that we're hearing today. Women weren't biologically suited for this, it's really not in the right makeup for them. And yet today, half the students at those schools are women. And women have earned their way there. I just kind of laughingly say it's like deja vu all over again. We've heard all of that. we've heard people tell us you can't do that, you shouldn't do that, no you're not welcome and I think women they're not going to back down. >> It's interesting times too, because the classic gates, the distribution gate, the financing gate, the investment gate, to build companies, to create companies, they've all been broken down and kudos or serendipitously computing is the vehicle that's broken down a lot of those traditional barriers. You used to be, you couldn't start a new company because you had to get into distribution. You couldn't be a writer, there was only a few newspaper editors that controlled everything. That's all completely changed and now ubiquitous distribution, democratization of software, open source, you don't have to raise a bunch of money and buy a bunch of servers. It's so much easier to go out and affect the world and there's no easier way to affect the world than writing a great piece of software. >> Yeah, I think you're spot on on that. There's so much more leverage out there for people that want to start something. I believe that will accrue to the advantage of women. I always end up saying women are going to do great things and then I have to stop myself and say they are doing great things today. I think we've seen that already with some of the keynotes. Fei-Fei Li, and yet you hear her story as an immigrant and as a mother, as an Asian woman. She's had her challenges and she told her personal story not like with a woe is me but with a clear eye towards the things that she had to overcome to get where she was. >> And a lot of hard work, just a flat out a lot of hard work including working at the dry cleaners while she was going to school. >> Yeah, exactly. And yet there she is, one of the leaders in that space and doing incredible things. So I think you're starting to hear more and more about those women. I think they've always been there. I think that we just don't hear as much about them. So, this venue is such a great opportunity for us to hear more of their stories. >> Right, and we learned a lot about that last year with the whole Hidden Figures thing that we had on here as well as the movie and that was again, in the 60's. So we're in October, it's kind of the end the year. As you look forward to 2018, what are some of your priorities for AnitaB.org? I won't put you on the hook to tell us where Grace Hopper will be next year. You can tell us if you want. >> I saw it posted at Pride someplace. >> Is it posted already? >> I saw that and it was like whoa, I didn't know that was in the wild yet. >> But give us kind of a look. What are your priorities for next year? I know AVI Local has been a thing that's been growing over time. What are you kind of looking at as you're doing your 2018 planning? >> As amazing as it is to have 18,000 people here, which just blows our mind, we hope it continues to grow. We also know that no matter how big this conference gets that not everyone will be able to come here for a variety of reasons and so building out the local communities and making it so that, empowering those local communities to have smaller versions of this type of thing and growing this movement to a bigger scale that really encompasses all the women that are out there because even though people here say "Oh, 18,000 women, holy cow" it's a tip of the iceberg. There are thousands and thousands more women out there, we know there are. We really want to find a way to reach every single one of them and bring support and connection and inspiration to every single one of them so that they stay in the field, can achieve their dreams and their highest potential. That will have an impact on them and on the communities they live in. That's really what our focus is. >> Well, Elizabeth, again. Always great to see you. Congratulations on a phenomenal conference. And thank for inviting us to be here. It's really, honestly, one of our favorite places to be. >> We love having you here. I would just end by saying all you people out there, come join us next year. >> There you go. Are you going to tell them where? >> Houston, Texas. >> In Houston. - Back in Houston. >> Good barbecue. Ask me, I'll tell you where to go. Alright, she's Elizabeth Ames. I'm Jeff Frick. You're watching theCUBE from the Grace Hopper Celebration of Women in Computing 2017. Thanks for watching. [Upbeat Techno Music]

>> Announcer: Live from Orlando, Florida, it's theCUBE, covering Grace Hopper's Celebration of Women in Computing, brought to you by SiliconANGLE Media. >> Welcome to day one of the Grace Hopper Conference here in Orlando, Florida. Welcome back to theCUBE, I should say. I'm your host, Rebecca Knight, along with my co-host, Jeff Frick. We have just seen some really great keynote addresses. We had Faith Ilee from Stanford University. Melinda Gates, obviously the co-founder of the Bill and Melinda Gates Foundation. We also had Diane Green, the founder of VMware. Jeff, what are your first impressions? >> You know, I love comin' to this show. It's great to be workin' with you again, Rebecca. I thought the keynotes were really good. I've seen Diane Green speak a lot and she's a super smart lady, super qualified, changed the world of VMware. She's not always the greatest public speaker, but she was so comfortable up there. She so felt in her element. It was actually the best I'd ever seen. For me, I'm not a woman, but I'm a dad of two daughters. It was really fun to hear the lessons that some of these ladies learned from their father that they took forward. So, I was really hap-- I admit, I'm feelin' the pressure to make sure I do a good job on my daughters. >> Make sure those formative experiences are the right ones, yes. >> It's just interesting though how people's early foundation sets the stage for where they go. I thought Dr. Sue Black, who talked about the morning she woke up and her husband threatened to kill her. So, she just got out of the house with her two kids and started her journey then. Not in her teens, not in her twenties, not in college. Obviously well after that, to get into computer science and to start her tech journey and become what she's done now. Now she's saving the estate where the codebreakers were in World War II, so phenomenal story. Melinda Gates, I've never seen her speak. Then Megan Smith, always just a ton of energy. Before she was a CTO for the United States, that was with the Obama administration. I don't think she hung around as part of the Trump Administration. She brings such energy, and now, kind of released from the shackles of her public service and her own thing. Great to see her up there. It's just a terrific event. The energy that comes from, I think, a third of the people here are young women. Really young, either still in college or just out of college. Really makes for an atmosphere that I think is unique in all the tech shows that we cover. >> I completely agree. I think the energy really is what sets the Grace Hopper Celebration of Women in Computing apart from all the other conferences. First of all, there's just many more women who come to this. The age, as you noted, it's a lot lower than your typical tech conference. But, I also just think what is so exciting about this conference is that it is this incredible mix of positivity. let's get more women in here, let's figure out ways to get more women interested in computer science and really working on their journey as tech leaders. But, also really understanding what we're up against in this industry. Understanding the bro-grammar culture, the biases that are really creating barriers for women to get ahead, and actually to even enter into the industry itself. Then, also there's the tech itself, so we have these women who are talking about these cool products that they're making and different pathways into artificial intelligence and machine-learning, and what they're doing. So, it's a really incredible conference that has a lot of different layers to it. >> It's interesting, Dr. Fei-Fei Li was talking a lot about artificial intelligence, and the programming that goes into artificial intelligence, and kind of the classic Google story where you use crowdsourcing and run a bunch of photographs through an algorithm to teach it. But, she made a really interesting point in all this discussion about, is it the dark future of AI, where they take over the world and kill us all? Or, is it a positive future, where it frees us up to do more important things and more enlightened things. She really made a good point that it's, how do you write the algorithms? How are we training the computers to do what we do? Women bring a different perspective. Diversity brings a different perspective. To bake that into the algorithms up front is so, so important to shape the way the AI shapes the evolution of our world. So, I found that to be a really interesting point that she brought up that I don't think is talked about enough. People have to write the algorithms. People have to write the stuff that trains the machines, so it's really important to have a broad perspective. You are absolutely right, and I think she actually made the point even broader than that in the sense of is if AI is going to shape our life and our economy going forward-- >> Which it will, right? >> Which it will. Then, the fact that there are so few women in technology, this is a crisis. Because, if the people who are the end-users and who are going to either benefit or be disadvantaged by AI aren't showing up and aren't helping create it, then yes, it is a crisis. >> Right. And I think the other point that came up was to bake more computer science into other fields, whether it's biology, whether it's law, education. The application of AI, the application of computer science in all those fields, it's much more powerful than just computing for the sake of computing. I think that's another way hopefully to keep more women engaged. 'Cause a big part of the issue is, not only the pipeline at the lead, but there's a lot of droppage as they go through the process. So, how do you keep more of 'em involved? Obviously, if you open it up across a broader set of academic disciplines, by rule you should get more retention. The other thing that's interesting here, Rebecca. This is our fourth year theCUBE's been at Grace Hopper's since way back in Phoenix in 2014, ironically, when there was also a big Microsoft moment at that show that we won't delve back into. But, it's a time of change. We have Brenda Darden Wilkerson, the brand new president of the Anita Borg organization. Telle Whitney's stepping down and she's passing the baton. We'll have them both on. So, again, Telle's done a great job. Look what she's created in the team. But, always fun to have fresh blood. Always fun to bring in new energy, new point of view, and I'm really excited to meet Brenda. She's done some amazing things in the Chicago Public School System, and if you've ever worked in a public school district, not a really easy place to innovate and bring change. >> Right, no, of course. Yeah, so our lineup of guests is incredible this week. We've got Sarah Clatterbuck, who is a CUBE alum. We have a woman who is the founder of Roar, which is a self-defense wearable technology. We're going to be looking at a broad array of the women technologists who are leading change in the industry, but then also leading it from a recruitment and retention point of-- >> So, should be a great three days, looking forward to it. >> I am as well. Excellent. Okay, so please keep joining us. Keep your channel tuned in here to theCUBE"s coverage of the Grace Hopper Conference here in Orlando, Florida. I'm your host, Rebecca Knight, along with my co-host, Jeff Frick. We will see you back here shortly. (light, electronic music)

>> Announcer: Live from San Jose, California, it's theCUBE, covering Innovating to Fuel the Next Decade of Big Data, brought to you by Western Digital. >> Hey welcome back everybody, Jeff Frick here with theCUBE. We're at Western Digital at their global headquarters in San Jose, California, it's the Almaden campus. This campus has a long history of innovation, and we're excited to be here, and probably have the smartest person in the building, if not the county, area code and zip code. I love to embarrass here, Janet George, she is the Fellow and Chief Data Scientist for Western Digital. We saw you at Women in Data Science, you were just at Grace Hopper, you're everywhere and get to get a chance to sit down again. >> Thank you Jeff, I appreciate it very much. >> So as a data scientist, today's announcement about MAMR, how does that make you feel, why is this exciting, how is this going to make you be more successful in your job and more importantly, the areas in which you study? >> So today's announcement is actually a breakthrough announcement, both in the field of machine learning and AI, because we've been on this data journey, and we have been very selectively storing data on our storage devices, and the selection is actually coming from the preconstructed queries that we do with business data, and now we no longer have to preconstruct these queries. We can store the data at scale in raw form. We don't even have to worry about the format or the schema of the data. We can look at the schema dynamically as the data grows within the storage and within the applications. >> Right, cause there's been two things, right. Before data was bad 'cause it was expensive to store >> Yes. >> Now suddenly we want to store it 'cause we know data is good, but even then, it still can be expensive, but you know, we've got this concept of data lakes and data swamps and data all kind of oceans, pick your favorite metaphor, but we want the data 'cause we're not really sure what we're going to do with it, and I think what's interesting that you said earlier today, is it was schema on write, then we evolved to schema on read, which was all the rage at Hadoop Summit a couple years ago, but you're talking about the whole next generation, which is an evolving dynamic schema >> Exactly. >> Based whatever happens to drive that query at the time. >> Exactly, exactly. So as we go through this journey, we are now getting independent of schema, we are decoupled from schema, and what we are finding out is we can capture data at its raw form, and we can do the learning at the raw form without human interference, in terms of transformation of the data and assigning a schema to that data. We got to understand the fidelity of the data, but we can train at scale from that data. So with massive amounts of training, the models already know to train itself from raw data. So now we are only talking about incremental learning, as the train model goes out into the field in production, and actually performs, now we are talking about how does the model learn, and this is where fast data plays a very big role. >> So that's interesting, 'cause you talked about that also earlier in your part of the presentation, kind of the fast data versus big data, which kind of maps the flash versus hard drive, and the two are not, it's not either or, but it's really both, because within the storage of the big data, you build the base foundations of the models, and then you can adapt, learn and grow, change with the fast data, with the streaming data on the front end, >> Exactly >> It's a whole new world. >> Exactly, so the fast data actually helps us after the training phase, right, and these are evolving architectures. This is part of your journey. As you come through the big data journey you experience this. But for fast data, what we are seeing is, these architectures like Lambda and Kappa are evolving, and especially the Lambda architecture is very interesting, because it allows for batch processing of historical data, and then it allows for what we call a high latency layer or a speed layer, where this data can then be promoted up the stack for serving purposes. And then Kappa architecture's where the data is being streamed near real time, bounded and unbounded streams of data. So this is again very important when we build machine learning and AI applications, because evolution is happening on the fly, learning is happening on the fly. Also, if you think about the learning, we are mimicking more and more on how humans learn. We don't really learn with very large chunks of data all at once, right? That's important for initially model training and model learning, but on a regular basis, we are learning with small chunks of data that are streamed to us near real time. >> Right, learning on the Delta. >> Learning on the Delta. >> So what is the bound versus the unbound? Unpack that a little bit. What does that mean? >> So what is bounded is basically saying, hey we are going to get certain amounts of data, so you're sizing the data for example. Unbounded is infinite streams of data coming to you. And so if your architecture can absorb infinite streams of data, like for example, the sensors constantly transmitting data to you, right? At that point you're not worried about whether you can store that data, you're simply worried about the fidelity of that data. But bounded would be saying, I'm going to send the data in chunks. You could also do bounded where you basically say, I'm going to pre-process the data a little bit just to see if the data's healthy, or if there is signal in the data. You don't want to find that out later as you're training, right? You're trying to figure that out up front. >> But it's funny, everything is ultimately bounded, it just depends on how you define the unit of time, right, 'cause you take it down to infinite zero, everything is frozen. But I love the example of the autonomous cars. We were at the event with, just talking about navigation just for autonomous cars. Goldman Sachs says it's going to be a seven billion dollar industry, and the great example that you used of the two systems working well together, 'cause is it the car centers or is it the map? >> Janet: That's right. >> And he says, well you know, you want to use the map, and the data from the map as much as you can to set the stage for the car driving down the road to give it some level of intelligence, but if today we happen to be paving lane number two on 101, and there's cones, now it's the real time data that's going to train the system. But the two have to work together, and the two are not autonomous and really can't work independent of each other. >> Yes. >> Pretty interesting. >> It makes perfect sense, right. And why it makes perfect sense is because first the autonomous cars have to learn to drive. Then the autonomous cars have to become an experienced driver. And the experience cannot be learned. It comes on the road. So one of the things I was watching was how insurance companies were doing testing on these cars, and they had a human, a human driving a car, and then an autonomous car. And the autonomous car, with the sensors, were predicting the behavior, every permutation and combination of how a bicycle would react to that car. It was almost predicting what the human on the bicycle would do, like jump in front of the car, and it got it right 80% of the cases. But a human driving a car, we're not sure how the bicycle is going to perform. We don't have peripheral vision, and we can't predict how the bicycle is going to perform, so we get it wrong. Now, we can't transmit that knowledge. If I'm a driver and I just encountered a bicycle, I can't transmit that knowledge to you. But a driverless car can learn, it can predict the behavior of the bicycle, and then it can transfer that information to a fleet of cars. So it's very powerful in where the learning can scale. >> Such a big part of the autonomous vehicle story that most people don't understand, that not only is the car driving down the road, but it's constantly measuring and modeling everything that's happening around it, including bikes, including pedestrians, including everything else, and whether it gets in a crash or not, it's still gathering that data and building the model and advancing the models, and I think that's, you know, people just don't talk about that enough. I want follow up on another topic. So we were both at Grace Hopper last week, which is a phenomenal experience, if you haven't been, go. Ill just leave it at that. But Dr. Fei-Fei Li gave one of the keynotes, and she made a really deep statement at the end of her keynote, and we were both talking about it before we turned the cameras on, which is, there's no question that AI is going to change the world, and it's changing the world today. The real question is, who are the people that are going to build the algorithms that train the AI? So you sit in your position here, with the power, both in the data and the tools and the compute that are available today, and this brand new world of AI and ML. How do you think about that? How does that make you feel about the opportunity to define the systems that drive the cars, et cetera. >> I think not just the diversity in data, but the diversity in the representation of that data are equally powerful. We need both. Because we cannot tackle diverse data, diverse experiences with only a single representation. We need multiple representation to be able to tackle that data. And this is how we will overcome bias of every sort. So it's not the question of who is going to build the AI models, it is a question of who is going to build the models, but not the question of will the AI models be built, because the AI models are already being built, but some of the models have biases into it from any kind of lack of representation. Like who's building the model, right? So I think it's very important. I think we have a powerful moment in history to change that, to make real impact. >> Because the trick is we all have bias. You can't do anything about it. We grew up in the world in which we grew up, we saw what we saw, we went to our schools, we had our family relationships et cetera. So everyone is locked into who they are. That's not the problem. The problem is the acceptance of bring in some other, (chuckles) and the combination will provide better outcomes, it's a proven scientific fact. >> I very much agree with that. I also think that having the freedom, having the choice to hear another person's conditioning, another person's experiences is very powerful, because that enriches our own experiences. Even if we are constrained, even if we are like that storage that has been structured and processed, we know that there's this other storage, and we can figure out how to get the freedom between the two point of views, right? And we have the freedom to choose. So that's very, very powerful, just having that freedom. >> So as we get ready to turn the calendar on 2017, which is hard to imagine it's true, it is. You look to 2018, what are some of your personal and professional priorities, what are you looking forward to, what are you working on, what's top of mind for Janet George? >> So right now I'm thinking about genetic algorithms, genetic machine learning algorithms. This has been around for a while, but I'll tell you where the power of genetic algorithms is, especially when you're creating powerful new technology memory cell. So when you start out trying to create a new technology memory cell, you have materials, material deformations, you have process, you have hundred permutation combination, and the genetic algorithms, we can quickly assign a cause function, and we can kill all the survival of the fittest, all that won't fit we can kill, arriving to the fastest, quickest new technology node, and then from there, we can scale that in mass production. So we can use these survival of the fittest mechanisms that evolution has used for a long period of time. So this is biology inspired. And using a cause function we can figure out how to get the best of every process, every technology, all the coupling effects, all the master effects of introducing a program voltage on a particular cell, reducing the program voltage on a particular cell, resetting and setting, and the neighboring effects, we can pull all that together, so 600, 700 permutation combination that we've been struggling on and not trying to figure out how to quickly narrow down to that perfect cell, which is the new technology node that we can then scale out into tens of millions of vehicles, right? >> Right, you're going to have to >> Getting to that spot. >> You're going to have to get me on the whiteboard on that one, Janet. That is amazing. Smart lady. >> Thank you. >> Thanks for taking a few minutes out of your time. Always great to catch up, and it was terrific to see you at Grace Hopper as well. >> Thank you, I really appreciate it, I appreciate it very much. >> All right, Janet George, I'm Jeff Frick. You are watching theCUBE. We're at Western Digital headquarters at Innovating to Fuel the Next Generation of Big Data. Thanks for watching.

>> Announcer: Live from Orlando, Florida. It's the CUBE. Covering Grace Hopper Celebration of Women in Computing. Brought to you by Silicon Angle Media. >> Welcome back everybody. Jeff Frick here with the Cube. We are winding down day three of the Grace Hopper Celebration of Women in Computing in Orlando. It's 18,000, mainly women, a couple of us men hangin' out. It's been a phenomenal event again. It always amazes me to run into first timers that have never been to the Grace Hopper event. It's a must do if you're in this business and I strongly encourage you to sign up quickly 'cause I think it sells out in about 15 minutes, like a good rock concert. But we're excited to have our next guest. She's Rachel Faber Tobac, UX Research at Course Hero. Rachel, great to see you. >> Thank you so much for having me on. >> Absolutely. So, Course Hero. Give people kind of an overview of what Course Hero is all about. >> Yup. So we are an online learning platform and we help about 200 million students and educators master their classes every year. So we have all the notes, >> 200 million. >> Yes, 200 million! We have all the notes, study guides, resources, anything a student would need to succeed in their classes. And then anything an educator would need to prepare for their classes or connect with their students. >> And what ages of students? What kind of grades? >> They're usually in college, but sometimes we help high schoolers, like AP students. >> Okay. >> Yeah. >> But that's not why you're here. You want to talk about hacking. So you are, what you call a "white hat hacker". >> White hat. >> So for people that aren't familiar with the white hat, >> Yeah. >> We all know about the black hat conference. What is a white hat hacker. >> So a "white hat hacker" is somebody >> Sounds hard to say three times fast. >> I know, it's a tongue twister. A white hat hacker is somebody who is a hacker, but they're doing it to help people. They're trying to make sure that information is kept safer rather than kind of letting it all out on the internet. >> Right, right. Like the old secret shoppers that we used to have back in the pre-internet days. >> Exactly. Exactly. >> So how did you get into that? >> It's a very non-linear story. Are you ready for it? >> Yeah. >> So I started my career as a special education teacher. And I was working with students with special needs. And I wanted to help more people. So, I ended up joining Course Hero. And I was able to help more people at scale, which was awesome. But I was interested in kind of more of the technical side, but I wasn't technical. So my husband went to Defcon. 'cause he's a cyber security researcher. And he calls me at Defcon about three years ago, and he's like, Rach, you have to get over here. I'm like, I'm not really technical. It's all going to go over my head. Why would I come? He's like, you know how you always call companies to try and get our bills lowered? Like calling Comcast. Well they have this competition where they put people in a glass booth and they try and have them do that, but it's hacking companies. You have to get over here and try it. So I bought a ticket to Vegas that night and I ended up doing the white hat hacker competition called The Social Engineering Capture the Flag and I ended up winning second, twice in a row as a newb. So, insane. >> So you're hacking, if I get this right, not via kind of hardcore command line assault. You're using other tools. So like, what are some of the tools that are vulnerabilities that people would never think about. >> So the biggest tool that I use is actually Instagram, which is really scary. 60% of the information that I need to hack a company, I find on Instagram via geolocation. So people are taking pictures of their computers, their work stations. I can get their browser, their version information and then I can help infiltrate that company by calling them over the phone. It's called vishing. So I'll call them and try and get them to go to a malicious link over the phone and if I can do that, I can own their company, by kind of presenting as an insider and getting in that way. (chuckling) It's terrifying. >> So we know phishing right? I keep wanting to get the million dollars from the guy in Africa that keeps offering it to me. >> (snickers) Right. >> I don't whether to bite on that or. >> Don't click the link. >> Don't click the link. >> No. >> But that interesting. So people taking selfies in the office and you can just get a piece of the browser data and the background of that information. >> Yep. >> And that gives you what you need to do. >> Yeah, so I'll find a phone number from somebody. Maybe they take a picture of their business card, right? I'll call that number. Test it to see if it works. And then if it does, I'll call them in that glass booth in front of 400 people and attempt to get them to go to malicious links over the phone to own their company or I can try and get more information about their work station, so we could, quote unquote, tailor an exploit for their software. >> Right. Right. >> We're not actually doing this, right? We're white hat hackers. >> Right. >> If we were the bad guys. >> You'd try to expose the vulnerability. >> Right. The risk. >> And what is your best ruse to get 'em to. Who are you representing yourself as? >> Yeah, so. The representation thing is called pre-texting. It's who you're pretending to be. If you've ever watched like, Catch Me If You Can. >> Right. Right. >> With Frank Abagnale Jr. So for me, the thing that works the best are low status pretext. So as a woman, I would kind of use what we understand about society to kind of exploit that. So you know, right now if I'm a woman and I call you and I'm like, I don't know how to trouble shoot your website. I'm so confused. I have to give a talk, it's in five minutes. Can you just try my link and see if it works on your end? (chuckling) >> You know? Right? You know, you believe that. >> That's brutal. >> Because there's things about our society that help you understand and believe what I'm trying to say. >> Right, right. >> Right? >> That's crazy and so. >> Yeah. >> Do you get, do you make money white hacking for companies? >> So. >> Do they pay you to do this or? Or is it like, part of the service or? >> It didn't start that way. >> Right. >> I started off just doing the Social Engineering Capture the Flag, the SECTF at Defcon. And I've done that two years in a row, but recently, my husband, Evan and I, co-founded a company, Social Proof Security. So we work with companies to train them about how social media can impact them from a social engineering risk perspective. >> Right. >> And so we can come in and help them and train them and understand, you know, via a webinar, 10 minute talk or we can do a deep dive and have them actually step into the shoes of a hacker and try it out themselves. >> Well I just thought the only danger was they know I'm here so they're going to go steal my bike out of my house, 'cause that's on the West Coast. I'm just curious and you may not have a perspective. >> Yeah. >> 'Cause you have niche that you execute, but between say, you know kind of what you're doing, social engineering. >> Yeah. >> You know, front door. >> God, on the telephone. Versus kind of more traditional phishing, you know, please click here. Million dollars if you'll click here versus, you know, what I would think was more hardcore command line. People are really goin' in. I mean do you have any sense for what kind of the distribution of that is, in terms of what people are going after? >> Right, we don't know exactly because usually that information's pretty confidential, >> Sure. when a hack happens. But we guess that about 90% of infiltrations start with either a phishing email or a vishing call. So they're trying to gain information so they can tailor their exploits for your specific machine. And then they'll go in and they'll do that like actual, you know, >> Right. >> technical hacking. >> Right. >> But, I mean, if I'm vishing you right and I'm talking to you over the phone and I get you to go to a malicious link, I can just kind of bypass every security protocol you've set up. I don't even a technical hacker, right? I just got into your computer because. >> 'Cause you're in 'Cause I'm in now, yup. >> I had the other kind of low profile way and I used to hear is, you know, you go after the person that's doin' the company picnic. You know Wordpress site. >> Yes. >> That's not thinking that that's an entry point in. You know, kind of these less obvious access points. >> Right. That's something that I talk about a lot actually is sometimes we go after mundane information. Something like, what pest service provider you use? Or what janitorial service you use? We're not even going to look for like, software on your machine. We might start with a softer target. So if I know what pest extermination provider you use, I can look them up on LinkedIn. See if they've tagged themselves in pictures in your office and now I can understand how do they work with you, what do their visitor badges look like. And then emulate all of that for an onsite attack. Something like, you know, really soft, right? >> So you're sitting in the key note, right? >> Yeah. >> Fei-Fei Li is talking about computer visualization learning. >> Right. >> And you know, Google running kagillions of pictures through an AI tool to be able to recognize the puppy from the blueberry muffin. >> Right. >> Um, I mean, that just represents ridiculous exploitation opportunity at scale. Even you know, >> Yeah. >> You kind of hackin' around the Instagram account, can't even begin to touch, as you said, your other thing. >> Right. >> You did and then you did it at scale. Now the same opportunity here. Both for bad and for good. >> I'm sure AI is going to impact social engineering pretty extremely in the future here. Hopefully they're protecting that data. >> Okay so, give a little plug so they'll look you up and get some more information. But what are just some of the really easy, basic steps that you find people just miss, that should just be, they should not be missing. From these basic things. >> The first thing is that if they want to take a picture at work, like a #TBT, right? It's their third year anniversary at their company. >> Right. Right. >> Step away from your work station. You don't need to take that picture in front of your computer. Because if you do, I'm going to see that little bottom line at the bottom and I'm going to see exactly the browser version, OS and everything like that. Now I'm able to exploit you with that information. So step away when you take your pictures. And if you do happen to take a picture on your computer. I know you're looking at computer nervously. >> I know, I'm like, don't turn my computer on to the cameras. >> Don't look at it! >> You're scarin' me Rachel. >> If you do take a picture of that. Then you don't want let someone authenticate with that information. So let's say I'm calling you and I'm like, hey, I'm with Google Chrome. I know that you use Google Chrome for your service provider. Has your network been slow recently? Everyone's network's been slow recently, right? >> Right. Right. >> So of course you're going to say yes. Don't let someone authenticate with that info. Think to yourself. Oh wait, I posted a picture of my work station recently. I'm not going to let them authenticate and I'm going to hang up. >> Interesting. All right Rachel. Well, I think the opportunity in learning is one thing. The opportunity in this other field is infinite. >> Yeah. >> So thanks for sharing a couple of tips. >> Yes. >> And um. >> Thank you for having me. >> Hopefully we'll keep you on the good side. We won't let you go to the dark side. >> I won't. I promise. >> All right. >> Rachel Faber Tobac and I'm Jeff Frick. You're watchin the Cube from Grace Hopper Celebration Women in Computing. Thanks for watching. (techno music)

>> Announcer: Live from San Jose in the heart of Silicon Valley, It's theCUBE covering DataWorks Summit 2017. Brought to you by Hortonworks. >> Welcome back to theCUBE. We are live at the DataWorks Summit on day two. We have had a great day and a half learning a lot about the next generation of big data, machine learning, artificial intelligence, I'm Lisa Martin, and my co-host is George Gilbert. We are next joined by a CUBE alumni, Ron Bodkin, the VP and General Manager of Artificial Intelligence for Teradata. Welcome back to theCUBE! >> Well thank you Lisa, it's nice to be here. >> Yeah, so talk to us about what you're doing right now. Your keynote is tomorrow. >> Ron: Yeah. >> What are you doing, what is Teradata doing in helping customers to be able to leverage artificial intelligence? >> Sure, yeah so as you may know, I ha`ve been involved in this conference and the big data space for a long time as the founding CEO of Think Big Analytics. We were involved in really helping customers in the beginning of big data in the enterprise. And so, we are seeing a very similar trend in the space of artificial intelligence, right? The rapid advances in recent years in deep learning have opened up a lot of opportunity to really create value from all the data the customers have in their data ecosystems, right? So Teradata has a big role to play in having high quality product, Teradata database, analytic ecosystem products such as Hadoop, such as QueryGrid for connecting these systems together, right? So what we're seeing is our customers are very excited by artificial intelligence, but what we're really focused on is how do they get to the value, right? What can they do that's really going to get results, right? And we bring this perspective of having this strong solutions approach inside of Teradata, and so we have Think Big Analytics consulting for data science, we now have been building up experts in deep learning in that organization, working with customers, right? We've brought product functionality so we're innovating around how do we keep pushing the Teradata product family forward with functionality around streaming with listeners. Functionality like the ability to, how do you take GPU and start to think about how can we add that and make that deploy efficiently inside our customer's data center. How can you take advantage of innovation in open source with projects like TensorFlow and Keras becoming important for our customers. So we're seeing is a lot of customers are excited about use cases for artificial intelligence. And tomorrow in the keynote I'm going to touch on a few of them, ranging from applications like preventative maintenance, anti-fraud in banking, to e-commerce recommendations and we're seeing those are some of the examples of use cases where customers are saying hey, there's a lot of value in combining traditional machine learning, wide learning, with deep learning using neural nets to generalize. >> Help us understand if there's an arc where there's the mix of what's repeatable and what's packagable, or what's custom, how that changes over time, or whether it's just by solution. >> Yeah, it's a great question. Right, I mean I think there's a lot of infrastructure that any of these systems need to rest on. So having data infrastructure, having quality data that you can rely on is foundational, and so you need to get that installed and working well as a beginning point. Obviously having repeatable products that manage data with high SLAs and supporting not use production use, but also how do you let data scientists analyze data in a lab and make that work well. So there's that foundational data layer. Then there's the whole integration of the data science into applications, which is critical, analytics, ops, agile ways of making it possible to take the data and build repeatable processes, and those are very horizontal, right? There's some variation, but those work the same in a lot of use cases. At this stage, I'd say, in deep learning, just like in machine learning generally, you still have a lot of horizontal infrastructure. You've got Spark, you've got TensorFlow, those are support use case across many industries. But then you get to the next level, you get specific problems, and there's a lot of nuance. What modeling techniques are going to work, what data sets matter? Okay, you've got time series data and a problem like fraud. What techniques are going to make that work well? And recommendations, you may have a long tail of items to think about recommending. How do you generalize across the long tail where you can't learn. People who use some relatively small thing or go to an obscure website, or buy an obscure product, there's not enough data to say are they likely to buy something else or do something else, but how do you categorize them so you get statistical power to make useful recommendations, right? Those are things that are very specific that there's a lot of repeatability and a specific solution area of. >> This is, when you talk about the data assets that might be specific to a customer and then I guess some third party or syndicated sources. If you have an outcome in mind, but not every customer has the same inventory of data, so how do you square that circle? >> That's a great question. And I really think that's a lot of the opportunity in the enterprise of applying analytics, so this whole summit DataWorks is about hey, the power of your data. What you can get by collecting your data in a well-managed ecosystem and creating value. So, there's always a nuance. It's like what's happening in your customers, what's your business process, what's special about how you interact, what's the core of your business? So I guess my view is that the way anybody that wants to be a winner in this new digital era and have processes that take advantage of artificial intelligence is going to have to use data as a competitive advantage and build on their unique data. So because we see a lot of times enterprises struggle with this. There's a tendency to say hey, can we just buy a package off the shelf SaaS solution and do that? And for context, for things that are the same for everybody in an industry, that's a great choice. But if you're doing that for your core differentiation of your business, you're in deep trouble in this digital era. >> And that's a great place, sorry George, really quickly. That this day and age, every company is a technology company. You mentioned a use case in banking, fraud detection, which is huge. There's tremendous value that can be gleaned from artificial intelligence, and there's also tremendous risk to them. I'm curious, maybe just kind of a generalization. Where are your customers on this journey in terms of have they, are you going out to customers that have already embraced Hadoop and have a significant amount of data that they say, all right, we've got a lot of data here, we need to understand the context. Where are customers in that maturity evolution? >> Sure, so I'd say that we're really fast-approaching the slope of enlightenment for Hadoop, which is to say the enthusiasm of three years ago when people thought Hadoop was going to do everything have kind of waned and there's now more of an appreciation, like there's a lot of value in having a data warehouse for high value curated data for large-scale use. There's a lot of value in having a data lake of fairly raw data that can be used for exploration in the data science arena. So there's emerging, like what is the best architecture for streaming and how do you drive realtime decisions, and that's still very much up in the air. So I'd say that most of our customers are somewhere on that journey, I think that a lot of them have backed off from their initial ambitions that they bought a little too much of the hype of all that Hadoop might do and they're realizing what it is good for, and how they really need to build a complementary ecosystem. The other thing I think is exciting though is I see the conversation is moving from the technology to the use cases. People are a lot more excited about how can we drive value and analytics, and let's work backwards from the analytics value to the data that's going to support it. >> Absolutely. >> So building on that, we talk about sort of what's core and if you can't have something completely repeatable that's going to be core to your sustainable advantage, but if everyone is learning from data, how does a customer achieve a competitive advantage or even sustain a competitive advantage? Is it orchestrating learning that feeds, that informs processes all across the business, or is it just sort of a perpetual Red Queen effect? >> Well, that's a great question. I mean, I think there's a few things, right? There's operational excellence in every discipline, so having good data scientists, having the right data, collecting data, thinking about how do you get network effects, those are all elements. So I would say there's a table-stakes aspect that if you're not doing this, you're in trouble, but then if you are it's like how do you optimize and lift your game and get better at it? So that's an important fact that you see companies that say how do we acquire data? Like one of the things that you see digital disruptors, like a Tesla, doing is changing the game by saying we're changing the way we work with our customers to get access to the data. Think of the difference between every time you buy a Tesla you sign over the rights for them to collect and use all your data, when the traditional auto OEMs are struggling to get access to a lot of the data because they have intermediaries that control the relationship and aren't willing to share. And a similar thing in other industries, you see in consumer packaged goods. You see a lot of manufacturers there are saying how do we get partnerships, how do we get more accurate data? The old models of going out to the Nielsens of the world and saying give us aggregates, and we'll pay you a lot to give us a summary report, that's not working. How do we learn directly in a digital world about our consumers so we can be more relevant? So one of the things is definitely that control of data and access to data, as well as we see a lot of companies saying what are the acquisitions we can make? What are start ups and capabilities that we can plug in, and complement to get data, to get analytic capability that we can then tailor for our needs? >> It's funny that you mention Tesla having more cars on the road, collecting more data than pretty much anyone else at this point. But then there's like Stanford's sort of luminary for AI, Fei-Fei Li. She signed on I think with Toyota, because she said they sell 10 million cars a year, I'm going to be swimming in data compared to anyone else, possible exception of GM or maybe some Chinese manufacturer. So where does, how can you get around scale when using data at scale to inform your models? How would someone like a Tesla be able to get an end run around that? So that's the battle, the disruptor comes in, they're not at scale, but they maybe change the game in some way. Like having different terms that give them access to different kinds of data, more complete data. So that's sort of part of the answer, is to disrupt an industry you need a strategy what's different, right, like in Tesla's case an electric vehicle. And they've been investing in autonomous vehicles with AI, of course everybody in the industry is seeing that and is racing. I mean, Google really started that whole wave going a long time ago as another potential disruptor coming in with their own unique data asset. So, I think it's all about the combination of capabilities that you need. Disruptors often bring a commitment to a different business process, and that's a big challenge is a lot of times the hardest things are the business processes that are entrenched in existing organizations and disruptors can say we're rethinking the way this gets done. I mean, the example of that in ride sharing, the Ubers and Lyfts of the world, deities where they are re-conceiving what does it mean to consume automobile services. Maybe you don't want to own a car at all if you're a millennial, maybe you just want to have access to a car when you need to go somewhere. That's a good example of a disruptive business model change. >> What are some things that are on the intermediate-term horizon that might affect how you go about trying to create a sustainable advantage? And here I mean things like where deep learning might help data scientists with feature engineering so there's less need for, you can make data scientists less of a scarce resource. Or where there's new types of training for models where you need less data? Those sorts of things might disrupt the practice of achieving an advantage with current AI technology. >> You know, that's a great question. So near-term, the ability to be more efficient in data science is a big deal. There's no surprise that there's a big talent gap, big shortage of qualified data scientists in the enterprise and one of the things that's exciting is that deep learning lets you get more information out of the data, so it learns more so that you'd have to do less future engineering. It's not like a magic box you just pour in raw data to deep learning and out comes the answers, so you still need qualified data scientists, but it's a force multiplier. There's less work to do in future engineering, and therefore you get better results. So that's a factor, you're starting to see things like a hyperparameter search where people will create neural networks that search for the best machine learning model, and again get another level of leverage. Now, today doing that is very expensive. The amount of hardware to do that, very few organizations are going to spend millions of dollars to sort of automate the discovery of models, but things are moving so fast. I mean, even just in the last six weeks to have Nvidia and Google both announce significant breakthroughs in hardware. And I just had a colleague forward me a paper for recent research that says hey this technique could produce a hundred times faster results in deep learning convergence. So you've got rapid advances in investment in the hardware and the software. Historically software improvements have outstripped hardware improvements throughout the history of computing, so it's quite reasonable to expect you'll have 10 thousand times the price performance for deep learning in five years. So things that today might cost a hundred million dollars and no one would do, could cost 10 thousand dollars in five years, and suddenly it's a no-brainer to apply a technique like that to automate something instead of hiring more scarce data scientists that are hard to find, and make the data scientists more productive so they're spending more time thinking about what's going on and less time trying out different variations of how do I configure this thing, does this work, does this, right? >> Oh gosh, Ron, we could keep chatting away. Thank you so much for stopping by theCUBE again, we wish you the best of luck in your keynote tomorrow. I think people are going to be very inspired by your passion, your energy, and also the tremendous opportunity that is really sitting right in front of us. >> Thank you, Lisa, it's a very exciting time to be in the data industry, and the emergence of AI and the enterprise, I couldn't be more excited by it. >> Oh, excellent, well your excitement is palpable. We want to thank you for watching. We are live on theCUBE at the DataWorks Summit day 2, #dws17. For my cohost George Gilbert, I'm Lisa Martin, stick around. We'll be right back. (upbeat electronic melody)

>> Narrator: Live from Silicon Valley, it's theCUBE, covering Google Cloud, Next 17. >> Hey, welcome back everyone. We're here live in the Palo Alto Studios, SiliconANGLE Media, is theCUBE's new 4400 square foot studio, here in our studio, this is our sports center. I'm here with Stu Miniman, analyst at Wikibon on the team. I was at the event all day today, drove down to Palo Alto to give us the latest in-person updates, as well as, for the past two days, Stu has been at the Analyst Summit, which is Google's first analyst summit, Google Cloud. And Stu, we're going to break down day one in the books. Certainly, people starting to get onto there. After-meetups, parties, dinners, and festivities. 10,000 people came to the Google Annual Cloud Next Conference. A lot of customer conversations, not a lot of technology announcements, Stu. But we got another day tomorrow. >> John, first of all, congrats on the studio here. I mean, it's really exciting. I remember the first time I met you in Palo Alto, there was the corner in ColoSpace-- >> Cloud Air. >> A couple towards down for fries, at the (mumbles) And look at this space. Gorgeous studio. Excited to be here. Happy to do a couple videos. And I'll be in here all day tomorrow, helping to break down. >> Well, Stu, first allows us to, one, do a lot more coverage. Obviously, Google Next, you saw, was literally a blockbuster, as Diane Greene said. People were around the block, lines to get in, mass hysteria, chaos. They really couldn't scale the event, which is Google's scale, they nailed the scale software, but scaling event, no room for theCUBE. But we're pumping out videos. We did, what? 13 today. We'll do a lot more tomorrow, and get more now. So you're going to be coming in as well. But also, we had on-the-ground, cause we had phone call-ins from Akash Agarwal from SAP. We had an exclusive video with Sam Yen, who was breaking down the SAP strategic announcement with Google Cloud. And of course, we have a post going on siliconangle.com. A lot of videos up on youtube.com/siliconangle. Great commentary. And really the goal was to continue our coverage, at SiliconANGLE, theCUBE, Wikibon, in the Cloud. Obviously, we've been covering the Cloud since it's really been around. I've been covering Google since it was founded. So we have a lot history, a lot of inside baseball, certainly here in Palo Alto, where Larry Page lives in the neighborhood, friends at Google Earth. So the utmost respect for Google. But really, I mean, come on. The story, you can't put lipstick on a pig. Amazon is crushing them. And there's just no debate about that. And people trying to put that out there, wrote a post this morning, to actually try to illustrate that point. You really can't compare Google Cloud to AWS, because it's just two different animals, Stu. And my point was, "Okay, you want to compare them? "Let's compare them." And we're well briefed on the Cloud players, and you guys have the studies coming out of Wikibon. So there it is. And my post pretty much sums up the truth, which is, Google's really serious about the enterprise. Their making steps, there's some holes, there's some potential fatal flaws in how they allow customers to park their data. They have some architectural differences. But Stu, it's really a different animal. I mean, it's apples and oranges in the Cloud. I don't think it's worthy complaining, because certainly Amazon has the lead. But you have Microsoft, you have Google, you have Oracle, IBM, SAP, they're all kind of in the cluster of this, I call "NASCAR Formation", where they're all kind of jocking around, some go ahead. And it really is a race to get the table stake features done. And really, truly be serious contender for the enterprise. So you can be serious about the enterprise, and say, "Hey, I'm serious about the enterprise." But to be serious winner and leader, are two different ball games. >> And a lot to kind of break down here, John. Because first of all, some of the (mumbles) challenges, absolutely, they scaled that event really big. And kudos to them, 10,000 people, a lot of these things came together last minute. They treated the press and analysts really well. We got to sit up front. They had some good sessions. You just tweeted out, Diane Greene, in the analyst session, and in the Q&A after, absolutely nailed it. I mean, she is an icon in the industry. She's brilliant, really impressive. And she's been pulling together a great team of people that understand the enterprise. But who is Google going after, and how do they compete against so of the other guys, is really interesting to parse. Because some people were saying in the keynote, "We heard more about G Suite "than we heard about some of the Cloud features." Some of that is because they're going to do the announcements tomorrow. And you keep hearing all this G Suite stuff, and it makes me think of Microsoft, not Amazon. It makes me think of Office 365. And we've been hearing out of Amazon recently, they're trying to go after some of those business productivity applications. They're trying to go there where Microsoft is embedded. We know everybody wants to go after companies like IBM and Oracle, and their applications. Because Google has some applications, but really, their strength is been on the data. The machine the AI stuff was really interesting. Dr. Fei-Fei Li from Stanford, really good piece in the keynote there, when they hired her not that long ago. The community really perked up, and is really interesting. And everybody seems to think that this could be the secret weapon for Google. I actually asked them like, in some of the one-on-ones, "Is this the entry point? "Are most people coming for this piece, "when it's around these data challenges in the analytics, "and coming to Google." And they're like, "Well, it's part of it. "But no, we have broad play." Everything from devices through G Suite. And last year, when they did the show, it was all the Cloud. And this year, it's kind of the full enterprise suite, that they're pulling in. So there's some of that sorting out the messaging, and how do you pull all of these pieces together? As you know, when you've got a portfolio, it's like, "Oh well, I got to have a customer for G Suite." And then when the customer's up there talking about G Suite for a while, it's like, "Wait, it's--" >> Wait a minute. Is this a software? >> "What's going on?" >> Is this a sash show? Is this a workplace productivity show? Or is this a Cloud show? Again, this is what my issue is. First of all, the insight is very clear. When you start seeing G Suite, that means that they've got something else that they are either hiding or waiting to announce. But the key though, that is the head customers. That was one important thing. I pointed out in my blog post. To me, when I'm looking for it's competitive wins, and I want to parse out the G Suite, because it's easy just to lay that on, Microsoft does it with 365 of Office, Oracle does it with their stuff. And it does kind of make the numbers fuzzy a little bit. But ultimately, where's the beef on infrastructure as a service, and platform as a service? >> And John, good customers out there, Disney, Colgate, SAP as a partner, HSBC, eBay, Home Depot, which was a big announcement with Pivotal, last year, and Verizon were there. So these are companies, we all know them. Dan Greene was joking, "Disney is going to bring their magic onto our magic. "And make that work." So real enterprise use cases. They seem to have some good push-around developers. They just acquired Kaggle, which is working in some of that space. >> Apogee. >> Yeah, Apogee-- >> I think Apogee's an API company, come on. What does that relate to? It has nothing to do with the enterprise. It's an API management solution. Okay, yes. I guess it fits the stack for Cloud-Native, and for developers. I get that. But this show has to nail the enterprise, Stu. >> And John, you remember back four years ago, when we went to the re:Invent show for the first time, and it was like, they're talking to all the developers, and they haven't gotten to the enterprise. And then they over-pivoted to enterprise. And I listen to the customers that were talking and keynote today, and I said, "You know, they're talking digital transformation, "but it's not like GE and Nike getting up on stage, "being like, "'We're going to be a software company, "'and we're hiring lots--'" >> John: Moving our data center over. >> They were pulling all of over stuff, and it's like, "Oh yeah, Google's a good partner. "And we're using them--" >> But to be fair, Stu. Let's be fair, for a second. First of all, let's break down the keynotes. And then we'll get to some of the things about being fair. And I think, one, people should be fair to Diane Greene, because I think that the press and the coverage of it, looking at the media coverage, is weak. And I'll tell you why it's weak. Cause everyone has the same story as, "Oh, Google's finally serious about Cloud. "That's old news. "Diane Greene from day one says "we're serious with the Cloud." That's not the story. The story is, can they be a serious contender? That's number one. On the keynote, one, customer traction, I saw that, the slide up there. Yeah, the G Suite in there, but at least they're talking customers. Number two, the SAP news was strategic for Google. SAP now has Google Cloud platform, I mean, Google Cloud support for HANA, and also the SAP Cloud platform. And three, the Chief Data Science from AIG pointed. To me, those were the three highlights of the keynote. Each one, thematically, represents at least a positive direction for Google, big time, which is, one, customer adoption, the customer focus. Two, partnerships with SAP, and they had Disney up there. And then three, the real game changer, which is, can they change the AI machine learning, TensorFlow has a ton of traction. Intel Xeon chips now are optimized with TensorFlow. This is Google. >> TensorFlow, Kubernetes, it's really interesting. And it's interesting, John, I think if the media listened to Eric Schmidt at the end, he was talking straight to them. He's like, "Look, bullet one. "17 years ago, I told Google that "this is where we need to go. "Bullet two, 30 billion dollars "I'm investing in infrastructure. "And yes, it's real, "cause I had to sign off on all of this money. And we've been all saying for a while, "Is this another beta from Google. "Is it serious? "There's no ad revenue, what is this?" And Diane Greene, in the Q&A afterwards, somebody talked about, "Perpetual beta seems to be Google." And she's like, "Look, I want to differentiate. "We are not the consumer business. "The consumer business might kill something. "They might change something. "We're positioning, "this a Cloud that the enterprise can build on. "We will not deprecate something. "We'll support today. "We'll support the old version. "We will support you going forward." Big push for channel, go-to-market service and support, because they understand that that-- >> Yeah, but that's weak. >> For those of us that used Google for years, understand that-- >> There's no support. >> "Where do I call for Google?" Come on, no. >> Yeah, but they're very weak on that. And we broke that down with Tom Kemp earlier, from Centrify, where Google's play is very weak on the sales and marketing side. Yeah, I get the service piece. But go to Diane Greene for a second, she is an incredible, savvy enterprise executive. She knows Cloud. She moved from server to virtualization. And now she can move virtualization to Cloud. That is her playbook. And I think she's well suited to do that. And I think anyone who rushes to judgment on her keynote, given the fail of the teleprompter, I think is a little bit overstepping their bounds on that. I think it's fair to say that, she knows what she's doing. But she can only go as fast as they can go. And that is, you can't like hope that you're further along. The reality is, it takes time. Security and data are the key points. On your point you just mentioned, that's interesting. Because now the war goes on. Okay, Kubernetes, the microservices, some of the things going on in the applications side, as trends like Serverless come on, Stu, where you're looking at the containerization trend that's now gone to Kubernetes. This is the battleground. This is the ground that we've been at Dockercon, we've been at Linux, CNCF has got huge traction, the Cloud Native Compute Foundation. This is key. Now, that being said. The marketplace never panned out, Stu. And I wanted to get your analysis on this, cause you cover this. Few years ago, the world was like, "Oh, I want to be like Facebook." We've heard, "the Uber of this, and the Airbnb of that." Here's the thing. Name one company that is the Facebook of their company. It's not happening. There is no other Facebook, and there is no other Google. So run like Google, is just a good idea in principle, horizontally scalable, having all the software. But no one is like Google. No one is like Facebook, in the enterprise. So I think that Google's got to downclock their messaging. I won't say dumb down, maybe I'll just say, slow it down a little bit for the enterprise, because they care about different things. They care more about SLA than pricing. They care more about data sovereignty than the most epic architecture for data. What's your analysis? >> John, some really good points there. So there's a lot of technology, where like, "This is really cool." And Google is the biggest of it. Remember that software-defined networking we spent years talking about? Well, the first big company we heard about was Google, and they got up of stage, "We're the largest SDN deployer in the world on that." And it's like, "Great. "So if you're the enterprise, "don't deploy SDN, go to somebody else "that can deliver it for you. "If that's Google, that's great." Dockercon, the first year they had, 2014, Google got up there, talked about how they were using containers, and containers, and they spin up and spin down. Two billion containers in a week. Now, nobody else needs to spin up two billion containers a week, and do that down. But they learned from that. They build Kubernetes-- >> Well, I think that's a good leadership position. But it's leadership position to show that you got the mojo, which again, this is again, what I like about Google's strategy is, they're going to play the technology card. I think that's a good card to play. But there are some just table stakes they got to nail. One is the certifications, the security, the data. But also, the sales motions. Going into the enterprise takes time. And our advice to Diane Greene was, "Don't screw the gold Google culture. "Keep that technology leadership. "And buy somebody, "buy a company that's got a full blown sales force." >> But John, one of the critiques of Google has always been, everything they create, they create like for Google, and it's too Googley. I talked to a couple of friends, that know about AWS for a while, and when they're trying to do Google, they're like, "Boy, this is a lot tougher. "It's not as easy as what we're doing." Google says that they want to do a lot of simplicity. You touched on pricing, it's like, "Oh, we're going to make pricing "so much easier than what Amazon's doing." Amazon Reserved Instances is something that I hear a lot of negative feedback in the community on, and Google's like, "It's much simpler." But when I've talked to some people that have been using it, it's like, "Well, generally it should be cheaper, "and it should be easier. "But it's not as predictable. "And therefore, it's not speaking to what "the CFO needs to have. "I can't be getting a rebate sometime down the road. "Based on some advanced math, "I need to know what I'm going to be getting, "and how I'm going to be using it." >> And that's a good point, Stu. And this comes down to the consumability of the Cloud. I think what Amazon has done well, and this came out of many interviews today, but it was highlighted by Val Bercovici, who pointed out that, Amazon has made their service consumable by the enterprise. I think that's important. Google needs to start thinking about how enterprises want to consume Cloud, and hit those points. The other thing that Val and I teased at, was kind of some new ground, and he coined the term, or used the term, maybe he coined it, I'm not sure, empathy. Enterprise empathy. Google has developer empathy, they understand the developer community. They're rock solid on open source. Obviously, their mojo's phenomenal on technology, AI, et cetera, TensorFlow, all that stuff's great. Empathy for the enterprise, not there. And I think that's something that they're going to have to work on. And again, that's just evolution. You mentioned Amazon, our first event, developer, developer, developer. Me and Pat Gelsinger once called it the developer Cloud. Now they're truly the enterprise Cloud. It took three years for Amazon to do that. So you just can't jump to a trajectory. There's a huge amount of diseconomies of scale, Stu, to try and just be an enterprise player overnight, because, "We're Google." That's just not going to fly. And whether it's sales motions, pricing and support, security, this is hard. >> And sorting out that go-to-market, is going to take years. You see a lot of the big SIs are there. PwC, everywhere at the show. Accenture, big push at the show. We saw that a year or two ago, at the Amazon show. I talked to some friends in the channel, and they're like, "Yeah, Google's still got work to do. "They're not there." Look, Amazon has work to do on the go-to-market, and Google is still a couple-- >> I mean, Amazon's not spring chicken here. They're quietly, slowly, ramming up. But they're not in a good position with their sales force, needs to be where they want to be. Let's talk about technology now. So tomorrow we're expecting to see a bunch of stuff. And one area that I'm super excited about with Google, is if they can have their identity identified, and solidified with the mind of the enterprise, make their product consumable, change or adjust or buy a sales force, that could go out and actually sell to the enterprise, that's going to be key. But you're going to hear some cool trends that I like. And if you look at the TensorFlow, and the relationship, Intel, we're going to see Intel on stage tomorrow, coming out during one of the keynotes. And you're going to start to see the Xeon chip come out. And now you're starting to see now, the silicon piece. And this has been a data center nuisance, Stu. As we talked about with James Hamilton at Amazon, which having a hardware being optimized for software, really is the key. And what Intel's doing with Xeon, and we talked to some other people today about it, is that the Cloud is like an operating system, it's a global computer, if you want look at that. It's a mainframe, the software mainframe, as it's been called. You want a diversity of chipsets, from two cores Atom to 72 cores Xeon. And have them being used in certain cases, whether it's programmable silicon, or whether it's GPUs, having these things in use case scenarios, where the chips can accelerate the software evolution, to me is going to be the key, state of the art innovation. I think if Intel continues to get that right, companies like Google are going to crush it. Now, Amazon, they do their own. So this is going to another interesting dynamic. >> Yeah, it was actually one of the differentiating points Google's saying, is like, "Hey, you can get the Intel Skylake chip, "on Google Cloud, "probably six months before you're going to be able to "just call up your favorite OEM of choice, "and get that in there." And it's an interesting move. Because we've been covering for years, John, Google does a ton of servers. And they don't just do Intel, they've been heavily involved in the openPOWER movement, they're looking at alternatives, they're looking at low power, they're looking at from their device standpoint. They understand how to develop to all these pieces. They actually gave to the influencers, the press, the analysts, just like at Amazon, we all walked home with Echo Dot, everybody's walking home with the Google Homes. >> John: Did you get one? >> I did get one, disclaimer. Yeah, I got one. I'll be playing with it home. I figured I could have Alexa and Google talking to each other. >> Is it an evaluation unit? You have to give it back, or do you get to keep? >> No, I'm pretty sure they just let us keep that. >> John: Tainted. >> But what I'm interested to see, John, is we talk like Serverless, so I saw a ton of companies that were playing with Alexa at re:Invent, and they've been creating tons of skills. Lambda currently has the leadership out there. Google leverages Serverless in a lot of their architecture, it's what drives a lot of their analytics on the inside. Coming into the show, Google Cloud Functions is alpha. So we expect them to move that forward, but we will see with the announcements come tomorrow. But you would think if they're, try to stay that leadership though there, I actually got a statement from one of the guys that work on the Serverless, and Google believes that for functions, that whole Serverless, to really go where it needs to be, it needs to be open. Google isn't open sourcing anything this week, as far as I know. But they want to be able to move forward-- >> And they're doing great at open source. And I think one of the things, that not to rush to judgment on Google, and no one should, by the way. I mean, certainly, we put out our analysis, and we stick by that, because we know the enterprise pretty well, very well actually. So the thing that I like is that there are new use cases coming out. And we had someone who came on theCUBE here, Tarun Thakur, who's with Datos, datos.io. They're reimagining data backup and recovery in the Cloud. And when you factor in IoT, this is a paradigm shift. So I think we're going to see use cases, and this is a Google opportunity, where they can actually move the goal post a bit on the market, by enabling these no-use cases, whether it's something as, what might seem pedestrian, like backup and recovery, reimagining that is huge. That's going to take impact as the data domains of the world, and what not, that (mumbles). These new uses cases are going to evolve. And so I'm excited by that. But the key thing that came out of this, Stu, and this is where I want to get your reaction on is, Multicloud. Clearly the messaging in the industry, over the course of events that we've been covering, and highlighted today on Google Next is, Multicloud is the world we are living in. Now, you can argue that we're all in Amazon's world, but as we start developing, you're starting to see the emergence of Cloud services providers. Cloud services providers are going to have some tiering, certainly the big ones, and then you're going to have secondary partner like service providers. And Google putting G Suite in the mix, and Office 365 from Microsoft, and Oracle put in their apps in their Clouds stuff, highlights that the SaaS market is going to be very relevant. If that's the case, then why aren't we putting Salesforce in there, Adobe? They all got Clouds too. So if you believe that there's going to be specialism around Clouds, that opens up the notion that there'll be a series of Multicloud architectures. So, Stu-- >> Stu: Yeah so, I mean, John, first of all-- >> BS? Real? I mean what's going on? >> Cloud is this big broad term. From Wikibon's research standpoint, SaaS, today, is two-thirds of the public Cloud market. We spend a lot of time talking-- >> In revenue? >> In revenue. Revenue standpoint. So, absolutely, Salesforce, Oracle, Infor, Microsoft, all up there, big dollars. If we look at the much smaller part of the world, that infrastructures a service, that's where we're spending a lot of time-- >> And platforms a service, which Gartner kind of bundles in, that's how Gartner looks at it. >> It's interesting. This year, we're saying PaaS as a category goes away. It's either SaaS plus, I'm sorry, it's SaaS minus, or infrastructure plus. So look at what Salesforce did with Heroku. Look at what company service now are doing. Yes, there are solutions-- >> Why is PaaS going away? What's the thesis? What's the premise of that for Wikibon research? >> If we look at what PaaS, the idea was it tied to languages, things like portability. There are other tools and solutions that are going to be able to help there. Look at, Docker came out of a PaaS company, DockCloud. There's a really good article from one of the Docker guys talking about the history of this, and you and I are going to be at Dockercon. John, from what I hear, we're going to spending a lot of time talking about Kubernetes, at Dockercon. OpenStack Summit is going to be talking a lot about-- >> By the way, Kubernetes originated at Google. Another cool thing from Google. >> All right, so the PaaS as a market, even if you talk to the Cloud Foundry people, the OpenShift people. The term we got, had a year ago was PaaS is Passe, the nice piffy line. So it really feeds into, because, just some of these categorizations are what we, as industry watchers have a put in there, when you talk to Google, it's like, "Well, why are they talking about G Suite, "and Google Cloud, and even some of their pieces?" They're like, "Well, this is our bundle "that we put together." When you talk to Microsoft, and talk about Cloud, it's like, "Oh, well." They're including Skype in that. They're including Office 365. I'm like, "Well, that's our productivity. "That's a part of our overall solutions." Amazon, even when you talk to Amazon, it's not like that there are two separate companies. There's not AWS and Amazon, it's one company-- >> Are we living in a world of alternative facts, Stu? I mean, Larry Ellison coined the term "Fake Cloud", talking about Salesforce. I'm not going to say Google's a fake Cloud, cause certainly it's not. But when you start blending in these numbers, it's kind of shifting the narrative to having alternative facts, certainly skewing the revenue numbers. To your point, if PaaS goes away because the SaaS minuses that lower down the stack. Cause if you have microservices and orchestration, it kind of thins that out. So one, is that the case? And then I saw your tweet with Sam Ramji, he formally ran Cloud Foundry, he's now at Google, knows his stuff, ex-Microsoft guy, very strong dude. What's he take? What's his take on this? Did you get a chance to chat with Sam at all? >> Yeah, I mean, it was interesting, because Sam, right, coming from Cloud Foundry said, what Cloud Foundry was one of the things they were trying to do, was to really standardize across the clouds. And of course, little bias that he works at Google now. But he's like, "We couldn't do that with Google, "cause Google had really cool features. And of course, when you put an abstraction layer on, can I actually do all the stuff? And he's like, "We couldn't do that." Sure, if you talked to Amazon, they'll be like, "Come on. "Thousand features we announced last year, "look at all the things we have. "It's not like you can just take all of our pieces, "and use it there." Yes, at the VM, or container, or application microservices layer, we can sit on a lot of different Clouds, public or private. But as we said today, the Cloud is not a utility. John, you've been in this discussion for years. So we've talked about, "Oh, I'm just going "to have a Cloud broker, "and go out in a service." It's like, this is not, I'm not buying from Domino's and Pizza Hut, and it's pepperoni pizza's a pepperoni pizza. >> Well, Multicloud, and moving workloads across Clouds, is a different challenge. Certainly, I might have to some stuff here, maybe put some data and edge my bets on leveraging other services. But this brings up the total cost of ownership problem. If you look at the trajectory, say OpenStack, just as a random example. OpenStack, at one point, had a great promise. Now it's kind of niched down into infrastructural service. I know you're going to be covering that summit in Boston. And it's going to be interesting to see how that is. But the word in the community is, that OpenStack is struggling because of the employment challenges involved with it. So to me, Google has an opportunity to avoid that OpenStack kind of concept. Because, talking about Sam Ramji, open source is the wildcard in all of this. So if you look at a open source, and you believe that that PaaS layer's thinning down, to infrastructure and SaaS, then you got to look at the open source community, and that's going to be a key area, that we're certainly watching, and we've identified, and we've mentioned it before. But here's my point. If you look at the total cost of ownership. If I'm a customer, Stu, I'm like, "Okay, if I'm just going to move to the Cloud, "I need to rely and lean on my partner, "my vendor, my supplier, "Amazon, or Google, or Microsoft, whoever, "to provide really excellent manageability. "Really excellent security. "Because if I don't, I have to build it myself." So it's becoming the shark fin, the tip of the iceberg, that you don't see the hidden cost, because I would much rather have more confidence in manageability that I can control. But I don't want to have to spend resources building manageability software, if the stuff doesn't work. So there's the issue about Multicloud that I'm watching. Your thoughts? Or is that too nuance? >> No, no. First of all, one of the things is that if I look at what I was doing on premises, before versus public Cloud, yes, there are some hidden costs, but in general I think we understand them a little bit better in public Cloud. And public Cloud gives us a chance to do a do-over for this like security, which most of us understand that security is good in public Cloud. Now, security overall, lots of work to do, challenges, not security isn't the same across all of them. We've talked to plenty of companies that are helping to give security across Clouds. But this Multicloud discussion is still something that is sorting out. Portability is not simple, but it's where we're going. Today, most companies, if I'm not really small, have some on-prem pieces. And they're leveraging at least one Cloud. They're usually using many SaaS providers. And there's this whole giant ecosystem, John, around the Cloud management platforms. Because managing across lots of environment, is definitely a challenge. There's so many companies that are trying to solve them. And there's just dozens and dozens of these companies, attacking everything from licensing, to the data management, to everything else. So there's a lot of challenges there, especially the larger you get as a company, the more things you need to worry about. >> So Stu, just to wrap up our segment. Great day. Wanted to just get some color on the day. And highlighting some parody from the web is always great. Just got a tweet from fake Andy Jassy, which we know really isn't Andy Jassy. But Cloud Opinion was very active to the hashtag, that Twitter handle Cloud Opinion. But he had a medium post, and he said, "Eric Schmidt was boring. "Diane Greene was horrible. "Unfortunately, day one keynote were missed opportunity, "that left several gaps, "failed to portray Google's vision for Google Cloud. "They could've done the following, A, "explain the vision for the Cloud, "where do they see Google Cloud going. "Identify customer use cases that show samples "and customer adoption." They kind of did that. So discount that. My favorite line is this one, "Differentiate from other Cloud providers. "'We're Google damn it,' isn't working so well. "Neither is indirect shots as S3 downtime, "didn't work either as well as either. "Where is the customer's journey going? "And what's the most compelling thing for customers?" This phrase, "We're Google damn it," has kind of speaks to the arrogance of Google. And we've seen this before, and always say, Google doesn't have a bad arrogance. I like the Google mojo. I think the technology, they run hard. But they can sometimes, like, "Customer support, self-service." You can't really get someone on the phone. It's hard to replies from Google. >> "Check out YouTube video. "We own that too, don't you know that?" >> So this is a perception of Google. This could fly in the face, and that arrogance might blow up in the enterprise, cause the enterprises aren't that sophisticated to kind of recognize the mojo from Google. And they, "Hey, I want support. "I want SLAs. "I want security. "I want data flexibility." What's your thoughts? >> So Cloud Opinion wrote, I thought a really thoughtful piece leading up to it, that I didn't think was satire. Some of what he's putting in there, is definitely satire-- >> John: Some of it's kind of true though. >> From the keynote. So I did not get a sense in the meetings I've been in, or watching the keynote, that they were arrogant. They're growing. They're learning. They're working with the community. They're reaching out. They're doing all the things we think they need to do. They're listening really well. So, yes, I think the keynote was a missed opportunity overall. >> John: But we've got to give, point out that was a teleprompter fail. >> That was a piece of it. But even, we felt with a little bit of polish, some of the interactions would've been a little bit smoother. I thought Eric Schmidt's piece was really good at end. As I said before, the AI discussion was enlightening, and really solid. So I don't give it a glowing rating, but I'm not ready to trash it. And tomorrow is when they're going to have the announcements. And overall, there's good buzz going at the show. There's lots going on. >> Give 'em a letter. Letter grade. >> For the keynote? Or the show in general? >> So far, your experience as an analyst, cause you had the, again, to give them credit, I agree with you. First analyst conference. They are listening. And the slideshow, you see what they're doing. They're being humble. They didn't take any real direct shots at its competitors. They were really humble. >> And that is something that I think they could've helped to focus one something that differentiated a little bit. Something we had to pry out of them in some of the one-on-ones, is like, "Come on, what are you doing?" And they're like, "We're winning 50, 60% of our competitive deals." And I'm like, "Explain to us why. "Because we're not hearing it. "You're not articulating it as well." It's not like we expect them, it's like, "Oh wait, they told us we're arrogant. "Maybe we should be super humble now." It's kind of-- >> I don't think they're thinking that way. I think my impression of Google, knowing the companies history, and the people involved there, and Diane Greene in particular, as you know from the Vmware days. She's kind of humble, but she's not. She's tough. And she's good. And she's smart. >> And she's bringing in really good people. And by the way, John, I want to give them kudos, really supported International Women's Day, I love the, Fei-Fei got up, and she talked about her, one of her compatriots, another badass woman up there, that got like one of the big moments of the keynote there. >> John: Did they have a woman in tech panel? >> Not at this event. Because Diane was there, Fei-Fei was there. They had some women just participating in it. I know they had some other events going on throughout the show. >> I agree, and I think it's awesome. I think one of the things that I like about Google, and again, I'll reiterate, is that apples and oranges relative to the other Cloud guys. But remember, just because Amazon's lead is so far ahead, that you still have this jocking of position between the other players. And they're all taking the same pattern. Again, this is the same thing we talked about at our other analysis, is that, certainly at re:Invent, we talked about the same thing. Microsoft, Oracle, IBM, and now Google, are differentiating with their apps. And I think that's smart. I don't think that's a bad move at all. It does telegraph a little bit, that maybe they got, they could add more to show, we'll see tomorrow. But I don't think that's a bad thing. Again, it does make the numbers a little messy, in terms of what's what. But I think it's totally cool for a company to differentiate on their offering. >> Yeah, definitely. And John, as you said, Google is playing their game. They're not trying to play Amazon's game. They're not, Oracle's thing was what? You kind of get a little bit of the lead, and kind of just make sure how you attack and stay ahead of what they're doing, going to the boating analogy there. But Google knows where they're going, moving themselves forward. That they've made some really good progress. The amount of people, the amount of news they have. Are they moving fast enough to really try to close a little bit on the Amazon's world, is something I want to come out of the show with. Where are customers going? >> And it's a turbulent time too. As Peter Burris, our own Peter Buriss at Wikibon, would say, is a turbulent time. And it's going to really put everyone on notice. There's a lot to cover, if you're an analyst. I mean, you have compute, network storage, services. I mean, there's a slew of stuff that's being rolled out, either in table stakes for existing enterprises, plus new stuff. I mean, I didn't hear a lot of IoT today. Did you hear much IoT? Is there IoT coming to you at the briefing? >> Come on. I'm sure there's some service coming out from Google, that'll help us be able to process all this stuff much faster. They'll just replace this with-- >> So you're in the analyst meeting. I know you're under NDA, but is there IoT coming tomorrow? >> IoT was a term that I heard this week, yes. >> So all right, that's a good confirmation. Stu cannot confirm or deny that IoT will be there tomorrow. Okay, well, that's going to end day one of coverage, here in our studio. As you know, we got a new studio. We have folks on the ground. You're going to start to see a new CUBE formula, where we have in-studio coverage, and out in the field, like our normal CUBE, our "game day", as we say. Getting all the signal, extracting it from that noise out there, for you. Again, in-studio allows us to get more content. We bring our friends in. We want to get the content. We're going to get the summaries, and share that with you. I'm John Furrier, Stu Miniman, day one coverage. We'll see you tomorrow for another full day of special coverage, sponsored by Intel, two days of coverage. I want to thank Intel for supporting our editorial mission. We love the enterprise, we love Cloud, we love big data, love Smart Cities, autonomous vehicles, and the changing landscape in tech. We'll be back tomorrow, thanks for watching.